{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91c682e7",
   "metadata": {},
   "source": [
    "## Coverslip 2 --> use same notebook as Coverslip1, coverslips 3 and on will be using another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f85b5273",
   "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",
    "import tifffile\n",
    "# from pystackreg import StackReg --> don't run this, run this with imlab environment\n",
    "from skimage.filters import threshold_otsu\n",
    "import seaborn as sns\n",
    "from skimage import measure\n",
    "from scipy import stats\n",
    "import umap\n",
    "#from umap.umap_ import UMAP\n",
    "import scanpy as sc\n",
    "import math\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57247dcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Volumes/imagestore/Brian/102022_D10_Coverslip2_Reimage_Processed'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c694b1b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "194"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load Feature Data\n",
    "MP_CP_DIR = 'mp_cp_output' # Cell Profiler ran just on 7 epitope tags + nuclei\n",
    "MP_DIR = 'mp_score_max' # MP score reconstruction\n",
    "DATA_DIR = 'max_clean' # all features including epitopes, cell heatlh, cell paint\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",
    "len(allFOVs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7dafd38d",
   "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>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",
       "      <th>ch_index</th>\n",
       "      <th>Split_Num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>DNA_0</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Blank_488_0</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Blank_561_0</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Blank_637_0</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>NWS</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>HSV</td>\n",
       "      <td>GFP</td>\n",
       "      <td>488</td>\n",
       "      <td>521</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>C</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>DNA_3</td>\n",
       "      <td>DAPI</td>\n",
       "      <td>405</td>\n",
       "      <td>445</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>3</td>\n",
       "      <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",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <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",
       "      <td>5</td>\n",
       "      <td>LAMP1</td>\n",
       "      <td>RFP</td>\n",
       "      <td>561</td>\n",
       "      <td>594</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>5</td>\n",
       "      <td>4HNE</td>\n",
       "      <td>Cy5</td>\n",
       "      <td>637</td>\n",
       "      <td>698</td>\n",
       "      <td>77</td>\n",
       "      <td>3</td>\n",
       "      <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",
       "    </tr>\n",
       "    <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": 4,
     "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": 5,
   "id": "23de094b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Calculate_PCA_Loadings(X):\n",
    "    from sklearn.decomposition import PCA\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 \n",
    "\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": 9,
   "id": "371c3920",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>F000_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.046839</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.205028</td>\n",
       "      <td>0.209091</td>\n",
       "      <td>0.365297</td>\n",
       "      <td>0.779707</td>\n",
       "      <td>0.151511</td>\n",
       "      <td>0.242699</td>\n",
       "      <td>0.212351</td>\n",
       "      <td>0.134020</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>0.811237</td>\n",
       "      <td>-0.068547</td>\n",
       "      <td>-0.035807</td>\n",
       "      <td>0.124406</td>\n",
       "      <td>...</td>\n",
       "      <td>1.264636</td>\n",
       "      <td>1.427556</td>\n",
       "      <td>0.622253</td>\n",
       "      <td>0.859885</td>\n",
       "      <td>1.152590</td>\n",
       "      <td>1.310279</td>\n",
       "      <td>1.159066</td>\n",
       "      <td>1.501676</td>\n",
       "      <td>1.628948</td>\n",
       "      <td>1.296938</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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.615654</td>\n",
       "      <td>-0.012243</td>\n",
       "      <td>-0.265879</td>\n",
       "      <td>0.844223</td>\n",
       "      <td>-0.329479</td>\n",
       "      <td>...</td>\n",
       "      <td>1.554100</td>\n",
       "      <td>1.656365</td>\n",
       "      <td>0.233998</td>\n",
       "      <td>0.607365</td>\n",
       "      <td>1.360672</td>\n",
       "      <td>1.455081</td>\n",
       "      <td>0.450738</td>\n",
       "      <td>0.510399</td>\n",
       "      <td>0.992994</td>\n",
       "      <td>1.329577</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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.279308</td>\n",
       "      <td>0.818168</td>\n",
       "      <td>0.357970</td>\n",
       "      <td>-0.071036</td>\n",
       "      <td>0.413842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.791320</td>\n",
       "      <td>0.877790</td>\n",
       "      <td>0.924221</td>\n",
       "      <td>0.485663</td>\n",
       "      <td>1.878052</td>\n",
       "      <td>1.593098</td>\n",
       "      <td>2.214764</td>\n",
       "      <td>1.084979</td>\n",
       "      <td>0.985166</td>\n",
       "      <td>0.597120</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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.060493</td>\n",
       "      <td>-0.118610</td>\n",
       "      <td>-0.089798</td>\n",
       "      <td>0.963343</td>\n",
       "      <td>-0.123567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.879008</td>\n",
       "      <td>1.753402</td>\n",
       "      <td>0.136123</td>\n",
       "      <td>0.380006</td>\n",
       "      <td>1.734859</td>\n",
       "      <td>2.109443</td>\n",
       "      <td>0.095089</td>\n",
       "      <td>0.185991</td>\n",
       "      <td>0.266949</td>\n",
       "      <td>0.423473</td>\n",
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       "    <tr>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128037</th>\n",
       "      <td>194</td>\n",
       "      <td>377</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.123193</td>\n",
       "      <td>0.625106</td>\n",
       "      <td>0.716346</td>\n",
       "      <td>0.985081</td>\n",
       "      <td>0.078066</td>\n",
       "      <td>...</td>\n",
       "      <td>0.951483</td>\n",
       "      <td>1.120351</td>\n",
       "      <td>1.744364</td>\n",
       "      <td>1.562882</td>\n",
       "      <td>1.423942</td>\n",
       "      <td>1.130583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.352642</td>\n",
       "      <td>1.257886</td>\n",
       "      <td>1.278322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>194</td>\n",
       "      <td>378</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.302183</td>\n",
       "      <td>-0.273615</td>\n",
       "      <td>-0.494512</td>\n",
       "      <td>0.981572</td>\n",
       "      <td>-0.385107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.663937</td>\n",
       "      <td>0.987070</td>\n",
       "      <td>1.615991</td>\n",
       "      <td>0.953780</td>\n",
       "      <td>1.174186</td>\n",
       "      <td>1.472543</td>\n",
       "      <td>0.334866</td>\n",
       "      <td>0.542341</td>\n",
       "      <td>0.737196</td>\n",
       "      <td>0.869108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>194</td>\n",
       "      <td>379</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.163972</td>\n",
       "      <td>-0.026100</td>\n",
       "      <td>0.077597</td>\n",
       "      <td>0.550540</td>\n",
       "      <td>0.141621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.426775</td>\n",
       "      <td>0.357418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.304040</td>\n",
       "      <td>2.434304</td>\n",
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       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>194</td>\n",
       "      <td>380</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.144917</td>\n",
       "      <td>-0.105292</td>\n",
       "      <td>-0.191567</td>\n",
       "      <td>0.988036</td>\n",
       "      <td>-0.215211</td>\n",
       "      <td>...</td>\n",
       "      <td>1.176655</td>\n",
       "      <td>1.813261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.296167</td>\n",
       "      <td>0.642319</td>\n",
       "      <td>1.134144</td>\n",
       "      <td>1.086141</td>\n",
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       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>194</td>\n",
       "      <td>381</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.205749</td>\n",
       "      <td>0.004237</td>\n",
       "      <td>0.073938</td>\n",
       "      <td>0.664650</td>\n",
       "      <td>0.094427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.842543</td>\n",
       "      <td>0.913865</td>\n",
       "      <td>0.398075</td>\n",
       "      <td>0.282012</td>\n",
       "      <td>0.520210</td>\n",
       "      <td>0.574197</td>\n",
       "      <td>0.356851</td>\n",
       "      <td>0.665661</td>\n",
       "      <td>0.858715</td>\n",
       "      <td>0.930806</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>128042 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",
       "128037          194           377  F224_max_clean.tif   \n",
       "128038          194           378  F224_max_clean.tif   \n",
       "128039          194           379  F224_max_clean.tif   \n",
       "128040          194           380  F224_max_clean.tif   \n",
       "128041          194           381  F224_max_clean.tif   \n",
       "\n",
       "                                       PathName_max_clean  \\\n",
       "0       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "1       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "2       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "3       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "4       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "...                                                   ...   \n",
       "128037  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128038  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128039  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128040  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128041  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "\n",
       "        Children_Cytoplasm_Count  Correlation_Correlation_C_DNA  \\\n",
       "0                              1                      -0.046839   \n",
       "1                              1                       0.103139   \n",
       "2                              1                       0.615654   \n",
       "3                              1                      -0.279308   \n",
       "4                              1                       0.060493   \n",
       "...                          ...                            ...   \n",
       "128037                         1                      -0.123193   \n",
       "128038                         1                       0.302183   \n",
       "128039                         1                      -0.163972   \n",
       "128040                         1                      -0.144917   \n",
       "128041                         1                      -0.205749   \n",
       "\n",
       "        Correlation_Correlation_C_FLAG  Correlation_Correlation_C_HSV  \\\n",
       "0                             0.388397                       0.218106   \n",
       "1                             0.811237                      -0.068547   \n",
       "2                            -0.012243                      -0.265879   \n",
       "3                             0.818168                       0.357970   \n",
       "4                            -0.118610                      -0.089798   \n",
       "...                                ...                            ...   \n",
       "128037                        0.625106                       0.716346   \n",
       "128038                       -0.273615                      -0.494512   \n",
       "128039                       -0.026100                       0.077597   \n",
       "128040                       -0.105292                      -0.191567   \n",
       "128041                        0.004237                       0.073938   \n",
       "\n",
       "        Correlation_Correlation_C_NWS  Correlation_Correlation_C_Ollas  ...  \\\n",
       "0                            0.345887                         0.139582  ...   \n",
       "1                           -0.035807                         0.124406  ...   \n",
       "2                            0.844223                        -0.329479  ...   \n",
       "3                           -0.071036                         0.413842  ...   \n",
       "4                            0.963343                        -0.123567  ...   \n",
       "...                               ...                              ...  ...   \n",
       "128037                       0.985081                         0.078066  ...   \n",
       "128038                       0.981572                        -0.385107  ...   \n",
       "128039                       0.550540                         0.141621  ...   \n",
       "128040                       0.988036                        -0.215211  ...   \n",
       "128041                       0.664650                         0.094427  ...   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_3of4  \\\n",
       "0                                     0.255224   \n",
       "1                                     1.264636   \n",
       "2                                     1.554100   \n",
       "3                                     0.791320   \n",
       "4                                     0.879008   \n",
       "...                                        ...   \n",
       "128037                                0.951483   \n",
       "128038                                0.663937   \n",
       "128039                                0.426775   \n",
       "128040                                1.176655   \n",
       "128041                                0.842543   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_4of4  \\\n",
       "0                                     0.153098   \n",
       "1                                     1.427556   \n",
       "2                                     1.656365   \n",
       "3                                     0.877790   \n",
       "4                                     1.753402   \n",
       "...                                        ...   \n",
       "128037                                1.120351   \n",
       "128038                                0.987070   \n",
       "128039                                0.357418   \n",
       "128040                                1.813261   \n",
       "128041                                0.913865   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_1of4  \\\n",
       "0                                 0.205028   \n",
       "1                                 0.622253   \n",
       "2                                 0.233998   \n",
       "3                                 0.924221   \n",
       "4                                 0.136123   \n",
       "...                                    ...   \n",
       "128037                            1.744364   \n",
       "128038                            1.615991   \n",
       "128039                            0.000000   \n",
       "128040                            0.000000   \n",
       "128041                            0.398075   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_2of4  \\\n",
       "0                                 0.209091   \n",
       "1                                 0.859885   \n",
       "2                                 0.607365   \n",
       "3                                 0.485663   \n",
       "4                                 0.380006   \n",
       "...                                    ...   \n",
       "128037                            1.562882   \n",
       "128038                            0.953780   \n",
       "128039                            0.000000   \n",
       "128040                            0.000000   \n",
       "128041                            0.282012   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_3of4  \\\n",
       "0                                 0.365297   \n",
       "1                                 1.152590   \n",
       "2                                 1.360672   \n",
       "3                                 1.878052   \n",
       "4                                 1.734859   \n",
       "...                                    ...   \n",
       "128037                            1.423942   \n",
       "128038                            1.174186   \n",
       "128039                            0.000000   \n",
       "128040                            2.645751   \n",
       "128041                            0.520210   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_4of4  \\\n",
       "0                                 0.779707   \n",
       "1                                 1.310279   \n",
       "2                                 1.455081   \n",
       "3                                 1.593098   \n",
       "4                                 2.109443   \n",
       "...                                    ...   \n",
       "128037                            1.130583   \n",
       "128038                            1.472543   \n",
       "128039                            2.645751   \n",
       "128040                            0.000000   \n",
       "128041                            0.574197   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_1of4  \\\n",
       "0                                    0.151511   \n",
       "1                                    1.159066   \n",
       "2                                    0.450738   \n",
       "3                                    2.214764   \n",
       "4                                    0.095089   \n",
       "...                                       ...   \n",
       "128037                               0.000000   \n",
       "128038                               0.334866   \n",
       "128039                               0.000000   \n",
       "128040                               0.296167   \n",
       "128041                               0.356851   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_2of4  \\\n",
       "0                                    0.242699   \n",
       "1                                    1.501676   \n",
       "2                                    0.510399   \n",
       "3                                    1.084979   \n",
       "4                                    0.185991   \n",
       "...                                       ...   \n",
       "128037                               1.352642   \n",
       "128038                               0.542341   \n",
       "128039                               0.000000   \n",
       "128040                               0.642319   \n",
       "128041                               0.665661   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_3of4  \\\n",
       "0                                    0.212351   \n",
       "1                                    1.628948   \n",
       "2                                    0.992994   \n",
       "3                                    0.985166   \n",
       "4                                    0.266949   \n",
       "...                                       ...   \n",
       "128037                               1.257886   \n",
       "128038                               0.737196   \n",
       "128039                               2.304040   \n",
       "128040                               1.134144   \n",
       "128041                               0.858715   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_4of4  \n",
       "0                                    0.134020  \n",
       "1                                    1.296938  \n",
       "2                                    1.329577  \n",
       "3                                    0.597120  \n",
       "4                                    0.423473  \n",
       "...                                       ...  \n",
       "128037                               1.278322  \n",
       "128038                               0.869108  \n",
       "128039                               2.434304  \n",
       "128040                               1.086141  \n",
       "128041                               0.930806  \n",
       "\n",
       "[128042 rows x 522 columns]"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "9b5de11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>ImageNumber</th>\n",
       "      <th>ObjectNumber</th>\n",
       "      <th>FileName_Cyto</th>\n",
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       "      <td>F000_max_clean_Soma.tiff</td>\n",
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       "      <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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>1070</td>\n",
       "      <td>1886</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>30.546509</td>\n",
       "      <td>32.055557</td>\n",
       "      <td>-40.571312</td>\n",
       "      <td>141.267027</td>\n",
       "      <td>0.916881</td>\n",
       "      <td>376.542991</td>\n",
       "      <td>26.094393</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>619</td>\n",
       "      <td>990</td>\n",
       "      <td>...</td>\n",
       "      <td>3.605551</td>\n",
       "      <td>26.246905</td>\n",
       "      <td>25.085552</td>\n",
       "      <td>-16.918501</td>\n",
       "      <td>112.225397</td>\n",
       "      <td>0.830872</td>\n",
       "      <td>402.148627</td>\n",
       "      <td>30.389338</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>1837</td>\n",
       "      <td>3360</td>\n",
       "      <td>...</td>\n",
       "      <td>6.082763</td>\n",
       "      <td>48.072381</td>\n",
       "      <td>45.233209</td>\n",
       "      <td>-45.965923</td>\n",
       "      <td>209.965512</td>\n",
       "      <td>0.794207</td>\n",
       "      <td>710.364181</td>\n",
       "      <td>35.610234</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>1146</td>\n",
       "      <td>1700</td>\n",
       "      <td>...</td>\n",
       "      <td>5.049510</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>33.133208</td>\n",
       "      <td>80.434796</td>\n",
       "      <td>146.124892</td>\n",
       "      <td>0.906646</td>\n",
       "      <td>286.285340</td>\n",
       "      <td>54.049738</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>128037</th>\n",
       "      <td>194</td>\n",
       "      <td>377</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>1060</td>\n",
       "      <td>1485</td>\n",
       "      <td>...</td>\n",
       "      <td>5.385165</td>\n",
       "      <td>30.857738</td>\n",
       "      <td>30.500952</td>\n",
       "      <td>69.686535</td>\n",
       "      <td>124.468037</td>\n",
       "      <td>0.971586</td>\n",
       "      <td>1906.205660</td>\n",
       "      <td>1955.836792</td>\n",
       "      <td>0</td>\n",
       "      <td>377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>194</td>\n",
       "      <td>378</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>443</td>\n",
       "      <td>572</td>\n",
       "      <td>...</td>\n",
       "      <td>3.605551</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>21.641274</td>\n",
       "      <td>10.842543</td>\n",
       "      <td>78.183766</td>\n",
       "      <td>0.956803</td>\n",
       "      <td>1846.625282</td>\n",
       "      <td>1983.584650</td>\n",
       "      <td>0</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>194</td>\n",
       "      <td>379</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>1069</td>\n",
       "      <td>1537</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>27.607882</td>\n",
       "      <td>26.292863</td>\n",
       "      <td>-76.740715</td>\n",
       "      <td>141.154329</td>\n",
       "      <td>0.934441</td>\n",
       "      <td>1451.477081</td>\n",
       "      <td>1983.122544</td>\n",
       "      <td>0</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>194</td>\n",
       "      <td>380</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>975</td>\n",
       "      <td>1457</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>28.318509</td>\n",
       "      <td>27.085701</td>\n",
       "      <td>65.877419</td>\n",
       "      <td>127.639610</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>1781.045128</td>\n",
       "      <td>1993.221538</td>\n",
       "      <td>0</td>\n",
       "      <td>380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>194</td>\n",
       "      <td>381</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/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>/home/imaging/Desktop/Brian/NAS/Brian/102022_D...</td>\n",
       "      <td>319</td>\n",
       "      <td>506</td>\n",
       "      <td>...</td>\n",
       "      <td>2.828427</td>\n",
       "      <td>14.336559</td>\n",
       "      <td>15.217468</td>\n",
       "      <td>-51.619469</td>\n",
       "      <td>71.840620</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>354.100313</td>\n",
       "      <td>2008.322884</td>\n",
       "      <td>0</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128042 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",
       "128037          194           377  F224_max_clean_Cyto.tiff   \n",
       "128038          194           378  F224_max_clean_Cyto.tiff   \n",
       "128039          194           379  F224_max_clean_Cyto.tiff   \n",
       "128040          194           380  F224_max_clean_Cyto.tiff   \n",
       "128041          194           381  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",
       "128037  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "128038  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "128039  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "128040  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "128041  F224_max_clean_Nuclei.tiff  F224_max_clean_Soma.tiff   \n",
       "\n",
       "                                            PathName_Cyto  \\\n",
       "0       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "1       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "2       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "3       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "4       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "...                                                   ...   \n",
       "128037  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128038  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128039  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128040  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128041  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "\n",
       "                                          PathName_Nuclei  \\\n",
       "0       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "1       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "2       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "3       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "4       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "...                                                   ...   \n",
       "128037  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128038  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128039  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128040  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "128041  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...   \n",
       "\n",
       "                                            PathName_Soma  AreaShape_Area  \\\n",
       "0       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...             322   \n",
       "1       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...            1070   \n",
       "2       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...             619   \n",
       "3       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...            1837   \n",
       "4       /home/imaging/Desktop/Brian/NAS/Brian/102022_D...            1146   \n",
       "...                                                   ...             ...   \n",
       "128037  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...            1060   \n",
       "128038  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...             443   \n",
       "128039  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...            1069   \n",
       "128040  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...             975   \n",
       "128041  /home/imaging/Desktop/Brian/NAS/Brian/102022_D...             319   \n",
       "\n",
       "        AreaShape_BoundingBoxArea  ...  AreaShape_MedianRadius  \\\n",
       "0                             437  ...                3.000000   \n",
       "1                            1886  ...                5.000000   \n",
       "2                             990  ...                3.605551   \n",
       "3                            3360  ...                6.082763   \n",
       "4                            1700  ...                5.049510   \n",
       "...                           ...  ...                     ...   \n",
       "128037                       1485  ...                5.385165   \n",
       "128038                        572  ...                3.605551   \n",
       "128039                       1537  ...                5.000000   \n",
       "128040                       1457  ...                5.000000   \n",
       "128041                        506  ...                2.828427   \n",
       "\n",
       "        AreaShape_MinFeretDiameter  AreaShape_MinorAxisLength  \\\n",
       "0                        17.846568                  18.291100   \n",
       "1                        30.546509                  32.055557   \n",
       "2                        26.246905                  25.085552   \n",
       "3                        48.072381                  45.233209   \n",
       "4                        33.000000                  33.133208   \n",
       "...                            ...                        ...   \n",
       "128037                   30.857738                  30.500952   \n",
       "128038                   21.000000                  21.641274   \n",
       "128039                   27.607882                  26.292863   \n",
       "128040                   28.318509                  27.085701   \n",
       "128041                   14.336559                  15.217468   \n",
       "\n",
       "        AreaShape_Orientation  AreaShape_Perimeter  AreaShape_Solidity  \\\n",
       "0                    1.617815            71.698485            0.917379   \n",
       "1                  -40.571312           141.267027            0.916881   \n",
       "2                  -16.918501           112.225397            0.830872   \n",
       "3                  -45.965923           209.965512            0.794207   \n",
       "4                   80.434796           146.124892            0.906646   \n",
       "...                       ...                  ...                 ...   \n",
       "128037              69.686535           124.468037            0.971586   \n",
       "128038              10.842543            78.183766            0.956803   \n",
       "128039             -76.740715           141.154329            0.934441   \n",
       "128040              65.877419           127.639610            0.949367   \n",
       "128041             -51.619469            71.840620            0.935484   \n",
       "\n",
       "        Location_Center_X  Location_Center_Y  Location_Center_Z  \\\n",
       "0              660.782609          18.130435                  0   \n",
       "1              376.542991          26.094393                  0   \n",
       "2              402.148627          30.389338                  0   \n",
       "3              710.364181          35.610234                  0   \n",
       "4              286.285340          54.049738                  0   \n",
       "...                   ...                ...                ...   \n",
       "128037        1906.205660        1955.836792                  0   \n",
       "128038        1846.625282        1983.584650                  0   \n",
       "128039        1451.477081        1983.122544                  0   \n",
       "128040        1781.045128        1993.221538                  0   \n",
       "128041         354.100313        2008.322884                  0   \n",
       "\n",
       "        Number_Object_Number  \n",
       "0                          1  \n",
       "1                          2  \n",
       "2                          3  \n",
       "3                          4  \n",
       "4                          5  \n",
       "...                      ...  \n",
       "128037                   377  \n",
       "128038                   378  \n",
       "128039                   379  \n",
       "128040                   380  \n",
       "128041                   381  \n",
       "\n",
       "[128042 rows x 37 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_df = pd.read_csv(f'seg/SegFeatSoma_.csv', sep=',')\n",
    "seg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f7eea54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(22, 2027, 2029)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/s7/fg8686z944s5332vtll29z5h0000gn/T/ipykernel_4963/2212913895.py:6: 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 0x2b3e93820>"
      ]
     },
     "execution_count": 14,
     "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": [
    "# Test mp output\n",
    "fov = '000'\n",
    "mpscore = imread(f'{MP_DIR}/F{fov}_mp_score_max.tif')\n",
    "print(mpscore.shape)\n",
    "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": 13,
   "id": "98dd313b",
   "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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.046839</td>\n",
       "      <td>0.388397</td>\n",
       "      <td>0.218106</td>\n",
       "      <td>0.345887</td>\n",
       "      <td>0.139582</td>\n",
       "      <td>...</td>\n",
       "      <td>0.255224</td>\n",
       "      <td>0.153098</td>\n",
       "      <td>0.205028</td>\n",
       "      <td>0.209091</td>\n",
       "      <td>0.365297</td>\n",
       "      <td>0.779707</td>\n",
       "      <td>0.151511</td>\n",
       "      <td>0.242699</td>\n",
       "      <td>0.212351</td>\n",
       "      <td>0.134020</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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.103139</td>\n",
       "      <td>0.811237</td>\n",
       "      <td>-0.068547</td>\n",
       "      <td>-0.035807</td>\n",
       "      <td>0.124406</td>\n",
       "      <td>...</td>\n",
       "      <td>1.264636</td>\n",
       "      <td>1.427556</td>\n",
       "      <td>0.622253</td>\n",
       "      <td>0.859885</td>\n",
       "      <td>1.152590</td>\n",
       "      <td>1.310279</td>\n",
       "      <td>1.159066</td>\n",
       "      <td>1.501676</td>\n",
       "      <td>1.628948</td>\n",
       "      <td>1.296938</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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.615654</td>\n",
       "      <td>-0.012243</td>\n",
       "      <td>-0.265879</td>\n",
       "      <td>0.844223</td>\n",
       "      <td>-0.329479</td>\n",
       "      <td>...</td>\n",
       "      <td>1.554100</td>\n",
       "      <td>1.656365</td>\n",
       "      <td>0.233998</td>\n",
       "      <td>0.607365</td>\n",
       "      <td>1.360672</td>\n",
       "      <td>1.455081</td>\n",
       "      <td>0.450738</td>\n",
       "      <td>0.510399</td>\n",
       "      <td>0.992994</td>\n",
       "      <td>1.329577</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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.279308</td>\n",
       "      <td>0.818168</td>\n",
       "      <td>0.357970</td>\n",
       "      <td>-0.071036</td>\n",
       "      <td>0.413842</td>\n",
       "      <td>...</td>\n",
       "      <td>0.791320</td>\n",
       "      <td>0.877790</td>\n",
       "      <td>0.924221</td>\n",
       "      <td>0.485663</td>\n",
       "      <td>1.878052</td>\n",
       "      <td>1.593098</td>\n",
       "      <td>2.214764</td>\n",
       "      <td>1.084979</td>\n",
       "      <td>0.985166</td>\n",
       "      <td>0.597120</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/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.060493</td>\n",
       "      <td>-0.118610</td>\n",
       "      <td>-0.089798</td>\n",
       "      <td>0.963343</td>\n",
       "      <td>-0.123567</td>\n",
       "      <td>...</td>\n",
       "      <td>0.879008</td>\n",
       "      <td>1.753402</td>\n",
       "      <td>0.136123</td>\n",
       "      <td>0.380006</td>\n",
       "      <td>1.734859</td>\n",
       "      <td>2.109443</td>\n",
       "      <td>0.095089</td>\n",
       "      <td>0.185991</td>\n",
       "      <td>0.266949</td>\n",
       "      <td>0.423473</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>128037</th>\n",
       "      <td>194</td>\n",
       "      <td>377</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.123193</td>\n",
       "      <td>0.625106</td>\n",
       "      <td>0.716346</td>\n",
       "      <td>0.985081</td>\n",
       "      <td>0.078066</td>\n",
       "      <td>...</td>\n",
       "      <td>0.951483</td>\n",
       "      <td>1.120351</td>\n",
       "      <td>1.744364</td>\n",
       "      <td>1.562882</td>\n",
       "      <td>1.423942</td>\n",
       "      <td>1.130583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.352642</td>\n",
       "      <td>1.257886</td>\n",
       "      <td>1.278322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>194</td>\n",
       "      <td>378</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.302183</td>\n",
       "      <td>-0.273615</td>\n",
       "      <td>-0.494512</td>\n",
       "      <td>0.981572</td>\n",
       "      <td>-0.385107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.663937</td>\n",
       "      <td>0.987070</td>\n",
       "      <td>1.615991</td>\n",
       "      <td>0.953780</td>\n",
       "      <td>1.174186</td>\n",
       "      <td>1.472543</td>\n",
       "      <td>0.334866</td>\n",
       "      <td>0.542341</td>\n",
       "      <td>0.737196</td>\n",
       "      <td>0.869108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>194</td>\n",
       "      <td>379</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.163972</td>\n",
       "      <td>-0.026100</td>\n",
       "      <td>0.077597</td>\n",
       "      <td>0.550540</td>\n",
       "      <td>0.141621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.426775</td>\n",
       "      <td>0.357418</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.304040</td>\n",
       "      <td>2.434304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>194</td>\n",
       "      <td>380</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.144917</td>\n",
       "      <td>-0.105292</td>\n",
       "      <td>-0.191567</td>\n",
       "      <td>0.988036</td>\n",
       "      <td>-0.215211</td>\n",
       "      <td>...</td>\n",
       "      <td>1.176655</td>\n",
       "      <td>1.813261</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.645751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.296167</td>\n",
       "      <td>0.642319</td>\n",
       "      <td>1.134144</td>\n",
       "      <td>1.086141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>194</td>\n",
       "      <td>381</td>\n",
       "      <td>F224_max_clean.tif</td>\n",
       "      <td>/mnt/disks/store/102022_D10_Coverslip2_Reimage...</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.205749</td>\n",
       "      <td>0.004237</td>\n",
       "      <td>0.073938</td>\n",
       "      <td>0.664650</td>\n",
       "      <td>0.094427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.842543</td>\n",
       "      <td>0.913865</td>\n",
       "      <td>0.398075</td>\n",
       "      <td>0.282012</td>\n",
       "      <td>0.520210</td>\n",
       "      <td>0.574197</td>\n",
       "      <td>0.356851</td>\n",
       "      <td>0.665661</td>\n",
       "      <td>0.858715</td>\n",
       "      <td>0.930806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128042 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",
       "128037          194           377  F224_max_clean.tif   \n",
       "128038          194           378  F224_max_clean.tif   \n",
       "128039          194           379  F224_max_clean.tif   \n",
       "128040          194           380  F224_max_clean.tif   \n",
       "128041          194           381  F224_max_clean.tif   \n",
       "\n",
       "                                       PathName_max_clean  \\\n",
       "0       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "1       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "2       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "3       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "4       /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "...                                                   ...   \n",
       "128037  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128038  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128039  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128040  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "128041  /mnt/disks/store/102022_D10_Coverslip2_Reimage...   \n",
       "\n",
       "        Children_Cytoplasm_Count  Correlation_Correlation_C_DNA  \\\n",
       "0                              1                      -0.046839   \n",
       "1                              1                       0.103139   \n",
       "2                              1                       0.615654   \n",
       "3                              1                      -0.279308   \n",
       "4                              1                       0.060493   \n",
       "...                          ...                            ...   \n",
       "128037                         1                      -0.123193   \n",
       "128038                         1                       0.302183   \n",
       "128039                         1                      -0.163972   \n",
       "128040                         1                      -0.144917   \n",
       "128041                         1                      -0.205749   \n",
       "\n",
       "        Correlation_Correlation_C_FLAG  Correlation_Correlation_C_HSV  \\\n",
       "0                             0.388397                       0.218106   \n",
       "1                             0.811237                      -0.068547   \n",
       "2                            -0.012243                      -0.265879   \n",
       "3                             0.818168                       0.357970   \n",
       "4                            -0.118610                      -0.089798   \n",
       "...                                ...                            ...   \n",
       "128037                        0.625106                       0.716346   \n",
       "128038                       -0.273615                      -0.494512   \n",
       "128039                       -0.026100                       0.077597   \n",
       "128040                       -0.105292                      -0.191567   \n",
       "128041                        0.004237                       0.073938   \n",
       "\n",
       "        Correlation_Correlation_C_NWS  Correlation_Correlation_C_Ollas  ...  \\\n",
       "0                            0.345887                         0.139582  ...   \n",
       "1                           -0.035807                         0.124406  ...   \n",
       "2                            0.844223                        -0.329479  ...   \n",
       "3                           -0.071036                         0.413842  ...   \n",
       "4                            0.963343                        -0.123567  ...   \n",
       "...                               ...                              ...  ...   \n",
       "128037                       0.985081                         0.078066  ...   \n",
       "128038                       0.981572                        -0.385107  ...   \n",
       "128039                       0.550540                         0.141621  ...   \n",
       "128040                       0.988036                        -0.215211  ...   \n",
       "128041                       0.664650                         0.094427  ...   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_3of4  \\\n",
       "0                                     0.255224   \n",
       "1                                     1.264636   \n",
       "2                                     1.554100   \n",
       "3                                     0.791320   \n",
       "4                                     0.879008   \n",
       "...                                        ...   \n",
       "128037                                0.951483   \n",
       "128038                                0.663937   \n",
       "128039                                0.426775   \n",
       "128040                                1.176655   \n",
       "128041                                0.842543   \n",
       "\n",
       "        RadialDistribution_RadialCV_Ollas_4of4  \\\n",
       "0                                     0.153098   \n",
       "1                                     1.427556   \n",
       "2                                     1.656365   \n",
       "3                                     0.877790   \n",
       "4                                     1.753402   \n",
       "...                                        ...   \n",
       "128037                                1.120351   \n",
       "128038                                0.987070   \n",
       "128039                                0.357418   \n",
       "128040                                1.813261   \n",
       "128041                                0.913865   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_1of4  \\\n",
       "0                                 0.205028   \n",
       "1                                 0.622253   \n",
       "2                                 0.233998   \n",
       "3                                 0.924221   \n",
       "4                                 0.136123   \n",
       "...                                    ...   \n",
       "128037                            1.744364   \n",
       "128038                            1.615991   \n",
       "128039                            0.000000   \n",
       "128040                            0.000000   \n",
       "128041                            0.398075   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_2of4  \\\n",
       "0                                 0.209091   \n",
       "1                                 0.859885   \n",
       "2                                 0.607365   \n",
       "3                                 0.485663   \n",
       "4                                 0.380006   \n",
       "...                                    ...   \n",
       "128037                            1.562882   \n",
       "128038                            0.953780   \n",
       "128039                            0.000000   \n",
       "128040                            0.000000   \n",
       "128041                            0.282012   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_3of4  \\\n",
       "0                                 0.365297   \n",
       "1                                 1.152590   \n",
       "2                                 1.360672   \n",
       "3                                 1.878052   \n",
       "4                                 1.734859   \n",
       "...                                    ...   \n",
       "128037                            1.423942   \n",
       "128038                            1.174186   \n",
       "128039                            0.000000   \n",
       "128040                            2.645751   \n",
       "128041                            0.520210   \n",
       "\n",
       "        RadialDistribution_RadialCV_S_4of4  \\\n",
       "0                                 0.779707   \n",
       "1                                 1.310279   \n",
       "2                                 1.455081   \n",
       "3                                 1.593098   \n",
       "4                                 2.109443   \n",
       "...                                    ...   \n",
       "128037                            1.130583   \n",
       "128038                            1.472543   \n",
       "128039                            2.645751   \n",
       "128040                            0.000000   \n",
       "128041                            0.574197   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_1of4  \\\n",
       "0                                    0.151511   \n",
       "1                                    1.159066   \n",
       "2                                    0.450738   \n",
       "3                                    2.214764   \n",
       "4                                    0.095089   \n",
       "...                                       ...   \n",
       "128037                               0.000000   \n",
       "128038                               0.334866   \n",
       "128039                               0.000000   \n",
       "128040                               0.296167   \n",
       "128041                               0.356851   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_2of4  \\\n",
       "0                                    0.242699   \n",
       "1                                    1.501676   \n",
       "2                                    0.510399   \n",
       "3                                    1.084979   \n",
       "4                                    0.185991   \n",
       "...                                       ...   \n",
       "128037                               1.352642   \n",
       "128038                               0.542341   \n",
       "128039                               0.000000   \n",
       "128040                               0.642319   \n",
       "128041                               0.665661   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_3of4  \\\n",
       "0                                    0.212351   \n",
       "1                                    1.628948   \n",
       "2                                    0.992994   \n",
       "3                                    0.985166   \n",
       "4                                    0.266949   \n",
       "...                                       ...   \n",
       "128037                               1.257886   \n",
       "128038                               0.737196   \n",
       "128039                               2.304040   \n",
       "128040                               1.134144   \n",
       "128041                               0.858715   \n",
       "\n",
       "        RadialDistribution_RadialCV_VSVG_4of4  \n",
       "0                                    0.134020  \n",
       "1                                    1.296938  \n",
       "2                                    1.329577  \n",
       "3                                    0.597120  \n",
       "4                                    0.423473  \n",
       "...                                       ...  \n",
       "128037                               1.278322  \n",
       "128038                               0.869108  \n",
       "128039                               2.434304  \n",
       "128040                               1.086141  \n",
       "128041                               0.930806  \n",
       "\n",
       "[128042 rows x 522 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpcp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c8e9108",
   "metadata": {},
   "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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "260\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "603\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "884\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "1290\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1825\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "2130\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "2458\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2740\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2952\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3222\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3557\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3847\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4246\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4744\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "5238\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5747\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "6164\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6609\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6971\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7343\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7837\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": [
      "8258\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8788\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9517\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9821\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "10232\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "10743\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "11333\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "11954\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "12482\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "12964\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13481\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "14022\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "14431\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14837\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "15412\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15973\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": [
      "16602\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "17179\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "18020\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18788\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19399\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "20092\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20696\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21367\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "22536\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23327\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24534\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25302\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26036\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": "stdout",
     "output_type": "stream",
     "text": [
      "26797\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27444\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27904\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28332\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28727\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29227\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29810\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30736\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31480\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "32192\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "32714\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "33112\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "33595\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "34044\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "34562\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "35106\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35828\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "36714\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37650\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38405\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39057\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "39668\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40186\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": "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": [
      "41565\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "43159\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44695\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46212\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47329\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "48481\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": "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": [
      "49997\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "51675\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "53405\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54773\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "56206\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "57563\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58740\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59764\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "60969\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "62258\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63407\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "64613\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "65634\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66617\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "67727\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "68862\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "70035\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71062\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71477\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "72117\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "72774\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73439\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74263\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "75215\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "75920\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "76532\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "76994\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "77543\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78177\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "78974\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79742\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "80786\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "81737\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "82695\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83373\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "84123\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "84713\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85254\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "85754\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "86569\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87008\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "87866\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "88389\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "89055\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89603\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "90286\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90855\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "91405\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91964\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "92475\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "92952\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93293\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93705\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "94227\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94626\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "95198\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95654\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "96039\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "96451\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": [
      "96825\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97282\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "97695\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": [
      "98053\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "98351\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "98684\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "99051\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "99890\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "100785\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101747\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "102796\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "103662\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "104550\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "105451\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "106258\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": "stdout",
     "output_type": "stream",
     "text": [
      "106991\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "107647\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "108107\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "108727\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "109374\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "110114\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "110944\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "111540\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "112064\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "112573\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "113061\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "113520\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113886\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "114319\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "114734\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115283\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "116161\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "116895\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "117518\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "118010\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "118499\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "119059\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "119692\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "120366\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121082\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "121666\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "122240\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "122695\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "123139\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123542\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "123856\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "124205\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "124646\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "125202\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "125810\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "126320\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "126905\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": [
      "127302\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "127661\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",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/decomposition/_pca.py:527: RuntimeWarning: invalid value encountered in true_divide\n",
      "  explained_variance_ratio_ = explained_variance_ / total_var\n",
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/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": [
      "128042\n",
      ".................................................\n"
     ]
    },
    {
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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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.191720</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.182375</td>\n",
       "      <td>0.951255</td>\n",
       "      <td>2.971174</td>\n",
       "      <td>0.263777</td>\n",
       "      <td>0.100066</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046995</td>\n",
       "      <td>12.486859</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.058255</td>\n",
       "      <td>0.012449</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>3.086518e-03</td>\n",
       "      <td>0.068215</td>\n",
       "      <td>0.026202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.450634</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.299142</td>\n",
       "      <td>0.663825</td>\n",
       "      <td>0.680385</td>\n",
       "      <td>1.279714</td>\n",
       "      <td>0.250318</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082236</td>\n",
       "      <td>2.514754</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.006242</td>\n",
       "      <td>0.026956</td>\n",
       "      <td>0.076686</td>\n",
       "      <td>0.025459</td>\n",
       "      <td>1.807716e-01</td>\n",
       "      <td>0.109060</td>\n",
       "      <td>0.049730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.123755</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.098491</td>\n",
       "      <td>0.795852</td>\n",
       "      <td>1.360571</td>\n",
       "      <td>0.814115</td>\n",
       "      <td>0.084115</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163706</td>\n",
       "      <td>1.245219</td>\n",
       "      <td>0.010843</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.020191</td>\n",
       "      <td>0.010946</td>\n",
       "      <td>5.893018e-02</td>\n",
       "      <td>0.032417</td>\n",
       "      <td>0.009245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.026527</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.279833</td>\n",
       "      <td>-0.945291</td>\n",
       "      <td>1.770039</td>\n",
       "      <td>0.029651</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106068</td>\n",
       "      <td>1.359534</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.017912</td>\n",
       "      <td>0.008027</td>\n",
       "      <td>0.002127</td>\n",
       "      <td>3.666942e-03</td>\n",
       "      <td>0.010847</td>\n",
       "      <td>0.001283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.069560</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>0.501994</td>\n",
       "      <td>0.007974</td>\n",
       "      <td>1.526264</td>\n",
       "      <td>0.055174</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098970</td>\n",
       "      <td>1.648263</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.023954</td>\n",
       "      <td>0.009854</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>2.236398e-02</td>\n",
       "      <td>0.018912</td>\n",
       "      <td>0.009059</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>128037</th>\n",
       "      <td>377</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.075780</td>\n",
       "      <td>0.590499</td>\n",
       "      <td>0.366030</td>\n",
       "      <td>1.466102</td>\n",
       "      <td>0.051011</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042147</td>\n",
       "      <td>11.524521</td>\n",
       "      <td>0.013869</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>0.007039</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>0.032047</td>\n",
       "      <td>5.027296e-03</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.006829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>378</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.126980</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.716923</td>\n",
       "      <td>0.636146</td>\n",
       "      <td>0.558675</td>\n",
       "      <td>1.369687</td>\n",
       "      <td>0.487330</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021921</td>\n",
       "      <td>13.807765</td>\n",
       "      <td>0.128915</td>\n",
       "      <td>0.324126</td>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.030898</td>\n",
       "      <td>0.329987</td>\n",
       "      <td>9.035076e-04</td>\n",
       "      <td>0.016441</td>\n",
       "      <td>0.098017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>379</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>2.374248e-07</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.000439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>380</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.062363</td>\n",
       "      <td>0.898025</td>\n",
       "      <td>2.175467</td>\n",
       "      <td>0.468172</td>\n",
       "      <td>0.048991</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124232</td>\n",
       "      <td>1.898480</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>0.033787</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>0.028677</td>\n",
       "      <td>1.301575e-07</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.008940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>381</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.116183</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.115965</td>\n",
       "      <td>0.998118</td>\n",
       "      <td>6.273389</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.066863</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098852</td>\n",
       "      <td>2.252959</td>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.043921</td>\n",
       "      <td>0.006482</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>1.666852e-03</td>\n",
       "      <td>0.031659</td>\n",
       "      <td>0.025177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128042 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1  intensity_mean-10  0.191720      D7  0.182375  0.951255   \n",
       "1           2   intensity_mean-3  0.450634     C12  0.299142  0.663825   \n",
       "2           3   intensity_mean-3  0.123755     C12  0.098491  0.795852   \n",
       "3           4  intensity_mean-10  0.026527      D7  0.007423  0.279833   \n",
       "4           5   intensity_mean-3  0.069560     C12  0.034919  0.501994   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "128037    377   intensity_mean-2  0.128333     C10  0.075780  0.590499   \n",
       "128038    378   intensity_mean-2  1.126980     C10  0.716923  0.636146   \n",
       "128039    379   intensity_mean-0  0.000000      A2  0.000000  0.000000   \n",
       "128040    380   intensity_mean-2  0.069445     C10  0.062363  0.898025   \n",
       "128041    381  intensity_mean-10  0.116183      D7  0.115965  0.998118   \n",
       "\n",
       "            LOGIT   ENTROPY    X_NORM    Gene  ...   PC3_var    PC1/PC2  \\\n",
       "0        2.971174  0.263777  0.100066    MLH1  ...  0.046995  12.486859   \n",
       "1        0.680385  1.279714  0.250318    HSF1  ...  0.082236   2.514754   \n",
       "2        1.360571  0.814115  0.084115    HSF1  ...  0.163706   1.245219   \n",
       "3       -0.945291  1.770039  0.029651    MLH1  ...  0.106068   1.359534   \n",
       "4        0.007974  1.526264  0.055174    HSF1  ...  0.098970   1.648263   \n",
       "...           ...       ...       ...     ...  ...       ...        ...   \n",
       "128037   0.366030  1.466102  0.051011    NCK1  ...  0.042147  11.524521   \n",
       "128038   0.558675  1.369687  0.487330    NCK1  ...  0.021921  13.807765   \n",
       "128039 -34.538776  3.091042  0.004005  PGGT1B  ...       NaN   1.000000   \n",
       "128040   2.175467  0.468172  0.048991    NCK1  ...  0.124232   1.898480   \n",
       "128041   6.273389  0.013692  0.066863    MLH1  ...  0.098852   2.252959   \n",
       "\n",
       "        intensity_mean-0  intensity_mean-1  intensity_mean-2  \\\n",
       "0               0.003344          0.058255          0.012449   \n",
       "1               0.000553          0.006242          0.026956   \n",
       "2               0.010843          0.006916          0.001170   \n",
       "3               0.001303          0.009310          0.017912   \n",
       "4               0.008465          0.023954          0.009854   \n",
       "...                  ...               ...               ...   \n",
       "128037          0.013869          0.031354          0.007039   \n",
       "128038          0.128915          0.324126          0.014652   \n",
       "128039          0.000510          0.000047          0.001783   \n",
       "128040          0.011701          0.033787          0.000291   \n",
       "128041          0.001968          0.043921          0.006482   \n",
       "\n",
       "        intensity_mean-3  intensity_mean-4  intensity_mean-5  \\\n",
       "0               0.038651          0.000044      3.086518e-03   \n",
       "1               0.076686          0.025459      1.807716e-01   \n",
       "2               0.020191          0.010946      5.893018e-02   \n",
       "3               0.008027          0.002127      3.666942e-03   \n",
       "4               0.009225          0.005172      2.236398e-02   \n",
       "...                  ...               ...               ...   \n",
       "128037          0.004849          0.032047      5.027296e-03   \n",
       "128038          0.030898          0.329987      9.035076e-04   \n",
       "128039          0.000949          0.000196      2.374248e-07   \n",
       "128040          0.002761          0.028677      1.301575e-07   \n",
       "128041          0.033927          0.002052      1.666852e-03   \n",
       "\n",
       "        intensity_mean-6     Delta  \n",
       "0               0.068215  0.026202  \n",
       "1               0.109060  0.049730  \n",
       "2               0.032417  0.009245  \n",
       "3               0.010847  0.001283  \n",
       "4               0.018912  0.009059  \n",
       "...                  ...       ...  \n",
       "128037          0.000053  0.006829  \n",
       "128038          0.016441  0.098017  \n",
       "128039          0.002034  0.000439  \n",
       "128040          0.002292  0.008940  \n",
       "128041          0.031659  0.025177  \n",
       "\n",
       "[128042 rows x 27 columns]"
      ]
     },
     "execution_count": 21,
     "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()+1e-15)\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": 22,
   "id": "f476c3c6",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.191720</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.182375</td>\n",
       "      <td>0.951255</td>\n",
       "      <td>2.971174</td>\n",
       "      <td>0.263777</td>\n",
       "      <td>0.100066</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046995</td>\n",
       "      <td>12.486859</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.058255</td>\n",
       "      <td>0.012449</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>3.086518e-03</td>\n",
       "      <td>0.068215</td>\n",
       "      <td>0.026202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.450634</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.299142</td>\n",
       "      <td>0.663825</td>\n",
       "      <td>0.680385</td>\n",
       "      <td>1.279714</td>\n",
       "      <td>0.250318</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082236</td>\n",
       "      <td>2.514754</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.006242</td>\n",
       "      <td>0.026956</td>\n",
       "      <td>0.076686</td>\n",
       "      <td>0.025459</td>\n",
       "      <td>1.807716e-01</td>\n",
       "      <td>0.109060</td>\n",
       "      <td>0.049730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.123755</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.098491</td>\n",
       "      <td>0.795852</td>\n",
       "      <td>1.360571</td>\n",
       "      <td>0.814115</td>\n",
       "      <td>0.084115</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163706</td>\n",
       "      <td>1.245219</td>\n",
       "      <td>0.010843</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.020191</td>\n",
       "      <td>0.010946</td>\n",
       "      <td>5.893018e-02</td>\n",
       "      <td>0.032417</td>\n",
       "      <td>0.009245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.026527</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.279833</td>\n",
       "      <td>-0.945291</td>\n",
       "      <td>1.770039</td>\n",
       "      <td>0.029651</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106068</td>\n",
       "      <td>1.359534</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.017912</td>\n",
       "      <td>0.008027</td>\n",
       "      <td>0.002127</td>\n",
       "      <td>3.666942e-03</td>\n",
       "      <td>0.010847</td>\n",
       "      <td>0.001283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.069560</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>0.501994</td>\n",
       "      <td>0.007974</td>\n",
       "      <td>1.526264</td>\n",
       "      <td>0.055174</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098970</td>\n",
       "      <td>1.648263</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.023954</td>\n",
       "      <td>0.009854</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>2.236398e-02</td>\n",
       "      <td>0.018912</td>\n",
       "      <td>0.009059</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>128037</th>\n",
       "      <td>377</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.075780</td>\n",
       "      <td>0.590499</td>\n",
       "      <td>0.366030</td>\n",
       "      <td>1.466102</td>\n",
       "      <td>0.051011</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042147</td>\n",
       "      <td>11.524521</td>\n",
       "      <td>0.013869</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>0.007039</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>0.032047</td>\n",
       "      <td>5.027296e-03</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.006829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>378</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.126980</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.716923</td>\n",
       "      <td>0.636146</td>\n",
       "      <td>0.558675</td>\n",
       "      <td>1.369687</td>\n",
       "      <td>0.487330</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021921</td>\n",
       "      <td>13.807765</td>\n",
       "      <td>0.128915</td>\n",
       "      <td>0.324126</td>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.030898</td>\n",
       "      <td>0.329987</td>\n",
       "      <td>9.035076e-04</td>\n",
       "      <td>0.016441</td>\n",
       "      <td>0.098017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>379</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>2.374248e-07</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.000439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>380</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.062363</td>\n",
       "      <td>0.898025</td>\n",
       "      <td>2.175467</td>\n",
       "      <td>0.468172</td>\n",
       "      <td>0.048991</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124232</td>\n",
       "      <td>1.898480</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>0.033787</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>0.028677</td>\n",
       "      <td>1.301575e-07</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.008940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>381</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.116183</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.115965</td>\n",
       "      <td>0.998118</td>\n",
       "      <td>6.273389</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.066863</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098852</td>\n",
       "      <td>2.252959</td>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.043921</td>\n",
       "      <td>0.006482</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>1.666852e-03</td>\n",
       "      <td>0.031659</td>\n",
       "      <td>0.025177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128042 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1  intensity_mean-10  0.191720      D7  0.182375  0.951255   \n",
       "1           2   intensity_mean-3  0.450634     C12  0.299142  0.663825   \n",
       "2           3   intensity_mean-3  0.123755     C12  0.098491  0.795852   \n",
       "3           4  intensity_mean-10  0.026527      D7  0.007423  0.279833   \n",
       "4           5   intensity_mean-3  0.069560     C12  0.034919  0.501994   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "128037    377   intensity_mean-2  0.128333     C10  0.075780  0.590499   \n",
       "128038    378   intensity_mean-2  1.126980     C10  0.716923  0.636146   \n",
       "128039    379   intensity_mean-0  0.000000      A2  0.000000  0.000000   \n",
       "128040    380   intensity_mean-2  0.069445     C10  0.062363  0.898025   \n",
       "128041    381  intensity_mean-10  0.116183      D7  0.115965  0.998118   \n",
       "\n",
       "            LOGIT   ENTROPY    X_NORM    Gene  ...   PC3_var    PC1/PC2  \\\n",
       "0        2.971174  0.263777  0.100066    MLH1  ...  0.046995  12.486859   \n",
       "1        0.680385  1.279714  0.250318    HSF1  ...  0.082236   2.514754   \n",
       "2        1.360571  0.814115  0.084115    HSF1  ...  0.163706   1.245219   \n",
       "3       -0.945291  1.770039  0.029651    MLH1  ...  0.106068   1.359534   \n",
       "4        0.007974  1.526264  0.055174    HSF1  ...  0.098970   1.648263   \n",
       "...           ...       ...       ...     ...  ...       ...        ...   \n",
       "128037   0.366030  1.466102  0.051011    NCK1  ...  0.042147  11.524521   \n",
       "128038   0.558675  1.369687  0.487330    NCK1  ...  0.021921  13.807765   \n",
       "128039 -34.538776  3.091042  0.004005  PGGT1B  ...       NaN   1.000000   \n",
       "128040   2.175467  0.468172  0.048991    NCK1  ...  0.124232   1.898480   \n",
       "128041   6.273389  0.013692  0.066863    MLH1  ...  0.098852   2.252959   \n",
       "\n",
       "        intensity_mean-0  intensity_mean-1  intensity_mean-2  \\\n",
       "0               0.003344          0.058255          0.012449   \n",
       "1               0.000553          0.006242          0.026956   \n",
       "2               0.010843          0.006916          0.001170   \n",
       "3               0.001303          0.009310          0.017912   \n",
       "4               0.008465          0.023954          0.009854   \n",
       "...                  ...               ...               ...   \n",
       "128037          0.013869          0.031354          0.007039   \n",
       "128038          0.128915          0.324126          0.014652   \n",
       "128039          0.000510          0.000047          0.001783   \n",
       "128040          0.011701          0.033787          0.000291   \n",
       "128041          0.001968          0.043921          0.006482   \n",
       "\n",
       "        intensity_mean-3  intensity_mean-4  intensity_mean-5  \\\n",
       "0               0.038651          0.000044      3.086518e-03   \n",
       "1               0.076686          0.025459      1.807716e-01   \n",
       "2               0.020191          0.010946      5.893018e-02   \n",
       "3               0.008027          0.002127      3.666942e-03   \n",
       "4               0.009225          0.005172      2.236398e-02   \n",
       "...                  ...               ...               ...   \n",
       "128037          0.004849          0.032047      5.027296e-03   \n",
       "128038          0.030898          0.329987      9.035076e-04   \n",
       "128039          0.000949          0.000196      2.374248e-07   \n",
       "128040          0.002761          0.028677      1.301575e-07   \n",
       "128041          0.033927          0.002052      1.666852e-03   \n",
       "\n",
       "        intensity_mean-6     Delta  \n",
       "0               0.068215  0.026202  \n",
       "1               0.109060  0.049730  \n",
       "2               0.032417  0.009245  \n",
       "3               0.010847  0.001283  \n",
       "4               0.018912  0.009059  \n",
       "...                  ...       ...  \n",
       "128037          0.000053  0.006829  \n",
       "128038          0.016441  0.098017  \n",
       "128039          0.002034  0.000439  \n",
       "128040          0.002292  0.008940  \n",
       "128041          0.031659  0.025177  \n",
       "\n",
       "[128042 rows x 27 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ebaaeb63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save soma_df\n",
    "soma_df.to_csv('Coverslip2_soma_df.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b270b8",
   "metadata": {},
   "source": [
    "## Visualize QC metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "188110f3",
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.191720</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.182375</td>\n",
       "      <td>0.951255</td>\n",
       "      <td>2.971174</td>\n",
       "      <td>0.263777</td>\n",
       "      <td>0.100066</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.046995</td>\n",
       "      <td>12.486859</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.058255</td>\n",
       "      <td>0.012449</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>3.086518e-03</td>\n",
       "      <td>0.068215</td>\n",
       "      <td>0.026202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.450634</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.299142</td>\n",
       "      <td>0.663825</td>\n",
       "      <td>0.680385</td>\n",
       "      <td>1.279714</td>\n",
       "      <td>0.250318</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082236</td>\n",
       "      <td>2.514754</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.006242</td>\n",
       "      <td>0.026956</td>\n",
       "      <td>0.076686</td>\n",
       "      <td>0.025459</td>\n",
       "      <td>1.807716e-01</td>\n",
       "      <td>0.109060</td>\n",
       "      <td>0.049730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.123755</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.098491</td>\n",
       "      <td>0.795852</td>\n",
       "      <td>1.360571</td>\n",
       "      <td>0.814115</td>\n",
       "      <td>0.084115</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.163706</td>\n",
       "      <td>1.245219</td>\n",
       "      <td>0.010843</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.020191</td>\n",
       "      <td>0.010946</td>\n",
       "      <td>5.893018e-02</td>\n",
       "      <td>0.032417</td>\n",
       "      <td>0.009245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.026527</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.279833</td>\n",
       "      <td>-0.945291</td>\n",
       "      <td>1.770039</td>\n",
       "      <td>0.029651</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.106068</td>\n",
       "      <td>1.359534</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.017912</td>\n",
       "      <td>0.008027</td>\n",
       "      <td>0.002127</td>\n",
       "      <td>3.666942e-03</td>\n",
       "      <td>0.010847</td>\n",
       "      <td>0.001283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.069560</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>0.501994</td>\n",
       "      <td>0.007974</td>\n",
       "      <td>1.526264</td>\n",
       "      <td>0.055174</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098970</td>\n",
       "      <td>1.648263</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.023954</td>\n",
       "      <td>0.009854</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>2.236398e-02</td>\n",
       "      <td>0.018912</td>\n",
       "      <td>0.009059</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>128037</th>\n",
       "      <td>377</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.075780</td>\n",
       "      <td>0.590499</td>\n",
       "      <td>0.366030</td>\n",
       "      <td>1.466102</td>\n",
       "      <td>0.051011</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042147</td>\n",
       "      <td>11.524521</td>\n",
       "      <td>0.013869</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>0.007039</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>0.032047</td>\n",
       "      <td>5.027296e-03</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.006829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>378</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.126980</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.716923</td>\n",
       "      <td>0.636146</td>\n",
       "      <td>0.558675</td>\n",
       "      <td>1.369687</td>\n",
       "      <td>0.487330</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021921</td>\n",
       "      <td>13.807765</td>\n",
       "      <td>0.128915</td>\n",
       "      <td>0.324126</td>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.030898</td>\n",
       "      <td>0.329987</td>\n",
       "      <td>9.035076e-04</td>\n",
       "      <td>0.016441</td>\n",
       "      <td>0.098017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>379</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>2.374248e-07</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.000439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>380</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.062363</td>\n",
       "      <td>0.898025</td>\n",
       "      <td>2.175467</td>\n",
       "      <td>0.468172</td>\n",
       "      <td>0.048991</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.124232</td>\n",
       "      <td>1.898480</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>0.033787</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>0.028677</td>\n",
       "      <td>1.301575e-07</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.008940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>381</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.116183</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.115965</td>\n",
       "      <td>0.998118</td>\n",
       "      <td>6.273389</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.066863</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.098852</td>\n",
       "      <td>2.252959</td>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.043921</td>\n",
       "      <td>0.006482</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>1.666852e-03</td>\n",
       "      <td>0.031659</td>\n",
       "      <td>0.025177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128042 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1  intensity_mean-10  0.191720      D7  0.182375  0.951255   \n",
       "1           2   intensity_mean-3  0.450634     C12  0.299142  0.663825   \n",
       "2           3   intensity_mean-3  0.123755     C12  0.098491  0.795852   \n",
       "3           4  intensity_mean-10  0.026527      D7  0.007423  0.279833   \n",
       "4           5   intensity_mean-3  0.069560     C12  0.034919  0.501994   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "128037    377   intensity_mean-2  0.128333     C10  0.075780  0.590499   \n",
       "128038    378   intensity_mean-2  1.126980     C10  0.716923  0.636146   \n",
       "128039    379   intensity_mean-0  0.000000      A2  0.000000  0.000000   \n",
       "128040    380   intensity_mean-2  0.069445     C10  0.062363  0.898025   \n",
       "128041    381  intensity_mean-10  0.116183      D7  0.115965  0.998118   \n",
       "\n",
       "            LOGIT   ENTROPY    X_NORM    Gene  ...   PC3_var    PC1/PC2  \\\n",
       "0        2.971174  0.263777  0.100066    MLH1  ...  0.046995  12.486859   \n",
       "1        0.680385  1.279714  0.250318    HSF1  ...  0.082236   2.514754   \n",
       "2        1.360571  0.814115  0.084115    HSF1  ...  0.163706   1.245219   \n",
       "3       -0.945291  1.770039  0.029651    MLH1  ...  0.106068   1.359534   \n",
       "4        0.007974  1.526264  0.055174    HSF1  ...  0.098970   1.648263   \n",
       "...           ...       ...       ...     ...  ...       ...        ...   \n",
       "128037   0.366030  1.466102  0.051011    NCK1  ...  0.042147  11.524521   \n",
       "128038   0.558675  1.369687  0.487330    NCK1  ...  0.021921  13.807765   \n",
       "128039 -34.538776  3.091042  0.004005  PGGT1B  ...       NaN   1.000000   \n",
       "128040   2.175467  0.468172  0.048991    NCK1  ...  0.124232   1.898480   \n",
       "128041   6.273389  0.013692  0.066863    MLH1  ...  0.098852   2.252959   \n",
       "\n",
       "        intensity_mean-0  intensity_mean-1  intensity_mean-2  \\\n",
       "0               0.003344          0.058255          0.012449   \n",
       "1               0.000553          0.006242          0.026956   \n",
       "2               0.010843          0.006916          0.001170   \n",
       "3               0.001303          0.009310          0.017912   \n",
       "4               0.008465          0.023954          0.009854   \n",
       "...                  ...               ...               ...   \n",
       "128037          0.013869          0.031354          0.007039   \n",
       "128038          0.128915          0.324126          0.014652   \n",
       "128039          0.000510          0.000047          0.001783   \n",
       "128040          0.011701          0.033787          0.000291   \n",
       "128041          0.001968          0.043921          0.006482   \n",
       "\n",
       "        intensity_mean-3  intensity_mean-4  intensity_mean-5  \\\n",
       "0               0.038651          0.000044      3.086518e-03   \n",
       "1               0.076686          0.025459      1.807716e-01   \n",
       "2               0.020191          0.010946      5.893018e-02   \n",
       "3               0.008027          0.002127      3.666942e-03   \n",
       "4               0.009225          0.005172      2.236398e-02   \n",
       "...                  ...               ...               ...   \n",
       "128037          0.004849          0.032047      5.027296e-03   \n",
       "128038          0.030898          0.329987      9.035076e-04   \n",
       "128039          0.000949          0.000196      2.374248e-07   \n",
       "128040          0.002761          0.028677      1.301575e-07   \n",
       "128041          0.033927          0.002052      1.666852e-03   \n",
       "\n",
       "        intensity_mean-6     Delta  \n",
       "0               0.068215  0.026202  \n",
       "1               0.109060  0.049730  \n",
       "2               0.032417  0.009245  \n",
       "3               0.010847  0.001283  \n",
       "4               0.018912  0.009059  \n",
       "...                  ...       ...  \n",
       "128037          0.000053  0.006829  \n",
       "128038          0.016441  0.098017  \n",
       "128039          0.002034  0.000439  \n",
       "128040          0.002292  0.008940  \n",
       "128041          0.031659  0.025177  \n",
       "\n",
       "[128042 rows x 27 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load soma_df\n",
    "soma_df = pd.read_csv('Coverslip2_soma_df.csv',sep=',',index_col=0)\n",
    "soma_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "076695bb",
   "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": [
      "(128042, 2)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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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-10</td>\n",
       "      <td>0.191720</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.182375</td>\n",
       "      <td>0.951255</td>\n",
       "      <td>2.971174</td>\n",
       "      <td>0.263777</td>\n",
       "      <td>0.100066</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003344</td>\n",
       "      <td>0.058255</td>\n",
       "      <td>0.012449</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>3.086518e-03</td>\n",
       "      <td>0.068215</td>\n",
       "      <td>0.026202</td>\n",
       "      <td>9.317850</td>\n",
       "      <td>4.936615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.450634</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.299142</td>\n",
       "      <td>0.663825</td>\n",
       "      <td>0.680385</td>\n",
       "      <td>1.279714</td>\n",
       "      <td>0.250318</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000553</td>\n",
       "      <td>0.006242</td>\n",
       "      <td>0.026956</td>\n",
       "      <td>0.076686</td>\n",
       "      <td>0.025459</td>\n",
       "      <td>1.807716e-01</td>\n",
       "      <td>0.109060</td>\n",
       "      <td>0.049730</td>\n",
       "      <td>9.702929</td>\n",
       "      <td>-1.126068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.123755</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.098491</td>\n",
       "      <td>0.795852</td>\n",
       "      <td>1.360571</td>\n",
       "      <td>0.814115</td>\n",
       "      <td>0.084115</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010843</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.020191</td>\n",
       "      <td>0.010946</td>\n",
       "      <td>5.893018e-02</td>\n",
       "      <td>0.032417</td>\n",
       "      <td>0.009245</td>\n",
       "      <td>7.678627</td>\n",
       "      <td>6.474439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.026527</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.279833</td>\n",
       "      <td>-0.945291</td>\n",
       "      <td>1.770039</td>\n",
       "      <td>0.029651</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.017912</td>\n",
       "      <td>0.008027</td>\n",
       "      <td>0.002127</td>\n",
       "      <td>3.666942e-03</td>\n",
       "      <td>0.010847</td>\n",
       "      <td>0.001283</td>\n",
       "      <td>10.278169</td>\n",
       "      <td>11.612705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.069560</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>0.501994</td>\n",
       "      <td>0.007974</td>\n",
       "      <td>1.526264</td>\n",
       "      <td>0.055174</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.023954</td>\n",
       "      <td>0.009854</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>2.236398e-02</td>\n",
       "      <td>0.018912</td>\n",
       "      <td>0.009059</td>\n",
       "      <td>9.708878</td>\n",
       "      <td>9.509619</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>128037</th>\n",
       "      <td>377</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.075780</td>\n",
       "      <td>0.590499</td>\n",
       "      <td>0.366030</td>\n",
       "      <td>1.466102</td>\n",
       "      <td>0.051011</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013869</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>0.007039</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>0.032047</td>\n",
       "      <td>5.027296e-03</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.006829</td>\n",
       "      <td>13.872565</td>\n",
       "      <td>8.909060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>378</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.126980</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.716923</td>\n",
       "      <td>0.636146</td>\n",
       "      <td>0.558675</td>\n",
       "      <td>1.369687</td>\n",
       "      <td>0.487330</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.128915</td>\n",
       "      <td>0.324126</td>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.030898</td>\n",
       "      <td>0.329987</td>\n",
       "      <td>9.035076e-04</td>\n",
       "      <td>0.016441</td>\n",
       "      <td>0.098017</td>\n",
       "      <td>4.925003</td>\n",
       "      <td>-2.935362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128039</th>\n",
       "      <td>379</td>\n",
       "      <td>intensity_mean-0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>A2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-34.538776</td>\n",
       "      <td>3.091042</td>\n",
       "      <td>0.004005</td>\n",
       "      <td>PGGT1B</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.000047</td>\n",
       "      <td>0.001783</td>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>2.374248e-07</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>0.000439</td>\n",
       "      <td>7.337516</td>\n",
       "      <td>17.292553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>380</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.062363</td>\n",
       "      <td>0.898025</td>\n",
       "      <td>2.175467</td>\n",
       "      <td>0.468172</td>\n",
       "      <td>0.048991</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011701</td>\n",
       "      <td>0.033787</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>0.028677</td>\n",
       "      <td>1.301575e-07</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.008940</td>\n",
       "      <td>14.045950</td>\n",
       "      <td>9.117386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>381</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.116183</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.115965</td>\n",
       "      <td>0.998118</td>\n",
       "      <td>6.273389</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.066863</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.043921</td>\n",
       "      <td>0.006482</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>1.666852e-03</td>\n",
       "      <td>0.031659</td>\n",
       "      <td>0.025177</td>\n",
       "      <td>10.418358</td>\n",
       "      <td>7.281187</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128042 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1  intensity_mean-10  0.191720      D7  0.182375  0.951255   \n",
       "1           2   intensity_mean-3  0.450634     C12  0.299142  0.663825   \n",
       "2           3   intensity_mean-3  0.123755     C12  0.098491  0.795852   \n",
       "3           4  intensity_mean-10  0.026527      D7  0.007423  0.279833   \n",
       "4           5   intensity_mean-3  0.069560     C12  0.034919  0.501994   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "128037    377   intensity_mean-2  0.128333     C10  0.075780  0.590499   \n",
       "128038    378   intensity_mean-2  1.126980     C10  0.716923  0.636146   \n",
       "128039    379   intensity_mean-0  0.000000      A2  0.000000  0.000000   \n",
       "128040    380   intensity_mean-2  0.069445     C10  0.062363  0.898025   \n",
       "128041    381  intensity_mean-10  0.116183      D7  0.115965  0.998118   \n",
       "\n",
       "            LOGIT   ENTROPY    X_NORM    Gene  ...  intensity_mean-0  \\\n",
       "0        2.971174  0.263777  0.100066    MLH1  ...          0.003344   \n",
       "1        0.680385  1.279714  0.250318    HSF1  ...          0.000553   \n",
       "2        1.360571  0.814115  0.084115    HSF1  ...          0.010843   \n",
       "3       -0.945291  1.770039  0.029651    MLH1  ...          0.001303   \n",
       "4        0.007974  1.526264  0.055174    HSF1  ...          0.008465   \n",
       "...           ...       ...       ...     ...  ...               ...   \n",
       "128037   0.366030  1.466102  0.051011    NCK1  ...          0.013869   \n",
       "128038   0.558675  1.369687  0.487330    NCK1  ...          0.128915   \n",
       "128039 -34.538776  3.091042  0.004005  PGGT1B  ...          0.000510   \n",
       "128040   2.175467  0.468172  0.048991    NCK1  ...          0.011701   \n",
       "128041   6.273389  0.013692  0.066863    MLH1  ...          0.001968   \n",
       "\n",
       "        intensity_mean-1  intensity_mean-2  intensity_mean-3  \\\n",
       "0               0.058255          0.012449          0.038651   \n",
       "1               0.006242          0.026956          0.076686   \n",
       "2               0.006916          0.001170          0.020191   \n",
       "3               0.009310          0.017912          0.008027   \n",
       "4               0.023954          0.009854          0.009225   \n",
       "...                  ...               ...               ...   \n",
       "128037          0.031354          0.007039          0.004849   \n",
       "128038          0.324126          0.014652          0.030898   \n",
       "128039          0.000047          0.001783          0.000949   \n",
       "128040          0.033787          0.000291          0.002761   \n",
       "128041          0.043921          0.006482          0.033927   \n",
       "\n",
       "        intensity_mean-4  intensity_mean-5  intensity_mean-6     Delta  \\\n",
       "0               0.000044      3.086518e-03          0.068215  0.026202   \n",
       "1               0.025459      1.807716e-01          0.109060  0.049730   \n",
       "2               0.010946      5.893018e-02          0.032417  0.009245   \n",
       "3               0.002127      3.666942e-03          0.010847  0.001283   \n",
       "4               0.005172      2.236398e-02          0.018912  0.009059   \n",
       "...                  ...               ...               ...       ...   \n",
       "128037          0.032047      5.027296e-03          0.000053  0.006829   \n",
       "128038          0.329987      9.035076e-04          0.016441  0.098017   \n",
       "128039          0.000196      2.374248e-07          0.002034  0.000439   \n",
       "128040          0.028677      1.301575e-07          0.002292  0.008940   \n",
       "128041          0.002052      1.666852e-03          0.031659  0.025177   \n",
       "\n",
       "        embedding1  embedding2  \n",
       "0         9.317850    4.936615  \n",
       "1         9.702929   -1.126068  \n",
       "2         7.678627    6.474439  \n",
       "3        10.278169   11.612705  \n",
       "4         9.708878    9.509619  \n",
       "...            ...         ...  \n",
       "128037   13.872565    8.909060  \n",
       "128038    4.925003   -2.935362  \n",
       "128039    7.337516   17.292553  \n",
       "128040   14.045950    9.117386  \n",
       "128041   10.418358    7.281187  \n",
       "\n",
       "[128042 rows x 29 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run UMAP\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": 9,
   "id": "d680c44b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1752392e0>"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "f152009d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Bfb5v55Jg4eet91kMGGWDUqFMor4GVVXn+3YuCXxt+swGxUIR07BI1NegKMp8384lga9Nn9kgn80jXYjXxvDyHfu8EHxd+swW2XQOTdOIxiPzfSuXBItBm77BaBEz1j/OEz94lkhtDMd2ePCrj3B8by833HsNt7/xRjZcu5pH/vtJ0mM59v3yEDe95lpufe31DHYOUy4YrNy6jFgiihbQUFQFXfe/DjORyEURV+qz8Og9NMBzv9hHJBHGsRx+8PkHGD0+wo2vupa73/FiWlc189h3nyY5OMHBpzp56W/cxrV3badnfx+O67Jq23IisRCqpqIFNN/IdBK+Nn0ulM7nutnz2EFq6mLYps13//FHpMezXP+yq3jrn74eTVd5/Ps7GD4+SveBPl733ntYf91aju7sQg/prLliBaFoECklwXDQNzKdhK9Nnwvl0I6jHHriKDUtNVhlk69/4n8wyza3vO4GfvXP3kApX+aJ7z9N9/4+Msk8r/v9l7PqipUceOIIicY4yzctJRgJAJJQODTfzVlQ+Lr0uVCklOx+aB/9hwaJ1UUpZIt8/ePfQ6iCl7/jxbz2vfeQGs3w8LefYLR3jNxEgbd/+A3UttZx6IkjNC9vom1VC5GasDduhoLz3aQFxWLRpm8hWKS4rssTP3yW0f5x6i2bx76/g4FDQ2iawv4njhKJRzj01FEOPXmEcCLKaP84D371MQaOjuCaFhMjWdZetRIBPPDNxzCyRbbdsYUPfOF3fe+kSSQ4C1/DPguMYq7Esz97jtRIlppyhPu//Cj5dBYpXA4+dYSGljpM2+TYzh4iNUGGu0f46ecf5PjeXsrpEtlkjk03riebzPP493dglCvceO/V/OFn3+Ovsk7ia9PnAkiPZtj7yAHSwxmMXIn7v/wIpXwJ6Uq6D/Xxs/96hGKuRNe+PgIhlaGuEb73Lz9hzdVHSI9mKWaKbLxxHWM94zzz013YtstL33E77/zYr8530xYOvjZ9LoCx/iR7HjpAPlOkVDT43y/8HKtkIjSFY7uP86PPP8D4QJKBzmEUVzLSN863P/UD1l+zmuHeCRyzwobr19Ozv4/dv9iLFPBrH3o9r3zP3fPdtIWBr0ufC6TvyCB7Hj6ApiuMD6b4+VcfxrEdhAPPPbyfUCxA9/4BxvrGUFWF4e5xvvDhb7Nq6zLG+8axKjbX3L2dPQ/vY88j+9G0AP/nX9/JjfdeO99NWxgsEm36BqNFRGY8R01DDEVRGO4aJRDSSTTWYhRMNt2wnmgshNB0VEVhYjRNIV2gVDJJJXO86JXXcnDHUUb7J7jjjdfTf2SYiZEUE2N5LAlqUGX/Y0f41Ls/z21vuJ4bXn7VZb9yKlkcboI+84vruhTSRWJ1UYQQjPUmCUUD1DTEqBQMtt+5lYEjAwhFEAgF6D7YTzgeJp/KU8qXeNGrr2f/44dIDaS48d5r6NzTx1DnKOMjSVzbAVx2/GQXn37fF7jhFVdz7cu2z3eT5x1fmz7ngm3ZGMXK1MpmcjCJFlSJN8YpZYtsuXUjE/0TqJqCHtToOdBPNBEhm8wQq4lx4yuuYf/jh8mMZLjits10H+hntHuM4ePDSAVs0+Ghbz2Ga0tuvPdqtrxo43w3ed7xtelzLpiGiWXaROJhbMsmOThBtDaCZVlUihW23LCBUq6IUAWKqtK1t4dQKEhqOE1zRyPX3HUl+584RKlgsP2OzXQ9183QsSHGBpIgFCrFCt//zE8Z7Utxx6/cxOorVsx3k+cVX5c+50q5aCClJBwNUTFM0uMZGtpqGe1Jogd1Nly3DkWTlLIGwUiQI88cJ1YbY6w/ycqty9h++xYO7zjKuitXsOnGDYz0jXP4qSOM9Y8jhcAoGfzXR/+bA08e45XvvouWZU3z3eR5ZbFo0zcYLRL6jw4x3DVKpWyy+6F9dO7uIRILEquL4jqSW19/A8n+FB1r2+g61E/v/l7aV7eydGMb+355lFK+jFVxmBhK8ZW/+g71TQkUXWcimaNuWSMN0RC7HtzLL776KEahzMRgmlhrHVfesJa65sR8N3+eEDinreDp4+PRva+XzFiOYr7Ew996nMxYlkAoQCgaRNNUbnrNdZRzRZqWNnL4mU6GOkfYdOM62te10buvl3KpDK5L39EhOv/qv0k0JtA0jfRYmvrWOmrqYzzzs+f4wafvp5gtMdI7TkN7PZuuX0ttk69NH59TIaXk6M4uSvkyE0MTPPnDnRQyRSSSRF2cQDzEVbdtZN/jR4jXxTnw1GGyY3muvnMbje0NZEYzFHIlXEdyfE8Ph3YcpbGjAdOwKOfLJGprqN9Yy46fPse3//b7ZJN5Onf3sGxTB6u3rSDRWDPfXTBP+Nr0OTOO7XD8uW5yqSJj/ePsenAvmbEcgZBOJB6mpinGppvX039gACkkXXt6yGfLXHf3FdQ311LMl8il8ghFZe+jB9j94D6aljZSzBQplw0SDTWsubKBp3+4k/5j91HKlVmyvpUN161hydp2aurj890F84CvS5+zUylX6N7XR3Y8R3Jwgmd+tpvUUIaGjnoc12XZxg42vWg9oz1jxBImvQcGKOZK3PTq64jX15CbKODYEjUY4PEfPYNjOyxfv4SxwQnKxTItS5upbYiz48e76Ts4gGPb1DUluO4VV9PUXkc0EZ3vLpgHFoc2fYPRAkdKya4H99J/eIhYXRSjZFApmCxd34GqK4RjIfY/dpjH/3cn5UKZ5MAEnTu7cW2brn393HDPVSxZ3cKxPT0YJROhQCAQwHYk2fEJ0iMZGtoStCxvIhgLYRYr7H/qOM89fphwbQ0BxcUyLD7+ww+xcuOS+e6Oi4oE3EXgJugzP9iWzTP3P8dY7zg1DXFyE3lUTaV9TRuariFxOfbscZ69/zkyY1kcx6Vrbw+arrHv0UPcdO+1FNpzHNvZiVGooGgqmq4jHYfhvjEKuTId61pJ1CVQggKnJNn94F6e/OEzRGqjCFeiBVU+9eDHaF7SMN/dcVHxtelzJkzD5Mkf7SQzliVeHyWfLqPoCkvXtSJ0jVKmyMDBAaSEwkSezGiaka5RkLDrwee46q7tGCWTY892YRoWkZoQoODYDsPdI9hli6tfvt0L39YAE3b+bBePfOdx6poSmBWLeG2ET/3ioyQaLi/Dka9NnzNRKpR55DtPIlyJHtIoFQxAsHzTEvSQznhvkp79QxQmiji2S3o0w8RwBqNksPvBvay/dj1de7rp3NuDrmqgCqTr4jg2g13DSCm4+fXXYuRNEAK3Inn6x8/wwNcKNC9pJJcq0LKsiU89+BeEIpdPniNflz5nIz2W4YnvP0MoGsK2bfLZIgLBxpvWoakqPfv7OPDkMRqaa7ArFqmxLLl0kXwqz75HDrFsQzv9R4foPzxIMBZEWg6aqlIulhjqHCIQDHLDS7YzOjgBgFmyeOibj5NPFXjk248x0ptk1RXL+eRP/vyyytu5WLTpG4wWKMmBJKVChcx4loe//QTSlZRLFbJjOUb7x0CCEApX3LkV23bY99ghGlvq6O8cxKxY2KZNMV1kpHcUB0m0oYZInYVrQn17gvGBFI7lYhQqHNlxHKPg4KgagcYazGIZJahSVx/BNC1GetP84//5Eh/71h94nhORyydh2WKw+vpcXIa7R3Ech75DAzz6308RrQmTSeYZG0iSHs6gKKDqOltu2YBRMNj9i33UtdQyeHwEKSE/kaNi2PR3DeMKlXgiTqw26lWDqY+RnchjViyMbIX9jx2hsaMBPRBEYFPIFtECOo3tdaRG0gx3jfMvv//v/N8v/B6hWIhA8PLJP+Zr0+dk+o8OgoTje3t44gfP0NhWz55Hxhg6PkoxX0TakmAkyPrr1lLIFtj70D7qWmvJJfO4rkspXSI1lqV9zQjBkIranEAiUQREYmHyuQKmYVHKGOx79ADR2hihQBA34JJLF9E1lZbljXQf7Kfn0CBf/NOv846PvoWa+hiqdvlMgH1t+pxMz4E+FE1l/+OH2fvwARo66ug7PMBQ5yiWaWGZNrVNCVZsWsqRnceZGEoSq4+RGc8iXEE2laeYLdG6vJVQNEgoEsSs2EQSEaR0KRcqVMomVtFm1/170UM6wUgAqQmyEwU0TaVjbRsTjxzg6K7jfOtT3+cVv3UXDW11l01uQF+XPqeic3cXwWiQnQ/s49jO49Q21XBk53HSoxmkhMPPdtK8rInmJfUcP9hHenCCSCxMNp1DUTUK40W6nF5C8RCRaJBYbZRStkjTmlbSozkqhoNZtihnKjz9013YjkswHkAIQT6VIxAO0L6mnd7Dw+x97DD3/+dDXHf3VTS2189311w0ZlubQogvAvcCY1LKLafZ53bgHwEdSEopbzvTOS/vJDULlGwyRzaZZ+j4EE/9YBebb97AnW+7DUURNC1pYO1Vq8hOFEiNZXjgvx4mO54nGNQY6hqhlCmjBVVUXSWaCOGYDis3LEEaJn37hug92M9o9zi58QJN7XXUt9fj2C7Hdh3HyBYR0sXIG+iaSiAUwK44XH3nFqyKycd/41958FtPMNQzPt9ddFGQeCI+15fPpU96NENuIk/Xnh72PnSAG155Nde94mqEIli6to1lmzqYGM2SHJ7gZ//5C4yyRSAaYqhnjHKujKIo6JEAsboYuC7rt6+iVCjTvb+f/kNDpIYzZEbzLFvfQU1LDKNYpndvP0ahghACs2h7BltFIFC44rYtTAxO8Kl3/Su/+MYvGa+u3Fzq+Nr0OZnkUIpitsiRZzrZ98vDvOTXbmfrbRvBlazetpyO1W1MDKUY6xvjF19/BMd20HWV4eMjlIsGWkBHDwepbYpjlizWbF9FPl2ge18fg8eGmRjNkhkpsGrrCqJ1npG4/8AQRrmCbdmYBYtYQ5xSwSQUCbHt5k107e3hn9/3Hzz030+QS+Xnu4suCr42fU5mfGCCQrbEvkcP0XtwgJf/9p2suXIlioAN16+jriVBaiRN/5FBHvn2E2i6inQFo91jWIaFQBCOhahriWOWTdZuX0V6PEfvwX4GDg9643Iyy7prVhOIBxgfTjF0ZJSKVcEsm1QKFRqX1jPSN068Psa2mzey64E9/Nsff5lffu9pSoXyfHfRnHO+uvS1eenjOA7JoRSFXJmdP9/LeN8Y977nLtrXtROK6Wy4fh1aQCWXzNGzr5en/3cXQV3HcRxGesYxDQchBKF4kER9HKRk9baVjPcmGTg6TNfefnKpPPlUgVVbl6OHNAZ7RxntGsexHYxcBbNos2zjEo7t7qZlWQNX3LKJn335Yf79A1/hmQeew6yY891Nc84cafPLwGkz/gshaoHPAK+SUm4G3ni2E/oeRguM3kMDVEoVmpc1EquLIRSVStGkc+dxipkSNU0Jbn/TTfQcHKCUN6hrSNC0opHkYBpF1agYFonGGsb6kziOpO/IENG6KN0H+lBDAZasamPDNasJx0Mc2tGJ0ASarqKGVPRomADgVCzi9TF6Dg0gLRtFgSUbl5IZSvHANx7n4e89w7s+9ibWXgZJBF3pD5o+Hsd2daFoCm2rWogmIsRqoyQH0wwdGyE3kaNjbRt3v/LFdO/rAyGJr2iiY207g8dHqamNUjEtahsTjHQP4zo2A0cHCddEGekdIxQOUd9ay3Wv2I5Vdtj76EGkhIAewBUOwVgYNIFj24SjIbr29CAkhGvCNHc0kB0v8MPP3c9j9+3g3X/9Npau75jv7ppzfG36gJegc+DoEIFQgKXrlxAIBWld1ULn7m4K6QLFfJnGpY1c9ZJtdO7uJpKIEI2FaV3VwmDnEKiCcrFCQ0stvakBZMWrwBSpjTDaM0o0HqN1VTMveu21jPaOc/jp4whVIRwJYCkO4VgYV3HJp0ogXHoO9oHjEk1EqWuqIZ/M852/+wE7fryL3/zYW2hd0TzfXTbn+Nr0AchN5JkYTuHYLhuvX4uqKazYspQ9D+8HAcVMheVb6tl4wzp6Dw5S315LMBSgeWkTg8dGEArYhkXzsgYyu3JUAg6Dx0fQohoj3WPUttTQsaaFa+/eztCxYQ4/00UgqKMpAkt3iCViGGUDs2RSKZmkx9K4DtQ11xKpCTExmuWbf/0/7PjxLt71N2+75HMb+br0mSQ9liU5kCQYCXLlbZtRBKzcsow9jxyglCtTzlfYfFMLy9e38/VP/A+tK1tRFEFDRy2DXWO4jkTVFRINcVJDE5RzZcYGk9iOw3jfOPVL6lm2roMr7txM38F+Dj55jFAshGpbWIpNTUOCYiYPQjAxlCY7nsV1JQ1LGlB0lYmRNF/8s6+zbvsq3vsvv4WmX9rmitnWppTyUSHEijPs8lbgu1LKvur+Y2c756X9P7DIkFJS0xAnvraVka4xcpkClVKFcCzEFbdt4dhzPSQHkpTzFbbdvImjz3UTjgTpPzQEQqIHVLITWcr5Msu3LCE7kqW2sYYDTxyhmC2jGhZjQiARjA5OYJUtzFIZy3QI10kiiQgr169h36P7GTw+hjRthKYy1J3ElgJhORRyZSrlMh/5tX/klW9/MW/+41fNd7fNGZNWXx8fKSWNHfVEasKkRjJMDKcp5Q0SzQmWb1nKwaePMtw1xlUvFmy9eSODx4cJ6jrH9/SiCAUtqJFLFUm7GZZvXEIxZxCOh9n78H4K6RKWYeMi2fnAPvqPDCFdF6NoYJsOQhGEAwprrljJvkcOMHh0ZOq+eg72Y5QquJZLIVOk52A/f/LKv+ItH3gd97zzJfPYY3OLr02fSYLhAI3t9QQjQXITeUZ7x5FIWlY0seXm9ex+aC+jXWNc/4qrWLt9JeViBats0rW3F1UVqJpKpVAhnyqwdH07ju2iKAq7frEHo2gCBcYHFJ68bwfH9/ehKAqlfBnXchEqRIIKq9Yv4/BTRxntnPbw6zvUi5FrxjQtipkS/ccGObKrk9/4yK9w+xtvnr8Om2N8bfpMEq2N4Loumq4xcHSY9HAGLaCy+spVxOuj7PjJboY6R7j6JVtYtqEdTdfJpfIMHBsGRxIIamSSWUoFg/ZVjQQiISplm+d+vh+zYlLMlhjuHecX33ycwWPDKEJQKlZwbQehKOgRnZaVzXTv6WGkc/p5qHNPDw1L6qkUTayySe/hYY48c4zf+ft3cNWdV8xjj80dvi59ZhKrjaBq3uJF175e8pkiwZDOmu2rcCybXQ/uY6QryY2vuoa21S1EE1GyE3mGuybAkei6Sno8R6wuTn1bPY0d9RTSBQ49eYSKYVNIF+k7NsD44DjDPWMIqVAqlZG2RAQ1YrVhGtsSdB/sZ/T4dNTKgcePkGhOYBsWplGh7+AAx/b08IefeRdrt6+ar+6aUy5Qm41CiGdnvP+8lPLz53H8OkAXQjwMxIF/klL+15kO8A1GC4Ch4yOUixWGj48QCAcIRgN0PtdH26pmjJKJHgpMeRY0LW2k78gI3Qf6KeXKNLR6VZKEplJM5kg0JijnDJL9ScySRTaZZdvNm9j3VCeVokE5W6SnUMa1XWzbRboSITyvokq6gFUqEYuFKafLgABN4FYsUgMToAnMsoOiSlLDOb77bz8nGA3x2t956fx24BwhEVjy8sk74fN8Bo4OUSmb9B4aoK6lBimh50A/bataKKQLNC1txCxWqKmPoagqex45QO/BAUzLpH5dPa4ARSiUCmUSTXFMo8LEcIZy0SCXzLH5RevY88tDWCWLUq5E175ezFIF15XeKCJAWhKrWKFSNojURakUM97NKYDplQqXLjhlByUgGB1I8l8f/RZ6OMhdb71lHntv7vC16dN3aIDsRJ7x/gkal9bhmJKu/b0sWdvKSM84a65cRS5ZoLa1nlg8zKPffpLkYAqhQPuqNgKpHLhQLhkkGuO4EtLjOco5g2htiE3Xr+PgU0dxLJd8qkA+XaSUK4PAyyGogLRAWi6VcoVwPEylaHk3J8AuS8aGJ7ANGxwQARjrGecLf/J1guEQN957zbz231zha/PyxnVdjj/XQyaZw7FsYrVRjJJJ76F+mpc2kB7MsmzjEox8mURzDaGYzs++/DBGwUTRLJatayeTzGMZJpZlk6iLY1YsKkWD8aEMNQ01rL96FYefPY5VtsmM5MiMZCjnKwhFIPGyx0rLRRECu2Ki6SqVyRtUwSpZpAZTWBUHKSWKpjJ4fJRPv+9LvO9ffosr7jhlyo9Fja9LH7NicnxPD0ahgmXZhMI6pazB2MA40USUQrrI8k1LyE/kiddHCYR1Hvr6LzErFqJg0L6qlXw6j1kwMEM6CSkppPPYpkPn3h6alzSwettSOvcOUClbmINpJBKzaIGKN6eVIMs2wXAA25aoqoqF7d2gALNokh/PYxQNb5zVoPdAP3/3zs/ywa+/jxUbLr3iSxeozaSU8oVMIjTgauBOIAw8KYR4Skp59EwH+Mwj+XSBXb/YRzQR5dH/eYLR3nFe8Vt30ba8kSM7jqNoCr2HBmhe3sj2O7fyxPd3EK0Js/G6dfQdHSSXLrJywxKuqia/To/mGBtIkhpKc+CpI1iWTXo8S2NrDUM9FRwFAgEdoUnCtREqxQqW7VJMSyplg4GjwwRDKoEgWMEQ0nRA2pjZEgQ00FUalzaS7B3HyJf56VceYeD4KO/91NvmuytnHX9F5vImPZZhx0920bCkgV9841EyEzle/Tt3U9ec4PAznWiKSu/BQdrXtXLli7ey84E9tK5oYtutmxjsHCI3nmXV1uVc//KrKRcNhnvGyI5m6e8c5vjuXkzLxCyb1DcnGB1IggRFhZq6GMF4mGK2hGs7lNJlijmD4a5xIvEwuWAOx3LB9e7TLjreHxokWmpJ96cpl0y+9df3MXB0kN/4yJvnrxPnCF+blzeDx0d49ud7aF7SyE//8yHMksnr/uAeauqiHHrqGCB57LtP0762lWvu3Mq+xw7T2F7PDfdezdFnu0mPpdlw/Qauv2c7+Yk8Xfv7KWVLDHeNcmjHMSplCwnUNidIDqdwXYmqKdS1JdA0jUKmgKIIimmDfDqP2qcRiodgIgcOM7RZnQgrEImGKKYNjLLJl/78GwwcHeKNf3Tpeej62ry8ObTjGL0HBojXRbnvX/8XRVV57XvvJRKP0PVcL6506T80yOorVnLFrZs5/Ewny9a2U99Wy97HDpGZKLD5pvVc9ZKtZMayHNnZjVOx6Ds6yJFnurDMCkJAbXMNqZE0EtCDQWIhjXAwSLFQBgmldJnUcAojVyHeGKOUN5BSevoE7yEWEGGVgK5h5A2MUoXP/t8v88r3vIxX/NZd89eJc4CvS5+9jx4kM54F1+UHn3uAaE2YV/3O3QQjYfoPDeJISc+hPlZvWcna7aspZDwDUn1rHQeeOkIpX+LqO7ex4ca15JN59j16GISg91Avx3b1YpsOihagpj5KdiKHquroYY1ASCccj1BI5RFCpZQpMdg1ghbQiNfHMApVc2513DQKBgBKWEFXNSpFk2wmy9+8/Z/51T97Ay965XXz1INzwzxpcwCYkFIWgaIQ4lHgCsA3GC1EKuUKA8eGue7l29n/xGGKuTKO5ZDPFKltTiAE9Ozvo3t/L7e+9kY2XreWjtXtXHH7RlRF0Lm7l92P7Gd8MMVYf5qBowNIYO1Vq6ltrCGbzDPSPUa5bJEeyXoiNB2cuEOiKU56KAOawMqbIEGr0UgNTdDQkgApaGpLkEmXscsGTslEIABBeigNEgzLZqh3jEy+hF4X4z1/+tp57tHZRuBIPy/85UghU2RiKM2tb7iRHT99jlKujGNKkkMZVmzowDJMOg8N0X+4n7t/48WsefE2RnrHufm1N1DIFDm04xhHnj7GWN8E40MpDj99DD2os+7qVUTrI5TSBVJjWZKDXhJtu2BjYxOI6YRjYSb6J0CR2GVvBFWDKumxLHWNcRRNpa6tjonRDFgu0q7W43Qhl8yBUChlSozLcX7yHw8Sr43xhj+4d/46c07wtXm5kpvIU8qVuOW11/Pzrz2KWTZwHElyYIL69joK2SL9BwYYG07zxvX3smzDEjLjBV706mtIj2WJJqIcePIoo92jDB8fYfdDB2jqqGfNVSsI14TIjOfIpXKkhrPkknnsooONQ6QujCoUxocmUITANT3dqYpKLpUjFo8RCGrEa2OkRjNIG5gslVv1ZALIDGexSibf+acf0biknjvedKmFp/navFxJj2UJhHS23rKBh775OBXDIhhWGO0bI5qIUMoXOfZcD/lkgc0vWk/z8hUYRZPb33QjY31JFFWje18vfUeGSDTFObqzmyXr2mlY3044HiIzlqOUL5MazZEbL2AVHSwc1PoAmq4wPpxCVVUcw7MKCUWlWCgjcQlEAoQiIfLpPO6MBRdpuhhuBVTFS5yfK/LNT/4P7Stb2X7n1nnszdnG1+XlzMRwmrrmBHVNNTz2/R1UDJNIIkI6mUVVVMolgwOPH8GyHbbfvpVNdWvpOzzI7W+6id7DgyiaSt/hAbr29iAUwUDnMC3LmqlvSRCvDXlegWWL7FiWXHpam5oeQwLJwSSqULErnnHIrjg4pgOuJBQLoYd1ihN5XHv6nl3DpaJ4z6fpkRzlgsm/f+CrtK1sYdWW5fPTkXPCvGjz+8CnhRAaEACuB/7hTAf4BqN5JBAKYJsOqeEM0XiEd370LfQdHSabzHHg8SMEowG2vGg9Kzcu5aqXbmHd9lWsq8ZwFrMlitkyiE387Ku/5L7P/pjN168nnykSioc59uxxGlprWbF5OZmJLAFdxbZsKhUL13HJjOdxLAcqCmo4gCsES9e00X90gORojqVr2pkYSROLhTCDGkVZwBUC4UrsfAURDKCoKmpAo1K2ePA/HyIRDfDm991zyZQolYDrFxK8LAlFg5QLBqZh0dhexzs//qv0HRokl8qz//EjaKrG1XdtYc22pdz82utZtn4JG69bC0AuladcKKOoKg985SG++cnvcfVd2+g/NEgkGuDQzm7aV7ez9upVpEdzBI6pVComriWxTYeJ4TRUI1vQBUjB8vVL6D7YRzaVp2VpM+nxLHV1Mcplk3KuPOXV4JS9ibKi66iahlEy+M4//JBASOdV73nZ/HTmHOBr8/IlGAlQzJawKhZrr1rFphvX03fQC9E+9uxxIrEwN7z6GgqZElffdQUty5vYfNMGAGoa4pRzZRzH5aGv/5Kug31ccctmjj7TiR4NsPeRg6y9cgVqQGN8YALpSsbsUVwHjFKFcqEMFrhIUEFVFdo2tNG3fwDDKFPf0kAulaOutZZiukSlMF3hxa0uoioBL7dKIZXni3/6DVRV4dbX3zQfXTkn+Nq8nJFUihUc02HrLZvYeN0ahrvGyKYKJAcmiNUlvFBMRbDxhvU0ttdzxW2bAQhFgqSGM6i6yoNfe5ShzmFWXbGC/Y8fZvW2pRx8qpMr79iC67gMHRvBth2kTGObNpVSiaLtgg1SeNrUgxotSxvoOzKEE1GJJaIYRYOmJY1MDKSwK9UnU6fqdaQJFE0hHIswPpTls3/8Jd7zqbdfMjmNfF1e3timjeO4VEom227bwrqrV5McSDHaPY5tOTS213Pz668nGAmy7po11DbWcMMrvIgnRVEY6RpF11Ue+NqjDHaNsGLTco4+20nL8ia69vdy073XUsoV6T00gG3bpMhiWTZGqYhVroaJBr17CcUCJFpqGO1JEooFCUaDOLZNw5ImUoMTngc9TLregC5QgGg8wkj3GJ9+33/wmx97K1tu2njR+3EumAttCiG+AdyOl+toAPgLQAeQUn5OSnlICPFTYC+e+fw/pJT7z3RO32A0D7iuS8/+PoxShUwyS2NbPbVNNay+YgUda9sYODrEMz/bQ8vyRupaanFdl649fSQaaqhvrSOfLuA6LoVckZHuMfoOD3oZ5BWwKhbPPbyfjpWthOJhcpk8y9a14VoOrgvlfJl8ukg+VQRFEIzrRONhnIrDeP84juGC7TI8PIFAEIvqxINxlq1upetgH+VMGVQFLawhXRfhQvvqVlKD43zr737EUz/ZzT/d/2fz3cWzhu/Ce3lRypcZ60+STxXIjGdoXdFCoinOuqvX0LGmjd5DA+x79CCNS+ppXd5EZbXDc784QCCoE6mJYBQrlHJljKJB195uhrtGCUVC2I6NZZrs/sUBWlc2Yds2pumwZF07ruPgSEGlVCKXKlAYL4ECgWiAeCKGY9sMdQ4iTYll2owPjCMUhUhLgnBNlNrNyzi6uwur6mKvR3Vcx0VVFBpWtFDMFvn3D32Fp/53Fx//4YfmuYdnD1+blxf5dIGJ4RS5VIHsRI4l69oJBL0knR2rW+ne38/RXUdpXtpM89JGjJLJUz9+lptfcwOqpmBbDpnRDIVska69fYz2J6ltSlAulbEsh2d/vIvWFa0UsmXiTXGWrm9H4lWCKecrZJIZjGwFNAgGdWqbajHLZUaOj4ANRtYk6STRFI14IkowFKSpvZ69jx9iMkVDIK5jVWw0TaN9VTulQpFPvuPTPPOzvbz/394zvx08i/javLzIJnOkRzNkJ/JkxrMsWdNGKBpkxeZljPaOc3TncfqPDdG2soW61gTFTImnfvQst77hRmzTRigK4wMTGKUyx57tJDWcoWlZA6VcCbNssP+xozQva2BiMEVtU4KVW5aCIghHg2SSeQqpArZhogQVAhGduoZaSrkiI71JsKGQLGPGbVShUlMXQyBoXdbEc4/unwpRC4R1XMslEArQsqqJYtbgT1/1CX7zo2/hje9/9fx28Czh6/LyIz2aIZPMkRnLkk8XaV3RTG0syJK1mxntHWPfY0dIjaRpX91CtDZGZjzHU//7LLe85npK+TLBcJDhnjFUVWXvowfJpXKEoyHKhTLlskHn7i4al9Qz1DlMY0cja65cjRAK4USM1GCaQqEA2CghhVBIJ76kgVK+THIgDQ5kh/OEEyGEgMb2BNKxWbZxCbt+tm+qDcGQjmO7BEI6De31DB0b5YN3f4w//dofcuMrr52/zp1FZlubUsq3nMM+fwv87bme0zcYXWRsy2a0N0msNsr4UIpStkxfboBgJETryhZGesboPTyIHtKpGDa/+Pov2XTjerbduolyvkLSmUBRVQY7h+nZ149RMlm5ZRlmyWTw6BAuCopQqGuv4+kf7cRBsvOBPRQzZSK1QTZdv57ug/3kMgWwoTJeoJIuE6wJU9uSQCgKdsWiUjCRlstIusiaa9ZS01RDpCFOuWKDYWGbDqoeoJQ3qJTK6NEQq65YSW1rLT1Hh1mxrm2+u/oFI6Xvwns5YZQqZMazROJh+g4NeEaffb2EoyGWbVxK/5FBBo4Oood1yvkKP/nyQ1z/8qvZfNN68uki5WIFRRH0Hxnk2O4etIDGys3LMCoW3Xt7EVJBDaiEa6Ps+tkeFEVQLpuUsiVqGmrYfNMaDj5xHCiBC2beZCKfIpwIE2tKoAUMzIpJpWSBCwMHh1h/w1qCsRCx2hjpYhoAy3ARuiA7kaeuNYHQVa64dRNNSxoZ7h6lbWXL/Hb0LOBr8/KilC+TmyigqCq5ZB7TsDj27HGidXFWX7mSY891kx3LoekB0qM59jx0kJtecx3rrlpNdiyLoqu4jstw9yg9+/uI1UZYuWU5ZrnC0JFRpHDRFJVARGfXz/eg6zqlokExU6RpaQNrtq1g32MFDCreuGlbjBbHidZHidVEsCMupWwZu+Rguw69+we54o6NuMLzasqN5gEwizYISWokTW1zLZQVrnrxVjTVq7wWiYfnt6NnAV+blxeFTJFitoSUnk6LmTJHdnbTvKSO1pUtHN11nFLBQNNVBo8NsfvBvdzy+htZuXU5qZEMiqJglir0Hxli6PgoDR2NLN/cQaVskh7LYJUs9GgQLaDy7M+eI14bIz2Wo1Qos3RdB6u2LufALw8D4FZcjEqF4fQosaYYkZowoViIQqboac+xOPZsF9feexWFTIFYXZRCsgiAaXrjampwgqWbllASFa556ZXk0sX57N5Zw9fl5UduIk+5YOA6LqZhkhqaoJQr0bGmlXhdjJ4D/ZhGBYSg++AAo11j3P3OO1EUwfhQClUoTAxn6D8yyHDXKEs3LaFY8oompUYylAol6poT4MLOh/eRaEkw2j2K60rWbF/Bsk3tHH76GOCFlxUNg2LeoKYhTiwRwXEcioUy5ZyX4PrQ051cf+9VZEayhOtClNNeGLdlWrimJNk3TtvadhCwcutyBjtHztT8RcNi0ebCv8NLjJ4D/Rx48jCBcBBVUxnuGqWQKnr5hnrH6D3Yj1GssPWm9dS31HDHm19EMBygob2OmsYYI71jaLrKwJEhmjrqaV3ZxEvfdiuNS+voOzpM/+EBmpc1EImGCUdC5JI5MkMZrKJBQ3Mdd7/9dqLxCIqqIIIKqBpKNIBVssmO53AcSTAaQbouSAlCpXNXN889coBgSEeNBCGgIy0bu2IgbYfRzhHqOxo5+GwnAwcH+NG3H+Ozf/5tjLJ59g5Z4LiIc375LF5c1+X4nh72P3aImoY4QgiGusbIJnNkxnMMHx+h5+AASIVtt2yieXkTt7z2OrSARmNHPeFYiNRwBsu0GewcoXV5Eys2LuGlb7+DeG2E3kODDPWM0bqilXhthGhNmOx4jsJ4Add06VjZzEt+9Q5CEQ01KKqOo4AK5WyZwkQBy7YJhoPTeVGAI08d4+jTnWghDaGrXgUnx0EaNtJyGe9PkqiPs//xo/Qc6OG+T/+YL/zZ13EcZz66eVbxtXl5YFZMDj9zjCPPHqN1eRPFXJG+Q0MUC16lwcHOYcb7JhAKbLt5I22rmrnu5dtRVUHrima0gEpmLEtuIs/EcJqG9gbWXLWSW994A8FogO5DfYz3J2ld00KiIU4oEmZiJEUhWUTasHxTB7f9yi1o4QBKcMZ3SYdiqkghY2CZNqFIYCovCsCehw4xeGQITVOm9ex64S+uKRkfShKNhtn/2EEGOof54oe/ztc++T9eYt5Fjq/Ny4NCtsihJ4/Se3iAmvoY+Yk8w8dHMCsV8pki4wNJxvqSaJrKpuvXsXLzcra/eCuaptKyvAnHtMmn8qRGM5RyJeqaa9l80xpuetW1BMM6XXv6yExkaV/ZTKKxlmAoyGDnKKVMGVzByq3LuPNXbyEQ1lACM7UpKCSLlHIGtmETDOte6FmVZ360i2T/BKqigF59DKq4YLlUKha5VBHFEex9/CjdB/v41z/4Ij/4zE8ucu/OPuejS1+bi5tsMsuhp48x3D1KOBokOTBBeixDpVShkC2RGc/StbefhtY6Nl67mqVr27n6ritAQkNbHWbZolgoM96XxLVdEi0JrrhtEzfcczWKEBzb3UWpYHgeS2316AGNwcND2GUH13XZdssWXvyW21A0BRE48buUG8tjlE1syyUcmTGndeDp7+9iYjiNpmpTLi1uxasabBQtTMOkkClw+NmjHNnZyb+89z944OuPXNzOnQMWgzZ9D6OLSCFTZKR7lBe9+jqiNRHidVGGu0dR1DZuun0L6fEMyzd0cGRXF8mRDNvv2MzBJ48gFMGTP9rJ8g0djPYmcSyH23/lJvY/dpjO3T3c8SsvIj9RZN0VK5EqbLphPaM947hIGlrryahZyrbDYO8Y933mfgIhnbXbV9N3bAg75OJYFkpYIISCZdoIVYDrAhIR0JCWg1GsMNI5gqw4oAmErqFHgth5rwS4qkjsskXnnm46Dw1gFyz6u0b5q6/+/qLNaeSFz/o21cuB9GiW7HiW29/0IgKhAKFYiKHOEdZcuZzNN65jYiTDmitWcnRXJ9lkgfXXruLA40dxXYfH7tvBuqtWMnB0GJDc9eu38dSPnqXv8CC3vPFGKkWTjdetRQvqbLh+Fcd39yCBmuYE+Yk8FcOka38PP/j8/dQ01xCKhhnsHkUGwDYrqEEVicS1XUyYGlyFKpCOpJApUphcBRWAoqBHFKyi7RmAQzqmYXJkZxdHnu3GqjiMD6T54Jd/b176ejbwtXn5MDHo5Sm57Q03IRRBNB5h+PgwLcsa2HDtaoaOj7B2+0oOPH0EwzBZuXUp3Xv6ySZz7Lh/Nyu3LmP4+AgtK5q55Q038rMvPUxxOMX6a9ZglS223ryBQCjIuqtXcfDxo+hBldrmWrITOeySw+Edx3FtScvSBiKxoJf/xHGQjosSUREqmIbFCcOcQtVbIfu89ugRFavk4FoOSlCnUrY4+ORR9j5yAOlKyrkyv/XxX7to/Tvb+Nq8fEgOptBCGlfdsm3K0Dnan2TphnaWrOtguGuUDdevZc9D+1ixZQmx2ih9hwZJDaVIj2XoWN1K76FBGtvruPUNN3Lfv/yEStkgEAygCJWtt2wkENJZs30lex86QDAWoq6thuxEEbficPDJI+RSedrXthMaTpMey+DYDhIFoYOiCizLOuW9j/dOnLhBFahBHdd2cGwLNR7CKJbZ84uDPFN+DiEkQlV55btfOtfdOif4ury8GB9IEU1E2Hj9WmzLxpUw1p9i+YZlNLTXMdI9xuYb13JwxzFue+ONROJhBo4NM9I3ztjAOG0rWjjyTCcrNi/lyju38M1PfA8BSEsSqYmw6fq1NLQ3sHzDEp780U5qmmqwHYdCpoC04ZfffZz21W2s2baS0YEkmbEMriORrkQEBUKROKaNdQq/gmRf6sQNKp61wgLLsgkGQ0wMpnjyRzupFCv89D8fIhgKcOvrbrwIPTv7LBZt+gaji0isNsrNr71h6r3rSF7/h/eSnygSr4uSaIyjagq9R4ZJDqY4trMLVVVZf+1qJgYzdKxrY/21a8inCwTDAW59442svGI5y9Z38PYPvwlFg0M7jtN3aJAju3vo6x6jbWkDq7atoL8viVMycW2XngN9IBQaljQQiYcYOp4kENYoporYloWVL4GmIoSCW/YmwlIRYDjepMBVEKrmJR20bUQkjK5ohGtj5DJFWpY2YWRLPPfAXn7y1V9yz9tuncdefyEsDjdBnxdOQ1sdN73KK9VpmRaBoM5bPvQa0iNZahpqiNXFUVWF3oN9DB0bRg9oROIhWlc2M9qbZOXW5ay9ajXZZI5oIsLL3nEHW2/exLINHYi/lEhVcGxnF527uzm04zhDncN0rGmlZftKeg/1I11BpWBw/LleQtEArUsbEbpgrHeCUCRIPl3wknTOKCEhJ1dMZzokSEAR2La3IuON8IJgJEgxZ7BkXQsTgyke/e6TvPLdd7L5xg0Xq4tnGV+blwttq1poW+WFUebTBerb6nnLh15HPl2gprGGaCKKpmuE9vbQd6Afy7CIJMK0rm6hnC+zbvtq1m1fTXo0Q6Kphtf+n5czMZyhfVULsUQE27LpOTjA0WePc2THMcaH0yzfuIS21S107uoBJNlkgeP7uqlrTtC0tAnHsUmNZAhFQuRSWVyLE3XonqIhVaxKtYITgoCqEggFKJcNVmxaxsDRQX70bz/j7t+4kyVrF2tYt6/Ny4UVm5ayYtNSADLjWZauX8KKLcuolEzqmhPEaz1tRmujHH6qk6alDUTrosTrmpEurL9mNeuvXeuFaDYleP0f3YtRrFDbnGDN9pUUsyWGukc5+ORRDu04SjFTZOW2lSxZ187hp48jpSQ9kqbv8DAty5poWdaMY9mkRzNoQc2rPGycxZtWCM+bHnAsGywXDQVNVdFDOmbFYuUVy+ne18NXPvZtbnvTTdTUxea6a+cAX5eXE2uuXDn1dzaZY+2VK1l71Ups06GhtZ5YIkowHODIri72PnKQRGOcupZagtEAeiDA6m3LWX/tGtKjGeK1MV7/R/eiKAqBcIBrn9vOWO8YyZE0+x4/TOfubiplgw3XrMWRNl37BhCojHSNMdo7TsOSetqWN2OZDpmJHAhBpWjgVs4wUJ6AAqYLEnRNQ9FA1TUc22H19lV07e3i3z/wVW561bVo2mI0aywObS7Gnl20jPUnaVrSwFhfEtMwCcfC7PjJThJNNVz9kunynS95680UsyWC4QCBUACAZeuXTH0enzFYLd/gbb/itk0ArNyygn2PHSI5nMYsGGRGM4QjARTHwbRtDu7qwjJshAPZiRzSdtFCCpnRHNJykKoXhiYmv7yK8MLTrKqxSFFBVXFNE5AITcGVnveUXbEIKGCZNuVyBduRfOFj38EsV3jNu+6a496dffyqEpcHruuSGk5T3+atulimTX1bLb/87tO0r2qmacm2qX3vetvtlIsGgVCAQNCLM1m6rmPq80RjzdTfyzZ427ff6R2/avMy9j58gOHOUexljaRHswTCXrVBy6pwZGcnjuFSsGxUTSVaE0HVBOnhzIkPo2dDOkgDEGBVTKySgWu5hKLe5NcwbdyyyZ+//m/5o8+9m5urhrLFhK/NywPLtMinCsTqYiQHJnBdl0g8yBPff5YVW5fS1NEwte9L337HlLFXD+jPO1ddSy0AoUiIjtWtwLQ2V2xdTigaZODYCFIVJIdTBCI6mq5iWw7H9/YgbUiNZHEsCMd1NFUhM/J8D6IzIphKslsuVbAsC8d1CUaC5LNFrIqNVbb5ozv+nD//9h+x9aZN591n842vzcuDSrlCuWAQjARJj2ZwHZdgROeJ+55h003raGyvn9r3ZW9/MY5tE4qEUDX1eeeqb60DIF4bI17rzW+vvMObE4/2jpNPlxjrT5IcnGCs1wsfDQQ1irkiEyNeVdHR3lEa2moRiobQBbmxwrk1ZDIEVEqwJKiCXL5IMBFEQRAMB8hO5HElpIezvPvq9/P3D32UtuWLKxegr8vLh3KhjGXaaLpGZjyHdBykkDx137Pc+JpraWirm9r33t++C5CEoiEU5cTvhxBiSpv1LdPHbL15I9y8kd7DA4z3T7B88xKGjo8w1DNGY0cdmq6RGstglkxwIDkwQV1LHVLaCCkopUvn1yCnalhSIJcqkGiMeYWeJOQmMkgLRo6P8c7Nf8jndv8t4Ujogvptvlgs2lz4d3iJIKXk8DPHOPj0UQ4+dZShrlFs0yJeG8MyTA7vOIZZ8VxnFUUhXhebMhadD7FEhBtfcTXv+sSv8s5PvBmkpPfQAJWCQXNHPXbFAtNBOg75sTzp8SzSBde2vWdSRyACKuigxYII10EENZBeiJq3swtCARTCTQls16W/axTDcAiEQ6QGkljFCooUFMaL/Mf/+x7p8fxsdudFw5HinF8+i5NSvszeXx7kyLOdHN11nPGBJLZlowc0bMul87nuqX1VzSvPO2ksOh9qmxLc+sabeO9nfps3f+C1WJZNz6F+XMumrrUBp+ytoGBDdiTvDfSIE41FAlAEQlOm35+M620P1YawKw793aOUTQs1oDHaNQ6WC1KQH8nx9+/+t0Wbz8jX5qVPZjTLwaeOcuSZTrr29zI+kPJCoDUFs2zSe2hgat9AUCcaj5zSWHQ2mjoauPddL+UPPvPbvPgtN2OWLbr29iJUQawmgrTktDZHs+TTZRz3+VZcoYlTa3KS6iHBmgCWYTHcPUKlbKJpKsmeJEL1dJ0eyvLXv/6v592OhYKvzUufiaE0B544wtGdXfQe7GesLwkIpCIoF036jw5N7RuKBInWRE9pLDobLcubeMdH3sTv/eNvcu3LrqRULNK56zhaOEAoHIRqxJm0ITmQwTIsLMN+3nnU8FnWx11AAT2iUc4bjPelMAwLVddI9nvFZhCCZE+Kj/3KP5x3OxYC56NLX5uLl/H+CQ48cZijO4/Te6if0f4JgqEgQhOU0kWGjk8ni47Ew0TikecZi86F5RuW8O6/+XXe/bdvZ+O1a8ilc3Tv7SOaCKMHtKnFEWlBaiANUsEoG887z1m1CSAgGA9SyBWZGExTLhoIRTDem0EJevc+dGyE//fmvzvvdiwEFoM2fYPRRUIIwfKNS0gPp1m6rp3rX34V8boYtS01rL5iFa0rmy/oIfR0rNqyjFtfcwPrrlmD47pUTItCtuR5JylVF1wpWX/NWrbcsIZEcwJcB2wHBDQuayYcCRCtj1LXFAdVm35wFQJhWigBjYphQrmCUTQIJ0KUyiVcw0S6EK4PI5GYJZv7vvTQrLXtYiEROCjn/PJZnERrInSsbmViKMPyjUu45q4rCQQDdKxuZdmmDho76s9+kvNgzRUrufe372L5+g6kIzFMC6NoEKk/cVVk221b2HrjRkKJGdsVaFxRT6QuSqQuTE1LDadCBFSsig0OlLJlYjVhCtkiOC6KLgjVBAHIj+X5X1+bPguUutZaGtvrySbzrNi4hO13bEEP6LStbmP55mXUNp36+38hCCFYf80a3vbnb6JlaQOO7VIpeQmtg7EZY7MK21+yhQ3XriUUPXF745J6wnVhYk0RovWRU19IBem4njZzBrF4hELGy0GmBRX06jlHu8bYcf/uWWvfxcLX5uVBQ3sd9a21ZMezrL5yJVfduQ1FVehY3cayjR2zqk1FUdh0/Tp+/S9+hfqWOlxXYuRLCKGihae/Q4qusP2l21h39Vr0yLQ21bBKXWst4dowscYokYboKa8jNAXhApakUjaJRoIU0964qQdUtLC3iHtsx3F6DvXPWvsuBuerS1+bi5empQ3EElEKqSKbbljPVS/eiuu4LFnbQce6NhKzqE09oLPlpg284/+9lUR9Ake6FDJe2pSZRSL0mMZ191zFxuvWImb4QgRjQRpa6tBjOrUdNYQTp/YO0kIaruUiKy6mbROKhijlSyBdAiHdK+IE7PjJXlJj5+n5O88sFm36vwgXkURjDQNHh73ynYAW0Ghf3cbG69dS25SYk2u+71/fyU33XouueN4S+XwZoQlvJVNRGDo+xL7HjmIUDAiqgIs0TEqlCq6qgqJ6lmHH9WK9NRWhKF5omgs1tRFC9VFkySQ/ksbKV5CuwDUdCmMZUECoKvd98SG+9PH75qSNc4krlXN++SxOhBDEG+IMHBn0VhHxBsHlm5eybvvqOdGmEIL3fua3uPLWzQhHYps2pZwx7Z2gQvfeXvb+8iCuPSPO2wEjX0G6DmpAJTeSO+X5peVS0xBDjwbBcsmP5qDiWXztooWRNUBRQBV86c+/wX2f+emst3Gu8bV56aPpGoFwgOGuEURVm4qmsuG6NazcvOyEENDZIhQO8rv//E7WbF+JbbtICZXSjMS5Khx+4iiHn+nEmbnQ50AxW0ZBIKWkmDqN273r5TNE8xKIFlLFKS8Jq2BjFaev9Tfv+DSPfu+pWW/jXONr89InGA6CgNGeca/aWJUr79jE0rXtJ6ROmC2alzTynk+9naVrW3EcF9M0sU136klG6LD3F/vpPtDLzEV4p+xg5Aw0XcWyXUoTxdNcQRCtj4IAt+JQyBSRluf5W8lVsIsVUFXQNf6/l36Mw88cm/U2ziXno0tfm4uXcCyMZdmMDoxPTSkVVeG6u7fTsryFaM1pFjNeAGuuWMlv/tVbaF7SiHSgUjZxrWkvXC2g8vSPd9O9v/cEb6ZKoUIxXyIY0iikSpSzz/dAAnClS6zOu2+nZGNky2DjVU/LVJCT+ZBc+L3rPsBI/9ist3EuWQza9H8RLhKjfeOomsq9734pm65fB0CkJjwng+pMlqxu48Nffx8v/43biTfVENA14vVR1EgQhMpo11g1d4KFkCreSCkpjuVQbAejUqmGn0mUWAglGvDeVt2NshMFKmUbAhpqJASa54kkHRuhql54eEBipEv8+Ku/nNO2zjaTmesXssXX54Uz1DVCorGGe9/9UpZv9HKCRWsjRBOzP6jOZNN16/noDz7InW+9lXAshB7QSLTUgCbA8TwMSoUSZvHEMhKF8QJaQCOfLk9v1PAqSVRDWnAl6ZEclmGCAC2iTedqAM8w5bqgqRTG8nz7778/p22dbXxtXh70Hx2ifXULL//NO6fyDtXUxwhFg3N63WvvupK//smfcfvrbyAQ1FCDCom2qnHKhGR/mnK2jFU4sQJTKV1C01WKyWltKqGTwtQkpIaz3mQX4HSOxbpCdjTH1z7637PWrouBr81LH9uy6Ts8yNqrVnHPb7+Ehmq+ovqWWgLB80+lcD7c8rob+dgP/4TrX341iqagh3Tq2msBcIouE4NpiukydvFEbRayBYQiqGSmtalGNM/jvqpPaTpkxgrek5EQXsXgScuTlNVxUyKCGunBNF/4k6/PaVtnk/PVpa/NxUm5UGawc5htt2zint+8k5qGOAB1zQm0wNymLb777S/mT7/2h1x52yZURUEPaTQu934byqkKmaEMpayBY5yY7LqYLSJdsMvToaRKWDlh3HQNl/T49AKpEjxNeKsuSfZN8NWPLJ5xc7Fo0/9FmENGesfITXi5e/SAhqqplAsGO+7fjZSScDREfWvtnN+Hqqq86xO/ys33XEU4GqSQNb1iS4qAQABpuUjHc8clHPAMRNKlUChhlx0vVE1KpAJuycR1HNAUzzBUqiBtB1yJa3oJzbz9QaiaF/1mOARqAuQLFb74tz+c8/bOFpKFH1Pqc2EMdY1MhYEEgjqqppIZy7DvsUOAF6Y2V15/MwlHQvzeP72T6+7ejqZrZMdz4Mww7Ew9VM74qVYgN5YD86TcQ66o5hqrYrlTRiK7clJOB7062Jo2wUSYzESR7y4iLyNfm5cu/UeHKBe8h7pAUEfTNcYGkhx8+ijgFX2oqY/P+X3U1Nfwvs+8h403rUdTdLJjp/bm46R5a3b0xHx97mTFwhM2zvj75Krfk3N61yUYDzDUNcIvvrV4Flt8bV6aOLZD9/4+HMdBURU0XUVVVQY7hzm2uwuAmoa45z03x3SsauOP/u09bLh6DdJ1SY+eFIIymSRXmzFuOpAbP1mbLrgn6lNW7Ko+pZe7bDIpIAIl6IlTmhaBmjCHn+vh2K7js9y6ueF8delrc/FQLpSncoapuoZeHTd7DvbTvb8X8Io+ROLhOb+XTTes43f/8Z0s37gUx3ZI9qdO3GFy7Js5btpQTJVP3M1wnz9umnLG5yfNf3Xv+6oqGmpY44kf7yQ5PHHhDbmILBZt+gajOSQYDmKZNmN94wweGyEYCRBNRKhvrkWIi/sfrgc0bnn1NXSsbSfRXot0bBQkqqoiFBXpSi+vgitBCGKNcRAKYnJVJRJEFipgOYhgAKFqSEAKBVmueMebNlLXIBYBXSXeUc/Sde20rahDReAWK3z70/fz0E8XT14GF+WcXz6Lh2A4SKVs0n9kkPRollAkSKwuRrz+4pfLDUWC3Pza6+jY0E68IT4jV9iMnazpJ8xYc/T5ZbttPOOQpnleRpPHVnPV43CCJ0PbyiaWbW6neXkDOC5WzuDzH/wKBxeRi72vzUuTYDhAuWDQc6CPUr6Mpmsk6uMkGubeSHQykXiYF//KzTQvryNWexqPwxnz1mjDKfaZtNWe6Ws4Q5srNy+jfUMLjUvqsF0XI2/yT7/37wzPSFS60PG1eemhqAqhaJDcRJ7egwM4toMQgkRD/KIYcE+mpj7O9fdcRVNHI/FJbQq81AmTzAjnjjZEnjduSnOy+tLkMZPeRNUX0vP4FQKQrLlyOR0bW6lrSeC4EiNV4sNv+geSg4vjwfR8dOlrc/GgB3WC4QCpkTSDR4cxDW8VoqYuRk3D7Idtn4321a1svWUDTUubCNWcxkg1Y9wM151i3JyyDZ3hWXnGRxuuW0vHxjZqGmI4pk1+NM8H7/4r8plzrJY4zywGbfq/CHNIXXOCUCQAQlDTGKeYKRGJh1mzfeW83M/6a1Zz8yu2EwmoBONhpKJASJvKTSStatJroJg1UaIhzxtXF1WvB+nlPanY4LgI2wLLBj2AAM/TyLS8B1ddp1QoMT6cwihaNLXWInUVp2Dwv//5CGOD6Xnpg/NBSnCkcs4vn8VDQ1sdelAjHA8TCAUoZIrUNiVYsWnpvNzPVXdewbUvuZJIPIQS8L5LQjv1d6owUkTopxlETdsLNTt5ZUYI78FVUUAIJkYzjA9M4DguDe1euVSnaPGtv76PYq78vNMuNHxtXro0L21EURXPlV5KSvkyDe31dKxpm5f7edGrr2P7i7d5pXrPUuSpOFGa9hA6mZONvDOZ4WU0eGyY7GgO23RpqOZoKqXKfP1v7kPKk4W98PC1eWkihKBtZQvSlSSa4lTKJpVyhZblzbQsb5qXe3rFb9/FxuvXepURPZsOzx/8PIqp0nTI9kyNCrywsxmpFqp5F6qVEadD1noO9JMZzSKlJJYIgYBkzxg/+OzC9849X1362lw8aLpG89JGXMclXh+jlC/jui4da9tpaKubl3t64/tfzaotSwhMerOfwe5Tzs/I9/e8r90ZxrwZH3Xt62ZiJI1QBOGE5+XYu6+fB/7z4fO57XlhsWjT/0WYQ8yKRSFTwqpYuFU33vnm5tdcx3WvuBotpHlhZIbtJcFWvDxFk7HbslzBzZW8JNe28BZuggGUkAaOjZQuBDTQVNywjhTVQdX23Hldx8VKlymVTQKxIDVNcdpXt6KEgnR3jnHs4OD8dsQ5IXDP4+WzeCgXDSolk1KuhOs6CGX+///u+JUXse22jajVAVZa7ml/oaUpT1xJVUCEqjurnPo4SdWYJDEzBmXDIhAM0NCeoGFZIyhwdE8fPQf6ZrNZc4SvzUuVYq6E67hkxnMoqrIgxs2X/vodrL1q1ZQx94ycXNFbZfoB9VSHn+RtbJYsioUy0XiYlpVN1DR5Xo/PPXKAoZ7F4GXka/NSJZ8u4Dou430ThOOhedempmu84l13sWR9+/QiiuTUD6cu3vgHUxoVkQAioAISJTDDzW8ytFtUw7xdr+iLmTcpFsrEY2FWbl5KuCECqsqeJ46TSZ0mZHXBcH669LW5uMgmc5iGRS6VI1oTvuhRLCcTq41y7++8nPY1rd74d6a1jplj5qT0Zoy1SuDsbTFzFlbFIl4bYeuNa9GjGgh47pcHKJcqF9SGi8fi0Ob8z8QuYSYGUwx0DiOEQCjiosR2n43W5U289Y/uRUUBTUEgEbqGCOig6yBUrwpEUEMqCkKoSCFwLQeEihYJ0b6hg1hLAiUWQmgaWtVrAVeCI3Fd2zM6CYETiDA6lGVsME3zqmbCiQiFwRwf//0v0XlkeL6744xI/JXSS5Xx/iRDx4dBeu68c1E14nxZuWUZ97zzJWgzJ676GZIUzvQ2cEHTNDo2dRCpiaCEZrhCqOqJ+05iwfDxESYG0yzftIRALESyZ4wPvOITTIwsbA9AX5uXLmO94wx3j4GUBCMBQpG5TXB9Lqy/Zg03v+46z5NhEu0cJ20OBIIqSza0EYoHn5/k+lTatAWDR0dIjeVYdeVy1KDCyNERfv/GD2FWzOfvv4DwtXnpMnR8hNRIBkVVCIYCJ+phnth2yyauv/dqFH16zPOMQJNvTq9TaViEokGWbV6CqikI/cREu6fUZkXSf2yYzFiG5euXoOgqBx/dz/950Z8taA/A89Wlr83FxcCxIYyigV2xCcdC824wArjupVey/fatiBmmBjVybom3pe0Sa4rSvr4VVVNP1PRpcEoO/ceGSI/lWbZuCaDw5Pd28f6X/OWFNuGisFi0OStXFUJ8UQgxJoTYP2PbR4QQg0KI56qve2bjWouJ5mWNrLtqNU1LG0g0zn0C3XNFVVXUeBA1HCLRVg+agmuUPS8iVUWoKoquIYSCGlZBVRAIRMBLtmWUbBRAlk1EUPWS7Wqa93AbDqEFgihCgKoiVIFrQbbiEquLIbxlHqx0nr98x2enYm0XKgs9a/2Z8HV5elpXtrBm+ypaVjTNS/6F09HY1oAQoIZ14i1xvOz0JxFQPNf6md4OAqyKg1k2UVSBW5kRID65qioUUNTpCbTrgguFUoVAWPc0K6CSKfKnr/7kgp78gq/NS5W21a2s2ractlUtxGovfk6x09G0tBGBJBjViTVETkxOP8nknPakubpZcTEN29vunHzQKahqr1wwUDUNoXonLowV+dPXfPKC23Cx8LV5abJ0fQfLNy2hfXUL4YuQQPdcaV3ahJAQToQI14a99AiTSAma6hl4lecbhColC9tyUFXVG/Mk3hg5+aL6HjFVMRhHYhQsNE1FVb3zDB8Z5hPv+OeL1uYLYTFUYjoTvjZPz/KNS1m6oYO21a0LSpux+iiKKojUhgklQjjlkwuwVP892Y7kCsyihWM5ngPCyUVeToNrSBzbBlVFDXkFnI49dYzPf/BrL7gtc8li0OZsXfXLwN2n2P4PUsorq68fz9K1Fg2qphKvi6IHdOqaF47BKF4X5Yob1mGXK2TGcjglE1CQwvMUUmtCBKM6aCpKdRvREELXkQqULAdHurgFA0zAtb0JrVARjuNNmgWIchnFtpEBlfLwBIce2Yuu4IXACYWx3iSP3r9wE2BLBK4899cC5Mv4ujwlgapXUTAcnCo7uhBoXdHMyi3LcMoW+fG8l9tkxkqRGtYJRoLeL7cznRRFhHQv30uujGW4Jz6UThl+pKfNyfeqd97cQIbOZ4+jRQIIzXsw7T04yN7HDs5ZO18ovjYvXUKRIOFoiHAsvCC8cifZdutmGtvqqRQtChOl57nY6xENPRKoamzGBwEBQmIaFa9a4ZlyGZ1Eqi9N/6FBgrFpL6vDTx3j+L6eF9KUOcXX5qVLJB4mGA4STUQXhFfuJDe+8hoStXHKWYNysXLi+CcgGNNRA4o39s3QpgioCCRm2cAwKmDPGCsnLUtC8QxN8kThjvSMMtqfJBDWp3Ij7bx/L2P9Y3PWzhfC+erS1+biIlbrPWvG62KEo6H5vp0p7vmtlxAIBSllyhh543njZiQeqXrrnvR90wW262A7DlbxfBwLJF37+8mnsgSC+lQ+soe+/ThGeWGGpi0Wbc6KwUhK+SiQOuuOlxEjPWOM9Y3P922clj/593ez/IoVCCFQdN3LYeR4llxVU6lvrSdeF8EqGEjXBcvBLVpQtqhkihTSJaRjg+sQSERQFImiCC+0za0WnYiGwJWorotAkDo+RmYwhVuxEAEdEVD47hcXdrnghW7xPRO+Lk/NYOfwgq5q8jc/+zCNk0lEler3qmo0CkYC1LfXEomHTpgUy7IFtqSYKVApTietjjZEYdKVdzJsVMEboCc9JDRI9k1QGMl5lRI1Dcd1+cbf/e8ct/SF4Wvz0qP/6BCpBRoOqSgKf/vgR4jWn3r1NhDSaWqrRz0534IpwYLMSA67PC3aaH34jOEyAAgY60lSHJuu9GKUTb7zdz+64HZcDHxtXnr0HOwnM549+47zgB7Q+asff4hATWC6MtqktqQgGArS2Nb4vHBQWXFwKg4TQ7npSqRCEG+MewYiKb3J7KSX7glGI4Xx7nEv0b0EVJVC2eTHX3hoLpv6glgMXgxnwtfmqTm2q4tSfmEWK4klovzFd9/vRaOcwik3HA3R0FrrzU1nYrm4hsPESQWSIvUzjWGnGT8tl7GuJOVMGfAqfWeSGR782sJ93lwM2pzrq/6+EGJv1Y3wlKnahRDvEkI8K4R4dnx84RpYzpdyocwZ08IvAO7+tVtQYhGkbSOR3iqJ41DJlpgYz1JxBdKVXiscyxs4JXjxLw4Eg6ArKK6C0BSEpqGENJSQjggHUXQNVZEoYR1h2xCPImvjSEXBsUzciqRzXx9f/eef4rrnsex6kZCAK5Vzfi0izqpLuHS1WcyV5z1Z55kIBHRues01oOnTE9SqV1ApXSY1kqNUME48aDJpPcq0B4MqcISComkQDICuIIIB1GjE219RUOJBz/CkVsPcLAdsG1m22PmjZ/jZVx9ZkKFpvjYvTW1mxrMEwoH5vo3T0tjWwJV3XnHKob2YKpMay+CUz00vjltNXC+YMuLq0Rl5Gk6XKNR0+flXH2H3Q3suoAVzj6/NS0+bRqlCLplfUN64J7N620o2Xbv+FJ9IcuNZ8qmCZ7x93scSadtMexR5w63QdE+Xigsa6DXV36WgghIKehWFT/JYcis23/i773N8b8/sNm4WOF9d+tpcHOTTBUzTJrKAwtBO5uo7trHmihWcauCcGEphlCqn97y1ZwhME0ipTIeLKgIRUhDhamXhoHKSVUNMadQuWPz7B77G0PGFVzhisWhzLq/6WWA1cCUwDPzdqXaSUn5eSnmNlPKapqb5Kc05F9S11FHbXDPft3FGXvP2WwmGVKiYYNuIsIYUAqVaDcI2Kp4o1eoAqQgIBpCmhVQ1lFAQoasY+QKu7YW7uI7ENW2ErtK2qolofQ0q4ChexnpVFyimDRUbKW3ckslX/u6nHHqud7674xQInPN4LRLOSZdw6Wqzsb1uQU98AX7jI2/yvlGS6ionVQ8hl0q2+Pz8Ka5bzY/i4sWDghJQMXJlXNPyzmHbSMNE01TaVjVT01LneRYKBRHQvMqJk19jIUCo/O17Pk96Qa4q+9q8FLXZsqxpQYW6nIp3feJXT1vxxcgY57ROpIRUjKxRrVqIN1m2JYqq07q2mVhjfHpSrCknVIwBQAo+eO8nF+RCi6/NS0+bwXCAtlUtKMrCNiK8+YOvmtamlNM5xRwo5crPz5MCXl4/qtXQVAU1qFNIFZC25Z3LkmBLQsEwzasaqKmP4TpeHhYR0rxkvJM5jhyJW3YWaJLd89Olr83FQTgWom3Fwm/LPb9556mTyDuCYqp85nFTKIBADQjK6RJTO7su0nCpbUhQu7SeaCI2ZXhSQsrzClMU0wXe/9KPzkZzZpnFoc05+/WXUo5KKR0ppQv8O3DdXF1rIVLbVEMgtHBXSgE0XeVFL9sGoRBC1bzk1Yo6NQkNhgJeuXGJZ8mV0gtD01VUxUtq7eYNpIPnmeB6A6YjwS2bVIomDS01iEgIJRzyjEmZMq5wQVNRdQ2BiwP0HFp4FdMuxZXSy12XAPWtdWhnqj62AIglYlxxx8bpDTOfC1WVUGKGW67iDaZe1vrqQKII3LIDpu256EtA8fKSVcoGUkJNQwQhvRA0WTaxJ+PLFQWha965HBjpTc55e88XX5uXJs1LGxdEdZcz0b6qleXbOk79oQKh6NmrurmGc0qjU6VcQQsEiCbCqHq1uqHtIs0ZPwDCWzV1yw6DnYt/tXQxcLlrUwhB05KG+b6Ns3L1nVfSuKx+eoM747dExfO2PeH3RVQXYyb3d3HK5ozwGIHQAyAUCrkidS31BIJBL2enoiDLFrJizwjvVkHTKJVtCpkCC4nF4sVwvlzu2tR0jfrW0zpVLRju/s07iNSdajFIQgDQzvR9cwGJU5oRGjpj/MyO51iyus17LtW987iGOx2eOoVgon/hhbwvFm3O2VWFEG0z3r4W2H+6fX3mj7INSiSMCIUQEoSQiKCO0FSMgoGoJr2WpgNBDUdTUSJBREhHui6ymgjbdR1c00A6npuuKwWpvnEymRK6IyGkIYTEtR0c24GIwrINHQTr42hBjf37++e7K07JQrf4ni++LhcPriunkmkC1WovGggFozAzeV91v6lQNKr5jWaMqJbl5ShzvQfQkWMjGOUyKgpV4U8lB0QTrL5qJWo0hBLU+eX3dsxpOy8UX5s+88apkk5WHzxP0Ob5fvUsycCBAVRVIE/lo6/BiiuXIkI6Iqzx7M/3nucFLg6+Nn3mg6nwaTG1wftXV0DiVQ+dKvqAp1m7WqgF8XwjrpSep5HrIis2R3Z0ogcD02MmTP2rBAXLNrajRELokQgHdnTOWTsvlMXgxXC++NpcHKSG0whVef6YqCpgi+kcYjDtGTjJKT16pze6FYf9jxwgEguf0ospVBembX0bQlUIRPQFGTI629o8VUXB0+x3rRDCFkK84WznnBWDkRDiG8CTwHohxIAQ4p3A3wgh9gkh9gJ3AH84G9fymV2uum09MqCCkF6+ooCGqmu4lo00bQh7qyvSdXFNG7VcwSkUcS0XgVL9ARBTD7KTJUdxXVwtSLoviR5RkcUKVCwcx0V1XTRFI58vE05EELrKwX29jI0srNAXKcWCt/ieCV+Xi5srb9k4rS+AgILQhDfBNd3p7a6sJl3A+9eR3oT2ZNxqEk/XeyW7UkQSEc87UMqpfEnBcIDMRJ5IIgRS8twjh8hO5C9Oo88RX5s+88nWWzafuGEyZPTkyr8XmP5r6OgosZrY87aHoiFyyQLRuJd78PEf7CCfXmCeDL42feYJIQTrr1s7rTtF9cZNhGfknfmc5eLNWQVenhR5womm/5aTCyqeB/1w9yiRWHA6EXb132g0TCaZIxLRsB2bn3/7SYq50tw19jw5X1362vSZTRo7Gli+dekJOlNDSnU+etLiyMnj6LkgYbhnjHDs+R6+wUCAYq5IKBHCMi0e/tbjGEXjFCeZH+ZIm1/m1BUFpxBCqMBfAz87lxPOVpW0t0gp26SUupRyiZTyC1LKt0kpt0opt0kpXyWlXHgxRz7c9rJtRBvj0zlQVAWpaYigjlQUkI7nKhgJgetUV3AEUgikYyEdB6FoBGJejiMZCCKCGm5Aw9U0pKpjFiuexTioocZCEIxg58rkxjJUKhZ2rsjIsWH++78emefeOBEvfF0959e5IIS4WwhxRAjRKYT44Ck+XyaEeEgIsbuaxO+eC75/X5eLmu13biM0c/ATqjePDUwm9puZbXOGi66inPBRoC7C1ExZUUCtfldVQalYmZ5EqwqoUCmUySdzWAUD17A4fniAH//Xwqou4WvTZz656s4tKOEZ3ysXJquxnM+sSg2dJjRWKFROUQLYyBmUciUqxRLYsP+Jozzy3YXlAehr02c+ue7uq7xcRUKAW11c0VSmPHGnvI/wvG4lUwl0p1CFN87CtLGIqheu5WKbtrcPTHnmFnJFzEIFI1/ELRo88b+72ff4kYvS5nPhfHV5Lto8B10GhRDfqn7+tBBiRXW7LoT4z6qh55AQ4kPn1AZfm4sWRVG4/TU3nmC0dQx3Os/QuTq0ncpLaeqELpZlP+/zXCaHbTiYJQPXsPnhv/+cwe6x823CnDEX2jzHioLvBf4HOKfOWHgmZJ+LSjCgo7ouLqJqIFJQgiqhaAgsG01VEa5EjQZQaqKIhgToOq6UuLoOQiCkTV1dhPrljUhspJQoQkUqEjSFimETjodQa2PotVGUuAaqgmm5WI6NokikYfPDLz7CEw8emO8umYHAkco5v856Ns+a+6/Ay4FNwFuEEJtO2u3PgG9LKbcDbwY+M8uN8lkkdKxtwxXK9AqnaSEE6LoKLqghb9BQwjoioEOg+vAp8Tz+qjS01FLTlpjKfQKArgMKrnSI1McQiRB6IooSDIALlUIF03ZAkUjD4Kuf+B+OPtd9cTvgjPja9Jk/lq5re94qqBpSvYdIlymXenXyoVM99Qy3dWULodrQiRsVxfP2UxRCiQBKUEeL6d45JBhZw8v8p0icQoXPf+A/GRtYSBV/fG36zB8N7bVgUy0W4X2/gkHVW1Rx5JRhSIlUc4xqkwssXl5OFAWhKCzZ0EYgGpzhYcSUJ24wGiQQCyIimmc4FiArjpfGQfEeaM10kU+9+3OUigul3Pn56fJs2jxHXb4TSEsp1wD/gOfNAPBGICil3ApcDbx70pjkc+limeaJGwTEJvMazfi6iXA1kfypxk0JK7YuR4/o1Z2V6ReCYDSIFtERARUl7M2DZUVSShdRda+wSzFV4C/f9HcLqALwBWmzcbLiX/X1rvO6ohAdeCGcnz3XY3yD0WWOFJBNFb03josUAtewqVQqiFAAPR5E4uCYDkpQh2zRW6gRAkVRUSNBCAVJpYoYuRKY0ht7HQelZIBh4joQS0RBCs/QZDjgOMhiCWesgFB0tLoItunwL39z3zz2xol4icjEOb/OgeuATilll5TSBL4JvPoUl50sr5cAhmarPT6LC7tiUUkX8Wajnsu860hsw0ZoKnrIGzBdx/GchszqyopSXS2NhEBVSSfzlMvVgbpaLQ3LAuliFW3C8TA4Co7l4FYsbz/Lxs2VwZGIgI6ZKfEP7/3SfHTDKfG16TOfJIfSuOaJFiPpuF5oiwp61NOmI10UjedXNax+JXOpLNak5iapuudXcmWC4TCuY2MbzgnncEquZ7DSoJwq8ek/+M/ZbN4Lwtemz3xy6Omj3h+KMrVAYtkWuKAEFNSgZ80VCJSAXvUwYsrAJHQVKSWFVAnbrmrcmcx95BmP8slStaiNgms50yHgjotVML3xVxVkB1J89a++ezGbf1rOV5fnoM1z0eWrgckfp+8AdwqvqoEEokIIDQgDJpCbpab6LFAOPn30xNBPCeWCgVcBTUOEPJOEoKq1E8ZNMbXomUpmsMrW9EmkW02pICjlDDRNQzrSG6NnWDnMfMX7XXBh+PAQj9+3MLxzL1CbycmKf9XX58/zsv8IfKCaLP6c8A1GlzkjA2mEEJ5AgyrScXFKJpguWjhApeyiaDrCcnDyljcpdmxcwDEtLzGvpmGXTIyCidQ1XCGR1QdSEdCwVY10Mk+iPkqiPg6qTjQeQm9OIIMqjuWgmC5CFTiGhW1dSADr3OCgnPOLs1t8O4CZ2b0Hqttm8hHg14QQA8CP8VwGfS5D9j95rOoKPz1oShukI1FDGhWjGkZqudiTg+ekm71lVz2NBEa2hFWyqvmLqmODpnv5HaQgPZ4j0RSnpqkGFEG0MYaIhadC20KhAGgK5UJxAa3I+Nr0mT92P7Tvedtcw9OGqqtYuUnDK7j2KU5QlVF+tIBTPmm8m8qXAvlMgURrLZHaMCgQbgidkAg/XOOtzhaSCyv/n69Nn/ni2K6qJ+xkzj4ETtEBVaAqCk7ZE6RjVHAtezoHoHTBtpGu96CaGc5681s40TsXgbQcKqUSiZZ6wk01IAShhsi0Z68jCYY00FTGBxdOldHz0eU5aPNcdDm1j5TSBrJAA57xqAgMA33Ap6SUZwuf8VnkDHc/3xPWKdlT81xpePNTt3xiFTSPqmVXSnJDuenPZ85JpYs0Ja7iUNtWix7zvAijjeHpRNquRA17CzpdB3pnq2kvmAvQ5gvlGuCbQoge4A3AZ4QQrznTAb7B6DKnriFGrCEO0RCu0L1B0/UqmdmmiytUCGpIy8E1yrgCZDgAOiiO7ZUUtWwUXcd1JIrroDkghUBEI96CjGlTKVVQgzp6Ikz9qmZa13awfEUrWlBHUSROQAFXkO5O8v53nq+hdG6QXHSLL8BbgC9LKZcA9wBfEUL4Or0MufLWjQRrIxDQPePNZE4GV3o5FFwJ+mRuhZPiwKWEsoFwq3/bljeZVSdD1aorOAq4ZRNdUwgEFJpWttK2uo3lG9sRsRBCVzEN74F28MAgH3/3v13cTjgNvjZ95pPb3/wiRFA/MTluFcdwXtjMajIEBnANh1BIIxyN0La+nfaVbazYugSha6CAUfLyHO179DBf/+uF4snga9Nn/rjzV2/3QrRFNcRsUqIuWIaD0Ko5UNxJz13phb5Mhn9b9lT4J7bjLawIBYJBbxyuhrRZBgSEpKYuypINS2hb0caKLR0omoqiKpiWF/728H8/y9MPPDdPvTHN+epyFrV5Kq7D85FsB1YC7xdCrJqlc/ssUG574w0IVZ2uyDuJdL18RnDmXEbVUO1TFnWZxHUxcyZ6QKOpvZHWta20rWhj6foOUBWUgIJbrcj2lQ9/h/5j8++MeoHafGHXlHKllHKFlHIFngH3d6WU953pGH9AvcypqY3wpt++A6GqCNvGDgVAVVF0FVdVkYCeiKLWhEGRIBSEVHAM10tqHdBwdN0rP2pZSMvCli5CKMhSBce0kJqCCIcwKzbkDLL5MoOZMpmiQU0oQNv6dmzLwSl5SXb3PdmJcYqEn/OBi3LOr3NgEFg64/2S6raZvBP4NoCU8kkgBDTOQlN8Fhl1zQle8dt3egOsqs6Y3KpgS6QjCUVCoDO9yqKoJ5T79bzoq3lVHNszOKnaDG8jb6Jsli1cB1ITRYZ60+RSRepqo7SubsExLTAtcCUPf+MxbOtULhMXH1+bPvPFmq0refGv3HD6KmguhGqeX63lnHHdqfwrlaKJUASjPeMM9YyTGclS11pD8/JGpDHtnfSlv/w2jnPO3uVziq9Nn/nittddx7Uv3Tq9yCInK6AJcCVSCkKJ0PSDqahmqleENzYKUa0c6jL1iCSlF8YtvHN4YS0Sx7RxKzZDAynGxjKkxnPUd9TS0JxAmqY39joOn3jHZxeEd+756PIctHkuupzapxp+lgAmgLcCP5VSWlLKMeBxPI8Hn0uY1/3+Pay7fhWcKgqqGhIaqY2c+SSuPP24O3UucCyHUibPaE+SkcEJMsk8TcvrqWlMIB1nascPvuLjF9KUWWeWtXnKioJCiPcIId5zoffoG4x8uOdN11PbGENxXVQJMhpBqjogkY6LVXZwKiZCKhAJ4qpetSY7HPCeNy0HWTJQACUYQOgariKr47FA2hauIwlEApiOi6sKLCnJViyywmGkZwxRshCuDbEwQtH58z/86rz2CVCtUC7O+XUOPAOsFUKsFEIE8JJz/uCkffqAOwGEEBvxJr4LKaOpz0Xk1b/1EkKxgDdZBabyGVUTaxoFczr5rhDTuRaqyTtxvXxhgGdMAibzMEw6GQFEayMYZRPHlZiWRaFokMuVGemdAMetTpIFmC7/9uFvX7wOOA2+Nn3mEyEEb3r/q1FCp69WYuRe4KJHNXw0Vh8lN57FdQWVkkmpZJBJ5hnrnzjpgg6PfveJF3bNWcDXps98omoqr/ydlyFUxcvXNxnWLd0pg49RNE8cJx3n+ZXSUKaPQ04vsgi8Yi9I4q0J0hMlsB0qWYOK7TKRLDA+nvbyrzgOuC7F0TQ9hwbmpT8mOV9dnoM2z0WXPwDeXv37DcAvpGc56wNeDCCEiAI3AIdnqak+C5RQJMQtr7meGdbaE3dwXcqFs42bZ7EWCa94U7wuSjZVBiEoZUo40mF8IEVmJD19ClVlrDtJLpU//8bMInOgzdNVFPyclPJzp9j3HVLK75ztnL7ByAfbcjAciYyHkJriueRaNsKyvZhu0wRFwQ0GQVXRcCCgolYz00vXwXEcZDUnirBchBTIUAAiQYTuVWLLTZSoqY+xbdtyli5rxClbWCUL23VxVAUnGkFKgXBt9u/rwzCss9/8HDObLoLVGO7fB+4HDuFVdTkghPioEOJV1d3eD/y2EGIP8A3gHXIhLE35zAuObePYjpe3pGok8kYXx3OXt6vJcCeruIA3CXYnt834Xk4mxAaQjve360JAp1gwqG1KcP1Lt9K2tBG7aGMbFtK0PIPRpJeTHuDh7+/wvAXnGV+bPvOJaZjIU4SkzSqKQqlg0LSknhe96irqmuKYho1rWF6C7Umq9/GVT35vYXgy+Nr0mUdcqxoWOmkQmqSa02hqkcWtLoZAtRiE7e0/Gco26dU7tUgjppJpSwmmYdHcXsfNr7yaaG0EK1tGlkwo29OhpaEghEJ8+WP/c3E74RTMZtjLOeryC0CDEKIT+CPgg9Xt/wrEhBAH8AxPX5JS7p2DJvssMCzLrs5DT200kta5esme4vs5I9TN+f/Z+/NgWZP0vA/7vZn5bVV1lnvuvb3PdM++AgRAAARIWgBMyAHaDkKKkEjJsoNWkGbQi8IKRVik/zBN0wpZDipEURJFmQsMgjRJAARADPZtAIwwK2af7p6e7p7el7vfs9TyLZn5+o/MOud0Y2a6e+aec+7t+Z6IM6dO1VdVt3Lq6cx883mfxwcuvuU8P/zv/ABFYVnttdDHVxpp58PUn/97v/r6P8AJ4bRb0r4ZuNe+ZMSbHcF7rBFM6dCVT60rZZk4raAEwCAxppazPmKcgbZLBoGFQyYVwbrUzqaKWkN0Nk3CRUE0hr7tuH59zssvXGPAIIsuRZpuTVCfJMBy0BJdQb/X82u/+Gn+7b/wg2c2Lqmv9NbWVFX1V0mmnMfv+xvHbj8K/Klb+qYj7liognOOvrCwLqCWZVYHxbQo7v0x2X1+UmHT4lckK4s0n7yQbluXTbEdDAP71xfEbuDS0y/je01OvSYnyNgs1zcGscLe1QUf/7XP8UP/1ved/oCsP+LIzRFnjEQN4Y+UTtdtMFb+aDra8cdfD2Lkxsu7TCYNLz/7KWIXX6EMPPaiIPD8Y5d56Zkr3P+2u9/ox7llGLk54qyhBsQYNPpXHZxk4jiTNo+albOHfNKjOS9FF6XHJfumGJdUu8ZAiFx58SZVYfnoL3+G0Pl0gGNN+gnhaANbFHziw4+c6hi8GmfEyxb4d7/G8+Zf6/4Rb36UTYkpDLFfJ5txxE/VZE79zeYeHePztZduYqzwP/7CJ/FdPlg9XjxeB8Ao/MJ/8+v8b/9vf+Gb/UjfMk6CmyeBsWA0gmvXFixaT4gGNWDKMrWJqmKNgS4emu1SFbjGEbqBaFyK8wa0qrDWEKOi1qGqaFTipML51J6mPtAfLBiuL4gbEygtEcESMZXFDYJOGqIqYpWXbp6tTBAgfEMHthEjThaPfOpJ2mV/NNlFTQVdJBV11BydZCpQGhhi9lswIDElooVjCWkxpuc5e7io1X7AhwK/GI5Of0JIvmUKVCWgaIzQduzfBqlMIzdHnCV+/6c/dpROeBzrYtDXKhYdfxxeX/FoiGnvunrVyashm/PmzSkC3vPS4y+dacEIRm6OOFv8xj/+MLHzr1LY5tsxpmLR8b+LPG+uEUNOPMtqpHXUoTNHz3MupfoKhINVVvYm7zHTFKjPhzUhpIPUoeepLz3L27/jwZP++F8XIy9HnDU+9gt/mE2nj6ebHQU9/JF505A8OF+BV6YHH/4tHKrr22WbntvqKy6XMvmBqs/enqoMg+fG5V127t6+NR/ym8CdwM3bv6Q14sTx3g8+QFk7wnwJqx5te9QYqEo0hFQEMiYVgawhxHRiY9A0gRbCUBliP6CDR9sWFaBwmKJASZvVIEp/c0kMAVUllA4blLgK6MIjRrDDgOlSSsXDn3iGEL7ZUvO3jnTAdHtLBEe8ufGj/96fhCIrfdYtacam+8SAz+1ikE5mNBt32iKdchbVsYk4F31dkdvaYrq2LEGE1c15bnGLqZgkcijZLybZR6ntMdbysV//whmNSMLIzRFnjT//n/74137gFW1qr/Hd+0Ytbes2UGC1u/ijLxmBQak3m6w6SkXl3/3Zj7/Gv/xkMXJzxFnjz/1HP3ZkjrsuyMbju85XFZLWxSIxR4VY4BVbJJM9jY6Z7mo/0O23hzw10ybZN/QD4iqqaYNqKvhSlPzeL3zqVn/U1403ysuRmyNOAj/yv/rTmUPrgo9843kw5mvWEPijEtvjr5X/HhTatf8Y6TBVQTuPBihmdT6wiSDKJ37ts7fg031zuFO4ORaMRgDQW0E2G3RawMYEmTqoC8KkIhQucdZabIjEEDHTBpk2qDOYusKqIRqDKQpk0qDOoqLYoUdVCM6iTU2MEWJE9ucUqx4JnhgDOvRsn5/CrEIt2Mpx9aVrfOFzz5zhqCSZ4Ov9GTHiVqMoipTooPmUpE7JS1K5VDRy7sjoOiR/Bqld9usUxNlUAPL5xLSujibqwqbrXC5A5bZQfG5xi0OS4PvA+fOTVGhCKGclzz11lSsvXv/a/+hTwcjNEWeLZlof+Z8cR3FMhfDqha079l10vGoT+yocnrq+anG43sxmef3mucmhN1nVlHzxY0/g/Vl6jI3cHHG2uP/t96T5zh5T4DoLlTsKf9Djm1aSitYaQBBjj7ht5cgLSQTK3OIdY9rMxnw7BCIh09USu5bZVp1a2boWVxZ89NfP0qbnjfFy5OaIk8AHf/Ddr5zS1nYHJnHva8LmeVBMSg1eQ9btouuikx5e98qpVxMPDyu9kc2dWX4Ng1jHb/x/f//WfMBvCncGN8f/IowA4D3f+Ra0SRvQYCAuh6QoakpMnRQJJgRMjMnLKCo4OVTDa+4dVQMMERMVQdO+tTQYI2gUdHsG1mEi0Ae07WDVIZOCwSjaD6iJ4By7N1f8t3/nV850XCLyun9GjDgJ3P+e+46MrLs+K4WAsshXrDeQKQFG+3A4b+oQskw+L3r7DghHz10nxAxD9ipavw7JVNd7mNWs5m1SFAqEoFx9/hr/xf/hH532ULwCIzdHnCWaac3Wvdt/9AH/DVSx/liBKLzW91JfdRK7ft76YYXSsjxYpeIv4L3n8jNX+Dv/0T98rX/+iWLk5oizxF1vuUA1q464mDeWEvJceHw3ud5sSko/FBHUx6NQiTVl12olr4eqXDT7sJgcStEF6AfoeyiE5e4c4wrEGfwQeP7Jy/z8/+c3T3k0jvBGeDlyc8RJ4N633w2VzYUec1hsBUnJhl8LUQ+Xua+YDr+e0OhrHbZ0R3NvuVGx2J+DEygsvht47JNP8oU/ePRb+3DfAu4Ebo4FoxEA/MAPvQ83BKT1yGoJfYcKFMGj8xZE0ODRYUAKgy9d+uKWJbo2DQyR2A8YDUhUbOkIhUNUiYD0PeIhbE8JGxO0aSBG7M0DuDnn4IU9ohGkqdIEu+x45uGXzqwt7QTigUeMeMP4wA++O7eIQTopSZG+EkP2IMoXRgXyglgkSXCFpB4ycmTCWZVIU6b7YzbRDVmBVFT5ZDYvhn2Ag5a9qwsimpSFhUP7wMN/8OSZJTKN3BxxO+Dd3/fOr3Fvbl15LawXta9OMqzyCWrU5KUCR+qG9TXr5/eB+e4choidVElROHh+86f+4Jv7QLcAIzdHnDVEhIc++JZ0CCJ57gshs1JfkaaUuJUPTpwkOwb3qnQ1kw5TZM1NjVmNRFbjxjyX6tHPKrC8uUJ9xJYORVEf+Uf/z184pVF4JU4iunvEiDcKAe59tceeSGrd/FqK3bU/5+Ht46pcPbxfirxGlmOPrYtGYo61mUK/39EeDIBgKgMqxBD4m3/h79yCT/jGcadwcywYjQDgE597ilA5wnaNTGfI5gQQ/BAwVUo/C5MJWleEqiROq+RtFCLR5daX7HGkkxqpitRBI0JhgBjQwhInDlNaZNUS/UDc2IKmws9qfGExW1OkKgj7K1xUcI6//bd/+czG5XaXCI548+PJzz19KNulKpPftWb1kM1eQ+vF7br9TGPq4T7cjGY/ImsxRQlA3bicFtOl5xclFC6fkPojr6MmpbLZqkScZZh32RAU/sn/60NnMCIJIzdHnDWefuSZP7qKOr7AfS28ui3GWIhQb00OOXZYVDJrL5ZXmX0GMLUhhohfDllJGPjXf/83vrkPdQswcnPEWcIYw7NffiEVdaoUa390OHIsmckca1kTk/ai61Q0I1ltm6+3DsRQbVb5PklzJhy2hCZlrxy+tikNpi6yCt+AtQy7cz7+S2fjZXQntL2MeHNjsb/i6gvXjwo7kttAD1vKXoXjqqEYk0/nIRJHRUzywG2SbUIyKzpSJaER4queZ8GVBh0isRsgKovrSx77zJO39PO+XtwJ3BxT0kYAsNHUBGuxBz0gaAyIBrRwqAr4SGwMfRtSD3dUtLRp7ZpPXoQKO+8Y+h4jgoYCsQGZ1ZCNrJkU2Bd308Q8BIaJwW3PiJMGGQJx1aNBCSrIpEJmJVHPSGHEaPw34uzhmjKbVTsOZ8/BZwNsAWz63fdpcWxNltKHQ0+G4z3ecRggQhcVqQrW9JLSoss2na6uC1FVmSb0toXBEUJAIkjtME1JeMVpz+lh5OaI2wJZ1He48VSFb2W+CgEChBBTIWrNWydwPJBtna4mQFBEDdEnXlMXmKpE5YzUfyM3R9wGcJajTaYxqY3M5hYYEeBYO4zk4k8/pHnW2VQgCuEVRSMNgptU9KWi3QCFQyzouvVNNRV9swF99BFXCgyeyACThrLZoh9Of94ceTnidsDOPduvsBtat3qqSprzAhyuWfVYcVePeRAdQg+VgxoN5+7b5ObL++n51iDOJJ4eh8lKpC4Qq1QwEgScod6o8d3p7zfvFG6OJeQRAPzl//BHUKPE4IkG1EeCeug8UUCi4g56tDCoFbTrkGWLiYrtB1AlNo5QOcTZpDqSSO89/apHuwGzv8LutahG4kYDhaPwEMRADNh5hyq4wiK5zzwetPz2b3+Bvj8bE8/bvad0xJsf/+v/+H+RN4b5hFTJMb8mTX7m2OYxKixXqXikenTiWZZpIWxyMUlAVx26tzxUIemqS5O1yfzLCWkEnyf0iK2Sz5kRJewv+dn/4XfOrC1t5OaIs8aP/x9/LN04bl79ir3gq1rOXieGeZtVfi493/tjr5v5bu2hOkkh+5Il36N4sOIn/9bPvuH3vVUYuTnirPEn/9z3Jc+w4A9b0hjyvCnmqJi03ox2/RGPuwH8kB9fq3fTvLm4OUcPlomTw3A0/63bRtftabkF3JUWM6nS/Lxa0feRf/if/+szGJE7wydlxJsbxhg++IPvSX+s+SZkPh7rKVt7g6HHCkfrA9BjB6FweBh688Xd/Jrp8EaHV+0b5fjBjlJNymNWR8pqd8k//r//9Al98m+MO4GbY8FoBAD33XeOZqdGnKDTErZmSF2lFhZjiKWD0uGMwbQDxRBwxiIhQOex8x67DGmjaSwiQqxKxAjS9WAtsSrS45tTXAjY0uArg2hAVAg7NVVdsnFumtIlCoN2EXlmn898/IlTH5M3GnU4YsRJ4F3f81CK6xVJG8hpg1R1agMFDhNgyvJIVVQWYIu0qG1b6Fa5v3vdz22PFrerFQDGWaiK7HuUvRxibnubVDSzitn2lMn5TTQmD4ewGnjiS8+c+piM3BxxO+B7fvg7oTq2aF1vGr9eStoar/Yu+noYhqwkzAtkA9R52bbe7DqhnlXMdhqm95zLqgjDcq/n6sunn2Q4cnPE7YA/+ee+L7VYk+fNvDY9nPfgiLOHfkW5eLROC11fu+bg4NMhSg6YIARMmdtgBKhzm7dJf9tJiS0MG+cmbO5MSQXdwJUnr7Ccr051PN4oL0dujjgp/Ik/+z1H69G1h9gax+fFdfv1qw2sD9VGx4pHrzDD1mxVtuZiXvMeew8zcRhn2L57m+nONPknxcAjf/AYwykLFO4Ubo4FoxEAlJXjB3/0A8jWjNh7TEjqgjC1yBAw3iOqSNdj+gAe1BmksMRpjRk8pveIFMny01q0tuAcUlfohiVsFMTKodOCYVLRG4PWVXr+dkWsDDIVphsVKyPU2w2mFGgqfvqnP34m43K795SOePNj89yU7/2ffhCpXF6kZlXBeg41Wbo7hMO0iWTwl1UI6xazssw+DHmiLUqYNTCpwQpRTEqpMBbtIzJpkpqotCCWYqNhtj2hn89pzk3TXF84fua//+0zGZeRmyPOGhfuP8f7//i70yZxDSuvNMz9Wgq8wxQXjopHry4gOY4SC4XUlhYF2pA8WdCUjhaVyayiKiqGVUuz2Ry2s/3sf3U2KaMjN0ecNe5/x72847sfSvOeMdmTLwJZqWuzsXVc98bkOdPnOXRdpBUOVblpcatpTq3LdBCqMc2pYkAszDaQssA0DTHCdHOCdY6h95hJAasWjZ7f/MnfO/UxuRN8Uka8+fH+H3w397/3XsTYzEk9LLIezpeHaYaZmznJ8Aiv+nsd9mLTelesyTWkfF1M7aWSPXejV6ZbE6KJDH1M82v2EHzsD09foHAncHP0MBoBgIhw5embxGWH3V8SJxVaOZwHrR20PrWbBUVsJMYIRYGf2iTdPXBgwDXJwV0gxXtbS6wMxRDpRYmbBTpEwkaN6zymsbgDj93v6AfPQRD6vRtoVNqomKxkkOIMKqrjKcuI2wBFWXDl+euoz6lJYlAi4lxKKAwKxqVUtIE0aQ5Zziscnt4YJ2iIqCvSRlMsxirR5Naz0qa2NJMTJSQiooeS+/2rcw6uL4lDz3A1taFKUWLNGUxeIzdH3AbY3Jlx5cXrqfVlXfAJgH0dbZprlYNqUgVqXhzbfOIq+f6Q/f/6cPQew4A4ixoLErn2/I1UtBpial11DnGGeBbtoiM3R9wG2Dw34coL19IGMobEoxihctBmX5M1PdYqIjFpbvQpIAKfPYwk3y5zumjfHymWnE0tbCIIgg4dUqbNqnrlylNXcLMK7znkpp01dKv+dAdk5OWI2wTGGG5eOUDXBDyeGCrrlrHcjvaKg5Xj13LUTppvS1YsaVQUQQrQLhdzLRAU4wzBB/DK1WeuItYmD7KsZpKq4tLTV/iOP/W+0xuQO4SbYwl5xCFeev5aIk1REIsComFQm2K06xKMYJ0hFBYpHaY0TFRwVUkoLaEo8LMKqQtiNyB9ku+GqKikNhm/GIiLFtsNiAMXIi4E2F3ibq4weyv6VU80gukjcVYxTAq+9OWXefzJS6c6HsnP9PbuKR3x7YErL15LC971aUlZpYOZdWtZimFJCqKyQGZT3NYUaZrDk1TRLM0dhkNvozgE7LpN7WCV7ncWYw1WBUGRboDlkjhfEhYtqAUUqpJohE/81sPs35yf6niM3BxxO8Bay80by6M7stkt/hu0ojUVZqM81iIqUJijpCVZR4Hnk9cArLIRpxFMndIKk6/ZkJMRI7Q+FY+dBQs6RH7/5z+FXxvynhJGbo64HTA7N2E+H16ZjqYK60KNalYDkgqzRjCzCjdrUtiDtVA4TF2lItLa0DpkRVHIqt5FT/JFApGI0YCEgK5aaFfgPf6gS/OuVaQuCAq/+s8/eqr+f2+UlyM3R5wUpps1y93lUctZDo2QQ18iOWolI/0plaFo3NHfIhSTIh3OxHhMyZvb1VTRNh51rlkLziTOxQgaUR+JfUj35bZT7QO/8Pd/61TH407h5lgwGnEEY9CmxG82GGuhMIiBOKTTF1cXTLYaZFIRrMEOnjgMiA+4nQlMHHHhkTZgbVIpxNqhBmLbI+0AXcAEwZeGsLei31+wjEq/VROM4kpLaYSI0teGMHhUI74b+OqjL536kNzuPaUj3vwIIdB3+XQTjnxSIJ18CthZw+TcBHHZCJtA7Hq0W6XFrrEEn/uyXZF+a4SuI/qYvBmsSX1u/UBcrghdT+yGVDw2BtmaIU2NlBaw6TnLJf1ixRf/4PHTHpaRmyPOHFdfvJESzdbtLcfVdvl2uTnB1JlzoohE4mqdcpivbX0q+miOXdO8IfXhlUkxIRI7n5SC/bFT0VmTWmSMSSltnYeuY7634EsffexUxuI4Rm6OOGs888iLqaC67jYritSGXaznUaHe3kAm1WE7i4gh9h5ZqxyA2OdCrEhOQFxz02c17jptLRIXLXHlCW0u0hYW2czto/0AfUxJpINnb3fBs4+9eKpjcif4pIx48+PqizeP/SW5/CHosbmu3q5TkceY5K5QGIZuQKqj9a9fDq/wLlLV5EWU/li/PMSItgP0gdhFUmCMwUxKMLmitPbK9oHdyzdZ7B87CDoF3AncHAtGIwCIMTJfdqCCkVSFVQPSD6llpRswi45F26GiqVWlsgRVQq/0ixa6DtN7NCjiwJBbXVw+ffExGWV7sIsBKwY39xjriH0gXtyicxaPQZ3BblaIGsygDCHy+c8+dapjMpp3jrgd8PLTV9MkaC1sztLvvsstLRGcJQwD7RDzCajHWkMkJlnvsk3qpN6jbX+UGjMMgDk0sMYW2Ysh+xv5kBbZbQd1cdgfLkWJnTbpMVcQouHhz4zcHPHth0c/+TgS1u1ocpSOZPPSyln6riceGuiCs7nlJZI2on9EZSCphQaOTmDtMTPtYzHfhKSSSB6gmkx3bU5EVPArz8MfPV0/hpGbI24HfPK3vkCS2pHms3WrtTX58EVo9+foEA7T0Gxh0RhTq6ezR2o/ODLNtSYVjNavVVVHPoFrk92oieN1DbZCnMNuNIjLLW3Osegin/+9h09tPO4UY90Rb378zr/8aFYTceRDtLYsyre7vf6w2JO8rdP8qV2aY1WzXUI83sqWn3+oXMpcfMUcq4d81yGpmqQ2icNZmXTtxT0+/3uPnvxAHP2L7ghujgWjEUCS982mDc1dDYoiEUwbk5pvUBQlVpbSWlyffFSGPtIvB2IfEGuRNhKDT0SmQLGoBBQhTkuoHcFZhpmjKIRyZ0bcmeKrJPcVY9FpgUwchTXsnJtw7h3bRBGsGr7y8rVTH5fbncAj3vyYnpswu7BJfX4CQ58nRwvkzWlU3GRCURbpBFQVP0R00GM+KPn4pCigadB1xHAMR8Ujl0zqTVXgtqfQ1Ic94hIBa7CFAWc4/8A22/dvp5ObGHn2icunPi4jN0ecNe59291MdmYpNjuG7P0lR+0vMWKLAlc3rFezw7w7eoHjRtdG0kZ28JmvHPkaZR6aSYVpysTVtZopKhIVsYJxlosPnWfj7s30eiFw5bkrpzIWxzFyc8RZ463vvod6s0EKk9RAMSQK+qN5z00r3LrgEz397jwp90JIBR8/JGVQ1JS4FjXd51yOAM9QMLWF0h55kgkwBCT6pCy0wl1vv0CzUSHdCobAy0+drs3CnbApHfHmx10Pnqc+NzkswioceRFlFI1JLZxZ/TccDEfzomSz+njUSoa+ai5d+xtZg52V6b8DZl2hOvI7whjKpuT+d9+TOGyTd9JzX3n5VMZijTuBm2PBaAQAy1XHzYMlq8sHiEIsDFomKaAETX4m1mGMoTCKmXfYPmLLCheUUDmGWYHWBeF8Q7/p8A40rGX0gVg7kEjpDX1VUF6sMVUBBmJdIEOHGyBOLRrh/GbDd7z9AerNChXl6Ydf4OmnTo/Eyu1P4BFvfty8tmD/5oL22uIwLhRroPeQFUMaBqxoMvYTSZOnye1pTQ11BVWJ1A4tbDbsJG86NS2Aux6Jqf97Mm3S4tdZ2JghZQH9gGZv0LvvP8cHv+cdWdJr+MJHvkS7bE9tTEZujrgdMHSexc05cdHlE9Ljm8i0oFWNuFJTCou1R95F2bfhUDW0PiU9jvVjPqSW8cHTTKssn49J3VCWOTHRIGJ423c8yAd+4N15gwsf/dVPn96AMHJzxO2BycaM1bV9tKhSIXcdxR1C2nhm0+qicdnDyB0FRSBp7qtKZDaBxqX7Cpc4vk5Kg6TAtWkDO9lojlpTqyr9JyEmRaBR4aH3v4X3f9fbyDGGfOxDp8fNN8rLkZsjTgr3PXQ3qxuLV7STAdnjOreHGkNZuaQiUs20NEdpaplnslbfrrFWFx0z1BYnuGl5ZJZt8/UhIGIwCA++7wHe+T1vR4ygQ+Ajv/CJkxyCV33sO4ObY8FoBAB1XRJXLaEsGMpIdAZvhXiuIWxW+NISgrLaW9IvWhQYaktwBi1ShKGUKVlta6fEYnGDIj5iukCcOExt0cIQXPqyr66uMFaxheR4UkcEfFGgU8d7PvBW7rl/hz/9w+8llkni+/iXT7nqe5ubkI148+PiPVvook3JLMqRh9F0AttbqSWtDyxvztFlB10PfYfE3KJmLBQFUjpm5zeQZZviTF2RZfQOW5dQOrQqidYx351jciFJB09UkkFgGDAG3vneB3jLOy/yfX/mg2CVoQ184WOn62M0cnPEWaOcFCkhKWR/sLVxtTGHp6exHWhvrGDIUvjjEvl14agqaC5sJBNdZ49a0BCkKg7bXVRhud8C2XPMD2kD3PcQApPG8cCDd/HQe+/j/T/wLkA5uLHg8S88farjMnJzxFlj/+YBlAV0XUpFk1zIKYvDwq1f9KyWIRVkhyGFRmhMfDYGigJTW+qNSQ6WgCQnMint7JjaL6qwagPiyB5kSV2vxoJ1TGYFD7ztLt71A+/kgQ88ADFw49oB1y/tntqY3AnGuiPe/Hj+iZdIrWGJU0IypRa7NqVWuoOObq899LAGjnl3pqJSsVli6gJZH6SSW9cQpHSH14VlxC99OrQxqU1Vc0abCmzubHLPgxf57n/jA5y75xyIcv2l6/TdcGpjcidw053Ju4647XBzd0E0DlEP1iEiaFUSCsEtOkzfEwtLbApcJ8RpgQwBMYHQOHxtcNnFs7++ws4HhjL1hApgRICIm1m6FsygtAZKU6BW8JVCFxEMtjKE3ZZHHn+RzVnNE09fxSpoUXLt+uL0BkUZT1lGnDke/ezTyQuhyMa5MR6pDhaLJJkvi1xMSkkQmJSepoZjcvpAu784OkE1pD5uBXECxqHWQe+JIeAkEjEpkam0gGAnJf2NBU898izRCE8/cxMpKoiRgxur0xuUkZsjbgN87EOfPvI2yQtd4MhjCI5k9Otr1soiOFIj+IBf9UftbEUBEiFG6knJSjWpioaADi3FtGQYslrC2RQXXBUcXJ/zzJefp+sDzzxxKaU9SWT/6sGpjEf6jCM3R5w9/uBDnz4q3K5bOzUHPCBpngTwfUo1tDY9HjUpcgWISmwDg8lpa8O6HS21gk8bx0GU5AO4WKHOUMwmDG1un5FUfLKVY//qnGcfe55l67lyaT9RXS17V/c4f8/2yQ/IyMsRtwk+89tfSjcOlUGpRezQi2+dSChZAY8kH8/12jXdg1+FpAjKd8s6bc0I5+7a4MYLu+mgJXsFFtMCn83qFU1qJitce+kGz3zlRfau7HNwsATrWC56rr5wnfvfcc/JD8gJcFNEfgL4XwJXVPWDX+Px/wD4a6QBPQD+96r6hW/0mmPBaAQAv/mhz2GsxU4NfjmgZTYYa/u02ayEWBqG7RJ74NEQMMuIlIKqYvpADIo1luhSddcYwVuDKWG2tWR/v0Gdw0RQsVgX2awLliEg8w4w9E3EzJVoLM8++gKlrTgIKRpRHHzu0gv8B6c0JmsjshEjzhIf+aXPISh2WuBX2d/EmFQoWrelFA45jAyVQ58GwRKtQcKAOAfGYpxBbZF8jGJEYsAvWnAO0czdaJhtlRx0gbC/gjYQqwJ/kJROj3z2WUQE33moStSVfOb3H+ZH//yfOJUxGbk54qyhqnz2I49BYSk3K/r9Pm1Gs7rgENakpEIfcjtLPGpFi5p8x6xBCgfGHxWNYgCB1f4qr5nlsADUnNsgzFfEhWTja0vo0mb4i598kjgMxC4iVWrn/vRvfp7v/dHvPJ1xYeTmiLOFHzxf/fxzIIZmu2F1Ix5Fb7vcgtbnItDavBrSJrU0iXt94rCKUm1PWHbZ18gZCAL9wP5LbTLUzi0utjbUtSNEJfYhc7bC9x2o4Ut/8GWGICgGipIogY//0qd5+3c8eOJjMvJyxO2Avu156cnLiLXYmSMcJI+wuD5kWRd9JPFS16mEkBVDBsnm1ChUk5J2CEfFpZyqdvPSfvY/Sk8tZiVFVeD7gPocVKFAFwgoj33yCVZZ0SRGGJYDn//Il0+lYHRC3PxJ4L8DfurrPP408EOqelNE/izwD4BvuIAfC0Yj8N7zEz/3cWg95XZFvVOxfP4ANYotLHanJl5fIirUN1q0LtI3xwnUJbEpUGdwvkVcpC8gVkpXVpjBY2Jk6FMKk6iABGIDsShpQwsyIFKgQ4TgCC6lNg2aWthsbfC9ByM89eRLxBgxx+OLTwiK4OPYtTni7HD90i4f/uXPotYwmTasWDIcRvoK5u4LxGUHarOsNxB7kh9DNFA3GCto3yPWErqeiIWqAC/QdmhVpsSKqEmplH0BF4uQwiSqEu08JsS0321q/GrATsu0EY4KpeXJU2x7Gbk54qzxmd9/hMf+8GkQw8bmjJvtHjFqaoEBZHuKHrRHLWbYbFpvkldRWZJXp4gV+oNlWhiXZVoMdz61jWoA1koJEOeYLzo0xKT864djp7KCn3eYiQWbRfdGePSTp5eUNnJzxFnjl/7hb3HpqUtIUbCzXfPijf00T3UDYgSZ1ESf1XmqmMoQl20q6pYFTKbphfoeJLI86HJqWt5kHlcIrhW9JqAelgctsR9yCmIuHIcU/93Pe+zWlBiUOGvA93zlc6eTMDrycsTtgL/31/4p85sr0Mh9D97F84+8lOYp1eRHhKARMIJm00zRVAxSkcRfBA0REaE9yPOtNWn+RTFGkqqIdYsaDK3HDzE9d628T+0vEJTl3gpbO2IbkveYwhOfexr+wx858TE5CW6q6kdE5KFv8PjHjv35CeCB13rN8b8eI/jDzz7NohvAGfqVT+3btUMmJRQO7z1+s0S9p2scQcGrEGcNag0RRXpPKC3cbAl7kWAcRgMSInYI2GCw3YBZtghQrJTiRkd3oPhJSd+kNhs7BKQu8NsOLUq6GBnOCXa7wqpy9asH/OwvfPzUxkZVXvfPiBG3Gr/+zz6Cv7YHITK/cQBRMaWF0sF0gq48kFJgog+oN0emnggaAtq3iBHifEmcZy+koU9xwj4mGa9LPiqEgLY9cdkS+iGbhYIQwQdsXWGqCnGWMG8hBlSTf8uzz+/x2CktfmHk5oizxc//3V9G58mI/sblGxiNyXS+cFCX6OBTkSdo8lCJmZcxppVXCMlvzPfoqoMhHi1i47HWtaI68i0TRbuO2AUkaNrgSlYU5hZTNBKXPptgp9jgr3zhefZv7J/a2IzcHHGW+MX/7jfQbgAfuPLsNYwTRBSadMgRFNSarM4zRB+P/ImCP5wjiT5xVyXxdb2T9P5IKRjTa4gVIoaQN6341LZG22UVkkERQu+hLjGADoFP/vbDSRl8CngjvBy5OeIk8Pv/6pNpLgRefPISUhvEGcQViCTrg0QzzWlo60CIFOqA6lGHqfd5zszFJY3Zn4hDc2xVTe1sYW1yBMnmOre2WUkevEoqFtUOikTt3/2Zj32NT3Ay+Ca4eUFEPn3s5698C2//l4Bfe62LxoLRCG7sL6lri60sqNBdXxELQ3DJ1Hqwgg8BNUIsHcEYKEtCoYSJwfiIhEgsBL9p02RaWqxaTOFYbjbM+yq1dIeAHHSoBoKNqCixUZyAlgZTWmaDpcbQ10qwoNc97CfD32Atv/eZ09uU3u4mZCPe3IhFQXV+C1saVAxDTCoDfDK0VuOSr0kIgObfsDb9k3aFHKyg86g7lgITA9QFujODIk/EPqakNCtZYi8YTcb1uBJpKlxTYAtBjSQ/BxVMXSUvpAC//C9Or5g7cnPEWeLut92L2aiTgs8L3qc0JNaL2EA2ys0bSx/IK1jShnLdfqZHRthCUgdOquSjYnN7jMYjXySxECLq83Osg6qgmNWpnQaS6igopirTnnYIfOTnTi+RaeTmiLPE2777IWxTIU4IUYjBopIKRGnDReKT5N2l91kpZNImte0Sh8jtoX2ffMUi4Czm3BZaFai16XW6Pr0+xzhcOGgqKCxVkxNHCwd9QFc9lIL6SJi3fPWLz5zKuNwJxroj3tx42wfvT76Y1hJ7Rdv0TRONqehDLhaFiER91bdw3YoWX3mfkAtKktav6/bTmOZNlfzUPs/BACYViuysSIemkktIbfI4EmC117LYX570kADfFDevqer3Hvv5B9/M+4rIj5AKRn/tta4dC0YjuLRcsHPvBuc3GmIcCA7CELDzAbWk6FFvwFlcSzq1XPaYpacSi04L/KzEREOcTggbTepJlYGAYoYBJwHxkfZ8QXehQYuCcjYhTCx2VVBKhaoSFfrOo/tLYqm5SGzoHJBPVff35qcyLpqNyG7nmMMRb27sHwxs37fN5oVpDoEQorG5bYX0X3CTTmco0iSM99C1GCfZryGbedYlYTqBySSZfHY94hUxBvUDUTza1IDgJjVYwZYGMy3QKr2On3fE/QUMAalKMEI8WKbF9eC5+dL1UxmXkZsjzhrtwYrz951n4+6t3KaSCquUZTa/liNTT+cOW8YAqIvcppYXu2XeUDoHXQurVSowudzy6UN6roLZSIa89dYkb4otxjqGRX+kUirSSW3YW+TFM1x9eeTmiG8P9MuOC2/dYbYzQyXzqqqTB5izGE3rSiI5gTDzzFqkzPdVZSr8OJdux8h6wxpXbboW0vOcAwS7USPGMJnViYN9CpHp2wA2yxaIsGoJl/eQrkcUnnn4+RMfkzfKy5GbI241VBXUcM/bLlJvNtmrCA5T0DJS4cemQs9a1aeKFPYw+SxNhq9uE81PEEltbjY9LiLpMEWEZrM+nJdVBb/XHyr8dP3ThfR+cCrF3LPipoh8J/CPgB9X1ddcINySgpGI/ISIXBGRh4/dtyMivyUiT+Tf527Fe4249Ti/OaUpalbBo30kGlBjCLXFBMWYSCwhlpYggeAUo6CF0ofkn+BLCKJIH4nWMNSGWBbEDcewWSQ/otJSLECdIZCuK8oSmRuGYAgTizORED3gsYtAMBA1Ip0y1BY7BLYuVKc2Nne6fHfk5p2Ni/dtUpWObtmjUdGhT/4I1mDqAtsNeXI1xDabeIok/oY0kcaqSG0tXYd4nxa5IimKtB8IxqFVhSnrHDOaPFbECSp5cpK0dtYhpFYbA1qWybxT7KEK4uK9p/dVupO5OfLyzsfOPecoC8tqv8uqoghtTjqzAnE48ikasppIs2H9QG5ZIRd5c/sKWW0UsgIJSaqESZOURM5ixGCrAo8QwtpAW49iv61J3keHr5fesypPz7Jy5OaIs8TO3edxRcEyrk1zQ1IJmdyKMnSkRWxuGVt/DWNEvaT5DQFC4mvIproxJsVRCEhdQ1MnvuViri0rTFMzGJNMswWI/rA9jrJE6xpdF4CbGs5vszg4nYTRO70lbeTmnY3F/oqL955HBbpll1rFlLS2JZd7JPGPV7RppoMV7cNRVYesvkUOfYrWPFTy7/SCALjKIZWlb7Ny0Api9EgVnIMo1rWnpHZSbrx8Oq3cp81NEXkr8PPA/0ZVH389z7lVCqOfBH7sVff9deB3VPVdwO/kv0fcZlBVOlHuvWeTZT+g1iC5q8VERRYB7SN2GRgmjjATRAz+XM1wvkF9RFYdWCFWqYhkBo9bRLAW0wl2P6DOEK1graE56JDaUd1dY60w2IiEnmIR6KaObsMQW8GGPHHHSDn3ScVfOl56+oCXLu2ewui8KU5jfpKRm3ckum7AlI7tBy6wCgaxFqoKIwLBE/uBIKkNLVpBqtSaIk2DTJq0ObUWmhpVj3jF5GISqkkNAagoiiBVhUURUbZ2NjCTKm1IjUViJIZI0Ija1AaX0tokt84U6HTKE8/eYH/vNCS8dzw3f5KRl3cs5rsL6o2a6YUNBp9SzrAu+yYk4040L2ir5AVIWWRVoEkmuCLHTOaTgpeyyiqk/J0NMUntyyIXoZSLFzcRaxi6HilTy0sMebltTS445Q3pZJKTmgoe+dKL+CF83c906zByc8TZYffqHufu3sLOKnQ41lYWFQkh7TVjbj2bVLmomwu5NiuNNHse2azQXReNijKnHQJdNpt3NokaHNy9VREWS4aDFgqLbE6Tsqmp02t0ffIJzJxUAa0qnn30hVMYmTfGy5GbI241VvOWc/edS23c2bQ6wjHFj6YwB0gFHdYFpMTNw1rR+qt5TJ6U2trSFSKSlLeS77eGux+4AD7i+wGVxHfV/NrpzQ9VRZij9tTnv3rpFEbm1nNTRP4F8HHgPSLygoj8JRH5qyLyV/MlfwM4D/z3IvJ5EXnNnvVbcuT0ddy4fxz44Xz7nwC/x+vokRtxulBVvvLyyzzz8jVkz+OtEktDEQLBWgRNPiYoKa2lQE0kFBBKS3lgkC5S3hyQGFCXNpdhBsEMWGtRl54jCEEsaMAbxb80xw8RbwQtDBRCXwjuQHHq6LYMNoBRIdQwVFD0SlFELp7fOKXxuS0nzdeNkZt3Lvb2ljz55Ze59Mz1tDgdhnRaKoIMebHa1Kj4dFJTFiiK8cORhN6C9gNaVEgFpuvAD+BcUhDhMKsOppO0hrZJtnuw3xIGn+JHHckUtO/T4nlSEYPHRMWYZHqvOcEi+shsoz6V8bmTuTny8s7GC1+9xJVnrnH1hb1sHh/AJKN52qwWqMqjAo5wFA1sBGyV1QoheaMULqWr9UM2vJa8eY2p3SVvYrGGGzeX+GGAIab3Nib5iZmcxtb16TlGjpRPGAoTcYU9lfEZuTnirPD0w89z7dJ1bry8T1weSyXUmFpNgk+F2nUbWoyH7WiUZeZgAC/E6QQxHln7kvk2PddatO+hLJDCpc1t1XDp6kH2Kksm2GpzQEU3QL9K14eYXqP3xKpAy4Jicjqq+TuZlzBy807H4597hmsv3+DGpf1sQq2IkpQ9MRd+TDoYEZu8MnV9MHm8b005VBMdVZD00IdI116BYoDklXv5uetpC0t+7pBVg5C4j+RutvzcfAYz2Zyc/MBw67mpqv/+azz+l4G//EZe8yQ9jO5W1Zfz7UvA3V/rIhH5K2uX76tXr57gP2fE18IjL73E73/6S1z6/BW8gBQFDkMoSrwzDNOCduII9zYU3uKGiOkDIrCVTTeHRjBdj9nrkEFTslpj0umJyYqjVczVX1JxSGGIAWny6cygmKDYg0CYFbhpwUyhqhvMdsNwV4OJEATA8su/9JkTHxvlTevFMHLzDsCXPvoVPvqLn+LKV19OxRjn0gK0KomFS5PdEDDTrAwafMp+KGscCvMlMaTNrLQtxkjefJJMOoMSrSRjzhgQ44h1hYrBK9g6+bEoghhDaGpwDlsVFOc2qGYlUhq0tGkB7AzaeX7rX33yxMfmTcrN18VLGLl51vjiHzzGb/3sH3DzpRu5BS23gWXPBCAlEDp3ZMDpkk+RGGDV5jYXyYqFdVEp37dugSkLgORHlr0bQoi4uoa6TsWmwWcvB7Blgd2c4kqbFt7OpWsE5tfn/P4vfOrEx2bk5sjNs8THfuUz/PbPfILFlf0UArH2PMkJS6m1LCsJYk5WsgaqEmslpaP1A7Q9sjdHhpBbX9Ladm2kixHUmXSfLdCoBO+xs6TqU42wbGF/jvghJ0EZymmV/Fk2ZjBpUANXX7zJJ3/j8yc6Lm+UlyM3R9xqfPhffYL/8ec+Rbu3zAUZSQp2MYiRRFNrwOUCksZsTn1YBkq81dwamtVBh21kUbNPUp5zTVIQiQoxRqRMAS+iuShE8vCUwiKl4KbJW1BMajNV4JmHn+PLf/jkiY7LncLNUzG9VtXDOuDXeOwfrF2+L168eBr/nBHHcG4y5b677yNsFYSNAp1YsIpWaTLsG0VU6YDQCENjsAL1fiBcawkuLXRjU6CbNeKEdpoqv2UwmHmPWQZcANsrcWaIhQErmKbi7nu32biQDHZFIs5aRMCdK9BJSZhGyk2LG5RQOMKm4/LVJT//K5/m4UdO2ChQD1WKr+vnTsTIzdsXD7zrPu56z31IWWBsgS0shTNIkVpUgk08ovWoCDIEpEtxwH4IUDgMgimKNAln9YKpS5hNUDHI4MGDYlCScbX0HaY0PPC2i7hZldpCqwpXFJiiYHLXFhJAC0fVpBNWrSwYeP6lG/zcT36EZ09axvsm5+Y34mV+fOTmGeLd3/12Lt5/MRVllNSqUuSNaVHmNhZBly0giXt+ADT5l2ST3EOfoXViWl1CVcBkevRm1hwGpDF4xFrue8dduKZAjGCmk8P327pnCzRim5Jqs0GKXMQaBp5+/BI/83d/leuXd092cEZujtw8Q/yxH/oA2/ecQwqLsTYZ5eoxdd/WRirodF36Aq7b0JYtYbFKByplCnSQtf9RyJvQISTPvhBQH5B+yMVdoB0onOO+B3bSe4b82jkGfPvec5i6Stw8P8ndNCmp7ZE/fJJ//p//HN2qO7mBeYO8HLk54lbje3/0O2i2mlQkKmwq1six/8OMSfMjHIqGEEGDHl20buGGlKS2LvCIHAmNIN23/tJHpZwV3Pf2u6FKKlux9rAwtHnXBliHq0ps444VmeFzv/clfuo/+7mTHZg7hJsnWTC6LCL3AuTfV07wvUZ8k+hi4I89dBflds1kp4YKoolEIuCxvWBXkSIIVCBWCM4hnYI4onUMjUVF6S9a/LkCJ4JTYTARP3GExrHcMAwbjiEo0gfUGPrVQHtlRRg8ZWHwRUGwYD3Me2URIq3Cqhuw89QCY/uAF+Wxds7/45/++omPz5s0gnTk5h0AZw0f/K634nZmVFslUjiixpS8EiK2SP4KUuUNalWgxmaTz7xAroq0Oc0qCLE2FZmMSRYrTUm0kegEbT0aIIrBryJ71w8QTQb32BRFLBpZHqwIIgQvdPMBCbk1ZvAMB0ue+dIL/Jd//WdOfHzehNwceXmHoKws7/3+d+I2JzTTIm9K7ZGayCS/MZmWqR3GGsBkJZDN12TPFMhph1mpRL5dloePq88R4AJD3zPfXaAxItmLIT3XsXtlj4Chm3f0qwFjc7FKYbG74PFPf5X/+v/8kyc+PiM3R5wVphsND33ng5iNhurcBDTAZII6g5Ql+BQMIU3y8JMhm8vHdDhKjvw+9BUjgAExOS2ttGhVojub6W9JXigaA4P3KYY7hqO2UGsR37N70BK8Z7W7oD9YZQXTAtMF9g96Hv3YY/yTv3my8+Y3Ed19J2Dk5h2Ci/ee476334VtCqpZdaTG1Xh02xpE1sWcY2rA9ffRJDXSodk8kubP/HwRsv+QQDh6XjcfcsK2guWoAKWwfy0l/3YHLWGdkJYLTVdfuslnP/won/i1z53o2NwJ3DzJgtGHgL+Yb/9F4BdP8L1GfJPYmdX8wPc+yDvfcRfv2dmi9OAngnilXAqmsviN5KEgewolyMTgNwv8ZtqImiESSoFBAIM0BtsYYmXpNy3qciCFFWyIWRlhGIxwY6IcGEG94gqQyqb1bx8pusjGPGAxxC1HnCR3eG8jk9rg25M18Ewyxzs3UeIbYOTmHYCNrZr3vf8tfPB9d/PQQ+ehsqhX6AfEZTNrY6Dt0imLkBbH2TchFY8keaOgMG2QpkTKAtWIqk8tMj5ihkAsLZiQDmSGnoODFT4EjDVpn+skLYTbAYxgZhUiiiAYn7xUonWUOxOijNz8JjDy8g7Bubu2eO/3vJXv/sF3cM+DFxI3/JAeLMvc+hlSqotxUBdHRaHgs1Hu+jjU5OfYVASWrDyyuV1tGNL1IasdVh0H1/ZSWqFLrytFgTCAj9iioN6aJK+U9ULcWILCZGuKNye57Bu5OeJsceHebb7zT7yLP/5D72Nza5IUPiRFbPL1GpKSLxoIHmkyh6rM2zJ7j61VDFWd1EpNtnz1Ck2FdAPMV8ljpUtJa3F/xXzeJkGTTepeM2uQ2iGrNE+X0xJMgTV6eJAzlJZi1rB/Y3Fi4/JGeTlyc8Stxs49m3zX/+T9/LF/4z1MpxU4ix7KZSSZVaumtEKNSCm5DrT+Lsas2EuHJ2ItuPx7XSgy5rBtdC03UxHoA+1BOjzBGqR0qXBkSKomaygbl56xLl5JOigtpxWPf/6pExuXO4Wbt2Tl8LXcuIH/Avg3ReQJ4Efz3yNuM8yv9zz6uatcv7LHC8s9SmexQ+rx7DcdgygmKkOMSPDIPKCVYCpD9IoJkTAx+O0ShoCbD3RVYFFqNjET1EkyvZaBWAlowDJQmEiN4GIgFkpEMRqhMTR1iZ8ZejyFj4TCYi2ESvA+stwdeHl3nlKcTgy3f0/pa2Hk5p2LG1fnfPnzz/Li05e58txVKg1AIM5qorNEJ1A6BusIUVFjMRuT1H6WT12064nGprQXhbg3Jyza5FmUazrRmNT77RyqgmhErKNwDhlSqoz0Id0vSl1ZogqhG9DSpg1oYcH7FB6zt+Lpx0/6kO/O5ubIyzsbzz1xmcc++QzPP/4y+3tLphMHTlIakmjyHirKtHhVhWgwk4rDAlHwqfi6VhqJgfmScLBKnkTDkFpjhFzwMel2UYArMKZAkLQo7jpUB8RaysYRY6DvhuStEjQb/iYj3uV8yWMn7McwcnPEWeKrDz/Pk59/lqcefZEhRKbbU0Bh1qQ1qXXgUkIZ1hHbATut0iFKjKmgpJr4k3tcVCGuDbONoN6jdQ1NiaBI8Kltpkp+fnajRsoS7Xs0BsQUFFsTqGqCDxhniGWFzCZEZxgKS7/o+cMPf/EER+aN8XLk5ohbja9+/jme/8pLPPvwi9iypKwkK3AtYk0qnBiTCkIq0INpHLr2JOKokHOoEIpCXKf2Hu/VMiYfmOTrnSFEn9bHATR6UMU6QzEtQaHv8+NGsk2KIAH6gxUf/5XPn+DI3BncvFUpaV/PjfvP3IrXH3FyCLXw1I0bdL1n92CJD4oVi58KwUK5irBKyqKhNlgRtI0kFa/S7liCs5g+4tQQnCZTscFQHASsU6Iz+AaqhSIaMc5hxKMKyxfnzC7WHPSBKKAVuCFy171brJY3U494GyhmSjcIrguYQYgSoVM+88izfP93vu3ExudO7OM+jpGbdy5s5bi+bFmqYb67hOUA21M0JtWQBEVz6pH0fTLGDgH8gGKTmfWyTakPdZGSWoxJC9gQ0cKhrkCGpEJQQCY1GiIhepb7yvR8w3KvTwvlqkDqivNvvUj3/HVC73F18nqgL1CJiFiijXiFK5d2ueue7RMbnzuZmyMv72wYZ5h3nv2Fp725T2g9dtoQhpBaXkxMG9N1ApMqcZXaNllbd/qckGZTsTUZeMYjD5XCwaJLKiIRmExg/wCAbghs7Ew5uLlM9aQoSOHYeWCHS09fIyoUdUH0pOd7n4u6hm7Rs1p0NNOTS2YauTnirND3Hq+wt9/S7+2hA9itDUIfkmqvGxIFI4lXZZF4m/2G8CFdU1c5fVBScSkXbmPw4GqM98k81woUBaoDMUT6LjA5P2OxjJi2BWdxClt3b3F1tyMeRIoKghG0LFKqok+EaRcd3aqnyu1ytxp3Mi9h5Oadjt3rB6jA3s0F3g/QRbAFEuOrlEYmK4kiYRWPDlbW6liRo/ZvhEOT66i5FTSrjKzNJSbNXp3J3y+0/rCQ5MqC6c6UG5d30V4pNhzee5Q8H+d/1u61A0KIWHsyCt07gZu3pGA04s7FL/7uF/jUF57GGKV3jrhp8csAVpEI7VSwpaNsI2ojvnJgFdOBdUKB4m4GhIhvDGhA5gYXNU+oFj81CIqfFKmy24GPQmEgTC19IcRpMrZ2GwXhumcSDUY9OhNWYtNm1keiGHTTcM9bt9nennDQnaBJINxJstwRbzL8yr/8GA9/6il01UNQtCxSvH1VoEWDBkXEghPiymJcMveUUOAkEIxNp6BtD50hTsskPCpSuhqFSwXZIm02zboPvC7zwlkZVh6dLzEbU8zFTfTmHKeK7zqCs0QjIJZYgNgKrPCWh3aYnZvx8okXjEZujjgb/Oo//X2+8snHCSESVj2oIbQDOJM2mv2Q9NtVnWLuhfS7cDlpSVJ72TCkjWrhoGmSzF4ltZp2HYeR4C77I1Vl+h087cEShgFTV6nVtBuom4o49FBUxF6JWbavpcMAD7znXnYeuMil567ztvfdd2LjM3JzxFnhN//lx3j2hT1UFHpFnEOXXQpJK4u0GRWSv4l1UDk4WCbFXpEOVPADMeZEQ+egKWHV56LSkQm2Vi7NsYtVVkUA8wXttAGUaqMmrnpElNKaZKpdlumQJvikppjUGGN46K4p97/lAtdevM7977z3RMZm5OWIs0KMkd/+6Y9y4/I+McSUqL0u/BhIkvjsHbRuQ1v7EhmTUniH7C+EHBWP0Nw9mpX1MaZ50ySFUL4k/U+E0HtQqM43DKuAOMEagQHEGob5cEyVZBGEB993Lw++/y3sXtnj/L3nTmR87gRunkpK2ojbF88+dZXlzZbpTs32tGajVYqDAL0mG4UeyghhyxPq/IVeRlj1xDZgDwJ1ELAF0RhEDUYUNWkylcJQrAImgliFPqYNbmGJE4MRofNKfGtN+ZaauUT685YXZA5lTJJFr6lfvI+YwiBqGfYGgo/8/V/6KL0/Gb+U5EZ/e/eUjnjz4pknX2Z1MLBz1wbT7SnlzgbGR6KSCjU2FWjxmk5BjaDDQFj1hJhlqy75GQUnOboUaPt0bVNhvYeYUygwRCOEJkeLokRV3FZDtT0hHHSoOK6teuzGjMq4tOheZgNPQNQQUJbLlp/8u792YmMzcnPEWeK5Jy6zXLTc+9YdpndtU9x1Lp9yZj+FukoL165PTxBJSjxN7WdSFFBVOY2JnNQUYRiSaX2ZvRSMSQqhEAHNwS2JyzEotnEUTUlYtMSoXH5xF9NMKYwQAPUD6vvDFBaxwu6lXX7qv/zQiY3NyM0RZ4kXnrnGwf6Kh95+N82FDYyzyY9ekhGunstm1aQ2bAaFbkBXHboakim9Tb5fWmQe+1QMUifEjQmiA1qVUFcp7SkErDFYYxCb+FrUFiOC7wYGH7j+8h6uLnECcdVD36HLFul77KqlnFS88ORL/NO/9a9OZFzeKC9Hbo641Xj52SvM9+e87X334zaqI8lKJPthZuT5UnLHGpqCXETWnka52JTnXGIuIBlYt5Ee+hCtXy8/T5zBblZIFOIQGYbA/u4qpQdbOXyvo2hSZXqu4alHXuD/97d/6UTG5U7h5qgw+jaHn3dcLGvumk545NIBpos4ExnEYhBUlIDS2wJqgQrqlyNihL4ApoaiF7zVXCwKDCo4Z2DD4UuDGxRd9vhGKKIQTCSWUN8c6JwBH6m9YELALsFOB7rasVzWbCw8UWFoKqwOSBeJM6HvAy8/fQNzsUnRpyeE27GPe8S3Bzos2/dusHlugxde2MUUqd2TELFR0Kw6iCZtIDUGbO9RFK+RWFS4IaI2pE3sqkOGIfWK1wVGA2xNkgLJCOI9LFKyku09GiM+GmZ3bzEMER084hy9M4TG4lYtZt5CUxFMOrmxM8f+QU94aZfY+xMdn5GbI84Cqko0lvNvvZuiKlktPG5qDyXzqjm5jOyD4nMr2rpoFCIah3Tf+pS069JPU0Hl0gHppCb4eBTp3fVo1KS294GgjvP37LB7fXno6aAoUlvC3irJ/esitb2FgNQF16+1hLalmhUnOkYjN0ecBbq2B5R77tpgCIGuV8pZzWp3gU6LpLgrLGaR2lXUGqJRTOGQtj/mg6LgCkQD0nVoU8FGjQkBySEtlBFZeiAXhY3FWJM8xbxns2rYvXQjzZuTirgxwcyXRI3JhLdwiChx3iE7M158+irhYMn5e7dObHxGXo44Kzz3xMvUk4Z73rrBctHil4FqVtPdWGUF7boIdKzYE47a1Fh/d5V8+ALJrZ5c6JHkIzjk56x7vOJ6Po6gBm0DG/dtsHtlP7W9SfJNwkboQw6cSOlshAil5YnPP4uxlv2bJ6P8gzuDm6PC6Nscly/tc7AaePnlfWJliIVw3wMXUWeIMeJnSmjARoM4QyiUUApRoWyhWAlDJWgh+DIy1Cap7x0s74v05z0alXZbiQ58LcRaUkLE+RJbGoohEncH6rqGbP/QLRW75+kHj9qAcSARit5Te6g3LJ2DRgyFO7m6p+rr/xkx4lZi9/I++zcWXLu6D5MS4yP3vvMiybUo9WjHwiFZNisHS8RYTFkgXrAxEIc+bXDVQ1WiG1NiVRLEppCYaYFvKnydTOtNYZAYUp+2K6Eo6SNMNqv0JR964spj9nt8VFQ0pTEVBTiHGGU2K/DCifV6rzFyc8RZYDlv2b2+4GB3zsF+i0wcOgzc8667SaZBJM8ga4+KRENS+aAKvc+KIdKi1CfzTeo6LXoHRRFilYw4qYr0uEupLqpAWWAKx7L1TGdlMqR3kgq7fSDikpxeQGyKAzd1ycZWwxAi1tkTHaORmyPOAo994TlWq8juYsVqfwGrFbJq2X5gG7XZO2y+QlcrghjitEI3p8TCpU0nMal1XQHqkQhqHYpBu4Df3kQnNdqUqQ3VCtGapFSqsirQOWg7/LyjrAoIikHx82UKiBg0Gd53A6Gu0O0JdlIy2ZnSD5561pzY+LwRXo7cHHEr8cWPPE676Lj24g0gmVBbYyl3JlnN8yqfIkhz51oeu/YpWj++/oKaXCxat7FZmwo+66AIm0NZXL4fUCJlY0HBGmFY9cR2SIp5I0ctaSKUk5LZrMZ3ns2d2YmNz53AzbFg9G2OD/7JB+lsRAtD3abF5vXhAD+L9HcpfksYdhJpo1EkJL+TuGURo0BANIADnVgIgqLYPmIvBdwzPRo9JhiiJZH+3oDdjCkO3CeTz85HVvMe3W4Zdnr8kGK6+2CwPqAhEmeWOCuJ28oNlmxtl6xi4LFnL53Y+NzuEsERb054H3jo/XcxiGBKwXhPCHDj6k1CjKgBbXvMKnl4iQOjkWiFWBZIaWHwKaGwThtPrVw6NKmKnKCWTkiNHzDLDh06qGvElsmXhYgYx3DQMywGgiZRRL8/x0clFiWxLAjWoDYZAcdlx9XrCzbqkvn+kt295YmN0cjNEWcBPwQeePtFhqXHOgOrntD23Ly8l1rOBp+KQNEnPzCyAXZRgEkpSqyTC3OhlTIbUNcllAaGmOZGkdTW1qVIbiIpAjgm+X57c8ngldh5/HyF7zriEBBnc4HJpXWvEfyy4/qNfaZNyd7NJd26Xe4EMHJzxFng3M6UC++4wApDXRWoCF3n2W9jMpiOkjyK6gqZ1Xn3FTBlQdrEpnRDrUu0KFKRqXBEDQQDuljiX7yC6WIqArctulqCpITf2PfoMKBDYL7qGIoCRRnaQIyS9sVliW3qtPsqBCYlXTtwsOyZbtRceeHGiY3PndD2MuLNiXd994NsnpuxXPYUlUV8oF129MshFXOMZd1KJoU9UhmZnFq2Ri7ksE4zWx/ErBVBkgvDMberWZc8O72mQApV9q/PGQZFRBgWIf03Yd2pYkgFKGugKOjmHb1CUVtefPLk0n/vBG6OBaNvY8SofObLzzHESCkR2gBG2Z13uACxECrXIvsgUbEDSIBoFXwgAL6waC9EBO2VbtvQbylMFOMiiiG2HjMYDEJsoJDA4AU/L5hWAbGC88pi6Ym7Dv9sRVwqtB7noN+20BjMpoFNQxBFVpH3P3Av9919jueu753I+Ci3P4FHvDnhQ+QrT12HzmOtI/rE1/kqonVBwKMxEGc11KkQFDen+GmNWfWoTyoGYyxaFWhZEIq0mTXBoyGANYShR9RCYdGqRjRg2iUUVerpdoAqi/kSnEWN4IeQ5uwQcnpTgWsKxJIMQGPkfT/wTi687wG++vjJFHNHbo44K4jA81+9SowRsZIUCF7pVmkjqutT0qpOZp1VDU2dCkVrk88QoMxFHQyyVj8cO+WMIXvzGQPWoe2QFrxliakKsAXGGdqDVfJMqkq0S4tkzYbaxhpM5dLri8EUjg/80PvYuGubl566diLjM3JzxFnBFZaXri5QNQTNSUteiXsrTB9Qq+iiTQo967DtgHv5OtJm83kFYkSdyeESBcymaF0m/5QhIPfsEIxCjEgEEDRENAJFiZlVSF3A4Bku3SQOA6xa/MECoif0S0I/IHVNURisQKhKrCt4/w++F2cMbXvri7lvlJcjN0fcSlgnXLu0C4GkhEWIXnPb2ZEfUequzp5ENithNXJogn3cm+h4alq68UoJjjHHZDlAlVvFVdDWpxJRjDBoSjzMxSapC0xdYApBTFLqvuu73oatT6ab5U7h5lgw+jbGx596hudv7KHnDAcaac9Z/H0GNi0SIvXcUwyCVUkS2nKgmiXDXD8RfGOSka6zaGmIs1ThVStQDYRSWd1f0p93xFoZKiWIoKGgWBiohSUlGhQzKGGieCyxingbofcYGxisIIuI6QIX392wstCK8BJ7nL97wkNv2T6xMdI38DNixK3C7/3Wl9h78SaucsznPcYKFAYqh+kCro/YokCcJRKJkqTxrhuIhuSdMpuhRqBdpfZwFeKkgN6jzhI3JvhJTTQgOZ3CLDvMbApFMtZWlwxAtffErgMUayzqI36jJDqHKpgbK+461xBDwM9bbjx1mfsubnHvvdsnNkYjN0ecBT70Ux9hsTenqAzL/Q4TIlK6lJA29IhLBSOxJm0kvQc/JHP46NNJalWmU8zVKm1QQ+DQ8dOQvIzKrE5ae6r0bSrioumu0qIiKTlmGEhy/GTsSzbVjhH87gHnZw68p72xz43nrvLWBy+yc/EE5fVv4GfEiFuFf/Z3f5243zMJnvmNAyRGTOkw3mN35zgfcE2Z0kH3FwQRQuES55zL7aMDMm9h1aIxIjFiO48sk3qPskqWDQJalRgjmOgheNCAdkNKPysMcXNC3N4gbtTE0qaU37qBENDlEnNjyeasgX6gXQaWi473fd870vucAN4IL0dujriV+Mf/2c9BgMm5htVBi5jUrQKkOU6yB6BI8v1bF3pEkkroUFF0rBXteAHJ2qMCExzzQ4rZxF6OmWOvza1fxTNrAUlzdlSqSUol7RYdMSrv/OBb6FYno8y9E7g5Foy+jfHxLz+NiuF84WgihEFhIchg0WDwxtJeL4hlRI1Q+Z5CeuJMUSxFp5ihZ+YUu99TXfFoCaIR7S2mc7iVIq3gC0UqTW3iWHS7YLirYKVCtVHSzgwrB5TKZGGJjTBccHSlYGwy2PYdXL7cYg+E2ApffWGXD3/6Sf7lb3z2ZAZIR2n9iLPBxz71JDJ4ZhslZqNIUaCimKDYZYu2PXGjSqczfTo9USBWBdQlKgZiwDUOFYcpHGbZYmMkFA7jwfQecQadVWhZoIXDiCLrGO/FirpyxN4zaIooLY1JsachoD55M8RhwJeGy1f3QQy60fDkU9f43X/xUX7v1z9/MgM0cnPEGeFzH30SAWYXppi6JISAFhY6D8s2FYfqCu2HdFJaOGJdJYVRUaQENGcx0zrdrsrUdmZM8vUMmczOoa5AbVIimbrCOANxQNuOsi6Iqy61vyGJsz63w+UlpaJQFVy9Nk/tNpOGx7/4HL/1T3+Xz3z08ZMZoJGbI84IX/nMU4hRphs1xtk8L0ZYrlDRVJh1Dp1M0ElJnJaE7Rlx2hDWvl6FwzVlskzYP4Cuw2ASl5xBQ0CqCtnewEwbJCoGwZYFJgQQQ2mESDpskZDbS62B/UVKWju/RdzeIkxrru0viKUhzBxf/sOn+OWf+DBf+cxXb/3gvEFejtwccSvx3KMvg1Gmm02KPlMFspefRsSYnIJmjoo6cqw0opo4tPbGXBvUC0eHKsqRMil7GIkzuGluAY2RYlqgrT8qNuX30qhJyWRsal3VyOrmMtWVVHn4Y4/zr/+H3+L5J05ANX+HcHMsGH0b47GXr+AqYaOesOcHCmuwJmIHCN6AcagB0xqMCmFesDqo8ec9heuJpWDuq2mdIBZiaVExSW3Upcm6CgFTK4JBFhbEUt60VDci03nA+sj1CxE/S+9R37ek2rFsLwI6WMxgiEEIFfQTQ7ebJt4yRpxY4gDPHdw8uUG6xSVfEfkxEfmKiDwpIn/961zz50XkURF5RET++bf+IUbcaXjx8h6yPWN6YYuD68vU4r2/wsTIsFmjmzUaIdQFxkpS5gYl+I5gwEikJqUTGmuQqERriMGTRbnYrsO0Q+JrWSDTEs5tEJsK05SItSznS4ZZg25U+ElBWTnEJQWDUU2nrMYQSosaixl8MuHebNBzM5558vLJDdLIzRFngOtX9yk2pky3N7l59QDTFDBfAQGaGnFlWsTOJggp2dAoaVFbpDSYuoDYdkkJqDH9RpOiz62VRwEmE6RJ0d3MZlBVmKoBEVb7C3TaoFsbUFiapkDqKpvsZrNQ1mkyBcSIqQqkmaBlzROfPYFN6RojN0ecMrwP7O93mFlFWVn2ru0j0yr5X7qUzks/EGNApzX0EdMHTFDC5oS1qW6xNUWbGp1OUoFXBDQQywJjDG7IBdqsRjBVVhKu1YNlSesKglEkgNQ1VWGwywHjwXYDphswPiBek79KP1A2BWItAcuzX37pZAbpFssYXouXIlKJyE/nxz8pIg8de+w7ReTjma9fEpH6W/+AI25HvPjUFdq2p57VlHXJ/s1VTjSLpB7sBFVFYy4E+QAqeW4EDLgmG8sf60BbT3Mg5Ik2F4PATsrc4q252iH4VVbz5tZxV5v8fU8qJ1E9MtLOZtuTjQbB0i0DNy6d0H7zDpAYjQWjb2NsnjfsuJL3bF7AV0JrYWWFuLHATj12GZANJU484lOBqIwrjEbabUt0wqAdugH9OYs6kDbgBqCORGtgpcnfKCiiga6MrPxA2PesOqXY8TTXNbXRqNLfbJi3SiwKbOlhCa6LlAtlsu9xpSfagDsXmG4HpihPfvkyi2V3ImN0Kyu+ImKBvwf8WeD9wL8vIu9/1TXvAv6vwJ9S1Q8A//Et/1AjbnvMasvWuQnvevtFBicMVUE/KRlsSnaJm1OkH1A/wKonqmL7AdSgBoIInQ+EpiE0JQFF+o5oLGFSonXBYBwSFdpk4KkDhKUnrgZiVHxRgCnAJH7aIHReoSjxG2WS3ZclNoBZKZQFflahpaMxKSXtcw+/cGJjNHJzxGnDe89kUrJ11yZvf+/9RGeJKtlQl5R0VlfoqoNuSD5f3icjbFkvQiPtqs8Gu9lsN0QIMZlVl1VSQYSY1EpdMtKN3uNXfVIP1k1KJVybe0YYugEAacqUrFalhbZIbperHURlo3G4jYaPf+SEFEaM3Bxx+jjYnbN9zybbF7e4/733E8qKuGjRVYtON9CNDcL2lFgXyO4cIWL2lkkVaDKHjTIsWoJGwtYErQvoPMEIVA6Zt+jNObGQlHY49MQuot1AnHdEH9GDJbLqkqdYWYLGZHbtgKZCYjK1J0TUFcikQSc1QYTppAAr/Pa/+IMTGaNbqWJ4PbwE/hJwU1XfCfwd4P+dn+uAfwb81czXHwaGW/lZR9w+UIHti1tsXtjg/L3bJFNqkgJXcivZOgFNX1UVUXI7GfjOp9Y1m4tIUdP8t25LCxw9VyG2AQ2KP+jTP8Lk4pC1rzTSLuTQQFvXrWpBD8MpupVn48IEKYRf/InfPZkxGhVGI25nzLueYOCRl67QTGpk8Ng9wxAdoRVMGaiLniZ4qAS/LQzn0gLZAL6KmIXBlB7RCMYjxhA7Q5wKbh6RzmL3oRjAHXim+55Yw/xuy+DAbwjDTPGVJlPCuRCHgaEHWkMohFgIbjbgig56pZtYdm3JohHYNsyLgYdfPhklwy2OOfx+4ElVfUpVe+BfAj/+qmv+d8DfU9Wb6f315Gz5R9yWUFUWUQnAY09dYvPijKikBLMYsUufijV1hXhFC0s0QthsMM5iCgchJKVQ14JJUcFhMkE3G3xTHkl3ixJZrTCLDqfJN0w7T/QB6Xp8ZVBnsb0iMeIJqPeYbkCdSV4O0YNVvBHipMTHwNAP2DCwP1/xwvMnZK47cnPEKePm5X26tiPGwFNPXWXzwiylukwmQPJeiChS1ynJ0JikXMjyd82mnKasIHiiMclfYVIRNhri2hjbWsRapOuSN4otkgfSMKD9AN0qS+cViYpakwJgfI8iRNVUPA6euF4Ye0V9ZOg6GHpuvnAjpZGeAEZujjhtPP/EZWI7MPSel68v2bh/G+09GIf0PWZ3gfiIwaI5eReNGDHERQthSJwKHh36ZDxvDTKp0a1ZShxEE4etQ1crWHVIyEbzIhD65FNWFHDQon1HiOD3F0jZQFFkQ3pPdEIsATH47Yp2CPiQ5tlLT5/M1/cWR3e/Hl7+OPBP8u1/BfwZERHgfwZ8UVW/kP5del1VT+Y/RiPOHE9+4TmsNawWPQfX5jRb1SH/DmtDhU1emiYFNCRPo6NCDsYcdrChetR2VpapgHT4pU0+ZGhMxR/y68d0W9fFqfx36CN40vsdfuklCZdEECMMfSRoevPnHn/5RMboFnPzRDAWjL5N4WPkvtlFosALNw6Yz1sQgx0i0pVIcFg3YGaReG+P3zQMtaEPDdVVoTmIGLXIxNFcAekEZpZQpcUr+wa1QthQuguR1V3C8j6La2qKhdBMWsxmZBmm6MQlb5VKCU4J1uT/VqRTGDuP+LbgYDohxIJqL1LvK5utQKcwGfjdxz5/y8coqRTfUMX3goh8+tjPX3nVS94PPH/s7xfyfcfxbuDdIvJREfmEiPzYLf9gI25rDD7w0EN30cbACy/us9xv80QpSV3UVKhCqAxxYok2tYGGrQapK8RVUBTJ8NM4QuXQaYlWDnUGaXsoBJFA0ICWBVEkxQI3BbEuiM6gsxpnQI0SSsFXFozDzLK6ofeY3hM3JgwbDQZBFh0SlObeLfqqwreej/za52/5GI3cHHEW2NiZ8eAHHuSg9Tz/1CX6gy5x0xpkNiGWyQQ+Sl4Ix4CqgWlqeRFI3LEg1mHKMhnQmwIpKyRkOXzboX0q/tANGKMpCU1JXkVNDV2LakxbWAUGj51OEWsO44WlrtEyeakgoNYxuesc6gr6Zc/Hfv0zt3yMRm6OOAu84wMPsHP/Dvurjq8+d42+7ZP6wApaV7AxxW839BObUgObGuoypdvfXGC8AAZbuFQ4mjaYsiJYg+0CLFqYNhAD5toeBov0A2JianGxFikr2Jilwm3tIHokDMRJifQdZjnPasN0MBQUonroBvxWQ1mnxMWDvSXPPnZr1blvlJevg5uvh5eH16iqB/aA8yS+qoj8hoh8VkT+01v6YUfcVnjwPfeyddcGi705Tz/6Er73Welj0sGIzcWgdcIo+XbU1JomghiDqe1hsUhM9jMSkiLIGCAmzufnipOj4pJ1KZAiHladsndRqhWBHv571gbbGrL5dtCkUIqwe33B/o35LR2fb5Kbp46TyYgbcdtjMfQ8/ORLXI0tuhUxjTIVx1JgOii7m4opbfpiBiX2ipkEbGHwg2PoAoUkpi2kxLYRWvA7EGtw3uD8AHVErMGpx4ljZQPlTInGIkvD9Hqkv6AYDDQeOwM1nuKqIM4wTByTPjIEwfbCMIG+SHLBa9d6dkxJ4YRpubr1g6Sk/0i8flxT1e/9Ft/VAe8iSXQfAD4iIt+hqrvf4uuOuEOw7AYeeeQFVrtLwqBoCZPNin7e4Z2lt4IEzQtc0oZz6rCth90DaFKSkpmUhKDYkE5iZNmialNyWudh5bFN8inDWobgEZToHKjFTB1DaREf0OChV7yx2B6cNfi6wgTFWMFaGJxFFhFV2L/RsrFd472y70/gOGTk5ogzQLvsefSLz9FdOwCFsJGSVPoQUmHGp7RBDclrT+s6t4z5QxtqIZ2gajbrFGPR5SoVfqxAH1KhyMe0wC0L4qpPC+OcwGaaBmIkhoC2bWpTM5pORU1S/hmb5m6pHIQUJ64+0s47ZuemaO+5+uL+rR+kkZsjzgDtouPJr1yiQwmFwSwGivPbBFLCqK8KzLyD6YToHHbRJs8veiAm5cEQid4jnae4so+2Pdq1RONgVhFXHWIdWpdpU7kwKdFMJJnX9x6jipZVMrG3BcSAaCpCibUgBmOEUBY005KFQLWbWk6HCFv3bKMx8swjL/Dgex+4dQP0xnkJt4abXwsO+NPA9wFL4HdE5DOq+jsn8F4jzhhihSe/+DwxNx0WhcM3JRoj6gNgD22IDgtH1kCflEJYm0ypQ74oxuR1tDauPkxEs8fSRiW9tlmvP5P6SKyg69a1qEj2O5LSpvsNSBRM5Qht/geHgEaYXWiwZc3ezTmbO7cwZfSb4+apY1QYfZtiq6r5kbe8nW1TMTUFm1WJbgpsRbp7IkXVogtFr8ABNcNU0M5Rtp7i3ApHRFSYVjBsBsIsEKeKG8DZSHnQoVVAekNxSaifE7gJQ6ms+ohcUaIahkbxqngHfW1gGrhnMjC5uMLWBZubBRcf3GHzngnuQoGdGcwq4vC85Z4JxiovvVjSr3ZOZJxusUTwReAtx/5+IN93HC8AH1LVQVWfBh4nLYRHfJtge9bwvd/1IPVGQ3muZKtwaUPYlFivSP6JBugGolUQQ7SG/tyE4CziHGYztb24/QXS94gPyBAoFz0MHi0NpguYYKC0EC1DH1FrkEIIub2GAGE2we/MKCYFoS5xDjZK4e53XmB2fkZjSGagAiKGtz60w9ANLJcdO+enJzJOIzdHnDa2dqa897sfoj43obo4Y1Y6xKSFqLY9+CG1pjiLzhdpUVs4VA1xUmdPBMWUJXhPnM9hCIiCeI/0PqmLnKQ2bz+k1woxvW5dpdS1riP0ISWxlRXUNbYuwRiKWcHWxQ3O33+e7bu3qZ0gAtElpdHb3/8AbeuZd5H73nPPiYzTyM0Rp41zd2/xjg/eTzkp2ZhVTKsSMymRzWmOzzZQFMQY0s7HQBQlTEraezbR2iHeY0pBJyXDxIIo2nsk9Njr+6gTho0aiYosOqgL4qQiOEsMAdDUDhoiMii6MyXOalxTYMqCwglb2w3b56dcuHuDouuxewuCKuIs7//T72F+0LLYW3HfO289N29x28vr4eXhNdm3aAu4TuLrR1T1mqougV8Fvudb/4Qjbkc88La7eeDtd2MrS7NdUVUFRWEQY3P7Wb4wJPWPAIet3NYmlZE1mNrl4lA89gXVzG9N92c/wCQbMhCTM7aIpAKVJrUSzoG1mFkFxmGd4/wD59i57zznHzhPUbpUTMqqp/f/wLuY3+xY7s2Zzm69P/vYkjbitoUPkUefv4SLhimWfh44P3VszCyNFYwzFBc95SwylZ4KxajiO8vmqqMpPa43zJ8b8F1InI2GggAaiPcLsVRUFL8B7QWHqRQzRKx16IZJXC4twRlYCebA0N+wbIYHmWzVbJy3FKVhsVzS3Avzvqc7CNhg6KVg+8IU3Vjh8Pze5790MgOlb+DntfGHwLtE5G0iUgL/HvChV13zr0mnpIjIBZJ096lv7UOMuJMwP2h56vnrFDNHWVcs5y3npo6yEIqs+MFArBx+q0rpLV6Tz5CAVBbdmjBcPoCVp3eGaCKxKFCBsNEk9UNT4psyncYS8Y2gTYlMa6RwGBWkHYgETIiYNvDWd97D5lbD7OIm2pSsDlqanYZdoNfU+W0nJre5KSqGX/rQZ09moEZujjhl3Lw259q1A4pZRWUtq4OWnXNT6tJQVmX6qoUAroTNKdT5PmtSIXZSYqazZFA9BKQsiYVBC4cYSYtaFKlLosmeR2KSEsI6cIKxKXo4qqJVma6J8NAH3sqsKZhOarwIMQRc41j1ELPptp0U7B+0SX1khZ/6b3/rZAZq5OaIU8b1S3vc2Gsply3ltTnDsuPiXVuUlcU1FTHEww2qIsRpgxpH9BEbFOkDIkpoQ1LgOkt0FnEWnU6SMbWxMKnTYU3tiFWJOEecpOJUSkoTghHCpEjG2H3gwXfcxaS0VFsz1CaFhPSepQph1hC2SiYXGi599Qq2LvBtxz/66//s1g/SG+Hla3Pz9fDyQ8BfzLf/HeDDmoxlfgP4DhGZ5ELSDwGPfkufbcRtiysv3GD3+gFlY3HW4vvA1t1bFLXBrpPPjKDWJAW8McjapFoMOItEJcy7NL9Kbl0TIaWjZTNrV6Q3NOawBsW65U00KZAyJEZEI2959z1MtmsmGzWDD2gMePV08z5VZkJg5y2bPP/kZcppybAK/MO/+XO3fpBuLTcRkZ8QkSsi8vDXeVxE5L/JCYZfFJHXLNiOBaNvU3TBwznHEAOLxcBu6Ll8Zck7ti5wvpphbE3fW4aFQ/agPD/gysBgDFf9FvuziqGxDFOHaRXTKVpGsJGtnSXuYstQW4qHWsxmh9jIakPwM8fqouJ8SaGGKYaq79lxC7amKy5O5ky3vsBQDtihYqqG9kbH4uqAWk9fRvymYjYA2/Ku73gX9z50nq3m3AmMkqDx9f+8FnIP9/+JNFl+GfgZVX1ERP6WiPy5fNlvANdF5FHgd4H/i6peP4EPN+J2hRVCZRhCpN1vmS891y4veM9Dd7E5rTAzR6gttu2IQ49bdYRK8DNLmDXE0oJG4hDREFJrqSmgMulE1ECYVWhpCTFC3xIXPTqpMJJif62zuIsztCnBgZpIOSgvf/UKy+ApqoK6LlhePmB1Y4G3gregPhCLNDl/9w+8i7se2uHCfdsnMEgjN0ecPorSJW8+Lyx2Fyx3W25cOeDt77+fjZ0ppqnQpkqSeYC2TZxrqpS4FEI6HRw8am3iinPZLyESiKkI1KX0w7Th7KEukSFgc6tbdfcWzBrURxgGJAw8+6Xn6H2g2pxQIuxd2cOvhtQa4wpM8CCWgsB3/cA7OX9hwv1vv3gCozRyc8TpoyxdSgIMhuXNOfvXD7j50k3e9vaLqQVzYhimFoaUYBhLS8z7TLP0CJEoiY8SfFLlGtDtKbFyhGkFTYnsLTCLFcFHiAFcgVkNiasbNcX2FN84tGuRxQrXBZ595hpdU9JsVYQQ2Lu5IHqlt5ahTAa7gxcmWxP+xL/5HVx4YIe3vv8tr/WR3yDeGC9fi5uvk5f/GDgvIk8C/wnw1/NzbwL/Fano9Hngs6r6K7f4A4+4TeBKg1hD6CP7NxYcXJuz2F3yrj/2EHVTHqWk+ex7LkA+QBGRo+JPyJUSjUmYJNkU29k0jwqHiWkqHPohaVRwBW6WVUOAikGN4bknLhMDNBs1cYjs3dgn+lTASgonw/ygY+euDb73Rz7Azj2b3P+Ou27xCN1abmb8JPCNvPz+LEmF+y7grwB//7VecPQw+jZFFwJbFyZMLja8cK4jDgPhZcuVGy03ug5Z9uBLimlPsbli9+UtdBKJ5wK2h1nbMwyWWCi6YVBvMV5QcUSEsOcwnWG11+DFwYbHWE+5a2kLw+Jei7sZ8eWSyvX0ahiiUu5G+k1lcWCwfcvk7hVRJoTBULqSYD3agO4ZLl3q+aF3z3j06ef5/ne/99YPknLLzcVU9VdJ8tvj9/2NY7eVNLH+J7f0jUfcMWi7ga1zEyaTkuXlOWY5oHXk0ku77Hcdg0291qFJqgQ/MYiSon+NMrgCe60lWtCigKrGdH3yTjElsVZMl7yGqtUKX5TExmF2V5gYkcoxdC3a9cSJIVYFogHfR2wAQmDZDbQhoFsVtg/UhbC0LqU+IRwEz7vv2WL5pef5zh/+wK0fpJGbI84Aq2XLdFIxqS2rm3vQBTqtufLcdeYHS9SVxILsg+CQusgmmwrGEgrBDj4veAUXhdh2yUdsnbY2eExQ1Kc0RKxBlh0aBkKw0EJ/5SZSOGJpcQZQIRCgsLTtwKrt0yI6JNVELC0a08JcnWHz3IRud8F3/fG33fpBGrk54gywvztn2pQ092zQHSyIhdKteg6eu8z8+hLjHCEK3XZNURZYH6FxGB/AB1RdUiH0XTo86RWz7JMfWFkSS4u0HRJBhkjcKoirDtd3UDioC7yAbyxmiISyQvdbRCKoJaxWrDYrFpslRiy2shgixhqiEQY/MLt/BxHL4JXv/59/960doLPhZQv8u1/nuf8MOAEZ1YjbDdcv7bF5fhP1gf6566jA6qBlvr9isbdEsnZFquIoCU1ScWiddCaaizgkZZHGCMQ0z7FuU+OwyJPMstMBCxYYPAGXFEyRVOxF0Ki0bYebWzrvscbhXOqA0Zja1/pl4O4HLnBwsMBH5Yf/re+/tQN0Mtz8iIg89A0u+XHgp/Lc+QkR2RaRe1X168bAjQqjb1PcWCz59MNPcXl/n42NFhpl+TaYbUwpaks831NMAu1Qsrw6RbO5/OSGwb1k6RYTIhFrFNsDJp+cnou0weGuKuICYjyT5YCxCsuCbiq4OlKimCG1ua16R9c54nVHtyj56uW7iG3F5MLA04uSpe+Y73fYrqXsItIpG5MDVu0N7Erpyl2+sP/lkxmoWyutHzHiNXH52j6PfvFZbry4RxkUaZfE1nPhng1MURCNQSTiC0GdJVjBDB4XwXZgW080im40SOHAB6RICRJqI+ZggD4ibU+YzdCqQOuC0FTEqkCdIRqLWohiQUEGgRDwomAM3in7JSxKw822x7eBqo1prlfl6qU5ISrzm0s+9fEnTmagRm6OOGU88/RlHv/ic9x46lI6ER164sGcC/ddQLtIDAPap+RBnVSpLbMfoHCYHL8dYsBvNigGHTziHDL41E7aDbBsUd/B5gwpHVpVqCjqHNKUqBGigLqU5hLrmmgEsSVGDF4iKxV8UXJzr0VXHabt0aCEfuCFF3bpVp79K/t8/AQSDIGRmyNOHZ/7xJM8/ZXL3HjqCmoM/p5z6KSiKisIkdAU+ImgOzX9PRuEicX0yVhe+qQoikZhNsUsWmLbQ+GQISII1oMsemSIKUFt5ZOCoaphY4Lm43eNEF0BA4i1xNVA1EAxqekry9DDoML1F24i+0uq5YAOgd4Znr52QB8GDvY6PvPhR279IN3itpcRI14PPvGbX+TZx57n2qXdHPYA1gnOSGq1Ro78edZPipp+NP3OZaOj/y1sauNWyJWitEkVSc/LCiExJhWkjKQi1LoAtb5u8FR1QdCIX3qGfuDmlf2k3hXAp+S1Z594iaH1HFxb8uQjx8MBbxHeODdfK130tfB6Ug5fgVFh9G2Kc5OaVRfxGnH7FedjidRwrdqjvi50+wUHtcG7SFUPzLrAwbKhC6DnoVhZYtFTzgLhRUE2LOIUezMRr98SrImUd/UELZDWMR16FvcI9UbP5HJLLAzWKe5izzDAQbGBLhwDjmXvmT8LF9+7S12XtMOEdrujZUKIDrc1x5SeX/zIl9nvZrx9unFCI3X7O9ePeHNhUhX0S09EKWc1bN9D2XquPXOdYrOhvr6ks2AGjy/BDTA4R7HsMUGQEkLtKEg94aE22KhQlsQ+kg5YFCkNOgAupbbIsketIwwBLSwyKbGisOrx0wbBYH3AX28Z5h3xXIGzFmeELka8c8mbwUe8dnz4ww8TVNiYntQ0M3JzxOmiNI6h6wHD5N5zWFNi2xXXX7pCfWGTfn9FjIrvB7SMyAAYB/uL1E5WFwRTYNsBJjVaFIhRZFIR2y4VjNzabyHCtEZigIM+rXVXA1oUGGewy4GAEnYmGI0QAv1Bm+LEVZIXmQHtBvzmJL3PMNCq51N/+FWYNpy/Z+uERmrk5ojTRVMYOj9AiMzOT5GmwA4l83bA7MzAgGk74s0I00lKRFOPXQyY0GM8hMISvYfCEDerlGh2Y0mvAZxBdzYwxhAOVlA58D1iDdJHhirSn2uYXV+iTTqs6S9MqQ+W6HJgeWMB7YDfrChnDjMEGJS+HJA+EjdKbr5wnc8+/hLa91w8EW6OvBxx+oheCZ1CjGzdvUkIyS5hteiwNkXbq5DMqm3WsYRcGVkXgVQPf6sIpi7RZZffID9mzFGxKJJa3YzJz0kFJmIuSbkCgkcVur0WCgNikaJAfUiKJkivpfD8s1cwT4ESmW40JzBKt02C4dfFWDD6NsVTe19hOgvsE+ldy04T6W+W7LFCr0E/A50p5xgIN0vq3tMFRTXijcGoR2vHcNMQN5UwSyZjKh6zKKDzGAsb84Foe5Z7U0yjVFegHgaabc9emLEx81zaP080K2zp0UG4sHGNS3s7hEapz3d0Lxcc2Ii75rCqaDkwX9X4JVgXaJzl3/7eE+LNeMoy4pTx2AtXkA1HnBfM5wPnqopBlJvtwKqPxAC2Nrj/P3v/HW7bed3noe/XZlt199M7AKISIEGApFglyqIsqrhIth5Ldm7kxDU3jh0ldqw4vrZvYjuxFDv3WrmyryNLVnGlZEmmRBWqUKJEsYLo5QCnn91XnfUr+WNugBRNUSZ5NiCQ632e/ey191pzro2B9TvfnOMb4zemofVNKDx2GKNqj7QOpw0iMdjGEYzBpQo/KzElCAS2r5GlQ1UeHwJeqjYRlBpcosCAdoLQV3ivEHmDyBukFzRSIGXAxZIgFb5xNJVFVR7XV3hEe5M6rmiCJOoa/vAfucXluy+y0OaCl5lLF2+gEkOtBfmoZGXdMNWaYlJRSYdrLEIrokkBnQQfPCHLEIWEpgYTIYzCK4k0khAp/CxHSglJ1BrYNzXBQqCtwBVBEXpdIBB021JGlkASYJbjp1Nk3uB1awDqohhZN/hKIGsHxiCVwoeA8BY7KiijmHS1x9d/+5sPJ1ALbS54mdm6NkYTaLxnWjjWu5pR3lDPcnJlYJDSmQPjClHP8EYhXCB4B40n+IAqa4JQhCzCLnfx+znpQUup1BJvPRjVfry9RQXRrnmuJkhNMBKdalxw+NIhxpaAgrrC9gc0A43rJqjrU0TdtOtxN2u9CG+OaDanCCS9jT4Pfd39tz5IC10ueAWYz0swIErJdK9g7eQSOzf22d2a4CvXmlRHCpqmTRS92H7maBM86qBKyPuX2rRDXr3kV4RU4A4q/kQ4mJDmfsffICREnZh6Vrftao09aHPjIBF10OpW2/Zv0KLNYh14K7k9i+9GDDf6nL/nxK0P0suvzf+UKYe/g0XC6CuU33h8l8t5QyosyjTs1ZZSScKeYmVosOMamYDbkKzalMmoRMaWqHYUdTvm2zhP3VWIypFsOhQCu94gI4HsOHwVMS1jjLFEF0qy9YKdGwNEKrBrnuDgxvaQykGUSpSo8cIwLToEo0m8Y/xsn/nEoGNPHQw4QVx74mGFLWKGJuPhk8d56MQheDHAYoFd8LLz4UefZ5xbvAho79jdnGG0hI5BGUUzd3jnSZYzYgGlLfG1xSetv4lQCjNvqJYiQuFQ0wbhFDZup01IGwiRRM5se5OpFS5W6LlFzEtE45BLXSgFvqnaC2TnCRJ07glaECGQ4xp8oEl0m+CyoTUOlQoTKTaODbjrgTPcdveZwwnUQpsLXmY++vErzGw4uEBt2NmaIIxGxQlSK3wQeGfpHu1jfaCZ1vi8QChBiGOEFkjaljUvaXdUTTupCWS7mZkliHHZ7sIGj3QgQ2gviL1HrQwJNhy0s7U3o3gPTiKEREuBU7I10c5ipFbgAgKLUx6VRJw8v86dD53j7J2HcOELC20ueFlx1vGR336OuglYJfAKtq7tthYmViBVQBQNjbMsdwy1a6h9wElBMBJpNWiJKCqCB+K4nQzqHLa1xSZIBbFqbzSlwKYaHyfEkwonJHrmMLLGbk/b6b8rA7yGEClwBjOriJyj2s2hCrhuhh2kqKKCWBESCEnEmbMr3P+mOzh5+7FbH6iFLhe8zBTzksc/fglheakCaPvyHniQEQfJGsAGBseWmO7N8KVrdSja69l2YfSEg+oj4fynJ6QBL3oE4izCmE/7GB20gQOgJfW0PpiyJgjioOXtxVNpf+B71CakEHy6ukkGCILbX3uaN7zzHpbXD6H67+XX5r8H/qIQ4seBh4Hx5/MvgoWH0Vcsx/spcRyIxBQ31uQjRd/F9KUmTiTKGWTtyG8k3H7bgGhVEHcaiiQiXsuRPUdUBDppjULQrIAdKrSDMtM02jDszDDdhtvO7zMdZ9g9g/GBDgVH1BgzaxiGKbLwDDozjKqJ8HTjio2VCdXcoEuPlm0WWK/mJEmBtwLfCAZZwV0ne7z9ayRjsXfrgxQ4yDD/J34tWHALWE6ydoEsGkIkcKmhu5yS9lPMwCAShZ7V+MZx6vajiJX4YGGVhKY5aD/TZLsVybwGI/AdBSiEc0hnkTbQxArXjwndCK8U1TChORJBFGi8aEd/B0HT1bhBjEs1TabxqaLxApVbhHXo0iPzGolHKIFT4Lsxp24/wpkTq+ztTG59kBbaXPAKkMaK1qrWQWJAS7J+Qme5QzRMW8+T2uGl4vyFI8i09RDDeigKhFRIoRCTGbKoAE+IDCgBdUOoWlNd5y1KAEsDgpL4fhcbt0nZgGh9kIzCaokcdAndBGcUIRL4JkDVELxvfVnGc/AOl+rWB6mbcuG2IxxNFFuXD2GQ2EKbC15mpJKYXoqLJHat204KjTTpUkYyTDBHeriOISiJSAwn7znd3mrWDbJxiKpG1A6RJIilDiiFyhuENIh+h5BoRFEhPdQKvBaEWBOEx1uLC57QWBoXcL0M1+tgewbhAtLa1jhbKRopESLCI1q/pLpBNoKgoOrG0NHc/fAdJJFhvDe9tUH6QnW50OaCW4A2+tNVPC99SUzXkA666NS0xl8hkHUTTlzYaBM2L7aEfcYxLxpht21r4tPT1JxD6LadrK0ykkD4dOIIDqqV2mQRWr30XSgJpt1UwQWw9tOVS9CeTxtkx3DvG2/HA879zuqlL5lD0KYQ4seADwF3CCGuCiG+SwjxZ4UQf/bgJf8BuAg8C/wT4M//XudcVBh9hTIYXOFkZ5uRBb+vCEmEjsF1Z1y7aZB9Re0jVnXOs9NdnOuSdWK8LglNTMgERSYwRKwdE2zuKFywqLEm3QvYdYFIPCWGUZmiKsvmqIeMBVFI6JYV89LQLQXxSk63P2O3WcZpz9GVOaOtIWJQsVd0EAZU6WlsRJASpxR1ERPLOVOl+MlPbdEZ/hr/2ztvu+VxCosdmQUvMzMsvqkRUoIX+KEiGEOxP6eKDEIHSA3Ow1NP3sBrQZQa/F5BiE27W1kHZBqRZjG73rWVBa71VAkEhPe42LST0yblweN28ppdiRDzBiFTqgRcHNA1NCKgEk2QHmGh7sh2zHBpkQGsUW0VEgFZO6wL/OpvPsHlZ2/wPd/zR295nBbaXPBy4/Yn+L0xQuq2nUxKTKQZb+/johSFw0eaelbzzCOXEb0M3Tj8rIIsRRIIzhP3UsgSCudbrwUdEZSHoiL40Poz0CaRfByjnMf3uweTDgVBa5wGNXbtBCepUL7BpxEhSEQWI3VEsA3CSGw3BiSiaW9Si+D54C89zmhryp//e3/ilsdpoc0FLydCtHOWfGhbxmRZwbTEWM+udThrMXhcJ2baBOZPbaG0JOgYxnlbeasDoWrIVnrUvQy7vQ/zCoRHLA2gaZBFQ2QkwijEvMEbQ21AuoByAVW2mzUhjXGdiMh6vNI4HIqAOPAHtCeXULMKUVuaYYwTDuVAa8N8UvHoBx5BSsGf/J4/ckvjtNDlgpcbE2mkUp9VQRMwkWLv8m6bTNLt8zevjgjOYrKIBguuaY/TAipPZ6WDtYE6byC4Az+ig7Fq3reeRd5/OjEEeK2RB9W5KH3w+9CaXouDxNRnmrxr3bbGGf07PJCiKGLn5pjnn7jC8kaf93zHW29pnG61NkMI3/57PB+Av/CFnHNRYfQVSuLPMN6LUBKKFIz3dCLNzHqEDMi4IB1UuMzjgkK4iLnNWFGOZs8grnnSqaAvE4IzHFtLCR3H8okxXjt8LdmcDcn3El7Y72NTjfKCrizIG8nOfg9dRMyIGCQlnbTiaDIhUZbKxRSxQipHVHvoOeRyjeg4iq7Apw5fQyg1/SiimSuu3BwfTqAWEyUWvMwsZykhBHwksRGoWKG7EU1poXbtRXGARgqckcgGpNSQxrg4wlYNmsDgZJ9mOWLtaIaPJC5TOOnwscEmB1PQlMB1DCKTBOuQE4AMkRgaHXApFKuSJhMIBDISiFih6vYC1xqwmaROFS5udz587UEKut2Y4BzPbe4cTqAW2lzwMmPWhvg0xisIWiKyCJTGlo5gHaF0yMbinGureeYVWgnI4nZymvfIWNI7voxZ6bYGoInEG4mw7cQ0fCAo1e56JgkiVnhvwTlkN2uno9Ga3vthgpcHF8xJDEIh6xrhWj0TaCceEhCNbXd204heN6NpPM89v3U4gVpoc8HLjEgNPo7wWtIkCjFIsInBJwap23HdsnI0CogkoqyJlGzN4Ttd8AoRSbKjfeTpAf1TS9S3beCWhsh5jQyCYC1iVkDemtO7WEIcQZZCZkBDuZTRHElpeoIGiwge7YCqQU0q5M4MOSkQjcdrQz3U0InAB7QUDIcJ+bTi2U9cvPVB+sInMS1Y8CWjRbvRAbRVQZHEvWhi7QOtuybQeJCG0Fh0Its2M++hsRArusMucTdGdkybzHmxekgcjPFumjbzog78iLREBNdW8AoOqoUUIY7addQfPCc/o2JHiPbc4eCxb6uJTGI4emqF2ajk2U/+vpiS9rKzSBh9pRJ5Xnf2CoO+IE0ly3GClw2uMEQbBWrDk/c1c59RjAa4StFFsL+/hOhJsiTiDXec5r67jiI6FRNRIYRju+hRr0TI5Qq/HDArDr2tSNKCpNswI6Gou8zKBLXawHpDhWG806dOFPOupriYYayCA68V0UiUDRgbkF6gIo9Q0CSaI2sr1O4G3egQ2l5gUb674GVnVlUoqYjXMkxmWO4mlMHihgnErQmfwyMyRZJqjAv0M92OGTWSJIu5/+GznL5wBGzDftEgrEe6gO8YgnOARRa2bX+JaBNUqQSl8A6CFNiOQNlANHH4ROJ6IGsLicZ1NT5SCA9etSNLpRAI75FCUqealWGX69tjGnFIq9tCmwteZsY7I6RryLodVGJYGiRUWiB7CTI0BNcQnEdmMaYboRNDf9htqx6kJOklPPD2u1k/tgRVw3haomyrGx8bgm0TQ9QNxG2FEN5jM4NUmtA48B7XNCBke90oNTI1hCSCQQ86KUQCnypQCiFUW61YO4QQWKOIBNy8tgP+kLSx0OaCl5nZzpSocaRKkaQxS5nBSRBVgwhtCzfzGaGjiIxCJ4ZBRyO8IxhNZ7nLV73nQTrHl9GlZ8+HttpvNkdOckJRtpoLIKRgfmZIMKBQhCwmOI8dpnjlYV6gduZI61E+4PoJ1ckBmNZjTOcNyglEmRO8h9zCIMVeWMVVDfv7M1QS3fogLVrSFrwClJWDSLWTBWPN0kofaw9awiINIbRrmZIkiUZFhl4/bauAtGCwMeCt3/h6ok6EUopQHYy8D4BSbfLnxYohKdtKohdb0hoHof19CKGd5NvY9n1f9CpSqv1uXjy+3ZR5ySdJCPorGfmsZLQ/JenGtz5IrwJtLlrSvkJ5ZPsF8k5GZ7yBnnlGoiJK5/hZip0qyCGNLP1OjtiN8SbjwknH83nOOEiqjuDy7g6RM0ynkqm12CRCTyGSkKaSUgZsLQldydmlEZMbAzpZgT0CvjJU1zViYpjpiGqoOJPuoUvJHXdf5vEbp1haGTK9UtPUYKsu3a6iVjl+pNE4Gu+4nm9hd2ecHx5O7vOw7nUXLPjduFZMkKlkPcnYnFl2dnJkbWkyjakDTghEN0LWUPqaOJJ0tWHiK8K0oIkl167sovoJReGoaPvDZRnQkcZmEicCLgqgwCaCeD8gg0NkCqlaf6LEldRZhPQS7z0ERTlsze7XTvbYu55DrElKSzTQjLRANb5dzBvLc09epyo8/Sw7lDgttLng5aZsAloEBsf7bG3P2bs+JuQ5oddFBhCJJogGakcdGeJujJIBgcLbnDwPXL+yQxCCybw1yxVNAwhUpA9uPANBBIQBn1uElMiqIeAJSLABacB1Dd5oQt1gADGvkYMOy6dW2N2ZQ6LbSsBEMlcgg0TkNbZseOYTl7AqYvXYIZh3stDmgpefqXCYYcJgOePa/oTNnRJlJd4oglSEQYSLBVHpqKynt9zDKQ1RhZzOmZUVz37iOZqlAZN5QUgNZlYimoBSAacFddeAyVBFTfABYSU0DXqaExCI2qPyCtdV2NuXCKOKeOKwg5hm3XA0Xmfv2ohmrUdnf4pMI3aOJxAkvasV9bjkUyOLE4bjZ9dveYwWulzwclPkFfN5TpxEDJY7bF3aZf/GiCBkO6XMH7SEyTYDVM4rltZ7WOsPNkQU490Zz3zyBZwPjLamL5lVQ0CYqN1keTHpIyVetBVLIoS2xcy5NjGLw2Nar0HnEUof+BjB4Nga46sj0K3/UdDtkKUXh63t7075+AefREjFifMbtzxOrwZtLhJGX4GMih0+uv0MVzePca6Ief2bdnn22h43rw0xOxK55ElFhcgFab+C1BL2JI9+VBOCxqcCPORihPVRW/Y3CSS1x2WedChoEKx3p+Re0TQx480lrHUsJxXNVLE/TXH7EPdKQp1ivKIsE2S34ZnegMjWOJcQ0ookDsi1hJFuSKcSOazoDOZM91bY3ZtxxUCvSG59oBZluQteZp64vMljT95ApYZ+ZrCiw81rI7wHM7ftKGAPqnEIo6hdQFnPld0ZKCBSBCXYG5VEzrWGuZVH1mAjQYZHTyuKqDXM1R4iIfGxR0+AbiAUbdtb2UvQHoSRBOmwHQVGEmuHCO1kNRmD7kUUtUeEgO1IvJR0jWY7L5gJT6UOQUQLbS54mfnwBx/nhUvbmKNLrC51aXam7NcNIo7bi0ol2wogAcE5Ql7SuIitomp3MrVGKsHujT1MPwMj20kwUiCDQNcWZ2uc9ZBGuMZCbBCNR5QNITUHFUEe3+0hPe3NMNAgUEGghaeRAa8USglUz2APzOtxFicc/dUe29Ocqm6ob7F3J7DQ5oKXnZ/6xU+yszNDG8XamVXm+znzRiDKBhUbvGtwWhG6MWUP5HTGdNzAvEZ0EkKkoa7YbTxxonBzgRxViFmBTzWkpq34SxWyLfIjuzolhIDMy7ZVQytKEWiOd/BlTpOCeb7EdiNkEPQmgkZr7EafVEvkyoCirwhGgwj4pmY4s+zMcuxkTlk2tzZIC10ueAX46R/+dWbjkjgxHD21ys7mCDez4GybZBUH7WLiIAnkYX9repDQgdDupzDanZH2MgSC4OynzbBlAMvBwLS2Al96R1DtdF+EQ1iHkIKgFCKEdtqaDwfT1wJJJ6Oc1WA0KpF4F17SitcaaR1ZmnDt6ja+BJPc4tTJq0Sbi5a0rzCcn3Fl96d43bIgI+a6m3D5pmSaG6yQqMijS0FeRFQCdOQQKqGOYiZRoAF0E6OdYb+TcCV35GWD6wkcgriwiKoiz1O0g17VIBpPuj4juz2nGEqMNYQaessFJ9WYpY2KcWR4QfdwFdx8ag3hu2Q9z5mjgo0EpkVDmFtKLxj0ppzMRrzzwXMcCUsEIcjzw/go//4vEVzw5cPebM5PPvoox48toWO4Pp8zLXKcAteVBClQXrQTznSg6ghi1ZbPNkWNqixxN2mnxXQCW85SCQeJQOh299PZQJNppHOYwuETTTWQNB2F25CEocNmApsqpGhIUES7FemVmnjXYqpAR8QkfcPSWoLuS0bKUfnW6BrvUcbzrrffw0ovQzmBK+whRGuhzQUvH5tXd/mtX3icU8eWccDWeE5RekJsDqacybbtS8r256ZBK4kvK9y8QDlLlEXE3YTGaPaLBisVIJBIaCxeBHwcI4RC1h6hIzwO39SENMIv9fCmLaPXs4LEWszeBHV9H1k53FJC1onoxpql5QQVK+YhUDUeXXtUbjE+8E1/6MG2Tc4HanEY2lhoc8HLx/PPb/Hos1c5c26DMpbsTefUsxIQCFuj5iWqbJBK0ASLrDxxN2stD5xDx5qo38GcWKVc7bK9O6XRApnnqEkJiSHEMU7Z1tzaOurVFLvSbf1NyvaOttaAd+hxQ6c2DD+xR+96iWoCsrH0haAnJd3jfdxywjxYKu8x0wY9KlFLMd/yh95IpBX4QNK51fv5X6AuF9pc8CXy+Mee58oL25y8sE5RNUynBb60BCEOppl9+jMmkAQpiVIF1hKsw2iJThQmjalcYHRz0loqyE9PUQvO81K25SCRFJRCCAlGt1W5sm1XE4CIJLKqEN6BcwgEJlYM1rukSx2C0oTaQ+3bSaM+oDoRX/utDxOrCIJnMExvcaReHdpcJIy+wnj65o/yyRf+LQ+vBmrG3PQ1VzchDz1U0Fy4f5PuUUm8UtNbK6lniqa2hG0BSnPhriM8fNtxvKkouwWqY2m0wPcd7lRDWBdYqemakp2tPpNZjzOJo2cbQmEY3+xxfatPUlhELNhTHfavd9DbAT0SxDqg+oHZPNDdm+GawOZNiMsaPQ1k/TkzEfGpzRMcXTnKA3eeZcUE0vgQKoyAz2k29vvIhGzBlw//y0//Iv/21x/hwslVxqVju6zIZw6NQmiD3UiQA02VSXymoAw4B7LxiG7EHXcd5XX3n6TAMtKCIAM+CFwsqBOB0IAAlxhcFqNiiTG0ZfSAbxRhrAGJzRSmEoRpjZl7ksIjDOiiYT5vaGYzmrykuVkjpp4gPUVPtga+wXLm2DKve+g8OpaY7BC8GGChzQUvCyEE/t7f+Df84s98jDOnVsjzhs29nFyDSAyiE6E1qFThtSRYj40iLIKQRKgs5q77TnHPG85QFJbq4Jw4B0WB847gHdS2LZ9PYrSSmKpB2wNT3jhCBggdg1ca3TjCpMaMGmIb0EJgJhVF6agnDbPRnKKwhNK2U5mCxXYNrhtz7MQq97/1doyAKD6kS8CFNhe8DOTziv/pn/4UH/jE0xyNNfP9Oc/vjGlqj/QOvKA5kiG7EVVHIoMkuj7HjQtcaJDC88BbbufCHevsr6TMEk0oLVYHxKTAioDXAiYzxLxB7s1Q4zm6rts20U6CzwyEgIslwnmi8RS1W5LuVghr0VJSr3YYR1AGx7SoGNc585WY+fGEpi+xXQPA2fvOcNdrj7dev80h3BS+Cox1F3x5MNqd8X1/7d/y2x98iuFqjya3XHzsOiCRSiGURnTidkLai+bV1lI3rWm17kS89u13cfbuEzS1g7r1FHupskgdJFQltBe2rj2XbBNDSAGurXx/cTIaBMirdq317bCIIKDMK5rKUYwL3LxqB6dJidAHbW7A3Q9d4Mzdx0FLdjdntz5grwJtLhJGX0HsTvf49cd/i6t7R3h6chofO2yiSV3JMBnTPzaDqKQZjskGc84cv8HMRRQhJShL70TFmWMpx85n9JfH9GWBkAETPCZYmGiCD/S7E1ZXdxnGBXEMSWdEFlWcTsYMKeh2c4oQkS2VTF2GkRVZVJPVgdloiJ1DERTXRU6T3qC/PKFrcja6Y1LfkDUO3XFc2b3JkpI0s5hZfhhVDPy+F/CCLw+evH6JJ24+zTCGJqmxQ4eIBTYEylRiM4FznkI4bKZoOpJgAlIHnHCIQcyRu47B8S61gRAE/qDcFlrzeKfBynaBlBKElIjSgYKqJ/ECnJKE4Ag6oCsJs5JQA8ZgaoEooGkClyvHjrCU/YAXAWsCuEBwAWcMl67vEyEQCkq70OaCVy8f+rUnefb6HvHpVbaGBructBuClcPVDhpHU1qawuEJYA4mCSaGgMBkEXe88TyN0tQSvAuEALJ0kESETnTQMeaR8wLhHFQNsrLAQfm8c/imaeVcVwevb8vuiaPW/9MKagKbRUGuwStPCA6vAk62ExHxcOm5m62PZxozbw6jJ42FNhe8LHzgVx/jypURy0nG7Noe8f4cN68oOhJf1uAt1mgaoXHmoOrAOrT3qAaSbod77z3JHh5nNEF4bKzaNpm8RguBmjmoLEJIlFBIITCjglBbqp7CR4p6mCDjFFU2qIlDFhW4gMoSQmVRe3OqKudS3zLpQL0a0cSOOguoskE4iZx6rl3cxCQJIjbs735l3pQu+PLgff/6w9y4ucf6yWXmpYPkwHT6xdYxINSOgPi0cXUAoQRCSZJuxpvf/QDbO5PWbP7g90KIg2miLxpfy9aOQR4YWPNigkggCG0LmhTtNDQfWmNsANl6HIm6oakse5tTvBAEeZAkevFLKVwT6x3K0QABAABJREFU2Nuc0u+nIGHz2v6tD9irQJuLhNFXCCEEfvgj/z2N3OQtd5zheL9PogTxtCTBkdshHVKKypD1cqpxwnxzCa9Sjg88ogvd/Ziu7fGJT/0mpl9TighXenrzinhLtIa8PcGkych3U9b1lLWep9eJWe3O6fXmCKFwGCINsgKd1vi+p+wZvFDIyqESQbdb4OOSsoxolhumdZfdNILYkQXLkqzodzydwRXuOldxaqN3SIH7Ar4WLPgi8MHzJ//lv+b5KvC1bz7DiZUYYwJh3u6ApLGk6wUhWLwUqBriUqA7ESY2uEQxTCN0DL/6oaepM0Uw4J1H1A6VB2Tj29aXADQeIQLZSka81LaOKhuQMtAYiw7t62TwhMTge5qgBVZCsxzRxIJKB6SXyFKglMQZwAekAKUFOtVMaYiOp2wc6R9O4BbaXHDINLXle/7+e8kry5vffhf9lQ4iEriynfqSLmUoCSFux9gLKVFao/oZKnikkQzXhkybwMc/dhEOpvV64XCpIqTtZDRZNsgATiukFqTHlonWM1ReI/OmHQUcGYQUuGAhQIgVLtOgNU4oZNdQi0CtBKqokaMc300ITUA6gbAeE0ukUCgXiJYTNpYX6+aCVyejSc7/9O9+iToRvOPh19AddpCAGZVII+j1UnQ/RU7qtiLXCOIqEK0NET4gI8lwvcvF57d4ushpEokeVwQF8ahEdTJ8L4FYYdMI24kJyx3S9QFxHBHnDhkCQkuabky9EuHimJCleClwWdx6skiJ7iWUvZgikph5Q7xjCVmEcA4x8qjK0dOaIq+JlzpkiWblMK5pXwU3pQte/Tz5qcv883/0fkysefPX3ItKIrw92FQUkmQYtw7KL/oXHVQBqV6MVgKhFYOVDh/+pUcZbebtSZ1vp/kK2k0UQrv7aS0gITWk/QTTiThwxAba1wYh8KKdfCa0BqPBHSSWMkNwHHzm3UGl0sHkNtu2u3VXEm5eGdFfG9AZdFhZ7976oL0KtHnoCSMhxAtCiE8JIT4hhPjIYb/fgv+YEAI/99Q/4gM3DE/kJzm68qe5uJvQbFkaJ1HnKl53e8S9R67w+tNXubB0gwuntmmiNVQ3xWpHlnnsXkKcjLguLJtbPWwh0dMYq2B5MCeVgWqsmY5jylgxVobrleejH1ph8/kT3Li2Qhk0dZCsn97FxDVmr4sMfdTUIERE0wGMpYwaaqWxTrGWFJReQGOIc03uFbaXInSHS/OYuD7K20+86xACB7/fe0q/FBbafOVpnOO/ft+PMtqrWRUpf+S2t3D9kqfZ9DTKYzc0a3f1qY41zE8Hqk7AdwS99Ri5pnGpRBmBrAP7L4wpyxrhIYQ2QeS1IKohLkK7EyMCNhNUiWJvXlNtVRgHagYgiJzEJiCCJ+4mRKsdVKKQSxEhUojS4wARJAFJiAROgbaSuA5o7xjoCJko5nXD2eEK3/aO19/6wH0Za3Ohy98fFHnFn/nuf8aMwNHTy7zna+9j9vQefrsmCIdWgaWjfZyWBB/wzoGzdE8PkesdRGraMdpJxM2nr9G4Bp8afMdAkrTm9dMaVXt8rNsd1thQG8lkNKWoJGKQIb0/8AR1+LwCbRgeXyFe6qLjGNXP2mmHRYWqLKb0BKlBaYKRaC1RtDusyypCKLCl5dT5Y/zhP/X2Wx+4hTYXHDI713f5ru/+P/GjkruPL/MHH7qTnSAYXViijiWDzYK1lQ7To10qU1PGHlLB8GgPM+wg+jEyQBQCN6+PKPsJdqioVmJ8P0GPSmRmCN5jhcPTatAZyfjmiGZ3jior0qd3iecWVTjkrCZ4x+pqhs4STGWRNDQ60IwKTG5IZgJVBWxHUZ5QiFgQuiDyio0bc1xe4aclZ19zkm/7b77h1gbtC9XlQpsLvgguPnWD/+kv/BChdrzp7a/hLV99J5Nru8iDwRC6G7N6fIVgDMG07WhCKfprPUyaoiJD8IGsG7F5ZbetJJKi9QhUCoRo28zgpXaxFw2zi905zThv80XOta+VbZpDWMfSsSFogTASkZhWEpVrPZW8b49TiqA1Xmu8aNva+llM42rqouaO157iPd/51lsbtFeJNl+uKWnvDCHsvEzvteCz+LGn/t/8u09eY//aEn/wdMq55T5XjvYQNsNVCTOv2ZnfwIQCSYTVmnnQ7FrDqomxOzXFpice5Pzi5cscPbZP+eQpxFhC5ChUhO8Yms0IVCAWJVHU4J/RxEuWJrNcKzuEnoYYhsSUQlOGCN3VHN/YYncvZWIhzA1SCDqFY+PkDtvjJS5dW2PQLbG5J+5XHL8w5fLeOr/27FM8dPwEsVvjnlOHs1P6ahh1+CWy0OYryJ973w/y4Q/fJBsL7rljnfOn1zhyuU8iFTaAMIrLN0c0xuIjg48loWo3Ro7YhHE1pZwHJr7iA088T9PVJArc/GBBiTVNAmLu8EbSBDBFQI5LQqLBSULQEAtk8HSDZBw8ptHIrsE1NcIJ7L4lmIBPQVTtVDU5B5dCiAQidzjjYT3QmUp+6Vcf5Y13n2Vl0OWeO44fSuy+zLW50OUrzH/x136QZ67toV3gntee5vxrjrG8MSRJNE2saKTg6s0xNA6yGOkFTjmct6yJhP0gqMqK3b0xVx67hNISvRRRHZjABymR0uOEJnQVNgREUaJEa+gp8oLQjUEolFIYo6itRfYyGt/gpgUiNjSjGT7WeKOQdUAECAZCGoP1ON9eOBsXcAR+4Rc+wRseup2Tt61x/PjSocRuoc0Fh8lf+uP/B9eVpYPkLevHOHVunZUTy8AU2zVUteK57Rn1hsGf7hONBc5JRHAsScn+uMCWgc3rY3ZsgzjVY9AoxpnENJYQJKKqIdW4QQfRy5CFxRUlRoI1EgYZcgQkmo6CXGhUZpgq8MtduLmHnTaomtbbrylxXQNaIpuAnNeoXYftJQx2Cqqy4oPv+yQPPHSes/edpje49VUMX+a6hIU2X3H+2p/5Z4y2pkgRuOvBcxw/u87aiSWuPLXVWg1VjitP3Wg9gqR6qRhIykCvG7N3Yw6N4+rFHYpZA0JiuhHNrAYCMoTWbJ6D6iSjwQawB1MFXQDdJnqIFEjaCttUUuQVMlb4/MA3ENo2tQMj7bbDrW2dE8G3HkYG5rOSX/uZT/LaN55n/egJlFK3PG6vBm0uWtK+EmimvP7YRY6nDWeWH0bIjA8+fpOJ6LJ6siTdL9ndD3SigmPJhHNmh3gW6DClEyeYzjI+ypiqADPH/PkOD5yIkL6hEgprJfWuxvYqggqIjkV3C5ZOj6mVRkpLNddIBFmSo/ScVAeOuhxn54SkQKYWg0dlDl1Kwjji6u4KEwxxp2ZjaZeehBtuyFNbd0MC29MJP//kI0zL65xYOXY4sft9XiK44NVNIUakx6f0VMR77r4PgE996iqx0ZiugNJhJx6rNCoN+G5ABYefWPpZzEoWobWibBw2FmjrObu+jHcBbyCoduelHgp8onCZxMYS29E407aSqcIhZaCOPK5w6KlHTRrm85JCC2yk8AYkgqiRxHmARuCMwxvw2qIjiZARR6c9yiiwuzXnJ3/9CSbjKVm6ML1e8Opjty6weJYSwzsevh2Ai09fxxhB5APee0LjQUqkkYRYI6IIMXd0hin9RKNMxLyyhDhCdFJODjuIIAiRIkSauhPhOwohJV4qaBp8CHgj8UohLITUUCtBKGzrrTItmE1LQvB41W42SusxFqSU2ETgvcPGAdk4lDaErmZ1rUsVCbZujPkPP/Ux/Kggig5pz3ChzQWHhG0cNzsWazzH04T7HzwPwFOjEYNZIJt56iRQDBPKjQi3FFGsGCSCRikGJ1foDDooAlVV471DlDUbVmBGDl3SVuHFrZl1MILaCILR7ZrqLPVKis80GEEpIQiFHRiKgaGsPNY24BxOKUSkIHiEdSTPjgnzCp8IOlcCSzcFK08WnJ0ocgfXn7vJ+37sN9DRwbjxW82roO1lwauX0f6c6ayGAMfOr3Hhzva+7PKTN4hS2SZyRCAcVPKgFWj50sdt9UiPONIgJeW8aXNCShBFB6kKIQiItt5PvdjOBshw4DckQQmEar2IQtNe/yIEoYGycHh7YJodAi+2wgnanz2i7SL1odWfCCwNO4z3SzYvbvH+f/lheku3ekLaAa8Cbb4cCaMAvF8I8VEhxH/52U8KIf5LIcRHhBAf2d7efhn+nK8sLm3/IMX0CbamK9x26jTD5TvIbc2DJ7oMM0W+L5iPY0bjPo/cOMrT20s8eukU62sTLpzRLBHIepJ3vmODNz9kmEwSbuwt8dhWwGfgk8DJE2M0FuqIblyzlJZYq9mruoRKEWU1dQpL6RwhJVcmfYpxF4sHq7i8cxylYtiO6Nka0cuZDTX5JCFet9RGsFsvk8aO80d2mc0qbr5gyURDOU256vPDWVy//Flo8xXkvc/9EJPqKnVIeMPrz7He6QBw7NQSSSfB+7o10SwkeixQWxDNPPWyZ+VMD+Eg3ejx9rfdxpnzq4BE1ILLW6ODtVQStMQpj/QSvEcQUHOLLgKmAt0EqAIiEcjKYW1AdCJs1t4UR0HSSTVoiTeSSgeCFujSkR/XNB2FlKoddGEEk3HDaFTTxO1IthvF/BWN8auUz6tLWGjzsPnrP/YfuJZ4VGL4qnfcw9JqW8G6fmwJOYypfUB6j5fgOhqrJUQaKSRHN/roIOkfW+Fr3n47x9d7uE4MkeHSjRHKWWRlMXlNULL1VEgMMji8UIgQkK414wzTHFnXhMrilUbGrZG2jiPUoEPaS3H9FIzEhQYfLEF6ypWYuh8hlUJ5R5Jb9vcKxrMajCF0Eq5uT1/hKL8qWWjzFcQ5z1//+/+crQcTkiTlrW+/i163nZC72k9RHaiEo+4qVNW0fnylw2UO4RvOdzL8tGTl2Apf/a2vp392BTsw2I7m+u4M0zhk5ZACPIIgNcIYiDSiDpipw+QOdWOEvDFGNgGVO2ReonMHKhDtT4gaSzLooZIE14lwvQRlBb6XYAcRXkC2H5BSkljP7taEepzj4wgfSbau7r3CkX5VstDmK0hR1Pz1v/wjND6guxFvffd9ZFmE956V40ttZU/jXkrS4DyhadrEUaQ4emKFomg4+ZrjvOWbX0e6nB4kiGC+W4B3bSuaVgddWRJhorYiyMNLo+mdJxQN6NbnSAiJkO39oVGgtMR0Y4RqJ6p9uq8tIJRCqda6AakQwjO6PobGHrwW9nYOwYz+VcLL0ZL2lhDCNSHEOvDzQognQwi/+uKTIYQfAH4A4MEHH1zktG8hefUsl278MCezHfb4UxwbPMzrV0+ipODm7AaD+AU26yG2GwjKEpqE0faQR68e59idMx7srrB8pMNz24L96zkbJwWccCwnu2znXfKdDD9WKAdxYrETQ+M7mDP7+FqT1ynZsCBVnqIM3LiyRDrwnDy3x23qBslgxgt7p9jozymbhpAaktCw7zp0VU2mPR3RUMiYaRMTRV3me7A02MGlGr0amOcJiTwEA7IDXg1lgl8CC22+QuwUT/Gxq+9jtSO588LbeXjtPK8937ZuPbe1xY1qArWAqN19ibA4pzBbEG6LWV8ZsL6ecXN/wtbmlDIN5McVamYpvEd1JLp0qAasECRTh48V84EkSIOetBUJ3jtcV6M2HZFTiFRhDVQ9g5lJ0plnGjc44ZBBEDKFRSCEQ4jWF0kiSTJFnlfkChQSvWEQYzi1cjgtL/Blrc3Pq0tYaPMw+fD1F/jJpx+lzuCPv/sBHrzzFOfObwDw/As3Gd2Y4pEgFBILQiOCwBpHb63LxslVOpHm2iRnd2vOvhLUHY2ZNwdtpQKBRDQWUTVoC05LfKwQyx1C7RGVBaMIxiBFICorRCMJpW0TPkIgRlNmLiXY9hrcLfcQ1r2UcFI2gBJkww753pwwKZEmJupFNLHi1JmVQ4vhQpsLbR4Gv/bLT/Ar168QDQPf9u438boHz3HqtiOEELj8zHX2XI6XAq+hLyRybjG1ROBZXuqyvjHAR5rRxZvcvDRir6/xmaa/66jiGDEviQqLiyNkXiC0wUlPE0sS3yBq27ZypxLnNbqqkEoTIkMTSWxmkJHBzx15rPEERF4jhjFqbltv3p0KuV0jTEwnOCZFg0EQ1Y5sEGOnNafvPHEo8fsy1iUstPmK4ZznZ3/m4zz1xHWIBN/yR9/I6992F8fPrFGXDTeujZhPGgSB4NtpZUGBcB5wrJ4YcuTsEZqiYndrn/HujKIOBKVoO7TbCZ8Ej0Ahm3a6L0KBkL+zYEC2kyWkbZNRIpJtB5sKICU+OFxuCS9OT9P6YLhaOwwG13ompV1FsVdBAKEUpm9oCjh391euxcKhJ4xCCNcOvm8JId4LPAT86uc/asGtYHc+4up0mTjO+Przb+K53S4/9YnHeej0cX7xymNsCUW0WnLfxjUu752kzGPKJHDb+i7V/CQkZ3l8dwdGDbu54OOTnNFKl8msT3Xd4zOD6liuTAZkM4VcUnSHOVWtMCagl0qUFuxOOthGYLse8KhQ8cJ0SLrfYznMKHckuTRUq5q9KhBbS9atSbOauY1JguaOlaPshBnPX1niWHeb5RM5ptvl9vgyZzdOHl4QX2XGf18IC22+clzcvgFlynGR8YdecxvXduBnPvoE9505woefvUaNQww0odfQmUbgYpwNyCxwMnQ4Kjo8e3UblXvyec0L9QTZERgZ42c1QgkkATW1JEpBGuGEQ8iAj6AegqpAeI0sPDZq21i8A1UIVCzAOqapolGSYBTOWmQT2ulOXUgtGAG3H11lczyDvRp0QGaSbmKYyxq9cohLzJepNhe6fGV59OpN0lhxttflHQ/dxv72jJ/7tSe46+w6T1/ZxWoBShKVFjlICc4hxjUIwep6l85aj4tPXMdXjnHZsJ9XhMS05fe1Q3mFrC0iCCIpkZnCzwuIDKGx2FiSBIEzMcxLGimQjSWUNTKNEVIQKovrZrhYtUllb8ECBEJHEyuJEoLzp1fYuzZiLiUkEZFwLHV77I/GxIdZlbvQ5oJD4BM7l5FJxN1L6zz8ltvYenqLX3zqBifvP8PlqqbYMEgriUc1cSfCFw79/C5yrcfJs0Mi67j4+HWEFNSzgokqqde76FFA6IB2oGclclJilCJ4S5kImmFEPU0xdQ3Oo5zEzwu8kATn2rV5Pkc3Etd4/DQnDLqApxmkiCyhEQHV1JgGUqE4edc6N56+hgJCGqFXhwwSwTgviLP4cAL4ZapLWGjzlaSpLU88ehUTa+659zivfeg8Lzx1g9H2hDgzjG9OWu8h1Y6sV2mEaxw43/pxHlvGVhVXn9tEEJiUEyibl1rFCL71GgqAbRC6bT0LSITkYKqZIwRBsBYhAEfrTWT9S+1q1lpAEQgQ6fb44A9a2Qz4QNqLOX5unSvP3miTTwpUFtEdZIxdztLqIRUovAq0eagJIyFEB5AhhOnB4z8A/K3DfM8FYOvHeerm32c6/zArg9dw5/CvgDO89cxprHMYqVGyYd0UXH5+g/14mbjbZVQX5LOM9aji+SsVdF+gpibuNhQDTXRTsF7myKrHbgj4PYk0ARkLgpL01Aw7Bx8SfKhZ6czZfOE4al3QH26DCezsrJAmNUtLIy49fYp8luBwSDzRcoOLDyr/XMpqaiirhqpJ2ZnNuOu+Hjt5yeZ4QH9qMKbH1Gyy7YrDCeSXcR/3QpuvDOPi47z32f+Nx3fmxJ2H+Nq1byDG8PC5Y0RKIiz4xCFrT7zjCMqwlKTs7ZaYyhMiuHlxyse5znhaY2xox3IbUAh0FXANyMKBb8tv6SjsrEZ4iTaeIAVBCSIh8KmgUA6vBKlXOKDsKNKadnJE4ZA+4AaaoAQ+FqRdQTqE/Zknqw3j7ZzTdy7xyLxEFxD1FCoWFFOYzevDCeSXqTYXunzl+ND1Z/kr7/8Jtq7XfN25O/iWtdvpe8OZO04y6CTMRjne6LalrPYopVjrZWxf3gHaHdCrT+8ic8dkZ0YcPGXj0VKiK9tewCIOLno9JgSEFPj9OdL51ttIAuHF6WgON/MIHxBZgigsXinkUoavHa4JYD2hIwhB4HuCTtJBNILCN5ga8t05K6eWGT+9BUC0lFB6RzWvEN3Duilloc0Ft4wQAr/5oWf4yz/zXnYry5947T284ehZhr0Oxx+6wNrGgMcev4bTClV7EIG0FiwvZVzfmSC7KWJS89z7HsOfX2d3e4xSktp6omWJujZF1YJKpW0Ld2KI92eILMKO5iRPQXluCS/AJRqfSETtkbOA1xIJhG6C7Uo6jSU07YzuUFQE01Y/iLKhu9ajVpIwCYjCUlYN/duOMLu4jagCvWGH8eUt6mlJf5AdQiD5stQlLLT5SuGc5zc++CR/+2+8F1c2/OE//hCnT6xw+sI6x06usH58wC+996MEKcGHNrmjFMvLCTvXxqA1vmp47Def4dRt6+xsTdEyYH2bPxFKQB0Ivh1vL7xvk04SQm1BuHaqGQLhQSYSL3WbJDrwKApStIbWWhGqBkJzcJMZEAd+R0kvoWw81BbnHbbx9FeG7JR7IAS9pYz93TnYwHDlEBJGrxJtHnaF0Qbw3oNyMQ38aAjhZw/5Pb+i+diVv8Uz2z+HtIpaDJgWPc4tl5xYvp80aReh33jut3n22hDvegQVs7PTY319izuykut0eXx7jQqY7pUUowjdaFaPw26/oScTmiYnWfU0o0AoJDIOUEpWTu9BLrg2GWJVwGmNNBVxVhP2YxoROL3hqEJgVHdZW9pltz7JaA5VLBBO0EkdVdKls2x44NwjrI8Ej5RLrK7NSUvPt9y7xlNXx4S8QE1nzJOYohwcXkBfBSL+Illo82UkhMDPPP1Xubj7UcazLtfHQ4bZDE5GnF45QS9ttfnvP/Jx1L5AO4NQEjcOjNISvVLg0LgdcEYx3S8IlcMHTbRksM6TlYFZ8IiOwJQSWVmqVBCFQL0mMYVDOEmTSJQX+EmN6yoUEmUhSTSVaCscqrRieDOinjS4VLVGhZkhGkqWeh2atRG9UYNxEU2qQDne8abzfOrJm8xCxdQVEKDX7RxiUA/v1K8gC12+zDhv+Z8/9Hf40OYu+fYyyU7Ejc4+wzu6XDi9TpIaAP79+z6KDyBjTSDQ5Jb98ZRmJUNoSZi3O6LzoqL0gRAZZNfjnSSZl8x9IPiAVq1TZ50ZjGwviF0nJmQah0fbQBhVrUFoJ0U6h7IeGymCEtSxptdYKGpc1+ClRHUNYpiykqWMdufoGlKj8EoQrSS85S238cgjl6grS9nUuDTmzIWNwwvqQpsLbgFlUfEd3/39bA2vU9aKwScd02sXOfLf3su5k2toLWkay/ve9zHwgdhKoq0KXzRs9zUkCXYY4fZyUhyjaUVZW2Ij4MwAqyydGYxTScARqhpZ1IQ4QQPFakY1iNDO4hKJ0ZIa8CsZikA8t0ij8K7BasO0E1iODcFEKOGp04goVUgTc3yjz1MbOfLJGbGJKAUcP7nC7UsDHvnti1RYmgMT/Tsfvu1wAvrlqUtYaPNlZzwp+M4//f1Mtgtk1RBcYGd7xle95TVsHGutCMa7M372Jz6M8AFSDZUH69jdzwmRQQDCtRU/k9GcKi9R3RiVGETukCrgGt9OU3Ou3W+RYIzCetG2fArZeiKJQKgswui2rUzJ9hgE4cALSQQHXrTuzUoiUoXSMSfPb/DcpR2CEKRpRFnWnL7rCOduX+Oxj71AVVtC+/asH1s+nIC+CrR5qAmjEMJF4LWH+R4LWsazR9ne/162R08wlI6n83soomPcf+II6ytvJI2HAGzuv5+L5S6m20XLbW4WGfFqBLMT1MlVTpl90g3HJbmCcR3OdzdQvYuM/HVUMyRUMZWIKGegew26ipg5jdSOMt+gu7RLVDTEnYJECObDBFtp1rMxW3mXWeNorKFQ8NqNHVbMiA89s4SPBMNuwXo/Z12fYKeq+bVnj/DAyiZ/4Nwldk3Kx6anWC49W/sVu7VBR457OoE/eve9hxbXV0Nf6RfDQpsvH5fHH+TZ3R8lnz7CQAiuzN9C1lnmzWc3ePj4BRLd3pD+8tVf5GN7U5IsYu4c0gtUBmu1YWJLhKwJyzFMINaa071VLk72uTYZ0yhJZCI0AVcHmggiK7Fa4GtPzyTMfIGYe5SV+BhCR4KSBBEQ1tFY8F2J1xa5bMkaTVkZVAxWtoaBd6+uMK3mXL9q6K41xCcbnPVc3i3JU8GoKMgrT1AeIyR/5l0PH1pcvxy1udDly8sjmz/Pr994Hy9MbhDqJV7TP06zZviDd97Og/efQQiB957//wc+zLX5mKSvcbnDSkFqEgbGUJY5obb4LCJyDhVp7rqwyrOPX2Jn12K7EWknAdlA45DTEozGaQk2MDjaZTapcEWDcRZJWwEojIQGgg8Io3FRoEkF2llUFFFlIBONKGpUJLlwapW90ZxKQNwxUIJKY25sT3BRyrjyVL4dHxxHgm/42vsPLa4LbS74UvmhD/8y9Y2f4tS9N9EuJlIPke1Y3vn2e3jd688BMM9L/sW/+hXCiiZONd41pPt1e9N5ImNSFDQpJKJGVYqkn3Lm7CpPffISm6MJ1VDTWRmCD6i9HL1fIZsGj6QuGvonh4zqCmYe5nNCGpNemULlqZXCaocKjlBV4MEOY1QvgVGBTQ3KQ1J5zt95hJ3r+/Sen1Kc7lA5R7dxXHt2m7x2TOY5Te4xQRL3O5y98yvXJ+WLYaHNl5fv+5EP8Ou/8QRbkwoj4O77TqC15qvffR/3v6HV5s7mmJ/+t7/NyrEVhLkGSIL0pN2INInZGRWtWXWsURLiNObkmXWeeOQKzV4OSOQgwpYCEVxbneQB52kqT7bcI9+bggttQkketJdJSUAcJI1MW50kJMQRwgdC3SaVAtDtxBw7v8HujRHYgOlEeCWQUnLlqRskacR8UhN863+ULqf0h4ezCfpq0ObLYXq94GXgke2/Q5E/zfPFER5YTvimc/8NkV7iSLrWZmCBn3jsH3J1/1HefPrr+fMPfBX/5tl/xcQF5luejRPXWVsTJGIPOdXsjxPq4Fk7e4FHdxQ6RJwcjpnVfaalIEkcxXhIKAMigbAMOwVUacRwaU6aNhgJe9MeTRPzbLWOKjyxDpQqQe7GfGwrpaMThIG0o5lWCfn1hCbb4cQpxXNFxtVwFpoKmcccU+sMhMQvT6j3xmx0R0xGXe5YO8R14lUg4gW/v/m1a/+Y2fwGRb7EhbUj/Knz/znLWZ+jncFLZn3/4/u/n0cnE/7i699JWmb88CceQe177NSjTiUs9wKzYkZTeMoqYrepeMs9a3ziN7eQWiENzPAEIfBK0KklBIdqBCjN3Aa8F0gESI+eVtgQI61HW/CedvrZPGD2PSZX5FLijMAsKerKwV7N7nCXbEURBYFmiaJq6BvNetKn30/YnOwzHVUE4UmF5NzRwzPWXWhzwZeCC56fvfYjXN6tKIsObz5zP9984hs5Muix3mvLzr33fOf//iPs7875W9/xdZTX5vzGxy4i6sDcW04cG3KjdDQTSxQETR0Ylw1vvGODx5+6gg4CWVjmErzWiMYSsuTA2NpB8MyUoREe8gqhFeQVfilDOYdoAl5p0CBcwOQOkTkqrQixRncS6gCucOxtT3GZJuorOklCPSpIU8XKSpflQcblzT2KSYOqHdFyH20O01/s8E694MufeVPzI7/wKxzrFawvG95m3sPDr3+I2//ECr1uO9a6bBr+1N/+Z6j9hu/57j/KM9d3eOLqDrOjht6zc85tDNhNHbXQCKVpRMNsWrJ+70ke//jzaKvxKMZlg6gDctYQCQmj4qD6wJDvFpAITFPiCOjagzGoRiCFRzQB5T3SONLNCmHBSfBZgurG+O0xtbXMZwVNL4KlHr31HrYpSM8PGaLYQHHlmU0aAXhPb613eIFd6HLBl8jO/ox/8/6P48c1JhZ813e+nfvvPMHd957AHKwpe7tT/ts/889Ikoj/6q+9h6vPbXP54iYIQTGznDp/jJ1mE2oHDXjrKWrH8tFl3Mcvtd5BEprCttVDUrbtZbj2mlkqiknVlhtB237WtNe/oqgOPucHLXA+QN36a+JDu8YqCY1jtjWlPj7Ey0AcKwbLHeajnOX1HiqJSBPNlUu7hNK1rWmHlCxq/xsO79S3ikXC6FXI7taPkaS3szX5QfLyInvu6wjq3WwMXs/tx76OE/2jaLX2O475xLN/l+fGH6GbHGMYR3xqtMl4lmEHJW6eUMQRtdyh9h2apmLJVFyyq1zcuQHzdaaxIdFjXFSQj5aJ3Aw59jhAFop+VNId1pwZzjkzvMLVvS5b4xWyTk6KZ542zGzCpNCogUCbgkJqxG5AdCxRp0u101B3HPs7Cm/6OObQKYm7FTZaYjy6hHU1Z1NPvDwhLyXJsGK5t354wX4ViHjB7x9e2PwXrGYP8NzOP2BWT2jCu+mIt3FiXXGq/9Uc6y8Rq99Z0vo//8r38cToMrctHUcIwwujEbL25F1P3CiaNCdoy9rqjKs3NVFq8ELw5PVNlnsdpkJTWMvYW0zTtpeRt8Z/ovGoSCKCIxw3zH2NzAWRjxBxIMFQzBqCF1jXILTBJ4LCRYh5wHUE3ViTV5aQQL4DuomBirgrsSImzQxP7WyiJ5ooiUj7U5o5xEOBUurwgr3Q5oIvgCeu/3NODt7CB6/+HSaVoKfegSi/gYeO93ho/V5ODod09KcvCEMI/LHv/SGe29rnobPHGc8qdn1FlQis9Bg0m5TYRFAogZs5etLgXODZK5v0Ty8zrxuqSUUhLEJIdCWQowqvNC6SaBcIIRAtZTSRaW2OkhgpPN00YmYLghI0TQNGIawiOPCyLfFPMk3ZKIL3lKOKqLIIJUmlQBoNSvH01S36mxGx1kgpAImKDtlgc6HNBf+JeO/5+PV/wTD5aj7wqe+l0xHsN1/NO848xLEq5u1vuJO1I0M6/fR3HPPN3/v/YzeUfO3J4+xdG9MYQSlqbGaJTmRcpvX9q9wEKyOiSGBnJc8/dY3V8+s0yxakYD4RxE1DZD3BekRkDiYy0RrVn+gyNyBLS9itME1DemKFqa+IRnP8rESaPk4HyA1+XiIiQ5QZCqNohh3GWOpYIGPNYOwo+hG2qHhhc8ZWbJDHB7hxgbSBODaHF+yFLhd8AVR1zT/9pd/izWfP8rd/8GdZTlLuO3mEb33TPQy6KV//dffR76VkSfTSMaP9nD/3J7+f+bjiHe+6h9HOnKAl3tJOHNOSyzf3COLAfJpArBV11XDpuU2OnlllZ2uG91DlFcjWqsjbg+SREO20NSUhzdqkk7WtV5EIZP2EfL9uf9e4tnRHAM1BpVEi0UrRHFQrFaMcVwfSLGqno3ViirzmxjNbLK11iTJN4QLYQNZLDi/YrwJtLhJGrwLs9Cmq8TcysZA7z9DAc3uSTd/hRtmjsgWvO/U27lq7F/M5btD+z1//S1zbusYbzsBdp76RY8O38/iN72WyB8d6Y3JjaXTK2NacSvYRS4Znd+/kmOlR6hnnTu2zMykoiorxThdRCoKP8bo16jSVg0IQDxo+dW2VZ5/vcuTIhHGe4kpJHkfsRxqZQaZq6lwxGMzQGcwnfSK/hM/HdCPP4OiEo4Mu3RObXFjpUdUdhiv3cWl3gnIlKyuSzesJia7IRcT6YRl3cvDvzKtAxAteOaq991LNvpttKxlbx0rk+OBOh91yyG7Zo6MdDxx/G687cdfnPP6P/fD/i7K4ye2nu3zj3e/kq47ez9+89Au4sUUNHEQQgmLcWLr9mrifo0frZL0umzan6TnmRY2d5yQzTUAjG4eTYCyIcLBYBk/YksRSQxqIvEOU4IzARYoQBHLaYJ3DHrUEFai3DWlHMS5qlG7wa5CoiujMjIeWMmw5YHn1BC/sX2G5jjFZxmxzgnE1viNY3Ti8ndKFNhf8Xsxv/F3q+ge4Uht2vUBJxyc3+1yarzGaZZwbSP7AiTfy2lP/8ZTNEAJv/Gvfx3S/4d6zG3zrW17HO+8+z//w//kp7MwhRUDEYINlTo3rgKo8RzpdfA07ozl2ktN4R543mABNx0IQWAXaWQIGh0A7TyUVpDG6aQ7K6SWVETitIVIwKnBJjE8gKEHwnqyfMS5qvBL4XkQVgVuLWFvqsB6lHDuyxMeev8JRkRFnMdfn+4jK0SSKY0eGhxb3hTYX/F74m38a+FVyBx/dXAbteKH4J0RlwhPPLXP0bs23vvl+Lpz5j322Gud449/4ezQv1Lz1NWf5lre9lrtfd46P/7ufpjimUaJDSDRF4pkJQSglEs+pTkpVWOZbI+ZGk68rqm1LOpOIqoFG4K1HSQnO4qzARQ3zJYEoJEIHkp0Z3ihmskFUB5UPgE0l5WqMbiSRFcTrKfP9MbOjCv/WmEyXpCSsuw4bZsgSEY9d2uK2qEM1lMz2c0II+OA4fkjeYgtdLvhP4See+k7ev5PzSxfvorzURRaCf/zeDxPlnhthn3fefZ63vOECp0+v/kfH7u9P+dZ3/X0Inq95z+v42m+6nyNHBlx6/AbhwGQ67cYUtcdbh1MC5eHk6RXm44KmqpmMchpncXMHwbUT0Hxok0UIRAgEL9rnyrYFDS2hqEEKisK217xatW1pUuMOkkyKgEoMzcwSjIRuxMxCb7VDt5uxdqRHnEQ89/h1Tp5ZxRIQ2wp8A1pw7jWH1yp6q7UphHg38A8BBfzTEMLf/aznTwH/HBgevOavhhD+w+c75yJh9CrANx9EB4uUmuDhaqEZuSWUEhCd4+vv+E5OdI59zmN/4bmfYzt/jnRguP34n8bKoxAg91MqHRCJ5XRSESlYT0aMyUi6BZ1QkqrrqGzGo9c6+OkS+WCKdRKUbQUbCVAgpKOqFVOrCMJSVTE7bkjv6JT5Xky52wPvmHtJN4fIChqVUonAuE7prgYGqxDVE052Z2wkljE5kRhQNncj6lUG6nmODEriNHBTTxiNe6z09sFpdooZq+khjTr0v/9HHS545Qj1L6CFIxbtrv7z0w4xA7zsMo/u5dvu/mMcyYaf89gf+K1/yazegjTmW+/+RhAD8rJm7mq8Bp207V5ZNGNj+SqpCdjlismVhp1yStWAKhyRl+RoJBKsQ9nWVNcmAmkDwQWqHujaYxSETGJTgQT0ixNHi4BsApEUyFLSJAKCR3UNkfHMlae3XBDLisncsby6DMkxjicDqmzM8HROWUXk0wZz01AOHZPakzcNmTmkHdOFNhd8Hjy/jhGeVAa6LnB12mVgIiZhhSK5jz/x4LeRmehzHvvnfuTfsl/XJEuGP/muN7DUTRlPc5oqEKTHB0EvkjSdGtnJkYnEyZjdmxUqtzSVBa0xRuOygCgtMvfI2oGWBAKy8SAVlfKIxuITgxcSVVSAoPEaqUU7XjgIpHOEJAIjCFYQRYpgYsahQXRjGimYb0146MxRujLl+FKfm8WQ0PfYwmENaBTOw7itZTrE4C+0ueB358rWNSBiba1G1hXb+xnbboVnLh1B7t/Pn3vT1/2ux/7nP/GPafKGXpbyrW98HcvLfZ69vgMTh+gDpaVfaEa9mqP33WC8m+I+lrH97ARKS+0cfjkmRBHBSJrUYaxA1A0iLxBJjOsYpBPYOELt1mhpEFmEH3YRdY3aKxCpab1TjEHWEFUeMatQ1iMzjRoOCOuSqAd+VrG7a3nrfa+BrYoz51bZriqi/YK9SBOcI92ZQ2Epxoc09RcWulzwexLCZTbihNgEKgdiBnEi6AbFm+86w5/4o2/8XY/9M//ZPyEAS2t93vbVd7K81ONjv3WRQCAEEFIRpYa9yhJSA6HtPtvcmhKsY2enaZMnUoAU7eTQxgK8NCEt+EDAE7xEeg9Kt69VbQlTcL5NMHEwJc07hHrRCBviOEIITdV4km7KrGwor09487ffxXx/zskL64z356RpzN7mBHFwHoSmKMvDC/wt1KYQQgH/X+BrgavAbwsh/n0I4fHPeNn3AP8qhPD9Qoi7gP8AnPl8510kjF4FRMvfhe+9jQ1zG+t+jPWKp/Z+haVkiTdFHdLkcyeLrk2u8PzWP+T0YM4z1VtY6r2ZRMUkOuJUr2RvHHFzNCCKBUEoTg4CrtJs1R1mTZckMexuKmyTsx9pogoEAr0ncEFi+hY3j6iFpLPUsL62w/PzNXIiOmWO9YpaS3wGq50ReYgodzpoGpZXCm5OexjpWTeKezaOU4yvcSSzeHEH80mFj07xgec+xfHdmK+9952sxPeQ2W0ei56iLyc044gXiozxvDi0hNFiR2bB5yPe+EcE+1c4Yc4R/B4XQsoj2+/nbelRutGQlXT4OY/71ObT/Ob1nyNZkvT0fZxZeh2pMaTa0F1qyKuIehJTRxG+nmGHMa6oWfEFY6FZy1K2J3MKXzMnwlmB8gE1dQQEMlbIOuCVIKSBcikQVRLtJaF2ONqJL0GK1jtBChInkQRM49FjkFZyTGX07+zwzI2nWe1Zzg7Oc/FiwPlj/PynnuT8ypBvePB+el1NWe/zr/fmbMUlMnfkYkZR1oeWMFpoc8Hno7Pxb8Df5DZ9mtrtcI/P+OjNn+TPn3kAZPq7Jot+9tHH+KWnnkNoyRtvO8ODF07TS2NC8IQliZu3LWBCS3xRQldyVE+wywqzf4JuJ2Xz2gjnPfnBBa90HlWGtpJPGlwKiIAInjrTrbeYlqhZhRMBkRh0ZKgqi8gkQmukdTCrEVIivOP0ncfxKzEfvXiJzrrk9pUjlNemKBS/+JGnuOPmCu9+1wMIA/v7c7b3Z+R1ja0c16YzxvOSQedwSuwX2lzw+Vi78BNMpgXZxpA7shl3N1Me+9RHeffXvB71u3hrOef5dx/6ZaY8QnrHMl8TP8Cd951iaanDlZv7aBEIQhIaQRDQaXa4//gNLk9WubKpUUcGdIqazWv7THuBSZwg+g0qDcQ3K4QD0U3xTdNOLkwiquUMpMAqR7RVEbAg24mhPjTQSZBCIp1D73iiaQVZyh13nGDUF8z2r3LimObo3hnK/Zrxc7t86gNPMrrrON/8n72DWV5xbXOfPVtzo/BwZcTzl3eYj3M6g+yWx32hywW/F99020/wjdT8jdevszWdc2084bGbmzy0cZxu+rk7OurK8sM/9Mvsbk4RSvHuP/hazl44wpFjS1y7tkvQEvyL1kIe5yw+iRBSohuBiSUmUZSzqjXClu1kM4JojawBlEIcDIRAqnbjJRwkkvyB8bVoK5G8dAh38GEPoZ3GFjwBxZ2vPcXW7pQXnttl+cSQ1W6MLS0vPH2DJx+5zBvqO/iWb38zk/GcS89usT+eMqsdWHj8k5eoq4boENpGb7E2HwKePTCDRwjx48A3A5+ZMApA/+DxALj+e510kTB6lSBNO2ZTyAFGwpH+W+lHGZH83P8Lb46e5ANP/wU2kn2m9Eg5yqrOkar9oNfJbXTWH+E1/S0ee+Y0o70BS0ur6DGMiw5390quzntUMqceBGRlmdcR2ilkZImGgeVkzixR5FGMNB7dlSznJduzGHscylLRhJhoo6bc7eBkA0ueM+dvEBrF/EpCoyQy2aXMZ2xuHWHbldx5epv3vPab6KslLp9vsLs3+eGPvI9pgDvWJ4xE4Pixiu1pzJ3mEld3f4Tzq3/pcAK/WGAXfB6EUAjTToUQchkDHO+/g7Wk15a2fw4eu/4xfvCRv0eWeTaLY9yeHKMTzYjVEkpKtDyCX79JHDVMrkCzn3Jsw+D2JXWecSzLqKYCKxxlTxJmAQxtD3ck8B2FHnsawKeSoMAEgSodAkfoCIRsx30TBDoHG4GLJfWxgMg9qtAICXM2YatHvJVR7hYUZxv+2Dsf5njS4+p4wvjqhP/j5z+AFJ7shGW3lnBMYguNMgW/ePkxvu3uhw4n+AttLvg8SBmDPA1ApFZBwbnlr+dYZ/i7HvNvHv0of/Xnfo4QQa+JuefIGk1libopWhuyVLEnWz+D8ahElwK1VmNvaNR4wLGsw2iS440kj8HUATKFR6K9w3pLtFtiI0W9HCEq0LVD1w5vNEEafNeg8JCXCNnq2gmH72ikDXhAesnVp2/QOTlgZQ7VxTlRVvN1f+RhluKY6ztjJptzvu9f/DxBQega6rpBDg0hlxS15cnnb/LwPWcOJ/gLbS74PGRZTJa1N5/rgy4hdHjNfW9lbWXwux7zT3/xl/jxT/40yWmDLIbcfmqFPHasG8X5U2u4MxE+rcAGtqoZyyOJ3ANz2dIXmvUsYrSfM1uV7N07pLflKYaK2kmIFGpn3lYTCEk1iLEDDSKQ7FbYvoI6UC8nmEmJM4EQJLKqCN4yu32JeKcg5A5QPDPbI3MR6zcD4ycDZ1+7xB84fxJZOcbXxmxuzfm7f/3H8Sc6xCJhnDnMckY1qRjParZv7B9KwmihywW/F0otvfR4o99luZNyYtBnvf+5N+VDCPyD7/1p3v9Tn0QG6PQ0py+so7RESsGDbzyPNBpnHSIERts5MhZ4AqGs6GvN+tqA7Sv7BARBCmQIL5ldCxnAO8KBKTxKI5QEBDTNwV/RHiegPUYcJJm0bCuQnG9fbz1XL++Ah1QGdp/b5LXf9DrufOAc89GMYlZx6ckb/C+/8uPE/QQXAlVRk3ZTiknF3k5JVdlDSRh9EdpcFUJ85DN+/oEQwg8cPD4OXPmM564Cnz2y+G8C7xdC/FdAB3jX7/WGi4TRq5TVpP/S4xDCS9OWAJ659IM8uvf93KyG3KiPcGHjXfzZY+/C1b+KSL4OQYfIPk0oIz6yeY5SR5R9y970PN3OjNecP8v26CaTTcs96xdRvuT5zROMshKbGNxOjHKCYhoRLBxNcog9NycZJ5dLam9IsoLukQZ3o4/WGtWvmFzv0ZkVDGRFaVKkhcQFrowizq7l5HFgXAKXNW+4p+DGruOe07fzTHyF1dEO9bbBT0ribsxvPbuKinOc6nGtHB1OkBc93wu+CI5kn77o/WxtfvTp/5WfuvFLbBYbTJsBX3vq6/mOe89yY/oxTg3eBsR4tUky8vh5jBeCKo7Ru0cJiWft2ClubE3xIkJkc7IAPu8xjz2VFggUommtBIUO7Y5oovDaowYGO7a41ONjQ3ciUUqQhECZB5Rt2ioHIxFeIERg95KgUYq9WmHnKaoK2DdYNos5bzx9hp9rHqWeWvy4wsw8WkRMNsF6T5JZ8vLG4QR5oc0FXwSfmSz6bG3+xZ/9v/iZZy4jSkOvivlL73wLD7zmJJ+4eJWvGdyO1oqpBzUPiBqqWCBtwulJBJHk2Knj3NwaY5ckVRVQLhB5Ra4lQVuUCGgvaLoaJzXKSkIMjVGkNlBPC1xiUEajkwglBWa/pAkgaoeUEiTIOiBCoBjNSHoxdR4I84aJ3qV6q6dQlteePs6HJs9RzBuqGjoiUAdoKk/jPF7CdHZI5fULbS74AhFC/I5k0Wdr87/+nr/Db9dj7DzDzo7wP77rPawd7fDI1nVO9Yd478mlR4QG3RjqYUx9dcgLv34HqJhzawN2duaE5Q7FSjuZN7o5p+imSF/jk4imXxLtV+0Nq3DUHYMIAeXBT0tsJgkaUhugnyI253ja6UzGKogUPonAB+zHd5BnV5mPYfa8YmelZHSnYLDa4Z77z/Gbv/AIzgVm+zl6qKkiyGno1R5XeuLsc1c/fkksdLngi8Ao9TuSRZ+pzRACf+iPfB/7WzNU41lazfhL/917iJKIpx+/zsaRIc9f3KZN10i8bdqkkgMjFP2jXYZRwt7WGNWN8PMGKUM7lUxLsKFNFLX9bAQlEEIQDn4WWrXG1gpEbNAGpNJUTdPaabuAMKI1sW//YMY3RnSWu9TzBoJn6+I2R06ts7re5/RtG3zity5SNZZ8lLO01qGeBnxetn9CEMTJ4SSLvght7oQQHvwS3vXbgR8MIfwDIcSbgB8WQtwTwoslXf8xi4TRlwGf2L3Omd4SS3HGzv7zPL/5j9lIcuZZj1P9/56uWaKbHGeUP03abNNTSxzLNhnnGRM8ddVwNURs7w1wQ8Vjz14jZ4lBNqfKl1i2+5zpbXBx8hxb20OMt8Q92L3cQ8qGmYuQzrYlvbki1iXDUDGIJ9wMQ/Y2M/qqRq0GOksVm2WH/crQ68zpdB1RVJNTsmdXmVUdLqz0sTbiGx+4n6e2t/nlJ3s8eDzjiXJKv5eQi5rpimBua8Y+4cbOIfZlLxbYBV8kIQQ+9PwVHjx1jEhrPnX1N/jQtZ+kF2vWUsV33PbtrHT6ROYoT+6N6KeOtSTQ7Y7Iq5gQW8gjfKnJJ0PqpmG32sUISdy1ZEVEpgqiNcXTowq93/aERx2Frz0ITzASa0AiqacAErCQe/LC0lStdmxfYonwBTS1R+OQUlFKidwqsKXHJJJeP2ZV93jo3tN88pnrvP8xwdvuOMXjz2/TX/NsjhvUMmwXJbmM2dvbPcQAH96pF3x5My9rnrq8xetuPwHA//XRn+MDl54jQnF6qc+f+wNvY33QZy3LuDzawrnAvKqxyuG6IIJClx7nBX4/pVTwbLSHiBwmgUgrTAjQk8hxQ2xbPwbTjSitBgJeQNPVCMCOLEEIgne4aUMBqNC2mXolCb0YeWClIMsSqTWutoyv7+M8JIOMTi/ljrUlzp7fILjAbz19mbeev41Hbt7EZIJkbJnrmp3CAYLd6fTwArzQ5oIvkv2tMVuXtrnjDRcA+Pln/wbDd3yE4y+cpLN1L9/6+q9iPctY7XfZfXaGPeXInUXVDnOkxmQVTdGnXDaUNyKaWU053SMMO0RKIdYU3UTgVzzxvkePPMoF4n4PV0uCBG8FzUpEdrXGz0qK21KsrcmeL8gbcIUlagQhUeA18V6NDwFvFCE12L0ZY9t6lq3GK2Sl5MG7TnLkyBIf2JwS9TMe/qoLfPLZq2TdmJCCGFcEowiDmL3tMUdPrf0ekfoiWOhywZfA1uaE6aTg/G2tMft3fNf3szcuQEkeeus53vk1d9MbdBn0Ey4+fRPbfNorLygQaIJv8xGpg/nmnFqVEAIqUkgF2SAh38kJziNCQChJ6ESEyrXzW4BgFMI6gnUEpVqD67KhKUEI2/oBRab1yQ4cLJzthNBi0tDUE0LV0F1pW07f9Q33EcUR25f3yDoRb37nXTz22HW6/RgtJTubM5zzhCCYTXKWV/ufHZovnVurzWvAZ07zOHHwu8/ku4B3A4QQPiSESIBVYOt3O+nn7plY8KrirqUNhlHK3o3v5xee+S+4ajN2myXefvsPcbR3b1uajyeO7kKq9oPeUSmj7R7FOIG0JtWeJgqEJObOk/fzzjvvZ5DM2SprLvmIS/tbrLiajsiZy5i9MkJ2GnqnpqymM07GY8K+ZraVsFV12VWGI2WgIyrSpEC4wMbyBDuoWduYsp5V5CJlc5rx3AsbfPTDR5FTi3KC5/a3+JlP/ixJZFBB8sDqgAeWjvP6e05wXSY8eb0hVpvcs3qd1c6MIjxzeMENX8DXggWfgRCCB08dQ4vAc5f/AT/+xPdytRySl8v8d1/1v3K6fxYtDZHSXOidQAuNEIJ+CPj9mHoaM40iJFCaQJrFvOPMa3jba+6mlA17ZeDyqMOl/YLUN7jYExRUtcPGjnpJgHGoANGOAu/wsafRijhtENIjGgdBInXABrApmLlDeYGXIGaOerNCl21C+Jnnt/jRn/wtlJT0spi7z2e87uxR7rj9GONasHezYC5HHDm6Q89UPLXz3OEFeKHNBV8knSTinrNHsG7Ozz75v/PDj/wsisDRLOVH/+T/g/PLK0RKMRx2uHBug+ADnbitHjANqNoTRLvTWmaS3jDjXQ/czlfddTuTwlMVDVXuqcYNQSpqIQhdTW09TkCjIThPtFtiJp4qi6iXE2zPwKBDsKE1u7Ye7xu8s4hJgZiXYCQuVthOSj23COdpjODxT13lJ37015FCsLrU48iZZV5/5ynuPHeUIsDmbs44tzSJR0j45ceePbwAL7S54ItkaX3A2ftOs7c/42/92I/wL3/+Oapc0/cX+L7/53exsTokMYoj/QGnh0OsdQzSBGEsrooRNxTxVoXyGis1x5eX+Oq33c1DF44ymU4o6gp7LdAIKAfQaIuoLX57gpUBmxia1ZhkJxBfnVGvGJq+wW70cUd72E6E2a3bNhnrcVlEtZIg5zWqaAjaUA0yysbjpYCrYz7x4ef4qV95FBFg/ewaa3ce5U0P38Yddx9hNp9y8/IuzbU9rPTgHB/9lacOJ7hfiC4X2lzwWayt9zh5eoVrN0f85b/+o1x+YZ/g4aE3nOa7/4dvptdN6PZjjp9e4ejxVpvrG32coPUkoq0QElqBkhw/ucLXfMP93PPAKfZ3ckLpKLby9s2EOEj4CHABRNueHQAah/C0E9SUbD2PtCYECM6CFHg+/REWBxVJiIBoGmxeA9A0jk988Bl+8b0fJfjA6okhx86t86avvouzt6+zvzNl88YUK8AfHP/oJ658dlhuDbdWm78N3CaEOCuEiIA/Dvz7z3rNZeBrAIQQdwIJsP35TrqoMPoyQDbbfOLKt7BXO7zsYKPX8cD5v8mJ3gB6AO1ORaQ+XfJbJm9EDR/hGDmFD6yu3+TSLsT1GkO7ynSzYd2uc9HN2alijqQ5F/dXcVkgzATGWcgCK90JHeW4VC4RBJxe3uXyeJmqTsiHnuP9GbNZjIg9syKhnBvK7hjTz4nClHJzAIlnuL7LmeUxT1xyKOeY1hMuXr/CylLM68RxRvkK+/OPc0zlzOKKmUuQjebe/5u9Pw+X7D7re9HPb1hTzVV77nlUa5ZsS5YHPGFswNhAiDHgQELIdG9CcnIYbkY4QJJ7EshDcpMAiU8OU+6FECCAMcbYeMRGlmTLlqxZ6rl7955rrjX9hvtHtY0N1tytbSn1eZ79PLurVq31W2/Xd6+qd73v911YYy5MrlpsZyW8M54PefEw733077GdCxLdJIju4G/e+kPM1aus1AGmd2qubf2ZX0FYO4FsnGdOG7CaYK+kmEj2M09sQnQaUC1q5EWJ8BKtHeUghsgjMo8IPUZrZCPH47GFRNQNXnnUCHAe1ypJjKWcVEBJxMSirMdXPHlFEziPdAIXK4pg2g6jN3PQgvtOr3Jps0+tLXnldSfIRxrvLlGjQMYOaT3VwGCXx8hW66rFdqbNGc+H86N7+Pl7/yOPjloQRLyleZh/861/Ha0V87Xql7a77sTKl37fv9Lg3OoQUXpkailDS1YWHGo2oXQcaDRojBWTQFEoSZh5tAebgEsdLpj6LIiqwhYOA6hSTkv8C4MMFYWafg5mrPC+RHkAiamG6F6Kj4OpXxlQCIdwHnGxj7eWT334fv7mP/gmkrmI173mGGbsSEtL4ARSC4wGEUEZePreXLXYzrQ54/nwR4/exeqlf8WxlZLVi/upnfpu/vU/fDcAt73i8Je2u+76P7uJvlCJmZgBVMFFEpM7TD8nOdbCe8eR4/v4QO8cTjuczZBWoqxEO8Wk6QmjGsKAwGGrelppVIuhkhCMJFJ48j1N4iCDixO0deSVaOr/V9GYVkxQSnwcYDohk1CiLbjHt7HbKR/58Bf4/ne8ilajxtd98y2MRylj5fHzArU2Qk9KvK2ihGRtrXdV4jrT5YzngxCC//xrH+PXf/deROHQCv7WX30d3/d9rwfgNW+47kvbXnfL1D8wigPCWkA5MXg8OElpHVle0KxXyLOCw9ft5U8//hjIaS7EeaYeRkLivZn+HoTTSiHtEH56wwUpENZN8ydaIYSEAoSSKOtxHryeViMJIcCJ6Y0eBHgotid4JXj/r/0pb/ueV7O0f47XvuVGtjeHZEVJEOlpWY0UgMMjuHRp5+rE9gpq03tvhBA/CPwRoIBf9N4/KIT4KeAz3vv3Aj8M/F9CiP+dadi/33v/lKuYJYxexHi7zerGx/jY+Z8miMDbKn86/g7+9av+DrXoqaefvHLlJraGj5OkjgcvClSlZBIUFEWfCzsbiMom3fhhyo0W6bDGaVsQCoOVkrBTUvcZw41FehPY1IYiF4SFZ0DEvr199jW3EUBRJOyLU9ZchEw91gs2NpvUkxH1zJDMb9LbaSOs5onVBTpxRtTMILT88qd/mX/xl3+MxcRzsj/k3OhuenGfaw7mnFzbS2+rSjXO2RxcvYTRjBnPBW83uO/cB/jQuf/GyDUBQW/ybn7mm76TUKmnfO3rD96Es1vsbE2YdC0iLtlxBTv5iEvdLsMgZc0O0KkmLgSlc9Mh2U4iOh4tHXE/pEwrlHFB6Dx6KDBVj5zzVFtjSgJqiSNfTEknMT4VCA+qP51KIa3HxR4nFWKat8I1FUEGvoCf/aUP8h9+7LuYbyTce2mDtdOPM0zGJMsetx2TbUfEiwVba1fn4jpjxnPF20u876E/5DdOfZRRUUP6hFc3vp6ffPs3o5/EqP6LvOWm43yw8iAbTxQU1mIE+MIwHKdcXN3h1No23X6BziEMIBfTPjKtFaYhUSWEEkTpSesaOTKorATvccJjlEaHGptopLAw8chxiZMCoTQuCsA7vHCIUEIucVGIygwqLzATwa/8/If4kX/xTpbnG3zqkTM8vLVOP89I64LqyGEmUDYFD2495c3EGTNeULz3nO/1+e27fwMXv5egVhKOqty69J387e/95qd9/ZtuPMEXVk+y1t8ii8ANJwxSy1DB6Us9HisnDGo5qpQ0eyHjCsjcUDGQao9PIO5bwvURttpCluAaCtPw+NygZYBMC/xRhbMRds1DUWATkKlBXhoiPUhj8ItVbCshjyXetgnXRwy3J/yP37+bv/lX3sC+/R0+/P57eej8Jvm8wtQi4qGHsyU+0jz4+dMvQMRnzHhmGOtY6/b5yX//Pu5/4ALSC+oVzfe97dX8le993VO+VgjBK199nLPnNrn42NTPCA+TsWE8yTjz2DqnT67hCoOQl6uKLGAdIlH4HBDT/eh4mnjCWHAOpzQiUNPqIemxQqMwU88ibxFC4q29PHHNI5RCuOlrvRDTVjbv2Ty7zac++AXe8PaXkR2e572/cQ+nTm5icRBqpHF4J/ASLlx4cVw3vffvB97/5x778S/7/SHgtc9mn7OE0YuUtNjk/fd9O8ZnXMjajCYRR+a/j//jjrc/bbIIoDs4T90+wfmsQUCEKDxtlXJgriQTnuHoDMtuk0YZ4MoORqQExuHSiMlEIiLJyoIjXM4psxKv59nZLMiyKouLa6yNm7RMSZZqcmGo+pS+S9jT6lKbWJrNEav9DgMCbBSydaFFUivQrYy1XhMzijhfjDHOEehljncW+MCjLe7fhmPtDerxFi52ZBPNxF7Ft/HsjsyMZ8kwPc0v3Pn/RKqMezaOoAPPWw98H2+//Y6nTRYBrI4uYs0ZChKEjCmNxjlH2LQUATzSvUgxHiOJCVWVsiwJjUWXEaWz2FDSWdBs4giEoBVUGG5mGAc0Mlyh8VlAWXjGfY0Lp19q5XKJzKAwAcIpvBJIb1G5I48kSkAZgqvApx+9gPCKZtjiNXsifst6Hu1J6klKFlnSiqA18oT6KhgEfpGZNmc8S85uf4b/96f+Dco7Tp05iq/CP3jFd/JNx2582mQRwKNb65wfDrCRQHrACEQGuiUxEh5++ALFqERJiUw0PiuQxhNJTZEZRKipV0JSkyO8ZKmR0BuOMXhcXSNlgCosSjFNFAmBiwKklrjSEUQBXimENTgcIo7xwuOVoFioI6TgI596lH+mNFGiuf3Yfoy3jISFxDKRFhGALAXV9sz7b8bXDnee/Rjr2z9GUK3yodOHGY9r/PO3/FVe8erjSPH079VHe2ucTbcIvcEeEOQrAfaJkNBaUmv5/M4Gvh0SjEu8lmQVQzDMCEaWwkuSUUGzWWNcS5GRZ8kHbLYcopYjBxKfKKK+QfeH2B2BzwQKS+ArMC4Q9RifFmjjyGyO9wqVC2w9xI5DCgm/9bH7+MEf+AbiKOC2r7uW/J67yHxGklpSG0LLo6Vmfn/7ac/3OTHT5YznwC+890/4xd+8C1F4osyiteanf+KdHDu+/BXm9E/GY49dYmNjNG0rw00rfPDU6iGbl4acObmJNw7BdAoaeLywUHh8aRGRptpISCcZMlLUmwGDLYOQEs+0NVxYhzAG58W08ggBSuKdm05Vs+7PzLNx01Y2z+U1wa/+2z/km9/1KiqViFe/8QS/+d8/RZY6jPAID1J7EIprrt13dYL8ItDmzMPoRYYrNvn8yW/hzpNvohQFlzaWSSc1SnuAgw3FfPTMpivcsnwzfRExcApiw7qrkLsQHdZJagFtZWktZyzMCSrxBKUKcq0oa54yEPjEEkRbxEage556Z8ye6zMqoWTQj3lsY467t/Zz7mKDfhoxLgN0pWS+0ufIchcZSRpByUIwQYYFrUqKcoJhkZDLgNxLlJF8/OEvAKCE4vZD72BfQ9Eb1EiqGWUEg0Dho/JpzvY5ctm5/pn+zPhfG5Od5kMPfgu//dj3YgWMN5skuWCvXmCuljNXfWZjcm9YOcLIBgxcgEOSlhFCKZJqiG4FBNpT318w3xAkcYlTHqdhohylktjYM6BgqT0kyFKyuiM6Iam1DL4UDHYquPUQe+lyhUIqKGrgEoteKBFaIKXHRpYyFhAJVAFIsLHHaoFRggdOrQIQBQnfduubWahBMHbUFweYALJIY8KZNmfsPun4IX7xc+/gPY/+HyAEuq+pOsP17TZJaGnXn1mV6on9y9O7jYB3IBSQaGQ9JFmu4GONXQkJ2wEitCAlNpSkl82rnXBMSstkTkPpsVISzSe4+QgXamSRoycGBgYMOCWxkaQUDuHs9EOxAhMphNIoLRFSgpa4QGC1Jneei5e6AKzU6nzfm15JJRZ450nnBD72uNBjouLqBHumzRnPgq3eZ1h9/Fa27v9xsnMKPwgxOxHzvSOUF6DeeGba3D9fp9nuoxYNOrKQCGRHgdB0DnRgrqRSSwlbFWwNfODwWjGWAhEpyAxFb8TglnlKqfAIasMAVULRkdhQEGxl+JFFZQbpwVRDTDXAK4sal6DE5XHgHhlrvBNQlJSJxoeCop+xsz0CYO9Kh3d9820sqgKXe5jTiI7Ae8f2zuTKB/pZ6nKmzRkf+vwj3PwP/x3/5Q/umd5U9B6k4toji+TWUK09fXECQGOuPjWFV2p60VQSAkXhBIt723gtINGoSoQP5HQymhA4A0JOq4NKZ3BO4EuLDAKCWoJX07Y1zGU/TuenLd/eg5TTKiKlwFoQAq8FPtCIIIAv3iC67K3U644Y9qe6O3R4kTe/41ZECARTc3unAryWnDqzduUD/SLR5ixh9CJh0r+TU6dv4pMnX0foH6cTlCzV6hxdCWnH89y6+A5et3gUnqEvwb7mIZyvs5gMqCeGATGF6XAhS1hqZWxuKj71xGG2M8EoKUmWSkQ7I0wK4uoYlWt8r0IapsjYMx9WGFYtG9LRdwm+Lgi1Z+lgl72HdgjaJY3WhPO2w3aRUGaCiQroD+tUuxaxL2X/9edYObjBy/de5I5Dp+m0htx5/r4vrfmO5dv4wdt+mJAbCIylYgwHdIG+muq5ciZkM16CeO/pbv0+9zxyE7/5yLtI1AX26RHXLyQcWAo42Kny+v3fxKv3753e1XgGvPngzUgZsVid0KjnWBvSFDH9kWBu3sNqyfBCjfVxwIZ3mI6grEucAhd5glQQj2BTgEwCWhVNXhszzBTaOlTDQ80j9ubE1wwo21NDQrEjMUYiiwLhHUEuUCVM5hTZvGQ0B3bB4VsWXxV89IHHvrTmNxy9jX/+9T/AfGWFwHmW3IADcY+aukpfSmGmzRlPifeGC6u/xB9+4VZ+/r6/T0evcyTa4RUr0FqIWFqY493XvZGb9y8/433+rdtfTRB6knqBqHqk0zQrIQOTU6kEmNSAF2QTz3ji8Ymc3uX0Hi89QeaJS0cmQMSCoBLSVwZjHaJeUjQDTFVTNgT5HoULJMJafAhlM6a0Bb4wSKFwoSZLBBiLUSCNR/oSLRz33jM1tBZC8O7XvIwf/0vfyPHFkHZlgKIgDC31ylX8+DfT5oynwPuc7qV/zfs++2p+9+5/RlVOeMXxbW44EnLynkPULh7lb7329RxdmnvG+/zhV7wFHQtOHFtj71KXmi6Yb0SMzm2hD2jspI9NMya5Jx9khCOQhYLSooYFSgqCUDJY1vhDVVRFkw5TvDaU1wcURyUiCmBfiN8fIIoCkVusNJSRmn6bqlawoYBKFTGeTkG0kcLVAqTxqNJy4dy0pUUryV9/3av4K2/6FpJkHm+gLCVlTbF33zM/72cX+Gf5M+N/OdKy5Ed/8/e55kd/lr//i3+AkRYXCzrzCfVAceuxFf76X30dS8vPvAruB37gjXgt8aHChRIRKeYWamxtjiicwxcOnMMUdjrwAfGlSiAhJaK0RFrhtCRqxligLKefp52WX0pA2Uhjtbzsg+Tx3iO++Eb2bjo9zTm8UlMD7UBBFE37x4XAFNPvz1Gk+Vt/+8189/e/jqQVgRJYPZ1sevSavVc24F/kRaDNWUva1zjF6AFWN78DIQw1YaioKp/v7WexfoI3XvNv+czqnexb8dy68uZnVBr4RaphyCsOvI47z76Pusqopw2CSQXT36SSnWflwGlG3UU2VpvoZsYen3HJ1aCWkW20cCrhZbcIHnZnOL22TLG6A4lAFLDUGnC4tcV6r0opPXPVIZU4ZYsaRSp4YtLiutY6PRUROGglBRMbME4Tbtq3yulLSzQoEdryxIUHvrRmLRUrtXn+7hu+i2ONv897Pv5dHKmv8rK4eTVCP2V20ZzxJOTDj3Ny/W8ycgFzwYSmldy/fZhjjRt5+w3/lI+e/iS31uq8bOnVz2q/9Tjilj038PDap4m0Ix3HBEVAtz9mkFnC63twvo4YK8RiRqOw5DbEVAx6GFCpJBy9MebhdJvesInxfQofYI1gsTkgr45Zy+ukJFTtEB86fKDIRiF6A7TzFFVFMJp+4RTOgwMZTe/E6NjAGH73U5/nf/v2NwEQqYATc8v86Ld8L8dbc/yb938vB9prxPUTVz7wX2SmzRlPwqj3uzyw9v+ib+osxWO2bIUnto9zTecVfOMNf4fff+RO3nbzPl6199pntd9mNeblK3VODs9jqprI7SOyiu72mNVhl8EeT7jtECUUFQgzPy2rFx4RKhpzMZW5CnbQx6SejfEYoUFaoGExDUlxyiInFhlZ1EhgI40XEjUpUUJiaiHaOJwFjYRAorzASQ9KY43nN/6/f8o7vvV2ACo65LWHj3Dgu76bPdU63/5r/5Kg0edNR19+FSJ/mZk2ZzwJ5dZ/Yzj6SRJlubFRYUUV/Nqdt3PN4W/mFa/4dl5x3b3cdHAvt95x9Fntd19znqqfR/hNllsDyslRfAFruiAI17FHobgYU/lcn2JF4wMIup6yIpC1Js1KjGhlJIOM+MGCHTQqg9RFZA2F0pB5Q/igwe0NMY0IF2vi8ylohY0CZC2G3KDHFhcIfKgp5iFcM9g6OCf5xV/7E/79y6bG3fUo4o033sDNJw5DaXn3j/0ywRDe8o6rpM2ZLmc8Cd57/u1dH+PnPnEPWIGMFdoL5qzk737ba/j6W6/lgx99kNtvOcj1J55d0uRVrzpGVNVkuQMB880YO3b0syFGerwGUjttVBNy2jkmmFYHIVg+1qE7KsBB1ssoxHRymgN8orEYSKdVRGgJXkzb1aybtqRJOa04wgFyWq17uQLJ28utbQZ+/b/8MX/3n38HAPV6wlvechNvfvMNnL+4wz/7sd/CIbnjlcevbOC/yItAm7OE0dco/Us/xGD8e+igoCIFPRMycG1OuzdxoLXBcnUfSiS8at+bn/Mx3nn8L3Nu6zw1afnem7+Hn/3T32ZQKN5/1jLZPEY/kJRCcZCSS0GEzQXD7QRnLROdkoon6A8q5FlAVqsihxPeeuIRNm3E9qiORNIIxlzabJJHAUcqWzQTy0QLjJTMO09kDbUwZaUy5JKocTZtkaJIgpJDS5fI8j1fseZWWKHVmbb2tKNXMeBOvvHAP3hesX4ypqWNV2XXM17EXDz/3YzTe5BhQaIUm2WFM3Yf58zr2dPeZLG2DyUrfMPRtz7nY/w/7vgu/s2HtjnUrHPrK9/Gz3zo97DO8enP5+R5m6wIULpElIJeoKmUHjGUGO/ppSl9cQqfJkgHkavh+ykSy8a4Pv0woBXGStJBlVorJ1AlRRgyyUN8CbVMETjLJLeU7WmJsAhAOIFB4Wrg4q8Ux1xSYS6pULqCRvQyuv5x3n30f3++4f6qzLQ546vxxJlvYFycRgYlsQpYLTU74+s5nd3Gcn2HdvUAkaryzhu+4Tkf44ff+i7+1Qd+gxuO7Odlr7uBn33/B0lagpP3bxK4qTuDC0AKQVoRRNjLU11gs5+SN3IEHuGgUw/oXZigR4adegKhxKvph10KTdGWiHEBYYAvLLaeEGuJGFtymwHga9HUJDQAJwHnyf9cpfFCrcpCrcrWZMR19f1Mok3+zk3f8twD/RTMtDnjq7F15g520jEHawWhEJQu4OT5V/B4tp8n+jVWxkdpxTW+712vf87H+NFXfje/89iv8o6b9rF+zU38/PvuZG6pwcaD6/gTMRaJaYLqS4qanbZyNmNsL2VjZ0SaCXzkUaagtZiwPU6pnTIUey16KDGVCD1ysGYo2wk2lLiVmGAjR8UC7Qw+1Bjv8VqTbJbkdYWQYGOBGBSMtsdfsea9c0320uRkb4t9N7ZRZ0q+7utver7h/gvMdDnjq2Gt5Q3v+0esTxRlWgUZ4K3i1pU5GDkqhaJVqbGy0OSvves1z/k4P/LDb+cXf+XjvO0bb8anJb/13++iXovZOL0NQiGUm45Hu+w/5IVHlA4HrJ3rUUQSrCXEU2smDDdGSCnxhUE4j1dyWk3kHE5OjYmkVuDF1EBbCyinN2+m49i+rMBCTDNUaxe/ckjLwQPzABSlZWGpQbUasbDQeM4xeDJeLNqcJYy+xshXv5WxuY9ASNqRYGgFPd8m6PxbDjRexU2qfsWOVQsSfvz1/+RL//6nybfw6/e9j8+dXSeoFxxSGaeKBTZNjBtC0zhc3WC1oUgDPnJhmTIUVCuOv3TjLfzmp+/CTRSLSUqhY7QsqDczNsOQubDPPBOaDr4wWaAXOXycM0gr6BVPFDi6G1XGWxWSxDCY1BjHMfNhyWfOfZbbDrziL6z/r37dP7pisXhSXgQinvHCMDj/eob+LDUBSaToGtgRhziw8tMs12/kzeqZeRQ9E5YqTX7226ba9N6jq2/lPZ/6II+MurhQ0ApHdE0NUWiksQTSQqywVYMfSB4/NUeuFdWK4k3XHucPP3E/vmNRTqEjA+2cXHrKXGICpufkctI8oqgLUufxbYEvJMp5vJXI3OMDiRiAVI5RUHDywjpH9y19xdoDGfIjb/unVywWT8pMmzOY6mP17G2MfI9l7ahGgq5RbHATNx/4VxyoHSNU0RU73g3Le/m17/8hAJzziHrBL7z/TtZkH+sEwjmcFsjCo4Lph1IhNUXsCcYSdhxCQ7Ua8rJjB/jIxQfBCur9ktGeECEdKIVVgAevADud8OKUoFAK3QKxnk6nF8ZVZOmwoZh+KLbQzQrGaUE1+UpPw/lKjV/63r9/xWLxpMy0OQNwoyGMbgM8jQCcmxYCfODkN3LHrT/Mm964hzfLK6fN1+89wev3/isAjDOwEfPeT36Iwasn2KwCjZDxEU/r8z3KWpWyVMQ7EFzoUnRCnKtgpaR6vMaRZoudi2N8HFI96zBVcHWJSyvIPEcUHnyOUBEiDPBZCoMSmlXQGm8N430h0kCBxMcSV4s52x1jrUOpr2wJPdqa5w9/5O9dsVh8VWa6nHGZdPAY/+PsX+PxvEq9eowJVXZyz6v3HuT//MZvYn+rhXoGgyCeKW/9+ht569ffCECelURxxO/9//4U6/xlzyEFYloR5AOJKCwikNjcTCuJzPRGysqhDkmoGW6OkHgs4JRAmmlFvEcC9nJLGwgvp/vFIUINhbvskQRIAX5aaSSU4ImHLn3VtZ84vsL//PWrU5jwJV4E2px5GH2NYdyDeCEpkcStP2b54FmOH/woh9tvIX6OyaKiHPBHj72Pfp495XY3L1zLdZ030InGaOHob1U4vLDFXHNI0IN8ItmrW8RYji9e5LrqRebDlOv27vDRxy9y5Mg5Mg3DcQWZCfIiRg4VN8c7jLp1Pr56gpGTNPWYoohJgoxx6Tnfn+PeJ44jygBVakovWG70CKUly0s+fv+Hn9N5P2+uggmZEOKbhBCPCiGeEEL846fY7i8LIbwQ4rYrdToznjuu7FP6c3gPqQ+oL32aw4fP8vJD/5PDrVeSPMdk0cV+n1+58x5Gaf6k2wgheM2+G7h94eUEcYnPPVk3QrcKfNVSSy1uItjXjPHOU5mbIKMSLTyL1ZL7njiHPDIgak6wFthRiDSiU2bU4gw5DCi7CUmcEqiMuHA4AVkhsKEimGgiYUmsxUce2SzQwpCnOb9258eeW0CfLzNtzriMH78X53sIIRi5BvNL93Ht4cf4+qO/zLHmDc85WXTqzAa/9YF7yfInN26XUvDNJ27lVfuPYRSogUWPHVYLvHOo1OGEoDMXorzHBxbtQGaOiil59OQqNvRMFjVkEHctAQIXgAs83jtcJ8LUPCbxmIrA4smlwC5U8MstBBIhJFYIvBcYLRnnJb/zO/c815A+P2banPFF8p/ii9+EtNrL/P4HSJYf4B23/TSH5w4jn2Oy6K61M/z6Y5/Duif3BdRS8z1vuI2bDy1TbU1QQ0PYTWk8OoZc09I5YbtkKY5xizWUl9jAUY9SWlt9Vu++gKk58qRAbeQEmyXagfMZZSNCOI+qCMKGwSqwOBwe50GlBqcdpi0oagIbA4nHS8vOMOPuzz7xnM77efEsdflMtPl0uhRCREKI37j8/F1CiEN/7vkDQoiREOJHrth5znhG5ON/Tl1MiITn9c0F7nn7T/PQu/8J//Uvv5NDnc5zThbd+dGH+Ngf3feU20RxwHd85x3s2d+ZTgI1Znqt+6LjkPN4Leks1RBa4YVEuqlPnysMG5e6mFBjpECmBcJM29mQEgRI56Zm2RLAT/2KEPgv9rpJCYEGBF5eboNznp3VAcPBVTCcfzqugjavBrMKo68xqvtOUv3zD4rOc96f955v/8Ufo4g9Pxnt47UHb33K7U/U4Y+9YWVuSN7UtKMxW+fmiJpDGstDFut99gz6XBjWmJufECqPNW1U7SxHatv08gon8ybbwyrVeorvVxnnisG4yh4xJNCORFiCYMRQxWzVDaGOyJIKkyAnEQXLjR6pksgypCsjFsurNGL0mXAFhSmEUMDPAW8BLgD3CCHe671/6M9tVwf+N+CuK3f0Gc8HGTSZO3Dmqzzz3LVZWMM3/J//CRMIblxZ5hWH9j/l9hWREQcZwcKYzXEHAkeSOVzNY9pAvU/Qc7jUcWBlh4lJsDsLINYImpYsk0wmAeUYyga0coEYSeIeBM2MILGEVUuWgcSCkjSiABc6ekphvcf7Eock0BJSTZ79hb9WLxwzbc4AZO3b2F/7tq/yTOs573OSF7zzR/8rTktedsN+ju5feMrts3GGjzxlnakfgvI4CT4BG8G4MARjT1r1LB7vMRhGsNVhZzDCRgE4Py25Nx4npmN89cChjCUXGtucGnrGA483OV4L5loVJh4G3hMPHTq1kCh8pJDeMjFPfYPoqjLT5gxAzv0M8DNf8VgUQBQ/s2m+X42ddMT3ffQ9YCVvWDrKnuZTt4i4t/SISkN4b05wjyRfVNCU+MAjB7ApCop9CaIYE2QpPvGYPKLXHYPTKAHSenwFRjVHtasIeilIj5BQDkp0DkUzJJwIhBS0OjGZDvCFJxCCYGgJ+h4pFKoasNNPn/P5Py9eeF3+DaDrvT8mhPhu4N8A3/Vlz/8s8IdXblUznimtlf/Bd6zAd3zZYxWlntc+H/zCGX7i7/4qKLj9tSeecopaGGoGoxT/xWNKEM4hhL/8mGN7czSddCY8+VyMHpbkhWEwKEBpsCUohXBcriZyYBwef3mMqZhOUvMe6R3IgNpixKif4b37UnuacGaaQBKwdn6H+g1XrlvgGTOrMJqx2/z8fX/MaKjJezF/cP/dnNvpPeX2v33/nVwq6/THVTY26jyyvsAgj9isxjzRXeSRdc12VmWuljHIG6SjgLAfcUAILp1eQlnQQoKE1Ieczhvcs7GPbpqQ2oiNYZPl+SEH53tcWO/gnaAUEhGVGB8ShSUjE5KFmrlWQrMF83O7kPH9IlfWtf6VwBPe+1Pe+wL478BX+6bzL5heWHfxE/+Mq81PvP8DyKEk6El+++772BqMnnRbYywfe+hRBlnCeFBDdj12RyMKxTBMKHoRFy9C1ZY022MC50kyQ6fmabdK5LrCo6hGDhVbpPCkowpmEBDUSiIEm+tNwuaE9vI2IpP4AIqGJ4s81k5b12QucBoqrRg9p1GNZ260f8WZaXPGVeL7/9WvIIxAZZ4/vuthhuMn/+8ejjLuOnUWaRxhv5xWHuSOwIMLBTIXZEOP0RLTFBRaIoF4UVHuCZBOYiOFDAQukBQVhZQeEkneCVECopHFG0GeSCQSkoBhrCiSqb+Yx+OVw9YV7TggTEJCHbxwAfvzzLQ54yrxDb/3s4hUYDZCPnTuEVLz5BWAW/0RD44uIFND2VWUgUAODSL1lFbB2DJsCkwD7AGNPxagLjiiTh0qIcbl2PkEMRcjh5akZ9FlgexPpj5+WiFGEhMLdCkQsSZrBaTCExYO6c10OpRxVM4b5ieCWgHa79J189no8um1+Ux0+W3Ar1z+/beAN4vL03mEEN8OnAYefD6nNONrhx9693twHig8n/rwg7inqAB84tE1zp3vTxM7HnBAYcAyrfpxDn/ZJNvG4eWkj6DariMqGukcKtAgHEI4hPRgLELIaXubF3gPXkm8Y7pTAaVh6pkkACHBe1ABST0iroaUpX1BYvUXuLLavCrMEkYvcQ5Engolduj5g8fW+Q+f/gCjYtr+YoxlcPmDsLUjfuVPfp1T3XMMdgJOb3XYHjbRGtqdEa/oXOT2ufO8bv+juFKSCIMdQa9fI22V3GMMpysVTmZzuKJCEitevXSWem5pC0OrMeaa/Rc5MbfJxaLKmazF8cUNVhb7WCXZG2coIbC5xFvNHiu4odNhb9OyWH/qu7tXk2dZIjgvhPjMl/387T+3u73A+S/794XLj/3Z8YR4ObDfe/8HV/O8Zuw+m8NVdGAISsMH7nmEf//Hn8DY6QW2LC3D0VSb64Mh//q9v81D62NG2xWGoxrWK1QFXOI5srDKfHOAqUtyF1MWMavrHSZFTF84Pr+T0LMVRK6pW0GlZVhY6BKbgop22ETAnhFxsyB3AXiB0wJXUSgHtQii0iOsx1Y9rSjg+J429VBwfHFx1+I30+aMq8XO1nD6gdLCf/uDu3nPH9yJc9M3Up6XTCbTa+jZizv8i1/5Q7YvjmEgUEYjpMRFEhNLbM2ilMNrCBKFcoLhmRo2j9nJDMO+o2hIRE2hqwI7pygbYKXHRwIbQFYDEwh8TeBDj+iPUYUjUIIAgTIWp8A2IipJyMpSg0Zds//oTJszXnrkLqPcifADzX/84J/wS5/6s4KySVaQXm4hffTkGv/yV97H6FRB/4EEacDMxbgoRlUDwthQHlBQd8ylBt336NMl1Y2M3lZKJhyuUyWIBFEuKG+PsTdL1BKoRoyvRZiKQMgCJz1BalH9ESo3mMN1smVFMBKIUlA2NPVWwt5Ok7kwYnn/c69Mfj48h7aXp9Lm0+ryy7fx3hugD8wJIWrAPwJ+8iqd6oxdwPtpXsbh+c8/83v80e/e+6XnxqOM4rI2H7z3DL/wM7+PGU4QWY7EIRAQBrhQ4aVEKIkPJVEtQDpP2C8QwtPrjbATiwdURSOiCFurYMMQF+hpViOQ+EhAME0SiS9mWRQEkQIN3otp9VKgqc7FLO1t01xo0J6r7UrsZi1pM3adX7r7bhABSctSDnLed9cpNnv/gVtXlpm3x1hqdrjl2j30sgt87MwneXTQRgHCQVQzrG/ViesOEdUYjjWDUQyuQqknaOtoJQ3i0PG2lfvIrOCunUOEMqMW5VS8YbnVYzOvkOiSYRxRLUZs9htMtKLXqzJ0EaWxFJGhjGClNmSxklOvaVrVwzBucLBxy+4F8NkJc8t7/5y9E4QQkmmJ7vc/133MePHw+Ye3CSJLGQv8JOf3PvwAD1+4yM0LezkYtjh+cJEjRxc5ub3GH9z/MP0sQCLQpUeEEtezxEnJKK0yzkNk6kkqknp1wsDV0NESpVfs2Xea0SBgMGqRhxk6yNif7HBpAbaGDZx2+ECTyJzRxhxj4aaX19LSK0pEJHGRp9EcUQaKek1x0+ISbZ9w4/yepz3Pq8ZMmzOuEr2BwYXTiUnpxPAbv3sXD9x7lusOLbFYqXLrLQdY3tPhc4+c5UOffZxSgCynngnCTqe9eOGQhcI5h3ASGQh8xSG6kjlZww0sQpVYBFJoxtpTiAJZdeRNQbjjEbHA1CXGS5SZejPYZhUTh2RpgQ/ldLKaFEgk1TDk1pcfYXWty76FF00r90ybM54RxlnwDiUlQljGZ0r+Y/9PueueU9zi66zUqtzypuuZ69T5xGcf5ePnH6ZlFH4CerVAohgtKFwTchLqaxMqWxl6HOCvccg1T6MxR97Pcd7jIyi1I73FEXRKdOmwoaNYCjFxgD/kCW4NyT8bITczSiXII882OUSeMhRI4dBRSKMVcP3LD7JdlDQbye4E8Nl/0Xxe2nwKfgL4d9770eWCoxkvcoaDdFrtKhVCC0bbBf+fn/wdPvFHn2ff/kX2H+xw4paDtOdr/MFvf5r77j7DdKoDYKcVssJ7QAAeZzzaeoy1IDxCC+abFdI0w4US6TymtDgpppVMWqBKgVMClMJrOc2sGBCYaZuagf5OhtBMvYzU1P16fqHFtTfuI8/NU7bRXVV2KQn0bJgljF7COO8J/UUW9g04329StYJxElG6c9x7fpXbDq/w4MZFfvnRO1moX+RQ7SLbIwgV+LgkyzRpnjDsVrl+5Rwnx3XOnNnD/uUBUeAwhWZOj+ntLBDOSSqVMbW1gmEv4euuuUQjmWCDeUo0urAsyRHGRdxY3+QL2TwustgJRLUR+zs7nFpbQNcL+oFnaCAcPsFNe05w3fL87gTwypf+XQS+3Khm3+XHvkgduBH42OWL6DLwXiHEt3rvP3NFVzJjV9kZDohqfQrl8d0AYwQukjy2tcXZzSE/8Jo7+PW7P8fgEwVZvE5eMSTGIpSAhifMHXKsKMYBrcWUUa4JuhWarXWU9FTCklYwJBtWiWsF0XJGfiohyAzXHD5POy44uzVPGBi0cVSrKfWhJVzYZrXbhPjyl926JWoPMBsVJkGAQbI2sDwyuMArD93IgeXW7gRwps0ZV4l7nzhPFoO3oDKBzAwmCXjw5AYXN3u8++138Iv/89PkwnI23aFUDmEsPgabOXxFEaQgjKCsglWCxMAkNFBK4hh0YMiNw9ctXgb4wbSkXh/MEEbi+wG+InGAbJf4gcCJAOUVVCTSOVygMNqjjQArKDXsDDIeObPO6199gn1Lrd0J4EybM64SH73wUTpqzIaNsd6idzyVdcG9nVV2ZMI7b76eX/7x36JsVTjTXKcV7VDdLtAtx+BgSPyEobYJJvJEw4IikcSlJ7+mpJJlqEAi91ZwTwyQSqGrBUk8otAx1ShltNYg8xXMfIAQOXubPbJRhGtInLcEcw1EFKOcY9KQqLSgetJgWiEXCsvKpS5veN31rFyF0dxPywuvyy/f5oIQQgNNYBu4A3inEOKnmZrNOSFE5r3/T1d0hTNeMP7zf/ogTmvwBvLLLWVGcO+nTrFxqE9n/lbe869/H50EfOGRtWklkpN4JVBCTBM4xn2pTUwAQkusA4RASkkUaYYjj1MShEBYg5dgI42yHi8BNTW4/uLkNO8tSl2uWPJMq4u0Qjg7TVAJwdrqDst723zdm2+g1tyFZO4utpk9G2YJo5cwnzz1WXIh2SlrZEVMLgNEWHIxrcJoEVW5l7XNCePUsFZkLPh59kYpEyXpBpKiDOmmCfM+Q4mS6nxJUBqGMeTDKt2NFlpXkJ1V+qnnSGjQQ0vUKrFOMs6qdIcJydwECoEsAx4cLbCZVuilCXVREscTltoDesMqmdV4CXka4eUc2+F5VrtPEB99067F8AqX/t0DHBdCHGZ6If1u4N1ffNJ73we+lB0TQnwM+JHZh96XHv/u/f+TolDIwBE4iReSXHhUCpGC9z7wBXrrEya+QHQK6rqg1h6xUzTIRAhCkA40gchpxBPKeU1mDSMR4wYe1wuwKmC7OWavgxO6x8akg6946niyQUw5DLEKmkHOXjHiXLpCf5JghwFaWCahRzQsbhhivaASlpAq4qDJ49sb7Izu4l2vuHXXYjjT5oyrwY//0vsRDqSfGti6UCOcB+8wGt73kfvY2BkywZG2Bb5ikHhEBrYucYkiyC2ldvimw9QlhVKgLMnYEUrLcCAZaMi0IAhKvPcEDcdcZYQZaFJTozQCIhBOoK1AWJDp1EPMeIELBKp0IDxOT++4NvaGPHBhjeEnCt7xhpt2LYYzbc64Gnxo7Zc5MOfYfnSeoNBIHGogiJRlPO/5ww9+jt6jG5QKsndK1NhjhKM8H1C0EoJWTrxaYOYF5bKmnIREFzTFliU8nRMIQd90SdOc/OaAZK8l6pUsVXKura5xv9vLBbmAD8H3SopMo89C4/EUpKQsctS2IazUSBdCbAvSoyF+4qm3Eu5cX6f4rOAtb76F5zYf7vnxQuryMu8F/hpwJ/BO4CPeew+87ktrEuIngNEsWfTi5oPv/dzlClsJ2k+rhqxFKkl/MOF9v3M3O6sDnBeYaDr5THzxDRlosA7vweGQSuEjPc2hlAZhPSIQ7GwOSQsLwbQyyDuPDdS0osiUWEAZPzWkn5YuIQEvJQ4PXiDE1FPM4aeG2d5T61S56+5TVBoV3vSOW5HPcULc82G32syeDTMPo5cwd535LPU4ZyA0lyYNfNOxVOtztBlx+/FLOLWDjgbcdGAViWbkWzTnh2xmCVvbbZpyyFJtxIGD2yjnOXepQ1mGjHp1Mh3RtzV2vOH1K6eohpJPn97P4sKIrz/2BJ35Lnkl58jRbXSmCGsZ9/eX2LjQortWZ7AZ0BUag+LC+WW2Rgn7W5t0ezVEYFhoDKhGl7DuAo/0Xhqm15d7uH8Q+CPgYeB/eO8fFEL8lBDiW6/G8md8bbLeX0eGloGJmAwrTKoSKpbD+2vMHRsxEn1MNGF+75DISCKp2bvcR+ZQjkNklNNpDansz1AFjM/GyFIghwJCGNsam4Xk5QdPscCEM6ttDi1vsnfvFmVF0a1F1A8NCCyMYsnj5/aTr1awqxq2JKVThAKStZA8DaBqURMIgpIozhgMC1a7W3TzXZ7ENNPmjCvMZtbHRA4np9ORXBVs5Nh7vM24Dlt+QiY9VAWVApIY/IrFSQCFLAw0Hb6tCCaOeB00DlJBpgNMGjIeWsoFS+CmSaDKckGlNaL0koGOGDRCpHKYEKK1ED0IUJsevWMwgURGgjCbfsC20mNDD4HA5JZsWPL4uQ3Mbt6ynGlzxlVgOy8ZjGNk1WGdoFzSdE8E7D3RYdg2jDJDlmhkrKlfzGjvs3CHpn8kQTmLLEukNpTtAJ8HVO/PkXmJyR02DHAjQT4uyfc1KCsJckcijaBhcmRNYauaKBiQbA6p+ZL8wQrjMxGqn2IqAeViDaoBwcaIhhyhO1DssShryZxjp+P5dHeLJNolQ/pno8un0eYz1OX/zdSz6Angh4B/fIXPaMbXCM76aTeZkF9KxqAUe69dokQwGpRYOW2hFgJ8JLG1AKHU1OTaA6FCqgBvPbIwCGvBetTlVu80MxAohPXgHDYO8BqcBBdIRKCmBtc4AuORViCyEmksODFNyhTl5fVNq5TwglFvgi8Mn/vMSYJgl+porqA2rxazCqOXMHk54lhtk7WLR7h53yrbRcD6qUWWozbH9p8h2RlxblxjbsFwx9wmpVpic1IhFQEV51gipbGS44qAixs1VtSYsfOsU4WixokFi65vkmUJH374GHtaE5YbG5wbtjFxyUKcc13cIykcF/otzm8tsNLeQciUwdoceiJYXBzhoxGLesSKTHmi3wYTMNyCLbfE1vyAbxK7cS9mypXO+nrv3w+8/8899uNPsu0br+zRZ3ytUMiUpXYXc3GJ5Ng23Z0qbjOivq9Ne7mHLlPO9gXX7O1C1icP5pkMY5wW1FxBTZf4BTBpgJsI9jV79MdtNk2FII3Y15DktW2GvSpnH1vi4PIOfsHSIOeJfoM9zTHXdnZ4fKzZ6VWZ9Bu0FrqMuwmiUAjv8A2wImcp6BM5yyCr4L1C9/ogE0wCiXx+Y1ifDzNtzrgauABM1SELSdpWyMwRWcHyXIvUD0kv9CmlINgnUA5CkeDLHnmkUAacF5QKpHcI5dFtR9kTaKmoGEUr0WyEE1RpCHqSYNkgamBDwXa3jmhA2HC4zBLlAQJBWWFaYWTAyuldWysFOIvXHiEsPlD0RwVegQnVtCpql5hpc8bVQCMYFAlRNWdQVJDKEj2hOXDdHIWDfNTFT3Ki70xgoAmfCFi57Rxnawv4hzQCReEVUWmZcwXiWsfkPghdQqUMqLQUfeEphaN5tkBVBOImzZpu8NjFeUoV4hKBHBSoUwaRlOTVCBkGyKwgWwghjvBWoykJAoPvQjiRDHe6cCCmjBVZaajsQtLohdal9z4DvvNp9vETV3ZVM3YFKcBOp5oRBjhjUEqx/+gSRm2xfnIT7yGar4L1DAsDgI8EIrMIJbF+6mkklIBQYjOLDBShVkShop8bRGkRzmOSEKTAW4vKHVoICBTeeYRzID3eegQCj4RA4L0EO/VJ8kpN9aA8+TDHIyhKs2vhm1UYzdhVLo4eJJOSGIMOFcOiim8Itoqc//HgCo9uxLzy8Bnq4ZAsi0mCBm/ec5bvu+U+4s6Esgp5FlLXJReG8zihOLBvFdGymHGHc6WjGuVcMh1a7ZLESDaHdR7rtUmLgLRwnNqeowgUy80Rc9Vt9tW6eAvzS31ErWRYaiqtMWXN8WjRYe7IDovNMfVWioPpxBmzuXtB/BrP+M54cbJtNyidRAmLCBUYjagGnN/u8pnP15nkBYeOr5KWJUUWUQ1DXnX4NG+99RGCZoFQniC3NPSE3nYT4zVyZQhtT9WHdN2EqF4wLpvMz+WoMuDR3h4e6K4QSMdoHLC+PU8Za+bbA5zMicMUAke2x2JiB8YSVccMVcxERvj9hrBVYqqSukipKU9uXhoVRjNmfJGuNEgPUnhkIlBOQhxwfq1HenHMQDomey0TY4iEpj4fsO/QGstHulAzlDWHVxYVGaI+SGuw7ZKyJYgSyToFJlSEPiCak4ROMOjV2Nnp4ENBMYJgBC6MKGKPyR2udDgNRUPjhcR4T45HIrBVRd4JcZWpsbaJBFKDULv48W6mzRlXGO8dh6I1FmWKSkHWwfoAV9Wcf2ib4s4+pReU11bh4JADt0BtXvGmxjle1lynqlKcDDF7ps0qHVIawwIOC1RToRoJ416BLiGsBKiqQ3Y861mTvonZEw9Z7mygRmCiBlm7RVZ4vPOYVoJVEqSliDVeBLiBQscWNZYEThC0HMEEtPFEepdutLwIqhhmvPg4d3YLq6aee15OW7mFVCAljz+4Sn99iMXjqgHjcUmrndBpJrhIXa7MBedB4MAZMA7rBbYSICshMtb0xwUy1CgtkbFCA6K0BFxuHZceZS1SCVASV3iEEIhIQ6jwiKkptxDT5JaWIAVCS5ySeDm1htg1XgTanFUYvYQRlZTH0zkuySp6soMsPPXOkFiO8X6JRZ1yS21MEhZ0olXOBYrf3zpBttMgcyUHlrYYhXUuDFoMCBClpVLGBAUMU0tNBcwHQ+7ZXiLGcWkS0hhOSKolcWIwNmArkIw3G2gNFedwwlC4iKO1LbppBeIcRhHV5gQaOZeKDjUpaLsA66CYSPy0xvCFZ3bRnHEVcM4xCSWTNGGiQ/JJifUCVy1AT6iZeSI8ty6keNFn0PCcKRNOnbkFuxOjZUmn2ceXkp1ug9QqikmIbljiHNKRpV4xLNe3+fTZazhe2WJ9q0JLDbFNha8o7EiwZgPG23WkazNf61IgKIuIWiNFRoJMCXxeQdYMwzxCjgRJAirxFIUly2NqyS5OlJhpc8YVZpBlyNAjkZQ1gXeGUCiK2NN3QzqyyrgsaM9r+iqjsJbt3MDje/FjjQosSWwJvCHrVZCFx1kJSx6dOtLM4+se1ylRT0g6SztsXmji5w2+BmXsCUeSUnmCfoD2FuIcoyXxWGPF1MOorEIAlLGEUqKNJ6kH0/Gm1hJZQb2yS5W5M23OuAoM09MkqqRay2BPgRor/EhiQsvgTJ9lH3Ax0iwerLD3wONUzi6wvuX51N2H6fuApJ3SL2okRyVq1aEGBVv9GkZpgvWCMgXfjHGJRsUZS+k2F9abqGpJLRtRjSdc7O6HakF40qFGBeNmCJFHbaVIFSF6Ei8tcjmgeW2frbUqripJljRqccJg4qioALUbydyZLmdcJT796UenE8e0mr7FrEAogcUz7I9pzVWZTArmVxpsbI/xQjKZ5OiiRDJN2DgtEVYgMzutVHIeohhKQ+kcIlYY4ZHe4wuHdwYRBljEdIqalxipCJzDAU6A8GJqbK0uT+ITAioBwgq8cXgpiGoxeVrinSep7nKr6Nc4s4TRS5SPnLybrUGdILBURY4XAUlh2XEJojqh5cfsW9ghrljSPGBHNTnfL1FFznpP0whLTKZ5ZGeeflGjvjJhzhYYFGv9Kn7HI1dyTg5biEzRzxJWljc5sNBlLkzxNc+frC1TuohIOBZVSn7AsRKNeHxcsj6sM9fqc25jkR6ekpAjzUt0pMK5Dof214jEJilLHF1Y2pUYiss/M2ZcSX7uY3+IG4dUZE4hQrR3GDOdGCGTlFCkzNUGLNXG3Lu9xNAts9G16DFs9WrU4wGlE2xsdUhtRLhcUNGWiQ0Y7FSIBo7uSsjpCws0ZMrGoEFrT5fO8iaNWDIi4Atre6BRMl/rg5dkVQglqG2P7SnKtkP0NTZwKCStxoRIG5pBm0ozYVuvkwTzNCvVXYnhTJszrgb//Fd/C1kIjPMoBNOOS4fKIQ0tmTO4hiNuSNKNgKqK2Z4MSHJHthEimim2BulGFRsJ/PLUf8EVAbYvUKlBaUvYKzFNRXezjtnv0PWSljJkOeQ7FUxD4usWaRQuUDCROC2gsHgpCCYeFwicUEhdYiuepbkGaewptiYcPbzIbo2rnmlzxtXg9KUfIlRQePBOYScBwbrCl5aiLJlMSoqKJ16xZI/W6X92hXLzAucaDQaPefI9dcKq5+B8j4fzJZq1ABVJ/EnIS0/pHMoJSmWgDFkvlrDXOGq1gqVkwqQXkJ+WeJEgIouNa5hOgDdQv9SndAavNEVdoSPPsIwQHpSGlX3zbKUhle2Ul1+3sivxm+lyxtXiV3/lk9NfpMR7j1cgSwdSUigY9XNMMJ1UphD4oiQfF3jnITcQS4Sx4CU+kng5NaOWpcNmBgdYpsVHEkAIXBTipEdIzzRD5PFCYKXEaonHT4dCMF0Tiqm/kREIwEsJCg4eWeL8mQ2yScGtrzi8K/F7sWhzljB6ifKb9/4at8+tcyn01IOc9V6HsA57zZD1nQq0JlQbOzyyc5hJmrFTnGBkL9AJDZXAENYMj/SWGRUhYxMwHMVMnCUaFYSFp9Pu8jde+Sl6WcwX1CJi3MXXHa36NrlL8KmjJUoyIUhiTyPI+OD6PnpxRLs6xERwYWsOE2mMhkKNGZQV2lFOHC2R+Cc4MpdzqbwDpXfnAgu8KLK+M15c3H/697hpccR9tkNd59h+QtiZYEpLt1shXOhR1ks+ffZ6onyDuFggcueoVjK2KgbVtOz0m2RlgLGK3NTIJVA4PNDev8k7vu4eHlrby5lokTmTUjY8Cwspg7SCyWGuNsYKQRg5RCHY2G4SyYxqe0zXVvADkFqThw4hLctqQgQcqNUZxmeY70iO1Y4SqF28hMy0OeMK85nugyRzin5aQQagUomreUrvUENBr51hq55kQ1HmBUthTFdsYaqWsgZqzmP7AcYIpBTgFQUKPbGgPOrohIVr19lY7WCHDhla4gVDvTOiSANUTyPqBZmNcdphc49VmkA48paAUiIKhwoUNhRgPUUo0IWkVamyWmwQzknuuOHQ7gZyps0ZV5iTk1Vc2aBPleGkjiumU3X12hixaRiiUblnaVAyPCdJXEDy8jEOS/exDsxBWTjOPDFHEQZs99r4viQ8XyBKT5mEmEOCKJ1Q5Bo177nuUBc75zClZu3RNmq5xD4RQRRSSElZF8RrGXJcwEqV4VJAaDxi4snWI5L2kCC1LJSwZUa0mxVecf3+pz/Zq8VMlzOuMN57huPySwY30gumKRmJLy12YBjFbjrWvjRIBc1mjQtbI7xzyFLghJj6DjkLgUJKOfUfKg1CQZZEEIIwHpMLTCAhVtMsiwOVGbyUSC+mLXHW4sW0O8UHGtx0QIRQ4BB45/BSIjyEscJ4T7OTcOvtR3YxkLt36GfKiyJh5L2nzEvCONztpbxo2CpztleXiJsTtgYtFjoxQe7Y6WlUJFBRF+Ml54YdMjaI3YSFyphmZ0gcO/ZE26xvt+iKJuVI4uuel7c3WE2rHGxs8dab7yMlZDJo0NMhuuZoWsN8YHh4WxMFkm89/AgfPnsCoTSPrM1zy9I6N7QvcdfaUbQN2B+l7EQlKocDQZ+eCPE+Ymu4yXxrFVtEtP3u2myJXeqGe7HgvacsDOFuTf14keG95+wwwvSq2AbYIiTq1CjSgnHPIELHUBUcVCmXNhOUj5mzQ1S9pKgJFoJtjtbXWb3UZljWKScaVXdct7TJ6mad2+vb3Hbz/fSLBF9EbLoqQhr26R6tcMLmahO85FU3PsZdJ69jnEeU24pDRy4xXx3z6IVD7FEFUWfC2bLFnBpTiyf0RRVfhJxe3eHE8T4iU9SquyuOmTafGmst3nn0bk39eJGxNRwyHmjKMkBEjkBJah1NNgAxKilCCyon9J7xJGRsoFRjciREGrGU0dzbY+fxNtopXAom8SStAmcVty6fp3Jig1FaY31QZSIEOhnTrExoNMb0NtvoxNA5vM7Z+w/hhgG2yDDzUHqP7ipqdYfDUaQaJz0IhxKKoPDcdXqVfMUQDzWB3d1YzrT51JjSIKRAqd0bGvBiYtL/EFkBHzu7Hy89FeeobWhGziEKRREoTCgIpaNfjjij51laECzqEm8le09YuvsLsh2N2FaEjRBxs0E+GOEuJNPR2/USFQv8KEaOLDtorlE5Yqi5eKlK2CmZLEUEH88JU8dkuYawY1SrxNQCaFaIEo3eHGHjgFIo/CBCFjF/bNeoCYdfyKmHya7FcabLp6fIS4JQ71qF5ouN//J/f2SajLEeD4hQEoYKk0JZepDgDQQB9EcltvSMJjlOSISW2NhilUB7pm1oCJyUiMDjkZhWgpECnVm08DimlUQuVAgEclJiatP262RkwBl8CVQFFonC48IAYabeSDgHUl3+//Xc9/lzABSlodGo7FIUXxzavOrfxoUQ3ySEeFQI8YQQ4jmNVHzss6e4832fudJLe8nywMaDHEo2GVtNf5RQFZ5rGj2CMMVLhS8UUWTZSFu8dt+dLIQjCjEiDkfsDGucGjUIHVSaBrDU5yd4D30hEGGDVx48S5WSs6OENT/HxnieejLGR5K7enupNgw3zl1ibafJaFKjNAmRV9yxcJGmtphenfW8wkKry7GFLY4vrlNKjUZxpCY50sihhLNpk6Dy8O4G82vchOy5ciV0CfDgnz7KZz9035Vc2kua337wE7RqGeM8phiFKKFYbuRYBUJpIkAnlrEJefXRT5JUMjbcCG1L4sIyHkfEwlGplojQoOYLSisYGUWnEXPzwXM0sDwwWGRLzBN6SWd+RKY1924eQM6Pmduzw85mg2ISENqAVmI4vrRNqByuWyEVDrVQ0Foe0VkcYbQikI5rlz375yZUfEZZaCbR6u4Gc6bNp+T+jz/E5z/6wJVc2kuan/vUh4lii3KCwEgkgrgiQIMMFFpLjJZEcUp87ByqnnMpHaB1QTXMsXikc4TVHJs4ypZACEcQWPYtWZb3bRCLgrNr88hKQCChvjjEC82lbpu8LvAVw2S7gi0DktIT1AJUyyGtoDLxpEIyqmncnEO2DTYEH0maKxV0Gzwei2e1GO9uMGfafEru/fD9PPipR6/k0l7SbAx+Hivh2vYWcehxHuZvzZEVj2totARXD3nrOx/kO775Y/jUsfWFLqfPdOiqmB0RMtxu4Fox41FA3Ctx2xLlQxpxiGkbkoWc+bltWrKAVsjrXnWRYMWxrquY1JBZhzk39ewLRIDbY8mP1xATiWtXIM1BGtyeJvNLfSqLGe5AhdqBFlEMvUTRR7Hp8t0L5IvAWPe5cqW0ec8H7uXhux6/kkt7SfNHH75/2u6lBF5PDajrzSpeCWQkIZAICalSjL1BKbh4fhvweDk1oRaBmiaCPNMEE44o0lSWq2TK43FI65Fa4jXYpsZLgVdiWrmEQxkLAiKtoaJB62lFjJ8m/pzWUI2mLd4SRKJoLFWmk9YQWCcYDF862nwmehBCvEsI8ZAQ4kEhxK893T6v6q1HIYQCfg54C3ABuEcI8V7v/UPPZj/HX36Y/Sf2XI0lviR5bPW/c/viKrV2zufX9nB60sJdyqlUEk7UD3CqeJyOHvLo6hw1nzFymsJvszmp0pvUEM0cGoas79GxweQRYVBgTMGoGLOO4ky+n1pljlQ2eWNwka6Q2CgjLxNcFvCJ3lHuu3SARhVUOc933vgJ5pMtPnDfjbSiMaNI8FixTDgqqZYTgsCwXOux3gvY24SPXbiOeuw5vvLq3Qukf3GMOny2XCldAlx3x3HKYvdGUb7Y+PT5D7Gy0kNWHWtbTVb7HayFWlLh+nbMhfgkDTVie7XBqs/YsjF9YzHbLZJKgddQaWb4QQMbAhZ0XDIwAT4tOOti/qT7MtrNGkPV5LXxKc7ICOVKbKYJfcgDoz1sXFyi3TLU/DztE/ezmHS598HrMKFFSkNv0CT1ihoZuQ5x2vH4puVIE05ePMRCUPDKA9fsXiBn2nxarn/NiSu9vJc0d56+n3hPRhZLgkGCGytMBovNCou1Cvf3L0BkYODJg4BJGuCGDlFqTCqJk5xqJ2W4XcEEAiE8puIZppKAjPvG+xjttJifryONJJbneXxYpUaGyUMqZKxvrcAoIqoqlmXCedsnISPcTsilxwhF4MEXnkSWmCQG4en1UqrzUIw0SSPiNTcd3L1AzrT5tNz0uusJwlnl3zNFmYe4oeHxgWSwHjLozWEu1Di4UEW2qzz++UsE3Zz5eIfuWkx+yuFNiekGNFo95GLAzYs9NtOAi1sBbllB11OcAd9K2c8mbe8Id/bAMcHe8SVOPdGg11XENUG6peEzkmQhIDpYp10E7IwHxOcywrSKTSSTliOQHjeXEez1HGjusBaW9E4Jjh8Y0vcl/c393HFw7+4E8SWqS7iy2nzZ199MXN2lgQEvQra6GT6QIB3SgzcOk2Xs3TuHsIbTZ7amORtn8dZTZg7vpu1hXgpsJCEWCKsRFrybJpLKtCA3glAoarWI+bmYwsOFnT4qB+csSisQgnhkkEJSqUY0KwGr22MwZtqiJgDvUFrinIdA4ZF46xh2M6JqSDYx1GoRr3zN0d0J4hXW5jPRgxDiOPBPgNd677tCiMWn2+/VvmK9EnjCe3/q8gL/O/BtwLMSsZSSSn33yjhfbHx240ES3+Fc2oZU06qO2B63yEaOQfIYNx6+iNIF+/Qmfem4UDa4dv4CLVHwkIpoJkOCwtIhZW8np0gdx4Iuk3HCte0N6lXDuNzDuHgtL08MI7PG6mCe7TKgv6PI9gcEhedgPORsvsB8c8Sp9Q53jes8Ol6mSBMOHdpkJEImI1jpjNC+YK8a8bHxEfbUC27dE5Ko6znWumN3g/nSvMBeEV0CKK2mf7RnPCMe3uwR+xb9ospwUsUFHgoBpsfp2DC/1EfHBUlYcDqrMXQhxw6sEyrL2e1FFip9lJPMJyOGCy0uDTX74x6TboW9cxtEiYfRHC3/Mo7VBuT5Y2xvdnhsJ4HMcWGuQygs1SSjb2tUaxPyzYTPnD3O6c1FtPR0mgVWNTGDlNrSGGMtjTDnUrfN8p6Saq3O/uQaXrbyit0N5kybT0k0a+F+VpzrWmIRoJzETwQFAqsdw3xETw6I5se42DFINWKsEdoTHR+jEIw2KwTRhNwGJHFO2InYSgM0hlaWUVnKsdrTMjVe1rmemtzg/KCguzbP9nZCLgS5aOCFQiAxHiZtQ7BVkl0IKHOND0Arx4KO2RlMSFvgrEVIQ1EoFuM6rYbk1j1LvOHYsd0N5kybT0lS3aXpki9CnHNYYQmNpDepcenhRfSwpDxfsJEOyZIJLvRU9IThKOQTX7gWUVVol5MYh7KO6p6Smw6tcfb+CqvJAeZWuiQXSsrRgNpxzzXzO4wuXsfiNa+lXT9N/+yjuIfnuXRfBdstiLTEHJKMqw5hwbY9YtOg+jmpTAgmOTqF1pE646VVysWcajChMVHsZE2OXFdnZyg5Wr+eWw7v271gvjR1CVdQm7Pvms+c7Z0h3nq89wg3nWBmPZSFZWN7QFFafKTBTQt9RGHw3uNiDUog8BAKEAIbCpSXiMJOrS6QGCnRsWRlrsotNx7i1NYOF9MxVefIhhZRlAgLAomTAoej1AInPX5S4lB4bREypDNfYX0jnS5cOnzp8d6xsq9NkZe8/vXX0W7Xdi+YV1abz0QPfwv4Oe99F8B7v/F0O73aCaO9wPkv+/cF4CsyAEKIvw38bYADBw5c5eW89Pn86oeQouQLq4ukNsYngmbQIqgLjlS32QkEa8UylXKDps9ZCAyVoWVf21JNhmyVQwJb4rIKj3fnWGg7VnYyEhGQzI3ZGta4sL6XhjxEpXqSzwwjqlHEMNtHUF6glWR0yxhtoRhUOd5O8ZRs2hrXLjxO3lZUwjaPrYcEoaUVlUidU/MGbx2HFkYEFcV82KVdPUkz2mWflJfmBfZpdQkzbV5pfvm+/0q9MuHSmTmMEKgQ9oaaWHvqccp2ELKermCHI26IV1lqDDh9OmZpbkQeC+LqhFYyZtyvsN5vcbAzIelLVsIhOys5fVPhc+eup+L3cNZd4JQrkdEio8EC2m1jtcOlGiMkrcLSSSx9U5KgqC2X1KspNZWwvR0iK544zpg4TSdKqQQp5VzCMFTo0FJvrNGMd/dLz0ybM21eKf7O7/8nKs0cdyEG6UAL2jVJ5BXjKMNpjc+a+NGYdmdAEWv00CHDAmM1ZeKoRY6oD1hNszmk6gStTo9xWWEra9DYbFEVdU6uXWKjGDAxxwkHkqzMMB6EBu1BSUPVJuyMc5rzGTJwFGGNMBb0R4aJs7ikxAcgkxIxkSQLjiwpqIQheWSIw931lJtpc6bNK8XmxdeyVcYsBgUdOaG6p6RYXUQWMbqpqI4ErvAsL2doKbAV2F/r0rhuQrwNFys1OlHO1qDK+SfmcK2QA0fX2XtDj/tP76H78RaPuWWCAwuMTq/S99sMt26i3BagM1i2BI2CJPToEDx1hqtjKs2UIhbUCoENNJvHAoI6tOZTmqQMJjUGowphpFhQl2i0E/KR3VVvnJeoLmGmzV3h2//mL+AViHLaTiYCTT0OiIRgYh1hqLA43MThEonSAufFtCLJgRAe6wTCeuKKpiws1gtMqAlKT+hhTxyhETzx2CXObHaZs4rJqEAVFiemE9m8ChB44lgz6qfTtrPEk8QhY1PiCk/ppompabWTAymQcUh/lBFGAWaXPYSegzbnhRBf7tXzHu/9ey7//kz0cA2AEOJTgAJ+wnv/gac64K7XxF4+wfcA3HbbbS/dP2cvEF+49LNEiWKuMeZYY4MnRoeRUZOhGFCrHObEwiEGg7Oc729zvNGlW8TsaWasFQmjYYvtPKZZg8P7z/Ox9SOEvZTXHD/NpAy5Z30PNk8YOYUuVllqdDGq5NRQQXgJEVtsITjY6NMfVhg2RpjIUwiPDXJW2iPyScTJkcaJBoc764SuhhZQk441XyGzAYQhabGEjhZpJbs4IQ1eyndknpaZNq8sazu/TyVsMdfsc2J+wMWdw1CN2XE5hxuHec3SEU5eeJjV0YDjCxusT+rU2hPOFm0qE8fWsM4enXLk4CUe21ihHI+45ro1JmPN+voS3kb4QlGkOyzNDeimju1ME0VrRLEjz+HIwjaDSRWZg1UeKQ2ZzrimsUXmKqyNLZlIWF7cJJgk2MAxF444Pe4Q+hAvI0Q2h68dINC7fCfuf+F35EybV5bNyWepJS1c3XDNvj6blw5BK6EoPdfPL3BkaT/3PnY/60WfoF1QdgPCOcPIRURjRzkMWV7p014e8tBn51HBhP4eQVo26K630T5iUkicHxDrnJ3cMvAhAotqOiyCSnuC2QpxaEZRTmkKNmyIbGfo1NAzChk48k5BMIoo9YQ4KinKiChRLCQSNa5xJPkaaN//X/gdOdPmlWWQb7OioT8JuHv9GnK5SKW+Qr6v4HVze9jXqfPJ9YdZbp6nvmhhWNDB0B/VyLslS2XG7ddc4vqbenz4Vw4SXW8I9+Y469go5sirDdx6gb7YJR8NGUwynApwNY+7wbGHIVGt5NyDi7iepJr1GNQs1gqkl5jUMlgR6KQknS85HMDNc1t8bj3m/KWY0EtO9vZjRg3eetMua/N/8XfjTJtXDu89pfUIKZCBhKaiE0fUkwibe64/usj8Qp1P/uljrG4OEApwCq8VXgpwFmEFtg4oiRtYvPAgJKoE5aeG1b1RyXDYxwsYGQvWI43BxAovQUiJLyxCKbqjElcYUAKEJLcW7xU+EqTeQiLBOKwXSCdIYk2zUUFLyXUnlnc5oM/6FVve+9uexxE1cBx4I7AP+IQQ4ibvfe+pXnA1uQh8+QzJfZcfm/Es8N7zucdO4BV0pOXwkZNPum3sqzTUkHixTywTXhaP2AlP0S/3MKTDuOizdukMWVblM3ofD4/muW3/Orl2GDx1U5CPQj567lr2twLiRpdHRnMEpaUTpOxYj3UBg9DwhfNVRJyx7WPieMLeypA0SEiNZrEyYk+lS5bXuVQkHG71GAnFMA9JizoHmiM6wYjQKsa5ZyNr4GqGeSV5VbXO4+Zh+t1VlNzdtoqX6B2ZmS6vEGU54Y8efi2BLulowe3XfOFJt02EpBOO8IueOIy5dV+Xsy4g8ssUssKouETePU29lNx95jgXJk2uPb5G6TTZUNEUBeNhxENPHGC5oRnUQvKdeZQtaAQTCmnpGcUosQxOVkCXGKeJOzlRkjOmgvCeKMpo1SeMJy2chIX2NoEX+LGk6iXh3IQ9ekCrXvD4UDCeVGiEhvm45Dv3B3zy5D2srm8A3/HCBfqrMNPmjKdi2D/Pbzz2LkZKsieKeNcNH3nSbdtByUiV6L2eJEw4cHjIRpkQ2A5SC3p+g1FvnUgpuucajNOE9okuo0Ljc0s9zjCDmG5aodlO2LCC+mjMpIyRkUO6jEEZMhKC6JIkc5IoENgkh9iRFB7lYZwIVKVAZQIZG8qapeoMPhXEsUS0ChqJRQUR2Tb4PCAMoBUpXn6kwvvuPsdnTpbwpq97ASP9F5lpc8ZT0d/8Ez5x5h8y8VVu6qxw/bHf+Krbee/puRAlC0qtuX2lz8nthJOLi1QXKjhRsr3RZ2M0xtYW2Pq859xGnW966+M8/gQ093TZDgNMTfDQpTnCG9oI1Wd4psF2UcdseopAkk1SrBbkywHBJU9QFnT3BDRswWJQsDPSqJqjHFnKcURxXLK30Ueccox2JJWlKsWekvrigI7MkdJQrUyopJpKVzJ/zQJ/eGaDRv0s3/P6l7/A0f4zXqK6hJk2rxjved/H+A+/9Rm0gW993XX81N/5lq+6XW8wAcAFEpUo6oHCCIEIAxpVjQ4kFy526W4PqYSaUVaCELhqgDMWIcBbj3QCFSgarYjB9mR6I9MBzmGkwE5KhJRI75ClQwQCEyhsoJDG4Z0DJfDCIZ0ELXFq+l5XSkEgSQErBGiFsw7hBEoJlBa02gmfu/c8Dz96iTe++cYXLtB/jiuszWeihwvAXd77EjgthHiMaQLpnifb6dVOGN0DHBdCHGa62O8G3n2Vj/mSwhYXWb/0LUTakXpJr6zg3Qgh/2Kvpfee85MBRmrQGdZ6vJQ8liois0G6OcHVtqCm2XaKiYjYV+2zMa6yN0qpRRk9G7Mv6HF+dDtlpPH+HKQhF7Y6bKZVfCXhyLzk7EaVvMworGRoNdcGXa6pDHBZwahq2Nips+Q0mZYcb22xrzomEyGlXGYntgRlTL1Xx5WW03aJsox4bfVxOkHKpilZDteZj676EL+n5kU4KeIZMtPlFaBMH+SJ1e8kUBG5DdnxTbx3CPEX37feG05NFEI6hHacLRMagePzI088HhAXXXZqQ5J6SG9ombiAZqWgv1VnX2WLoOJQBuZqPXZ6r8XURsAWtdxzfrNNd5IQtzR7mzV21jW9oiRzApcJ9rVSDjY2Ceqas2mCzQNCn7AjNAfam8xFlh1ZpZXUuJQ7irTK0GcYZ9gpKmykLU7sOcdi1OORYcKh9jkqQf2FD/hXBJSZNmc8KenwT7j70j8gVhEjIxmLw0+6rXMZ5yYtKmGGcRFnxgk6NKwNc4JJj5H06O0hSTukN87IY0knKHGbmpXqGNW05KEF5UnMDQz0GGuHNNyIYqeDHgtEK2HfYo1sI2dUOqw3+MwTdVJqSxlVV7KzXaEiAqpYxkGGbwnmggneS6K2JC0y/KDKxI9Q5JRG40ch8fKQiSz4/FpBZb7L0T2tFy7QX42ZNmc8Ba7/PzH9f8Rc2qSXB/TV7U+6rS8ucHMywXrPwAYkckiZ1xmyhdWw9XCd8XlH0ky48brHWGhuM9ps8NE/2M/yQpcbb9pmVAvo1Cfcc/JtCAS0x3QO9aiMDWfunGe+a5lvNVmfK3HCYsoc6zTVxzKOLg254bp1/uSeJeSGY2XJoq9LsQ3B8doAqT2fHR1jLD2VkSEJhmTjgoeLecpQMKgp6I/YshcRzTrLc7vskfLS1CXMtHlF+PmPfIqf/fhnCKvgenDD4Sf3Qb734fM47acTxkrLpISJMGS5IUawenGHibPU5iqs76S4QBIiKEqHkx6XCIQX6FBz3UKbc+e3sQK8lIjSIgpLc75CtRLTG04oRh5nHJRArBCRwEUBclAicZAEWAvSe0SsEF4gPWRpjq6E5LnBGzv1U/Ie6zyTzPDAw6s4D/sOzr9gcf4LXHltPhM9/C7wPcAvCSHmmbaonXqqnV7VhJH33gghfhD4I6Y9cr/ovX/wah7zpYD3nv7Fv0RafoHUl+QEPDRqM5e0ufngr37VZNEURysuOD8O2egd5A0HOjyyU9Id5wRmRFWDVjH5ICJih0kW4FzAQqWLsyVzYU6jmtGqFRxt1dgRAz6+1mY8DOkPa1S1ITcWbzeoz61wW73Lx09HzPuC/qBKd6FKFIy5vjpkzmUMigqDnQYrtW10VnCs3mdTXaQ17qA85DJgbRQRBjljBOeGc8zNj2C0yoaSNGt/5QWN+1flJXiBnenyueOcobv6Fgb5WXasJ3MhnxvMU4+u413X/YuvmiwCcGbEop6wNY4ZZx2+4UiDj55zjCcl1naxrkZD1jCDkkhuk1pNk5JaMgLhaYc57YUJquq5daHCg4NN7llvs5k6xoMK7bCgLATeXETN7eOVR7a450wIGgaTCkZUcGrCrXsvMBhX2dmpo3oJer5gSexQbxZ8cNAhySIaPgOrOduv4nXJ2ARMJnWi9gDTX+W8aPLGPT/4Akf+qzDT5owvw5UT1i++ga2ix8h5xkWdB3aWCZKv53tv+htP+rrtwYPsjYYUE0/PL/DKo/P88YMlvaxEuREuS1hI6oh0gpYTAhsxJybYJCeWKc1azqQ+wYUJr612+J1Tm5RpyFm7hB+ExHGBsDmjoiRZqLC8b8KpSx43kohSkQiPrqQc7HTZ7M7j1jRBmmDqljAZkzQsp7ebSOPBZchCMhpNPRu8cCgPLooZppso4O+/6gdeuKA/GTNtzvgyXLqD6b8R6VNK77i41uBTnzuCaL+Tt7/+7U/6Ot/7DTya1EHfLtGKX0k361OprtHbaaK/AM0DbUzDcKx9kT3zOR+pepL5lEw4tsIKrT0jyrTFTdfcyic3P01dllQ3BWmvRjGMGOyTjIxiLvXYzDM4XoUh1C54ilHAvrltXn6H530fqxPGhmJTsj0OOHatYNCskQ4zlJW4LU+nHHO4s0naC1gbV3CxZf5GePnSA9TkPP/4bf/yhQv6Vw3o7h7+ajHT5nPn7M5F/tIH/zPb3QDGEh1FyJHg7377K3jba2960tf93h9/Hi8EeEmnHnFwb4fHHlxlUBgmDlpS0G4kpFmBlQ7loNSCEo/wAoKpUbUKFd/w1hv5hf/rYzglkXiE9wgt6I8NpcyIk5DCeXxpwXu8EAgPZeAQNYkoBKIwiNLhQoX3AqcU2SADJ/ClwwcCLyQCCwKQgrgSsDMq0IHkHd/yshcu6F+NK6jNJ9ODEOKngM947997+bm3CiEeAizwo9777afa71X3MPLevx94/9U+zkuFYvXb8f4+cBaDoESCF+xtvJ1XHfpx5JN8IQVYHZ1lLinYMo6DvsJGv8lmPqTmMlb7dU4VNXTU5ZrWJkUvZF91gEo8W2WV04M9+CJDRTmnup68bxGqTxKVhLElbEDuBE5oLhhJyogDw5JDnQmRKzg/6fDZc/t42+HT6CJgc1Bls9/CJ4JqJadSG5MLx7jw7HQbhImh0hjwxmMX2XIBd28cZMM3ebhYohF/DhTMN592yt9VRfDSLeGd6fLZM7p4A9pPSA0M0HS9pong5QvfzluP/b2nfO39Ow8xV8/YNhVuCqtc3K4Ryh5zFGwM6/QnkuXmDtVORtGz3FztIxuCs6M5LnbblCZDBxM2hyFnuxmX4pI4yIisRTcFmfSMXEQ3rxAHXVyasdzOmLiQzZ0qXzi9wluOPkzDwcObTYb9JrLjyAJNt55QeEnVjFnfaSIqOS6YcPu1q5weNxmvVdjMmjw+XOC6dpeRDVlotV6YoD8JM23O+HKGF44gsPRcxKaL2C5CFoXnzYe+h7cc+66nfO0fXzzLQn3MhaLBy+sLnFmroKItqpOCbBDTLTz1lS6hMISpZ6W2QdwIONOdZ3O7TelyXDCgt1bjAbbZLjxOeupMMM2SVAZYB93UoV3BjcGIRgOGqsooTcjPh7zqyA6hdPz/2fvvaEuzw67z/u795Oc5Od0c6lbOnYOkVpYlZ+OAMQYzAzawBgweD2uY931nMMzwDnjgHRsMw2CTBxhsjG1kZGzLsnKr1d3qVF3VlatujienJ+79/lFySx7bHeSWqwufz1pnrVN1bz1V9bv3t+45+9lhvWOhI4e4LnGcCMsQRLGFa45JooB8bkjqKNx6hjM0GO0HELmUdUAqdxjHPoF7d4+EnnRz4quNdo5hptBRBv2BixiD49s8/sSf4V0PfPg1/6zWL7OTWDzVKrMv/yg3DsbsxynjEYSrAhn0SKSNHApcqdnr+NCz6e6XKT5scKVVYnglIu46DPMHLB/bJGf2iIWDJyIiBxIhiNfajDoZ4fkCZhKiM0DA9mpAbgTvWNjjc9PziEFGaviIdcG1uIA7rUisDKMgyC9FyExQiyPU0pBeZDC9NWLBdkEaDLLiH0zgv4f/knsJk25+Lf7JhUe5MarQV4+AkAgTcr7B//TtH+K7H/69B4sAdpr9O7OBBLzzwRWuXtslMQRSaVSs6CtF5hrE4xhMQYrANQRBCkmisDzJaJgghhmf+dRllBZgCDKRIQxxZ4VMljHoKyJfkRoa7QhEAiJTWKkgNgWZKyBUGAKUa6HknT2thVQoadwZLhF3ZkJpS6ANA6EEwoBc0aU1TjDMu7cRPXx9uvm79UFr/de+6rkGfvTLjzfkrm96PfH/4P0EyehDSONvUynN4iefBDnHinv0zhGEX0XriDTrs7H/dzHSXUTyKcy4xrw1oJZ7hp+9/s28Z7HLK+0BtWqfbi/gQ7XbKFNgODbSEIzSQ0TjGRrFC4SxoNPzmK03wdplqXqVtnJJLEF9bptnby+QpTYlO2XFaGOlOZLEYDspMj91gKM0e1GJvZ7PhfEUc4WMmrnFXjvHrO5DLqbVLBJYY4aGQdeUbMc5fGPI6dwWs7ZgNSxiu2OqtuDRxum79EX46pDv9j9g4u3CMv42UfojeN4/5rBnUw8/h+1Mc9b5nXcmtA6Jki6X9n8SEW+Qpk/hqRqnik3G4ZCnt97P/Yd6+Dpm2eyQDSTvnVlnRxcp5jKGqoCjZyApUqldYRQJmu08ucoACpvMlddpjnPYTsjyUotLN+eRITRyYwqGYKxcRqFklBlUFrvI0GFjNMsrzYwb/TrTvo1wV4n7JrXcgEjbtA/y2OaYjuEwylyCUYCUgqXSPit+SpgadA2bfGBxpDzZWHfi7UOKP0Wq/gWV4r9i1uiwN36Roj9Dzjv/Oz5XqyHdsMuX9v4RKr5JPnsezzjEyakBzRZ0Ow+xNDNijyGB26aQpJxe3OF6r0ZBx+wlU5hqChR4U6sMYhh2ipCPObB2sPJDgsTD1GPy8wPim3V0JnALIQXToq3zZFGGUibRVIYXG2x2Zmlvw2CYp+EZ9Mw+1khwdGqXtV6ZZscAM6MtPEgTCsmITGqC8oiyD6keEUqTStXFsd4GL+sm3Zz4MouHGZvP4Dn/ipeuSPrZJU4/WGba/J37tQ7CiMF4i/XWz/D5zS6WtY5v1Xm+22AQXeGhis/eNQG+Ish32S4F+H6bvK+ZmRnxsecfwk+rjFsHfOTMVaIDl+e7BUZGyH62x4dqm7T2SuxXh9hFyH8mJdpLMY9CvmnhTvfpPgu0JUQKq2DxqeePs58zGMoSjZpk347RQvHoI7tsrHns7kXE9hhbZuylPhe3p1nRbbLMpzYfM6LPr20cpViauqsnpAGTXk78NlOyRtca82/f86d4Za/NZqvDialp7pv/na/velHEhbUt/vWzz/PKpW32hiG2BEPCJ5++xny9CIYkyxSprwldiCyNTjSZaVKyDWb8HNu7XYY+JLHCUJpYwO3tFsrQOKbBWGli28TpxhiZRjkGxYJPvzsiMUzQCUoLpClxNSQJmI6BI2CYQaY1mS0wxhlKAvLOIBEatAK0AAGGabKxP8RAsLxc/wPP/ne4B7r5NnhlMfHV7PIh7PL1V3/t63eh9AhBhhAGAL3N/41U/QyWbDDSA+KkxzC1aVgZJ0tNnm5PMcgE3778KxzPt9jtH2VzXEEouBI5NAc5iiJlrHyENMjn98kZQ0LDZC5oszkqU3NDbC3xU0lXaW40G1xrVzhc7WBYMQeRx/VmniQ0qc60cEaaej7FMT0M64CzmFwflpnNaVCw7I5oeGNS6ZIZCkdq4ixjJ3UxUpd5v80wO0bO2uNWVKGeX0Cad/eODIDQ90CLJ/5AONPfisO3vvrrIP8ESo8RpMCdpaS76z+K0h/DM2bZS/YZJZp+ppmxIpZLLb7UOozrxnzjkV+m7o1Ys44zDj1UKnlxGNAcBlRlSF/YRLbCyfXIuWOkAQulHmutPF5+REmPsJRNG5PtPZeDbo6gPKanLXpjE9o54tjAqgwpxBl5N8HxDMruHmUDLuw1WDAzAifjoWKT1Mr4NOeIbAlZxhBJL/SIM6jlhwg1g5ZN9gcFztVPI+Vd3l+MSTcnviKY+zHgx/itnxj54gdRKkGIr3Tz+uofx+E5bHOZG+Nd2rHFMMuYchKmik1uNJeYK+xwf+PniEz4Yn8FEdskseRCu8h+N2DWCyGBsTVGWBLtakpOzHyhy0a7SOhFVMQII4OBloxbFlFqYTopYWrTjWLinoOOLEQR8iqiUNBYpmC2sIfuaNY3c1iOxhERp6abyFzC+o0VUlJUosEBnQqy0CRfiJgyAkaiSZxafM/hE3fta/DVJt2c+C3W9L/B+vLzD34IlD5HphSxujN4olTKaOdbUOkat1tnyOeuY2Ww4Pv0DQvHiHiosIOkyVl/l9JKnVbb50vxFONxjshJOWFucvlLLnPzG1hdg4tNSf+GAXbKgws7fGF3kUKhy+21ChtPTvHA91ylt+OgtgVZDfLzCdXDbVq3ChgiQOYi4lzGwrfEHFseYvQMwhdq3HxZMJxyCKwRh2o9+ioH5QC5ERNst3jkxBoLMmQjzFNWCr8fY5YPONCLfPv9v+OU9z9wk15OfLVvPf2xV58/PAdhkqK/auRiFI/5hl/+KXZ7GdN2ns29EUbXQIwE0ockhciCsZmyqpuYRY3X15ihRMWKJMuwIoGhFYltMEgSUpWhpUbZksw0MIYZYZSC0jhCEGoQaXpn8MnQIOCgOcROFaYUJLaFEmCVXUZxgkoVjpCMeyFYd05O055EaZDDjEwptALlmEgEOlaYrsS1JWGYYdiSH/jj77gb8f8290I3JwNGb3NCCAwRAJCNu4xaTxClA4RQRKpFP85Am0g5x8XRNmvDEkbi0ygfcGOvxlYUcDmaZxgf5cH6i+T9MQupRjJkR1RQrHB1F/AlY2UypsClToPD1pi13jRmkrFU75DEGU4+ZSvO84CI0GrEVhLiyjqWXWIg+oi+STsR3DcDs1aXL46nmNEemRWzTY7hQLIwa3C5leI7YxpWxP4wR97rc75xwO2DEugEM1Qko7t8xCH8l75J4MTv051u+gCk/ZcY9L6HJIuwBPTSHoNY4WrIjFk+08rY7hfII5grddholnmxX+fFzmFqSY0zy08jgpQTwSajWBBmRaZ1ked7KZl2SGKT3lhzrdegFYYU9CxRlmOlscp+VCR2JPQDDvkHaKFYjSx8o0ShEDNKJX445vLQ4ZGljLwcM2wa2KaBMBUv9KYQhmBxXnD9ICXnpxTMlE7PpVFu8dDUBqsbJWw3RpFhZzN3OXkm3Zx4TVKYSOPOy5uo8x/p9X4UpUEIxUE8IE5SGhL2meM3NhbY65VYdkd4+YS1TpGbowrX94+yIPMsHnmanqm4r3CLNDToDGzmZY6D8ZCh4ZImBgPDYqtZxRxocjJAUadav063H5BmFqpvkKuOUVIzjkxKno9VGaPTECeN2B57nD82Yj9NCG3JjDWmHITcPCiRRgXqc4K9fY2TC3EMg6jr4DcGVEs9wt08g5yFVBmOuosbd/6WSTcnXoMUFtIAywDV+inC8KdQKdgCGHXxvQEF10NEHo7cpODHUNSMUpvuyCROFM8+eQS93kAWQUchh1b2GQYWrvLR8yUWsi1qMwPaTcnWSxXW98v45RFONUavZCzn92nGJbRvYmhBo9pn6cQBt9IaQTuPVhFR3qTkNFmLoVwecVUkOIMEtWTizkhe6RaxXBO7ZDC2PDiwqQ0kL4+nqSw0ET3N9nPH2TvnkgaKul24u8FPejnxOtyvmp36C7d+ip9b/xJjswRujtZIoLTGLUtcWzC+fmcyQFwFNFg9sCOoS4vZ6QKX9psgBFGgkRnkHZu8Y9MyBNoAogwJGKkm0hlac2fLFS3QSODOCWoZoFEoIck5ko4hSJKUVhihtEBYBpHKSC0TZUm00IhE4bsmQhuoMEHbBlKCSBXKFMRaUXIkOgGNply6i5vRwz3TzcmA0T0k6TyCSUxOgMZAyoSxkOwqG1MuEsZQt3tcGBUYxad4JZmh21nHkiajLKRS3cbNFL+xfxjPOWA6UBz2rnJdz+JhUHBHLAU9drsFhhbEIwutZykywsgU+RF0sAiiAltxj+PTezS8Jg1nkYHTwchSTqltDA07yWHOVFusBLuEmUPFHOAZGb422RuXOJ+/jjRS2sql7u8x1grBkMUcGGHKWlfd7biB/7LXfE+8dZL+H8HRKaYhAEFOthgkHu3MQXOGQF2i4Iy52TlE2zzP/qDCwWANmQhCFVMotIgil4+tnSVXbFNzJYXcdVxK5IXCL3WpWDHdJMCUGeORjSGq9OQuUiT4SUqqDQrSZC9UzM/vUXMGPFSvcUUcYCZjzskYlQp21AzHZ3ZZqu7QDIvM51q0swIqtRmFJjP1bbJU0hMulmGyFbvY9oCpQogVjmk3e3c7bmDSzYnXp7UmHfy3uEIxIwVaQmDsMEgChqmFyk5RkZdoGTEXh4vks4dJez6b+9uIDFJCpDvC6RV5ZvMEbiFkLhhhezcpt2x8I8LLj8gbIcORT6QEWSppBD6RYeDKFKEUwtR4fkK8D+5cm5lgyPHZHK/oFl4yZkkoBqGJ7U5RWTjALfRxMs18qc1mz8EMDXRsMbIUWSYZWgZWbONFLlWrw1QhhEHEsL93tyMHJt2ceH1qeAnin8KVsNbyCJyUamMVYQBZxEKjgq9WaYWS//Dyg0izxoXOAt31dUalAGtNkgD5tsVnX15m/r6QB2aHuGt7+PMDxsOAbsenHMT4rkErEVzdaJCZCwj9DDdX84wWTayxIgby7pjpM5tc9+ocGRzHiS7ygaM3ce2YTzy1RMWp0r1vl8qxhKVhh3oQ0dyDXE+jyynJkskL6wu0I8mZQ5f5XOcU9aldglLKFZ3j9iDkibuc+aSXE2/EfuvvUYj+AYeCo3gq5Ioxw1ZoIgyXRGccFxVuZ21IFTOhQ872MbKY7dEAJTPa3QEYgrwhGbQ1OIKQlP1I39n0OtMYWiDQ2L5BHCmklAR5m1Y7RqYKrTXSMFAoJBCZmsC3mZkNuLbfAQ0i1RiJYm6mzLWNFkJ+eTNtS4CQuDakKgN9ZysjpQELBIJxnGEIgdZf/py77F7o5t1fVzDxhhliCaSFsI6grQ9gGj/C3OKnWGn8KhuDy3RjzdVxnb52eXpLEGYddJYRpwm1wpDtyOap1iK2GHI036dmJEgRMe3uUlOCsky5uNtg2inxQG2Dh6bXOT53C8hwnYg0s5hyQrrDkJIbEmWwqyXP7EYsGcdwQ0XFzGiPfBaLLjJM+fitk0SZQmfgmJK8OM8JL8W1UvaHHsrIGKUOe1FAKS+J3SZGLuX8/N2fIgh8ZeT3jTwm/tASYgotHYR1HmW+B9P8MU4c+jS14Ce53n6e1thkbVwnESbPb2r6aZ9ebBAhEH7GWujxws4Mi7kOC7kBU14PQw6ZDppMWwmOTrmyXmUp1+Dk9DZn5zaYWbhGrCXSBKFMSoFiPMzwvTFpAtuZ4OPXNWfcJcqJpmiOGQ7zzNZs0pHkc5dP4BhD0kzgGpJF9xhHSimOmdAaOGgrRSiDMM1hFgy2ZYaZN3jX8ffc7bjvmHRz4g0QRh4pbITzTpR8F07uJ3jg8KeJxY/y8t4lmgOH3WGVcWpzdSNmPx4wiCRhZtCzTNYTnxt7FRaLB0yXOtSLLQwrIpcbMOsNEalmc73KXGOexnyT0myPdnGfobLIkBhISkGMNVTk8wlqDNshXLhp80i+RiHVFMwxYuiRKwWkfYOt67MoP6WvHEzT5mx1jvnKCMOKGQ9BmBm2kWLJDKui2VWaahm+8eT773bcd0y6OfF6hAXYgEWt+C667rvJVf8Z7uwFyv4HKPI8YQqt1Ge5eECmDtgcR2zv+8QHLqNpQVpOsYMQ5gRPHN2kIof0+hndTRPXtBC+ZK1XwPMOUZwZsL3rc7OreLo1y61OAd+IcCoZjp/xUOmAJTGgp0cMhwlTM1P0uibTos98JaRa8dDTkGnBfH7A9ks5WjtVHv5Qleq5PvlGj+pyByMfM8xc3ErGzNE2S/YujzV6fMvyI3c78TfXy0k3/9ByjRWKlkvBdrmv+h7uz53kF7/pT/PSn/8RHu42uH25jUxBSoNhN6W926MfJqhhgiUkfZ2SGgIVKRJXovMmqVSMVYIWEss0AQWhZnauSmYbpCZ04xR9Z3IRQgCeQBvyzswhpWmOYtwIpqu5O7OOTDANA7/gILVAKIgtiZKSqZkChw43ELYkMyEWksSE1BQoRzKWkArN1HSeM8fm7nLi3BPdnMwwuodY07/+6lrwr3az9edJpWYsFGF6CCOFceoxlc/heqvsjR0OkiJXd2s0vIRGkDElh7w4WORi9yQNnsXOdXFMl3JZ0k6mOZz/da52p1lt1wnkPr4dQ2BRLI3o9y0WnBRbZOwJj54BsXiCkdigHXVJDJcroxGYQ+6rjHiosMUwdLg6OEnXMLFcjdQus3ZIiQwrVli+JJ+fYidsUw9iztbfBpuQcW+M+k7cfe7Mk7/r77/Q/EsMtEdf2ZSzJaIoQ4Uu1bKLXdthq2MwHJtc35yi7Ap8N2bR7PHM/gO4+gi29XlMr48nHdK6ZiQcFsv7PL21wM5gHoMdfEsj8wbaz0gTSdEbY2YON3FxTEGWvY+B2iAbGQxUwM32kNROuL/Y5mS1xSB0eKn7KK4A5Uj8RHIo1ycWEtdIaBgjnNz97A8H1EtNFmvlP+B0f3eTbk68HiEE/syLv+vHPr/7K0Taox06HLer3O5rrMhlPmeiqk2aTZckCtldr5K3bExPM+21ubT6GHPOHJb8DKY3pkyAZwpGekStNOLqehGR+vRERMEEOydJXRtLhZh2im1a7CZ5YiEoZO9H8C8ZDgJMbXNxt03OG3OksUNQ7KNGku29U1QNSHFZlHtk9T6JMnGskEVvn5z3TjrDy8yVB5QL7h9wwr+7STcnXo/0j4L/MgA54MhXfUzxcbJUc6EfUHaO0ig0WR0GnDEUG+OIzWsu0bEQM6c4lo/4wMnLTOeG/KsL3w6vVDh18tcoTA2oKJfRfJ6DZzsccru4A8nutGR3VKDcMMl1LcYFg5WpJkUz5HStxVOfHKNo8ejx0+T4ZdqGi5szufrUNqO8gzuIeWk0w/6Ow+yRI8Ra4YwkdiCw04Q/9+DzJH7CtmxSLpxiJ7xMxc5jSOOu5PzVJr2ceCPyxW/l4eK38vDv8rFLq22EAJFkLC2V2GkPKJo2toSBaRCPMkYSpFQE1Tyd4ZAURR2fnOdwYdQECZ5nYedN1na6IAVGrLC+PFJkFWxIuDMtaJyi5Z1BHkMKoizjXQ8e4uc++zIiVZiO4OL1PbQBSkqkAWSK2SBHGKbYjkmcZmRCo7+85C4TgnLVpdmMmJ4rkam7X4x7oZuTAaP/AjzVsukOSvRx2Q89jpeO8Hh9l3EYczsskZma/GiXvSQHdsrx0hrSkjRTSJMBc9WYmheCSqmaBq+op3mxM8tw7DHvhyRWHxfNh1ckY1Vn1Q65NFDU8j0ennqAUdFG5zscc9vUsy0uRbP8xlaNKTOgXu6wr4o0dEKPeTw3Q6cHWIwoBQlWJPAYE2uTmAq+1IxSB2Xc3aOBX3UPlHji7eszmw0cEXKQFBnHJY5WZ2hU2+yMQjaHNUwjIci2aY48DKEpTA0ZC4/tVFGVQx6t9rGdBFMllEyHV4bPcvmgwTj2yNkDYielbg44Vj3G3shkveVyY2CQy4V8ZO40cWwxLiQc8ZrMscdzPcXl9RIlw0SWU0Jt4IcWgZzHclI2RylH5Ji8oxhHDjk5ZjeqsVJMmXMGDKOAgfJ4W7wtnXRz4muktOa5zSkKdpPdUYGqmGGxUmK62ma9H9JMykhDEOsOUcsjqGTkgwgV+2xHirwdcWi2Ty/TaDWgYOZ5rnebUbtInARY9hBpJQREPLYwy2bPYn3fZG/UxynEfMvCKWKR0vEVy16Luu7yXDNFrpbRhk2/aDFnRXhdQcObAxnSEpqCaeAAeTVmBOyFC9SrEUu6h4gsOrFD4+3wqm7SzYmvkVIRAFEGzcTFc6dpc5rtgyIXnlUMO5DUTOLbA+g4jBctsszm1rrDi1fgm48P+dC71+knJiVvwJWNY0wvXadkRrRnAxZPvsJCsMfQznN8a5YtK+PltRN8qjjCExkfOnQfqdWiHjd57GST7ZbJbe0QFhVHZMpu7NJxwJ8Lmc6V2OuPGNopo6HPzX144tAtrnVLpAfz1OtjnhvWkFGO5G3wpnTSy4nfj85ghFYald05xv7odI3pSoEoTNm53cQRd46ASaMEQ8PAHqEFGGgOekMKR/Oo2ESEGakhqAY2/d4YDEmp4tEKM5CQCDh/3xy9gxHbmy1GSmNYBh948AixoWgORsQFAxlqBj1FkGQI00AakizTOImmXs1xa6vLMP7ykjRHkCqB0Jqpuo+0Jegxg2F8908vhHuim2+HlxYTvw+j4fOM0qsI1+TgwOVcw8CyDK70R2wONTWrxJJ1g7EXcnu8gjYatNQOraFHwfYpWB2K/hZCeRj0+c2DBQpui7VWA1NLpoIeM7kxg9igG43JB6dR7StMWUO2R3k2269g2DbzccJ2qMl5JoEx5FR5m3ZcJxGS53vTTFkhjcJhSk6FF5oFDoSJM4jJOSnHSx0W7IyGdYMhI4ZhAcs+fbejvTMt8h4o8cTb087+x8jsJgNdYLdT4PElC1tZPLs3oDMSHMprpmu3OOgb7AxzaLvOVrSLHrs0vBzTxhqu18dMTdJswCfXazSq22zu1XFMqNgdil6bg5FDlg7wzaMY2SU8M6GdeGwNLzOI8tSCLXYii3zBJnAGHK7s0k9r9McBX+gdYtkeMVWdxtAmabzKi9EC07JPyY+wA0nVyah4tympDiJtUHPfBrP/Jt2c+H34+OWfxPc6d04uG+Y5fzQgUAaX1/sMQslsXqDzW0R9zSD2Se0im6MOcugyX8hR8i4izYQyBt005tlVD7c2prsbUCtHCDWkHCQMuy77Q40vppDyKoajGCUOF8eXcZVPpbBBLyxilFIyM2Gqvk+UFhkOAl7oHWLZ7nF0tkIYJSTJHhutCnWnh+ElOLmEmpdi23tMJwNsOUXDv8ubd8KkmxO/P3vfCYCBQGCy1VlmT1lcudjFUAJvaBJ1xsjUIHMl+aHkldsFwnGB+03NUvEpLDPheGXAYAhPPhfjzBTpNm2OHm/SH4VU/JiXdvIEScr0SomD4U0O6R7bowJf9G9T/rxiKtxmFY+rO0VGGNw3t8P+MMCJJYMXUpxDNou5Mlt1l0yEnCzu4XgRN4YebeVzc3Ma2ctYyHcY2S5V17+7uU56OfH79JH/8f9EW/rOZjZCYjsmchRy6/YenmHi+CbdQYIUYCWawLfoj2IKls1UJc/l7QOELVCZJIwy9vZHaCmQqSLLFFpnIARZprGRVGs51nY6ZCJDC7hwe5t80aWtI2SUgr4zzpJ4JoYBxApzlGHnTKamKowTxcVrkElQAoShyKQEDWmSIaSmVPDw3N9t7c4foHukm5MBo3vc5y//BWaFIJSK60GZTb2F111jszNLYMOHgs+DmfCZZJGlUpu8UaDk9DjQZRadJV5pXeCcEdLKTPb2G7TbBY4tbJErNOlpk24ccG1rkQNlYiF5lyGpmXWEuIGlUrq9KZpJRJJuU3Ek2+0KjmUxkxthjmKcgUHdjzlWaJEYijQZUHE0u5nicLFLzehjKoGtxxwMD9gI89go0uQA25662/HeE6O+E29Pn7r8tzlrWdzOPFK/xoXhKsXRDTZbc5RsxdnCU2RGxhcOzmEFgpITUQ0G7CceC36Na60dFkXIXhIwbtZgZDA/38EvaXqmx16zyNZohq3UJRA275n2mfWLjPUOeRUy6Pjsj9t80RxRd3JYO4K8D9VcTNKHdOwzHxywWO6idILOBFUvYzsVzDljikGLgqmoWz3icY6rnTpFI0FrPbkjM3FPu7T+MY57FreTCqIwzbPNKxRGERvNafJWRuxtkgrY7VTJ+wLPHJBzh3SiIrVSleut23gZtAYWcgheopjLtQmShNTzaW7nSAcprSggGyqemC3S8x0C9umHHrLvszbo0FYxc3mP6FaN2kxGJ7wzs8/KTKbzt2lU7/RNKRPXMSAbsxh0EcGAuhthmQaDyGGr2aDsju92rF8x6ebE10DFEXCNRGkiBCdyI8bxz9NZnaa/exoLi95qlyRnoKcNqoEmyLWwZxSDmwVmFgNy5S1GoWRD2dxcKyEKLmc/cJOgF4HyuPbSLLP3jxl2XMZRwrlCiaxnUzcirm7n0DsRLxFzLFinlQb8p2fPcfZ8j2ygESOJtB2Of7hJ0ckz6sZMz6ZsbAdsa4/KQHJ5WCDnRDTXU1aOH/ByUkQOdu52tHdMejnxNbq4usVgkIG4s9RbWpJffeEadqgwQhiLhPFIobVAKhNZFHTimMQVlAoBpWLApVYbpTRGKpEZSAmZLVFpxihOELFEmxq0ptMbceboLBevbTBOgVCTZoobV/cZVkEY4LUUdtFBpxo3g0xkdD2Dcs2nNRjheOad4xhRiDuHuoHWNDtjlAtKS168vnV3g/0t90A3J5te38Pa4z1uKI+8k7IXl8hLn/VugYI9okabE/bLWO6Y7VGBKafL8cIeoblBJ8pTEQa94SU81cdSGaQJqRRUCwEDZTO0QGkP16twauo2Z+tbLJRWuZa+yNBfp1bs8+GlmxScfVLLY9rqY0Sa9eECs4Vdqs6QnF1gJtfntLWPFymuHOzQD5scyncpZjH9gU/OU8hUYEjYHU2zNaxS90tYVvVux4vgzqjvG31MTPyW6/uvcCMrEriagSpSMmxGA5OC06VsNTlV/RKmlbLRLjGba7FS32Rf7xONfFZyEd3eVYp0iEKT9sgmFYJ6vUgvdekYFrkMKoWAmcY+c5U2peIGT/YvsmYOmW60+KaVG2gGIDUrbotoJGkP5pgu71A1Q1Ycg4XCHueLG3hpws39NUy9xvHaNpVsRD+xMWyTft9BK4ud1gxaOayU62+LwaJJNye+Vv/58i9woAKqbkas85RMCz1WKCPC8nukxT0MAXHXoF7rkqu1aaZDdGwxXxiy1blCYPZIIsEwcomFSWOmQify6EmHepZQzXsE1SGVSg9Z3OOTrWtsmZLDM3t84ORlUhKmvC7Hgj3aB3nMrEKp0GHGGnNfPmautM652g5BHLO+fxlpNSk2elha0U6LWIZDf7+ANh0GnRJCwpmZxbsdLTDp5sTvQ+vPAJAogRSgVMDFfoFbmw5GM2R7fcz4lI3/zgSnmhAYQ0ZJhOWabDo5Ll5ZBSmIUsXzrRJhWXH4voBTiwdobfLiwQLF2Oc9y9ucq3c5SIZ88eVLXF33ibD5lvuvMSP3eeL0LWZkj08+vUJSzdNY2mF5YYibq9A2BOf9XYK0w9NbWwwGgmTLwjJhairjAwurnPY6+KsGgaExM8mDtQfucrBvvpeTbk58tb/4j38J7QACbFfiepIsgzDVJJFi3E9RWoAhSB1BqCBMwBCC0Sjm4o3tOzcbU9BaoWxBvZ4DU4BhYTgmpgOpL1CmZKvT5VMvXCeTksySxHlNmMUkrkIpjdNUBIHNGLAdSc61GWYKYUrSccyl69vEaLQDItOYlkBbEgG4voGUoEz4Y9/20F1O9t7p5mSG0T3s0u5nuTUu0optLOmyaG/hZmVCo8Dy9AhzaPDC/jw5f8ChoM8otUhlnVGYp6ccEtXmZKHJpcEUw0RyvNKhqvfoDCS7UQ5Sh8dKKTdDk1RI5oMhnSRHnEX0sgVWB7DazzHIUp7vzmPGkoZ1mEG6hSUTdNanmO9h6iG3RzPMVGa4nawRxJIjwQGf21jiWLBFrdyn5M1hmd+Itn6ZE5XjCPE2+dbUk5+aE2/es5uf59KgztqgxKybYNrb9GWZ0K1wdLZJMYZbu2WEB6fLW3TDHNmwziDKkwwzMn3AdGHItWGdUFk8Mb3JNjvsNXO0Uw8VmpxdiPlixyYzBVU7pZQGxFmX/WEdMzLpDk2GieBSexox8Kh4VURyncAU7BOyWGrR1Ypbwwb5qTk+096jYcGx4i4XtuZ4oHabrKpYqh+m7jzKavopHpp5GywV/S2Tbk58DS7tXuNLzVlKImYpP8aytxlbVSDPEX8LEkU4ENRKmnxuzGiYJxIBWezTHqTk5B5WkHGrW8FKBQ8tbHFxvEfWMYgyiziOCXIm2yMPDI3jQcGwiUXMemeKxGwxHGQMY58wN4XuF8j5HqXkJgNLsBqPqJX7rKqAvf4i841lntlZZzrXwSkqdltFDtUHjKsm9zXOEjoVttIv8vjcybsd7VdMujnxNdkHoJvCZljnqetFdrZnGfgetaUhO+065EY8MnuDtWqD5o0GK4t7DLZyjNeGPHr6NicXu9xYL9I78BmOfA4GY7ZaeTZ2AqaPdXjiWAfTTRCliMXKmIEbYOwI1po1ppxV9j2DFSUomzE7vSqLhwpYqaAURBSsXcobJhesBkltnne/02RPPU19ZsTywj6HnQFTuR6vCIPz73mQKXeLkfkKHzh27C7n+mWTXk58jcI4urOuizszTQZhRsEzwBX0oxhDSFKtELaJcsDTAuELUNDsjFDOlwdsYnVnlpIl2e0O7yxJk3eWjWX5O/sbaQluwcWxbCzPYtDqo2MIxwmZCdoWlD2LajXPaNBHC0EzjdG2gRISJ+9Rmy/y7LV1lCWIHIGRgLI1OJIPvecUt9b2uL7X5v6Tb48bLfdCN98m78onvha3uv+KITaWtjFEjpyhOVRwyNt9NocjlJ2xn7pUTThIcxwcTLNcPcC0E6RcoBeZzHtjumOB0iapHNPPBKca2xxNHX5t4x187HqZlcYellQEZp1jpceJ9afwtMlqXzLjxuwdlBjicKSa8J6Vh7m4G6PFRRYLYw7iWWbtG1j2mEu9T6FI2Q9NisqnaI2xbI0tMw7Uo7zY2aMedNjor3L0bof7ZZO7LBNfi/XBxxmpApFwyEYujqs4FFgETpe27tJLDW7rMqe8XQajAD0s82B1DU/GqHSeNMko+l32+zaeztjVFpFOOTO/wbEw4JM3HuYzlx3q9QMMEVLTizy8cJK98ZPEY5PWSLMcDLmwU6XfczlcVTx+5AG+sBUzlDusFCRZWiKTmkikXNl9Bk1KOLAoKJeCMybWBkjYGL6XKwcb5PMR11trrMzc7XTvmHRz4s3SWnMQPk+ipxhkFu2Bg+lKFlxNzeqwOhzSjAMO0gKH7V3MscmsNMjP3MSX0BvUKBuazOvxSrOMbaZcCcsEuQFzxU36/QaXbh5DDAVHy11awmDOWuGJwytc7FxgNDIYjRTTuSG394o02zmO123OnbifX7kh6comJ/KCInCgTNqk3Lz9EsiQcE8iU0XRGaHRxKZJe/AOrh48TSEIubCzx8lJNyfuUSpuArcAmHJgc2iTr3lEbYeF/Jhf3XUonOsz7ll8sXucRavLw8tNFu5vMu3G5Moz+LkpnPgGe32XMJJculpn7r6Q9x/dplDx+NzladZuDpk+Y+CZCQ83ZrH8kzzbvs7zvSWOTW9xpDLkledq7DxfQFSWeOf5k1x4epuRu05hvsKc1WI6inn++pBPhlewK11K030MLThRbyMlODsZf/QbFFvdz1A224xHF4B339V8YdLLia/N87fX6YR39i4ypMS1TWxbYNo2UmcMNmKUa6CFRpmgDfArNnsqpWSaaMegmHO51R9iJgKNxlKa1JNkQlO1bcQooxXFZAIkgpNz09RnC1y6sc12e0BiCSwlsIcZlhbMz1d45zuO859+8wX2xyELMyWu7HZAaw5aYz63d4MIUFmGAJShvzybSTAzVebTL95kEKdcvbnHY6eX727A3BvdnCxJu0dd3PwYNnscdVuczhdIsyXKhkO9eAjP/wBVQvbTPNNORMNNWHH7vGPhJnl6rI0CvtjLsTmeoh85PF7YQ2Ym+2EVU6wjteZSa57uKGKqnDHKbErmiAfn30k3loRZiaGsEcWSw/4uZxpdyqbCyXx6vU+QhKuMYoOKm7KYk3jCwFMWUaw4GewxFxxQyY95V+M2vcRBKot6cJ6TjQOWrD0K2eW7He8d+k0+JiaA/3j5Z3DFATP5Ed8w4yONGgu+QaF4knLh3QQ6YZAElL2Ykic4Xd3l9MJNXHnAtV6O53o5NqJ5ZAYfmVrFUjAe5oEdkgRe2l1glETYOcEotgkM+PCxx9lrKlRWYygLjBOoOwcszw6oWwklJen1P4OKd4kiKLgxgS8pGoKiFMRpxsnyLr4/IijGPDS9Thha1IWgkZ9lrhFxwl1Hqmfudrx3TLo58TX46af/Oo4YUA7GfPhYhdCucahkUC/cR7H6BIYWSGFQKmTM5zSnZnYp1w/QasiNVpmNYYHdcIZpo8375m9hpQIjNMmSFqPQ5dJWg5gYjIRh5OLojO84c5693Yg8FVIhGYw9in6H2YURU3ZKwxnSHn8BJ91CJSmVIMVwPZadiLqZAgnLtRZ4imJ1xEJ9n3Rkc8RXVPKSYlmzEGwSpZ+92/HeMenmxNei9e1ordFac2nwYX7p+gM4g8OcOn+Kk/UzjEoS75WMXMciaRVwjRi/2MFnhIqhuNyndkzh5mMeP7xB2rdIS5LlmZt0Ww6rtz06uwZ9w8A1JSdzbd792P1sOWPypwr41hjfSTgkD1ipaowZG9sdsHnjBQLZpiXy+H2fxnyKIVJkXmPWR8wWQg4NezREH9sCGyiaAZkwiIXLnGxSTz56t9N9872cdHPiy/7sP/l5lNQYjuSJs4v40mJptsqjJ+c5tthA+ZLE0liewMyZZK7gII5JM5BSkgiF7VlgClIXZCbIDM1IChCC/iAms0Bp7qzPciQffOcJhu2QmXIBlEZrgeEbmHkT0xY0iXjy0nU64xHagGLJR1mAECihiSRk/pf3SXJB2XemMc1UA5TUZDIlVJqPP//KXU6Xe6abkwGje9Sndv4eL8V1Ep1nOJ5mpTACbdLqVjCTXWaDDiUzZN6b4WThEL6VcbFXwpchFWfMMLMp5Ers6Dx7sc+0lZEzQgKdsZ8U6CjI2V0GaUwPQSv20NrB5DmaNNkbjrm6Nk1r7OBZA45WFZYzx2p/gbVOAd/2SNM6BVosBCOU6aDSAv2kTM4KUBI6pkPPNNHO42hlcDg4TyHWGCq92/G+Sqg3/piYAPjs7sd5arxAVRjI0GOpFjNIfdyogJt8gdlcD2UIHqrWOV9uoLXkRrNEzhrjmHcGaIu5GjeTGpvDgKqrqLpdfK3oRXkSneDaXcZk7OHSyiykkZJal9hUfdr9mN3NKt3IwDYiTjcEvjfHzWaDq3s1hC7ikuOIu8658g6uKzBSF6k8craLkIoWHn3XpFH+MCpNOV17lIYY4STh3Y73VZNuTrxZn97Z5Jn+AkdcgZk61GsJO1GZivZIxr9KJdcBLfjwQomT1SK92KLXFQT2GG0otLCpF4rc6M+yP/So5GJquT5FlRBHDp49JDGHDAyHNZ2nrXNoPWJk3eJ63KM3gNFujmFqkDdG3DcnybuHuLpeYLVZw6ZI0c1xtnido9UNEiOHk1mUbQvTMQmFoGvYZC48vvjd6CThsamTLJg9ivTvdryvmnRz4s3bAyAj4CAs4BQcvnRzimT/ED/1f+zAwGCY2ZxaOsr5o4pe3eNg2mC23ub5/Tq//okHsDfrXN6vcGurjms5nJ3e4eH8NgfbebZfMBlsKe6v9nGNjGa7ztrNjIPPbzJe2yI2U660C8hRn6XZNkfKJepinluXFOvNMp3hNAe7Ae+dvcrp+5roeoUZI+NYpDhzOGW+ETHKQJuKxdN/ASvVLPIgc3aIJeO7nO0db6aXk25OAIzihP5QIQyoFh10nOHkLfQo43C1ylNfuo02BZaUPHbfMpVpF6kFsQJlQZaklCyHSsG7M9BhGqicJBSAVGgFY63ojhOEZYAhyOVNDoZDVpstbu80EalGxorOOEPbBkvTZY5P12nvDbAwqRZ9lJZkviQuCnJ5C9sV5HMW2rdQhgEClCX4we98DCEFhxYaKFMTv02m9twL3ZwMGN2D2p2nqIk+p51tZv0yhaDPMGnQSqa4Mf4Ct7ufZzsMcOUUnfgwq2GM0BWKIkVnBku+x7un65ytLXAzKvAbnRVyuTny/oCuttgNA1byB2idEZsdGJuEPZ++DjCcJSI1pqNv8G3nn2emdkAuMxlxH8frC7T1i+wJjaFGXO82udFN6SQWjmHjCIedZIGaU0WnEqkEBSNjNjjH6cIGi9Yuu0mOVtq42xF/xdt8xHfi7WVn79+zbO1zv7/KXKGE8nrEyRSRsvj8wae50tpka5CjbB1iZ7jI/jhG6AI1GWJnFvfVbR6cmeJ0tcrL7Qaf2D+Om58mdDJaqcVOmGe5vIcQmswe4KaKfJjRSXPY7jyduMPA3efB89eoNdrksBmJh5itL7LDFSJnhG0Nubjb5mrPo5eZGNLHN22aap7jRQsr03giwxcpM7mzPFhb56h7g41BiTFzdzvir5h0c+JNuLz6kxzNbXOqsEG5XKRv9HDUHOMo5KPrn+f6/pjuwKfsnGWrM89oMKYgHBasPnUB75hzeHB2lsPFEi8czPDpjZPI4gx9DHZTm73QpVzoo1KBckaUk5AZlbAf2+DO0kva9L0xh85vkq90MVSBMDuHl5ti3Vxl5IdkVsILq1vcaBdIlIFnmpiOSy+d40xJU5FjCiKiZCfMBcc5M73DvLfJeqeBb79N1qPBpJsTb4ra+SGUhlgJ+ulZVgo3+dbDmgcXLvLyhX+LM7fHytQej5xdYnG6TWNpj6CsOVfbwRaaUrDAycUTHG4YPH1tjgubiyTFKr5MSLsGW8MAuxQSVQTNxKBmh2jPo7UXUZm22brVZqbVpl6IWHqsw57f4GDqDMHhOn7wCp0kpn+5x5dubrA79LiuSiShQ69bI+w1KFh5SvaYXmJyEDqYxnmuNgNCW2ELkN7y3Y74jntgFsPE28v3/dN/TWZAJmBhqsxuMubwTJXVfoef/OXPE2YajebhU7NEsSLWCmVD6oOQsDBX4Z33HWGkFDKEminJ5wy0CZkFVnZnCZtIIRMaDMFsJU+/H5L3Xbb7A0YuxDlJ5kDRcTgyN4XtWmz0hsRxyqA35sLldZTK0AIcyyAwDXIFl2LBenWwyPMFR+en6A3HeJ6FtgTHl2t3O+I77oFuTgaM7kEX9/4WLeUzY48Z6zyKDoveKq20i5klCCOh4PY5lNOcqMxzprTMkr/CSjDEcTMcpfnGmQbDaJPOWDPG4lRpCh/J+WCPQIBWEAqXnMxjiQzTyTjiH8ayc8i0SHvgkWqBbad0HcGp2VPMVk7wcG7Id82/zPFgHxUXWe3kGKUCoVc4VbdZ9g6IVMzJfI+ThR2k0DzT7bIRBdzsP0UsUnpxdLcjftXbfdf6ibeXL2z9c7qpS85J6KoKkR6xlN+kGQ7I6SGRNkg9qOQUp2bqPD4zz5nyFIfLfQwzI6dT/sTRRTaG12jFJlraHC/7GFrxaHmdgqUBRS/xmPFs0hTGAg77s2ijgMh89tsuhshILE3LdHhg+RhHaod4Z32P7zj0Mu+sruHKAtf3C3Rjh7o9xel5k7zZYaxjjpf3aRS7uMLiyd0dbo0CLux9ga6QbIfZ3Y74VZNuTrwZn9/8NZQ2kbZJP60ySrs0cnuMxgNK8gAMk6HjkS+YHJlx+cDKIg/WPZaqfWJDU5Vjvv/MUS72brA/tDH9gOm8gzRTzpV3KDrgiYhE28zbCaES9JTB8eIcWgfoxKTVcrGtmL7h0bddHjuywv0z8zzQOODDKxd5pLGKQ5FXDioMMpvDpQLH5ixS0SOUJnPFHvXiEDsr8p+vr3KlU+CpnS+yKz1e7rh3O+JXTbo58eZ8GqXgILIZSkmrE/Lp35zio79Y5YsXq2wWizTuH3D/kT5lUWeh+CgPFxze0zhg2kv4o+du8Ff/1Aw9+SKdXYvt7SmevXmCHdMjqCc0hc/5+TZ2bFBgTMFUnJ5qcfyBebzpEkUrZj/yGYUOgQDqHo+cOcq5w7PsdOsURxGlSkwp5/MfXjjJ5d0GZwOX7zqzy4n7rlPKj4lCTb/r8+yVY3zx8jNsNwVP777AWuzxXOvt8VbrXjiJaeLt5dJGG22D4QnSRNEeDekPQ/phShppsEAFBrl8wMxSmYfPHaI854Mr7wwwGJIPPHacW3tNDKXwCx55w4FMoCzQrkYLsKUABBpJo1Hi7MosfuCShSmkAr58Wlp5NsfZswscXa4jHUmUk2BIPEtiRhpPSOYbVbyiSzhIsG3zzpI0Ewq2xRefvUmvH/L5C7fRwKD39pg1fy90c7Lp9T1orbdJ3YRWVkSKHrupJpelTHkei8ZVOrgMmeVY8ZupBRk7zTEFR2AbMQ2vT+b8eV4ZSXqjT7Hi+RTthNWhQd6S+LFBkvoU8n1Olve40K1j5cGRMO/dRgmJCtuo3oBUWRTNjMPFfabdl7jWcmmNhiwEQ1pjj5K3Sl/n2E1nyQUhZrbMaq/ATiwwnQ1O+TFCj1hPXubp5ogpaTCTj0nlobsd8R0a7oWd6yfeHpRKud0dEViCoW4Qpgf0MkkujSl5AVPG82TSxBTLPNp4J9LeY7s9xLVMLHvMXG4I9g/xhVYHlXyJE8Uii0HM5qjB6fyQZGSjsxJm0GWp1OFar0wcuJS9mNPFHQ7CETJqc3XUx9QwZ8aYtR0c8RIv7SZEA0UjN6AbxrjOBoE02Exm8V1BLZkijmscdPpIZ5cnGjdZlYLb4QU+cfswZ/IOC8UIzPN3O+Y7Jt2ceBOStMflvo1lZAhZoxnu40pBNxtT8KrU/UskmNTNE7x/9hgtfYtWP0Rpk8TKOFFsY1iP8aurGxhcZKVc40g1ohcXuC8/YK+bxzTyWN6AUmHMtUGJyDE4WR1wurzLeq+PFbW5GPewVMqs28Z0t+lnF7l9ENGPExpBjygNMW1JzoTbyWFcV9OggY7L9JttvOAmj82ustuN2R49zQv9QxypWCyUxtTct8kJhpNuTrwJqvsUAFqkuHaDfrzFrbDGlb0UadQZqQ1mrV3m7BOcWHoHT99sUk1v8U3HLmEQYeAR8yhPPf+bLJVvcup0jY//0hGycsLhxpiclxJGRUrODofrPar5A6SQZOEcjZUEXtJEXgFdjYlSzYwxpu4c0Bm+xMVPjTh/Zp2zZ2/zdGuKa1/I4bkR71noc/1li831s1TnZ/jStT3O17awy7DRdJHDmwwqUzRkgC8yTlceuMspM+nlxJv29z7+m2SmgkxQy7tsdruoTDEYx9Qcm41wTCoEjxyb5hsfP8mnr99iHCXgC9JEUyzbHJuf4qc++mnaKsWyIUwzJCAdgcwEhiOItSJDkAmBsAVWYGIVHJQHViAhyVCGRhiCgyTi8vYur1zfIbQ0mRKM04ycK1BSUil4eEWHQ7k6g86InWYfkWq0JWmFMa+sbRNlisA3GWUZ3/jw2+Dn5j3SzbfHsPfEG3Z5/a8wX+xxKOghzEPcHhZIlKJqlTldUAxiA09FnCy/n5y7hC1rDMMeBblJzqlRss9xtPEDHIyukrf6nMy1eaa7zBcPTGpyRE+4LFWXUArqXp9aPmReRBQyi6cPpvjCbkQjOMIo8/hit8F/3jvE7iDgeqvFiwebXGotYUmBcBIwirxr9iVO+bdpjvp0hp8jHAUcLQkS4fPR1gqu3eSMd5uj7mWW/BaHvJDF4G0yRZC3/4jvxNvHr179PgrllOXCAGke51bPZ5RYzDoFzhX7RJkkygTnp76Boj9DzfKJwl3q1j51t8KC/Q5OT/1xNgfPM+t2OJPb5wt7K1zalpRFQj8tcrQKmRBMFUNKzpAjZodCmucT63le2jWZCZYZJQ5P7i3w8c0l2gOTm80ez221ubI3j2lJYmFgOA73LVzhfHCL3UGLje4z9AcBR2o2e1GZn986jes1OVm4xanSl5jJ73EoCFku1O92zK+adHPijfqZF/84hVLMVHFAKo+z1/foRT4nczYnijeIU5NRZPPOpfdRLSww7yr64SbzfoclP+BE/t2cmfoI2/FTLAQtZr0Oz20ss7evmLU6uIbPYnlAbMBieUDeHXDYblPVC/zadZvVps9ScYEwtLi4s8CLm/P0xwZ73YinbrXZ2y9jmZpxauAUYk7P3+Sx3E32R/ust5+HqMhyw2VvWOXXNo4TuxGLpQ2OlZ9ittDjSG7MTPnts5R70s2JN2z8AwAkWrIVn+Pza3l6UlKczrG3PcDJaZackENzH8avHKF8tsMiz2AgEFQwze9gL/xO1tpXEDIjS21CI0W4u6xUD+gMpnj36V16Uw75mgIvRWuFqj3My80WhULMmYcWqJRCps0hbpZyxN5i68aYm8NtzEBxfXeG9eenqOciZoMRCsWVp9b4xH/cIN2ewhlVuX67hklModhn63LCjd0BNSsEnWc2mL3LId9xL8ximHj7+Imnv4QyNcKEE7U642GM59gU5jy2hiGKO8vJvvGBU8zXS7RVxOXmHqZvUq7a/MA7H+RDDxzj5WGL1BNoIQgHEe0oJvEl5cBEm6AMA8c3wNAorTndqHPtyhaOkpxanHt1GZZOIM5SNve6HBz0EQgyW4PW9G1FnNOkZsazG+u8uLFJtVqgXAmQqUalglRAN4lo6RBlm3guzM2U7nLKd9wL3ZzMMLrHdAefQgoQxiG0mCNUNxCkdESCiK6Tw0YQslz+IL45xaWt76Pk73EQRzT1Msdqf58rrd9gPFgnLwSRVCwEPYaJiWNGjMY1xqmDkwqW3AqtxGE6t4OXSPrZNmNtcbVvIZwY09BUGGMph44oMpW/yQdrHQZji91RjqlAESqbUFnM+h5KxTQKHqa8TS7pspgfUTUySrZCuidZ73+Utk5w1e7djhkAwd3bXGzi3pNEm5RERM48jkochtkeYQJz2qCRvURFSISdcHrqfvJS8czaP8Jwu6xGCU11iLP1n+CZnV8n6u6T2QbCklSdISEjpJGyq3xqmU0+S7m/0GAw7JHzmyz4Br3sAMPwea7pk7h3jhGtyAQDi80kR6VwwDvmD+gNbPaHOaZzMamWHIxcZv08dU8wKlv4dotTxX2EMaZgxMy4GdI+wtX+FTZTaHBwt2MGJt2ceOPiLCWnu9hSkXfvYyNO6KQRYSzYiguUrFVKY5dqHs5On8NIt3mq9bNYYsj1YUpfnuWh+t/lN1Z/iUGrQzknMHISN4qxjS4jabKVBUzrjDpjTk/N0B9uUvD7LNQXGNIHkefJTY/Q0xhoqkaI0nU2+oLp+piHii06XZf2sECtkhEqi4Oxz3yQp2aGZCKm5LUI1CZ9Icg7Q455Hq47wwutm8SZ4IzVvdtRA5NuTrxxKvvKRu2+8ThlY5fLHZ/Va9PUb1jkz+xy/vQ+peI8D519hJu958lWf43TR14gjSAW70BU/ic+9eSv4I6GXFkt01zPcez4JjPzA6pGxO2uT5JIFpw+5uz9VJPPEivBwdBkMI7RdoH161c45tn4AoqOYsYu0NocM2uYLJUjbuxKVpMp5swdrIHC6oYcPlIm1SZZ7KASyZGjXdRY4UVdig8ucayRIczbYPZI0yL23YsZmPRy4s3Zane+/E0D7zu6xPW9fUaJwhiO0b0SYzfD0PChc0f5hkdO8AsvXeST6zcYotFZxgcOHeIHH3qEv/Pvf5MkubPPkZmXZCFEhkYosC2TkZGCo3nn4RV+6aVrIDXtMCawoez5XLi6ReZLlNKYSnN8tsH+QQ+v7iJGCWKQ4RiCkbpzwmIcJsw2yvimSWrA0MpIfQPQJFqysFCjNRzxwnaTTMBMqXiXk753ujmZYXQPGQyepCs0t6NptDQx4y9w2uwxjgwMsUtJ7mI6KXPVb6LozLK286OY6RbbcY2dOIcQPWyzziu7fw+R3qZihWSZw4mSxffOPMfVbo6hPsx95Sr1QodWVqSXrJNqyW5Uoe4X+PD8fcy4CYmAshXz3qlVHm/sM++l5OyQULS53psiVg6Z7rEb5hhhMm1tUXSKnMiPmDfa7IxNVsc5OpnLF6NvYzvOE9vfRn9YJI73XzeLcdzm5u7Po7Lx1y9wrd/cY+IPra3mz9PRLmvjKcI0o5g+zQmnQ5ZmSKOJYcRoWzFf/15q7hxXtv47XLHDTlhlb+xh0yVSJi9t/5/UjE3yjsIwTB6bF3z34nM8vdOg6B2hXlDkgiEHap7IboObcb1XYzFX41sPn2Iu6JBKWAj6LJd7PD7do+ENMO2MA1Ku9hsk2iIWfbaHFbrSYc7bZ9q1ebjSoso6+7HFpUGFdpznmf43sZNWiXgv4SCHEa+9bha98Q5Xtn8R9fU87XDSzYk36KXNv89BXGQznGWcDqjyAiv2gDSN8J0eNhnC1hyZ/k7m/Gku7f2/yJkHbIYldsYuLi12x22+sPVvmLV38EzFlK95ZH7EAzNX+PzmHMv5FSwvRnopm9k0djBGO4KLzRpHS1N8+8nDVIM7+5TNF3scL7f5piOKar6PdlL2pcnl3hSxYTJKR7QGRQ60xWG/w1Te5b7pDCe8wuowzyu9aQZhgeuDR9mLDzNOzpKMXGz98utmsdFf5Qubv4HWX8dXp5NuTrxR+z/05ScmG+MtPnpd8Pxnl1j7BYPiksXZc7tgCj549lGqhYCr6z9JxeoQOAaWAZ7d5vLmFv/y1lN0HMWFZIbrLJAeznF4eZuNUYUThw8zY3Tx+orAbLFm5dlKLIxWxDGxxPseOsNoaBLGFnuZjxYGkfluMttCFIeUzDFhZGEYIWFe8ODiHtOzBzz4wC7nHhYk9iLHjz3Fkdk+h+djposWBTPPsSDHfSWXWSuB9OrrRrGxeZXra59Af7068WZ7OenmH2rf94s/C1JR8E1eam6yHQ0wHU0/zChWHYRhICyDD9x/jFLO43/79KeJRYZpaKSZoN0xH/38S/yHz75MIjOE1Bh5SRZAhsKNNWePzZG6kswSDGQKlkCbkCQJ9x2d410PHSY07ixHU44gDiTnj82BYzJSKZhgZpLQEiQSzFTQ0imOZ1ELcizPlLh10EFLDVJQKxgYCo7O1vC9O9/jB4Ph62bxheev8+mnr3z9wv46dFMI8REhxBUhxHUhxP/wGp/3XUIILYR46PWuOZlhdI/QWvHyxp9n0RrjCos4zbjcP0JFVqib61R0j7rdJhKHWSg+jm1UcNUFbJnie33KUuHYc5BlZLqDkCZP9uukIuCwvceMM+K53Tor9UV24zxTZoQVNHmyU2AQW9zqLxDeusXW6LOcyF/AC4a44s4eD1fHDrMlSaby+OaLfNfymJd6RwicU5x0fpVhKrgRnuPyoIWhXuR48AqeZVAyIxQNOvENhpaPLaBop1RyC6+ZRZik/IMv/Fc8VFvjpe2/yUPTf4X56T/xdcl9Mi134vVk6ZAXtv4WZ4KQdWOGWMWsDQ5Tsgoc1hvMs0XF6XGg3svpyilcw0HoG/gy4VjQY9rp4VjHiKILaDFkaMAzrQVM4XBf6SZ5AZv9HO+dXaEf3eSQ0wX7Op/escnskNVWmV+5cpHt8OMczb+ENiWOEzHHkEs9n0M1m1akqdjrnF3JeKlzloXA47D7FPsjl6vD41xtN8mJF8l722BKTroDEnGIjXAVx8vhkrGQ6zNVeOI1sxiGMf/oyT/N4eoun139X/nOEz9NpXT/1yX3STcnXk8S7bDa/FnOFTNuxbOEiU1rdIjpvEs+3GBOXscORvT1B3igduzOH9Kb+GQcKwxYcnfwvSe41voPBGZILzb51O4RKrbNQ+VXEInNdjfgvSeOcbN5m+O5V1DxDXqRS02MaO3Dz7/wHMNsk8C4yhCXnNvFTiye2mmzVF6m0wypOBvMH7HZ6R9nMRdzxLvOarvEhd4xrjZ3KIvPkdkhli05YW+iWebl/iZLdoGyJVh0mlSC73vNLFqDET/12R+mVu7xyzd+gv/3I/+KnFv9uuQ+6ebE61Gtp4HnvvwrB0tmzOiAxazAjVyfZ29dYnpc5o/cf5RG6RwAC4V1FpwhYCENgXC+h6sv/yTfv/wyi9Uh//5LcySXA84vP83RfIeLQ5Nh9l6Ef5Mzc+s4rSbXujkCI6brDLi+9is8/5wiHa2iopRbeznmqkPS/lPUau/nocOf4NTKBklFYV6a5dBCl3OVA9bHedp6Hse7yEA9z7jQQdqw3ha88OwZosIVtoMiKwUXpTWBc/Q1s7h5fYdXut+PYWR8/7/9Bj7/V/4mpvnWvz2b9HLijfg7n/4Et0dtcEGYYGAwFxSxTM1ec8SvvHILpOJbz53g3MI0oygmNBSGp1kpDXlw5Srfd/KP8iP/4OOEnkZbIEfg5k0O+jFI0Inm8cPL/NrBdaKyppdEd05bSjWDQcRPf/RztOKIjU4f7Uh0BoaGz19bpVYNuLneoudoShXJXCHPK3SIZUaub2LG8PzaJp944QaxAmVKDASB5XF1p8m8yMh7LgMVkvdee+7fcy/e5s/+/34eHIn1Dw2e+5f/3dcl87eym0IIA/iHwIeADeAZIcRHtdaX/h+flwf+MvDFN3LdyQyje0Rn+4foKYtRBmsJbKYGh4unOTtT4QdW7mPWuI5pSKaqf4pS7p0IIbAND0tYGDqj5/5F0sKP8eLaD9HXJjO5ASV7hEyOkYYJw0wwU2lRL80z55tY0sCzz1DOulha0dV91tRnqHhDHCemYYScCTpUnRGeHHHEnyMKPbQWtBJB3j7D3vgUeZkxUnkO0gqBDDGyPUYpLAUD8nZG4Pic9zsERsp+OmZf2KTJzdfMQgCd2Mc2MvKmYKv7j9E6/voEr9/EY+IPpVfWvod2atHPJJdDg27qslQ6wYOzVf7c2ZM0rA18Cw5P/RGWSo+hdEhgePjYZFlK5v11KP41PrH248RCMuvG1Kw+BVYY9DJimRJUQwq5OnNBEdcwse1D1GSfHBGddJd1/SRTXoa2MubcESt+j2oQ4ZpD5r0ZbHwEil6cMp2/j/3BSUpmxn48RSvNE5gDElq0UoeaG+PYUHMc7i/toRHciFKupQUOohdfMwshYD/0CYyMki24uPvX0frrdLLapJsTr+NzN76XZmrTTk0u9R0yK6BePMF7Fsp8//kTuEYb39Ccnf9WjlbuI0lb+Hh4GChl4JV/EvL/Dc/s/2cSkTGTj6nZPYp6iUFfkJkKq6bw3WnmCiU8aWA708zZPVydcSNaZUs8R8VyUW7IUq7HfG7MVL5PosfUnQVcUSADhIqoee9kHC1TtRIOwlm6sY1vDRjTYz8MmPVGFLyYxVyOo9V9Im3w8tjnQtxgrfeZ18xCCmilATU7JmeEfH7tb3wdZzO8icfEHzpaZxB/5SbfMB0SsYDonOfxkwt84MgKtfIuGQEPnnsfjfxpdlqbNLyEshWTkCPO/V2S4H00m9dZ8fu8a36fP/3Qi9SmG/gqJXAyakXBaDDL8rRDLpCU6z6PVvc4mR8Qhp+mmvtNzhxROKWQ5VyXNKdZdCIeqD3Pw4c8gjDP1qZLPo5w+QjR9iyWmXDp4Bib4wpz+X0WG2uU/BjbgNmCpjZbJ+sqDk33+VRnhm5moEa/8Jp52LbBbifPlBvyyJFV/v5v/LuvU/Bv8jHxh04vDvmHL38RkWSgFf0w5ki1xqlCnfcdP8aRmTKxUBRqDt/5yDkO1at8eu0W0lcoLwXR4PsP/znq5jQbYUhUztCeJvNgzi6glSY1BUHNZrpaoFbzCXyBtkHLO68hf/XZy7x8sEej4DHOQVTUqKJCO5qNdptjlSo5x0RkgtSC960cpmFbaE+zXMgjbUk/i4gVaAFCgrJgZrpIZGVkWmNikNqan3vhwmvm4TkW2hVkriA2Ff/kV5/8+gT/1nbzEeC61vqmvvPm+N8B3/67fN7/Avw48IaOipsMGN0j4ugpjrk9FlxBTuaoeac4WS6wVHiC7cEqnqkZ6GUOYrjR3yGOVtlNmygZ41o5DtW/m1ruHFPGl3jYP2DGHHPU7/Mnl7s8VN/g6qCBUlOsVD+CGV+gM67Stu4nF4ywpeJseRdDexRzKxRyi3hGhkoMPrNxlNawyOowoWgaZFqynvqEpgHqeRKhKNgZOUPQjqsY5gJKu+yEAb3EYtbKEyuHnOlTtwxmzBaZ2nvNLBzL5H9+79/i5qBEL7NJ2WZ18y9/XXJ/u29CNnH3VcR17ssdcNiJcJmm4Z/gfL3A8dr7udy8imslbCb3045iLrU3aXWfZC2NUCKj4JWYr307tWCFh/xLvMPfZNlvc39xn//qyBoPzKxzqzvFjFVnsfR+jGyVTjRPk0VMWxBYcP/8OiItUghWUH6dnKlwMpunVg8zGrp0xzElc0wnCXgpnAYjIcqe5SC1mMrZBFrRj8pY5iHSzKWZ2ISpwaJn00tzTLkuh3Mpy/42Uq2/Zha+Y/PX3vtjXO3XiZRFR13hxtZPfF1yn3Rz4rVkWcKys8n9+X3O5FqUjQYL3gkeaficnf5mLu1+CUdG3Mq+idXhPq90dri8+8/YTDKUYVF3q0wXv5Epf5b3FS7xjmCLo7l93ju1w58+8TwL1X2agxyPFhc4VHgQO91lGC3TTGqE0mUqH3FoZgs1LlKuL5G5DQpmgp/kuLC2hI4hSjR5u8/muMLF/gyW0+YgvMraOKBa9gkYMRjXse1jZKnJTuxgpCYN12MY5Zj3LI4Uhsz7u2i1+pp5lAKfH338L/NKp04gM9ajJ9lq/+rXJftJNydei/6qbQfSDJ7ceZRfeuo7MP2jvOeb30Fv7vOcP73NI0ce5z9eiLnRbOJGP8aMOcC1IWWZXP6bsIw6jZkeQTBGaKgUbGQp5MThfTQC4X0zx6aPo7MBN3ZOks9FWGYMCEZdj5dfPkHOO8IjJ7rM5xKOp5q9do5mR5O0LG5serQjl6inWTvoY+QvkxguR2sWxw/dRukiJX2Ek/kY0wQlYe60w0ZQYWd4mBlbUDcVgldeM4/5xTqz+sdQHZfHZ7f44vA32dt86/fyvBc21p24u651dsCLIQfS0Rwuljg6VWFpocJ3PHCKF5tNMAQ/8PADfGlnm73+gL///OfI7BTTVZyfnebs9HtIkoQ0l6FzCuUr5uZt9GxGmtdIU/OXvuEJzi/NkNkhK4UClm2ibQPlGyhXUPU9FuZrmL4BRobrCOQYRlmCYRmkJmilURlc77XYjkZgaIqNHJnOqHg+K9P+nU21BRgS6kWfRGWUSwFeySSz4Uurr/2a9sTxWf7Ee+4HZRDnJT/+6adIs7f+JujX0M2aEOLZr3r82a+63Bzw1f+xjS//3lf+PiEeABa01h97o//GyYDRPWCz9euMSIgzuD4uctRfoqO+nWFYwUgvkyWfoa9s3NJf5P76h6m6RfYHnyWPwgBmpn4a0ygB4JqCihHhmBkGhxgbDTpZDgPBtNnh2u7/RTf8FO04YqCHeIGL0opu7GBhMYoPiMf7lK0RjnOMvinZSyTNxOL+4kVCbbA9rvGuxiMsBiN2I4dUFTnkpUwZClMmxKJIGJsUsbGdM6zkyizVf5jF0jeTocnU6/+kMqxlvvvsR1GJg4h9wvDXUOot3s9IA0q/8cfEHzovbf5DRkgOYo+XRzPcX54mlR8mi0y87AVs/SXakc2Rqe/jI/MPU/M8muELVIkxJRyd+xdIaQHgmoqcGWKaKa79KAdU6WY5HJkxZW9xZe8X6UXP0xn3GGFjBSbdxKQX2sR47I/38NM2ebtP3n2AA2mwHvtE1Dicu0Y3cxgmRd49d4KCZ/LKoIqUNqdLikA6dDIX13KxEs2UtJDWCY7m6zwx9+c4WXovttDI9PVvRBSCs/yx43+L/jggSB22B//srZ8BOOnmxOv43PW/yBiLl0cVXhoucbZyCHiEIgk6fJK8eZ04s3l07t1858r95CyTTtqjYcS4QnB66WdevZZnZnhWCEZC2f0mrmWLaBzyZoZh3OKlnY8zjK4yjrp0RJ7EsdgJHcgEmeGx0W9RFXs4dshC+TG2tMvGyCXwGkz5q4wSj0FW5INLs4xFwPVeDdMo8VDNQiqLvdCnmtP4MqHq5hFyhtPlKb5r5U/yQO0RfMYI1XrdTE7WH+O7lj/MsF9kjpiL+38Drd/ivcYm3Zx4Pe1v+MrTtMZw/3FmnTxxyeMTL1xityS4vTXLE8tLfMfZc0wHA1wiHAsMAtzGnW7a0iLwUkInwDRhc/ADVKsOkbRxdMLlJ9f43POXseV1Ur3HldUGIrRQCZStMVZOUui+wgeOXkc5gq777RhezPM7VeqNGR44dpWZmS5B0eChqTl2wgK3ujn66jCDvSN0mh5qDO0hjEIYpIfphjOcu/8Y33Lfd/PQ1ENICaHeeN1IPvLB9zA7vcR7G9v84MrLPLf+w2/tDMA328tJN/9Q+mO/8i8wHCCCRuDy3uUjTNsFyrbL3/3MJ0ksjWfDkaU633n/afpJDLZCujEzvsf/8p5vAWCUhOhKAjkFtuZbTp0hcG20hNSGn3/qBT558TKJscN+2KLkuXeWjyhBZoNpGNxqt9D5GOlnvHNuAS0Fw75iebpMnwS0puAbnDs7h6HAilOOz1SZm85jatCGgbIBA04t1siXPN5//1H+24+8i0eOHkJkmme3t18zDyEE/8Of/Aa8qkC5mrSQ8AM/9+/eDt080Fo/9FWPn36jf50QQgL/O/Cm1tdNBozuAfHwP9LOYCuTXIuX2ciKvHuuTtWL6PV+gpoxxCbPnKUo2xUaToXADAiEwENiWNNfuZg2yYBeonlk+W9iWoe5PawjRYqWXWT4j1nwh9xXERzJrTBjdHHtjMCOMOQB750KWHBfZNrp49v7vKO6w6O1TWa8NaToU1Nj5o0uifY5k3Oo2w6ef45E7yDNA2YdA4CiE1Nw+kj3vRRK/zWGHuOYJUIlQCZv6A1mqDrsyDkCO8aWJjub73nrw9dv4jHxh44c/SfWYpedzOXC6AjbOs9jC0v49piN/f+VmjUgZy2w4hrU3RI1p0jVLFGUihwm0ghevVamLDIc2rHFe1d+GGkd5eZgBiU1Q92B8Y9zPNfnkXrA2fwU03afzJAoS2GYPZ5YDph1rzMX9MBs8v656zxUXyPnHJC3uizLLqetHTJl846a5JFCH+meo5W0UFaLeQ/iTFD3QnLuED//QY7Wv4Us20cYDYaJTSzeWC5t6bKRTVF0IgoyZXPjtfdX+ZpMujnxGmT2EpfDEpHweL5/igMR8PjiSfqqzY2DH6fmDCl593EksJjyitTcPEuOS9lICKSJENar19KYCGnSS3zed/i7sIxDXB7M0BUGnXiTePw3OF3q8XCjzrsrJWbNHkPtMJQmiR3yzgWDem6L5XybUTrkw4cuc7q2DUaTonnAaXebx3IbZInNt831eaTeQThHuD7qYXtd6jlFHEPVDSm6IxrlD/Ho7BOk6oDYmGOgPNI3OCVAB4/SJUfOzvDos7r1V9/68CfdnHhNd248DDPJzeEcubrNd37zGVZ31ri0/y9Zqhxwfv59qPw8U5Uivl3GFhpbgCHKSOEDd97MPT61x/FylyGwdOib+eDJEctOSD+0+LnLFv/Xr/5zPBPOLr6bI0tLVMoxrgUL1RZHF9uU6nWmvT4PTu2zeUHhmgmz9YjjJ1epVg642qyw2SyDlpwsSI6WMtyDgNHWGgu1fY7PD3AlSAk3WzOcOvMYH545Tnu3Q9707mzO/QZ/bl7f+u9ZXSvxWKnDocrL3Nj7p29t7G+ml5Nu/qGUkJImmqJv4ac+yoEPnDrKF6+t8fGbayih+P9863tYyJeYLuSp+h7aaBHkQs7VS3jGnT2BFioVRCHECiIqgeSbjy8TZBIpAK25fLXFv/zU/80D82v85Ee+mZlqidTWKFMTAYZjUp8NiEsJWT0hH9hEJUHiguWbDIwM5WQoRyItTXFJUJ0zGMcJtw+6DGWG4xgooUgdRT5w+N6H7+N9J46y0x8wVy2SOoqhfGM3TH74W95J5oEupjzZvcFv3nr9jezflLe2m5vAV28GPP/l3/steeAM8CkhxG3gMeCjr7fx9WTA6B6QUx+jZo5oZSWidMwg2WaQbFHwyuzGkmbqs1D/QfL++xFCYEqLpPczuIYk7/0VhCy/eq1EnGakXCzDx3WWaPU+x4y7StGOUEJQtSLypb/DzPQ/xRSCKM0oGCkzXkzejrjW/zV0ZiAyQWs8ZiM7TcVIMfUqU3ILH8V9hQ6F4AzD9AZ5o09kPMJ+3KFmbXGQXeHWQJCXCXlDMkzHKFEFDPKGwDVyaCRv5KZnzmnw/kN/gQPmiZUmS/cZbf33b2n2k+m7E6+laF6jbIxoqwJ7cczuqEUn3sM1Ha5FBXbiPIen/xQF/zEAHMNhOPzHBCZUK/8MIQuvXisSRxkpE0fksI0qO51fZzFYwzETEi0pWYpa9f9gceZ/xzLAVhHzTocpL6XgN7nQ/iw2CjfV7A9GXBufo+xGHESXmTN3KEjFQ9U9bPs0cXSdoj2kpc7RjgdU3HU24hts9yUlY0DeMghTG0QdScaiKxDCJcZ6Q3dWloIFvufMX2ArriM0dOPn6W+/tS9+J92c+L1orZl2m+TFkN2oyH6YcLMX0kn7GFnIK/06G+MSp6a+nZL3AACeYTMe/Tw5CXMzv4KQXxnM7ag5RqmLZ5aR0mOn9zGW8jsoDUJKfCNmrv6vWZ75H5GGQcEYsuId0MgleM4BzzSfJyAlh2K1H3O9f4JyoNge32La6lC1Ux6o7GK696OyVcrOgGY0zTAJsew1bgzXaY4dKtaQvDRIdQHENEKHnA5s0DZaGW8omxOlZb7/gb9EMypjo9kcfoxB50tvaf6Tbk78XpSKXn3eijwu7Vlc2wwZZIrRcJ3wikeyLpmfe4SzS3fe8+hMAE8DIKZ+DSHujMBorRmPXfIyRuj7SaXCcv4zvqMIXPCWx7RXbFTxF1jt/xDTXkyooZVJ6lMRl1oxP3thg0EM4UiwcnKDp64cp9SNKcbPMUhtCkFGdRHOPLCCV1lDuLAZlljt+AwGbQb9G0QaDANKxTGGlaNWL4GTgnXkzv/5DX6fH1o6TsI/pTMQVO2UqdGPo8bNtyz7yZK0ideyMTjAzWsMqYjjjO4ootkdkrcdvniwiolGGjBXKnOmMQXAOBnT5YCGG/H33/Vfv3qtQTbCFBmOnfJnzjzEOBzz6c1bZI4CQ5M6mhu70/zVk/8zdXMGzzbRQGaA8CS3ex2eunqb6coBD0ytMowH2IbGzms+c+MmmCnS0pTLAWeq0/R0jG0ZRHFGJx2xE4Zc2exCdmf/Ip1pTCmpBz5CwJFGBSH0nVlNb8A7Dq/wE9/7ETBSTC/mR577JyRp8pZl/xZ38xngqBDikBDCBv4Y8NHf+qDWuqu1rmmtl7XWy8BTwLdprZ99rYtOBoze5pRSZJigLM76grMlH2QdA4dW76eZsgYEhkGp8L1I486bzzRcJ9WXGGQZdu23nx5m0qabGuxHmlGSAM+SNzWzvolCEioXL/eNWOYUhjkDAhIlSJmh7hZI9RTPDX8QR5mUiTFExEhKbHbxjDFHS01M+wglp0I/MhhmLt1wCxFrVkcpl7o1qkYMyqQVecTxLXT4KTqjp9gdXsIx58kwGPb+zetmY0mf46XHOLv0zxGiiiEgTX+W8fYPv3VfgMkRpBO/hyjcJ0WCgkdzEe+oeHhmDVeE9Ec/wyG3Q84q08h/y6tvPsPOLxHpfQ4SG7tw32+7XpKtsx+6bCc5BkmIyS1cabDiJ5iGoqc87OB9GGYN15rCkBEZYJlTzNgFRFZhc/CNVERG3WhikJBgUpCbWMaIU5UdtPsNTAdTDBKXUeKTJVcIk4j9ccp+3+FIrolWNoNQkkQXyMLfYLP3JGvDDTwzIFEu2fj1jxf17CKnKw9w//LPoclhS4ji/y+jnb/31n0BJt2c+D10h19AaYNMZ3yg2lrNal4AAQAASURBVOd8xWXeM1HJKqP0FzmR36foPUoj/yGEcADoH/w1BmrIblrAdCq/7XpRtMde5LCRPsDBuIkt97CExYOVPlJIhrqK4z+MKSvYGOSNEaaZkTPrHAoCDJVHj48zRUzd2MEwIrpK4KSXqDkdTpS20P733+lm6BGpPIKrjKIh/QR0nLGY72BmBt3xmDS+xHD0S1xpX2R9sI+DSaxsVPr6+56U3CrHy+e5f+nnUdoiLzO63e9m3PrEW/cFmHRz4vfS/WevPm04BeLuYdQgx5c+eY2lqc/xjifWOXHiB/nguWOvDgzR+pEv/wnnt838AwickFAbvHjr/8/efUc5Ut35339/KyhLrc5xciCHIUeDCSYYG5xzDrvO9nrX67W9zn527d2f8zonnAPYGNtgwBgwGDAwhBkm55nOuaVWrqr7/CENdM90T+du9cx9naPTUoVbt2r0GUm3bt06h0o2syTSQ8QweHq4jmhdhjNXOARjJxGOVpDMd+M3FT6lWBmuoCUSZ6mpOLC/GS+vqK8bYktrJRufjuMZG2gKD7M6PEhN3T/RtKSSaMYllPeTbN2Ple7jz0+uYWNnFXiQylt0DiY48EQ/f/7rZgZT63Hz+xn0IOWC8gYnPDQtzVWcee5aws1/ImArggGBofPxho48PtmkzfKtu7Wjy5c3/QlTgWF6LG8xaa6O4PeZ/HXnDhJ+Bzfi8r0bbuDClhXPrPMfT/4CT/JUBsIY8myTQlBswv40qCy5jOLB/QMkCxaBqEDcxamAE5dWs6RiFdFAgLbhRPHyMQNqYwGaamPEVJgLgl2sDPfTGPMQT5H3FOuTrRD2cKPCf1x8CS3xSoK+LJUBi/2pIfpTGVxcsraHFxL8nrCjs49t3T3cvnEbD+zeS086g/g9DL9DfhJjEq2treGylav5zTVvIBLNUhHK8PZ/vBHHmaVLumcxm6p4nfm7gTuALcCvlVKbROTTIvLC6VZRNxiVuULmb4gyyBirCEauJx58Hk3BEziQfJCQ8xQ+w2TtqqcxRpwNzfS/lgiKuP80DDM0qrxEYQ+W2MRNgy3d38RWeRzVhEU9w04lGTkT1+2jbXADP9rwBVAW1XaegFnBSdUvZk0owCXV99PcdDM75L2kCxYhM40qbCfr2SijjtUtv6TgedT59iLkyTmPcmCwC8mGuLS6kytq9xOwswQsCNs++of/Sl8hxQM9j3Mg007BMRjM7pzU8THEojawhONWPUxd1Q8Q08Dw/kSh48Wzcvxn+2yMiFwtIttEZKeIfHiM+f8iIptFZIOI3C0iy2ZlR7RZl038HFNB3r6QyujVLIldyNpoPRt61xNgHxg2F669B8t49rad+eQnqDJcWkLnITL6trm5Qo6gbVBrDfJox1epMnIEjOMISS0ZtxKM8/HcbnZ0PcD3Hv8yEcMibuap8DVyTv01nBHLc1rdfpY03MxueRuOVyAoGSJqI1nlI2es4MTmL5AuFKjyt+OoNIn8k/QP9WA5Pl7UvIfzaztImR5iBsh7BQ4k76E9X8nvDzzMrnQWV1yS2ckNxmkZPloijZy29nGao5/FMj0M58sUOj85K8dfZ1MbTzbxC5QHQd+LaYg8l5Oqz2B5pIZHO9cTtvsoSCXXHPcVDOPZXjm59K+pswqsjD/32R+qFHsxGEDMEup5iEc7vk6DmWFp4GwqjBB5N0rAfg6u085jrXfx800/Je4XfIZDbWgZl7dczIWVfTTXhlnZ/Gu2q+vJu3nqjQQx82lSXoCUeRqnNH+AZC5LXbCTbCFBd2obiWQ3QWXwmqVPc1x1Nz1ugIAVJFfoYlviaTpzVfxo5/3szJoMKwtX2WMcjcP5zQDLKho4//inaIm8irDhQeotuEO/mpXjr7OpjSt3W+nJSxg2X4A/chHBqjXc0fsAHXk/1YFaXvOcazGMZzOYyv6dQgGwXzGqKOUOEQpABpu88zCF3v9HxK/oGXw5J4RzrA3kuGLV2bT1DvKrJ+/idxsexvFMMpj4rJO4cOXFvPe5D7PulDVkl/yWvYlTOaFpH2tO7sJydhEJKtLWOaxoeAH9yWFq6hMk8kPs3b+P+qrtvOKKjVyyvJ02FaHgeVQRYFt3D1t27UGMBHv7v4+voPADijCTEQ77qapaTaR5B4ZcUpyYuRIv+eiMD/1s9zCaRC79IvKr0vx/iMjy0vQrRWS9iGws/b1sxjunzdjOxAF8kuNtq87humUnct7yFayqqeaLD98DfpfnLF/J5cevxRjx+bgjsYN4IM9bVl85qqwDmX20hBNEbOGRwa38cMujEHR4/oq1BIImliW8/Xnn0Z0b5HN33sWtGzeDX4EBZy1v4QUXnswAGXzOG7km/ilSuRoyhiInHu3JIXwRuGDZEi4+fiWbutoIhDP0u11s7O8m6eYgZOBFPcDFQVhSF2N9Vxvre9vZnxjiK3+7D89wcU0Pnzm53rkxX4Czly7nyRf/L1c0RDklsp/1e9aSSffO+NjPdjaVUrcppdYqpVYppT5XmvZxpdStYyx76US9i0A3GJW9RO+/kgMM3zVEQxfjCy4jGohipe7Fb3oY9prD1nG8NvLKI+A7+7B5Mf/F+EShRLByN1Fj5VgSeRlZZy+rg/1U+RSJ7C7wXGqDg3TlYuzPVNAUVKyKpFhj7SHCk+zrejFxtYEV0RgRs4Dn+ThQ8JErJMHrRzm9hO0CUTNPNHg9CTtOY6VQY5t0ZwN4+FBSRWXFy6iqfB+2fRx+1UZGCbZZwOc+MuVjZcWeS7hyAxgmwtB0DvdoaoqPCYiICfwfcA1wIvAqETnxkMWeAM5SSp0K3AR8YeY7os2FwdQPSHkBwsEXUhG6GGWtJmDHsLI34YmD4b/ksHUyDOJ4LtHoCw6b1xSsIixQIIivcBcNvgxLKl5AV6GPE0O9hC3FYH4fSjJUh4fYlqyiPRvn+GiAVZF+TvBtxy08yWPtr6PJOEBzJFy81NS12ZOJElR9oAbxCt1U+l0q7ALRwCUkfFGWVSlCpkFv2qJCQFlrWF33Elqq3k/E9uOni34nRNjMksj8YcrHKlTzaqL+X6JMC5iFa791NrUjSKTvIadsItHnEQ9djGuuIBSoRNxbyHtCOPi2w9bJqBQeeapibx41XURYEoSgeOSoJOT8g5ZAmpro+XTm0pwaaydsZBnMtaHUEAF/gacH60hkI5xbW8+q8BBrA5sYyD3JX/b8E6t8/dSFbKoDeVzHx4FcnGqjA6UGMAo7iQRdKu0CsdAZDAdirKrM41khMlmLOl+WUPgK1ja8nBNr30rIGgQG6cuFiVg5BpIbpnysqho+Tdh6P1g2FP4+vQM+ks6mdkSlHqrR51BdcS6n1EFLTYjo8GMMDwgnrvzXw9bwxCPjARXvHzVdzAoAWoJZKgTSyTaUElpzJ2MwzHXLd7MktItCoZNl4X2siAyS8jwMPPqM6+gK+hCjH/L3kup4C/GgDSGTyvos3fkKtnRH8OW6SQ6mcJy7qQxBXUWWi6+oZ+2JOaJhHz7LwG84JJWisuXlvP6F5/PKV1wAUmAom0eQ4vvcS075SBkN3wVKA4QXbp/y+qNMNZcTZHOSuXwLMKCUWg18ieItvAF6gRcopU4B3gD8ZGY7p81U1s2RcDuJ+XOcWb+G8+tWU2tHqQlH6XKyKFf4+HNGNwp5yiPuH6baHubapZeOmrc2dhxLo4O0RPqpMXN4vm4CEZclMYesP4tb4/DXvTvoSw2TKRTwiaAKCs9QXH/6SaSHcvQ7GX65ZQP/fPtdxEMBTE8hojBMg3zWJSVJsk6aB4ZuIRbOc3FdgbUNlUjAwh8yMAIgZvH/j/df9hzeef55vOmsM1GBAgP5NIgBSnA8b8rH65Nn/BfnxBqotB1U4ZYZHHlmPZtzRTcYlTmhDz8OzbF1VIQvYmn4BE6seiFBaw8DjkGX/TLaUvtHrZNRdfS5flK+5x9WXk3T9zmx6ZtUBs5AeXl2pBqprryMjBck5ykKKkXI8rEpNUTWWEd9yGR5MI3jhEnm+inIPnKuSdK1abT+yJrAFoayYYQ8vYUILgY4u/FJEtMQMGxOrqznjPozqPKFyTmVeK6JiRCLvgDD7SHkb6AuWMU51WdSH6jCEpesu3dax8sIRfE37sBqnHn3egFEqUk/JuEcYKdSarcqjur9S+D6kQsope5RSqVLLx+mOFiZVoZsSRCUNGvixxOPXMQZNSs4t/5K6sxOMk6ITq6ibbhz1DpZr5ouJ4wTuOiw8hqWPsCZLf9FRaAZ5bk8lVhDbfRUsspmsGBiM0zAsNmYCKCsdTSFc6wID+MU/BTyA2RopeApMkqotX7MqeFWhjMh8m6eASeMgYLCdkJ2CsHFNGwurGvmosbjidlhxK0kBARwaah4CX6vg3igieZQJZc1nsKaCBjKIZ2ZXrbM2vMINu7Abvj5tNYfSWdTO5KQlcYvKU6rWkZV7Bwua1jD+ZWnUufrJ+WE2e800ZUePTZIyqumuxBHfCsPK2/5ik2cu+TDVPs8sp7FhsR5REPLyHo+Bt0AfiOBJUE2JGqxrTNYHkmyLDxMugDZ/HZ63D5wHTJ4LAt8m9NjPfQmY7g4JB0/PqMA+a1EAn78SvCbfi5rauKyJSsI2wEqvBB1ZpaglaWh4rmIt5vqwHKWhau4rulU1kRTBIw8Hf3/M63jZTd+gGDjDsyar05r/ZF0NrXxeOnuZ18EziJnncfKVRdzQp1FJhPF669iR6qd/mzq2XW8HH4fBPxgWJFR5YkIRsN2hvgckZpeqqoLmMapRIJNKE8wbAUkiYYNnru6i6XVZ5LJhAhbHq3pDIb5CJ1OhoG0QVN4gFPjXyHnhIjaELPyNEQckimbbet3Y1kX4SnwW3Fy/c/BVBfQ51TS71QhhoFSPmoip9PeNsjxK5ZS6WtiRexKlEBBwVDv/07rmBkNX8do2I5R9fFprf/MsWJquZxENifMZen1jaXnNwGXi4gopZ5QSrWXpm8CgnLw2mBtQdzf+QBBfx4xFRcuXcXaypW8/JRTGMwPID6PsGXy0O69JPPP3ik34wxxaryLFeHDx/LxmTbfuOBm3nP8lWweGkR5BpfWr6K5KoqpPDA9sgEHHAtTfFyyejVYHirosrW7m0f2HyCTB1DkAwW+1fkP8IH4PHzVOQyfwvRl2Zd6ije2HM91dU9wftzHOatbOGt5I1G/TcgURAQxoSkepTeV4fSmJk6qr+EF69YioTxmwOHB/TumdcwuWvEb1i7fT6jirdM86kVzkM05oRuMyphSioIycFAM5/eTyHWTzPfR0/U/LLGyBM16qoMr8ZuHdkPPEvUdT1Xs1DHLNUPn4XlbaAlkWVV1Ca3pAbozVSwJQINdx4FMCojRGDqeloBLtwPtqQ4SueXEK79JwA6yPODR5HPpT3czkA+SzVtcEF9JU+1XMIIXgllN0FpC1DqFcOACrl7yOoZZiqu6CfhSVAaStMROQMwoSISgEaM28lzi4Zdgo7Bllm/DPV3eFB5QIyKPjXi8/ZDSmoEDI163lqaN5y3ADE8raXMhkRvEARyB9uF9dGe7GM4P0tb2WlqCaSL+dTSFGvBZz2bT8woY4lIXPIeQXT1muVb4EoLeDpaHUqysu4HdyX6yuRDHRRV1vgra0lnEjNMUOYWaQJa2vJ8DqR2k1Dqi4c9SE/SzNpynxsoxlOmiLx9CKcUVtUtpqPshRuB8PKOCoF1LzL6AhuhzeeHy19PvNNLnDZKzFcp2WBGLIUYEJErcF2ZV7BLioavxi4fJ1M+UzgmdTW0Mnckt5JWQUyY7BvfRkeohnU9woOOlNPpytIQvY3mkGp/1bDd0J9NB2PRoCV+HYYx9WVcgcg0RYz8rg0Msq3sh+5MJKAQ4NeJSZUXoyChsu44VlSfjGC4dBR/bB7eTVC+gKvhO6gIGayvS+FSe3nSenCf4vAJX16+kof4mjMAFOEaMsK+CmP/5rKm6mutXvJqeXD07Mg6Dhp9hw09zJIZlxDEkRK0/xHFVZ1MbOwc/eSxv13wd5iPT2dTGkr7n2ef5HXR29ZNIZ7mr7aPUn9bPeRc9j1ioEWPkT5OBv+MDfPY7xi3WX3E9yyvaMC344/D72Lcty47tdeBWkEyuQMiD0YTPvogENo8P1dJo/Jkz664n7L+WtD9K1jTJKpOTl7eTVdDT6+F4l7P6lJ9x1uWnsKRe8PuCKOP1XHfdhZy27nXYpo0lvTSYaXziJxSMUxkP4SlF0l1JKHoJYq8l6VmE1R/n7rhO1lRyOXE2J5PLZ5YpjasyBBz65eclwONKqRzagtnUfwDPhbAE2DvQx4GBIRLZHJ998h7weXzwvOfQHK+AEZej7en8GSv9A7xm+evGLffU2htoKwhu0ODy1SfQ0RcgbEAkZtIcjJPKFlhaWcGFx69ELIUUFE8eaOcTV1/BBasaifh8YAOuQoJecWDsYYOXnnA8v7zinRxXcT7+8DrWhLOcUfcy3nXaxfzTGefj4JHMuLimIhaxiIeDxAJ+MoUCyyO1XLvqRJpiJlbI4Stbbhu3/vNm6tmcd9bEi0yPiHwSeBvQU5r0EaVUGfyrLB5e/gBRcUljEfbVMOxlgCUo51ekFDQ0/hzbt2LUOm7n/6PaSFAwloxdKJBJDWB6/XhiIL41PNz2ZQaGDVZF8kSNPvy+k7i4JcL2xG6yfRZxQ5H0dZM0XCpizyUYuo/2nm9jFG6mkCuAuGRFMPxXEYgUuywKHr6a7yNGGOXtp0AYn63oyxnszdawShJE0620hIqXLjsqT0chSdzbQcFwCMicvTWnZIotub1KqSPelnDS2xV5LXAWcPh1TTMr95PoXM6Ylfk7YfHwRAgEIqQdAbOJYWcXFgFWt3wZ224YtU6u8zpqjGFM88xxy030PoaPBIYbxDZMfrv35/hzAVZEU4SkQDB6PNcsD7FpYA89XUKFmWdI+kkbAZrrX05F7nJ6+/6PXOZWUoaNY7gMOybxyBsIhIt3gzKBcO2vELFQXicJVzANj93JKEKElnCStbkELcEaAJIFj+4cxGU3hukSNxaoP+whdDa1sdjJnwIehmni94UYLtioPPQ4Q0QMPye0fAzbjI9aJz1wBTVmmkDF+F98Ozp+QpBhlNSQ9TLcsvPvNBCiPtpO1PBTGVjKS9asZsPAHnJpD+U69Egvhi/GCbXvoyH7Svp7P09f6gHyRoGMYTHkGtRUfBB/YHmx7gJW7e8AD6GfrkIBlxzbh+MEcOmKFLjSs6j0F/9v6SlAyg0QUN3EfQ4VMksDcM6QzqY2Juc3I154+ByXXO9WejeZ+JtDXH7yywn5D2mwLfxz8W/F+A1G/R0fwXbB8S0hN9zNL299kpaVS7i2YQ8n1xhUhmuR6BtId+9gtTdEvWeQTmXw+epY0fwFErl97O/5//DlHyNsFjjQU4HPUaw5/mMYvjgASnlIzZ9oz5nYRgZ3aJgTo+0k8+B50JM3WWbWUFttUnBduvoN0v4oqcEAkaiFEnXEVs75MI2eCbOWzbGIyEkUL1N73gzK+CQ6mzO2J7sJU3nYVpa85xExTf6w51Fsz6EqWMHrTjrnsLF+3MJ/c0bI48Saw69mOeif7/sa4VCWkwJr2N3bxS2bNuKZeYZ9JsdX1bC6poY1NbU82tGGMgAbur004YiPH7/6VWzu6uaTf7+TJ/tbcX0ulqvAUHxi3Q3PnJANhoJcEL6TtoSHlyswlMuRoYAYCvFcDNdPxO8nGhDShQLpnEvWzeDz2fiNBPvzB8at/3xZqF5DUzHXPYy+pJQ6vfTQAZ6ifP9XcUWhUPjttdTYAXx9l+Ioj7DvBYc1FgEovo0yhEjj78ctd2fX80g6Bsp3IYZdR9Sf5Tn1B6iyXHxGiqXhWkJWkOMqVhP3rSMtCsdxWRkpNkIZRpQ+eSWB2I9YE6ngpGgfK4IpVPo/6dh/DV5+D0rCiFkHRiNircRQDp25FJX2MMvDgzSbDo4Xoj83gOd2YttLWRI5j4AMYYkPUwRn6OE5O7aTMvvXlLYBI1vyWkrTRhGRK4CPUrzN4VycddG5nKFs8ktklYdIlGr/iSwNWeS6L0A8l7rouw5rLAIwZA8Ygq/h/eOWu3XgXSQdG1/w/bhGkNpwkksbdxG38kTMBEsiVYStACdVriTuqyatDMxChjWx4tvKNGP0yZuprPoaayMGZ8Z6WBNNsL/nneza/zK8wi6UVBWzabYg1jLwChxI56kNJmkMJFltDzBQCNGXGyDv9hEPrmVNbC1xcxif52Gj8Ao94+7DvNDZ1MaRz99WHPfPqKclupJVMYvUwBX4lMeKqh8c1lgE4JMCGBZG+KRxy92d+BoJz09D9fdAsrRUJri0ZTtBK0PMTNEUriBmBzmtcgVBw2DQ81HlDLM0WgeAbcbp5/3U13yatTGPMyraWRPrZ0PbS9m89114zn4wqhGzFrGWgrmE/nQfezIhGiJJamIJzox0sWc4TW92gLSToCVyMqsjDdRZKYKui0WxZ/KC0tnUxnVwjK1V5I1TidRW8aeNXySjLN7+3B8c3lg0gmEFxpzuuS654bvJO36q6m6isC9DbXWOi0/dw+r4EGYwjGE1IEYVdXVrCQYgYnnUBHPEohFEBL8VpbLyfayp+SBVlkFjIENLdQZr+Hzcro/gOe2IWQtmDU3BOmrsKpLpzWQdm4gJtqFwnQAP72+leyBJLl3g7OOXEo/HeaJ9FY1WmkrfAnegmf1xUiaTy2eWkeJdPiqAvtLrFuB3wOuVUjPtGqmzOUODuXZMw+P0qhUsraiiLhbk27vvwufL8ODLP3BYY5FSiiZTUW+Y45QI/akke9Ld+MThW5e/gZzZS01TgUjjMKFQmnObllMbjVATDXP2kmYQE7E8fD6PaCCAIUJVIMinzr2S951xMaHKPHZdmkI4xaV//jg/2/pnBvJ9RK04EauCNZXVVPj9bO7sxlKgQh7KpwiGTR7d20r38DCu53Fuy1JCPoN40KYqnKIyPDzXh/fI9BhG2kwl3PvIo7CsKxGrnmTfD/DUMDFxiDV+ecx1DOut+Ixrxi1zqO2/qDEHMI0Aheh7iRgBLm35AFH/JaRdg8KI3qJB08+g1LI3X0HSUyyJLAfAMkwsw+FAvo+YcRw+0yPjGXhK6Hda2dT6AYaG/oAYFcXrR8VHZ7qViDKI2zn84mIbNlnDxlb9KK8YVjHChOu/SzT2FUwxyKY/MGvHcnoUs3wL0keBNSKyQkR8wCuBUSPWi8g64NsUv/R2j1GGVgaSXgd5fISCL8IwPDq6/ouA5Km2FLGa94y5jm28CL/92nHL7Nr/ShqtJAWpJRO9gAZ/PS9d+UEccx1J18ZRxz+zbNjy0+EtZ2e+mqybpzoQByBo2eRVmo680GDFMEyXjGthG4rWzF6eOPAR+hIPIEYEEQMRm32JVhr8FhV2Ab/lUGX7KLgOhtdPwT2YzSj1jT+jNvQ2LMMg1/uu2TuY06KzqY0tqbJ4rp/a8KsxyLPrwHuJWXnqTYdo5TljruMzLifgGzu3AAf2XkSTL0PWW8OAEWJleBmvXvMfDLmr8JTgty99ZtmoHaQ1s4I92VoK7hA1/jgAEdtPwk2QUTW0mHlsE7KeHxGXXcObWL//Y/QmtyJGGBELEZP+TB/LgzlsyyWkHOotcApJIEHOLY7zYlrVHLf0JqrDZ+G3FLnu6Y1jNHt0NrXxuR6o4L8xlBZuvPuDPJlqoK7ZIBwMjbPGOWCPP4b57i3PIRTIUxlZiyWtnH3OSj7wnrdz2gkZokaOePSqZ5YVo5KCF6I7bzFcyBMLVAHFxtzeniSPdZ2DWB71VXnELmABon4Hve9FZdoQCWAZJrZl0hwrnTSRYq/dYNaHle0lm8wxPFxsHGqoXMJbn/95DLsaC/AST8z8AE7bFHM5cTYnzGXp9RtKz18K/FUppUQkDvwJ+LBSahZG2tdmypQCMSvHe45/E4lchitu+y/yyqYhHB9119CRwr4zqaka+yYoSin+Y/07qI4Mc1XdabQOH+Clq1bzufNeQwjB78sRDj7bCByxfZh4GJ5gBqAyGASgIhhgd/8AZ9S0sLqpB78/TyFro5THjXvu47+f+B6ZXB7LsPGZJjF/gLyXxbFdMBQYQjjgJ5HNknHypAvF8ZbOazyRW573QU6rTtIUSpHKp8bcj/kx69mcE3PdYPTu0m1OfyAilWMtICJvP3h9bE/PAp+1LjOmcRVDToj2/C6EIE7+u2Q9CEbH7z1k1HwIo378gSudwrcIGbCy/k80hlYQ8C8jEjgPJa1EDI+AGj2A9pnRRi6OdLLSn2TIGXhmelOsCsP00ZG/C1wDV/noyTWyouZTVJk7MRP/Rt/+Z+/gZptrWRmsYolt0GCZVIReS134NMJWGNNePWqbdmQdjvIwvAEW2izf5tAB3g3cAWwBfq2U2iQinxaRF5YW+x8gAvxGRJ4UkcNugTgLJswl6GwekZxAshBgW6oNXMjk/sCgK8Rrx7/1rVH/eYzaT44/31tPxFCcvvIv1AcaCAdXUhE8jYB0UWsWCKo9o5a/tCLCVVV7WBZJ0Zd/dlyhFZVV5Lw87bmt4IDn2SSyyzmt7gNUGxuxht5Ix95Tnlk+aK/g1KDirNAApwSy1Mb+maXRJVT4ooR9y0Zt01/9CgqA546uy0LQ2dTZHIshjQzkAzwx3IHK95P0HqPXsWlavnX8deq/iVHz3jHneYUCFt1EDYfz1v6eeKCW6vDxVAdXELfaqTVdfGr0oJ8vaMxyTd126sM5enPPnr08vraejsR2BpwePM/E8Cxc9xTOqX8VlcbDeAPXcWDfec8sX+FbySWxYa6Ot3FefJiWmg+wKlZFlR2h0t84apuRynfhKsFx75zOYZtVOps6m2ML0ZczaB16mIBxL3V1W7hk9QHe9fxfjbuG0fBTjOobxpznDe8nHugjYhUIt/wK06piRe0yGqta6OoErwDGIT0gwmYVWcdmU181qvQd0xSTkxprSe28hWwqx4NtNQw7ftKcAr6XAxsgcQ1e57NXTnn28YgVwzIgZ8Y4/qR30xyopjIWoqGxYnRFA+9gcMAP6S9O66jNltm8dfckc/l9oFpEdgL/Any4NP3dwGrg46W8PikidTPYNZ3NGQoZPijAna1PcXf7faS9HBHL5K7rPzLm8iJCqPFWAhWnjzn/sfZ7qAn2syTSx3+e+zoCdow1sTU0BSuJhRLURxKMbIbymxahOhPHr7CVj7zrAuCzTJbUxLl5261YkkKGfDjDAc4yz+TKpiV0FjbwmU2v5TdbP/RMWadWNVFVZWCFXCorfHzy0itpqolRGQpRHxk9cP4VVaez1N/HA52/mNHxm6nZzOZcmdFAMSLyF+Dway+K3YK/CXyGYuepzwD/D3jzoQsqpb4DfAfgrLPOWqDDUJ4M7y/klAKJkO2+hLBZR0X0/djx06ZV3kDHtZimgUMUX2QpAMqoQOWfIkw/A8qgTtaMWseIXUbD0L9RZ+YwlEcy10bU30xtoBKj92uYVoE+grSlLKqNAidGnk+dGSEz+C6yjqK19RxqG28nYPey3P8UuDmqbI+qmtdgWE1j77e1BNusADdBruNz+Bs/Oq39nRWz3JJb6i572yHTPj7i+RUz3cZs5LJUF53NcdhqGxllEFJJcr3Pp9FfRSD6RaxQfFrlJdrOBRFyRjOWGcAigFJhVP4pKqWHbs9kuXnyqHVCwReyLPsTcmYaxxlmMG8Q99XSEKyh0P2v+H0F9npxtiejLLdSnBl/FUHTT2rgg6RUlq7W5xGp/ym1oSy+wJNYZKiwfNTWvhLDiI5ZT9O3BktMlOoh23+AQNX4Y6XNOZ1Nnc2xuO3kjAD1HCAz8HpW+6sIx3+FaY7fdf5IhrpOQQEZ8zJMMamwK1GejSpsJGYM0+36qI+MHpfMLy2s8D9I1h8gme/DNiDuq6A5VMuwfJxYwGVfoYpdCYu1/j4uOP6fCJk26eSnGXIGiba/EaPmv1kWyxFIb8IkQZ0/TFPNDYiMfdmO4TsbH4JT2D2t/ZxVOps6m2NKE/Qb5HN7eXr7I6yJxzlp5U+nX9zwFVSGQeQdxV55VjP5gWHa93eypjpJwAbDN/qfVMQg7s+yq1CJKuxG2WswjBh2YBUXnPJdDAP8vQFq/L3kC3uR+ptQvSFwvw/sxev5GB2J92BQR324FYCIWYcZeiFNy8buieEaz2cw8TWiFf8gOP29nbn5z2UWeNkY630W+Oxkt6OzOff8Vj/BQAUbhx5kf3aA8xrifPu8sRuLJuOBnk8QMk0+3PA/2KbJ8mgdnttLlb2fhsAQqbwfv/lsE4SIYFgKgi4h5eexjgOc27QU2zQ5vamRX7TeRq0XZVsugjiKTV09/N+17+J7Wwt05//AruyjbOi5mbD/EmrqI3Q8kcI1PE5vbuCC5cvHref5Ta+gLfsrDiS/BczsbmczstCXkk/CjBqMJvshLSLfBcrgFgGLi6g+opZD0FhPwfUIV/0CK3TxtMpyhvtRhc0YhklNy4YRcwTIEhSPvOlRUJtGrWcYQQI4ZAEPhYv7zLwAD5EDqit/Ql3ubcStLO39f2NZ7VUYwa1I16sJ2ZeC00lu4Ivk8/uIiEKs5nEbiw7yhe8hkziLnHcHfhaowUiBLNBo9DOhczn3bFJUWBAyHiDjmFTU3YUVWDutsvJDf6fgdWIafmqX3D9ijgEqS9gCz3PJe3fw7Ak68IeXYg965ERwULiq+GZVyqGKLoY9k+UN36c7/3b8Ro6OxGaaKl6CGbgSq+f1hMOvRzk7GOi/Ec9tA/Gw/GvGbSw6yGd9llThPyhkPkKAn0xrn2dMZ1MbR9DMUKU8gvJXknmLhqZ/YPpqplVWpudHFFQGy4zStOQHI+YYoNJU2gam55Aa+Bzx0I3PzK20zyJZuImhQpaC8nC84pvVzQ/RaBRIuTbrGj5FzvkPcsohk08QrXoz/uCV+PveSTT2Rrz8o7QN/Rm8HhDBts8at7EIwDRDOMZ5FJyHSfZ+n2jNW6a1zzOms6kdwXDGYs+eJ/BUmPPW/QXTmt7PEK/z3QCI1GA0PDt8get6DCdzrKoFwwCv93aMmpHDNMSosRSrQr2gCqCKA8V7A08gpouISV3jm0kMfYFguHiZilH773iZa2HoIxB5BbXWXRzof5JsoVBslLKfM+5lOwAVlbXYy/0YXhJv+K8Ykcumtc8zskhzCTqbc81Vefymohd4dHAfYcPHTy7/2LTLu3PXK6j39WOa57FqyYhxAVUWyNEcHMQKeaQLfaPuGGybKfwRheUX8p5HwXUxDYMtrV/llGgnu9PC81adwO/2baC1kMA0TP7pxPexs/9CNvV/iaXR8+kZ+g29XQfwDAsRg0tbjvy9vDbYgo8QDikGUq1Uhlumvd/TtkiyOWeXpInIyP7SLwKenqttHY3cvh9ieVAjiqAIIi+ddmMRQHboXHyGwh/5+ajpIoLhP59w7W1UmhZh0qPmKy9Ha95PZz5GdXA1cX+xZ5Kb/BuGePjMFmKRFcQlS1/BTyb9OQBM0yLa9GvM2ndCYTum8whBhsgoG8yrmIhZUYVhrcBvLPBwBGV+TelU6VzOXKH3ywTEoNFyCRsWtvGv024sAsglXo0JxOoeGTVdxIcROI949U1UGyY+DrnGWmXZlwnRk42xNLKaan89APmuf0NMIeK7lkp/BREjS9KzGRwsjm1i+WLEmm/BjL8Yye9AOQ9hkSft+QkFb5iwvv6GVxKwmvEbT0x7n2eFzqZ2iFznZ4gYBkvtPFGxiPq/Pu3GIoB85hNYCmpbNo6aLkYII3ABVRX/R50BttE3en6kmV2pCNlChOMqVlMTKF4lkeq9AcsQ4qGPURWMESBDQbns7/kZAL7gEuItf8CMXIoq7CKX/xuIR9qzqYjeMGF9q1p+TtRfiVH4+rT3eVbobGojeJ5Hd/t/ksuBIR6xkMGZp39v+o1F6SRQvPTSaHhw1Lx4TYwzLzoOwyrdVc2595C1TyZgwfJoDiNwHoZZHMeI3FswxUACv2c4s5rtPUESOVCJ4vpG8BSMhj9gBE/ByW3En/wj9sFOi6EXTVjnUMOD2IYfhj8+4bJzZhGMkzJVOpszkytk+e7Wr1BtDxM0M/gMix9f9F/TLq+/625ajPtp8gmvOO77o+YZVgum/0yuii/h/PB+MgN/GjU/HghiOB4n1tVz6dKVBOziCRKV/zpLfCk+evofOaG2GhGFWA7tQ4MArK46i+tX/4x4oIn25KPc172Rsyr3UeUf5IXLxr+RxUFvOuGvHBdIsb3nTdPe7xlbBNmcyzGMviAiG0VkA/BcYKFHMF4UvEySTPvpuNnPoCjgKYUZ/R9izV+efpnJXRi42EYtgfi5Yy5j+CrwmUuRQ5o5DTMGRoCgb/R08S3HMs/Brf4rrsqQNNcR96UJeL2jluvM9HHACdFZUKSVD0sViFWPP/DvSP7Y5zGUh9P54ins7SxTU3gsDjqX0+SlO0m2rcXLfRVXORSUYFf+mFjL+IPlTlhm339iiMI2T8IqDY57KCO4Bsusx5LRd3Iw7CbyhAjbo89smtHXYVgX4VR9EfDodFsImAUsd/SlKvtTfex2aujIe+TFIkyeQOwFk6q3L/AviMpT6HznpPd11ulsaiXO8CYSbavA+SEFV5FxTYya3xNrHP8GEBPxOl+MIeCzrhy394AVuxzbimGq0bfl9YfWkXLDhMzRYxsFIx/GsK8gX/EqbPGxN1dD2PYwnAdGLbcn2ce2fD3tOfAwiJLFFxr7s/tQPvvVGCpBoXMBx0vR2dRKvOS9tG44AX/uVzgO+D2H6jW/IFa7esJ1x5UonXQ0xv9nMOo+APiA+0fPqPkgrgLcztHT/R9ArOuQiiYqQ3EKIUVlzENl7h21mJvfzi0PPIfMkIHjKPIeSGDZ5OptXAp04/Us0DjPU8mlzuZR7772u3j/+tfyZOIfGMqjyU7wtbM+RtR/5F7mR+Lk3klA4PSan427zNnLb+REn4G43x01/b/PfTmOZ3Ff1+ZR05si/8wpkZeTzjusqqkFD6xwnif6nx1zN19weHp/B48nn8+tPSdjWoqW4DAR/9h3VxzJMAwa/SdQxXZSicQU93iWLIJszuiStCNRSr1urso+Wnn93ySf/TyeAldsPEBw8IcumlG5RnQVAe/zGBWHXUqMU2gjldtGReQy7Ia78ZzMYcuYcjxDzi4ODPyZ2uhFBKwIhn8pRsOv6BrqJmbbNMcvo5BYjyEGWSdNwCre8UJ5HQwMP0CUHEHDA0wMe3Ljnpjhs1BJePZWrPNPFslZlsnSuZwep/ONuO594Clyhh+PPKIKREITn704EqP6MwSHTh0zm7nsTtKFLiqjF+JveADlZkfNFzGwjUZ6C/3sHvgby+IXYYqBFT4DK/xTtvd20BCKcFzl8yHzNSBHwS1gm8WzNnmnnf2JB2iQPCFAWUEMs2JS9bYqX4yb/TCe+suM9n8mdDY1gELHBXhuB0pB2ojiGEPYXp7aUPPMCq76JeHMrRgVLz1sViq7kYzjUBNZh7/2EcAZNd80/PitKJ2eYu/goyyPnw2AHbsKIlewvbuDZRWVnFx5BX73RnJu/6j1E7l29gw+xCorByhM3xLEGD1Y53ismg9S6Pg/lPoWxXFm55/OpgbgdZ6C46UxPIvdg6s4rnoXjgOV4SMPSTCh2B3g3IkRe8lhs57YuZdI2GBN41KoeRxk9Hlxw6rANIOEKeBlHsQIXlCcXvk6PO86XOcAbipO/8YrMBpvYsDoYGQfxV29vYj5J8LNDrYNyNmITHJkoqovQe+J4L4J2D69fZ+Boy2XoLM5XT/bfDbtToi820Cdv4lKeydB5dIYGXfM8EmJxW8nxm4CsdMOm/fA5j00VkVZ1VBDddPTcMgl1mc2LCfgs2hPD7O+Zx9n1hYbYuN1/0JvMkXX0DARK8gJNZX0G3vpSTzbs9e2TDYlOri78wkqwhmeTtbxhpUXYRuTG7twadUXyPZfgpM8D2KbJ15hli2GbM71XdK0qVBpBBNbXkKkaQdZICuKvDfz2/2N9YMUoLXz9XT0Pnt3GMM6/IPPsldTcG2GnTTFu2c+a21FHQ2hJqqDawgbFsmcw1Ntn0KV3vwxXz2p3AaipochHlbsk1Oqt/i/iZgTd/edM2XeRVCbL0E8MfDbXyHWvI20B4PKR9qb+X+h42Vza/ub2dr1QeDggICHZzPga8JTBik3y6F9IE6taaQ2WM/S6ArilmIol+KR9me7CFf7ayC3gQpTocQjHP/mlOpt+j+Oab9hSuvMKp1NDYBqXBH8obupbN5AzhM63BBZzzfxqkdg+HxjNhYppXjswHt4pO0/istZFoZ1+FnMqK8OnALDbm7UdNswObuhhUpfhFNrT6LRztKf7uLh9meH3qgLxPG7G4maLqI8Gup/OaW62/Y7Mf0L01gE6GxqJUEQk8bjn2DdutsRgR4vQCg4/lhck2GEomM2Frmuxwd/+gM+8tNi7wbD8mGYh58XN6UCSzIgo7NpGJXYvpOJVgRprL2egoLNbRv52yNbnlmmLlrBDefcQ2UIDBGMusmP42dYFpivhMCkx3ueXYvgshdtfoQNg5jk+P6Fv+L/O/OLKA8G8iEC5syGZQ/EVhKIHT7kVC5f4F3fv4V/vbF4GZphBTAOuRGFiBAxbIYLOQru6PVromFWN1TTFI3wjnUX4rMcvrLldp7obn1m3WDQT08+h9/OEbIs/nXdDZOudzC0Cr/vJQSi35944bmwCLI5Zz2MtKkzqj+Inw8+8zqkBMHEb9fPyfaUUvi93fgNP4lcL4MD/0dD5evx+VeMWq4xfi6eezv1/hB+c+wv4bHwZbT2NdFNO7nsdrJujqAVQCSA39lJ3rAI+yJEKl8/pTqalZcDl093F2dGAYtgIDJt7lkN3xz1n2XEUISVR3iMRpzZ4DpDhOUAPqOOruF2hhPfZ2ndP2NbtaOWqw8ux3GeZGl4DYYc3nglIlTHns/TPZ+j1XEIpHaglEJE8DwL8fbhKJtKu4GKiudOqY5W1eQuLZ0TOptaid34B0b+/AyLR9DMErLCc7K99v6bqDT3EzLPYU//FnK537G6/oNYhn/UctW+StLeflaOcaYVwDYj1FVcy/0dn6ItF6QluQW4DoCe1AC26sVxberClxEITO1uhFbNv05r32aFzqZWYjQ8wshvjH4bVlhZTJneHQsn8rN7v82pK/ZQEbsSb/jPkF+PVH7ksEtKM9Th5LPE/GP33q9trqb2xdXs2G5Taydp73gIOAGAmPsYmKVfs/73YBhTO2lk1H56yvs1K3QutRFuOP4fo14vsYaoCM9dQ8SbfnATORPOPK6FXz/8JN3JYd595eH5q4r5GcrkOa9h7Ms8W8I1tIRreP8Dt+AzXNa37WNdXXGg6k19uwj6UgTtAh899YYp1zFU95UprzMrFkk2dQ+jMuaYHp6AYmZnSsdTyO8gSRXbnPfw0JYraR28GSe347DlDFmL7bkkh+8fo5RnrW2+jahZoEK2YLpJPC9BMvk74laWvoKNL/zlOdmPuSIoRE3+oR078oBjeIB/okWnZTi9iT7VyBbv/Ty040Vs7bkFzxs4bDmxjiNmpEknHjpieecuuYUKO4Xp3ofyCuQKXQwnfkrY9GjP2YSrfz0n+zFXdDa18eSAgulOuNx05T2hNb+Knd5reGjH23nqwB9xvfRhyzmylAZziHRq4xilPOuChq9Q4cuQyv4BgHS+DS//U8SE9lyIqvoF+hI7TTqb2pGY499MbMbObjZ4+5k93HCWQaL33SRTP2KsX2I+4iiVRhW2HbG8xtjbWRrPc/rKLwHgFbZB9vZn5huV0x+/cL5NNZc6m8cOx3EYJoAjc/e52Vgfoa4lgOdT/Pudd/L/7h/7O2vYMMlKij1DfWPOP+j5y9bgs11+deBuADYO7mZL6mlcFyw3wlXN5836PsyVxZJN3WBUxkKmhd80YY4ajHZ3vBnHG8Bw/0zSC2MpRTB6eFfCishaQkYGs3ALShXGKKnIMgM0WzZrAsN0dL2IzNDPkOxfqfPbrKl6Nb741HowlIUy7yKoLYyIKQSMwBFvpTsTWzrfB14vhdRvyedtQoaL33f4ndiWVl5NxMiQyxz5R6XPquQ4O8WZoXa2tr+NbPJXGO5DrAgJ65o+hBWsPeL6ZUlnUxtDzBQiMv1BOyeyvf+/CEgHfT0/AgVhn4vfOnzch7UVl1JlJUkMHXnw6XjoRI4PdrMuupvNrd8jm/w5IXZwXADOWfZlDGNml+8sCJ1N7RDqmX/r+JyU73kevuFvYBc6CaW/RNASBJAxejOZxjJsM4/bf+RLPVXgZZgmRKI5+jrvheRvgS4gCPHbj7huWVoEl71o8y+d3U2tPcxK3wlzUn7PcJKb922iVQ3zg82PYIQclDl2l5qKoMI1h3mq5/DOCyO97bjnYIULpK1eHunezPreR/GMIVZWWvzysgW65HMmFkE2dYNRGSt4BXKoKXd5nYx89mlc1U7OC1DDXqr9w4QDlXgqd/iyrsOQgoLKHPF9qrw0ZuRddOSD7Egl2Zp8FMvbijJPIFzzkVnfh3lR5gHWFkbBc3Hm6FYFPQN/JK2GSXlVLPPtoCqYoTp6zpjL9ucT9DqKvNc34gv5GFQa8b+RvYVKNg9uYUtyK5a3F8M+h2DFAl5aNhM6m9oYlAeFOTpTuqXzKyQ8j6TXyNroTuqCwyyrHftWvJ0S4IBjMFjYesQyLSODbT6f/dkaHuz9CRuG2jC9NghcSyDynLnYjbmns6kdQnkHMzk3I2F0dP8LsWiOaEUDS4MJlCf0JccZj8RexoGEzdO9Dx6xzGTCx9O7TkEUhAtvh/xTwCAE34IRWDXr+zDnFsGPUm3+9fTfiIlBjX3tnJT/+jt/hQq4rGmIoaIObsjlV29+1ZjLVlBJ3rX55d71RyzTsoJUYGHg8t6//YineveDZHnt8tcQtid3g4iysgiyqRuMypitBFvNTQ+GVN/7qBThhIYb8akcLb40xy+9E9M4fEwWB4WFi6scRPWPUVpRotBPyivgSC2rgilC2fspuCmiFe8dd52ypij2Zp7sQztm+MXE9ubmP+388Gdpth0uWv5jolaOZl+Sk5Z+bcxlLSOCIKQ9D7zxs9mebiUptQgWx4cHsdL34jlZ4pUfn5N9mHM6m9o4fIaJX83NP7qR+T5LfS7Xrv4+NVKgwUpxYuPYDUYVVj0FT0h5oLzhMZdRSrFzcBcSPJOQeJwS7keyf8PyXBprPzwn+zDndDa1sfQcHNJgbnr/xfN3EAgHqGz8HoYIngfNq84fe2FrDTVSYIk5/omWQt5h4xO72d75diyL0k0lHgdMJPbuOdmHOTXVXOpsHjOy3u8JUCBeNfvjxXpKccDZR0O1wb+tO5+KWAp/OMfpjWPfKXFZtAVDKXoL43+fzRQKbB5o4xXLrqE+kGJ5ZSvtmW3UBk2ubD531vdhzi2SbOoGozKllAsCapK3BJwqUd0ETcVgspWM4SOvfJjm4Xd6AQhbAdpTJ5EsREhkxr7FfXd6D23pXhoq38Taun8nIjmyhtDnVmGGFuGlaCXieZN+aMcGpfIoAc+am/8+fV4/Faawse0bpD0fGQKYxthjJcX9YfYPryFbiNKX6x1zmQOJHbRnLdbUvZa1Ne+iQvIMickAJ2D5V87JPswHnU3tUIX0AEoUBXNqg0RPlk9SxE2HzfteTsbwk5Txs1kbrOTAwHJMz0dfPjvmMtsHNjHgNHJi1Qtoil5PtZliWCnSciGmObPbGy8knU3tcHeV/s7NuD+CC/ih/zL6XBvbV4/PP/blnElZTetAJZVxhfIO/9xUSvH409tZsqSWV770BHJcVJoOmFcgY9xgYjGYSi51No8dATGJGhALtcx62QLEosM0Vio+se3bGAa885Tz8Ftj9zS8uGEFca9AzMoxkM0cNt/xXP7RuYfVkSaev/xEmkJ1BIM5DMlycc04DcSLwGLI5uL8X+9YoBKIIVgSmpPiHbUczzyBQOETNNspIoEbjrj8qnCWqOHwRMeNY86PWYpl4QYsSeMWnsKTZqJmgYi0zkHt50v5dxHU5p9yujEMwS8nzUn5nrEU7NNZIbex1J9kafz/jrj8CZEeQmaWh1p/Oub8uK04uaIanyQw3e3kiFBp5wgbYzf+Lg46m9rhnMGvIQaEjHfMSfkiTfit59Bi9rA8MMS6VXePu6xhGJxW0YlFmgfafzPmMvUBOKO6GpcEYXuIIc8i7ssSlH+MufzioLOpjaXUw6hm9nsAeJ5LXmrAeiEAmYKJUX/vuMtHAlVQKJAYzkLy8M/NvFcg2hhg7SlLwU3iN21cE1wPcDfPev3nxxRzqbN5zKiwM8QNgPHHqJ2utuE2lkfhbasvoblikJr4IO8747pxlz+5oZq6ikH68/3ctuepw+bnvBwNkQAn1zWQcQdZEY2Da1DpH8ZTOptzSTcYlSlFBQrBoG72y1aKiNWG6Q1QYJigr4VlS488SFht5fW4hoN4Y//IDPhWErTimF4SL9uBp/qpMoP4DZd05/tnfR/mhaLsA6zNv0LPTSilMOTts162UnkiVhem10baUET9J1DTcPER16kOnkFOFKow9ngM0eBaApYPy8tTyLZh4tBsmlhelnzfD2Z9H+aFzqY2pttRShFoePmsl+y5A9TY3fjZQsq0qA5eSsB/5MHio4EgSbHJpjeNOT8eOhmf4WCrHOnMTmKmxTLbxfB6cCe4u1rZ0tnUxlS8xMSw5uAGC7kNWAyB+gu2CSsqXoVpjt8737RMAiEL0wSyh5/U9Js+TmxciUMPKTcB7iZMsXANgK14+dTs78Ncm2oudTaPGabKYxqCMU5v2ZnYnniQ1dF9bBj8JkvCab5w5pHHzLTNCBF/HleZDOYO72EUtkIcX7kU19kPzhD7h/YiRiXNoRTbk3/DU3N3p7c5MwfZFJGrRWSbiOwUkcOubxeRfxGRzSKyQUTuFpFlE5WpG4zKlNf1Gorvoo/OSflKggzlehl2BH/dnyZcPhR/Fz4p0GIPUSh0jb2QsweGf4Zb+DMWBazIuwkaFo5767jXiZe9Mr+mVJt/iq+jUNgNczFAoCD46cv1kS0YBOsnvt19TeNXsYw8ywP7cN1xrvvOP40a+iEF5++YkiEc/yphS5HOfmGW6z+PdDa1Qyh6UHM0GD3iR2HQkeknmxf89d+ccJWG+DcwJM/KwCO43uDYC+UeJNv3DRxvO6JyxCtvxG8oBgdePbv1n086m9phZr/3wrNsDKOA5bQBILX/PuEaq5ufRyQEaXaRc8duANo29Dhbu76FQzcKB3/gf4sz+q+ctZrPq0UwToo2/8xS94S5UOevoJ42xD1Ak5XljJojnwAFiNotBAMF8rKPrJsfc5lk+i/c3/o5xGqnoFJcUvkGqsxhfrN97DEFy94sZlOKt4b8P+Aa4ETgVSJy4iGLPQGcpZQ6FbgJmPDHgG4wKlOGPInPsLAbL5n1skWErNtFyIKA6ccwY5Nax/X8OMpiIHdgzGUM3wkMZ2/BZygMFLGqd+KzXgeqQK7jtNnejXkhSk36oR0bDJE5uXMhgIhNwemnwvKIWI2IEZ5wHdP0ozwfecekP9sx5jJG4AKSmV8QMA1sZRCJX4nPvADcNKn258z2bswLnU3tUCZgjHEb7dkg4sPN56i2clT5z0Zk4rOxscp1mJhkCxkGsuM05gauJp3/A2HDISAhIhXnY5tLUe4gifY3zPJezA+dTW1eSQzcgwNTv2NSYwwZpUYlw9uMuMkxl1kZPIdTrIewAFuWYlS+EAgBvXid35ulys+fqeRSZ/PY4RMD3xyNy9VkOZwe6mJtoJtXNn9mUuu8ZsXzqQkO88TA3WScsRua+wpnsyufJWTmOTN6NhctfS0FN8Cws5n+wV2zuQvzYpazeQ6wUym1WymVB34JXD9yAaXUPUqpdOnlw8CEA1jpBqMyJfWbIX7fnJTt5QcJeg5BydOwZOyu8mPxm0LE9OhMO3Rmeg6bn+/7HThJlCdEYp8GINDwCQyxcbwhCsnD1yl7uvuudgirfit21ZFvxztd7uCv8OERxKNh2b2TXi9ielTYLr156M72HTY/1fFpPBzE86is/llxnaafY4vguHsXZw9AnU3tEHbDJnzVj8xJ2U7nG/FbHgHDpmbJTya9XoVZIGrl6ct59GYHDpvf3/YyPPGwlEdd073FdRr+QkAMXPfeWar9PNPZ1A5VtR4q52hsrsHrsE3I2GswGj4wqVUMu3i3toABlteFcg//fhpOXsPB39FSe3vxSei3pbmLsHeuviRNG4O/Zj3+mrkZ/0cl3sEqn8Py2OU015w1qXUuaLoIv8qijALtqUH6c4f3APzV/o+QURZZz8/7Tn0fABdXvZu4lebvXTfM5i7Mj6lns0ZEHhvxGDlGRjMwsmdHa2naeN4C3D5RFXWDUZkSEYxA49wUbgUw7QoCvr8gIpNerdr3IoKmgz/9VjwY9SNTKY9M+t9AwPZdj7/y9c/M8wW+gqcU2cELJr0tN9+OW0hPvOBcUoCnJv/QjgmGYWL4G+amcPtcDCtObMn2Ka0Ws5oIWAUyg/+Jc8h70VM58oUfAEIk8FECsWdzKPZLAEh2nD7pbbn5/XjuAl8nrrOpjcEw/Bj+mrkpO/QWxKyiftnkT7IAxAw/fsshmbwR55AfYYVCElFPYGBSFf8FPn81AKZlo6xTEBQDU+gB6Ob3Taluc0JnUxuD4Yti+Ofozn/m+zHNOJUNEw+vMKbU3xg+pCODl+sChrAtIHY/hlm845oRWwkUx2HyOl8y6U14C53NqeZSZ/OYYfirMfwT92afDp/9ZoLWEs5Y8vUprRe1HCK+PB2ZHTiHfN/sSmyk2t/LEl+C/zr95md+x56+5GX4jChBM8VT+/51UttRSpHI7p9S3Wbd9LLZq5Q6a8TjO9PZtIi8FjgL+J+JltUNRscgwwhgNzyF1bB6SusFGz/PsGPgFnL0ZtL05RPPzEt3fJyU6+ESxF//xVHr+apfAGYMQ3KkOz42qW2luy8g23ECidaFvE2iPhujzS8zvBx/45NTvuStuua3pB0DL7+ZjvQwAyMG5exveykJZeNIA4GGfxq1Xqjhi4hYKHeQfM+tk9pWqusiUq0rSLa+cUp1nF06m9r8MiueS6Dx8SmvV+37ZzKeSSp1J7sSA+TcZ3+ZdnVeypBr4xqnEqi8cNR60aY/Ip6J5+zFSU3ubqOJzosZ2reUZOu3plzP2aOzqc0vo/bNGA3T6Vm4EoBU5l52DBzSi2Hg0tKTizFC9aO31/D30rONeJO4xbVSWei/kvTeteQ7d06jnrNhirnU2dRmQbTxk1Q0PzTl9RoCecKmw/6+A3QOj87mnw68lYxncVLFa7Ft36h516x4AKWgK3vzpLYznGvlvv1XcNeOlaTTC9VJYdaz2QYsGfG6pTRtFBG5guJAyS9USuUmKlQ3GGmTJiL4rBAmfuIMUeOveHam93MCJkRjXxvzx260cSOeJ2QKP57UtgqcCUphqA7yHa+arV2YOv3hqi0CdqgGS3yggtTbOSp9xbNFTj5LQD1NUBQ1tT8fc11f5BHySjGQed+ktpWkBoWAugen9/eztg9TprOpLQKx5ncTFPABK8Mm/lJPhUL6ADE1QMhQNDT+bMx1rfAPyXkGvT2XTmpbGWWBCK73/+F5Y4/LMi90NrXFoO42AMLWbk6vebYXg9d7B1B6XfftcVZ+XfFP9/ETbkYkgKfANMHNXcuCXQKuG4y0ReLda24kYmXoSN9NffjZ8QI37PsvqqxBKowMVy7/58PWsyybavtcLMPgsd0nTbidaGAJRsGh1srS1b12VvdhSmY3m48Ca0RkhYj4gFcCo84Ii8g64NsUG4u6J1NF3WCkTUk88BqaQ2mqCr8EQCmP4bazETyCZgNWxRVjriciYF6NbRgUOtZNuJ2qlpsx/Q8jhkLk+bO6D1OiP1y1RaIheD6rw0PE3fsB8Lw0ya7TEKWI2WdjBFaOuV4gXoNIPQFRFCbRONu85HEwP40IiH3yrO7DlOhsaovE0sBq1oT6iPI0AAWni6Hey7DwqPK/HsOKjLleuOZSTMOHz3DI93xtwu00LduNZ5yPIQJqAW9zpLOpLQLFk5v1QArcXgC8wn5w3lNa4vMYhjX2ug3/+cxzLzFx7yarcTu2XYPfDzD2nZ/mnG4w0haJWLiW5QGTeGQ/plEcl7NjaBt9hW8SM7Lc0HLLuOuesfwXVBl5ojJIJjPOXb1HeP5Ju6kyogTnaODvSZnFbCqlHODdwB3AFuDXSqlNIvJpEXlhabH/ASLAb0TkSRGZ8BID3WCkTYkv/gEEE895GIB052uwVC+uRAk2HXkg4GjztwmYQTw1iNs58Wj5gYYmws37sBteOyt1nzKFvt5bWzQCsfdjiODk/gJAsvM6/JLDtRoJNP7iiOvWLHmUgGniqIdwux+ecFuxljcQadmHWbFqVuo+ZTqb2iISCL0e04RC8h4AMp3PwxYHx1qHr+HjR1y3ful2ApaBW/hfnNTYt/8eqbLll8SW7MMwK2al7lOms6ktJublxb/DpUF/+64qzbgCo+FFR143vqH4Nz2576hGw4MYDdsndYfFWTfVXOpsagusJbSSmDXMcLYDpRT7eq7EMhxWxT5KdcWSI667rOoebPHo6j59UttaumwbDcsWaCyjOcimUuo2pdRapdQqpdTnStM+rpS6tfT8CqVUvVLq9NLjhUcuUTcYaVNk2GEsowrbaCXbeyt4D+KKQbR54+TW992LKSbC5O8ys3BU8SztZB+atoDM0On4jBA+tZFc948QbxeeWMSaJnf9uMinMTHAe90c13Q26Gxqi4cVfyUBw8RWf2G4/bO4agBTTGJNv5vU+gZXYWBAcuLeuQtPZ1NbRKpKPYWc7+F1vpbipWgRjIZvTLiqEQjw7ADYE1/+srCmmEudTW2BnVv3IeLmMHv6v87GvRcjhkPMWMWy+rdPuG5l5Wr8ZoQKwyDTccY81HYmFkc2dYORNmV5VYVy87i594IIPvsTk17Xqq7HkOKlMV52UpdNLizdfVdbRHLY4A5TyH8KD8Ef/OWk1/U1vgbLqEAQlFrgu6BNhs6mtogUlKAKA3jud1EYBKofnfS6gaZvYhuhxfOFTWdTWyQM0yw96wGKl5ZJ/eQH0C4OgC1AYaJFF56+JE1bRKLB5RgUyLuP4ai9mIQ4bfn9k16/eckO/JYfxfAc1nKWLIJsjn1xrqYdQTD4HTLDF6CUELJfjVX3+imtbzTciVLqmVshlq2D3QQ1bZEI8HoS3lcwFFQE/hOr6qwprW80PKGzqWlzwJSTSPEk4ioqY7/ECk7tFuNGwyYWbLDcqdDZ1BYdm2cafCr+jsjUfhoZDdvKP5s6l9oiVHAtPExCkmXN0sldyTJSqHG3zuYs0Q1G2pQolaeQfR45z8AywKz70LTKKfsfpAeV+380mlbiZXvx+AZ518AyDcy6l0yrHJ1NTZtdTnITwkYyLgRMCzN+wrTK0dnUtNnlDf2JZ3sH+RB/aFrlLIps6lxqi8hg39c5PZDgwVQtm/ONHGeYE680Bp3N2bFoejhr5SHbfiqi8oTsNZgiDHdeu9BVmltl3kVQ0wCUUuT6zkfwiAWWEDLyJDvfsNDVmls6m9oiUBjsJTP0AlCKmK8On+RJdx55oOtFT2dTWwS8nvsh84HSqxCQR/X9YSGrNLcWwWUvmgYw2PEtcun/wmcKIdMixh4GU5sWulpzZxFkUzcYaZPmdB6PITkM1hJq+gt44DmdeN4iGO9kWso/wJoGkO9YAzgYXE206V6UZ+A4U+++u3jobGqLQz59DuBiBz9ArPEBUEK28PuFrtYc0tnUyp/nuuC+pfgi/DWI3Fx87h6tjblTzKXOprZACrkBPOezCEK88h5OqXg3EcthR9f0es2Xv8WRTd1gpE2K13k+Cg/TiONruh2AHD7SQKr9qiOvvFgpwPMm/9C0BeB1noIhgmUuwdf0dQCGgZTnkWn7+cJWbq7obGqLgNe5FhsD2zgJu+qdmKZJAo+Ep/AyqYWu3tzQ2dQWg56Dl4VeghG9CiOyakGrM+emmkudTW2BFPrPxBaLoPkeQpE1rGp8I45rkvecha7a3Fgk2dQNRtok9WECVsMTz0ypXrKNkKEwZOfCVWuulXmLr3ZsU0qhyGGKYDf87Znplf47CRsK+M+Fq9xc09nUypjnDqEAyzAJNN32zPS48RYilkuuf93CVW6u6WxqZcxLby49i2I0fHfEnBXF+Z0XzHud5sUi6MWgHdvc3s9gAT7zNKJN//7M9ArbT8x06W59+cJVbi4tgmzqBiNtcmI3Qd3mUZNEhJDpwxKh0H7zAlVsjpV5gLVjm4ggsV9jNGwfNT3YsIaQZaKkgDMwsEC1m2M6m1oZEyNWyua2UdMrlnyciGHgkV2gms0DnU2tnAWOg9hvMBrWj5psNNxRetY7/3WaD4vgR6l2bJOqD2FW/BF/4+jLtk9ZsZ0G28Pj/gWq2RxbBNnUDUbapBihUzGMw2+qZ0QeRQHIh+e9TnNPFW91ONmHpi0AI3T6mNNN+QiCgcqeN78Vmhc6m1p5E5Hxs8k5KASn87nzW6l5obOplTfDMDFCp40zNw6A1/maeavP/JhiLnU2tQVgGH7M0IljzhOJYSqTTMf/zHOt5triyKZuMNJmxIzGsQ0bEUGpo+yMqQKlvEk/NK2c2I1vxWf4EFEodZQNTK+zqS1ivuZfEzT9QNtCV2X26Wxqi5jR8Ejp2aMLWo9ZN8Vc6mxq5aay7gkipg3qOwtdldm1SLJ5eJcRTZsisX4DzntBuSALXZtZps+yaIuZ8RnE+z7gAeZC12Z26Wxqi5nxZsR7eKFrMTd0NrVF7Tqgb6ErMft0LrVFzPKFwLgUiC10VWbfIsimbjDSZsyoOQ24b6GrMTf0ddzaImbWvQJ4xUJXY27obGqLmFl3NF7GXaKzqS1iRsMXF7oKc0PnUlvkrIYfLnQV5sYiyKZuMNK08Silby2qaeVIZ1PTypPOpqaVH51LTStPiySbMxrDSEReJiKbRMQTkbMOmfcfIrJTRLaJyFUzq6amLZAyH7V+PDqb2lFPZ1PTypPOpqaVn0VwJ6bx6GxqR7VFkM2Z9jB6Gngx8O2RE0XkROCVwElAE/AXEVmrjrqRV7WjnVoErb7j0NnUjmo6m5pWnnQ2Na38LOJcgs6mdhRbDNmcUYORUmoLFG8fe4jrgV8qpXLAHhHZCZwDPDST7WnavFIK3PIP8Vh0NrWjms6mppUnnU1NKz+LOJegs6kdxRZJNmd0SdoRNAMHRrxuLU07jIi8XUQeE5HHenp65qg6mjZNypv8Y3HQ2dSODjqbOptaedLZ1NnUys9UcqmzqWnzZxFkc8IeRiLyF6BhjFkfVUr9fqYVUEp9B/gOwFlnnVVeF81qxzQFqDK+1aHOpnas0tnU2dTKk86mzqZWfso9l6CzqR2bFkM2YRINRkqpK6ZRbhuwZMTrltI0TVs8lJr1llwRuRr4CmAC31NK/fch8/3Aj4EzgT7gFUqpvWNXT2dTO0bpbGpaedLZ1LTyU2a5FJH/AN4CuMB7lVJ36Gxqx6Q5yOZcmKtL0m4FXikifhFZAawBHpmjbWnanFGemvRjIiJiAv8HXAOcCLyqNGDfSG8BBpRSq4EvAZ+f5V3S2dSOCjqbmlaedDY1rfxMJZcTZXMmuTxkoOqrgW+UypsOnU1t0ZvNbM6VGTUYiciLRKQVOB/4k4jcAaCU2gT8GtgM/Bl4lx6xXluUZvea0nOAnUqp3UqpPPBLigP2jXQ9cGPp+U3A5TLGKH8T0dnUjno6m5pWnnQ2Na38zO44KTPJ5TMDVSul9gAHB6oel86mdlRbBGMYiVLlc92ciPQA+46wSA3QO0/VOZJyqQeUT10WUz2WKaVqJypIRP5cKm+yAkB2xOvvlK6ZPljeS4GrlVJvLb1+HXCuUurdI5Z5urRMa+n1rtIyC3psdTanpVzqspjqobM5RTqbU1Yu9YDyqYvO5hzQ2ZwyXY/DzUo2p5FLOEI2Z5JL4JPAw0qpn5amfx+4XSl10xTrN206m1Om63G4hcxmr1Lq6imuMyMTjmE0nyZxUB9TSp01X/Up93pA+dTlaKzHfIexnOlsTl251OVorIfO5rN0NhdnPaB86qKzOTd0NnU9Zmq26qJzOZrOpq7HTB1r2ZyrMYw0TTvcZAbne2YZEbGACoqDBWqaNnd0NjWtPOlsalr5mUku9UDVmrbI6AYjTZs/jwJrRGSFiPgoDvp36yHL3Aq8ofT8pcBfVTldN6ppRyedTU0rTzqbmlZ+ZpJLPVC1pi0yZXVJ2iR8Z+JF5kW51APKpy66HhNQSjki8m7gDoq3If2BUmqTiHwaeEwpdSvwfeAnIrIT6Kf4IbwYlMtxL5d6QPnURddjAjqb80LX43DlUpdyqcdhdDbnha7HaOVSDyivujxjJrksLXdwoGqH8hyoulyOu67HaOVSDyivusy5shr0WtM0TdM0TdM0TdM0TVt4+pI0TdM0TdM0TdM0TdM0bRTdYKRpmqZpmqZpmqZpmqaNsugajETkkyLSJiJPlh7XzvP2rxaRbSKyU0Q+PJ/bPqQee0VkY+kYPDbP2/6BiHSLyNMjplWJyF0isqP0t3KB6rGg749j2UIfe51NnU1tbAt97I/1bJZLLo9QF53NBbLQx15nszyyqXNZfhb6+Ots6myWk0XXYFTyJaXU6aXHbfO1URExgf8DrgFOBF4lIifO1/bH8NzSMThrnrf7I+DqQ6Z9GLhbKbUGuLv0eiHqAQv0/tAAnc2DdDZ1NsuNzmbRQmTzR5RHLserC+hsLiSdzaJjOZtj1QN0LheazmaRzubhjqlsLtYGo4VyDrBTKbVbKZUHfglcv8B1mndKqb9RvOPBSNcDN5ae3wjcsED10I5NOpvobGpl6ZjPZrnk8gh10Y5NOptlkk2dS+0QOps6m2VlsTYYvVtENpS6ic1LN+6SZuDAiNetpWkLQQF3ish6EXn7AtVhpHqlVEfpeSdQv4B1Waj3h6azCTqbR6KzuXB0Nssrm+WUS9DZXEg6mzqb49G5XFg6mzqb4zmmslmWDUYi8hcReXqMx/XAN4FVwOlAB/D/FrKuC+gipdQZFLsrvktEnrPQFTpIKaUo/gezEPT7Yw7pbE6KzubY9PtjDulsTkpZZnOBcwn6/TGndDYnRWfzcPq9Mcd0NidFZ/Nwx9x7w1roCoxFKXXFZJYTke8Cf5zj6ozUBiwZ8bqlNG3eKaXaSn+7ReR3FLsv/m0h6lLSJSKNSqkOEWkEuheiEkqproPPF+D9cdTT2ZyYzubYdDbnls7mxMosm2WRS9DZnGs6mxPT2TyczuXc09mcmM7m4Y7FbJZlD6MjKb1BDnoR8PR4y86BR4E1IrJCRHzAK4Fb53H7AIhIWESiB58Dz2N+j8NYbgXeUHr+BuD3C1GJBX5/HNN0NnU2j0Rnc+HobJZlNssil7Dg749jms6mzuZ4dC4Xls6mzuZ4jsVslmUPowl8QUROp9gNbS/wT/O1YaWUIyLvBu4ATOAHSqlN87X9EeqB34kIFP8Nf66U+vN8bVxEfgFcCtSISCvwCeC/gV+LyFuAfcDLF6gely7U+0PT2URn80j10NlcODqbC5jNcsnlEeqis7lwdDZ1NnUuy5POps6mzmaJFC8B1DRN0zRN0zRN0zRN07SiRXdJmqZpmqZpmqZpmqZpmja3dIORpmmapmmapmmapmmaNopuMNI0TdM0TdM0TdM0TdNG0Q1GmqZpmqZpmqZpmqZp2ii6wUjTNE3TNE3TNE3TNE0bRTcYaZqmaZqmaZqmaZqmaaPoBiNN0zRN0zRN0zRN0zRtFN1gpGmapmmapmmapmmapo2iG4w0TdM0TdM0TdM0TdO0UXSDkaZpmqZpmqZpmqZpmjaKbjDSNE3TNE3TNE3TNE3TRtENRpqmaZqmaZqmaZqmadoousFI0zRN0zRN0zRN0zRNG0U3GGmapmmapmmapmmapmmj6AYjTdM0TdM0TdM0TdM0bRTdYKRpmqZpmqZpmqZpmqaNohuMNE3TNE3TNE3TNE3TtFF0g5GmaZqmaZqmaZqmaZo2im4w0jRN0zRN0zTtqCAil4pI60LXQ9M07WigG4wWkIjsFZErxph+gYj8VUSSIjIkIn8QkRMPWSYqIl8slZESkf0icpOInDtiGSUiq0XkWyIyXHrkRaQw4vXt87GvmlYOSnnJjHj/D4vI10XkjaW8fOiQ5VtLXzyPmCERWV5a/+C0vSLy4TG2/0YR2SgiaRHpFJFvikh8xPxPjih7UEQeFJHzReStIrJFRPwjlq0WkW4RuXpOD5qmaZqmHUJE7hWRty50PTRNO+z7bZeI/EhEIqV5V4nI30q/K3tE5D4ReWFpXqOI3Coi7aXvscvHKNsnIr0iEinlPlvaTq+I/FZEGkcse46I3Fb6DtsvIo+IyJtK884TkbtK03tE5Dcj19XKl24wKjMicj5wJ/B7oAlYATwF/F1EVpaW8QN/BU4BrgNiwAnAL4FrDi1TKfXPSqmIUioC/H/Arw6+VkodtrymHeVeMOL9H1FKvbs0vR/4kIhED11hChmKl5Z5KfCfInLlwRki8kHg88C/ARXAecAy4C4R8Y0o41elMmqBB4DfAt8H2oCPj1juy8BtSqk/z+BYaFpZKX0h3SsirxkxLVo6KfLSI6z3o9KX3XNGTFstIuqQ5a4rfYFNiUifiPxMRFpGzH+jiLilL8MJEXlKRK4bMf9g4/ATh5RbU2pM3jvDQ6Bp2hSIiLXQddC0MvGC0vfHM4CzgI+VPjd/A/wYaAHqKX6XfEFpHQ/4M/CSI5T7HOBJpdRw6fW7S9tZC8SBL8Ezv2H/CtwHrAaqgXfw7G/TSuA7wHKK33+TwA9nssPa/NANRuXnC8CPlVJfUUollVL9SqmPAQ8Dnywt8zqKob9BKfW0UspVSqWUUjcppT45drGapk1gC/AQ8C8zLUgp9RiwCTgdQERiwKeA9yil/qyUKiil9gIvp/jB+doxyigANwINFD903wq8U0ROF5GrgMuBD8y0rppWTkpfSP8J+LKI1JYmfwF4TCl10wSr9wOfHW9m6Yvzzyk2ttYAJwE54AERqRyx6EOlL8Nx4BvAL0f2BCwJicjJI16/GtgzQf007agjIpUi8sdSj4GB0vORjbBVIvLDUg+GARG5ZcS860XkyVLj7K6DPWZF5E2lXrVJEdktIv80Yp1Lpdj7999FpBP4oYgES43GAyKyGTh7Hg+BppUVpVQbcDvFjgVfBD6jlPqeUmpIKeUppe5TSr2ttGyXUuobwKNHKPJa4LYxttMP3Awc/Cz8H+BGpdTnlVK9qmi9UurlpeVvV0r9RimVUEqlga8DF87SbmtzSDcYlRERCQEXUGwJPtSvgYO9Fa4A7lBKpearbpp2jPhP4P0iUjWTQkTkPIofoDtLky4AAhR7Cz2j9OP4Np7N9sgy/MAbgQOlD969FM8K/QD4FvBOpdTATOqpaeVIKXUH8CfgqyJyKcWG1XdOYtUbgVNF5JJDZ4iIAP8P+KxS6udKqYxSqpNiQ+wwYzS+KqU84CdAGFhzyOyfAG8Y8fr1FM/gatqxxqDYS2AZsBTIUPwheNBPgBDFBto6nu2NcA7FzPwbxcbZ5wB7S+t082wP+jcBXxKRM0aU2QBUlbb5duATwKrS4ypGZ1PTjikisoRiI08aWAJMdLJlItdS/Ew+dDs1FHsmPVH6DXv+FLf1HIonV7UypxuMyksVxX+TjjHmdVA8I0rpb+fBGaUeB4OlMzTb5r6amrao3VLKy8HH2w7OUEo9CdwF/Ps0y+4VkQzFnkrfAG4pTa8BepVSzhjrjMw2wMtFZBA4AJwJvGjEvK8DBYpdg29B045eHwAupfjl819LjTsTSVO8ZPRzY8w7juKP2VEnZEqNQjczdqOtSfHHagHYd8jsnwKvFBFTimMMRoB/TKKOmnZUUUr1KaVuVkqllVJJivm7BIrjo1C8HOWflVIDpd6195VWfQvwA6XUXaVeD21Kqa2lMv+klNpV6qFwH8WhGi4esVkP+IRSKqeUylBsVP5cqVf+AeCr87LzmlZebil9f3yA4mVhXy5NH+t35aSIyCrAUkqN/H351dJ2niqV/S8ULzcb7zfsWOWeSvEk6L9Nt27a/NENRuVlgOKH4FgDgDUCvaXnfSOXUUo9qZSKAy8G/IevqmnaCDcopeIjHt89ZP7HgXeISP00yq6h+MPxgxR/7Nql6b1AzThjLYzMNsCvS/WqU0pdppRaf3CGUkpRvHROn5HRjmql3nObKPZM+O0Ei4/0bWCpiBw6Pt/BRtmJTsgAnFf6MpwF/hd4rVKq+5B1WoFtFHv8vp5iLwpNO+aISEhEvi0i+0QkAfwNiJcaXJcA/eP0hl0C7BqnzGtE5OHS4LiDFHs4jMxoj1IqO+J1E8WTLAcd2sCraceCg99vlyml3knx9yKM/btysq6leHnbSO8tbadZKfUapVQPR/4NO4qIrC6V+T6l1P0zqJs2T3SDURkpXWL2EPCyMWa/HLi79Pxu4HkiEp6vumnasaJ0hvO3wEenub6rlPoixR+bBy+jeYjiWCkvHrmsFO9gcQ3PZlvTNEBEXktxfK+/UBwsflKUUjngM6XHSAcbZSc6IQPwcOkkTCVwK6N7Noz0Y4qXjb4K3WCkHbs+SLEH37lKqRjFy0wAhGIjTtUYY4BRmrfq0Imly7FvpthYW1/K4m2l8g5Sh6zWQbEB6qClU94LTTv6bKOYsyMNaD2RMccvOlRpTKKHJtqWiCyj+Ln+GaWU/txcJHSD0cKzRSRw8AF8GHiDiLy3dGeYShH5LMXrQj9VWufHFD8cfyciJ5e6xAcojoivadrMfYripSjxGZTx3xTvuhZQSg2VyvyaiFwtIrYUb136a4o9FfSHpqaViMjBcU7eRnEA7JeLyHiNNmP5IcXsjmyg3UYxa6NOyIiIQfEL7mGNtqUxxt4BvE5E1o2xnZuB5wO7lVL7p1A/TTuaRCmOWzRYGv/vEwdnKKU6KPYk+Ebp+6wtIgcblL4PvElELhcRQ0SaReR4wEext3wP4JR6Cz5vgjr8GviP0jZagPfM6h5q2iJU6pX+LxTv2vsmEYmVsnaRiHzn4HKl35AHr1Dxl14fHFv3HOCeSW7yQ8AbReTfRKS6VMZpIvLL0vNmindR+7pS6luzsY/a/NANRgvvNooftAcfV1McsO/FFBuF9gHrgIuUUjsASt1wnwtspjgIWYLil+GzKfZE0jRtfH+Q4i2zDz5+d+gCSqk9PDvY7XT9iWIX3YN3ovgC8BGKZ00TFMc7OQBcXuoVoWla0deBW5RS95R+cH4I+G6p58GESmOFfYIRY5GVvjj/K8XbDL+6dJKmAfgexYF1vzROWf2lZT4+xrwUcBnFgbM17VikKI6TEqTYS+9hirfoHul1FMcB20pxMOv3AyilHqE0oDUwRHHMlWWlcZDeS7ERaIDiHQhvnaAen6L4fXkPxfGO9EkYTQNKdxd9BfBmoB3oong30d+PWCxD8eYPUMxppvT8Mop3DR15+eeRtvVgaZ3LgN0i0g98h2d7KL0VWAl8cuT38OnumzZ/pPgdStM0TdM0bWGJyA0UB4w/USk1OGL6Xyl+cR3zUlER+RHQqpT6WOm1AWwATlJKyYjlrgc+BpxI8TLRO4APlQbKRUTeCLxVKXXRiHVaKI61cjbFxt49gH3oIPYicgXwPaXU8mkfAE1bJETkceDT+gYMmnZ0EpFvAE8rpb6x0HXRFpZuMNI0TdM0TdM0bVJE5CTgMeB4pZQeYFrTjkIi8nbgD6WevtoxTF+SpmmapmmapmnahETk8xQv+/p33VikaUcvpdR3dGPR4iMiPxCRbhF5esS0KhG5S0R2lP5WTqlM3cNI0zRN07RyJyKbgGVjzPonpdTP5rs+mqZpmqZp5aR0Y4Fh4MdKqZNL074A9Cul/ltEPgxUKqX+/UjljDTjHkYiskRE7hGRzSKySUTeV5o+o5YsTdNmRmdT08qTzub0KKVOUkpFxnjoxiJtVuhsalp50tnUtMlRSv0N6D9k8vXAjaXnNwI3TKXMGfcwEpFGoFEp9biIRIH1pUq8kSm2ZNXU1Kjly5fPqD6aNpH169f3KqVqJ1ruqueGVV+/O/lyN+TuUEpdPaPKzSKdTW2x0dnU2dTKk86mzqZWniaTzanmEnQ2NW2m5jCbm4CRd677jlLqOyOXEZHlwB9H9DAaVErFS88FGDj4ejKsKdVwDKVrGztKz5MisgVoptiSdWlpsRuBexlxi9uxLF++nMcee2ymVdK0IxKRSV1z39vv8o87WiZdrt24q2balZoDOpvaYqOzqbOplSedTZ1NrTxNJptTzSXobGraTM1hNrNKqbOmWy+llBKRKfUYmnGD0Uil1qx1wD+A+hEDZXUC9eOs83bg7QBLly6dzepo2gwpXOUtdCVmhc6mdnTR2URnUytLOpvobGpl5+jJJehsakeTectml4g0KqU6Sr31uqey8qzdJU1EIsDNwPuVUomR81TxurcxW7JKI7CfpZQ6q7Z2wt7OmjZvFOChJv0oVzqb2tFGZ1NnUytPOps6m1r5mWoudTY1bX7MYzZvBd5Qev4G4PdTWXlWehiJiE0xvD9TSv22NHlGLVmaVg48FvcZGZ1N7Wils6lp5UlnU9PKz2LPJehsaken2c6miPyC4mWaNSLSCnwC+G/g1yLyFmAf8PKplDkbd0kT4PvAFqXUF0fMmlFLlqYtNIXCVZN/lBudTe1opbOpaeVJZ1PTys9Uc6mzqWnzYy6yqZR6lVKqUSllK6ValFLfV0r1KaUuV0qtUUpdoZQ69C5qRzQbPYwuBF4HbBSRJ0vTPsIMW7I0baEpoLC4z8jobGpHJZ1NTStPOpuaVn6OglyCzqZ2FFos2ZyNu6Q9AMg4sy+fafmatpDK+TruiehsakcznU1NK086m5pWfhZzLkFnUzt6LYZszupd0jTtaKKgLLvlatqxTmdT08qTzqamlR+dS00rT4slm7N2lzRNOxp5U3homjZ/dDY1rTzNZjZFZImI3CMim0Vkk4i8b4xlLhWRIRF5svT4+KztjKYdJaaSS/25qWnzZzFkU/cwWuSy2QKuKJKpLPc9tJWnd7Zz3pmrOP/0VcRDQXbu7iIxlAafySnHNeP36X/yyVIo3EXQTVArT5lcAS/vkMzkuOPejezp6uf8k1dy8dlrsP02O/f3MOBk8CmTdWtbMA3dfj9ZOpvadCmlyGayOHmX9FCGP37nTvq7Bzn94pO57DUX4RZcdm/YR7J/GH/Yz6kXn7jQVV5U5iCbDvBBpdTjIhIF1ovIXUqpzYcsd79S6rrZ3LA2vzzPI5/Lk886ZBIZfvf120ilspzzvHVcfMM5ZFJZ9mzcR6I/TXVLFWtOXbbQVV409GemNhOO4+AUHLLDedKex88+/zv8YvGc68/k9OecSCqRZsdT+3CUUNsUZ9nqhoWu8qKxWLKpWw8WKaUU9/19K/fct5XoygoGh9Ns3tTGQDJF+0CCrr5h1q1t5v6HtlFIOPx9y06WraznBRefyqb9nQwWspyzvIX6ijD3r99NOB7gZVedSTQUWOhdKx8K3FnOsIh8AHhrsXQ2Am9SSmVndyvaQsoXHO6+fysPPbmHer9Na3+S7Xs7GRrOMDA4TMLNs7S6gr9v2MuAnWXjYzs4ecVynnvu8Wzb2EY6keG0c1cSrQ7z90d3Uh8L8/KrzsTn9y30rpWPOcimdvTLDGf4y8/u56l7n6ampYYD29rY+eRO0sN5Ej0JfAEL22/z+F830Hugj+1P7uHMK0/nvOevY+M9m8nnHU679ERCFSH+8af1tKxt4gX/dBWmZS70rpWPWc6mUqoD6Cg9T4rIFqAZOLTBSFvEUkMpbv/hPWx6cBvLTlrKlkd3sOup3XiOQSaZIxj2kxxIsfHvm+nY3U3bzi7OvuI0zrrmdB69cwO2bXDyhcdj+03W/3kjq9Yt5crXXYpp6hMxgP7M1KZtsCfJLd+7m86dHaw6v5JH7zvAtg2dWC5kh7MYfovWvb1svH8Lg4kcbVvauPxV57N0WS2Pbd7F6hNNmltOJzeYY/29mzj1nJVcev3ZGPokadEiyaZuMFqklFIo2+DKq05mIJHl7gNbySuPmvoIeddh/+4edu3vpq19gIBlU19bRX8ixS2Pb+TcNcsZ2pmkvX2Q9Zv38cCjuxh08/z+zqf40Jsu57wz1+gPWYotOrPZ9U9EmoH3AicqpTIi8mvglcCPZnEz2gJzHJewbfG8849jIJPmqbu7MTxFXSxMIpNmw952nnhkL+1dA8RqItSF4+zf0819iRzHnbCE3kKG9sFhtjy6jce37Cffm+aWPz3Bf/7LCzh1bQvFO8se22Y7m9qxIZ/NU90Y5zkvO5++zgEev/tJbF+A2voQw4NJHvzjY+QzBVp3dlDXXEVVQ5wd63fiFhwaVtSRau2hY083T9zzNNse20F6KMPtP7iHj/78/Sw9rnmhd68sTCObNSLy2IjX31FKfWesBUVkObAO+McYs88XkaeAduBflVKbplYNbSFlUzmaVjdQv7yOrn29dO3pIRILY/t99HUOce+vH2aoZ4ie9j6qWqqI18fYsn4HrlJU1EYZ7BqkbUcH6+/exI4nd/PH72W573eP8J6vvImm5fULvXsLTn9matOVTWdZs245y9bW09XVTdeuIWoaK3F9BgdyaW7/7T9Idybp2NlO3bI6apZW8fCdG0mdtYKK6jB9e/rIJ9v5+61PsHfTAf78swd4/OHNvPaDN9BQV7nQu7fgFks2dYPRIpHJ5GnvGABb6BpI8dTuVjo6h0AJRsbhuSevYsnKGoyCy0B/ms5UispwiFhlkPa2AS5dt5ZHN+wmky+wob0D03Z5vK2drsEk4TVh1voa2bqtjY9/44+88LnrePsrLiLgt4/xH6eCO+4NGabNAoIiUgBCFL/caovYcCpHV88QohQH2vrZuaOT1vYBLJ/JcFRx1RWn0LqxAy8k9Pen6N/Yy8oVNSQMk4HWQU47byWPbdxDNpFj26N7MWImT+3bRl9fmiWORWxtC1t2tfGh//41L37+ObztxRfqMzNzk03tKJPoT9Lb1o9SigNb29i1YR/7t7YSrYiQy+Z54T9fxYGtbSiB7tZ+eg70sOaMVXTv66a3Y4CTzjuOjfdvYjiZZvtjuwlHAzzxlw0M9iVpXF5PuDLMjkd38G+Xf4KXfvB6Xvr+647xz0yYRjZ7lVJnTViqSAS4GXi/UipxyOzHgWVKqWERuRa4BVgzlUpo82uwJ8FA1yDKU+x6+gA7H99NT1svtt/Gtm1e8oHn07a1Ddcw6G8boPNAL2vWLafzQC/D/SmWrGliyz92kE5l6O/oJxAJ8Pg9m0hmc9Qf30zIFjY/vJ0PX/ffvO6jL+WKV15wjGdTf2Zqk9PfnWCwZwjXhm2bWtl5/w6yBuQGMtTXV/CSN11GW3sf2aDNUOcgfds6OOuaZbTt3EMhlSHeVEVyRwfJTJ5ozz5sq5JHHn2ElCiWrK7Hbwj33vIETz22nXd96jWce+FJOpuLIJu6wWiRaO8YJDmcpTed5p6HttPWP8TKlmr8tkFP3qE/laFtfx8NjZVsau/CyXjk0wXWNtSS6MuwZX8Xacuht2eIfV39LPXHyBgeknepr4sRDfgZFBdnOMcDW3fxxKf2EakIcEJLPXXxKC+77uyFPgTzTgHe1LoJHvFMqVKqTUT+F9gPZIA7lVJ3zkZdtYWzp6sfr+DSdaCP2x7YTD6dZ2VjNVnHwRzI09+dpH14mFo3yPYtbVieyfZOg5Wra8lt6mDP7k6cbJ7dnSmUcqldUUW+Jw8VJtWhOFbAR7ovxZDf5fF7n+aN/9hKbUWElpW1nLCqgavPPXmhD8G8m0Y2tWOMUoodT+whFAmw/fE9/O2mhzBtg7olNeTTecQUuvf20rGnG38wwI7HdmL7bfZtaWXJCc0c2NLKjqd2k80X2Ll+F67r0byygcRgGn/AIlYZxTCE9FCe9FCe+377EHf9+B6WrG2kblkd6y49mXOuPXOhD8O8m4tsiohNsbHoZ0qp3x62zRENSEqp20TkGyJSo5Tqnd2aaLMhM5xl98a92D6bbev38MjtjyOmSeOqWoZ7k/gCPg5saaW/a5BUKk/Hni4MBe2xAEuOa6Jrbze7n96HEfDz9APbUEqxZG09gz3DWBVBovEYtueS7k+THkhz241/5eav/pFVZ68lEvRx6UvP5YSzVy/0YZhX+jNTm4zBniTte3rIpbPs3trKg/dsxDQM1p6xggPdwyTq/Tg7O0gOpuhJZBkczqIGhtm7oZslq5vp78uTyfRiRf08/uAOwpJn1WkK5RvENiqojERxh/Pk+tJ0ZPP8/Ed38d1//xXnXXoCWUdxw9suZ8kxNv7RYsmmbjAqcwODKW6+9TF6ehMEa4Pksw7D5GmKh9m/qYPjTm2htz3BfXsHaaqJsW9bF14yTyZfYFfvMM2BMFVVYXqHhqmNRqirjJJN5xnqS7OrMMBQf46hTJ7Lz1hNZcgkaRp09SQQD0IJmy37u0glcvQOpnjHay9d6MMx72bzTKmIVALXAyuAQeA3IvJapdRPZ1RJbUF0dA7yi/vXk0/lCbkmw7k8voIQbgyxJd/NymgN+3YP0JZNEa8IsWtgiHSVgduZoetAmsplFYQbwgwOpGioq6L+hAg9TpZMJsf+LW2kWx3yS+s5+ZQm/E0hohlhVyaBZ3sM9iV44un93Jx1Sb4jx8uuOvZ+mC6GMzLawti3tZXfffVPBMMBlILEQJJgyMYzhdbtHdQvrWHvpgN07u4kXhuna183hiEMdA3R3z7AhdefQ6AixGD3EA3LamlaUU/H/h68vEP7jg4K6QJrz1rO8pOX449Z5IYd9m7Yi1JCZjjH3299jN9++U985Ofv55KXXrDQh2PezWY2pXjq+fvAFqXUF8dZpgHoUkopETmH4h2A+2atEtqsUEqx88m9/P6bd1BdV0E6nWeoL4EvaOFkHdq3dxKribL9iT0EIwGiFUEGu4YwbR/dezpIDCQ559qz8AX9JIczNK2pI161ho7dnSCKvZsPoDw448pTCNbEsBoqcIcz7Hx8N8pVuAitW1v5ww/u4X///B+ceOaqhT4k80p/Zmrj8TyPjQ9s5e4/PE58TT0D/QMkW5PEa+IkuxPsWb8Xf2WIrbdvJhbwEwyYZHN5DOXR1pMk95jL8WesxOvvITtk0VQTZdWaJrq2dzLYCU/f3wXhflquP4e0crGCFsq22f3XPYjj8thfN7GvPcEdN/2DHz7wSWobqxb6kMyrxZBN3WBUpjbv7GBgMMW+1l4ypseK1XU8vvkA6Uyejv4EmZ4UAcvgoQ27wTNxDIf+Axk8BelCDuUDN5sjpzzwFPWNcfZt7WBnazfLaqsYzOdZUVXDru5OkskMv314Mx4OgbhNJudRaVusaIjj5eAAfdy/ZTexP4S4+OxVLG2oXujDMy8Usx7iK4A9SqkeABH5LXABoBuMFpENmw6QGs6xcXsrPtelti7Oxk2t9A4P098/THooT7DSILE1AZaF15Wmzx7CE0XKziP5PIWoTW4ohxW2qc2H2dHXy879B2he1cxgT4plKxvZeaCXnq4Ef+nsx1MQCJjkPI+Iz2bFqnoS6QIdHb389S8b8RsWZ52yjKaG+EIfnnkxB9nUjgJP/HUjQz1DbH9iDxW1MSLxCE/dt4m+1j6GB4ZJDg4TrgjTva8bwzJI9Cq69/fiOA6FrEuhUEAE+toHqKiKYRkme7a0sm9TK00rG+jrHGD5SS3s2biPfduLl7i5SuEL2uQyBSrqYyw7sZlwRZDu/b3c8eN7SScynPW806lt0Z+b03Qh8Dpgo4g8WZr2EWApgFLqW8BLgXeIiEOx9+4rlVKL4JztsePRO5+kt7Wftp3tNK+qwzQttq7fyUDvMNlEmsGBYWLxMO27OlEKhvsSdDgeBdcFnx/HdfF5MNjeR3VjFfbgMFse3kkg6KeyOsrQUIbV61aya+N+tqzfQz7nYfgFn99HfjhD9ZJKmlfUoURI9g5zyzf/woFL2jj/2jOIVUcW+vDMOf2ZqY3noT+up797kLbeBHXrmlFDBQ6s30u6L0M6lSM9kCIUDeIGBFwhE/GQHf1kXMHyCnjJLER9pLsGWdJUTU/vEJsf2EasMgTxEF7aZfnxTezb3cU/7n2ajOsRtA2cbIFc0KaluYaa5XXkKsNk2gf40f/cymlnruLSl56Hz28v9OGZc4slm8f6QBhlp69vmL1tfUQjAeKR4P/P3n/HSZad9f34+5xzY+XqPN3Tk3ZmdnZmo7Ra7UpCGSEJASIYEOZrMCAZbGOM+QK2fiYYjPkZMGCCyQZLYIGQEFFplaWVtHl3NszMTg6du7or33jO+f5Ro9VKm7U92zM79X697muqq27fe84z9el773OegBGCA/um8UsBxsL2mRG++/U3YISlmcU0mx3CkkOpViRxDNoYZidrCCwFozi53mTP3klOn16lKWNKIwH1ySqz03WsK6gIl0IX6GSozCAAFWk849A+2+F0t8PundMEFZ8Pf+4Bfuf9n+W9H76bOM0321TPC8aKZ7w9A84ANwshCudXTV8HHLqgExiyYSyudzhztkG9VsTzHGqlkBuu3E4xd9Gx5rortvNt33oTea6JGj1W6VOuh5TLBejmSAWzXhXpe+i6ZPXMGnu3TXB8ZZ1krUfV8ZjxilwxMYITGZzJAumUR79ooCgwGNIAxKjHibxJ49waN1y9jayb8v5P3MsfveuT/N2n7ifL9Gab6nlhg7U55BLFWsvKuQanHjrD2NZRwkqBia2j7LtxD1hDnmS85E3X8w3f/1rifkJnrUe71WVseoRiJSRNclxXMbVjAqUk0nVor3aY3jvJqYfOIIDqeJWpnRPM7p0GJH7RH9zkZRbHUxhrIIfQ9zn90FnWl5vc8NprWJtb58N/9gn+6D+9m0//zRfQeqjNZ6tNa+3nrLXCWnuttfb689sHrbW/f95ZhLX2d6y1B6y111lrb7bWfv6CT3LI05JnOctnVzl6zwm27p2mMlZmcscE266aRVuDtYJXfetN3PTGG0i6Md1mj343ZnSmTlAukKUG3/eYHC8jpcJaS6vRZWbvJKcePItyJZWRIlM7J5jZOYrVlqDgYbXGWo2LIE9zjLYUCiHHDp6hv97j+lv2cu6ROT76F5/lD//LX3PHrQe5HPyLz0aXw+vmC5s0Tjl3eoVT5xrsODBLZaLKzMwo26dGSZZbTJQNb/zOF7P3wFb62tLLNOlSm4lqEddk5DZldKLE2Ow4ju8NnD9xypZdDp31MzjlkNpEjZ07JpmYGUFr8IzAtBLCpE9BZeh+inEEYb3IiaPzJP2MG19/NScPL/Dhv/o8v/sz7+GeT18ezTAvBW0OI4wuMvpRiuso6tUCSZSxb8ckK2kf5QqKjkPzbJuJ3du45eZ9zK018YsucZTSSTLGRyo05lus24zxSoWWiJG54dbPH2JluUtYdnASxVS9zMl+k6SVoIsCExk8JKKiGHUKrCddMpFxupHQDVyaJ89xdXGEUiXkxPwK9x89w+mlBj/6Xa8hCF643t+N9vpaa28XQryPQYHOHLgXeMJuMEMuPjr9hLLvUq8W6PVTdl8xxeLcKk7BgbLPnO1y7Y5ZXrRjF+laH1EVpEbTEznjoxXmux2MziiPF3GSDErwkY/fx3KvT9X1MOWQrVNVjt95FpvGuCojEYKQkGKqKW8vsbzYx0QJK52IwqmYe+KU6etmqGrJg8fOcP+xM5w73eBffe+rcNQLt9X3pbIiM+TCk0Qp3WaPymiZyliZ1nKLXdft4OjdJ6iMVUAI1peavOJbb+bAy/ejHEiiHAMoFya2jrJwaon6lKRUL2O0Icszbn3Xp2k2OtTGymBh9soZ7v3EAwhpkUJitcHxFYVCQLFeZPVcg367R3utDwbu/dQDbN09TRB6PHDbIR655wSnHzzD//Mz3/mCLvA51OaQLxH3EpIopT5Vo1QvUhktERYDDt91jOk9U9z90ftpNnrc8s03cvju49TGK7RXuyjXwS0GjM1IFk8tM7V9jLAcEvguUS/h1j/7FJ31NrXJGuSGbfumuPdTR7AOOEqRRAlKKsKJEUo2p7PSYW2lRaeTIozl4B3HmNw1jtWa+z/9EIdvP8L8qa/nrW9/3Wab7IIx1OWQxxL3Esg0xXJAwXWojlRwPY9zR5fZdcMuzjRO0LU5b/n+13DmXIN9N9TJogaLi0X8xKE8o1h5YJ3p2SnCeoVC1aed5Xzsbx+g2c2oqBRjYPsVU9zx0XtQUuCNVonbPWykcGfHqNMi0ZZzh88ROwKWDfdGESMjFYSjuPsTD3P/xx7gHb/8Nl72DS/abJNdMC4VbQ4dRhcJq+0evXZMoeDhOoqPfeZhrtg5TprkTNRKbN9d5/67TpEEkrOdNk0dsdrssndsirXlLhpNvpyhYkN7uUutWmU09/ASQbsskBi6UYZJDLd+6iFWgz5JQeIlEhkKtNZMdBxuunErH86OsbyaYAILqUH1NA3Zozu/SivIEVrxD5+4jyMPzvELP/VWpsdfmG0RLQK9wUF41tqfA35uQw865IKyutymG6eMlH3iJOfjtx1mx/QI/ShlZmKE2X3j3HbwGKIFcw+v0lnr044ido7VWVlrDKIBS4Ich6X1HmNjNWTJwfoKZz5GGEu3HRFL+Kd77qV1aBnVFiSTBdCS2IdgpsTu/bOsnXyIZl8jU412XTqeZm21ySP9FE63EGWPv/rondy7vMivveNbqFWLm22+C8KF0OaQS4/5E4ukccrodI31pTb3f+pBxraN0lpuc80rrqI6WeYTf/k58lRz8uGzdNc6SCWpjlVYPruKTjPy8/1sV881qIwOHEZeGCDafawxtFc7pFHG3/+vD7M23xzEZT+mB6477rPnRbtYW2jSXu0/+n7STWnMr7G+2iLr5ShP8Ne/9nccufM4P/PenyAI/efVVs8XQ20O0dowd3QBqSS1iSpLp1e4/9MPUZ+skec5t3zjjVgMynVIo5gjd50g6vQRCPzQpd3qE3eT8wczrC40KZY8XOWAEuRaY62gvdwi6yZ84HdupbPeQSiJSdPBE5hyCHzJrn07+MI/3kPUiQfv+4puu48602BtqY02BmUNf/Iz7+HwHUf5yd9/O+oFuNgy1OUQgDTOWD7XQAqob6lx7pFFHjixRLVewA8UN37dPhrdLusdjyiX3HfXYXSzx+Jxj+rWKkmq6bUTuqspSSOlHfYIXIsq+ziupHU6J+pmaNWhG2ecOHiKqN1H1IogJLoboSsuO+olarvGufNTDxOXAtxmTDE09BYSbKpZy2LseozMNL/0I7/HG3/otfzoO9+22ea7IFwq2hw6jDYZYyyfu+c4QdHl9MllTsytcfO1u3Acwd33naJUCKmYAp0kZXqmTqPZZa3dR2hJoRKyNL/O5HSVuuezdaLOcrPLqfkGSavPctxHN3Jes+cq7l1OWTYx0pWoxDDS9Uh9hzTMsSVBuprRaSc8eGKJsXWJcRVxM6NfNrRcQz7fQrqSVEKhLokjy+Gowe/+xWf419/5dcxMvzALlA3Dci9fkizntruOMVIMeeiReVbWOrz4JVcQuTlfOHGGWhASex7dg2fYPTVOsx+ztNCkWHbJ+33mem22lquMba8xNlXn3CMrLHU6NPIe64ttGp7km77hGm6972HSpQQPSB1LsH0cfzlD5JoggxUy2lnCiZMruFfVcE+u061ZvKMx2UrGfG5xLJjcUM0s/V7MubvP8jvv/xTveOvXMTFS2WxTXhCG2rx86ax3uPvWg4xsqXH/px+m3+zxoq+/jvXlNqtzDQqVAsYYls+usOeGnfTWI848dJaJHWOsnmnQXmszvXuK6R2TVCYqHL3rOO1Gh167z8KJJcJSgZd/+0u47W9uJ4kzlK9IspRivYAA0jTBDT16qxG9VpfjB89QHS2xHK0NYkeBtJ+xfOrLdZc93yfqxTxyz0n+4Cffxff9wndRG2pzyAuMhZNLPHLvSerjFe7/5EOkac7VL99HY6nDynyLkYkKjaUmk7NjbN83Q6fdZ3lhjW17Z5g/vYqUMLV9kqntY5TqRY7ceYyol9BaXmfx5DKV0TI3vvF67vjQfaRJhvQdjIVCJQDlohMHx1V0Vzp017ocveMoE1NVFlINRmO1JV7tEDf7gycg6eAVXbI45+Dnj/DHP/8+/sV/eith4YXn0B3q8vLm5OE5Fk43KJZ87vvkQ+T1Anv3zdBaaTN/fImJrSOszrcY2zrCzMQEURKTS83s3mnmTzfIlMvI1eNcU9hJqeDzyBeOEnVj1voxSydWmdgzwnVvuIZ7bn2QXABK4JdC8okAJzLkShAmAe12n85qk+biGhMTVRZXO5AlJAUw6zlRJ0ZUA0Sa4boO2kpu++RBxis1vuOH34DjvPAcupeCNi9+l9YLnJMnV7Cppr0a8YnPHOaugyc5u95kerROhOZko8H7PnQPDx9ZYP91szT6Ebt3TfLK63Zy5ego9WqB7WMjfP+3vYzcE+zcNsarbtrH9dftYnpmBDdQPDy3jO85mNjQ6sT0ohjXSoI+eLkk6WqUBe3AifU13FqIs5JQqwe4qUVmklxYMmUJcoXvuXSKhmbRcMehE/zUf3s/pxfXNtuUG86XwgSf6TbkhYMxlhMrDXJ3ULvoE3cd5vaHT7Iwv8bWrSPodsbJQwv89cfv4ZFHltgzVqO72uOl1+7gxpuuYPbAVibrRXZdOc33fOPNmChn/4EZXv2Sfbxo53Z2jhQZDTPuO3iKYtEnlppGFtFqGqzrkE0G6JpLlxQvEYhGxtLxFUqZIi16jIQBad1FWomOMnJrcMd8PKtw5lrohRafuf0IP/5bH2B1tf30E77EGGrz8iXPchaOLyOk4OTBM9z2gTu469b7WZ1fZ+fVs7TXOzx82xH+4fc+wrlHFth21QxRL+Lmt9zIDa++hq1XbaU2XuPqV+zjjT/4WpJuws3f9GJe+R23cOWNe9iyaxKh4JEvHsMvhMSdhNZih7SX4vouQoLVlt5qBECcpKydbSCFRClBUHv8g6bwwAgNGtqtFp957238l2/7Fbrt/uP2vdQZavPyJY1T1habKCU5fMdxPv23t3PvJx6g14nZcfVWuuttbv/Q3Xz6vV/k9OEltl89i0Twktdczf6X72N65wTl8Qo3vv4At7zlRVhteOV33sKL33AdV798P2Pbx9HacPKBM3ieS9yJaa60SaMU5SqEzsFCt9UHpYi7CZ31HlmS4lqLVy8iPBcwiFwjrMT15KCDWpLSaff42F9+nl/8V39E+gKr0/lsdTnU5guLuBfTWm5Crnnoc0f41D/eyT33HkFJ2HLFFHFm+MxtD3P75+7m7NEltl8zTWrg+t1XcOX1O9i2e5JK6PPal17FDdftwDOGl77xOq5/9T5ufOV+6qWAfGGVtZPzeI4kSaCXWFJtcEoepiRISejnGUpIVla7dOKMLNf4UuKFAXpdYoXEajCJxAkK5Nqi+4bkdI/3/+6t/Pcf+WOMMU8730uJS0WbwwijTSRJM9b7MVfvn+Hw8SVecs1OFhfWSdb73Ns4jZbQbkX0bMauLSNcPTuF8+Yb2Tc7Qa+VUPIDHj65wH3H5zhycoGtk3WOJSm37J0lWY0ZLxaRuyVSW+Z6TbQvsNaSui5BpOm0IpKyBCNIA6i7DhrFou1SCzy0UpSVQpLRCSHzweaGTj9COAJcQT8wLCyv89O/9jf87n9+G6O1F1IKjEDboU/1cqTdj0n6OS/ZPcvBh89x0/W7mFtvk6mIB4/N0e3FdLsx6VrE1d+4nWsObKUyWmb/VVvZ2eoxEoY8fHyRe+8+zsETZ9k6McqJo4u89GVXElvDaK3KaASrmaS10MRpZghtcYuSaBe0OgmllsF6HjLJKI4V0FFOZ77NyD4Hb80gyyHGUXRXexibkQtBa7GDEyro5zirCefyZX7yN9/Pb/302yiXg8026wYy1OblSme9R5ZmXPOKq7jvUw9yw2uuZm2xyeq5Bqtzq3QafaJ+TNLPePEbrmd23xYmt05w1c17WDy1TFAOeOhzR7jt/bdz14fvY2xmlLPH5njld9xCmqaMbhmhPl6l2+7TbrQHd3MWlJJkSUpvPfqK8dRHavS7Pfr9iPpEjSzJUDVBmmqyfgaATQfXewCTWEwRHrnrOL/8tl/nZ/76JwleUNEMQ21errTXukhXsfu67aT9hOtetZ+4n3H2kTkcpchTTb8TkaeaF7/2Ksam62zdM83VL7uS04fO4jqSI/ec5KPv/hyVsSKFcsjy3CrXf91+Tt13kvHpUdQVk6zMNeh0+oNC1alBVR2yJCdqxeBLyAf3p8WyT7/RxWhLabyIFg7+mEe0DlknRgiJ7mVoYxCeIu6keGWfg188zG/+1J/z7//7P38BdWka6vJyptXoUqwUGZ2s0Wt1ueamPWQVh0OnFyn0LLiKUK4zWoEbX72fYLTAudOrXHvNdibuP4XjKE48eI73//6tjF0xha8t5XqXvftnOfnxg0xfuQWVWeZPLpH2uoh+jrUKOVEmfmSNLM0RrkLHOTKQlCoe3WZMoRpSKAWgFL4bEbVj8lyDgDRJIMlQhQJRJ6E2brnjMwf5s9/4J77vx96MesFEGl0a2hw6jDaJtfUeYejiSsHpMw2KrsNb3nAtcZTyyMkl1tf6TNQDrr9yGk9L8jTHEZIb98wSJRkjI0W2bK3Rkhkfe+goWSuiVApYS/p84qGjyL5mZqLOZLlOtRIS75jm9gdPsNqJ6MichrKYWBJqRWXGR2WCEb/APf01cgylcZ9upNm6fZS1+RZ+bmhGKZ2CgQg8JfFigesbqmMh7SziR/7rX/DWV1zD97z1ls0274ZgATMMwrussNay3o3wHIU2huNLDSrjRd669wa6ScKR+QWa/YyZHWXqLyqQFQQxAhzJ9TfsJE1zarUi2yZGaC90+Oxqm7RlmCwVmV9u8Jk7D5FYzbbRKoVwggOByxIR99x9lOZKTL6S0DuZ4AuNKHtMFQWUSpQ8n9MHz9LPLMUtE/S7ll0TY5xJulTKVXqrMXq9Te5LhOeTjoYUA0k9dFlbafFDP/9u/vm3vpRvfsW1m23iDWGozcsPrTXd9R6OK4m6MfPHF5ncMcHVL7+K9eUW5w7P0VptM/myCSa2jZMlGZ1GG6W2cuDlV2K0oT5VY/v+rSyfXuUL/3Q3YdGlWCuxMrfGp9/7ReJuxPYDszhKEVYD1hab3P3xB2mtNkm6MXE/BcAv+ZTqIX4xwFGS9YUmSSejukUOCn1es4Ozh8/iFzy6q70vT0IAEhxHEY5VOPbAaX70Zf+RH/rl7+Wlb3rx5hh2gxlq8/IjS3PiXozODXEvo2kHKZ9Xf91+1hbWOXNknk6jw/jsNl757bfQXe+wvthkZvcWrrppN9YYRreMsP3ANhbPrvHFD95Lv1Vhy65xVueaRL2U3lqfK2/ahbWw/yW7WV9qcs/HH6C10qbf7WFyC0pSrhTxp1wKoU+OYL27StKPqUzWEY5lx1XbOH7PcYKCT7cdDcqRWYtUEmEMvuOiHMH9H7+fH33VUd75f36E7Vdu3WwTP2eGurw8SZKMPDfEuSYzEEc5267fybVfd4CV1Tanjy3Spc+VB2Z42esO0F5usbrS5JodYxSumEJrw9iWOtuvnGZxrsn8Fw8TIdi5Y4Kz956keXyRXqbZ94qrMKlm77WzrM0tcvALJ1hb7dBrtDG5JZ8oUCuGuOsRlS11Oqsd8k7Kej8j3F7HcRx2XbWLR24/TsGTNNf66MAZ6LKgEGmOFxbJ8phPv+927r71IP//9/4HypVws038nLlUtDl0GG0CxliWltvMLTWJbcZIrciIP+gAoY1ldLLCwVMLTErD3Mk19u4co9VPOXRsgfpoiTw3kBluu+c4azYlzjKqtQIj9RLxUsLaSpctIzXum1uiqCWVQsh9a/OIksNkUMLRCefaHaQSRKkmOdenkjv0nQi/rpDkLEUJQsND3VVeOjVJ9UCROw+dJV4frOoUPYusCGwf6vsrzDW7ZHM5n7jzEd78+uuplS59EcOlUbl+yMbRjVOW17ucOLaE0pbR0TJqxKVcCsi0oZr5HDuxTFoOOdlrsXfnBKvtHkdcj6oaRAl0k4Tb7zrGysnVQTH5SoliEFIYLbDc7TJSLvHwkbMUt5TItWDu7jP4qWHqyglWadNbaSL9gKyVciawBE5Io7mGLPqYJGH9bB+k4fShNXa+dh+jfsAdzTM0HZfME7gVF9fmWK/AmFZ0l3usl1w+eNuDvP7FV1J4gRTbHWrz8qKz1mXp9ApnDp1DSEFd1giLAeNbR4l7McpVLJ5eoR6lLJ8Z1C46/dAClbEKylU4rsPquQb3ffIBFk4sDYp+TtSoT1ZpLq2zcnaZ+mSduz9+PxNbx+i2esw9Mo8fekxvn2Th9BK9xiC6KOkmZEmCF3rkmQYFaGgtd0DDI3cf48DNe6mMl7n9n+4l7Q4cTW5BIZBYoDpWodfu01xu8+H//QlufMP1L5hiu0NtXl501rosnFxm6cwySZQzMVOjNlmnNlYm6kSAYHmugTGW1TMH2XH1LGePLVGfqmG0wQ99Th86y6EvHmPh5NKgWPZUhbHtE7TuO83acpvqSJkv/tM9TO+ZZuXUIstnG5RGSkzummDhVIOo2UUoQWepRVRy6SqFFhICCbml3elDqjkRn2DPi6+gWAq449YHSNuD9LWgHGBSg9CG+sQISSdibbXJ3/zxJ/jxX/0Xm23iDWGoy8uPdidm8dQKjcUm7U7C7K4xqoUipZEiWsDJ+0/TXF9H6Iwz6xmzuydZPrtMdbqOXu9TqhZ46PYjnDm6THttmSAQTE5UKU9WWDg8h/ZyqpWQT//DnezZOcXJ+0+yttZjdMso4ztD5hfXyfo9ZJrQXewR1AKihRa2HSMdibWG/moX088Rac6VL7kCz4G7PnUI4/oYnRGELibVgKBS9BHA8lKLv/vTT/K9P/bmzTbxhrDR2hRC/BjwdgbLVH9krf3N53rMocPoeabdjbjr8Fleds1OVvo9+ispp040eEhmvGWsyJHD87S6ES970RUkzYTTvYzcU3zDLVfTiRKiNKdQ8vnY7UeojBWQTck33HQVDx+a5xOfP4xwBTO1KjMzde47sUISRyRHM1SsmbxylG9+2XX85cfvwY0VRlpS3+I4kgjLuB8i0OR5SGIyTCshL1nuixaY7VWo+S6pcol7GVGg0ZnGb1pOP9ygfn2NwqTP3toki632C8JhZO2lESY4ZGNYXWlz5JFFXvrSK5g/ukzHtxw6voA6Clu/vszdj5zGM4pXXLOLFRNzZm6dUqJ4+Y1X0+nEZLkm68UcvPs09aKPuHKcN1RC7n1kjo9/4kGCUDK+d5zZcomFTx9n7cgqjYIl83Ku3TLB17/2ev7P396G8kO01YhWhmMUcimhMFVD9mPyJCAzOd0AhDEcueckE/vGKZZ8uk6RODLE3R4iy2kXBIXMMi4l05HL7toojcUWhZ0Tm23q58xQm5cXZx+ZZ/7YAte8cj9nj8yTZzmH7ziKH/jM7N3CfZ98kPJoiZd9y40snFxhbW6NQq3AW374DbRW2ggpWDqzyqmHz1KsFdl13Q6K9RL3f/ohPvmXn6VQKTA1O874zAhH7jxG49w6veagvtDOV2/nxjfcwF/92t9+RYc0o8FoQ6lewhpDEiXE5x1DeT/n8J3HmN49SWm0yFqcQg5ZTwOatJcRFDyCQsDM3immd2+hvdalPl7dFPtuJENtXl6cuP8U7bUuM3u20Fpt0V3vc+iLxxiZHaU+WeWuWw8yPjvCy7/5Rk4+PEeepozNjPKi11/L2sI6fqXAqYfOsXBimfJokf23XEm1XuLgZx/m1ANnKdZLjM2OUZsoc/AzTRrLLbrNLgLJ9a85wLa9W/nAH3wU6XuYfgzaoo2LFzgUfBdbKRD3IuI4xSYZneU2x+8/w9hMjepklVUstpvQb/QRUrLSiQnKIW4lZO9V00zOjhFlGaF7aaemDXV5+fHIvSdJo4z6zAiddoSZX+feD97Hnhu244c+X/jHu5ncOsrsnklOPzSHLQq2Xb+dmyaqNBabVCZrPDA/x1qUUCwXuP6WqwgLBe6/+xyH7zpKRSlqUzW86RFWzi7Q+vT99Ja6KN/l+m+7GTVd5QPv/Rwiz5DtBLQmSVP8ukupWEX3E6LMkMUZuclYO9dAS6iVQurlkLV2jNaafi+C3LKQ52x/8Q6ElVw5tYVqtUAcpQSht9mmfk5stDaFEFczcBbdBKTAh4UQ/2itPfZcjjv86/E8EqcZ9xw8w2ipgAWmxiocOrNAonKuuWKa5dUOYeAiEbhWMjNd57obttNMUx45ukCn2eeOe09y7twaO6dGqGiXxskmt+zfheM4BBakI9gxPUJxosi1I5P4SxrTM5hU0jvTY3W9S8H3KBUcXCNRvkSWJJGjaa/EJK2U0JEkWpMbi8otHWk41m/RbEb05aBAmR8rCg0LoSDBUtAehw8u8NGP3M+P/uZ7ee3bf4PP3f2cvpsXBQbxjLchly69Xsy9D51jZKQAwPhomUMPnEYKuGr3JAuLTUbqZSKTQ+Cyc3SUq7dM0lmPOHxyiZVGh7vvOUmzl3Dlniny0OWsjXnRzXvBA6ckUK5k19QEdeNx5Q27WJ+U5BZs7rHUTVnudBkplQh8HxG4KAx+lqPXY9ZMzHrRIuoe1lgIQDou+VyX+UOLrKc9TEfjVBRipEgyW8TpG3qRxikE3H1qnvc/cD/v+Nk/5Q3/+jc5fHJpky3+3Blq8/JgZa7BkTuPMTYzih96lGoF7vvUA1THK+x+8U4WTiwxNjtKrxWhpGL3dTvYfvVW1hdaHL3nOIsnl7n3Ew+Qxym7r99B3EtoLje55hX7cByJ63sEoc/2q7dSGilz5U27SePk0dpFS6dX6a53GZ+q4xe8QTQR4LiKtJfTa3XpdyJc3wP95XEnnZQzD8/RXm4POqeJ89t5olYfKQV3feQgH/nTT/Av9/07vmvH21k51+BSZ6jNy4Mzh+c4c2SOkek65XqJpJ9y+K7jTO/dwrZ9M6yeXWV0pkp7tYPruex/yRVM7ZqisbjO4TuPsXR6hXs+8RBZmnHFDTtoLrXIooQdV2/F9V2k61CoFbni2h1Uq0V2XDVDv9WDRGOt4dShebLcUKkXCIo+shwgQw9HCOJmRLcTkSQZ0lHYXoZQCpRDZ7XDuRNLtBbXIUrBEeAqhKsQStJr9ch6MXf+w1184I8/ztuu+0m+/5Z30u9GT2+Ui5hno8uhNi9tjj94luWzDUYmK9RHSzRaXY6dWWbvi7czOjPC+vI6hdAh7UcUiwH7XrqbsZk6S/PrPHzncZbPNrj7kw8iIs2eq3ewMt8gSS1TV0xSHvFxE0N1pMTsgRnGZytsCwP6yqIrBYKi4ZEHz2I7MZOZxZUCNeYhxjwskqSZ0GtFJLlBjATknkJKING0jq2wdGqVRjciJ0clGdJohCshN0StiPZal7s+fJC/+J0P8d1X/QQ//LqfJ02yzTb5c2KDtXkVcLu1tm+tzYFPA9/2XMc4jDB6HpFCsGf3FDMTg1VElcHsjjHCos9UrUxjvU9tssJDqyv0V1Z5aWUbq+daTJQCDp1d5kVXbWPfrkkksGvLKM1aGaFh1/Qou/dNcdXuKbpRws56jVvvfYTDCw221stM7PY5t7ROF8Od95wgWutxxVidhoqgLJlb61CWHkyCbWY0lrsgLMIXOEYiW5q0rFhPUqy0aCHQrqBY8liXOa6wBG0oeIIsNngmJ19O+OXf+Ad+/ee/iyt3T2+u4b9GBpXrhz7VywEpJdfu38rkRIUs01hPctXEBIWSz8hUjdWVDuNBwNm5c5zLDHv3z9BwUkqjPgcfPMM3vv4aTG5QgWL7FTUKywX8FZ+d20a5dmYK78odtNOMLRN1PvOPd3Lm3Boze8aoey7Hz66Qtrp8/vBxlr2YmWsnaJ1tUyiELCy0KU065EsZPSlYIYJA46QKOeKSlS3Oek7HxggLUmSo1FCSHtl6GzHuobeF+Kkg78XgBywLzY/9/vv4y3d+P/XqpVmkfqjNy4dCOeS6Vx9gfGaUXqtHqV5k3417KdWLjEzWWTm3yuhkjeP3nBxcS3dMkMUZcS9lZa7Bm3/o9biBg3IUM7u3kGeabrPH7J5p9t20h8pomV6zx/i2cT78p5+ksdBgcuckYbnAyYOn6DR63PGhe2k3u1xx3XZWF9YR1rI836A6VSZqx6RRRtbrPm7sOnlMNxc7+McruaTdDITAcR28okPUiwhLAWtnm/ynN/8i/+vOX8HzL81V06E2Lx/qk1WufeV+RqbqtBsd6hNV9t+0G893KFcLrJxrMLltkhP338XIlhHCko/JDa3VDq3VDq/89pcSPHQWYyx7XrSLfivCcRVYuP5V+wlKBaJeQrVe5IPv/jRxJ2HXNdvQAk7NrdJt9vnCh+4Z1A3bM8V6OyaPEtaX1qhuqdLvJvQ7MaQ5uApcB9IUIVyybga5BqMRvge+QilB3kqQSHzPwa2VSNoJrqNYOLHKf/7O/8mvf/A/brbZvyaGury8GJseYXRLjdpomeZKi6mxMlw9A1s9ipMlFh9YZOeBbXzu7++mMjZ4JhWJZv3EEo4jeOkbb0AqSeh77LlhJ63Ty1THqiQiwpQM7SN93FKIOxXwN+/7FM6CZtuOafwgolI4zNyxBnf9QxtXOeyamaLTiUhsj06zxfS+MnEKjSWDnu8iiPECQZ4A1hL1UnSeYCQoqxHKwZWCLLU4WuKOuqybIp1c4ynLqcML/Jd/+Xv80v/9d5tr9K+Rr1GbY0KIux7z8x9aa//w/OsHgV8SQowCEfBm4K6vPsCzZegweh7xXOdRZxFAbgzbChWUlIzUS2yfGQXAdSXnltZRSrH/6q3UagU8z0Gpr/xC1etFds6OAfCT3/Va0jTn4WMLfPq2I5hmjk0MbRmzy68gQokMBb00Z0n2Od3psc0UKfYDSsqhG+SkfUOQGqxrIAEcQSItTgSFdY3taPDAGIXVgl5P4xoB7uBfN4a2ZygIF39Wsroe8f4P3cO/+8ERSoVLsUPTMIT3ciEMPcLzYa2OI1GuQ3XvKL5yGKuX2DY9AoD0BcutDqHncuPOrVRKAa+8fjdSCkZr5UePV9sZctXOKQB+7P/9JqI048Gj83z89sMkZUXuQD9KGK2H+MYghcE2I/pnVjkeL1PfP4H0BYXYIVtOSBODng2xUY4WFtmxxHGODR3SbS5Z6EBPo4wEISExUCqSKXBChZEudj3Fn3KpmZzWuRb/+0Nf5Mf+2atxLsm6KUNtXi4UKwWKlUHkX1AMkEoxOl2nPFJmfHaUrXu3IKXEWOi3elTHq0xuH6dYLeAXfKSU7L/5ykePd8Nrrnn09X/4gx+h34l48POH+ORffR6EJUsz0jhhbEsdz3dxA0kaJ6wttVhfbTE+M4JFEBYD+q0+WaQfN+anIu0OVkKz3OD6DmiBznKK1SK9bsTc8UX+7y//Dd/3c9+FEJfiKv9Qm5cL5Xrp0ddB0Sco+hRrBWpjVbbsnGDbvhmEEKRJhjV2UDx33wxhKcQveEgpueqlex89xs1vftGjr298w3V0mz0e+PwRPvquT6OEoN/pk6Z1xqerLDyyhKy7JHHK0ukGzdUutakawhEElSLdtYhcp5BqUA4CsEkGucHY83XHcgPqfOhfClmWQGbIlUC5CokgNZqJyRq9XsKxB07zd3/0cb7l7a97/oy8YQx1eTlRHfnyYqBf8AkE+BomxkaY2TLOtulJANrNPkExoD5e5oqrtxEU/Ue1ed0r9j16jK/71psfff2SV7yIdqPDXV88ykf/4jMEizndRkxhahymQw7ePkvNUaStiGanSzEQ+JUKnrWMjLs4Yg0dBySei8hSVC8h6yisEIiCC75CdAQIi9UWXI+0n0Hgobt9XBxkKyKpV5m6coJz957l8EMn+ejffo43vPUVz5+RN4yvSZur1tobn+gDa+0hIcR/Bz4K9ID7+Ir456+NocPoecJay/33nuaK3RPc/+AcSMvoWJmFhSaB7xJ1EsoFHyEEu2bG2DUz9qzP4XkO1++fZc+OCQofuotd7Taf+9TDnDi2iI/Ca1qO0sDNDWlJMa8zql1L7hn6WYbTMKQGrBAoYzBC4PU0xlfYpsZKS1oTCC0opJJ+aKmmCpPnnF1vkCUptUJI2k1JkozmhOUDn3iAw6eWedf/+IELYNULy6VSuX7Ic6ObpBxZXOGKkTr3HjyLKiimqmWOn1hm25Y6rSyheL7t9d6dW9jLlmd9jtBzecmB7eyeGePPrWJ9eoqPPXKcZLUBsyG5b1k4Po/2JIVmTvehJvmWEjYxxN2EqBZCV2OkxJZyRMuSBQK/K0hqCtvPKDQjROIh/RDRjLDbSyQmZW5xjdgzlMMSayRkywlyvse7br2DR84s8wc/9baNNukFZ6jNy4N2o8PcsQWq4xWO33eKsBxSrhU4+cBZ9t1yBf12RFgcLEZcddOer+kchXLITd/wIrbsnORvf/tDrC2tc9dH7yXqpChXkaeW+aPLANgcGvNNitUCJjXP2lk0eGoF6YKOMlbmG1hrKddLrM43yHuD4/3FL7yfxRNL/Md3/djXNKfNZKjNy4O1xXWWz64SFgPmji8SFAPCos+ph84ytnWUXjuiPjFYhLnuFVd9Teco1Yrc8uYXUR0p8qn3fZG5E0sc/NSDtJbW8FxJ3Ivone0ipCSNEprL6/ihT57kZGkCKLAGshx8F4wAzx1EFiFBCiyDNRbSCKSDGikRtxPWRQeBoVYJWZlbw2QpUc/yOz/9F/R7MW/799+4UaZ8Xhjq8vJh5dwazfUurpTMn12lUC3guA5nDy+w48A2+qWYynmH0k1f/7V1zq2MlnntN76Isq+47zOHeOC+I5yZW0Cu+BSNQ2uxTZrmCBTdTkbUb1EbcbDknDyiyLAYP8HpaWTNIU9AtjUYA20NrkLlGuG5iGKIiHOUK+g2+7ixQ5hZVDtisdHH5Jr1M6v86g/9ASoTvO6fvXwjzXnBuRDatNb+CfAnAEKI/wace67HHP71eJ4QQtCJEh48NE+lErJzxzjF0Ee7AhEq+v2ELHvODkAAigWft3/7y/mp734dhVJAM8tJuhn1chHpO6SegKYl7WYEwrJzZITQOGQVRVaUmElJseZTqgd4WjBlXRQCjELGkjzQJGiCVCAFyHN91la6qAIkSUp3LcZpC+qRwDqW4ydXuPOBExsyt+cbbcUz3oZcmgSO4myjydH5VSbHKuzZOk7ouXiJRfdyonTjcqPrtSI/+oOv50d+4PUERtJb7ZMqw7hTxm8KRKxJKx4xEBQUkzsnEb6HSFJoJZS6GRNukRIBKvQIDxSRmUAJCZ7A6UbEuUbWfFzP0ps2NIlwsowozYjXNWqsgJ0OIdLcfeQcZxZXN2x+zycbqU0hxKwQ4pNCiIeFEA+d7zDx1fu8WgjREkLcd3772QsysSGPYq1l4eQyjbk1pnZOMnPFFGE5REhLe7VH3Is37Fyze2f40d/+If7Zj38TikGdE200lbHSV+ynY0NltMz4tq9c1BEeFEdClD+4rSqOPkHjBwsoUJ4kjXPaqx2stfQ6EWk3Jyh/uYPhp97/BVqN1obN7/lkeN184ZOnmqUzq7TXu0xsG2NidoywFCCQrM2vbag29998Jf/6176PV377LQghaTXaSM8lLAdYa7Bxik0NSTdhctcEI1N1EA5oDRK8epHyVBUZKoQrKExUwNrBwykCjB04cn0XUXCI13v0+wnGCrprXeJmn0IpQPgOaMt7f/dW8nxj7tefT56NLofavHRJrWXh3BqdVo/p7eOMbalTLBcwStJe6xFHyYad6yWvv463/8J3c8sbXozNDP3FCM9qtu4JMEKgrcEkmjwVjO7YhvDGsZnEKYLnScJ6kXDrNLZaQlUKBCMlZJyg+ikyNZAb7EID4Qqk5xJlObEQ5I5D1M3I1iIq06PgSkzP8oc/+1cbNrfnk43WphBi4vy/2xjUL/q/z3WMQ4fR88iBa2Y52WyRO5aZqTqFwOPlN+ziLa+7lp27JvC8jQ34KoQe3/SmG6j6DtqxNHTEmFdAjzpEU4K8KNmyb5RW1VCsF/BTcBKD7EO9XqKuPXaOjBBFmjy3ICyqb3HWIS1CFgwWb8RMSLNiiWJB1DfoHDqhQbQNacXSmLH80ce/QLvd39D5XWgsAo18xtuQSxNHKV62dSvHH1hAWcuW8SrKk7zutQd44+uu4Yrx0Q0/5/homW+4bi/FeohdjuidbVIdrVCcy1BzXcI0ZZsfIk+3qQYOlFxMRaEdyUipTOnKGtuuHiNbzcmx5IEhqXnERR93PUbHBhl5hAuC7IE2qRH0bY4G8pU+xvcIViK8hxr8wZ9+gvgSKxh4AbSZAz9hrd0P3Az8GyHE/ifY77PW2uvPb7+wkXMa8niqYxX2vWQ3h+44iuc5bNk1iVCSN/7L1/L67/k6ZnY/+2i/p2P/Lfu48U3XEVZ90iiht9alOPrllGo3dClWikglKY0WHn3f5lCbrDGypcbWfVvorT9JgVwDnjdwDCWdDJ1rsjgHIO4kj8Z960jzf37ury65B9PhdfPyYGLbGNv3zfDA5w5THa2wdc8WEIJv+TffwKu+42Vs2Tm54ed8w/e8ggO37CEshkT9hDzOKI2UwAWwFGpFlIFSKaBYcc87hSw601TqRcZnRtgyM0J/vQtCgBAIJRG5BisRShJYCY6g3+yijSHpZxhj6HUihBwUIuut93jPb314w+d3IXm2uhxq89JlZnaU2mSVIw+doz5RZeuOcXSW87Z//2ZufO0BJmZGNvyc3/nDb2Ln9DSuzeg2E/Ikp1BwENoiHIfqeAHbjamNFgkm6gjpIvKMJHAYrdSYLJYZ3zZK0jBY6SAQCJuTVSVaSKQFr+iiM028HpFHOVknxaYpndPLeEohFaw12nzg3Z/e8PldSC6QNt8vhHgY+Afg31hrm891nMO/CM8T/Sgl9By2T9WYnaidf9eijb2g5337W17Gb7zzuynvLZOFsNaOKDRABpKsYLjvgSXOHluj0e2jUg1aIJqGhcUW8XKHM60mbZ1jfIlrFZ62uPn57sKZIfQclFY4TUEWWQwWU4QcS6QtRkpUQXHXwXO84+f//ILO9UJgrHzG25BLk14/wfNdtm8fY2Z2cCEVniLlwmrzJ/7dW/jZd3wjU2kAfU2rk+BOVfATjW5n3Hd6kVPrLbrrCbogUJmh70tOt9doF3ssH1slSjK8xKBchSg6OCEYR6AdcB2Jkh6iWiBzLAKDinIyNHmWIXKNnxs+ducj/PB/u7y1aa1dsNbec/51BzgEzFzgKQx5GnrtPl7gsfOa7UxsH0T0uK5DluQX9Lz/+T0/wY/8j++jNlLFAL1GjBMMan1lUcaRu49x+oFzdBuPWQQxMPfIAt31PueOLoB54mNjQUj5aMc0m/GV+z5mav/wv27lZ9763zZwZs8Pw+vmC59eu09YCrnyxt1Uxwb1+9zAI40v3OKDEIJf/sf/H9/xE99MZbRKmhm63QSvXAAB/WbEkXtOcezBM0TtBBwHLOgkYf7MKv12wvyZNWw2aFsoQg/hOFg5+B5ao1G+ixASDOTdGKyG3EKUYHsZSIUouPz5r/4jv/ET775gc70QPBtdDrV56dLrRIyMFNl91VaKlUGkq+e7RN2Ni/r7apSj+M0Pv5PXvP3VuNeO0sNgZIrnu9g8p7na5tihOU48vEA/zzFtjVmX5FHO3INniJs9lo4sYaMIJGTjBfA9TLkAjsR4LnLWQzgaG8fofop1wUqBzTSmowe5pSMF/tc7/4I//52/v2BzvRBstDattV9nrd1vrb3OWvvxjRjj8C/CBaTbjdF6cCe4vNym00l4yVXbSaPBBXVstMyO7c++VtGz5ert0/zaD34rV+2aplL18aSkaCVGWjKpsRqc9iB0VzgWp2cxiSapCeIQcAZ53qEnsZ6L4ztYDSK3NNZ7pJEmbIEbCWxmoG8J1lO8TFJoWoJFjathVXdprD2+k8zFikGQWvWMtyGXDu1OhDnvrJ1famGs5brrZmmfD9XdUiuzY6J+wcfx8lfs550/8x3suWKScuhjlYNTKUA/JYk1aUHRqw6K/xkk1vPQHYM6npOtpGgrcDJDAQ+v76Kkj0SgHU1Ht0hcS7zNIysorDakoULXJIwoku1F4u0lbBkWF9foRekFn+9G8TVoc0wIcddjtnc82bGFEDuAG4Dbn+DjW4QQ9wshPiSEOHBhZnd5017rPPr69MPnCMsB+16ym26zB8DUjgmmr5i64ON407/8en7ij/8NW3ZO4pc9HN951MnDkz0Ta9BZ/hXlJZ3w8eHj3Ubv0a5pPFVhawELx5ZJ4he0NodcIrQbA21macaJ+08zPjvKzmu20T8fPT6za5LJbRf+nvZf/Me38q9/5XsZ3VKlUPTxiwF8qaugNlhrMNYiPQeUBC0gzsnyHLRFSAUSnIIHViOsBaXASFrLLYzRIM87lIQzCKWXDoQeYDDJQI/Hjsxj7YVdWNoonq0un4k2hRBvFEIcEUIcE0I8rn2cEGLb+VTve4UQB4UQb74gk7vMsdY+qs32Wpf5E0ts3THBzI5x4v7ASbRt3wzjWzc+Wv6r+fGf+25+4IfeRL1SRzkV/LIHjoBUo7Mcow00u4jAQQiQzYjENeTSwRqLFoASeGMVbD3E6xmkthib0rl/CVP1YMKDoou1DFLbpi0ojTUZdFNkbjl+x8kLPteN4kJo80IwdBhdQNbX+6yv97DW4jmK8bESa6tdjh1bft4vMvunJ3nLgX2gBFlRkjQ0uWdJpzRxMafnWeK6gzQWYSzFUNLHQF8jMLiZoacM1phBd5eewUssylhUqtE6w8QZKlTYokNWcgkdxZj2KMcSz5U08ogf+YN30U0unJd7ozHIZ7wNuXRorPdotvskeU5xNGCkVuRco8XplbXnfSzXvmQXL3ntAbzcIto5kQEbJbjzXeRijOim+I0YU1TgSryyQ1vHWN9D5pa06qKbGca1ZF5ONuahi5K+MkQhJGmGdcCPDIWmprAqCIsF3NkicszHywTrpxr825/6Q7S+dNJfnqU2V621Nz5m+8MnOqYQogS8H/j31tr2V318D7DdWnsd8NvA317A6V22LJ1eodvsEfcTJraNUawUmDu2wPzxped9LDe+4Tquf+3VOK4i7iU8XdChW3RI+18Z/ZRHT/NL1g46Np2nNFrELbl4JQflSeYeWeRnv+2/f40z2ByG180XHjrXnD0yh9aaLMnZuncaKSXzxxeZP7H8vI/nFd/0Yq552ZVYV9HvpZDpQeSelNDPQFvsl0L3PIUquMS9fOCgtYAnyHrRYJFTSUBj0hTSHFIDmR7sF7qgHJCCylgZrxQQhh5CWY7efozffudfPu9z/1p5Nrp8Om0KIRTwu8CbgP3A254gjfs/A++11t4AfDfwvy7AtC57us0ey2cHtSilFEyc77g9f2KRuWPP73VTCMEbv+nl7HjxDnKj6Xf7UBLgKnAddJJhFBgsCHAqHk7o0DYZec1FOhLhOpgzawMdRzGmKDBRjG1b5EqOWMqgFyOkAGuQSlDeMoIzViGolLFW8Pm/u4/3/OrfPa9zfy5spDYvFMOr9QVkdnYEI+HIyWV6UUKeG6ama7zqVfs2pV3uG266ijddexXTTomSlnipxOBA3+K2LToDmQiyOvQDg0wdTGBxsSiloGuQkaXQt4RtkD3QypL5GhsojGORiynGMWQ+SEfgT4dUtxVwCw42lhw70uC33vMJjHmyeP2LB2tBW/mMtyGXDju3jZHEOSfPrNKJEqy17N0yzi1X7dyU8XzLm17MLa8/QH22iqh4iGKIY8G4gjwIsJFEGgfSPnQSXOFjoxi/DMpIMh+SIuhqkXybg3EUakUg2jHCGjAp8W4HY3JSMtwUpjqKKcehqCTkggePLvC77700cr8vhDaFEC4DZ9FfWGv/5vHntG1rbff86w8CrhDiwi+nX2bsuWEX7UaHs4fP0WsNIhf2vWQP171qcwK6/vl/+naufsV+RqeePuLwKReCnmhRUEhAfEVEkhe4jEzWqY3XcQuD2kn3fPggf/Nb//TsBr5JDK+bL0yUozjwsn3MHV1k7ugindYg4u/qW65k/817N2VM3/njb+bKF+9hZLyM8OTAGeRIcB2QEhvn51sQWZxCCenIwVNPIMEKiDIQIJUzeKDVOWgDwoK12F48+P3AB9/FqwaMjhYol0LcwEMb+OD//Swf/oc7NmX+z4Znq8tnoM2bgGPW2hPW2hT4S+Bbvvq0QOX86yowv6GTGgJAuV5i9/U7OTa3ytxKi05zkMlx3Sv3c+WLdz3v4xFC8I3f+Cr2bR+jNF5G9gQit4jMYDyFjXJo9hGpIUtSAsv5KCKNE3iDFNBYI/o5wggIfYzvk6Oxuca6CjIL2pKmLv1+GV8qRmfGCUMfaTJMrnn3//wwX/j8oed9/s+WC6DNC8Lwan2BGa+XqFZDjIDVRufpf+EC4nkO1++coeoGeCMKgaC0IvG7oBA4GYOq9rmim0iEAX8VRMsgMgM+EEgiR2At5AVIqg7JlEQXBLYsyAIHXTfkNWgpw0qvj0odrprZgutIUt/hjtNnOb24vqm2eGYIzLPYhlxaTE5UqIUBoqtZXt1cbRZ8lz3XbyOcKlIw4FlJtqtGPFvAhpa84qM9yIVHsm4xnkJvDUmVxXgaqUFFEtMxEAm8dUOwklI8k+I1DbYL+SLIVh931ZCciWivJuhSwJ4rpnEQOB3Dwdseod19kmK9FxUbq00x8OD/CXDIWvvrT7LP1Pn9EELcxOD62djASQ05z+T2cUq1ElEvYn2pualjKVYKXP/qA5TqBZT/1KHgeV/zpF+3Zxi8tzbfptXoIIRg7/XbH01Zu+NDd18i6S/D6+YLmckd45TrRdorbXrnnUabxfTOKW54xZX4xQJu6CM9hfAchHIGutGDaD+hNXGjjXXO1w/TAiEGaWlIMGmKSDVCKIyUg9QzbSDX2GQQlWSsZPXUCs1mjFMJ2XnNDEoJbGx48PPHN9UOz4xnp8vz2nyqVO4Z4Oxjfj7H4+v+/TzwvUKIc8AHgR+9gBO87NkyVmZkSrE639z0Rfn9N+3mqpdeSegGeDUPUXVBCZQBJ82RUmGxkEGcRTihRuUK3UvAWoJKAlJDlGGlixYKaTU4EovFZhr6MWQpKolYbUd0OylhILn65llUBVJtOXT3qU21wzPja9Lm887QYXQBWV7r8MWDJ0lyjRe4bJmqbfaQeM3LruRHf+C12AmL045JnBRbU4hAYIyhPypBCYQniZUhDyVp0SGWBumA6yu8ikexFuCXHNy+QSPI62D6oHoJ3hmN1JasmNPNDefSHqudLtdun8YZcXik3eRn3/tB0ou8+4tluFL6QuXswhr3HTpDFKWUQp/J8crT/9IF5i0vv5rvfc2LUL2Ifl1g1vp4UYaXS0gynLk2XmqRucWuJdg+JALySKOEpRwZipmhJlxCJchqDjazqJUUfBdTh2TWx7g5edol6vToHGmzvt7n6pt3UdJw9L4z/JdffO9mm+JpuQDafDnw/wCvFULcd357sxDih4UQP3x+n+8AHhRC3A/8FvDd9tJ4gr+kOPngGY7cdYxus8vo1Aj1ydpmD4m3vOMNvPq7Xo6Sj/kuPdnX6qu/Ea5A+hLpSwq1cFDP4dF9zeN/wRriVsLaapP2Wp99t+wBIbn71of4lbdf/Bkdw+vmC5ej957k5MHTrM6vsf3ALMVqcbOHxFvf8XpufN0BTG4wqcZ0E2TBHdQkUmoQKSQkNk2xGVhjsbnFpjmqWsbxPZyiQ2G8jAgcrCOwQoFUA+dvFA+aRGiDTQxRL6G51sIkObtu2g2u5OPv/yL/53c+stmmeEqerS7Pa/MZpXI/BW8D/sxauxV4M/BuIcRQ9BeAQ3MnmGufoxP12PeSK5Byc80shOC7/8M3cuCV+0iSDBNpkApnIhxk2BiNCRyM52CaOcSWrOyQlXyEMkztlziBS2G6SK2o8FwH6SlsokE6aGExuUE5FmsMeS+mt9ykt9bDVS67Dkwj0pS//o1/5O/f9dlNtcXT8TVq83lnKNwLiBe4jI9VqIU+ahNS0J4IJSXbx+r4qw5F4REUJbIMugg2ACe2CGUIexqZG5RQWGnxMzB9i001W5SHoy10Na518NqK3LFEkxYrFCKzCKMhzrHVnH6W0WtETJcrjCxKvDXLoTvn+K3//bHNNsfTMmxB+sKk5HmMFUOq9eIF7oX2zPFch217pjCjRdy6hxNA2M1RvQSpIK8HSCfHzIIIBVI4uMZia4rIF8RVQX1vDRFYglqOqPswVcBtZ7inYrQV2FqOdQzGNcjlHv12RF8ZRiYK9F9UpV8P+NzpOX7/Q7dttjmelo3UprX2c9ZaYa291lp7/fntg9ba37fW/v75fX7HWnvgfNeJm621n7/gk7wMKY8UqY5XqU/V0RfJokJQ8Nl65VaU5+CFHjg887unbFBJZWr7BGmWgRTnU9GeAmvIOvmj7cDdwqDb08f+7DN88E+G180hm0N5pEh9ss7EtjGSXrLZwwGgUA7YfuUWvGKAXw8Q5QDjOwhHgjtwHInAQxTC88F6FgEYrdECVClgcnacJM4gdHFLRYQ2YA3GddGBg3TU4KEucNFBSNSzZFZSLvlIKcnXY9772x/h9k8/vLnGeBo2uHX3HDD7mJ+3nn/vsfwg8F4Aa+0XgAAYpnFfAMp5gVFGKKQ1+p2Lo05sqVZk+qop8D38ggeeRMRgPQlGI6IIx1icRCBSidvRkOTkueDMwyX8MKBaLJK1IlzpgHWQNQ854mMdBYGL7hnSzGCVhFTTWuzQ6YErS0jPIV/p8Ef/9S85+ODRzTbHU7LB2rwgbMhZhRD/WwixLIR48DHv/bwQYu6xq7Ubca5LiVohYPfMGLVKgW3TI5s9nEeplQtUYp8shiB2UYsZ3pJGpAKZg6sUSilc7eA6oKWgV5eEoYNQiswYXGvQaUZmcsgMRkpkT2EDiRlx8fuSYFWR5wIbwkKnR7rUJ9ASVwzCCT9w2wOsrG1uKtBTYREY+8y3i42hLp+c+miJbTsmGKkXmZm+8N3Qnilbt4zijJXIU4lfL9Oe8IjGfawVCFeiyg64IbbuIwKFbAv80zmlEQcpJWmuCcqaQaUig9V24PStgGoL/OWQvFwgnayQ+wqb5sytrtEuS7yCj3RyEnL++r1fJMkuXHvk58pQmy9cxqZHmbliirHpEaZ2TGz2cB7lqpfsRkpLGqUEoQv5V+3gMCiee74s0ZeQgUJKQa41ru9Cas9HFj0BQn55A84en8cwqB8DgIX/84t/fdE40p6IoTZfuExtn2By+xgTs2OMzVw897T7b9qNsZYkMoSlENuOsWkGQiJ9B+U5kOcIXyEKIXgO0h1ELQjXwQQejpKYToJJcoSrsL6PRaCUg3IdpDGoNB+UHEsyFuZWQWvcYJBqk652+JP/+oGLNm302eryGWjzTmCPEGKnEMJjUNT6q3uZnwFeByCEuIqBw2jla53DUJtPztbtU4yO15naOsLIxOZHzH+JvTtncDopSadPOQTd6mIdD6SHcF18CVZJpDUIJb6sWd9DIJFhgEkFebOPzXKUNigdIaWDFAIVujgpOA4YR5B7sNjoYFJN4DogFN1Gm3f9+sVbAPsCaPOCsFFuqj8D3vgE7//GY1drN+hclwTtXkynf3F4eb8apSS/9G++BV0XtHROryJIPMBq0pIhsILKbIgtK3IrsXIQXtjPNVmcsWxS+nFG6khUZAj7Eq8xKDiYFgWqZZBNixjRFNsWfy0nLRk+vnaGZnHQacZUFLkPv/93F3eo4MXu8X0a/oyhLh9Hq9Wn3784Vke/mlIx4Me+9/Wodp+o0cM518dGBptmOK0EPIctuaK0IlCdHGEkYIjXcnQ/QZ9sst6BdtfFlnPSbUX0lI9MBLKTEVcc8prCWY7QswHZqIszF3PwvQ+SnWiBLwi6lrwf8673XdxRRkNtvvBYX24RX6TanNw2zjf/yOC/LO483pnqFzxqkxWEw1dkmZlYY2JLc6FF1IwHHwo5KM77dPd9Kdz14ftJoi/bpLvW5YN/+vHnPqELyFCbLzwaC+ukycW5iLDrwDZuefMNYCxxpzfQlwXSFJRLOF6lPjMCCGyWYwWgHNJIkzYj1heaxK0+JkoRlsGDKBZpLSQZ5Oe9w3mOMgakIF5scv8nDpK2uoNSSK7DytlV7r7tkc0zxNOwkVEM1toc+LfAR4BDDLqhPSSE+AUhxDef3+0ngLefT+N+D/D9zzGN+88YavNxLM+tbXrNoifj5tccYP+bdoOvaUYpOpRoqzEKrFAUx6oUqgFY0Nri5AaRavL1Dr1Y02wn5N0EYxnUEtOCfM0ipMRqC4aBoynOwVqMo2gvrHL0gTOkWqNrHnq8xInPnODska8OgLt4uGwijKy1nwGe/37UFzHL6x16UbrZw3hS9u+a5jtuvAFTsOSBwlQk1gcKoN2MIEmpGkFKhhIWmRi0awdFdtdTso4haNtBW8O9Hs4Wg3LPL7CWBYFWqL5CdC1uqvC0IklyummGbVl0ZkjjjM/cdoQoujgfEAbNNeQz3i42hrp8YpYWW0T9i1ebX/+G63n5667H6eU4XYHbTZHGIpIUN0qxBYuuCvJuhDQZeSgQ3Zwky1gKHXpaEVd9bO6w5ZzG7eSAQFlBVcWEWxR6zEM4DqbmIkOPxDFkcUaqXHKZEycZ77vzQbS+OG9Chtp8YbJ4aonkInUYAfzAf/3nXP2qJ+4IJYREOvJ8wstXYQ1plA86M52PHqpNVgY/c36TT+I9MmAf85ye9nLe95sXb8e0oTZfeFhrWTy9QnoR39O+8/ffzo4bdwGDukWDzWDyHNHvo3wHjIVcIzINxkCWY9KceK0HWYZ0FQIYna6DsQjHQVaKCKUQRQ/pukitB2XINJALcptjTDaoARrlvOt3PrS5hngSnq0un4k2z6dt77XWXmGt/aXz7/2stfbvz79+2Fr78vNp3Ndbaz/6nOYw1Obj0NqwPL9OGl+czlyAX/nTn2bsiq2ITCK0QaQ5yUQJqyRG5/gFH2MtqtNHGANxiu0lkKR011rYgRcXoSST28YGEbo2R4UusuAiygXILDLLcKMUrRW5FWSRRmPQytKOcv7wlz6w2aZ4Qi6ENi8EF/qs/1YIcfB8GOET5n0IId7xpQr8Kytfc6TiRYfrOnies9nDeEre8tKrYUShco1KLWSC4pylb3NW5mL63RRhDX7X4GSCrCBIqhanZ8mxyNTgW5csMSjHUFAGxyqSMUk+6eJ6Lo6r8B2D7WaEaxpXKmRoEeGg+8Sq6fIt/+q3aZ9vn3xxIdDPYruEeFpdwgtXm0HBw7nItfn1r70OtZ6RewaLJC/7pBM+8VLMYp4SyRRbkERbHHA9RDvH7WTk3qDjS15wcYs+aZxCL4OyRLkKfdbinlUUSj6qqigXcvIwJZ90UJ7AyfsInaNtTuPIPG/5ud8jSi/GG5GhNl+I2nQcB7/gb/YwnpIXv+6GJ3w/bsWsza0PWgI/EY9ZWBcudNbPp2MLMfAZWYtfdMEB4T31d3b+8CI//NKfvkjTX4bafKFpM4lSgoJPqbb5Ra6fiv037cVKCWk2yFHxXNCWdjOl0egyeM4S59NGBw+gKIHNNKYQQhDgFn0a8+vgSGTgIDwXHIdiuQDlECf0sGkOmYaRIqJQGqSnGouJEg599ig/96//9+Ya4gl5drocavPSoNeJqY6WCS7i66aUkm037sL6LsTgKBd/NUamGa25JmvLbZACmWuMBDProgNFjkanOWiLynLCWoml1S4CgwSMsiAVYS1EVAOCSjBIPW31UJ5CYfB74DUsWZZz20fu4X/+v+/ebHM8AZeGNi+kw+j3gCuA64EF4H880U7W2j/8UgX+8fHxCzic55ftk3VGyoXNHsZTct0VM+xSNdx1CBqWoA9pWZB7ihyFcCyqC4mrkAF4sUSUXWzBInwXKoqMHHM2RtyrSbsQK0OaWnIf9u6cZLJWRmgHHRoEgqCrUH2wmSCdkMQ1wanxlLsPn95sczyOS32l9El4RrqEF642t82OUq2Emz2Mp+S1X3eAwpY6ec3HjAV4oaKwbLGZQrcE1hdkoY/bEijfooo+xaaD2zWoWOEnGTrRrIwoGteU0A7kaUqaZMh+ypWzU1TGywOtZxaMxNQKiHoAWqDWIpyVmNbHTrO02tpsczyOoTZfmNrc86JdF/WNL8D3vPPbwH2SD7UdhNk+yvkbu68qcm0z0JGFLzXDsBYsWCzXv+oAtdEKqMf8guJxHL/rFOtLQ20+T1zW2gwKPldcu32zh/G0vP3/fTPCmoGetEb6LmAgTbG9FIkaFNv1PVQYIJUYSFAKlB2UXkjXu+goxRgwUUYep5g4QQjBja/YS1gNEYUAIwT0EkSaQp5BrgdOozTj9lsPEl1kJSkulSiGr4HLWpuVWoHZXRdPrb8n4yff+V2oKMVmepCx7UswAkIfYc9ncUuBJUP5Lo7n4wiJjVKsEOAq4naEbXQxicEqi80TTKNLEAS86LXX4IY+wg8QSpHrlHQ0wBqNbHVxI43I4SPv/cLmGuIJuFS0ecHOaq1dstZqa60B/gi46UKda8jXzstu2ImekESjimhCIiw4mYOoKeS4izUClRroGFw0TpJhpi2QDy6oRkAOVkuIwcXipRad5zx8cp5C4FDoWrxMkrqGrJOQ56CsQ024OD1FXoX7muc22xRPyMXu8X22DHV56XDTi3dQWIxwlhPk2Yi0pHCFQ7kNYdtifYkOFNqX5J4mkRa/2ceUINeCLLcYz2KKDiaErCyxStKXMQ+fOUfJC4iqAcoHN0mJCxFp3+JYSTBdRRc93EaPQ3ee3GxTPCFDbQ7ZDKSU3PC6a574s0Di+o/17li+7DR6qu/hIC0t7eUc/OzDVMfLX1nf6DE1roPSlx1qcycWnuXonx+G2hyyGRTKAVfcvBtRDMBRGA1fSvf0lEJYC8rBJhk2zzGOg3FdZOgjHAerM7TRICQ2S0EYpAHjunRbHR64+wTViRKiEmBrRay12H6KcRQikPhFb+Aw1tBqXnxR85dCFMOzZajNSwMlBDtePIYM7SAdVEpsSaE8S1BWqDQjcR106KFbCuVoBAJntIwtFjAScs9DuwrpOxghMblA1GC9u8qJh85RqhQQgQRfYpSA3KB9B6fg4VQKoA3aWDrr3c02x+O4FLR5wRxGQogtj/nxW4EHn2zfIZvHudUWmSfIR3Ki0KAQeKklque02yk2BBs6aBeoCNxEIBYdjNTIrsbt5lgXyC0qspjQ4Hg5rorpO5aH8zbejjKkDiobFPl0EkOYwzaqbKkHTHmS44118ousXoq14qL3+D5bhrq8dFhrtsAaZBwh17vYokCOgWh2ybI+spcgk0HLbpWCrnskEyFWZuSzBrZqfEC6kAY+3koK1pJnYM/lrH5xnqmOh/VLxK6LzSW5VDhjJcb3b2FsokqhVubBu05gzMWV+jLU5pDNZHXuq8poCEBITAJZ7zHt04R4GkfR4/czqeXs0QXKT5D+E5Rc6jM1SqMlvKLLnf9030WXljbU5pDNQgjB+moP4boMwhYM0ncRriKNE0yagaPOp6sJhBFIY9DWnA/yk1AOMcIM6hYZsFmOTFJMaohWuiyfWiFQEpnlkObY3CCynMJEhfrOSUama7j1And++tBmm+MreLa6HGpzyEYSFH36povMwWQZph/hTxZwpSbtpYP7WF/i9iyuEhRrYD2LiGKkTgap3ibDug6UAd9DOAFZ6kInYf3EMr1mn4LjYixkIwHGk1DwKG4fY3znKNXpEYJKgWOHL64AhUtFmxtyViHEe4AvAFcKIc4JIX4Q+BUhxANCiIPAa4Af34hzDdlY/uM3v54p6yA1SN+SFSQOEloWKwROFZTWoMAsGOSKJa5APOWeb0RhEBaUNygWaNsKkUoMFjSI9Yx4uUewmuImkrjiDop7xtBc6yLPJiQLOZ9/8Bx/d/v9m22Ox6GtfMbbxcZQl5c2P/RDb8SfrSE0SC0IWhavLjB1hWkrPJujvRyTZETjAbrq4C5kVA8nKAsy01jXoqQCF7JagCkFqARyBzplw1olId+VITwHZ0GTTjmkLjSTFv0ZQ6dk+fjnHuYL9x/bbHM8jqE2h2wWP/iL3wPuYx1B8tE6RIPY+i8Vt/7aVgJ1pEn60ePezzJNd7VLGsekvYy//6OP8PAXj3xN57iQDLU5ZLP4/p96C7gKoQZ5o8JRAweS62A9F+U5g25LscGkMTZJELEepK5IiUoNUiqEK7FCQuCCq7Cug3YlcaNHnpx3CkuBCBRCOuSthF67T7+XEq91+b+/+xFOHV/aREs8nmejy6E2h2wkYTHgW77z27FaQOgirMT0FLETkqHAStxc4gQ5xk2JXUHmu5ACVgEa2U9x8gwRW2yaDjoYZjlZ7pGWfDrNPnGWYl2J28qRkUE2e+h2Qm+1R9KJ6K+1+INfew/L5xqbbZKv4FLQ5oZUfrXWvu0J3v6TjTj2kAtLJQywqSJxBVYYDIpoEsIsRPViREFiU4usCfJEQDZoYZjKnO42gdcQyNxSHPHpWIMSmrTvYJWLLhuMK5heVsQ1hUrEoPRDQZBZWJYZQShQKwrTSfi7T93FN73kGjz34ihIbAFziYTlPhFDXV7alMoFdOiiOoPVF53kuMfBKbnoYoBMBh3SwshiOhahJCoHTYx3r8bWAmwoqAufaHkQHp8VHQQ+/lof1chxrqgiGiGxn0NFUVjX2Kal20kIfMimC6xnhr/4m89yy3W7kU/Wyel5ZqjNIZtJUHxM3QUAzPkWoQK0Od/9jMGS3FMEzhZrAb3mE9c68Ushme5hH9M0TieGTtIbFMcGeo2I9/3mP3Hgln3PdUobxlCbQzYTTyqQYKUF5WGFRJYDTDzoumSEAE+higV0r4dwAmyUkAuBk+tBMeyCS1D0iNajQeSf4z7azJBAEAYOmSwMnFDGYHVE0uqSeC5+OUA0ezQWW3z0fV/gHT/91k22yIBLXZcw1OaljislVoCMM5AG3WjibK+QWBfRTLDGUh21hPWEkyc9vCTF5hlaa5QjsMUQoTMqYx55ltBbVWjlII2FfgoKCkFI7uTIWIC25I6k1+kjAdf3sI7LyXsWuf2jB/mmH3jNZpsEuHS0efG5kIc8rzhCYYwGI5CxQrs5TkHgGIM1kmLi4FhDkQzPdVChRHY0QUtiAOEaZC5wQ8ms42NiDdqgEou7ZnASzZJNKSgXXRAUrEQWHZwox1nOyNoar29wu4JDdy3xzv/xt5ttkscgLnqP75AXLiPjFUyUYhwJRR/Rz1EIMsdBew7uRBlXCzylCQFpFVndI91SwMkMsqjRgaUQKkpXlUgcgRaGzDfEZUGuJO3FiHBEoFKNVwkJYsimBPGkoOMIkrJLUvD47MIiv/HeT262SR7DUJtDNo/ZK2ewX9ENTeIFzqOv3cL515anXJarTlUpjj5xAf7Oeo9COPjMKcivOE7S+3Lnws+97w7+9vcuplbeQ20O2TwmpqtYbTFIbJpj84zAV1grQcrBQ0+Wg6+Q1eIgikgppAFcF+EpsDA9O04wWsTECSbPsblGaovWEPVSAiUQoY9TCRCVEFwX2+6RrnUG0UmJ5q//+FPcc+fxTbbIl3h2uhxqc8hGk5Ijiw47rs8QrgDl4JsQFzOoMeQqGgsuC2cK+IlFSol1XJQdZLJYCWjL+I6dxLGHW7bgOeQOqCgh1zmJtoShi/TBdUBiIRtEEOosR4Yetp3xu+98D2vLzc02yXkuDW0O/yJc5nSihG6aUVgX2NSSj0J/KUX3M5zMEgkDyiWNA/oFS5ob8iKgLW4k6O110FOS7mJEY6mDmw+cSEiDnxos0B8H7QrstECXNHkvQ3kS1xqkk6LLKaWCj3EVB0+f3WyTPMqgcr14xtuQIRvJkcNz5GsJ6WwZIwVOM6LlWuKCRPgS09E4uBgbYBoxuY6gH5ErhS4qSnd2cRdyVvKIBhH4BidKUWlOVhMYY3DO9Mm6ltJCjttL6RU0QSiwviVNEmwnoVwKURF89s6LpybDUJtDNpMP/dknBylnX9qAtJsPilMLyJLzVaoH/SGelIXjS/Qaj089AyADawyOr7DGQg7S+8pd/NIg0uizf/3F5zahDWSozSGbyZ//1kewmUFhEaGLcT2iTjr4MPSxjgLPHdT+inLoR4N0NCEGJRZ8HysVpw7Nka71EFKBsghjoddHCsiyfFAHSUmskdhUI1yB9V1MbrBegFN1Ibd85oP3bK5BzvNsdTnU5pCN5oPv/xxaC9aiMjazYDX9tSbbd0UILwfXQUqFzTU205gsB2XRBQ9tLEpKLIITD5xBtzJ0BDIxOEKCC3kpIBLghQq/bsiTHBFlSE+Bq9G9BNY6uEUXm2luu+3iKHd1qWhz6DC6zGnpBDUpwDH4PUv4yKAodSwyTCBIPYHVA0eR6KS4nYwgEmjXkvoKeVbAWoZsZujMohLwcoFMDTkGkQGpodlP2ZlWmZRFPNehOlOhsruOMQq5LEjJEaGll+V86p6jm22WR9HIZ7wNGbKR+EoSdnOcXBCPKlpXV7EZmIUIv2ugn5BZS55Yohr0xh1ELcRr58iupTNZIB+V2EKKjQVZQZGXPJxigutnCCFAGTqdlMmXTeJvLRGGiglRZLIXUC5pnG0am4GTJqwu9zl0dG6zzfIoQ20O2Swmt4486WfKF0+ZhvZYbPbUn/fbCVv3ThEUQ9yCw9j0KGHtMRFJ51NEj9xzgsZFs1o61OaQzWP3gdlBFIFyyLGIfh/d7ECWDlKqNYg0xzR7mDgFKxCuQ+66kOlB6lqeoa0FqbC+g4g1mBzhOKAhzw1xP2F6skoxdPBDn4nJKoWRIuQGkaSoFMDy2Y89SJY+jdCfJ56NLofaHLLR7Ns7hqNS1tc98roHnkIbOHEkwPQtuSvR0mJjg840OtcIFWA8Fxxn4ABSanDdLLgkwkEC1ghwfJzUolc6dJcSRktTFMcqOGHAli11yo6PEAIhFY6bY3XGX//qBzfbJI9yKWhz+BfhMmd7tca2iVGyOqSzhnRCQe5C7tIrGdKKIawqlLT46xqUIitYHKtRPYMygBFkIy5xIBAKrGPRjkIoB7cNeQhJDeLlHr6RlLeFjFZCpsplKuUCbjkkRZMjiJsZ//kP3r/ZZgHAcvF7fIe8cNl3zTYmJ8qI1R7hiQ7BagYFD8oe+WKXoJUwUg3At5gJQRBb5GpK5gkyqZBIrJLYOYds1SLyQX0V2/MxBEQjLraRo5YiWm5EtSCY0B7lkQJj02O4zgiuLZM22iRSsC4T/v0vvnuzzQIMtTlkc/m6b7+F0tjju5gB6NiChOJIYUPO1Wp0qE1UGN1So1AL2bJzHLfo4BYckvYgciLppPz0a//LhpzvuTLU5pDN5E3/7KWEgTsojWDF+Wgi0FFGHuWIckBpto4QEp0O6gMa1wH0oCaRkgjfReYGE6WIOB6ksikHIwCTY61Fp5p+nlOfHmF05wTFqRpbZ8dwSx7eVIk0yzGdiOYjS/zCT27+dfPZ6nKozSEbzTd/z6up1QOc9QTRzkgdl7RaJA9cslI66I5WL4ExSCEwvgPC4MSDGoHCWISwkBtMDtIRYEEaA1mOTbJB9H23T9LqUp+oMTpTpzRWorJnC9IRBFWPaC0jz+HcQ+f43d/42802yyWjzaHD6DLHkZLf/M5vJ/B8ZEticsAVOMLBFgwqiYnXB1EOpiiJx0FrILHY0GIEWOGQeALjCbQUuGsaJ7GYTKKMwHMydNUgMcT9PsvrXR6cX+ZwZw0ZOoyOh+SdHGdxUAitG2Xc+rmHN9s0AAxG/cy2IUM2Es93+blf/z5KwkMUQlQMMgUlfVQ/o+toWtWEvK7wlxX+2T7W0diqRiqJcATWCmzboNrZoDBgVZAXHEziYD2F1ClOnBHUMjo6YuVck1MPnWPhoTkKKEquQ1ITJGMS4pilTpcHTsxvtmmAoTaHbB7FcoGf/aunaMZjoN/tb8i5lOfQXGqxeHyVUw+c4+wj85RrRQqVr6x9dPrQPCvzF0fnl6E2h2wW0zvGecfPfdugY2GSYsMQWSqgij54DibOiVKLzXNUGEDgYvspIsswoQNKYbXAZucL2TseVgqstQhr0dpgpcApBfjWsHBujYVza5w8tc6Z44vUx8pI38NmOUJKrIQv3nqYXveJi9s/nzwbXQ61OWSj2X/TNbzuu75h8BCZaWRqcBfXkEqi+4qsE5En6aCzYSFAKoWJUmySItzB99HmFpukyH6M088QWoMG60ioSvJagCwGSOUyN7/K0rkGR+89w+p8k+k9I9SqKTbJsSbHKMkHfvfjJFG6yZbZeG0KIX5cCPGQEOJBIcR7hBDBcx3j8C/CECYrJULXx4sVxjXkbo7uZTjZoClEqix5bkilwLggcoNFIowgLQnimkBGGtXN8VoxuZRIbQnjHBWmqLYL2qUrLFYoxlouO4tFZCene6rJ/OkmOlTookAHEI8pfvVDt262WQarUlY8423IkI1mdLyMX/DQIy4yTYAYmyXkBTXoNnFW46yDjXLINdmIReUS61hUo4e3mJDXBGIasl0WETnkjsaOWKRO0WMlZCSI1gUWy9gtRUZ3VchqcKrU5lzUx18F1bN4kcVbSPiNX/+bzTbLUJtDNp3RqRGc8MkrWtsNugcVDB5Wa9MVqhMl8lyzNteitdh53L7v+rn3bsxJnwNDbQ7ZbLbsnsSphIN6RI5Eu+e/Z6nGGovuZYO6YFIgfB8ReANnkrbYLEf3Y2w2uAEWYrCftgLhKPAcZG7Qyy2QDo6Q1KerhPUSedFn6UyD+Ow6WghwHGzBx2Q5H/vwfZtqk2ery6E2h1wIRrbUECUX6bs4OsJmGrHUoSDAkYZMG3IhIM+RViKERTkQVhKqUz1skgJ24Ng1ltxTmLIPFkRH4LcsOspxXRdPuIztnsQLFXmumTuxxOLpCOO6iDDACoFt9jl8z+YWpt9obQohZoB/B9xorb0aUMB3P9dxDh1GQ1ju9ZAMooWcngUtSUqQakXa9kBDJsC1mkIzQaZgPAevqQlXU5zEYtEInaANEAvILcZaREvhtCzWZjTHLNOjJbYEIXFHk3ctQivyqkImYD01CPn1NWv9Hg8cOrfZprnoQwSHvLDpNPtoD7QjyMouSilEJ8cRDnnok/qSXFmyiiGbVIjUhY6LqTtkWz3SusSYnCDoo40hrgqEVagwx9Y8skBidEJ6f8Tk3iqFkRqdJvQ9B4VLYS4jKUtkYjBCILKUI6eWOLG4+ZEMQ20O2Uz6nQiBffodn+t5un0md0xQG68Qd2Js8uTn/NhffY5+7yKIZBhqc8gmkkQp+C666GF6MTZKIdfIfLCwgs2xpQLWc7E2R+QaqRycbOBQEr6DsRajJFYpkALlD5w/eD7CczFKEff6TO4Yp1IrofsJZqWDdF3y0Mf6PkaB7MSYTsy7f3Pz66VcCmkvQ17YGAQiLIJSmHSwIII2pCSYVEAvJd1SJJooYsmxQmKFJOk6pP0QgnBQJRoBSQZphk1STLmAV8whzxDakKUJWyaL+GNFUk+S91MQPkY56ABsnCLyHGE0v/Hf3rfZZrkQ2nSAUAjhAAXgOacGPEXD1yGXDbmhk8eIQOK1LDaz5NVBMbJAWFRgyXIJ1kX2Fb4RaFdgHAfV1diCRfsKoz1sIKFuyKQgQ+FkgDa4TUiU5nCrQT+1GN8SdDW9ioPxBZ4B6Tk4YR+RCmxsuf/+01xz1dZNM8sgr3ToUx2yecw3OqRrCX7VIa4WyHJwE0tWAhkOIvlyA/g+qQ/KcbBtg81B1wSqZwFFu+1gEovb6w4K8h53EaGD9CGrhvSjjEfua5OuZtieJtkuCU5onNgiJy1OqrBpinUVRhsOnppn19ToptllqM0hm81DXzhEFukLfp5eI+JM9+ygNtJXI+CxPqu8lzP/yDy7b9h1wcf1ZAy1OWSzuf1TD6PTHCkVBB5Ga6wFYQxCANYM6vlZizQWqRzyfgJGQ9FHZDk2cBF+wKBOgxgUwNYgpB2khBR8ms2Ydv8c+XqELfkYKTFCIEsejmYQwaAUop/Q7iT0s4yC626KTYa6HHIxcPCO4xgX8rJCroNyJVY5WJUjrEF4HmotQToZjCnkvMFYg+pCkijw80G0n1CIXCKSHGEFIgBRzhBuiM0zls6tQSjRvZR0PMDtZ+Sug3I9vCzF3SrpxB60E5aWWuS5xnHUptjka9TmmBDirsf8/IfW2j8EsNbOCSF+DTgDRMBHrbUffa7jHP71GAJW4GYC0wOTWmRX46QGpQ2ZY8gcgTIGYUHEFuGfLzDmSJQjcBJQWqByhcwG+8lcQAqyP3igNTg42kKmSI3BFjXRLknuCqQWuBq83OA3HIITQEFxeHF5sy2DRjzjbciQjaZU8ckKisxI0lCQjEvyQobINaKt0V1NFgyiA00ZXJPiBhovtriRGqyEehI3kHhWI3MHP/EQ0iH3BGQWlSryksJoRZ5abDemeP8geiIvD1JF3Zog31+id6CMyi2n5zY/wmiozSGbSVDcmKLWT4sA80RBRWKwCU/8f+z9eexkWXbfiX3OXd4SEb8t96ylq3eSzWZzk0bUiKIWDj0SIZuwgRlIsOQZwx5BlsYQ4YFhGRh4DEm2x8Z4gNFYkExZEjWWLGlGIj2URVJsLhLXZrPX6r32JbNyz98Sy1vuvef4j8iqyqquqq4lf5mV1fEBHn4RL268eO8gvr/74tyzvHQnJxG+9FvfuDvn9QbcSW2KyMMi8msi8tVbNRn+8muMERH5GyLyhIg8KiI/cCwXtuG+oGkq1HlsGCBlGDOqhlpBxxHrCqIZGwa08lgw8II5wWdDEFxdAev23gCqhVIFbNoChmWF6ChjpuREdiBxXZg3GFgUnCt4LbjocSXzu7/+tXtql7eiy828ueE4qKODrqc6ygQTxgcmxA843DKQFcZ+IC473I2E3gCZBqgqhpOBvBWR2uGlrBcvJy0SK7yCDIl0MEG7QskKUbBRwRvNZESCI45L/IkamdZMzKi7AgKl4p53AH4b2rxuZr/vtu2nXjyWiOwBPwF8AHgAmIrIn32n57hxGG3g4dN7+CW4JsPM4UVwyfAjSBHGCeQo69aitZAEmj5T9RlrC/6kIMUoDWAeHQQGMBOcd7gQSDvC2Cpdl/ELZegr9MgxncMYYd5CbB0Uwy89cuT54s2L3Nhf3DO7GHc+tF5EdkXkn4nI10XkayLyB4/3Kjbcz3ziu99PaRySRjxgrVDqgJ2qIXqq6wm3MpwpsxuG9IVwOpFOKIwLfFVQGcmHBV8CYso4cTinVEGQHHHdgB0VVqsRm4GLNbJX0z00wV/pcRc7pghZDd2pYBb5rV/6Eikff3TF63Ec2tyw4a3wY3/2D7+zA7xRfPftd2YKNhrf9PvN1q9NZu06rP/W65/8f/86ZsefKvd6HIM2M/CfmNnHgB8C/pKIfOxVY/4k8JFb258H/tYdvKQN9xn/wz/zhyjDgKnCOCBeKG3AdrZQ77Hg0AJOwIZb3dO8x09aqDycmpGjxxTAIDhAESs4zThbp9KkVU/en2PewWrEeY8Xt45WOhpoZu26gHYVsdH4V//4d+6ZTd6qLjfz5obj4H/yv/zjWDJ0qyW0xp50+P0RF3pkp4WqQTBc7fDdCPs9vigu1BBrcBNy8RgJ9SCmoIaEiMPwswl2YoazjE8FenAvJHz0iINyuUePBoZuhnW67h68Kvz83//Ne2aTY9DmvwM8bWbXzCwBPwP82+/0PDcOow0A+D3BaogHSjoVEITpHMwLrqw7LIXksCogK6E/WaEeNAspGX40ihPMs26FGB0+G5UKGpRwYkBnEY1KPcD0cqHa13U7xKrgzXhkUdGkgFZCMxo3hwW/+Kl72S1tHSb4Zrc3yX8F/KKZfSfwvcC9XXLa8K6nXib8cqQ7GamvKUyF/OA6F2VoChaFcSugvZB8JFxxeBN05lDLUDtCD3VMNGNEeiNHIbcVMgyE6yPtdQNvdNuB4ZHA8L5AtIKooVuO2fdPqLcjokI+Gbhy6Qa/8Zl7GclwLNrcsOFNI/I2vle3vyW/wbFf7R0yEBHE3Yomuu3lyc4E/Pp1E3j+8Qs8/9i9XC29s9o0s0tm9rlbj+es58wHXzXsJ4D/xtZ8CtgVkfN3+so23B/MthpoAmVSoWd2sN0ZMa6LVct0goYKCYJDEBRRw23XWIwYDiuGVRWljrhpjQsegl9LLyulCiCGuYi1NVKMsOpg1UEesTEjTpnsbeF2G+xEBdOKbzx5heVyuEdWeWu63MybG46DvZPbODciusKf9ky80s0jZdLAToBxgOjAuXWZotMtdm4Ld5RwY6IEowiUqsK1Ho0RtQJyS8tlhRel3oVqknBOyEuDZSL7ybrjWojrhZYoaBT8PPP5X/sSqnqPrHLHtfkc8EMiMhERAX6UO/Bbc/MfYQMA/+GP/BD0kWgD9ZWB6jCTnTHrDb/K+C5TolDGjBTFhsI4FfK2I2fDGqXqjVILxYzsFNsV+u1CmRjeG6koyXtKdIShUB8pulKqm4LHuNgtGUwJzpBROLoB//d/+W/u6Wqprv8FvantWyEiO8CPAH8XwMxGMzs43ivYcL/zYz/++7CTLTb2dOccwyRgvaJnPH5P4UTBdwItqIOVCfGFjjA3/JHSzjM683SHAa0VGZXmcKS+1OFXBfVKJhOOAlGVvCP0ZyBFI51uER945umBcX/tQEpNYJUKf+3/9M/vqV3upDY3bHirxCrwiT/2XW/tTW90P3rb19ReIwftxXlQENb3gOv3XL9wA24F+4kJ/dHIT/6R/+ytndcd5i1q85SIfOa27c+/3nFF5P3A9wO/+6qXHgSev+35Bb7ZqbTh24TtnQkf+tgDSC5wMMD+EWXZo9ERKgHN6FhQU5wJ5h1FBQuCtp6ihnQ9bhjRvqBjhqQQ1vVWcLL+gVoUmhrVgoUKU48uemTRgVNuXrlJWSyhSagUDi4e8Jf+3H91z+zyVnS5mTc3HAfn33+ahz9yEpsb84vGzYsQpGAHir+8RJwnT2aYChICmDCkjKiSK8MWA6Eo4gJ22ONcD6cjJRQMoTRCmffMrwjzviF5KE2NFjAHFh3UgRtHC5hV+PftUcWB68/c4D/9s/+Pe2aXO6lNM/td4J8BnwO+xNrX81Nv+KY3wabo9QYAHr96HW2VfhrJJxyTuVFOZyQVmm8IkjNpL9AsMlavuzKNbQ3ZEcWAwrgKxMNEgydPK2JXqFJgCMp4rUaKQ2vQSpCUSDPBvKHeI11iWTu881TiSScc2QasMz7zhSf5/d//4btukxdbHb4FXrcI2S0+AFwD/r6IfC/wWeAvm9nynZ/thvcql56/gRuV6ZWCkBnOtMSLc0obWH18ghMjXnH4D/XEQ9Crmbg0rDekhhwKVYJhT9CpUF9zDJMGjcbkQLCzW0gS6BQnHj8Y8kzBBvDZ0eVMdWWk9RG/65k9OzLvC4eTkScvXeND50/fdZu8DW1u2HDHObh6eGcO5HipeLVEgXLLQfRGayUvFrwu6/eHJmClUAosbiy5ee2QE6d37sz5vQXehjavm9nv+1aDRGQG/HPgJ83s6O2e34b3PqrGTVO8Qq6FkCtwBkcrNDhICSkFEw+Nw2YTihl+yEgxqCJSVWjXQRXxrgJRZNZiRyvIrDuoAaQEu1N0UFwQvAil60ldS9UIcmKLphN0zKTFigtPXGMYE3V1d4tfb+bMDe8Gbtw84soiYlXAgic5QTK4krAhr+uAHfVISuAddSeknPFDoaxAsiPgcDcXiBo0FbI1oR0GdEwMvVvX+QkCk0hppjTXO0QSPgtW1/Qx0Ow43HagKh5JgeRGPvPok5jZywsyd4nj0KaZ/WfAHV052kQYbQDg+gsLLBnmPWnqWZ4W9GpgnNeMZxvybgNJGXY8VgX6ukWjIJrISSmHLXGuhGyEFbhBSQLBebaLx68XXNCpUpmhzmFm61QaKQwnDW0hjtD3mdVBQqIgU+Ef/sJnvvUFHAOGkNW/6Y03KEJ2iwD8APC3zOz7gSXwV+7yZW24z7g59mg2vAbivtJcGBjOzCh1TXwhogc17HT0l6HrI/ULAstCRhm9I+1OybtGaYVBKzpz6FFi0ja4hwPaetQLVR6oK0jBUYLDHxZUE9WlkVIS7mxm1Rn7OMJqJBbj7/3iqxf67w5vQ5sbNtxxjm7O39H7xb1ce0jk1uMCk73JK25axck33cS+lKIG4CGvMmV42cP0T//zn31H5/Z2OQ5tikhk7Sz6R2b2M68x5CLw8G3PH7q1b8O3Ic4Jy9VAio5QFBc8xXt0WmNVhUxn6KQh7TTk4Cmm0FSUacUYjVwL6gx8AHHk2qGq6MEhO7MIk0CpI2VWw25N7BKuH1Az8qxFt2fYtCLXEXGeNE/k+Ygzw5qan/2nn7rrNnmrutzMmxuOg8p5gh5hlaMeRxgT2Stl4hBnuKRYLXA+oA40J7wDqwNxofhFj5ZCyYXiwDrgckc3L+ydnmIhUDWOZlpwWw3NzQ7JmYxhKeHGgjVGPwr6xJKjC0ccHChOQaLxa7/06btuk/tFmxuH0QYAfvgPfgQtUIJBEZIEKCDF0OmIZQMpqLe1R7gDGYSwcsRxHTFkppjeet+ojHXgwHryIuEWUK9GqqSoAxMFhTBCWAlugOQMSYpXQ0UJKwhVYn/S3zO73OHw3QvAhVvhgrAOGdx0c9nwhvxbH30fZoKlDHj8qge/7lDor84xCk4KzX6hGgtlC8pU0K0anOBnI24iIHHd5aXxhF7Q5SG5guF0ZDgl4ARdOgiKc1C2KoatFieReGS4EPGThIw9+XRF2O8Znrx5z+yyCa3fcK/5wHe/780PvtXV7BXPueX44eXHpsZqf7V+7m5zCr0B7jXGDMP45s/tDnOHU7mFdRr318zsv3ydYT8H/M9udUv7IeDQzC7duSvacL9xZncL6oiJp4wJv8rImLE0olKQnBGE8cS6gYQkhaz4pkFMybvT9f5xRIaEakGLcNCNmBqhG3HDiFOHYlCFdae1ukImDeCwVU9wsnY2NTUym0LK+Pre/ODbpKRtuNds7U7ZfmgLbQpFFVkNhEWmNDUWAuIduupJvUAV179Lt2q0gtwGSu1hGvDVuiOhia3r+iwK114oYB6ZwnSa8DmhVYCsBDOcGmjBX1wQrq6op1P8kHDDSAwJxpHpibsflQv3hzY3DqMNAPxPf+AHaEogVQ41pVooLgMG0+2eMl3/gEQ8LiZmlxPttcTWamRPllSHGZfK2lMcBVOlsoKoZ4gQ5gZLAcm4NGKqeANJhs9Ge8lorxjmHNWkQvYqvAqjh0e/9jz7N+9+t7Q73e3FzC4Dz4vId9za9aPAvazqveE+4N/7iz+Gbx3VIlO80Z2dkisHK4W+0Nws+P2Ic0KQJeN3VpRHHOVcRttCN4nUs4RIZvRKqjJpCmn0DD2Me5B2PCYjk905sRiuAecctMLq44HxIxOGMTLtG2Z9QHygJOVff+ZxUrn73dI2XdI2vBv4D//qn/7mnbd93V5y9sgtx9CtqKBvjhiSb37PW0DLuqvM7fz8//NX3/Jx7gTHoM0/BPw54I+LyBdubT8uIn9BRP7CrTE/DzwFPAH8HeAvHse1bbh/+I/+5z+CFFBZN2LRSSBXcf2dk4AXTxwy1VyRRYekhDNbO3QWmbAYwK27kpbo0EmDxVs/YFOP5hG6AenXjSckeLwaYoppwh3OcaNSBOqtlugdTCO6GPmpv/5aQXLHy6ZL2oZ3Cz/2E38MmSvj9gRtKlxJVJeP0CBkpwQcTTcyOZnWU+NBh3UJPyRkVCQVaBvKzhQZRhAwZyQtlG6k6yPzixH2eyTltacjekwNUyV6T7B1Xd54apuwV6HLwvhsx1//9/7GXbfH/aLNjcNoAwB1DHz4oR2iCRKNvOXIlUfFsbw8wwyqo4xFR3UDqs6YHjlIMHaecRoRizgE3xeaw0w8KpgK1ZFQ1yOhLvho6CwSXMBbYHVOKZNMiB5p4HQTed9kynbvaIoj3ICg8FP/9N60PDyGjhL/a+AficijwPcB/+fjOvcN7w2mIXDuwR1KGwkrZXZZ8YOgE4+daNdF/k4YPcrw4AQ7Bav3TZCpI+8I2gdu5hlFPL41yglHmThyjpS2RlaZaqU0ppBrSu0gCu3zN3F+hfhIHB0nz9dsv79Bzkbk4ZbxVIUG4ad++bfviV023V423Gt2T2+zdXb6zS/IK51Fx0qAc4+cZmt3SjUNr7ir+4W/9yvH/OGvzR3ukvabZiZm9gkz+75b28+b2d82s799a4yZ2V8ysw+Z2feY2b3JY9/wrmH7oZO0HzmBiWHisDTibB1Z5DCkrpHKgxpOAiE4YjEQQUtClj0yZHwRQjbUCWVa4ZLh1KOTFh8D0gbcpEXMGOtALgWd1vDgDu4D25w6s8tkt6E90cDBAu17bL/jM5967K7bZNMlbcO7gYfOnWb75C7eOyTrukRJ8BQPTAISA9VUMTwSI7UovqwLX0sQiveYjUgoaFVBToh3UAznHeIgTwLOOdyYQRQrBfMOMTjxUGHnfRUn9qa0e1PaekLebdCc6K/PufDU5btuk/tBm5ui1xuA9ernB85u8djzN5ABYldIe44m2brTRAcmUPdp3YklGtIUtPLoEPB+nc4mlUfGhDXrEDsTRSaeNhZ8yJSDSBiExQmPHwvMjHhU0Fmgm3mu5J7TnSPXGRsy9cJI74PL+1fuvlGOwZNrZl8AvmWBzw0bXmS23XJiNuPiC4fkiUNmnunlnrybsRpidCykQrYqivf4qwWXMjYYKU5xWfCrjOWAm40wF2IWxBKu1Pitjh4QF+nGivaZETcD+gHxkXZfcVdHFlmZPtiQVMmdks/XNJcSly8f3H2jbFZAN7wL2D29w/bJLeZXbutbYK+MEjq2ApovFr3OcO3iTSbTmrHPr+jE9sLT7415c8OGt8reyW32YqSLEQ4WOO+xqUNPzwgLRcloViQPSNVQFHRak1shpoR0BQsOcYIhuOAxcZRJTe1Ax5H+5Iy6rhhLQaPDDQU3a/DewbwwdIWbi0RT18xFkBO7uHmHBePyO6x/9pbZ6HLDu4RzD52m3ZuyeGyOV0WzQuWJ2xUljehBYrFyMIXSBiQZeMPqsO7AvbeNm3eEUZEhwaTBxoTLGXduF7dYrItXz3YgHXHm7Ipr12vy6HA1BBsYzTg8WCG7FcN+R8SjdSDuOa6+sM9DHzx39wxyn2hz4zDa8BJP3lzgViN1J/jRE7SgW0ozOlQS2QdQQSJocLiiOBXGbQ9zg7ow8R6JSi6FhKATwQ4KSyBveYaZY5qVuFIqE+LFSJh7vCvkrIzeuJB6FI8bjTKNlJB44vI1Ui7EcPdyvw02edwb7jkiws3Lc8YZ6LQiTQw8hAXkWU1wI34Ei4YsBN853MSAlvqw0J+MVCtla09ZREe3De4gU8+F7nSmOqqoLq4YADmTSRNHfdqhcoZwGqpRSQmGZaF7ZiSqUEaj6cEtC1/60rN33SYbbW54NzDZajm8cqth14sOHHjrnVY8625nb3aswrrQ2No7VPrCvF9909Av/MqX4a+9+dO4E2y0ueHdwJm9LW5cOMQNCYLHvEOz4SxjwaNZYNqQVQkq625nk4jTQppWVF0H0eP3pqRhRFY9XjxgDNFhbp3iMqgiSZFVT9ydYi5QLXts0VGCp4uOYVUQcSi67srWF377X3+dP/XjP3jX7LHR5YZ3C9t7gaXbh2lgMhtYXhGIHm5mvMhL01uuHYittVlFbG+K5h5/c59qCe7EhHx2l5ILfiqUVWI66TnKAYqgOwV3ZJgEqvM76I2EBOPydQedAgfIAYCnqOHrSBmMX/5nn+YHfvi77po97hdtbmION7zE9dWKtBuxKkJ06JZDk0ezYR8wYixE7xl2POOWow6exgV25kaD4Uzo+xG6W+loKVEqIWelnTvyymNmaG2EeUKORpo0xw0JtzKqw4IbIajh+4wXJZzIqINLbeaXP/31u26Td3tO6Yb3PmbG/Noh9Q2jaIbgqPcjmKdkIX55yWQbJlIzfdao9j1hWhF2ItVHHVWdSd7TDZkSlOZMT6UJG5QcCunBihIj1hcsDeQHC53LDK4nTzNlB/KDAjvrYvVpyyFRkWvrkP35o9f5wuMX7rpdNtrccK+Z7y9YHnQv1SR6sevZK5xFr1rjcLUgAYi3jXkrZcAK6zvMovAtnFLPfv0iLzx5L8LrN9rccG+ZzztS12NqsN2ibY3EgPmImODqmqqtCLFGZxHxjqbyNMvMZHsGOzXaBFJKuFFxyVAxShByHQhu7SNOeSSJok2FLnvKkElOKHWEoviqwkxBDBPQuiZPKr7+jYsslne3ocv9UCdlw3uflANHc6OZFGYnM7o7RWMgO7DViAueam9GGBS3GCE62u0p9WHHlgaq3sHuFpjiVgMyJGwcoHL41ONHhWViFR1pt+XqtS1WVxO6GilHI66sU+Bkt0HMoVl54MGRUx/J9E748hefwcy+9YXcQe4HbW4cRhteYkiK2/eMjZHrQqnBxCi1xy57HELdZUKCmI0UIcVMP+0ZTEitI6hj2PNo5aiLR2sYT3nSkGgXBQkZLUI6LViTGUeh98bhTNGzRjotyKhU10c0Ct3K4y5H8qLw9LPX76o9NoV1N7wbWK16sgh5t6a6mQk3e/wiQRJiD8O5KSEnmu0E20KuFUZFD0byhTlu7qhGY7CKySWj7YydXaU6zEyfTZhbMZ4RLK+1Hp4ckWHEt4V8kEiXRnisR25m7IQxfighBiV40ukp86w8+uQLd9UmG21ueDdw4fFLL0UVvcg3RRbZq58KloH06je+jRP4Fje1/Xzgi//m7vZV2Ghzw7uBrz36LOI8UkUsRiSE9WJon1FZO3AohjZCCcCkAjNSHei7kewAHNIXXPBI5ZAYYNICjjIofqU013qaa0ukapCDETnqSNkYfYDKI6sRxkKZRLQK2LJHcuHwyhFf+J1v3DV73C+FdTe89/nsr3+J5lIiPTdw9YkalzM2jqAJ592666cI7G1huzN8iJhTitp64dMJNo6UYR2JhCl+cJgF9udTGEHE0T7fQ1asZI4+1DC2kdEJZorU1ToKqS9I5dkvFfsHHkW5+sJ1nvzK3VsEvV+0uXEYbQCglEKpIERBohA7o7quuCCMVUFWAkslO1t7dQejPioUK2TnsVoJhxnNGRuN5WmHjIkmJepB6c4GtDK8OsZacclD25LHitIE4gild5TGoQFoPW6vJYjgVMlF+MozF++6Xd7tAt7w3ufq8zfxBVwrOBTbCqRTcd2RpQwMuxWLJpAVJGTkhJJ3lLxQyn6AfqRsG856VgrjE7AInnFmDG1F+IaiCjiQboC9hqY0KC2sGoZQ4yYRdzMhRXCDp0mCbEfSVBAKX3767jqMYKPNDfeem5cP1tFCb4TyCmeQDfra497sguYbRRXVAvGVux7//FNv8sB3jo02N9xrLj1/g9D1UAVkmXE5wZCRPMCYscMVZd7jhpEwGJisO372GdePSAFZ9pCMshzW2Z9FMc3oJKKTCisJrT3aBOLRCjdrcM4j5vDm0WlNqQUXHDVQ1Q6XlXUzNuOxx67eVZvcDz9KN7z3eerJq7imQqcO9QHxHgsBCRXqBQ2eMq4dPaKGxop01GE54xcD6qGkjGkmpQKDkmPEmojk9YTrcsH1A6VSJI3UQ8YJ4COlipglrBF0Fqh9pCRPqI2mK5RrSx791ON31Sb3gzY3DqMNAPQ502RHsUybMkSHVevinWEOqXa42YSddkKVHDIoqRgsCtILpTbMg04jPhviHRYdch1sFOKhkaeOkh0+Cj4WZm1LqQN+MIo4XOdoXgAXBGsDH622+cETDzDrjTPtnMefvcLQ57tmE+PdL+AN7318cLidljzP5DaCOYaZrSN8JjWxM1qpaPdqqnOe5B2pN8pOoex4XE74Q8GvHGVb8EUo4hjON6gT0m5Fc72Qpy1uAdvbK7Z2I2pGag1EyWc843fWEMGPFe/74Yf44B9+GNtyKJlvPHvprtpko80N7wa2Tkxx4Y2/X752THcnd6ZbmkC9U/NNBxPWjqQCn/iRj/Pwxx966aWnHn3uDnzwm2ejzQ3vBh758Hl0uyWLYZqxVBBTdGsKPuImDe3ZbbbaCdEE7XrSYsS6kWKG1etUMkOx6HDiMC+Qbb3fQTo1wyFoXRPOtIQzM7TyGEaWTOwSfjrB6og54/s+/hDv+8BpnBZEjaeeu3vpom9Vlxttbjgu/sAffAB1hlb1OtJvGDgxXfHA6TnFrwMHJls1bVUTKoGjI7QpuDZQZjVSBJvW5GmNEyGIIBhppyI3HhyUPYEx01zpaXZmTG8UHIrrB9yyIywTHsH3RhU8f/SPfAff9wO7QARxPH/h7s2b94s2Nw6jDQAE55hfGfAnRmxWsNoo6khNQZzii5E1c3PoCWagkGshxRoRj85gOB0xMbbOt8TOENytFU+luEKaGr4XXA4MWwX2O9oEREU8qIfcCFZ5RBxnpzO+48wJHjx3kq6O3JyPfPnxuxvJoMib3jZsOA4mu1Ou24g7XOKuj7hDQ5LgVwM5JswV8uHA9ecKQ1l3cnELw8YKpMZf9lQ3Rpwouzkw1hULqQgrIQ4FH0bqrsdaIVhFc3CInhxwpz2TNFKVhK2MsG/ERcE7Ye/MDh/+zvOcO71F7IWLV464dvPortplo80N95q9MzuU/o1Dg8qgLPdX39Jh5BrHm/EqWSpr55DcdvtmgAgxBmbbDd/1+z9Ms90A8NhnHifnu7fQAhttbrj3tK1j6EckBooTSrV2+KyLVWdw0Pcj3cGcIhkZFcEQ59Y1UMSj2y2622LvP0Xeikif8Flxy4TkjNaCquHGhPaZguK2a3JlWOUxFxm8QS14J0wnFR/7/R+injRYzjz6u3c3+u+t6HKjzQ3HxRAiA0LAkKhYaFh0FfMbsi5K72C56BlWK+TkiFRGeFBQl6lcTxU6wryjKRDahrJdkfcaBI/HYyIMZ7bRWYtWNUPOyOCo1GNO199tV7DU48SQ4NBwgsnsE7hWKNvGZ7/49F21yf2gzY3DaAMAwXuKN/J+Tb+KlCQQFJc9+LAutHm0XnlJt1ZqcmuUnBj2bN1KOBRMBDnKhMEwE/xCcT3ktmLvOcdJHUhnR7QPzFPB5QKDoEVJjSNFT54IbjXgH6jYXw2c+cgJ+m4bccpnP3cXJ1jbhNZveBegRkgG2zPYiqhTZFUos0hlHl850naGkil5JIae3WHFtrvB7qUVFjKSFRXH9tkB3VMkGuNeIZ10yKIm/tAukzrhbh5xudvjxnUhRsFPwIWMXznCdcfsZmav3uehKx7mA9/ZnIRZjRXll7/xxN2zyR3Wpog8LCK/JiJfFZGviMhffo0xIiJ/Q0SeEJFHReQHjuXaNtw3PPuNt5Am/TqZaGsE7wKvlZdWbd+WY2YwrvI3FdJ+kazKbHeGmvLhTzyy3pfhU//yc2/+PN8pm3lzw7uA3/utr+OHAilDEIgV43ZEtwIWIZdM6QtZHIZjPLtFaQPjpEK3atIEyEptSnPU4UxQ77Ah45cdBdiqKnQaKNOGTjxpkfEGOMGcrR27vVH2anRa4c+fwNeOD37iYVxw9IPy+FfuUqmFt6jLjTY3HBe/+c+/TFjmdQ2+0cMwkHph3tfQTnAdlHlPHgvpaoVKQ3qqxlYFBsgWKU6oWkGDosEjFOJT12DZo3XN7kJwtYfKkYqSZF2DzPsaaQNWAqUT3NYE26mhCqgqD3/0PF4C47P7LA/vUuT8faLNb5V9v+HbhEcfv0BuwIlDQsZaxV2viUeQg1IV0MojATQKYVGwqdIosFTS4BEHWQQWEBcJA4ZZgGBMpSctA2lLkJUiK09pHLPgyFEZZZ3nXayQJoL3wqOffZa2rXn6sQU5ZmQGfnL3hPJiIbING+4lv/TJL1AdjNgk0MuAM4c/uYPOHHExUHYqpBNilUjRowZlzOikhgdXZJthJoSwjvLbiiOrSxV+iJgqVMqReqpg6/onU0czGO2JAC84FocTXILsMrpM9Ncrfu/gWVYXPFeeuYnEQjwaCePds8kxaDMD/4mZfU5EtoDPisgnzez2isF/EvjIre0PAH/r1t8N36b82j/6zTtzIFkX4nw5wuhlx9G4fHULNWH7xISja6tX7jawofDob36N5WHH/Op8fTy71b3tLrGZNze8G/j0V1+gtDVxHEl1jXmPFxAr6+i8EMmtp0wi1SBUfUHmI3lWU6LHZxC/rg1Y5QLjAEHQaX2rtgpkK1gMkNdREflky55zdGNAbnUh9f0IK2EVI49/8Sn2n7rJUZ9hSORJYPBv6Em+Y2x0ueHdwlNfu4BrItJ6bFiCAx8dJoGC4EKA7QlSR+h7Ssm4PkOsyD6AKt4VVBIBT1FB9jPmA2UaUSkUdYwOzBnOg+w6ZilwOO+wZgfTDpVCEqVcX3H5ics88Y3nWA4j7oSQJsZ84ZnuHL897hdtbiKMNgDwD37xd3FmbHtH3XvIHic9ZTejU0NjIUeHW4KrHHnPsEVgaGoGrejPO3ISKhPKLmhQum0jt4rPGR+VMgmspMGkwu+ti/9JgXqArWs91fWMG4xRlbTtuH5jztVLcxgVN1XGvcKv/fbd6yoBm5XSDfeeX330cUqlTE+2sNUiREx7wpUlzUEHJFxRunMe6RyuzxzWLat5zeJgm/D0iDoh1w67GtgLK8KFTCExRqNkI90U0iKiriHue/bOrnBdZjzp0I9llg8LJSgljZRF4QW9zk07AoxwlODKEf+/T9/dbkx3UptmdsnMPnfr8Rz4GvDgq4b9BPDf2JpPAbsicv5OX9eG+4Nu1fP1z68LY26fnX3zABHeSuEiF/xrF7R+9e9JB90qraOMXpGWtnYyXX7iCvNri1tpa+vj/cL/61ff9HncCTbz5oZ7yZVL+1x7+gYuePY+chafCm7RYSVjeKwKiICNA1VfKNGjzrBiEDxSDHUZ80Kpgd0aqQNpWiHZsKqCkhkXS3wxJCuhQHMwMBx1tONAmHeYGqWpyauM9pmLX7jAgYMiBqVQViP/4tc/e9fscj9EMWx4b/ONR5/h4OYRTgof/njAnXCkExVsga+hbjLe9biUkQbKJKwdFVFwlUMNgh9wtZFqR5hMcG1kbCBXDo3g+4IeDISDDt8X/BiJh4XFcoXMGrCC+yjY2XXR7SSer331WVbB47XgFon5kyt+7r/79F2zy/2gzY3DaANX5jf53QuPIk5B4fx0i+qqIy6F+tBzYqygqYlZsADZG5ocfnC4G9BveUoFWoGMSt8Zq92GtNtQgsf1ge6wWafWrApdLfTBkOiZx0LyIFWNNyEeKfVNyK1g0VNSYWYOVwLTNnFh/4ALL9y8K3bZFO/ccK/vKeT6AAEAAElEQVT5lZ//HF/61OPkIOgunHnfNpxrKdseHp4iD+9iuaLfCXDoYGmkqWBbDXo2UB0obiUwOCwUriyEa1/Zwd+MuM4hs4DueCQI2jraGxnLiZuXJ3CwohQjhgptPfmhmuWJbdK+MJ970mDUJyJx5nDOeO7iVVTv1mrpW9bmKRH5zG3bn3+9Y4vI+4HvB373VS89CDx/2/MLfLNTacO3CX/nr/xDrj21D4AqL9UMAkAEqeVVDqDXewyYMs6HW04fe41xt41XyENCTG4Vu3a3tlsOKrs1/La0tWfeSurcO2Qzb2641/z1/8vPcG3/CL8cwHvimRbaACL4AHUTwEMYFZYFcqHYiE08JgUpBfUR1NAxsThakQo4M0xHfCowa2F0qAfEr51L2ShRMZdwbcSrQysHVQ3iUB+pJhXN+Yjs1ASBx75wd9p33y+FdTe8t/lrP/nTLK7OoVvRpMKpcwEJkTyZkHcaJEB/usHUcPsDvnjy1hTZ8TAWpAlYA2Rl1UeOUk+xDirBKgddBu/JlqgqpQ2KN0OHTKkE3YLTZ+acmnX4FAmHPWnHyN1IrRBDS7hZIGe+8YUn74pN7hdtbhxGG/jcExfpS4MzZVgolw6WeDHSLCKDMM9GQUELaeqxTtZFxaYRotJeybQXPan12JHCUSYOMLmWcebIla5XYAaj2s/MXiiE7ChZyYNiq0IuhSJGfTSwdWSEXrAMxYxeR6Q1pnmk6xN/+x/82l2zjZm86W3DhjvNk89fwWVwAjdXyrXrK6wFKZ7FVsVynsmlEBZGvKIgih0Fqosey0L+MOSPOao+U20Vmq3MMkzoP9Ig6qi6RFTFV5n4XUr/PQ3ihNEJCxFWseJoBdWQac/3NB/wxN0KAbqc6JdL3JjIAa4erPj7n7x7KzJvUZvXzez33bb91GsdU0RmwD8HftLM7m4V7w33FQdXD156vLi2oD/qb3tVsAHWxf1edOjc/vKr54vbopFeGu9eHveiM+hWRJFlMLVX+pYcLxfDNtZ1B29x9anrfPl3vv62r/Wtspk3N9xLlkPC6kDZnbB/4YBBBdmdYFtTOBo4qpQhKHka6M9M0OjIkwlle4b4iAQPwVNmEekKGiL57AyfDPoBRUEgzDvizQ43FFynKEa/NJZbU4ZhxDThxOG3KnQaKZOKVTcw9gUfIK8yj33pCl0/3BW7vBVdbrS54Tiwytad0SYVX/tKz8GFTMwetyjoMpM6Ya8bydNIrgPmHG4S8KcFTRlbrlhNduhDjcRCaRxQU4+CFKV4gVLWutszCAVbdVg/okUpBa4/Jex/rSJeWuEqhz9KuCLo1Tl0A+xOwcMXv/QNzN64qcUds8t9oM2Nw2gDV5Ydp09uc2LRkreFxVljvuUoxdEHY5wYYQRzgqJodIg66oNMMIdlo3jBp4BuB9KWx4KQg0cEhu2Aa2QdhdR4fGJd56ip1hXt60gABMPHQJ2EamX4JFSrhNOC6zKLVU0xx3NX9++abd7tVes3vLexAuce3KH58EnilZ7m8Tnu8kguir8yIocFrWuKdzhVcg6Y93TvM9o2I02G7MiTinI1whWIriBLcHjqhVDPCjoRxqsVfRWQLY9JyypPqE3wjSDR024rdk4Ytits5pFziquVckogOJx5vnDh7nUxvNPaFJHI2ln0j8zsZ15jyEXg4dueP3Rr34ZvQ3ZP77JzbovQvEYpSDMwfVUNa3nZEQS3nDu87Bi6LYXsNXnxtRfv2l519yavVQj7tjGf/+RX3vB67iSbeXPDveTc+3c5fWJGEx22SjDvKX2iFCXvTPCD4LuCKcTDARtGXCmoFZg1jFFwDrwJtA2Y4LpC2Yro9hbs1Egx0nbLMIswibBd4ZpAngSic0QxpB+IQ6HqE1YSORRcEHTlkOJJk4ChfP4zd6cj0/3QiWnDexcz43v+4ISz33mOsHuacTCyC5Q+4bpErgxpAifPKaIGQyYcLvHPH5K/NuBnLURHWCYkKW5/oLrRE/qMDQrTiP/AhHy2ZqyFvPTY4JCtCb6N5InDtSDBUVJBg4OJx6lg0a3TVIeM34mkhyckBxefunJXbHM/aHNT9HoDdXI0vaeZ1lyt5+zaimQVahEvRnOoaABEiStBA/hVIa4yMqvRqdA3SsgOw1AVxpOO5tqALQzfGn0dKLXgR8M+mvCXwb8gRBzOKzkZBKEUIXU9khx512NnPKwE94wwnq5xM6Od1nfFLmb3RyGyDe9ddC8gJxoahLS4xtAoJTjCEtIZYZxVuDRioeDOCG5p1FPFTnVICeQU8QcFH5R8wtH4jnJNKd8B7dlCPxqyX+NGQWtl8vkbNN8tDO9vUFdoJxkFRh84LBW5jLhJwu874lPKYrelC0KTBuJhYip3Z0q509oUEQH+LvA1M/svX2fYzwH/sYj8E9bFrg/N7C610djwbiOlQjNtEYSDy68VjPYGDiAH6FurcfQioQ3kPlNPAsM8v7Tf0msMvi1DtNiri2cfD5t5c8O9RsUzndXYzSXLCBYFt+ypBDQ6EMVXETXWEUMnJ/hBsaj4ccBNA0UFhyA41EEQQ7sBJjWyGiArPsPybCBKoLreE05N0KI0g2O13VKGjBdDxxHxghbDieF8pKMQOkNr5aodHrtNNrrccK9ZrgaWXcNWU7NcLCkhYiJYWqFVjRsVeuWZJyvckAm7cV3rq6sgOsQromHdgdv7tTNYFW0DOQa8RfRSoYREGArsNdBl/NGSsLVF3QlVJ3RWKOKxaUW72yFXHDIKoooNieFmh8sDVXI8/cxVHvrQuWO1y/2izTsSYSQif09ErorIl2/bd0JEPikij9/6u3cnPmvDnWe7bvEWec56shfKfoV0HgkFPyrxAOIKbBpxA/hkVINSak8iM1aGiIN+hKIEHPVcceaQVpCgEB02dVgTcU9WjPOAmwq+gVV9a+WmEdrtwFgLJYAzIe0Z3ieKAysOq4zF0LFcvvtCeN+NbLR5f3Nmb5fiIvOvX8F1Sjk5QycB8YpOBNkL2MzhDgOHk8i47ZGZ4RfG6rohL2RQwU4MZHPMY4M87NAhkm8IpRfKXHAHBqVh9fFTLEOL6z1hBd1FJcw97dwRx5o4z1SjEnshXB+omxVSCoQIaaBLA6XcpTpGd1abfwj4c8AfF5Ev3Np+XET+goj8hVtjfh54CngC+DvAX3y7577R5f3PiXO7jH3i4PL8tr3yyoih1+MdSCT49W3bsHzZWST1t/6OL16RMne83M/z5kab9z+nt2eMhyPLowGvDikg9bpOphvWNU5EHGknMJxt1v7bWUWpHWnIZAxtI6UVysQh3oFTdGdCmdaUSU1uarSNbN1IxMMBjYKv47p7Wj8SD3vqXJCdisEJbqm4QdHg0KMeN1+hbcQNyvz51be8pjvB/ZD28kZstHl/0zaRdtjl6lgY5j2iCjc7nEQwxY2GzRpmDyvNbgJJaHH4AK4K6KiY2rqGWMlI0xBmU1wdCEVw8x7tE1Lc+r43gSZhdXobf3aGkBlz4eSDwrkPgJ96dG4U8xQn5Ogp3qP7HW65LotycPnuZLTcaW2KyHfcdi/7BRE5EpGffCfneKdS0n4a+BOv2vdXgF8xs48Av3Lr+YZ3GWrG03rEH/vBD9IMRjjIDOKxwYhLgSLkCqo04mcjpQJXlFx70vY6zzuuwI0jpTKGXY+JYiLken1f3PsKeiWs1hOmeSgTx2zWUo4Sch3yUCgBDj40kh5xiAihK8QLEQ4d9bJAMfrKc2O+pOvvRg/vd38RsjfBT7PR5n3JwfU5869f4w99/wfhZMvylMOvEmihTCvcyqGj4GImXk+4OQhKGR35BYcXh+4EXJtpDhKT6wOiwrgI+N6oNOGHiLbKuAdue2SmI/WhcboNbJ9Y0U2FIRf05oD9/D7yLNhMyScyw7ktdFqRzwjkQrvwPH3lOovhbjhz76w2zew3zUzM7BNm9n23tp83s79tZn/71hgzs79kZh8ys+8xs8+8gwv4aTa6vG959usXqarI9/+xj60LjL265tAxMtubvJzqduvjbPjWdRae/PyT5JS/5bh3zn0/b/40G23et3zlwmW2P7DLBz/+ABrW3UHxYMPIGMBSwa8yOQ2oFqyOlDqu08PaFhGI80Scj/hhhJRxN+fojSV4QfqEdCOWE74oNjp8V2A3MqsDTpWyHNAxkdXoUo+MGZcEOzlFxoJowveKWkGy8dxT1+6CZe6Pwrrfgp9mo837lq9+/lke/q6HOP/xc4wBSoDiBXPK9EHYPWfIwYp0zRhzxTBWZBUYDYrAckRShmKoeIordHViLBlJPSUILnjMPO7sFMOT68i09cy8R1cjedVz7UbNtWcidpAZDiL+oIfdGXlWrTuttZFcRQpw5fH7U5tm9o0X72WBHwRWwM++k7O8I/kDZvbrtzrL3M5PAH/01uN/APxr4H93Jz5vw53j5mLJL336K1z84g3KaMQthzQQZ1BWgjNFgsNRQ1+wqYfDTKkd1jhsmcE7muuQfcGaSJk5XDRkH0KvpGpd30Q7Q1uHhAHfeq48v2Q6KlQRiSBm2AqcM4IKItCMSpZAtyOoFx7cuc4p+cBLLYSPm3fjKstbYaPN+5dLVw/4V//mKzzhB8YqENoKzYbkW/XEtoSpV8atSJ4q1bIgU2Wce6osdBaI04zVNZYSDNAc9oy5pkwgn3XwrGEuYoDuO2Tosa7ihdWK5owwHrbIHAgdrono0ijTAK0griEvoL42R4ojpwVnmxllo81vyUaX9zfPfP15/sXf/SQ3n3+DVJIIvFaa2Dvk+rMHa0eRsV7ye7XcXqyPjawLY9/izPtO0K8GZjvHnza60eaGe8WXnr7Iz/7j32D5mavIOMIDe3DQYXVARSF42p2afOmAFCNu9DAO5N0ZVV4XzZViMF/iB3CaYWeL4aSBA9EAqri+UFJCvEAdKIuBgysr8B4rwPYMDYI4QeqK0K+P7aYNZoFxq0A3YoOxtzdj6EfqpjpW29zPuoSNNu93vv7Y1/iVf/pJLnxjSciKWUCmFThHyokssHO+4eDQyI2HVYK6QuuIlIzVEecjputOhk4UU4dse6zUUHnojUpHNHpcMlwayCiHVw8hecR7/OGKXDVY9Ni0RupAzCNukcmnZ5TTRnmsR9vA6e86SykF71+rSOCd45i1+aPAk2b27Ds5yHEWvT57W32Hy8DZ1xokIn/+xVbH167dDU/ehtu5sj/n2s05WRQTozoAfwWG6MkB0g50p4yshrvpCKMQjzLNsrB9oSeOSjDBJ6gHISSHdIYcFJwYCtSXB/xhwo0ZRyGlhnDNsyWOvdMTqugQVfxRxj8bsKNIu9PQVB4f1mGMNhPYLezbhEuXl/zXf/OXGMZjuBu/DYP7fTXm9dho8z7ghUuH3Oh6ZJXQqAwPT+ke3iIkAU1UVwb0yY5Bhf5che5V8CBMHknMvrswmfVorOgmkfmkoaum6LYRtjOVgfdQfyRRzhYCiaoe6aYTVo+0tHvCw7OGWVXwk4HwwZHhe6fkWcO2Tgk50u4J1a6jOgjEwxEbC49fucb/9Rd+lZSPt17Ke1Sbb0qXsNHmvebGxZvcvPot6o68xvQkTl7R7v41+RZ3ZfVWxYnz2+tj6W3HFZieaBEvTPcafPPKA33lU4/zd//Tf3LsKaMbbW60eS95Yf+Iw2WHOEc+tY2bD5jTdeS7QX+q4iAaw+kdvAoEo14qs/2eZp7Q6MmzSIg1qYWy3bCaCMUHfKew7KAv60K7zuODoIC0LfH0NtsP7mKzCKkQ5oW4zOhWzfaJCfUisb07ozrT4pIhVSSfmfCZ332Kv/83P3msdnmrutxoc8Od5pnPX+HG8x06GM4UpwMu9Tjp6W8EZgLN5BBxhapTqtWITAUPeC+od2Qr2KSG4AkhEnOmkrL+DbnskZwgF+h6GBJkgJb23B6753dwzmPiMM3QRGIxtremhKTsPDijnk5gIRA9jMYv/PRv8o9/6ng7c79NbZ568bt8a/vzb/ARfxr4x+/0PO9KlzRb96V7zWVnM/upF1sdnz59+m6czobb2Nlu+Hd/+DsYH6yI24HoPHHbga0LXWcfoBMKRomGDRmnYEMhF6GgpAi58Yg6wjxRz5VoEQuB1DrK1GO74KagCcYdpT8h6GGi0chEAQw9EcjbguR1mOLirGdZCeNUcJ0jLDPjPHAohd/47JP81f/iXxyvcexWs5s3ud2PbLT57uWjHzzDH/6Oh2mu92x10Hij7cd1Eb/OiKNidSSmTKs9uc2YN5IqRwKVDoSjROyV0Gbi2Z70YM04mTDkinQIrAp+YZiCUmif66iu9AzVQJhWzGZGbQV5vqbaL8ymBV04qicy6WCgPSiMJyq63QJtZHh+xS999jH+bz/3q8drnPe4Nt9Il7de32jzHvKJH/5uPv4HvnP95C0E7Jga8no/xIRXOIFej7FLtLMJvnr5OIYhIqQhgcJqvweTV9TUvnn5gF/8B7/Kf/tf/NybP+G3w0abG23eQ3704x/l4fNn0MYRdz0+Gt4JeWeCRU9zdUX7/AKNQFCKFXIQ3M0BjgacKqiR2wCzijyr8cFw4pA84Iugqph4tBXGOiCNQ7NhvTJzkdg6RAwq0MpBEFZ1oLSe6yyh64mLEXERl4Tn9g/5b3/5c/ybXz/GToZvUZcbbW640/zYv/8j7D54Ctlq8NMp/kSNFmE1enCe/YvC1WcmyOCgTwiOeLGnLFZYt6IEQ6Oi3qASNBe0ePTQMXrIbUPx645j5j3q11Ogj5lcG+78HpxsyDst5cHZ+kvuPKlPSK3MD1eknOBmwlUtDuHiU1f5h3/3V7hy6RhrGb09bV5/8bt8a/up1zq0iFTA/wj4797paR6nw+iKiJwHuPX36jF+1oa3SfKJD57f4wPbDQ/TYDNjcIqPeqtDWib2CWmgGjPTo4J5jwWHTQQLfl29vii5FmIbCBiNCIxKGB1pr6U7E5AMIoIfBe+VkcL1oyWDFSpxmAhp4vAZhqMRmStV8MiwnsD9NU91WdGVkDw8f+34i5G929scvk022rwPGBc9D7z/FOc/cJrtj05obmY4zHgreG/0D0ZspzDJyvapFbvnEulWu143OFbjBEogSKa6Epk6YVIpsxMGHobpjO6FHSZPFZqzA2loGJuKbI5hVJ65lOkFYu2phkx1mEiTyFB1rGpBXKBHkNaRH2np3z8lTwSWymNPH//q3XtQmxtd3ieUlPnQJ97HuQ+dZuvE7FuOfzECSNxrfBdv7Rcnb875pHDjyj6lGK6+5RRSMDPGVcbMkCiUXBCRlz6z9EoZja9/5om3drFvg402N9wrpFY+8ZGznP/QaVof0SZQnOD6kXoAJjWlgWiGjIXaPIZi0VNqD2qoGJZ6rA7UQahDzXSu5NkUrT3eOfLEYeIIQ8EUciMsyNxYDUgHYVqTtjyShXh5xWrVY8uOegBDsDailQJCKpm+gi9+9bljtc390Lr7bbDR5n1CqSve/7EPcvahXeppQ/EzxuhxTnACebslNxXS1khw68xrLUgAjYGgDi8B0kjVtOgHdpHTM2Qs+KqGYLimgkkN5nDFyCdaxk7oLnSMh3MkGXXJxMNEjgVJPSktGJNHT06xcaRaJWLMSFMxAjkrX/jtx4/VNseozT8JfM7MrrzTczxOh9HPAf/Brcf/AfDfH+NnbXib7MqMybLhzO6EM9OGPSdsNwu0F8Lc0OgoVcXoPKU4snfEWSBGRxGPKMgyYd6QYWQVlfnMsbBEiUba8lhW/MrRNZHSrldccg5YEtwsMgRHMaNaKNudo3KeMAkMO54hGsWBi4K0ET3v0bMLggiXr79WK+M7h3F/d3t5AzbavA/YOrsLOw2nTs84c3pGc7ImuYxOPP15ByMwVKTR00nFcuFp85QdZ9gcrIXQZKQIzakD+hugTzu6m4VwCOGiUXIhx4i+0OAPIC7XRTiTOrjhGQ+EQT1pp8Wdb6m0ohock8NESAMhKSEZLnmqSyu4eB2ZwPMHN4/VNu9RbW50eZ/QbrX44Dj7yEn2zu7QbsXXHfuiw0Zuiwh6xf2erZ09VuxW+PxrHeSVT1PKUEDTuq4KEXztXqptpMW+KVLJDFwlXHj84pu9zLfFRpsb7iVVitRtw7mHTnAiBuLJdTFbvypo5SiTCqoJcr2DwbCppzozxbYjJXjwhhMlzyqkUzjombcD3bYn1UZ/0lOc4Zyg24ESHSmuu6+pE5bbQopKXo2EJhBOtrgzU2oVpCvEmxmKp5ytGSujRCWtRiqMr3zl+WOzy1vV5UabG+40nsLOIyc4993nqR7eQlqPE0c4GshbYd1pcByxS9exroOZx+1MoYoMbr2aos4hVjMulvQoXRDS3gRiQHxExoQMAyfP95TTRt6tKLOak2fnPPDITXZPDlgxQhWYlHU2jEy3sQLu2oK4n5FJQxmVoomxNkI/8q//5eePzS7HrM0/wx1IR4M75DASkX8M/A7wHSJyQUT+F8B/DvyYiDwO/Du3nm94l1FMefb5fV64OufRvM+8V7pxyjDzaOPXN5nFKIBFR6mEMRe0S/gu4XJBglBupaTFFcgkYnjqmyPVIkNtjI0RsmB9JiyVZqnoxBiOeiZilInDDKRb4euesyHSDEa1KDSDUbwjR0GOBJvXLC0xX41cvzn/ltf49rn/87032rx/6ceRi8slz924yYXfuMBhpVS1x93MhIXDoifXQt87ulWFWxZGWVJiIswSmCHJ4/cdZScST2TSvKZkx/hIx3BSkB7ckEhUMMn0Zx15N9CuEtWpQ7ZPGxw5shhL67Gw4PSHAu6kZ3jAYacFXRSkd+S9llw3dIcjN4+6Y65jdH9rc6PL+5ujG4dcf2GfZ798kctPX6Xr0utGD8Eth9D4cqaEyMvpYuJk/dytx70UbfQax3pxv/ZKs1WtnUIKUgRVY+fcFhIEQYiNf8V7AWyES09fvwMWeCM22txw77h27Yj9Kwsee+YCV2LGriyI3UiJjrGB6vICrRx9G9HKoYMyJsUGQ7qBVAv96RqdBXxnIA7drdeLpfuJ+ggIEbwSDzoUJS7HdV3BRSFc75iGiLaOXBxlkZAQaE/MyB/exmpPqGvYH6hGw69G/NzohszT179FXbR3xFvT5ZvRpoj8CRH5hog8ISKv2Z1MRP59EfmqiHxFRP4/7+gKNtq8r7l65YDrN2/w2KefZnj2CK4cUZ9T2G3xTU+cD+hOgJ2ANYFxT9AywCohuVAmNVJVOM34xUhzeVhPbbnghhXSZ0IRrK2QCNEH/FJBHNcubXP1y1DGCVQVRYWxbrEzDW3lCLMGVopsVTCfI8uR0BfctQW5Kzx/rAstd16bACIyBX4M+Jk7cZZ3qkvan3mdl370Thx/w/Hx608+yaOHz3JIYlgkJjZC8UyGsA7TtQx4/CjYqAxbjrpTgnOEUlAcZVBiMhBPzmB9xqKsV2CSEnpDTSi+YtzziIPqkmEqjEXxJow5E5xQzyrSSphs16jrKW1CjozYQWkgFUEmEBphlgNPPHuVUye2js0+92Me9+1stHn/8ju/+mWe+91nGC4smZ9x9MtM5TxytsYtBI0FKkjbgVB64oHRNBmLhb2Tmf0k9CUS9ww/CqtlxDThO3BnHGW7UM8S2RtmMLmQGBsjnQzY6JgfrtiaZoJCM8vofgseqlChi0Pc04nhYYc/LbgbhmWoOrBWqCeR56/t88Hzp47NPvezNje6vL/57Z/7LM997QLzowWlt5ejiNytzmQvOoPkRU8N3xQldLvT6KUxt4176Vi3H+c2hpxfSnVzlYAJW1tTji4vAEjjyw7bF9/vKpjMJly/eJNTD554JyZ4Qzba3HCv+OSnvsqzN2/QHxnzHSM0RtUH8EIuStqqkX7EtzUZwzmHOJDtQIrriPhq4fDLgrag04rqYiJ0hbAY0WBYhH7mcaXCD4qOhrhCqhS3zDABVUdgrTnfDdQPTCj9ijFC3a0gOLRkXK9QOVrfsl3VHB6s2NmdHItt7qQuRcQDf5P1D9ILwO+JyM+Z2VdvG/MR4H8P/CEz2xeRM+/kMzfavL958vO/xeryimk8ICVHMU9FhXcGVzJZFbucsElDQXHmoAn4fqQWRxJgtaIUsCoiQybmAiL4kCgTI608WgUu2UkmYUWrPf2+oThWvSf1HXrQ0zw4I6kQ9xO7pwSVBf2JbUQVJjU6E3RfCeppdyMnHmwZh0RVv3408TvhOOZMM1sCJ+/U8e5K0esN714+/fXneEGfZGeq1KHC6ogcObIUcuXoTwfSFpAK2hhpEsjek8Sw6AFH3Zd1apoDQsEaISwKmJB2BXekhE7wBjp1uMEQFI/gfEDUmHbGrINhcBQV6lnLpDeC8yQzLGQkGR5hGjIfOzPjB7/vEX7ltx5D9fjuTt+D4bsb7hM++7mnefb3nmI6a6hDRaXrSCFWI2E0JldH6uuJIAV/CBxVLBY1rAxLgdZnpg+MDFQcdROijdSmyIOKBOXUtTmThxO6HdGpww4T7DjidCRPlEPdppRIeKjAA5EShOw8ai3lfIP4SFhkSBmNijRABR/5yBm+74MP8XOf/9qx2mejzQ33ii/+xtd47usvsLU3RepXfr/EycvOn9v2veT0eQ3nz8sDX+P5aw0Xh7/lpPK1R5MhXigv3nU64DUC/D76gx/hY//2h/mN//7Tb3B175yNNjfcK77y5Wd5+tI+7YMtcdIg4hmiMLqMpII2AqIw9MR5jywHVAzmHU0qtPuZyY0BC5AqQeYJPxh+SFAU0cx4Arw5fCmYCk4EcFgIWNuge4F0omZytiFvRYgBZwUfBWaerAlQ3CiE6GGv5WM/9H6+83sf4jc+e3y1Uu5w2su/BTxhZk+Z2Qj8E9Yt7m/nPwL+ppntrz/fNvWFvk0ppfA7n7rON16YU7W7rMoMVwn5sMdPBqwXtKpw04jtOmQnYiFSVgWtIj7WSDEcgm478tktNCgUB4uBcWFkC4j3gBLnA1zrsU7xwSO5kA4zTRD8VoXM2nWdv72WJA39OEOyko86RnNY73Gnp8SdCd/5Ix9i63vP8zuf/NKx2ed+SBe9IxFGG+5ftpwwv7bNh3bPMJ/dhH1H6BZoBSk4rBHUg87XEUTiFNfpOk1NDJyAF9IUnBnNocGip0wChuDE0SgcbBsUpboG1dLwyeEXI4hjyIWqrZDtSNYMIjx7ZZ+EJ64K46hYF/ACqRUOc8UTw8jlLz2PGej/6t/FHUOBPjMouvGpbrg3uFlEzm5x9sOnmT9zEzvXMK+XlJSRpaO+mimVUXYM8wVQdAbdpGI1CL4kQlLqRcLaQvNIYjil1AXoPdk11DuF9qsdi+1A+lCDnqpIE6E6tcJyoRPFb08ZLkWIHeOi4eKVJT4o+ognLRyOGmRExozvlCtfv8bzJ+Y8cf0GP/njf/hYbLPR5oZ7SWwDPjoe/NADLA6eWBd+795kCubrLCW+VhTRa+170YOUl4V2t2XsR1AoXeHmC/u3HFKv/RkvPHaJZ7+WufzMNf7Hf/FPvLnzfYtstLnhXhLVMUnCgw+fZPWNi1TqWZgxVgE/JOIIOVY4NdLOumGLOxjACmkUfBMo2TAv6zqBQbC9mjIN+C7hi9DuFzR1pGkkJqPMIrblsBsd2gkpT5ieqbH5HHWRcWJc60aCF2yEwdX4VlGfyL1AFl74/BUemwqX95f8qR/93jtul7epy1Mi8pnbnv/Ubd2YHgRuL7p0AfgDr3r/RwFE5LcAD/wfzewX3+pJbLj/UYPuMFIVY/v0A1zefxa3G9H9AUsF11a0rWN0LeR1193QGK4STCNpHMk7DZITfhSGmSAnI+EInGspraM2waww9hnXFbJGyGDbgRKEunOMK2hP7bA6WlBCxVKNYb+Dtib5gTytcD7SSMd4Y4lJxfOfeY60B9X+kj/yp37gjtvmfpkzNw6jb3N+69GLtPsVJY2MuRAVZk3kmmV8r3jvcLFQV4VhqFDAKofFiKQECBSh6pUcA1pGtPWkPY+Z4OpASkoOBZ8gFofvEuodkzqifaavhBCFgGM1LzBzaARJIKPgkhEzdNtKTOtxMQrjamTn/M6xxr/fz6H1G+5vvvbUFexD2xw2hTSMmAmTxtEXYdjyMDZkG0gYIQVKBkuefuloq4FxaOjxWA2Vd/hQiEtPuu5gEHKoCVcG/LzHzRTbqsitYurZqiqGekSuClu7gfkEcptpDxLOoETIneBFoSrYYmTcEtouIHg6lIdDc6z22Whzw71g6Eee+uJztFsTFocrymDEibxUcBpepxvaHSK2kdQlAOo2kodMQSGCOAdRIMFrOY3MG3mV2H4Tnd3eCRttbrgXPP2NFzj87AV233+S+XLFII7JdqC61qOhAm/kKKQIxIjJiJ6oaZ8eUFdRAhSDvBMw54ha0L7Hzz2YMZz0BK3wVztyiPjsyY1ha88S7XZL8gVGmGlg3jdIP1BvKSUEytGAhRZqIzeO5skerR21N0YtlHnhzPT45s23ocvrZvb73sFHBuAjwB8FHgJ+XUS+x8wO3sExN9yHfPJffpr5Yp/zJ2YcLY7gcODsIzVX55BWDV4SFSNjF3AS1zXFksMphO3MeF1JOxVuJxAHxaniV4KKx2Ih70aqXCjJcAoxK9mD1QHbikxyQBdHaBCmWuiSEPJI8B4/Kjn3yNYUyhJxcKY9ZKgCB4ua5eU5Mrac+OGdY7PP/TBnvvtdWhuOlQd2thl2OxbLFdMVyP7Ayd0d+lMR3TLqTqn2Yegj/bl121+L6xoNUtbCLLVDdzwEYzzVYA7wxtjAYAkN4IpHHVhKlNbjPMwn0IviBqNbZKg8eRtGnymLka42+hZMPD4VwlIolSOOoAUkOIacGMbXay3zznm3hwhueO+y/cAuw5VD9hkYH6hYbcH52S6Ti4UwHxn2PN2DER8C02dXhJwJKUGRdePNAHIEcR/83FGemNCbY+iF6qCnmR+xGgM6ndDODdck3NKoyZRnB9rSUbaF60c9PgSOLkLXd4iM2HOFdM2RvcDJQplG6uRhJ+CCw2MsVwN6rM7cjTY33H3MlJ2TW3TLjnHZg4M0Zk4/cofrdb3m11ZI3Qisi/EeXJtTT+L6eTKGYYRRb/mKXpXPJuBlXUNwddTf2XN9FRttbrgXFDEmHzjB9cZRBsMBw6owe/82uUmkRhlOOFwrxNVIXI3rTmg4nIf6Rqaaj4TlSCwFqwTxgVKU7KCIx/oeC0bZWxfNHvcc4jJVHRinBW8JvxhZXFsSQyGfVnrJlIMVo3l00REPlzTPL3Bq+OgpDvwsYE7oF8Ox2ecOp71cBB6+7flDt/bdzgXg58wsmdnTwGOsHUgbvs04ubvN1u4WhxcP2aoMN8L8qrLz4C7d+QnmPEcyIe/WWAPiFOuUsSuMlwZMYHK5Y3owp9aRaoxY01K8UILRt5HBBGvABY96IPp1A4jgGVXR2pMNroeCw1icb8kukbNiBXTV41YJt7/g8jMT9lcnCK1nenKbNCjLQ/2W1/l2uR9S0jYOo29z2rMV8511fSLdL0jxPN3NcXXBJpBFyNEjzlEthLgCPwCmSAFzoKosZzAGx7glMDWCV5orA1sXBwa3btddDUZcKlYrqgWZZ9QpTtdFd5f7S0yVsBLGLuOXihsUlxI4TzodYCpolVhp4pH3n0IMPv3o08diG+PdL+AN701yLkyuLnHPzwnZsTrpGX3ghS/tIyslHmVCl5kMgUqF4aEthrM1eRpoJgNaGUkF8kB1Y0nuC+MlKH2hksz29/fox4yclX6nYDsZN000qWPCEtvNWDLGRhg9qDvEp4ILoF1PfdKYnExM64RLgZ3LSrtM6LWBbn/kgydOkQy++NSFY7HPRpsb7hXdUY9zkFcFC27dqSzDtWfebPcxuVXw71vwmr7WV+0ssLixevl5lnVK2iu2l9+6Olrw4AfPMz9Y8vzjL7zJ831rbLS54V7RnGhJAunSEc4yfrmiiyPXbx5QZjCeqRi3FPOBOilOwWcFZ2hrpK3IOHWoAX1CVpnsPbrlccEx3Tfi5REbC6aQZ4GwMnikIZUef2FAnMeCY5CO+XKAuZKHyOpUSz0qgiPt1eRTLflUi048XSgwH3jg4R2ePzpiTHd+EfSt6vJNaPP3gI+IyAdEpAL+NOsW97fz/2UdXYSInGKdovbUHb2wDfcFDzx0ivTCdcpyf70jBgg9RW5SGYRtYMhUy46dUwPmHDaMiBkaIsOZhtVpR1rAqlRYVtyYsVKw+UB9dYXs9/ijRDQlb02wIIzntunHEQ5XOAoeRyIwDIr0hbQyqBwhJsKNBR5HOT1hOFshrZGCkLdrTj58gmur4+kwegzaPBY2KWnfxhwtez516Vn8VqbvDVrBkpI7Jd5wmCoxJOoDz2DgByXcanWvZmjlEIWgSiwOZ0qKAW09OjisciwfknWrwwNHaYU8cVRXCkEhDAU3iWgUghbksIAXZGlQwLagTAwWgnhh63qmeqjhqgNWRrsT+KHv/SAPnjnGbi/HduQNG16fK5cPeOaLz+NXCXlhRTztsdrop0Z9tsHVCZ2OLB+eUo9AEfJMkb2Ct0JaehxKs5dxWw4NQsbBjUxdOy7tbzN9IrP1yILu5BbDVU87X2HvC3RBOLt7SOgih4tAbCHJQBOFcbvi5gTa+Yg/6unqBlVj6/uU2ZUJR1/uGFU4tT3lu86d5eTO9NhstNHmhnvB1eevcfmpG+vIoj69yXfJGxe7ftPHuNVGLQqkWzUEy0t5cC+Puw1fe2IT6A8GUoGzj5xm9/Q2OyePLy1to80N94IvPvsC1/oVnGkY5xlz4KeCXIHQeEyhSTC5PFBUUR+RDMO2R0rBC2jbYGVkPDmBccR3grge2gArwU5VyFAQE9Q5ZAswwy88RRyVeHR/RRwdw5BQ5xFXsKEwmDHOaqRyhD5Tn67QALo/sKgSnzi5x+yhXYI/nrX8O6lLM8si8h8D/4p1faK/Z2ZfEZG/CnzGzH7u1mv/AxH5Kusy/P9bM7txB09jw33Co5/6HF1a4XLLjWd7cJmuCejTGe+XLAVoKuyocPCcYTOIRcF71AmuF+plxuoaj6c0cO6hOS9cbRBtcCKY80jOhNqoq8Ji1RALyDzjk8JOgxyN1PORPinhsCACrh3xJ6CPDaU4qrni92ZIExgvHrJ0Pd/7ox/hoz90xxqOfRP3w5y5cRh9G/MLv/0VlgfK1nxGexTIy5FiQuyNcBUsg+wKOYIbFAmJ8GBm7BtIDp8MKYb4zORqjzYg8y0sB6TLuEpYTT1Vl+l2HeIc9c3Muhu4o0wr1MCJo2+N3IAcDZisayS5JVjt6GeGt4TzxgwYopJmwlMHhzx18QY/+D2P8NEPvqNuna+NsVkB3XBP+Jlf+izZBfz3nCN/oEVXBXOG7EG3F/Eo7lCobhp+WrC8on7aGKZb9NIiJhjC9s6K8ZGR7WXPYTnDZKK4vZ72uiMcKat5TVEIKPW5QmyMRVNz8cYpBq2JlTLZX5HmFbPzh8ydkGjIuzUcZPzBiDOQYaCWiO13uDrwja+8wGPTwI9/73fwvlPH4NDdaHPDPeKf/Vfrmq3TnQYfb91CiQNTXnYM3XLsrL+ot/3lZceOyG2FC24LA5LbUsnsthD4FyOGjHWNIpF1JdHbiye9GjNKhjZGYMCS8pXffYJ2u+UnLuyzfWL7nRnjNT9zo80N94af/dnfJW0FzjoP3tFLy9QnBh+wlIhaAYWuzgTx9A9HwjwTh4pcOdxYcMuEtJGVGtXVAdv2ZFfhjoxCIZhgWUit4q3Hlh493eLbgWECtjLqNrI4V9FXE9qbed3BsFWqGz2h1OgiY/sDuQq4IJTFwHAu8DsHFzk9P+DS8/s8+Mgd/nF6DLo0s58Hfv5V+/4Ptz024H9za9vwbcwv/9LvITph6+wEiiFSQfR0tcMnXTuHssCu4CpIlSdlT7VUHOAOOrSKlHMthIi/vs/R5UC1FDQNaF1D9Aw+oj7ik1KVhHUelzK2sw15HbmnQ8Gcwx8scW1Nry1y05PDgE9KGgtOG6wf0DEDhS996mkOuwU/+keOOHX6Ds+b98mcuUlJ+zbmt7/xHJNDOJFahuVIwbBKqZcQj3oCDpt7xuhJrUdWjs4q+p2ABmBYT4SVr+mrSDKPG0eQgtOMDpndsmKSoJwR9KShU0fTVohCNHCrvM4znwoqwnC+pvUB55XQKcFGKi8MW4HViZobzyyJbSG0xuJgxbXlik/++lePz0j2FrYNG+4Qjz72AuMDU6pH9lhgZG+4ZSbveeTMwNhOWDUz4txR5p5xx+FHwz8v2GiEE5mmhaoObF3v2XXKtOqofMZJpq4yejIxbk+oH+uJVxXtPDZ1xBbqAK4ZmXjFLgpubqy+1lDvOZox47cT1aTDa8BfcuRnI8/oCH4E69kPI9durPjFz37j+Iy00eaGe8ATjz4DHiY7M+bXF+udpmtH0EtRRLf/vf1GUF7+E159g2jfPP4l5xIQhdCEl+7aXP3i2Ftf8Pg6J1yMxfXVS8ed31hy9anr/M6//Nybut63xUabG+4yqsrF60dUVWAqjsULh1T7A3IhIZXDggfN+APwrkYGZXJFMQ/9w4pf9rhFT90mqlKonj/ALwdKG3AhE0bBFUXGDkmZOEK9X2izp943wizgkqJ9Ib+vIYknDIptwcQlghOGc7uoF7SGfKJmWA70F3tiv468755b8OwTl/ncE88cj5Heii432txwh1itjnjumTkyawnOsTpcYmqMvSOIwKTCe0HnPZorrEQsFfqtSH9uSqkqaGvitKLuR0rfwaCs0pTSenyfoS9Mm0TsFUbFRvCziE3BPbCNpIKMBRcDYxopjTA+0BKjw/UJO1wR5oIfPa4odtgzXJ0jQ8Z54ebhii8/eoknv3HpeIx0H2hz4zD6NmayGwlnAh985BRjr3gDGYS0PbL6kGd8uKARgkJcgbshVI+BS4ozcN4RgpBSZvHRyOp8jV8Cg6KjYm3A9j15sLU3NxnRZ4Y+UYaR4iBFx5ALrhi5cugk4nIhdkraMZhHUE/VGfEgMZwT5AZUc88kRLx5LlzbPzYbvdtzSje8N9kZYWeR+a7zp+hzRqbKuGW4g57q05nqmY4qj6TGkWaR4XyDfmgCDooFGB1tNPb3A/bZwPxrgf604NoRSzDXCXIuMOt7vEVKhNUYublf43TdqaJixJU5/Y6DpccdBE5NBpqdxHTRkR5sGEND3nN055TqSmI83RJOTQiNwxp4/MrVY7PRRpsb7gWTWcNke8IHP/4w/WqE6tZtlOkbtzq5zaHkXkwpe+m12+oavXgMs5ejlnCQjNy/XNtE+1d+Vqziy76ml7LTbnNAiSC1o56uPUtf//QTb+m63wobbW6425jB1jRw6kh4eHubfkfgXMXiEaPsBvyhww2OcSciKaOijJWiu4Y3Q2soexW2VdOJwtaE8dQW5gNSHEUM3xmlCbDrUe9AIl1TCI/v455eUi8h7UE3Ks4LYTQ0CEEqQjHiqmPryor6co+YEChI5cneqCvHdBCq0fjqE6+uHX2nbPTur5Oy4b1Hzo5qa8Zkp+HhD5xlrAK65ymDUnKBGx15mWj2lMolpicLMSe8j7jB8LmgFJg6sJ54vqBTj8w7fJehdkjKzGeOciaSDHIVsDSgh5nSJTQ6EoXRG9p60ITVEVfV2KxCakfwAR0TeLdu7tQ0jHsVfqch+IjGgaeuHFNdzvtAmxuH0bcxj/XX0cbYH3s4G0kmuNEotUMWkMWRJ5ANJCtWO8TB9pkl8dQIY8KOMhIcokI1X4fP5xbCR4RKgMPIsB1x1xJys9BlhyJYNrRTnBjmhTAILo0Ihf5WqQa/uLXm2mXicsRHwxBKK6y2FUWpG+Hi1SMuXj4ep5HZm982bLgTmBlXvnIBscLh83O2X1ByLowzB4fC2EbC3AjXDZcSzmeqzuPMUV1a0Bx2zPbm9E3HmBzLxjE85BErTG6M+GcFE+HGdIbOHfaBzM4DK5oxUZJjLEqejfQ45sOEPgZS1ZHqwPM3G1L27FczjlKkPjGnObUiZccoEPcz+sIKp2v9P3blBv34Zuu8vFU7bbS54e5y7eINDq7O8TGwf+WIuo230sMcr9PW7JXOINbDdLT/P3v/HSxZft13gp+fuy7d86ZcV7VvmIYHQYIelCgaSRxJQ1EjadzuaLWr3dWGYv0qtDEbIWl2Ima0QzE0GmkkcklRILUSKUMSkiASBAgPNNAAGg207y776tm01/3c/pFV1YYN/141ujs/Edkv38vr8nR+694893vO4eV7Gr0kifSiErc4//HSQS03ElG2ds9v82sMc4kNaDN3KT36qSeJJySOhTYX3G6+9NxlZn2HkJFqWJMNI24SUCNNIMyb5I5rlPfgGqRt0CqQNO5Gw+uUEAK2aRBFxLgwb5I7dWAlYQBmFlHjiNlrSXdrYltjxg31ksBNStocUtuSjAO2iDjviFOL9QLvBHqvpdUajEC6kmTS4BNPWC2YpBL7QE5A8cmPP34iMfpWdLnQ5oLj4nOfeBrTEZhZyWTWEpXEX53Oyz+DRK5FWJJ4pQgxUu4rnElQbSQUCUFERAi0ByVYT/JkiXQgBwluoKj7BdKBmETUgUOFQEgcLksRSuLHltg6hNAEwMwcghQzdEjrUUTENGKrmmAEfpBgC4POI0JI6r0JRZESZp7f+cDDJxKjV4M2Fwmj1zHv3j7HViz4yvV9dC4RMRBkJDYGEQzRBYIKeBMIKiJ8QERJeEbg9hUojUzn3e70UBCjJgqBxIBVhCiIlUW2gW5iyeua1d2AKltcVxGJKAuaiMshbTy68bRaEpVAWUGIgaglAUlwhjiL6JWKpF8hNPhW4IGHv3Tp2OMTWdwpXXD7EULw4A+9gWRriaceu0pqFLGUyDriuikxy7AmgrCoxqF8i9WC2UDTvkXQrGoObAeTK5JlSbvUoxUJtdZcNBsMVQoOet2WoC0tEbVc0Xt7oN+fMd4rECagb5SJmiNPsuoRHUczksRaojR0WovzhsYZVJthEoE6qJD7NUpGPJJQwhcvHv80poU2F7wSLK33uf/d9yAEPPXlZ0k6yfMv3uwx9HK8qCrtRtNquJHseWEySb54Gzefqxc4haS48XjJDvw351W/mSQqy5rJaPoNl/9WWWhzwSvB2aVl3tg7hQuapw/HiDM95NghrJiXp7SemCtkFzijcecKbDfBTlL0Ncj2K9RoRn4oia2nXVOIrUB2dYIeWfS1kjaTxG6CzTWzMwUuE2RO4Jcz7AM5bRqYZSn1iibEQDK1mCNH3TaI2hETA8FD7VFeE1sFlSS4gHMOGSJhIDnqHP/47m9VlwttLjguzt+/xV3nzlA1FRenu8QzS3itCURkkMSpwpJQ6gS5KoizgCwb0nZKNh2jk5YgBImPuEpQp33IJKJy+FSD84TgEFOgJ4lnNHn0mCag+i3IgLQBlSgEAToKWVvk1NEID7VFGoloPaK2OK2JXYFKHMrNTQrTvqFJC5r90bHH59WizUXC6HXKZFrz5S9f4fJoSJOUlJMWlUpUiKT1jV4KGYiViLYChURIhRCBuB+RlUQgiEFCE1CTgLYS9A2X0p4heoWQEj1rsY1G1wYXQAiFcBFCRGiBkAIZI15DnPob1nkNmZrXnLYeE0A2HpEI9NUEORFY70gFiETwew+fwB2ZCETxzT8WLDgGDuuSh3d22Lu6R3Uwo/IOIyXGRMxmRMaAmbaI2mGXJFWW4pUkmEDcktBRKOlJRCBknnbZEG5MkSCLlOsdGqNppWdts0Z4ybOdNXarDCc1edoiY8QgSdJIR5UkFqJxmEagygQ1TCh6Nd5A25PIjsc0YM8NiIVGJiBXI0nj+MCHHjn+IC20ueAVYHI448mHn2K8O8VXkaZsv/4KN/M8N8fb3/wo+m9hpzH+4eX1zY2/BCXnyaSXaZt0k7yfYgqFUpJ/9t//DsfOQpsLXgG8gC8+eoVhXTFyLaF1yFSiGugdRmRfoDYVsQfO93ATjXOStIzIWiBDJCpD3ZeYkUDvtIgjyfRsDpkk9AoUAS9b2nWNTgN2o0ezkqGjIE/BjKA/jphhTTIFeyqlPtWhHSTz3imrhiAlpg1EB81qBxEielajxo7OTJErQbY64YOf/uzxBuhb1eVCmwuOifGo4pNPXqL0nvKwxOzOSBJNIhUxE0Sh8XmOWZXoDUfIwMiAti2ZrdAqoIJEDlKEAdoWERS+hHCtxAiB7hSQJTiVYuuUtsrwLuAHkZDNp6ElWcAIiVhNiSs5sVCEACLI+aAYrRBSoaOkzXNG2QBfGHxVUzhPP4Mz/as8/ZWLxxugV4k2F1PSXqd0ipSVokDuGqQCFaGTCkYrEmkFVkXEoQITkURi7XH5vCO97SQgA5qIUoJaC6QAPW1RU0vsalTp8YnEp4YoBHESaKMmZh7hHdoBUhKtJyqFnsKsY4g5yErSakHWCrJuQme54LC1rKwlhCyyX5fIaUJtI9vLHcKs5G0XTp9InBa23AW3m16S0u13MSZF5BGXQrrsya5HbGHwTU15qoNwHpVaVBLQswSRBeJEI0PA1JKlvGYyVETdYjc1uusQ1tDv1cz2UzhImIiI2ogkRMShR5qKItXUKBLVUjYF7R0a/1g7/857pLEDSbYWyNKCjuxxuN+wvtKhrRUT19L2E5YaT79bUK1a3vbg2ROJ00KbC2433aWCrJvPb7Xd6HOddAxta+dJnZczBrzQMXTjXgwKvqXmlTebat/EMReAFDcmpTE/Jh8RBpLEkOSGctIwWOtxeDCC8mZvJMjyDN963vPTb/lW3v43zUKbC243vSJjEDSTriCYjGgkyXJO27b4mUPVYHc9PoLvKfIjTzJrEYWlSjroJEF5CVlKmNWELEIlieua1kVU2dK5UJI0kekox5SOuiNoCgHeUdYJ2bQhKkHoJxgdqPME2UpE2pI2HrmUk6QFqVqi3pkyGBTsDce0MkUQ6SgIU0Xn6ZwH77pw7DFa6HLBK8HZs2tsdWquLmnEVJMmgtDLETHibI3TAUZTalHQqlUSpng0LsnIsimzkCC0It1oETXYfUHwHplIYkcSS0V1xhAlqD2Ldp6gHU3r4aLA1AoRIz6X+G6XOPF47ZG5RHUVYuYRRYesI0iaSHPUsOE119oZsa6JY8FgDfa84jpn2Dh3/FO5Xw3aXDiMXqdEIpunBix3DGu1YSAV+WZ37uzRAlMzvyAWgAmEQuAyCAjqTUM0AhQEF0FLbBppO4KYa3QQIOad6m0PhHdkQ0BF2lVNO0gQSoENWKNxeKwRhAJCT3BmuSAtNGkMJB2D7mecWe/jJo7JxRpfGupE8+b7TnE4K6kmjj/45BMnFahv/rFgwTHg28DdRZ9iJcfcvUwnkWyNNakPuAhtrlCtxjQaNRToGegDgZ1J0sc8WjhkCpNakc8i2ZGlu+dQB4q2FWS6YXNlRLIHMQ005yWDUSB/KlJfz3BC000cYt+gGo+Ygm4Fai9y57mM5ejIVgLJqCCZppzZXOGAlmHRzBPJPnL32W2GVcORcPy7x06oue5CmwtuM7b1nLvvFN2VDp2VjE6/oDPIEVI8nwx6IXOv+fwhbpSS3fz7N8sLeyDJlySfwvMbWju3jMk0OjWknZy8U7B1fp3D6yOoubX+qXu3qSYV5aThP/yTj31L7/+bZqHNBbcZV1lOba/QIWWFhE6ekpwx0FO4PCK6jkyCPNQIpfC5IdTz/pxLYUZ23WH2PKpsaAeBkARcRyCtRwiHtIEj26W93CU59FgUZmzxrYNRC1OPTwzNZoZPBGEMYhRR+w33riYUFwxaQdExpJ2C1Xef4lpR4ycR0QSSVHC238PFisNrMz708c8cf5C+FV0utLngmJAR8t4a/V7KymZKd2VArzAIb0mEIpqAaRrSsUMhCImj7c6/i1YqJ5QCSkd12VBf00itIAqCjGR1RDiP2W1h2KJqRzYtOdUdQ2JQTuFXMlyhaKYQj1p83SK8QNvA/fdrumc1MUlZ8gndGk7ftc3R0QxzZYKuAvl6TndrQCMl165W/IdPfvn4g/Qq0ObCYfQ6RQrBo5f3qA8a1I6lyjTBee6+o8OQhuFjDUMTkAgkjigNuvbEVGPaSBQSGSK+tkhrSdOEkEi0slRJgt0sUFNHyBTeaYge6UHUEj2LCK1QGlRHM9PzcjQzjogq0GApomDl3nVmrmF6aYLqaMY6YlNJP5kitCDMNlkucpRsuX44PoEoLeq4F9x+pBQ8/tQ12hVPc0pwMJMsnxacHRd0Ljt21iTySYdXAhsFOIXrBWRtaBOBbFq8iJSJw50CsyfBgVA12VFKe62DHdSIUsORYSm0NHlGvZJSDgIFLQhBKhR1LWhmiv6pms5GS760RzlYoTdZYjaesL8yQzWCctSiPJAEMgOlsGwuZxzuWZ69fHACUVpoc8HtRynJ01++RFM1EALlpGHl1BKnzm8x3B8xOyyfX/hmKVqMfziZFF7wGtxyLH1DbvS/FkYQX9I4u5q1dJZy+mtLHO4ccLQ3Qqdm7kYCkAKZCKZ7U5Y3l5keTnny4ee+g2h87YNcaHPB7UZpyeWL+4j9CqsDw06Xc0GxtJ4xyQT740iWW5KpJ9CgVyV+RxArjVpt8esdxG7EjlpMLvFFPp8IbBzNqqQ5n9Eb1ySrlrEukHWkGUhgXjIqEwW5QqUpYX9KdDXF1KGNIlhDlmT00i6jp8aUscLrBPmsQwho+4qBybiSeFZPLzFUYx763GV+7o8fZ4QWulzwyuCD58lJi3yqReNxK0NOr3TQ53Mmh4Hk4j42F4RegnIlPk+QEwutQPYcaSEIY0cYO5SKKNlig6FdS1GJR7YRGSFtG1qR4kLg4EqGkA6UQNUOVTlEJyEMZ4RCoiY1eUdQDTWJ7nB6kDN84gqthbELhOtjjEzwiaS/1KOsWu5YGnD1+hGf+/Bj/Mz73nGMEXp1aHPhMHqdcjCacWF9BbNWMCskk9YxnTrss570WYG3HhqIDsJYEHQkaIXVIL1A+vnEMuEcsm4xdSQ/tCQdixpErBDUqwLpAtmuJWk8OjrSypMqR5pYYiEgEbhMEDRoCyEqrvUDZes52DvAJTOc8qRekstI7AlcmjBJU642U+5+YJvlfs673nr89l3gRDK+QgglhPi8EOK3jvtwF7z6OZxWbJ9bJT8yzC452qOWqzPL8HxCe1+ClQLf8QQNzWokOlAVaOvxawKbCiKRXDUURYVfU8T7IJ22yHHAzTzySqTNINzZ4juRVJfEJYdygVxGxEziraE5lHO7vUgZlh0uPiQ4sC1De0i9VxMaR56mGK9JrirMRCOHiuHFireZ06xlBd9///mTCdR3+d2YBa89hvtjNs6ukhUptgpEGzm4fEQUEank8y4iKV6SEHqZi8Gbr4lb//n6SDG/xSdBm5c0xwZmByWj/SnD3SNc6wkh0l/uvGiZ0EZCjNz9jjsYbPb53p94+7ccg2+KY9SmEOIfCyF2hRAv2wxNCPHDQoiREOLhG4+/cTxvYsGriYOqZm1jgOkljJc89WTKzpcOSdIO8qLHXBKEowS8Id0PqL0KU0V8Ypi6AS6CLRT1uS7ORqhaQKBioNevSC636Kcd1YqmXVeI0x6yiBIgBsyb5haKwltS5TCnFf5Uwh0XDhksPU25c8Rkp6FpW0KE1WCwfUm7bPAp1OOanjNsvnmD4swKP/JDx/mF9AavAhfDgtcew8MpW1lGulwwXEqYipZnL+6yYZZxO1NUZTFBIoIn2EB0CSIohAc/UthRxFfgE4nUEVFFfCFREaZ5SjUNSO8QXQnCk532tBGEc7TLKUpppFEkJsN2E+qNHL/dZeuNJebMmENrGR/MaKRCKMFqP8We7dF9S4JZiZTllKWzy/S3Ck71u/zxn37X8QfpVaDNhcPodYrQguuTI2rbzMf8CklMIp2uYedqSZMIkipSdyJyXaEPQNmAtBFRe3QbCcEjEomwgFREKSmPJMwkaeYR1qJLN2/ZkIAUETdqSTYcepBS7Wpi3qLrlNhv8FFipgERPCIKfJJyFAIyC4RpPd+OipRTg9WaobHcd982H3noKZKru8cfpMhJZX3/KvAVoH8SG1/wKqe27A2HTG2FqTXBBfQUChT7YgaVw60q0ucscVfSbkpU4YlpJF1uiT2P8wrtIkVWMU6WgEAY9Gi9wUiLUAGRWILXlFUk67UMVqfUTyqOrveooyQ9cvg0IxmXVGlKtR7oRk8hHdpK7KhFHmom6RSTA3kklgHnFOKgZOOtPXa/8Bi73ZXjj9HJaXPBgq9JOS05uj6kqZvn/yghSTTlpPzaKwJ/yGZ0M6F0M/HzonKz+OJVbi5jb/woX75rtjZzq1JTtxAje1dext0XI4PlPjvP7nOwO/oGx/xtcPza/CXgF4Bf/jrL/EGM8aePc6cLXl08+8wuR3VFNXBMT6cQPNElxKlnmAtkzxP3BCH1qAm05KAdMQr0QQChoLKYsYBtg75i8YBNJO6yJjm0VHlCaDRJ47ArETmLiExQbXZoq4T0oEFIh8s08khi9qdc1B2yOwwqdEiVY3ezQB9Zdq+OkC7icoE5qBClIK8Dm+uaz1/eYSKPeYLh4py54BXiyceehKrGraaE2iOnihxJOaoI0mIThVSKzZWSo6nBuQapNThwM0l0AaJDeo31Coxk3vkkUgxrtAiUlSaWAhksrk2IBejSEYQkBIeTEjubQpaiazBHNc9MesSjDqlWqB4QBHYIR+UhqpX4YUsYeVrfsrzao8wcj155DqWab/SWvzVeJdpcJIxep4yrhkeeuMp04ugahSdAHXBtJMkMLvMECTLG+Vj7jkRea25NOBMuIHwkFMm80WaMhFSivYQQianAGwWpwFtN0gh81RILw1hmJGNBiIH0IBBEgxx76hWNEoF0FLAm4o4ElQC9EjC1oG0l8ihgZMTe6TicOj71hWcIbaCcfoNpNd8ux5zJFUKcAX4K+JvAXzverS94LbB3fcRTT+9QOoHuGeLI4kUgZAqZGsz1GUHKudOgijD2hFWHaA3BQaojjfBMri/TdgPeSfJxwBYSk1qKfYfRjjKmVFmBWPLUS5FwmOJbheyBkS3pOY9sAp1zNfVzikoo7JqgmQXKWUAGT7jkCWsJTiYkGy2+F8g/NGOkA4/evUObwH75jb5If5ss7oAuuM1cfeo6z37lCtGCyTW2cgghiBHSTkZZly92/rzIBfQyF4QvdR7dLF974esv7IF0oyTtRe6lF2Arj3Ml8UZiKco/vMxzj1+jt9aHAJMTKeXmWLUZY/yIEOL88W1xwWuRazsHXL54gM89azUMo6I8o/E+UAiorzd4m+C0QqceocE5hXKS4CNkhphZRGsR+xC0JnUSqwNKe/yqJmSC1HmCEfjKzMd5n5UMlieIT+eEyiOXDTKvafICOc0JM0l4UsGy53DN4r1COUcwAb9k8Dag1hSZnvHk/mWqTwnaVlGFE7imXZwzF7wCTEfP8dzkAGaKvoC68fgsQYiICgrpS2SuELUgBkXMPF4oBKCnDnIFIkEIifSWIAUdIbHOk59pcVcUwSpoW2JiqKcRoicOCqQVqNJiU00aJe2sQitByCTqoEFOZviO5aiTIqoWoSN1kyCjYrIriWsNLAUe/tKTLEVNGEaCMccfpFeBNhcJo9cplbX0s4JqOMG2ntPdAiEDu+2MOnpEEUhrUFduZHaDQrYCwrxeVLWeGAOyAScF1Soo79GHAhEDgQjBE0yCah0eiUoAHGoMITdEGeeNA7XCuojLImrmcYkiOvDS4YyioyDWEREdcQVUDBRjgdtzfOnpi4gQuH977YQi9S1lfdeEEC+chfoPYoz/4CXL/H+A/zPQ+w4PbMFrlOgjeVYwcSUueDbe2KGUjqNnhlSHAVUG1EDg3ybQ0jIVCSQK8kjakfTSmq28YtQM8Eiy5yr0wBPTDo2RpNFiRMOs6jHYGOFMQrQSFwPpSk096iKMQHU81muCMmSjhrgkKA96JEVDcBXdNhJSKFOIhcWPHVFAtWWo1wWfvHoVl0juvXfrhCL13X9HZsFrjCjIOhnVsMY6z/bda0ymDXs7R5TD8sZFX/xD5WLzdSPIm10A4te+QLyVFHrhui/Zrryx4Ev7HsV4K1n0fKnbS3bk4JE/eAyAs/ee+gZv+Nvl2M+b34jvFUJ8AbgK/B9jjCfQlXTBdzOpMZi+wfUiaup4cNBnr50xaibY3QqxJqGFpKxRWxJxDfRM0W5HXJSkI4dPFXLWIIOiXUuoY4ueSOShx28nZNLSX6mYXc3RU03UDn8oqF1K2vOY5YDuQVUl+DZgi4QkD6jYUMWIbyGe94goiUctauSgMRAlVWGw656jx3cQqwkbS90TiNLinLng9uPECsU+zEIk6oRzZ3vslxV7V/ZoJxU2KxAN7FUpoqfQVxtCdIg8J6RqPv2sUIjK0S4l5MsQxxE5dkxUj/VixGymcIOc7FpFVJGoNM5Ekk6N1ZLYBmKeYqTCyhbpPL6ToGmJo0AjJX4zZdA66msNrkgR0aEPgGA4PDrgsBWo9YIgkxOI0ne/NhcJo9cpDz1+iYPRFDkL1Gngaj2mmGpiHdAuEGVEeOgkhjBpoXagbtyJAUg1ygeaBIISRCHBB3yAaCTSCUQwxOAICkQWCaToskVIwXRb0d0J5E7TNgERJflBSza0WJUCnjhQiKCprnkSAUIqglR01o+oqwI50vSkoH9ulf/iP/mBkwnUt5b13Y8xvvNrvSiE+GlgN8b4kBDih7+zA1vwWuWjX3ySfV9TDRTysOZabWlPJ4Rs7jDQwtEMDKuZoB4EclGTJI56pglesDcdMK1zpI1IP+9bUisNwZEFwSTroEhZzsfYSWQyVqQzaDoKUUXM4wHe4ZC9lCZI2pixes+MM6emXDtQpKJFzCQNOXYj4A8dNjFEl6Muz/BdhR9o+huSs2GZ/9WPvOdkAvUquCOz4LXFZ3/3S1STGyPHPFx77gA8oMT85w1Eqog2vNgxdDNZJHnRsi/iZkJIvORvgvm2fAQl0YnC1V9rIze4ud+XcSN113LO3HmKP/O//4mvv41vl2M8b34TfA64I8Y4FUL8JPAvgXu+g+0teBXyiWeeo3EBN2mxaxWXpaDZ9RRO42tPVSW404bBOIFLFjJH6CrU0GIqT1jKkCbB5wpTtZhxgxMRYR3IBGENsRS4yiKdx90VsbkmOYq0QHUeOFSIOkFctZh1T1xxSBXQM4trNPQUcuSI+w0697hKIZ1DaajyHBElm6s5F85v8sfe8u7jD9LinLngFeDKs49RZDVVzIGG3UOPiRPq2MX7CDoiOoZs6gk7JdEFpHX4ACQpJkTqzQQxEejUocoZs6lBtaAI0Av0egE/a3B9QdKXxBBoS0HVT2EgSa7U815G2w35UaBsIGhJW891KTIDs8hsJDBnFGbSYicRpxXhKKJlZPO+de5+w1ne9cbzxx+k469mWQL+Z+BNN7b+X8YYP/GdbHPR9Pp1yrCsqLoRt1XhliNMARuRbWC1lyMONXEiqdvA+fOr6EITfSQahRCBeOPupbGgK4+aORAKp8Ebia4i0oFPFd4IdO5Jz9WYzNN2JbIU0ELrAqIN+K6CNYEsJCpEDIpkKOhdtKQjEChs1yCDYHrQxymBX4Gl1S4/8K676fTSkwnU8TYhey/wJ4QQzwK/BvyoEOKfHPsxL3hVU05r9M4U8+gR6ZWSuq+IGuhEllY01dkOTc/QaM/9K6sMkoivBTEXtEGDk9QY8oFDpxE30MhGYw4DTCFZqmm1pD7UTMsMUYJ/zGNmFnldEAaKqAyzMiK0p72WU17U7FUFIg9MegXVvQmzt0jsHdBoDbsJURs4k6GCxUwca0tL/NAb70GeVG32d3mDwAWvPcYHkxcnezwgBEJJkp6+1W8oKwxn7t/+w32Jbhp+bjbF/lYRAPFrJ4uEePHja/x9644t3v3jb8G7b2Y027fBbdRmjHEcY5zeeP47gBFCnJTleMF3Id55jp7Yx3lB3q2osoRy5PHSYI4ESS+j00bS1pKd6XD6zlVszxASj80NsztSjI/oyiIHERcDqrYkSqC6mvKMgdQjZWTSSai3NLoQ5Ost1msGq1OyXk3eBNrRjCACzCQqeJyXTE1B6Ocs7Tas1mPaJQlpgllOkMW8UW9YguQ6bJ9a5Ud/6DThllXwGHkVNNZd8NpiMpyxc2mEait6ckhwDpqWjdUSoxVyrUPUESrH+vYqG+c3kP0uMjNIoYnREvqQXp+RTC3hcsV4kuOFImQGNbWMnsjgMKJcREdBtunJtx1MW5pCEbXAFAF//Yg4DFgMop+gexLf09itHC1auu0IZQNMFFbkxNWM2NHINU3esZy5Y513/8T9iG/n3P2NOH5t/g/Av40x3g+8hXnf3O+IRcLodcrSKUOuBbVPUaVENoLVxHD+zAq1DMiewuYQViRV7ZFCkSiBnjlEkCgpwXuUj+h+ga4jUUhUjNi+RFpH2oDymvU3TYmVJJSCNk8xg0iaW1QdCAhcIrAFuDql1Bmuo/EqEgU47wkGgnCYYUv0AacVPihihHvfcJrJqOaTn37y+IMUgSi++cc32lyM/7cY45kY43ng54DfizH+heM/8AWvZgZlwJgUnSTYtQ6xA6c3M+4aLOEnkqxWmGlLvJZxYAMmn09nSXOLySJSCPLc0bicqh3glxSt0LTKYCYBWXiqdcPEJIinLX41IWSS+qhDMxYk0hK6DdNRgp9pOocNomuoZxmqCazLCYaWLKlhqDHOwVpNkJ54KIk2QeaRd65vc7Ec8smLl44/SMeszQULvhk6g84fcv+snVvi1IV1fHw+SeNdoJxUmNQ8n6iRvKjcTKivcfn1tS5GbyWBxNdf7huh4E3vuZtnv3qVxx96+tvbxtfjNmtTCLElblzBCyHezTzSL9Pte8FrFaUVq2/tkK2P0a2g+KIl3RFs5wXdu/qgFdQKdRnUvuXQgBEFYVVDJgkdje+noCJJBmbF4LcyohTIcSCfgjSevNug+yBGHZojAyODaCSDxjKwLX4vIkoPBLqnp6iDSLpXI2qLrKBqJc0ww4wUrgnY1Rbbk4iZJBnX5H3FG998gc+MDvnytf3jDdK3qsvFeXPBMdBb6lCf22C0MqCdpviDGtc4JtU5eut9jIekBLNXE9rATAr82YKwkqGUQOsE2QqEF6goyNICOjmkBk8g+EhIxLwyZcOTn51y8IRk1/UIK5KoBBaPmDk6aw5/QTFe6xInARsEvVWJaRRyUqPGLbG1+BKCSYhak4xLgjSk/SXuffA8n/7UU1zeOTreIB2zNoUQA+AHgX8EEGNsY4zD7/QwFyVpr1OqpqGpPBSB0EhsB5pE8qX6EFODsILUCaxSXCkn5LOWNDNYH+ZNrT2IIDGpQJ3v0g4loXK0ywZZe2RQBG+Rk8jo832SoaNCQ+JxVpNMI3rWzjvX64DtCswkABGhI1FFqECHiMsEMQRE9LTLEp8IpJSkCK5cOSQAjzx2hZ/5yeMfQ/oyfUUXLDhRJrOaduZQbUPbN+iho0wDV5+6TCy6aOkRRtMeesr9PbJzntx1GZcGIRwegw6BvEhYei5lhKfqWMy+pekrGp+DtLQbkna9i1xrWWoaEj/iIFmmWYNet6Ssc3qzkngkaQtDqCTKR6azhGlfoPMW1UQyGqYxw1xrkGOHsoaGhCcOjxgfVVy8fMhPP/jAscdpoc0Ft5vJcPLiu3sxUo1r9p978QVkO7UcTofo3GAKjasDMYRbuZ7uSofoArNx+YfL0174wX5pOdmtkrUXu4eEmJ8jvyk8XHlql8tPXOPw+hHv+LEHv7n1vgWOU5tCiPcDP8y819Fl4P8JmPl+4t8H/gzwvxZCOKACfi7Gxb8OrzemdYmvIxwG/CzFryTMMsd0NsQbQ9oRqHFLK2bIJkfXXbprglg31E8KZKJwQrGS94nTiunUIaRFJx6xE7DaMBtrGqeR0pJcEcR9QbJhmT1TUBWKaCVqGtCnA22mAYfaalBbOXrXIToNs6spbkPRuVYTbQJREoNAH3n8ZsPh9RlflBeJbc1b/vRfPNYYLVSx4JUgbYYMa0UnEQQivdxCUXJ1bwSVQ6xkuNJxrZwSBoa8dCAVLrTE2qGtxrvA4N5VaqeIO0cIYxExMkNioiQ2NfUTUHcLfGpIZhanBIPLLXHiaGoDjSWWgBe41BBWFP1TI3g8oa1TnIv4vkHNGmQiCVpg0xwmLWUiaA4rnnrkMf7Fv5H81f/qzx5rjL4NbX693n8XgD3gF4UQbwEeAv5qjHH2nRzjwmH0OuWBzlnCRJE6RyYCK9HQbZmXkRUB4QO0Ed20yI5BpAYyjRaCkGqCt2xvddg+P6C4sMddZ5axb7bYjYiZRWg9GE1HtxS6JXQNMkbU1JONA2YqYJDT9DV2LQEkZBERA/qwRUaD9AHTBoQHqSVRaXRlSbzDNwqfSLZXBwgXoXUnE6gTsu/GGH9/MQZ4wcux/sA2biXFDjRSB/qnCvLNiEkkIlSQBpKOI19u8Fc7lLsdCpuSqYYwTul0Gx7YSLmnSVFfnPDA2R7dD42xO5JqkEBu0a3GaIsiEkYpZaapVhPybYcvNM1wCXUkqK/noC2xctCxpOcCrTDkXUe6FIlSUqmUxijqvsd3JEFDbxY5vdnDa48136DXyrfLwlq/4DaT97IXlXvlq3MXwi1uJnJiRCYCgSDvFUQx/xAKJJt3rtPpZQQfOH3312kI/3LJIUBIyc3JaULK5+3xN8rchJTzZV7y/CbJkmaw2qNtPeqlU9qOi2PUZozxz8UYt2OM5oZD9x/FGP/+jWQRMcZfiDG+Mcb4lhjje2KMHz+ZN7Xgu5nVJpC1UPYKxLJivdBw5ODIIm2N36ppzilmfoleGpDRUbQdQmuQOaRWcKFXYEpLzCWdtQSZaEJf0Wwr2q7EbRrSEBFd8NuC6YrB15JpnqN8gu1p/BmJ6grk1OPXYVR1cY/miCNgmt7ob5ZglxV+IABJs6IQe5qBG5D1In7sCOIEGusuStIWvAI04wSDwJU5brDM2lpKs2cJEdyyJJUNbINylqxq8UJTSIOUCiU1UkvO3nuKdrMg3FnAakqatyRnHd54cAGtU8ySgSRjQGBlMqbTtggBWaGwawl1OqDTgI6BaCRm1zL+Yooop0gRcMsFsZcTuhl4SGaWCIQi49TdW6Alg86YteIEUiffujb3Y4zvfMHjhYMiNPB24H+MMb4NmAH/1+/0EBcJo9cpX37mGnmUnA/LCKmoaDncmZEfgak0IRX4TKKiRFsIAopuhsg1MkaSVDNY7jC4Y52jr/T4ynCfdAbppUgyC/OTogJvNT5qWiNwmUZ5Q/SKGD0OD7UlObSofUtEk1SBdtlQryWIrZzQTRASVCtIfEBiCFFiHPgQ2VrtcOloRHtS7tmFfXfBbebS7gi1lLD69g20kszKkn05pro7Qa9LTGXxO5KymyHy+ejRJSVRTYpSktSldNUyHbPOwf0FD33ogOlal+qBnHSjxFUJiIgzmpAL1Ajaac6eXGJkMmIambWQ6Ja4HWHD0HOWpY2a5rJBXjOEvR5HRz0aabC5Ik0lSiZgJe3AYGeBXCsOrk2R9oROMwttLrjNDA+mmNywfLoHQlAdNVTDBpScN76+iRLoVCEUJIma/01A1tOsnV2lv9ajqhuuPH79+XVersTshUmjG89vOYle8Fq84UQSQtw6jhf2WRA3kkcA7dgThWCyN5oPqzgJFtpccJuxvRFv/6M7bPdykpFgWE2pUot0Bd0LknaQIFYNyb5m+lROHloyCSoqxFpK0k/ZurDFoFE0uzOODkrqI4vdA1ODwdP2JUFH0muW9a19CmVxHUGW16ilCtkNeKdpdiT6AAYDR2YUBTOSIdAt0CGSNx6iRpQJxEgybdE6Mm5a4nSMfWJCN5zAjZZFSdqC24wPgXolsPlWQW+li46Si5cVE59DTBG9FG8DmBRdeUQlSJynWNGIrkRqQbbeZf3MMoPrLckTl0g7Y6qJpn7MUezW0LQwiwSvYN9RrxtCqii6LSFEZlagHRACk2fm3z3tcoLwkqoVhFIjewnKeaR3hCygtCDJGxJmaB2YHQ7Zm4w4dAGxmR9/oI5Xm5eByzHGT934/Z8zTyB9RyxK0l6nfO4rF0naiB2krCjBcHfINBVIGwjBI2sImaGxkVh70lThaotoPKZ2+BC5eHGfTHtqK4gCTAsuCpSShERQL0uiSEhKf0OIgpgppPMgJCFGiAERIKxooghYpQhFhmpgeSVjrwpEBEluSLRgdzUQMoGcBZIDxxe+eImZbFhbK04kTmJxl2XBbeYrB1eIy5HBLKEZdBi7CeKyROWOtvHYJUlyAHHoGS9FOrWC3oy1Xg2ZQucNO+0QMT1NDIKgDKQWKSy9jkDuNwyfTiEa1ArIToOJMBn1WOoOmbaGmAiEbMmNRF8TUASqg4xpXbAyaDiVd/liKXBWUqQFHR/xT4xQjaJastTnIp9+7jLtsGZ9/WROMwttLrideO/ZeXKPKCO9pR7V1FKPaiLhxXfjhYAAdubJugrXOoSSRAL11PPsly7SXS7gpT1tb05ae6E3/euUp4kXuJnmDbUFKBhsdhhdnQKQFBq0pB23z28D+PLHv4ptA2e+nsPpO2ChzQW3k1lV8cgeNDvnOBuWeO6OA3woscIRrMFfjaQykp1tcOuB2Y6iZwqaw4ZoIl5FqnbGM48+Q3J2iSpk2MajtIChJ9cJzaTFpR6LJG5p3EFK57DCvLGhdR3KicLPBDQKFQXVKGHsHPnpBveARH5F0J9Jhoctfk2TDroI7YnXJngJ9bLGaMHnn7iCyWruWz7+vu0LXS643VzZHXL50Ro/Trjz9IAnHrtOGT1yPAWpkBNL2wTshsaLgJpZljsF1ahBigSnPdX+lCfYobecY5uKuA+uzgirEpV3iOOGRkeYCSSRZhIRPcNIZTAEoT2UDiUkPhOc0TMqk1CeNhg03UlFUAnNQcQtZ+QJyImHI0WMAlm2NI3mka8+yzB0ON3bOPY4Hac2Y4w7QohLQoj7YoyPAe8DHv1Ot7tIGL0O+cwTz7Jz6YDNVYHozigvS4RXECPKBaIFpyJi3CLWIbWQDh3jSQtKIBKNSiSub7gaWlojsK1E7SVI6QiJwNQOkQnExBODJPYk8jCgG4sgIgN4CTGJ5KWnnSlkE/GFJPhAXgd0ksKyIY0SCJRHlqxSzJxHE9nu5Jiuxl8JjFVz/IFa2HIX3GZ+50MPM85mrHda7LUGeS7DjDzWOEISoBcRtcBtW/IjR3q3Y+V0y0Q7apeTJYI8kah9yXNfrmimGvKAv1tRhBl2P6enKxJX41YSYkxw1xXtBU/emdEmgUw0VOOEZE9z6u1j9q5mNOuC5tCQ6pZ8G2LHk35Fk6QSW7YMU0GxnmKvVyghWb2wgYqCyZLiki2PP1ALbS64zfzG3/sgTWXp9Qu8C0g1L/eKt6afveADKQUYQd04qqlFGIE0Cm0k3nl2nzt8ftlbiZ+vsWMxf03ccBnFm06i+Wlx3kD7RnMkU6i5HVgIko6mrRyUz5drCyG48OAZpqMSEFx96trxBegmC20uuM3826/+Fv2sYX+UY/sKvd3SXEngCqipAxkhOJorkN3bInzGqHa4NpAZTTpVpJ2MdlAyvDSEMqAUKOvxA41LW2RX0e2UtHsaIQ3VwFBtRkRpUPsJqmkxM0/II6k1NC7QrIGcJJQuZ/2cxD0Jel2RnMsYtaAqjykDzYYi1fDA2dNcrPYZ9lKulqPjDdJClwteAf7dRz5CNhM4BHZL0Vw3YFrkpQbpWjAJTkFsPPmKREw01WhGGE1RK310CBgBzahmejij9QkxTfGrBrca6F4PZH0YLSvUc2E+hDQEjtIUPxEkTYuaSYKUhEyTWUs7DLTWo0JLXDV41Z9r43TGqX5NPS5pshQbBe3ZjG4Wecsdp/nM47vUiQCOuVz0ZLT5vwN+VQiRAE8D/8V3usFFSdrrjI89/Qx//+O/zQ++9y4udwRPXouUlUO2AgWEXGKCwDhgXcJYE0eBGCSSSLeTcurUgOaOhGtnBG3jsN4iXEC4iPARrCckGnkkSaYgjMIajbYeAsTMEBy4IpA4j/KSladKBjuWZOrJy4asowg+cv9gQBSBmXWUhaBVsLoxIzeW//Qn38Ob7zmDiZKeOYF6bxb23QW3j9/+zU/yL375g/z4Xe9g8hnH4UHFiIrpGoR1gZpBpyMJUiOvGcZLBVWecygNWd5SZF3uKLqID0d2fjfSOI9xFUu9GpTHC00rYawy/FKC7ym0lyRI0q4n+IAtM0IUyElDslGie5H++0rSez3Zas3aRkURBTaV3HtXF1m3+FmNuB6RM082bhEo/uKPvZd33HkWbTS9XnYC0TpebQoh/rEQYlcI8cjXeP2HhRAjIcTDNx5/49jf0oLvWt7/3/0b/uA3P81P/uc/yHBvwv6VA+rZ/CaFuNF0WtxIIAkp5wmcOHcaCSlYWu+zenoFWzrqSUN84Tj7G4mmmwmh56eq3fgZuZUourUczB1JLyxjU4LUJCR5wtad6/Nk0Uv7YAv4U/+bH+eBd90JzCfYHD+L8+aC20OIgX/+uV/lM48/x/c98A7sZMLO0ZDqcU12tUInArMmCTdyunrVk+w1mFlA1RbbEyytFGwmGdWo5vAyjAuNKxS+DyGPGDxeCNyBxB1o1IEkMy2uANvPcWWCn0pcliCER4qA6HrceUlYEYRZoDjQZE8J+lKyOhhgn6vhwNK2ILYl65sVxSTwM3/6e7h7e5PcOFLbPeZofYu6XGhzwXdA6xz/8EO/TMUXeN+fupNpO+bg8i5mf0Z62BLWO6T3GViWCGOQEXLTYFxFbD0iSVnf7LO60iWst1g8dSbwUeFkILVTlp85QB9WCFGirEXjaLuSpp+i+hr2Ld5HorP0OiUr3QqfZEx8FzcDNwpInyOFZrA2YFkZ4vURjYOhTKi2ujSbmn4h+bGffi9nL6wgdKTcPW6DwvFrM8b48I3eRg/GGH8mxvgdj3ZbOIxeR4QQ+e3f/xRXn95la+M0MxcIUTCODQaQNlD1IqnRNCYgCk9yySOjImIRieENbzvH2fu2efjRT1EvK8yhxuUCJSJVP5JUEOuAUJH8wKGFBGeJXlBva7J9j7COYEDViqTyxMYhYkQQSCS0QjL2Hp1HntrdZ2bUPPlaSFwiOZp2kaXgma/u8oYHz6KNIt7sE3HsQTuZzS5Y8EIa5/iNT3yOx8dDzuwOCa0nUlFdj8SzGmUc2XVLWCrId8aERuBXc5Z7DqMt42rAj27dQdf3+DdP/T7+QBDeVRDGkiYxZKWA9UhMPTGPuEQglUQtB+orHvmoIDsf8X2PnEXESkq+WjPzkdJKlk3Nbr/LYaOZXYusiMDF544IpSIjUrUgZx6hM0SiuTbdZbNYJhERcVINxo5Xm78E/ALwy19nmT9YNKp//VFOKj74/o9yuDth/dwKREFbvkx/ESXAPX+bMMlvOHyk4N1/5C1U0xkfefL6S3oV3Vg+xlvOoXijvEwgiDG8qBQtypteIgG3jEPPu42moxppJOP9Cnx8QZ+jm/+J7Fw8YGVrCRSEk/pSuDhvLrgNjO2I3//Ml2mPAvX1PaabBj9uKcY1mIxoJGUnkFtDYmtsLSmnBZ2gUMPA2rLmh//kgzzz0ee4dLSPEIZ8t4ayoT2dEnsJxBKxbTB1SzkpMFsRz4y4H8mEJRlGtINyqmHVYEYBsVeRXhWYfootNO2sYSRT8JZZMyNUGpE58hAQZ2H0VIZUElc1FN0Cf2CgOQFtLnS54DaxM5ryyY8/yWrREK5fRUTB4aUKGywqz8hVYCl37F0VVEsJUUoOJ336aSTIhnStz/t+7kE+/vuPs3vlkJh49MjRLqckZxM2+lN2H+kTuimzEDHP1KiuJg2w0ZkSr3qO6hRBQMRAbXq0EeLUIgEZIGQpfrelHEicrRlePERVhnarh0kjibCYUeAoZnR6GR2VkleWENrjD9irQJsLh9HriL/5Tz7I73/yMsvqHN0LPTIv6I49Ic6nrigh0JUgjgOyknBkMBNHOmvmZWLLOUtbfT65swNK0L3q8LpFOQ9IdAQzE8RU4uPcSaSNgkSDFagNT0gaYoxENS+zkW7uxYtKQAiICDJEooqMtw6RWxOiCBgDZnlEPrKokUBIQakiTQxkicK1J1CSBs9bBRcTJRacIH/95/8Vjx1NeeAtd9GfREQ/oTxXEGUkiohrDHrFUyeBtpfhtjO2zgwha7BWIut1unKNj/zOVXQK2RsNg3JKVJ7Gpri+JKwk6CbCTKHKlEIpcAqcIX+mZS0MYRroSEt2tQUdiQ5CEOjCknUdKg/otubot/YQO1OaxlP5jPTyiNmdimorgyjxR5HQeEyQNPalzVqOieOdxPQR4PAbLrjgdcff+PM/z/Urh9z79gsoY5Cpmrt/4MWOIP+C35WgrQMg6S3lbJ5d5fMffvQPN7a++dm8MfkszmvPMLkGKZ6fgnbzEV9c+faHthNgvFuCf8HV54t2KWimFbYJSCOZHE6PIUJf43gW580FJ8xf+/u/xBd3U5aW301Iu1DldIYOgiAmAS9ASkfsNmQDT+48rcgpC4Vc0SyvSJbblEt7T6A2BHYzgRWHTgJFKZHjhryoUTuezf4hK0lJ7g1ikLJ99yHn77mOXRbM+gbhQa01WAt6OyJ6ArNTU+x6zCzSRse4O0WdauB0Q5CSelUyjobQ04Q1w6XDIWlQDFYtR2Lv+AO2mJK24DbgvOcX/vZ/y85lGNz1I8RWknYkrp0naZQCvOPqZECbGqQUyBa0bWkbi0ol/dWEfGC4cv0qtjK4IiF6j4qesBe5eGmJVhpi09AOUlSRonoZop/izkiKN3rsdkbTN7R9yUxHwrMNaE0MkSgVMkoU0DQNB/0hzbku7XaB7jdk2YwqTeAIpE/40tNX2bprheVzcGl4cPxBexVoc5Ewep3w1ed2+M2PfBFfRf7Y29/A2byPnUVq15ISuGtrieWVgjYP1MuCkESWDiJqJad3aoCRAkEkKs3D+3vMOglOJdQ9hSwD+VFADwPEgGhagvDYVNIYiRo1ZKVFPOnQw4isHLZvCF0DmUYgEQF0qnERvFY4BO3lhNlOghoHZkYxpUc0AtVEBuPI9kafo+GMjSLnJ37gjccftMjCvrvgxHnoC0/z+5/7ClYHfuJH7mP1fJcKSVu1iDzw9jtX2OwIRkeS7ElHHjxrb3bQDsjCOgpBoS1PD0uuffE65TMpw1mO3ZWwHeBCIGwEMtOwXgzRVYuuIqJq6Z2asHbHBPGgJF1vObNyRNxRJJUkJ6Wbe1b6DTomuDZSDxXDNmV2p0HfaQnbETESRFGgpmBzhXgwY7nX5dA2bJ7u8Se+7w3HH7RvXZtrQojPvuDxl76NvX6vEOILQogPCCFO4B+cBd9t/Ptf+yiP/METKKH5/j/+DjqdjNDcaHItBefedIqsn754JSMxhaG32kEaKPodHvv8k0x2ZwC3ppUBCKUQ6oY79saFoBACWztU+vw55VbZG/P8j+kk8ySTBJKvcRl3Kzl1cy3obXUp+jmubdk8s8pP/1fv+w4j9DIszpsLTpgYI7/zmX/JY9f2WV6JvPcd95LKSDtrEKYl60fuvOcOVgmkmaO7NmN2lFHrNYqVjPUtSdAGvW949FOPMh5GAhEqi6gjkogWJSoRzEYpU5Nw+coqpdFUbSQ7yIg7muE0JbaKMIkkMiAa6G6klE0P4XPkIGe2rJme09g00gpJqxXaBJIqUFSBpcs1sXRs95dZ2p5PYhuELX7qwfcec9D41nS50OaCbwNrPX/vX/8aX9mtOW9S3r11Gt/PmRzAujjiXK/i/LvO0TnjsF1Pu9JBR0PfpOSDHp1uBzUJFFPDp37rS7jpjLTnCCrQ5hEmAbUzIz3wmDKibCAZ1lTnUkoRyJxncgVmjyWkQ4vKBe2pPsEo8tUOom4xIaCVwBqou5JQecSexIwDUQvkSOBGHZIdS1SaU0t9ko0OR0g21jb56fe9PrV54iVpQohngQnz+28uxvjOk97nghfzex//Kn/zH38As1dzx+oSP/E9D/AvPvMIrg8uT+g+59mflBzkLW5doY4iOEmxnLOUpRw8dYi/YYv/0CNP4fsOWRviisMjYRRQIoLzeAW+Y5BRIBpLO23RJehCIEpQXqITgy0dwkU6KERHUNUe5wMSgZpZRAY+E8QkAxPxuSAIBTFAB/oXI1/47LO89c3n2LpjjXe8/e4Tid1rearEQpuvPO//6Of5O//qgzR3Ot7wwBY/+p438YuPf4LsoESPFeP7Ouw/MWVycUZxFLCDiFSS5bwgUXDohrjWcDqf8fAfPIerIawW6GdroknhboFMIZUtbhhxM4MqApmeQadB5Ra1nhJXIk6ldLKAXW7pnJpwZlXgROTIB8pppCstcVAxXUpRs0gz6wOCZByQHnwUJLMaezrlo9ef4U2bpznfW+G9d39XaHP/O/x8fw64I8Y4FUL8JPAvgXu+g+19TRa6/O7gl/72b/LrP/8BEIK3/dgbee9PvZ2vfPKJ5xfQcPXp67g6zpM3N8bdb5xbxbvAcHdEaMF7x8Mf/uqLEkXAC1xDL5hyJsT85mGIuMojxNx5dKufkQ8gIMkNEXCtn5fBzavNXrJtuGUvumFLCq3n4T94lDsfvIPzbzjLG7/nRD7Cr9nz5kKb3x386kd+iX/6bx7DXk/5wXe8m++75zwf/upDFJNIqzPCuuJiucv6nVcI3YI4i9CNbJ4dYKuW6bAicRUtCV9JL1FlwGWJkRaJQSxDZ8tTXgUfNboVeAHst3gjmeyCN8tMtcYXklSDuLNBPZmSXY9EwBtofSCpJHUiyJKKoATucoZwDSqALD1aePIVxxDLo5/cYXVpFTU4z713nzv2uL1WdQkLbX638Lfe/5v83r9+AjUWfP9Pfh/vuvM8/6L4GHLcEnoQdMIz1/bYXLcs1Rmjq/Mem9tLG0zalkldElykVnA5lMyaPnrUoijRM3DKES6sIMYVYDFdSXCO9mKLnkZqKSAzTCcC6Sy6TIhLnnwXTIgYo/AC4niG7OTEKuDXUqgketzQpoqNwRFjcoJy6KzLeNryyGcvsbysGbz5Hs6cWT/2uL0atHm7HEY/EmN860LArwy/9tufYVS2JNPIu+87y/JajyQ3DKKgqBQoyWzaEMeBYCVOQ34QKK9NYdrS76dQWUbWcp2aEGGlVYQatI2YQ4uqHSqCSBX11txiG0VECwiJwOYGaRQ+02T9hERH8nVBs6KZrARipgk+EpqWqAVJA5QBs2fRVqCrSHGtJZsGikrQWyr46jO7/PrvfY5g4M47j3/MIfBdbxE8BhbafAV5/yc+x8w60rOW9953BwMzILMavZbhNzOkU1xzDbaj0EEhidR3S3bVhNATZKogCM/TlwTlxRnCeLK7O4R+is8hbkZM0ZJkHmtzpk+lDA87uBjpCg+ThJ1Rj2AlbZUw88t4Kxle63FwMeXpLyWMpl2IHq8CrjXoDsR+Anc0mF7AdgTT+wV+S6PuMwxUxuef2uH/9/uPUDSau9dWTiZ4t1GbMcZxjHF64/nvAEYIcfxzj59noctXmH/3/o8Rmwha8M4ffhOrW0soo5Ap8ysnD656YemXACnZ3xlickNazJ1He5eOaCbzkunOanFr2Xnz6hsfzhsladyoORPqZuPs5/sQ3XIcRWjKFle1z3+2hQQlb7iObjxurn9zoUSiE80XP/IE/+6ffJTBWo+17eWTCd5r+7y50OYriI+Bz1z+NMFVnM0nfP8bt1gfdMmeEeQpmFQzGyXUoxpfCHxfYIYtyMik3CcPhjBR+DG0epdGTYlrcPa+FCUjQcFEFuzaJdp+Rsy7uFShBx5RK7SQqDzihGFaJ8Ta0NnyUILPAvK+ltX37cOqhQuC4AWd6w35rkccKuJRQ6tz2j4oGwi5pL+ekaeW3//0l/jkZx7i9OmUJDmB+/mvgrKX75CFNl9Bqqrlw08+iXM1eZtwz/1nWVrrkbWBbElTJSscDBXNtZInZysc+ozk0KMv1+xf2qOYOVSItJnkcmjoxCOyjUi/lyIR+I0MIQVq3NJxU5KOo9YZIFAqzM0FMmDGghA8omswucQ8axGtoHu3pXs2Yr0n1hZVVsg8IMsWOW4ISkE/YUd0CdcCuVQs6RRXOz784S/y0OceZ3PzuJvR3+BVoM1F0+vXOI89vcNhOUPUgZXM8Kb7z+Ct56uXdkhqTZg1BAsqCGQukV4SMmiWYLNJ6Axy6iRhXUrGh1NmE0soEs70Ui5OjmhtgVKKGAJRawIBXYFzAuMlwku8AFE7KAwuwHRYI7ckUjTYfY/UEiMhaDEXbOshUWgbiQrqviCbOcxMEJRguZKUsqE2gqjgicev0e+exCSmBQtOjocvXqWspsi+pddZ5vu2lgB48tIOetDB5Y5a3XAXNAqhPbIKcAWy9TVsmdPWgeWOITu3y/SSxbmcpc2cy/tTbJpjo8N5z7ju0TU1s15CojytEhylBhM9K9mEw2oJbSs2BmOm9YAszuiszohLUKY99mYdoo/YNFKWKVvFiKYyzC7XqPs0YqBgBslGwdCV+ENF1Tie6xw+P9XpVYwQYgu4HmOMQoh3M08ZnEAh+4LvBr7wscdoSotQilP3bLJ1fp4bvPLkdbI8pbQN83n2L2kqLWDj7CpaSpY3+6SZoZxV1Ec1ACYz88Vf6C66sXq80ej6plsoEp//PUR8/XxyKsYASqBzjZvaubvpZR1M3Op91FvpMD6al8VVw5q9q4uP74JXHw99+bM8N+7hi8D5pVN0VR+AZ/cmyCyDqiTbbam3FeOiQ7ujWW9b7KTi3Ft7NE8Hlre62G7KzNa0H9dkLlKeBm81sQ9aBOK+JHQkSdbQS6e4dYW/7nClwSUSMQl01j1RV7Af0DaQ0FCWS+zpdVZGNb6E2GvxLuKFYbSUohMwSIrUY4zEoRGXUkaqRVSGnWHL4cR9gygsWPDdx7969PO0zzrkYeDuu89TqIQYA6OdS+TdjMluhWgEK3d7RjNNqD3lqiEzirXVFYrGIhJNp5cxuTZi/7GICjXTrVVCFokdTzZStLsTJt0OtqeRbaTbt8j9GbO6AyrgSovcUFDViDZHa0srJcN9STvSZMpSrfeIzIdMBBvwZxWhjOBqVOxTJw3MJFoaDsWUbqg5enJGsl++0mF+xbgdDqMI/HshxEMv1zdCCPGXbvaV2Ns7gSZvr3P+4W9+lKcORqwqw5/6E+/i7e+8gEk1P3TPBaLwzLSn6kV8iKijSDKNpPsejWRta0DRy+ktd1i/Y5W3f9+9yPVAKhsuXj9EKIGYBUCA9fML3AAhl0giataiQkTEiFeB2AaSyqNTRagVB6YL/QTVCLyLhBiRKFAK6SCZtqA0ZhagUZioKLRkOmqYHjVoFxENpMacWPxE/OYfr0IW2nwF+W9+8bc5uDRi+0LOX/3ed3L/+lkAHvy+81jpEHsNySWPQBAGgsmplGZbk3Y1K50O652CZQru1Fuc6V5AXgsoXXPt4hViEFRLEikkGodRliz3JI0gpoJ0tSFoSEJgo5lSdGuyFUsgJV1uUEeKq9cHlGlB8BJNQCBxQZD0WpKVlv3QQSwb2kSR9QIm0ewe1gzbFj8IhATESU0v5Hi1KYR4P/AJ4D4hxGUhxP9CCPGXhRB/+cYifwZ4RAjxBeDngZ+L8WXbDx8HX1eXN453oc0Toqlb/tu/+otUk4aNO1f4j//KH+HON5wB4P7vuRvvw3yiyQv/90dulZQtr/TZvGOVIs+468E7WDuzcitBNLwy+pr7fWli9eY8tJuJH5XrW8+9jUgj8a27VQr3wmVvNboWct6kWwkmuzPiC/rPB3dyJ63X8Hlzoc1XkHF9wP/4xfdz+Shl69xp/uif/HG217cBeMtbN4ihIu7XUFoSHxg+MqC8WnA0WaO8sIwyp1jOCvo+5+1v2mBpQ2FWS8Km5MqwpS0EMjhObR4QXIuZOYz0pFmgvZzRpgVZYSnWpzQrkuTQoWqFmhWIbUkwGU2V0MssWlcED6IE3RiiFfQOWnpLEacFdaUwuSZuS3a7FtGpMBsBtELrk0kYfSu6XGhzwbfCc3tX+Ue/+UF867nvnjv42f/lj3L69CrBR95wxzZ1WaHqlsJUxIlFzqaIssXMLEm/y8rpAVknZbVIecv9ZxmcSYir4De6eNMgdSQVLc2mxnUTpIwIrbDLCdW+oTlMENYilKHpa5hKfJ2RSUlvvWV1raYaaaQyuE5GTFIoUuTUYZRHaoPYMKAlsnb0t1PavuLQ19BX+HMFeknR6ZyMOeHVoM3b4TD6/hjjFSHEBvBBIcRXb0ykASDG+A+AfwDwzne+89X3T9R3MZ/4/FN86gvPIV3kP/3Jd7Oy0uFgWJL3cv7ehz/BXqwQPU8cqfnNSSVRRx5VBwYrGadWevQ6Gc9dOaCtLA/v7FNtp3QmDdMuYLvkM4seN8gQSSPYRDHtKnwIEBLk0CMbh5m6+XWsFFglcBIyZdGtxBhJ7fytO6FSCIIWRJ0ifSQ9sHRP9TFdxeHBFJ8qZPD0jMGkmqWl/OSC+Npu/LfQ5ivEbz38KE9MDpB55C//yPdidM6zU88pM+XXf/kPmF3eJ0aJTAtiV0MqaDWEQnN6OedN2z1CA1fshFla8exnj/BHHfQ7a8IXFa6fEbdb+ltjlBDEYKiGBnEVkJGx67O6PaPXaynWZqgyRwTF1VEHXXkEgdokrAfN3hQgELUj7zhGoz7X7DL9bku+4VGjNfCS66HEtSlaBZZXM3yMbPZOyL4Lx6rNGOOf+wav/wLwC8e2w6/P19XljeNZaPOE+MCvfozDq1OQkj/3f/gptNIcXh8hleQDv/wRmpmfLxh53sHD/Pna2RUuvOk0w/0JypSMdkdc+eq1+es3ythexM3G1e4lSZ+bySghIMx35Et7KzEltERrSTtzz+9fyVvL3txfsZShlWS8N7u1+f52l2rcsHnHCVZUvnbPmwttvoL8q6f+ABdH3Hkm8rP3/FmaNrJ7NKZpLR/5yJeonWe6lKEHFh0FaS0wsqXyOaf6m9zJBvv5EDubcOnJmiuTEtY6tDZDHzpcqnHTwOzZBBMk0gqsF9QovAyIvkDVnrCnSBT4EEge9IimJCSajpphbEohGi6LJehZROUIUbE6qBAqpSZBm4RBR9EisY8eoe5tEYVh2eQ0ouaOjcHJBPC1q0tYaPMVo3Qt//j3P0d11FKs5/zMn/8hxuOKw/0x18oJjz30VUQtMBc0XTFl77k+5BZ94FG54Z67Vrlre5UrRlFem7B75ZDrjYPVAlGlJLbGBoXdTXAbCUnqkNIgpi0yF7DhqEOBCSBtIL/WILMCv60YNo4laRA6YnoptnaUZxXquRYnDL08EroaN5Mk+w3KQ3p2lSSUhKMSv9xlIC1Lepl9NWVjY+tkgvgq0OaJJ4xijFdu/NwVQvwm8G7gI19/rQXfCdZ5nnzuOv/1P/gAD2ys8qZ7trj3zWcYdHOObljSr00nuNyTNqC8YDnPmI5qQuWRMRKHNT5EvvylixipmE4qDkONOpAkpUEWEl16RIjQOtIbd1CVCBhhiQMIU42KLaqXYsdhfkFrHTbMbbhrokIgqGcSIggzH1ccfQA0KoEgAkpCmhiKzHB0NCPqSCZTikwzrCzhpIT26q3j/qZYaPP2U7eOj37xCf67f/nvecNbNnnvvac43zlLaiTjpmRbDxjvzmAciJspItOsr+Ycqim4SKICZhCZzBwXh3vkBhq7z8hE7HlNfwA7ZwpiITGlQhqLyRQmOmST4WOg2c+Z9SRZL1C6LsNZQtmm5JlFzAK+NCylU/rFGCtga+AZtilCR2JwtCFSuIBtA90k4nsZKonEWiCsZyUxJMzrtfxJCeg1rM2FLl8ZqlnDv33/x/i1X/h3vOm9d/Lge+/nrjefpZ61lNOaEAL1eN6L6Gb5V7FaUB5W8xyNFLRty9H1Kc88fgWjFEcHY3g5s4B4gXsoztedJ4lu/D1R8+SPu1lqFucXlGK+79gGWh+ffw3my0sBau74RQn6yz1s09zaVzbQSBS29iTmhAzmC20uOGZm44rfeOpf87uPf5H7uw/y5o0HeOCuDXZGM6rW0ckjl1ODKiF0JHIjozcs8PUeK3bKVBXEp47Y31jhmaPr5EuK8eUKdqAtU2QdwELIBa6QzPZz5EAQ8gBrElsqEuuIexHdT6i9IF8aEXVG/VyO6ljkJYG2ioMWjr7YRzqHSgxp3hDLkvGsoFkvcC7SKS2b55fYm1T4RNM5cuRnMyZKsHJuTNorjj+Ir2FdwkKbrxSjWc2v/O77eewzO7zjrrt5z5138aatTa7PdrHWI4XmsjHMgievDUf9DbIVRXtljOsXSOkZNyU7exOuuD2EloyOatzIIoUihpomeFQVEK0nPbKoxEDt0QL8qME1CrBonRFtpO0keAWucYQ8YTpq0ErCwQyXGcwBSKcQ0eJTQ5JEVA9alxAHkvU7+xx+yRK6GSoI+qsFFTOSU5H1O/rHH8RXiTZPNGEkhOgAMsY4ufH8jwL/r5Pc5+udyzv7/PX//rf4yv4edmJhv+KnfuBNDAY599wzt+4+8ZXLqJ2AyROKIchpoIkeWTuiEEQC1djx6OeewdaeqCW5gHRTESuP9YGYSuSRRdWBJFGkQjBtHdZGsqcizZJBhojrpNgMojeoKpBpQZsIgtEMr+cU05bYtghlIARkEMRc0c8N9X2S2cSxvCdJypbNU6toLdi9NqJKBCRgxw7nT7De+1Ug4m+HhTZvPw898xx/65//Lhe/dI2y52Ei2eyfptAF9y/Nm7Y/efgU9dkE0oh7Q45dFujWk67VJI0k2sDQNXy1fJpGVdjasN6tKOoey3eWuGFLeqRw6waz4eiahLVOw9WrktQ2+LdJ6gPI8sDkaoeOspRG0e1WFKniKChCMOhTBpU0QIvCY7ShUA2enLUth/1dwXUk1YPLXFg9pGfWGegBzyQlsXIEb7CtJZykd/Y1qM2FLl8ZPvw7D/GL/+8PcP2JHYiR2faAU3dskBcZ97xpPq3oM7/3yPMryOd7C90qAQsw3p3x5c88yXQ0Iy8ShASdKZyNNyac3XT/SEgliVK0N51DvODGhw3P/xojKEH0N/od3Sw78+H5ZJMQCKkYrHWoyorGenqrXULwbN6xRj7ocO2ZXaKXtM28WXaSn6Qz9+Q2/Uqx0OYrwwe/8jt87NkP8sjejHKqWOndy9bdOauJ4u57LmC95x9/4BOYaUAFSbbniTYSNgMiBK5eXCZEwXg0ZvLlZ6j3AukMZD9FiJQ897g9C0TkYUD4CFsZ2ZJAlAfUX8loEoMwElF5/MwjVwW+LTDK4WQklAnWBcbPdREDhS4rUA5vChpymtMFW72CuDwlXIz0BDTTllNb6/SDYm82JIwidgqXV7skSe9kgvka1CUstPlK8ff/w7/n4Uc+S7l7iWoqWD+6QD/NWd/qc/78OrNpzd/5u/8CO03odkvsLFAJxaCbErIEHRx+XLLz2Snt2pByOEVv5CRJRl4YfFS4GMGBaixRRpLlLtIJynKGCGCGLcorRBPpL0/p3RnZG3aY7Ft6QbC2vs/l51KYSmyqUPtT1KEg5oYYPXGwzPltycHGlAOf0GkNYsey/YPn6X9xj92rY6azFDttkD4gmuRkgvkq0OZJO4w2gd+8UZuvgX8aY/y3J7zP1y1V3fKf/V9+heG0IUkFK6Vg9Y1LnL2wfitZNJrVfOoLz6IqSIRCVS3KQlTgck3QkagUcuSQRtFLE1ojGceavbXIynXJRAsYe3QDWE+LJNSepJ9gNQjpkZVDRI1qLW0QhAxklBAUQQgEnvaUYtUlVJOWkAp8kZAYyeZKl6KbcmnngKIviJ0UQ8LR0ZROklAIRVU79pxHKMm9d52QRZBXZR33N8tCm7eRSdvwX/7ir9EOLUvrknxbcW8leEtvgzuX58miw9GMD3zpaUJfYZoUv2rRmaQMKfJIkVQK60EmFXlHkMQeTQM7TzQ0D08Y/KjiyqUu6VZLs6zRhWUyCpgQMLbDYZMgTIlemUGSsDo4QswEstUoZylihFpgvKN61HLPGxyVqbg0Waasc2Ta4cxSQQwdDs/vsHFJUzUeZwx7jaPTS1nTQ65c1kyOKoTU/MCdd5xYTF+j2lzo8jZzcH3Mf/O//VVwHjKFEIKllQHv+IH7WFqbf3G7/PR1Hvq9R59P+NxI1LSNQ0g5d8aGeT8/k2mWky7NrGV6WIELpIOEZvhidxJNoCUgEkFs5Yt7EcGLeyTJcCOJNJ+mlnQ17TjMk0VEdJGytr2ESQ3lpYq0Y5AIkswwOSoZrPQY7o6YDsu540kI3vdn33NiMV1oc8Fx8Mxol7/36d9CHnnwS9Rlh+52nzfd9R6KbP7F7fLVPZ7aP8QcCaTRuCVJTAPxWs1svYBNQxhFCJrlHYFFU8bA+GiGbSOrxrDfRPSgpekpYi1R1sMlR3pPgisDgYxuWqIuCMatZiAqmv2UajXD9yUxKtyahvOe4glJqSWilGAtyVqX9V6P5b6iHUViV8zd9kXGrKpZ7iUMRwb/2JDQ9+RTxfveev+JxPM1qktYaPO287lnL/KPPvwxtupDOtc61E2HwZsz3vcjbya7MdzhuZ0Drj43xe9IXD7AOUfSONphSa/rcDHDNwmaQKebIoykkYpDPaWJgtVaMUoaXC1oex1ibanGLUYpkkFGXdUEnxDzSLKUYduauOSQzpKGjKZQXJt68k1YX63Y/fyAoCWBiE2gd+cyg80Odk+RTKZ0lzWJLxAO7JUJeTclTQSHTYmKLRkJd1/YPJF4vhq0eaIJoxjj08BbTnIfC+b89b/1G3zo8aeYqIBKI//RO9/M2a1lHnjDKd58o2HnE1d3+bv/9EPcvbHOH33vffzuxx9Hak0Mlo3Ngp1QMXSRvAzEVFAieNs7z/Olr1xmMqvpjRvCsEu7qdGTiG4dIBE2ELRANB5hAxJQPUlMPNIGOkOLyxQ2MXjrMErDJBAk+MQgOymkitA6fANiU9IGj5xotEmwuaZsAz0hKLoJpfW0zhGCRKWG933/yZxcgVdF1vfbYaHN28d/9v7388hHnyaMLHpF8hf/k3fS1V2+58xpLqysA/CFx5/h733wo7zhrnO8594LfCy9grSRaWF543KPg4dmlJWnOZtQTzW7JPzA9j18efgl6mZMZ8NxbbSFP9KETU9/qSEVDlt4rk8Lel1Bt5nS5AJ13cFY0nugZuvOEc9dG3B5skRiGpKOwzmDNoqDusewypFoOlmJSSLardC4Fr2dUI87qAkc7qT0M02S5OzPYOQiOkLmFX/8rW86ucC+BrW50OXt5a/87N/l6UcuzZ1CWvEX/tqPYxLNu3/wgVvJoj/47c/xr//h7/LuH3sLd7/tLE8+col552m48IbTPPnFy/OS6hszRJwNvOMHH+Djv/0Q+LkDqRm/oNv0raTT1/kAKwHIuYsIAHlrNUREoEB7hFHE2uKqhqKXUZcNQimkFFjrCCGilEAlmumovlUeZ3LN+QfOHFcY/zALbS74DvkrH/6/8/SnLdW0R54r/qPzP87aPSnf94YLt5JFv/7Jz/Kxx77Aj93/Zq7ekXM0PWI4K0iBB+9d4fNXdmhFi84EybAl9nIefOMdfGzyCKps0XuBfVsgJTRWoRDEFmw3weeKWBbEfMZSNmJ0piCbObiScqgyZK8F55EoXKuRsxazJJDRERuJ6mZEWppJzer2KkeHNdSG3ETEoCFNSya+gI2M6isO5SPaCPrdnMSc0Fez16AuYaHN20kIkb/0P/3XXPFTxJLGP7PMH3vn97Fx3zrvetc9t5JF//NvfJgvfeUyP/FH3sZOdcDIVqhHaozUvPX+O3hk8jgq1jTSEPcj3nve/o67ePyZz1KVCb4UNHZI3ouMQkY+afBTS+xlICQqT9FOEG3ErncIk0PqYcK1hzo0eSBXFaZKGR2lzNY9SakIwRMNxCJHhIB7dsbSuW321YTJsx3UmqLqeMRGB+0kgyTS7M1Qo4iInuX1wclN/X0VaPN2NL1ecILsHU3YPZhQlS3JEN79hm3+85/6Ht7+1vNkeYKU8w/39dGEv/Q//DpmFvnhN989b4eQSupo0SEyPapZW80ZyxKGgSAjofFc3xlR2oBJDSoqmo0MA0gCsrJEFxAIZKqxcj6Rpc4jSjjiOCLaiEwT0BJlI8KBqhyxEcRG0MaIJ9LLEspJQwiwf+mAweYSrhCcW+4zGc7o5xkqk1wrK05dWGF06ZAY5/1C3/m2O08uwK8CES/47iPGyLWDEQdtxdg2+NMp73rTHfzFP/Ee3rN5lkwlt048j3z5Of7a3/1nqI2MP/FDb+Pg+gTtr+J2LWIQ2Bc1+o5leHRMFJC4QEynzOopTQMmyaj7Oc3TKUFqkp5lbWmXXFUcuh5JlWAluCrDjQJBCTrrJSM0ue1T+Yy026Jlw/rmmHGS0ljJUATAsVwYhjHicIzjDgOfMN3PuHBumUt+xpk1xUEV2N9rOJesc9QOiS2gYam7KHtZ8N1FCIGrlw6YDCuqSUuSG978vXfzs3/pR7n3wXNk+fOW89//jU/z8/+nf0J/vc/Ze7dY3V7mqUevEO38wzcazeiudJgezogiIhCUk4rh/mR+fkoFREloX2bsPYCA6HlRNRpSzpNKivk0NgR48XwzbBdoKw8xkmUpVeshwrWL+2ijyHLD9h3r7F4e0Rl0KGclbdVw6vwGV5+6Pt+FFid34QsLbS74tgjBcf1wjyH7TMsU2dG8e/Ot/IWfeA/39tZvfRkF+JV/+wl+9d//HufeknN+s8/6qT5XHp7OjXgicK2oEas5OoyJE4FPJAeF40pbEasEgsEsB/yRRoSIbBSqsnhj5tN8c48ksnTvDKRF7mh8YRD9gK4MSI+5DC6BxFnCzFAfCOKBhbUU1e0hrh3hrGP32SFNIUhWck73FddsYLke4EYNpgmsnC2YlSOykSPmnZML8EKXC75NQnBcG+3w+NUZtoKk6vEn73oLP/unv5ezm8svOm/+nX/9Af7Nbz3EXfecYutNm6z/XsboC3s0ytNdl4yLGa5ZJ8QR1gm0iExnJRcPjrDNvC1CN0vBV+A1KIlXEhklYtIiu4bmdIJNIrbVxIMGXWdELRBqPpnQTkB2BMVajds3jK4IzMwRlvskUVBXFa70DB87ZJZCdylhaSllJxNs6ZzDoyly0KF4cIVJNUVPLL1TJ5gyeRVoc5EwepUQY+RXfufj/H/f/1HGU49SknJJoZvIe09v8943XuA//sl38Oa3nKPTSV+07tNX9/jzf/uX8ULwMw/cy5vuPs2/fOhRDjsNogv9WiMTydW9KSIVhBDIo6CTSJCS0+fXYCgZDSua3gQ1KpAzbozwnV8ERx8hOOz5ApM1aDzNWGOFIO+n1LUFEfAShIeoAQS+cZg0BQWOiOtoOpspeUdjkUyPpqyuDAjjhsNnjxCdhCZVBA1eCowQyJe7ED8GXqWjRRfcZuq64Vc+/Al+4eOfpK6hp0E86fFrgu/5yQv83DsvcPqHfoS3nzpFYcyL1v3UZx/nr/+dXyVqzR/70bdz9/YGv/TwhxBf2CdJNP5sjq5bDmYNIoG2kWxtBVYkuNrRT7YRBTRFRff6PvtrW1TdhLUoiS7FRE+lHbm2dK/kXDvU5MaTbnu8lUjbxalIExVHs4SBbijRTNFYF1Cmg8eha0c5LshzyVJ/wDhrmaVHbHXXaIspk3YE+YCDcUSWAjERdM6eUFNdFtpc8M0xGk35Z7/zaX7lf/o41IFcBRg1ECXv/SP38zN/4fu46/4t7nnTGZL0xdr87V/5CL/w//h18szwYz/7HrbOrfDVh579/7P33uGWpWWd9v2GlXY8+ZyqOhU756YTTZNDE1okqoB+Ks58g2FMGIaZ0XFG0dHRAcM3DgyiMioKKDKggIIgIKGhCd1N566ufKrq5J1XesP3xz4VGjpTRVE9+76uXefsvdZ611rvtZ9a6/zW8/wefLGR9aMFWWroracb3cskUT1EaYWQMLd1ktXVLr2l/lAQ+sbv68mZRkpsiEMbOE9jukrnaO/EOnJjHQHeWXQcUDp7fJyoEjI+WWd9uUu3lTG3dYJ+d0B3LQMj6bS6x4cfnzwNxp3HTmsUmyMeA4tra/zDvf/CB458gLooKAYTmKwkGERcseVpvG7rq9h0+TiXzG0eGtaexNve+1He//kvMjEW88KnXo4SMV+/p0U/sMMOaWWdw2sWb3qMzXdY2zNOGE7gnSCRkh3bEx44ZOkvp2hR4mSE7JUoWeILj7ABTjuQlvbBCXwzpZIUZPsjkjilMhWw2vXYLRLrLeQSM+7QpUdLSyglLu0Q9ApcXVPxUJuoU4oeum7Zyhhr+0q6Kx08NfJuzoAAu6rY+dTgYWbsW2MUlyMeK3uXDvCxW77MX3/9syjpmJSSQW6ox4or6s/kh696NTObJ7jkvM3f9ODh1/7r/+bTh++nMT/Nc667BO3Brh2gLzVJwyFT6MgOWeHoElDRkqltlaFP0Xyd6uRW9F1dkvIoWSDpt2PClRRtPF5ppLDDRIRWn/qkJ99s6fVjxGqBUZpGrMjXS4oQtBJUdYHvCnQ59P9LpGRgLa6hkIVARZLNmzxZ2xKMSc7LPO37lummFomkxDI12SMfBNTnN52W+T5bYnMkGJ0FvON9/8Lb3/85kJKwXaKExHiPahdEqeeGF+3iu152FfXmNz/N997z2l/6U8rU8vzLd/HUnVvZMjPGA0vLUBk+uPRVjS0hC8BoD1oylsQETrDn/kUKBbZiaSeSYF0hC4suLViHkhJvHN57iAV+vaC0inK6gRZ90IpiUOAUSCPwSuK0o2wolJWQQxBLBgOHTzRmpyba2WX/4oD5zdu5dHozl124g89++k7iyQqR0hxa76EHBoQnnK6RZgVJfLqMyL7zWx2OOHO84X++my/ctRevFdlcgVKabtsTzxuCLZYXXXwON269jKr+5tjM85Kf+MO/wlYVN73kAs7fMcs5Y5McOdQiSD0+s+h2QBE5/IKHHCJjGK80KVzJncVBIpuTFJ7+3Qo3G2OrljDMSUtFZmvMJyt0gwqtdsL4dEEy5YlaAV5Cr1dhn4M8sxRxyEo/wosU1ZaUOqafZ2yqCNZzkJ0K+pYIVy5xqDJg+/ZzmN5+KZdtn+fD932JzcEEvibIu11aFUOhPFXZoLCWUKnTM/mj2BzxCDzvbW/n0IE2lX2CqgClBSa1KCERCm58xTU85annPOjJ6DHWllr8j198DwDPet317Lx0O1vP20x7eUN08SADSZmb46VlQgim5ifprXa5/ebdCOeQ36gSnXxzfVxEGpa3IeSDhKXOSn9DJPInOqjJ4UMcvEPHmrxXHh+rtzagu9Ln2udfyrYLN3PeZfP8zf/8OOdc3MB6T7/b51gqU9w8DV2YTmYUmyMegTff8hruaWsOrE9RKyuUJCy3Y3yrSWxj/v0rn8lF07NE0TeLJ1/fs593feJLbJtd5DU3vYDp+Fy2T88xONSCWYmqWeKWozwI3URgx2qkcch51FjI+9zeWyChjWwn2L6HDFxiUBWwWYCQJUG/wK7HlNMKtexgOaZ+fgkiRbcFZXeAFwEmUsSVHnkvIe6nZFFCUdOEMmJAht+i0dOSw64gDzs8/5Lt7JTTbDlvhr9Wn+LKS2c52PdM7j1Cw3VZURXCZOz0TfwoLkc8Cv/177+HdSPY/cBmysUE3bTkbkB3TxUjq7zqPz+d+flJwuib5YO/+uQX+PjNXyWe0fzAs67gnNlNjMcBreWCqNSIJKBMalg62CJkanOGsSHTlQkWjrZ54KsHcd7hOoZWHiCMIVAZTgUI5XGhpJQaUTpUz1G0LfFRKMYDvHd47Ris9YfXyLEIbT35oiSY8diuRvb62DzChgobaFRVw+wi3TDi+pddxMzsTqo5vP9Dt3HFpkkWD/WY9j3yTkF3Mmbn9Ols4vKdH5sjwegs4FUvvJw//diXeMUNF3DjlRfzpc/fTy8ShEqijOdV33fdQ15YAX7gN9+GVynjqsYPvfRpTE/ViashpREwkGgDDa3wpSOrCcIChLe0l3okkaLIDaYiKSKNd2CVJOwZ1LEbXe+Q1uJ9Sd6sYaUc3pK2DTgBiUYbh3MW6R0gEZkjxOO9R1qJjgJ2XDTNXUdX0VVBUlapHmkz19Dcc9dulm89zI4rtuNmx9AG1k1JoAU2c+S5o9fLT6NgdHqGHfHk4Mdf8Ay+fuchfvymp7N11zRfXDpIvmhpjktqNc/LdlyDFA+dafOyN/weNjM0rhjj+294FhNJhTwt8VZSTiTYmqQxoclTS3C0pJwKUbLExfvxokHhHP2DCXIloIj6SOGpT/XQ2iCFI5EZiTDMBR12d2L6NkRKh5yyWBvSXa0zPdlFtgxZIBA1CGVBbVMKPqMIErQY54JknN0PLBIrS3XbPMm2RbbPRXzZ3Eqv9QDzY9vBJdgoo7tsWIp7lFLSyXLK0yoYnZ5hRzw5+NEbrufXF/+J//qTL0ZnngfuXaS/1mOikZAkIdc/+8KHLct6/dP+E3jPpvOmeekPPp2JmSYrR9YRQuIZdjBrTtbpDiwEmmExGhzes0ylEmCyoZDkvTvhVfSNYtHJXdCOfZdP9rmOJG5w0vbH1nVD78DxiTqVHTF7bl8gqmnGNo3TXe0T12I+9+Fb2Xv3Yc69cjvNsRqt1S7rSy3aawMwns5KB2sdSp2mLMBRbI54BK6bfAUr2d/xM9f8OPcud1ht9Tg62WFLs8FE0ODK+Yf31/rRt/8ZqpGzacflXDh7A/NTY9x2+wHKqRhnFel+w/TmOod7faRQ1N0AT8EDK2tEYxpTDujel+D6njKUEELQtcisxMUhSlpcIEEJunVNrD0yhUFLouoGEyUoFSJdiV0HliAOC8K6oWx5fBwwtqPO2PQEC/sXiBox1W6CkX0qkeGfP/x1du7fxDMv3oW2jno54A4/zvKKp9Ceo/v7eO9PT8noKC5HPArXbruBuxY/x499z8/yz3cdpK7aLHZygovG2To3wa5zHtr02TnH337oLwgbAdecczXnVzZzyQVbeO/ff46FTkLFOGqNjNrUDGVnGeEEdT+gowL27lkhrIWk3YywV+IQUGqECDDG4CoGOQAbBEjj8UoyGIsI5YC0rRCrKVKFhKGjOgHtwwW6CxgHhUVNQpoKdFJh66YxzJYKh/assGk2IloZILWne1jxhY9+jWvP3cT1111AFAZEep3b7mqzvFeiJgXt3UdP38SfBbE5EozOAqbGmnzhnT9//P1VV+3ic1/dw6aZOhNjtYcVi/7Nr/1vbu+uE4iQrWN1Lr982/Fl589N8LXV/dhQ0bu/IFkrCcdBO0EwcGydbVCvJdy3Z5luQ2GFQHiPawSYzoCg5ZDG4UqLizU+0KRTGtXPkQjoGzwKrwUZCh9JXBygcovH4S1oNCZRxPUI/DDTKKtp4ovmuV7Pc/7F8/z9A1+ku9Klv9JhEEAhBL2VPj4KEFIyyEtuuXUPN73g9PjdnQ1pgiPOHE85fztf+MM3HX//3HMv4ONfv48L5qYZryQPKxZ97395G+2FFcR8jXO3N7l09kSnv/G5Bku3HUL3HK1uSHLBOuZIFVMBPWu5YHaSXlkn7ywyuM9TNAQyD0mOWMSmHF8BESjqpk/eiQmsoLnHU5nro1DEjZSjroIJoNUOybKQfq6pThUImVNqQaESsm5MECegPHmRwFHBZGM7F1/UJAjOo7n4Caphn4qusHAkpr8c0lEdlJSIEjqdnHsPL3PVji2nZe5HsTnikXjNFVfymiuuPP7ePucSPvnxO7jmul1EUfCwf5D9wHX/ibJvQAi2njfHuZduBYbZulFdkXfBe0drqQNe4eJhCbjAc+5l8wzWe/TbKb60J24CH+RRdCyNSJzIHBL+m24YXeE3yr6PK0snMpK8I0oCtNYEVY0SivOv2InWkslNE9z9lb2kaUGRGQ7cd5g8K+m1c1Qgsdayvthj8dAKm7fPfEtz/HCMYnPEI/GiXa/jRbteB8B1O2GtO+BLew9ww4U70Q8Tl1le8Jq3/iZ2a4bKYdPEdi7ZMSwRueqqHQRRRK9S4JVnud3DmBJpFGLdMyksW7ZsYmmlz3KeM8iGjcpk6YhWMjAephVJs6CbhbhqgHCGsCwRAqLAI6qefr8CJiR0HqkERU3iBpKwYigQ+EghXMZ4rMl7OVWhCDLJpVdsJck1tU5I2liibwfsOSCZoYfZ2mJ5jyOrVHHLKQfLFVY7faaatVM+76O4HPFo3Hj5z3EjPwfAOdt3sriwzgMLR7n48h3E+qH/1lxca/Nj7/gt2lGEbwZcvGOSK244H4CbXnIVf/R7H4VmTouA3t5VclunCGGwGKBswEWXbOHoA8v0On36SiKSEL3mUbpAFiVFGeO1hjQDJMSCKC7xBy0+AWoKmzpUE2xmEE5jS4sgwMeSbj9CGI+LFfVLpun31glrni3bV7lg5+W4dU031Yjc0EoLWnccoXJ+jYVKj/7AoqdjyDM+f0eHwuaEKnrIefhWOBticyQYnaU8/aqHN3o2xvLD/+V/c89diwRCsLNS5aUvvIxbv/gAW3fNMDld50jew4cKSsiEQwuorpdYIZiv1ujnlnRtnXQMelsklcMCmwichqqRyLLEC48PJUJLnABRlASpR5QOH2nKiYBg3ZBUNDKFopsjgLIegNLIVoHRkuVOihMQlODznIPtJV73kudhDLzkZVfzTx/4Ep/88K3klYigHqClHJp2S4HH80+fvOO0CUZng+o74juLGy87/2GX5aXh1W9+G/tWWrirErZfWePlV13NzUf3cU5jmulKFbO/T5h5sglN2Snpd+o0zhkQa8HcfJU7j0jmGiuIuwy1PSntp2pUX5BLSS3K2TK5Tq+ooJyi52IoHbqWY2cl1lnSLMYvVpjeOSByVcxkSs30iKTjyN5xfKIIZvqkPmDvap9OtcROS1LRZ/f9R3hm9CKE1Dxt6/XcsvZFHlj7CisrDYyrkXvIlQLl8dbzj/fcc9oEo1Fsjng8KCW58cWXP+zyQS/lh5/+n+gtpSAE8xdv4sbXPI2vfuZuzr9iO7VmhTw9ZjQk8CUI5cFZUJLp2RprR9YxrsSX5rgmdFwtEuKkrCK+WUSyJ32hA0FcTcg66YP9j44ZXwMH7ztMY6qJkjDo9jlwx36+940vRWnFoDvgS5+4gztvvh8cJPUIU5RY/HEB6s4v7jltgtEoNkc8HibqFV58+cN3u11vrfG9b/nv9LxHhyUXz5zP86/dwacXdvOMTbsItcZaizYlzkrytYxQavqBpL06ztz0BAsrLRgv8XtypJB4qcmrCjVQUIMgcVQne6yvjuMDCwOLNkAoyRsWm4RUpMESEdY0HC0xy5a8FuJ29RErIdKUDFSVe46uMtlMMGlA0O9gDy9y9XOuRaaKtN3iltv2sOgNcdvQuCiilwUI7whqFj1uaOVtpjj1gtEoLkc8Xma3jDO7Zfxhl9+3fIBf+P3fpdWSyMNVrrlwF7vOP4ev3XmQp1yylbxwhNMpzgZM6pS8SLHrFfLtCf1uk7nxOnvbbeycoNgscUZh10JIS5pxzsAE6NxhlGNyqqDfCUmtwpWKSIZY50FpqltzipUETYISXWxeImsh2UwF0bZ4Ad5J7tu3RN2D7iruuk3S1CVX3ng5OvesDxx33neQwdIK5d1VgvEKOsyoRyWduGRlW0CeecLT4Ut/FsTmSDB6ErB0tE1zvHI80+i6n/9d3KolHjh2zo3zr3/g6Tzj6Rfxzx/7OjvPH2YyDNIC31PEeYl0kjISzIZ1QiFJgMIYTEVjJ0PCwjOP4EA/ReceuXEvrLTGIpBeUEwF2Kqmsp6SSYefLFEd0IWlpxTCGXwoKTSQO3zVUyoJHpzxODz5Sp9gINDNCiaQ1Coh1Z3TzG6dIplu8MChNSSKymyd7mqHMrUIKRhrnnq1Fxi2Sj4LgnjEdybee/a3W2xtNFEbxuzP/L4305uLyOcUV+7YxE8/4xlcPL2FTxy8n2tnhhmAhSgoxyOKmQDTUChjmdhVoxo6ZpOMpV5Aa71CvlYh2+yYO9fR6vcpJyV6rMAHjtxqPIaolnL0y5vIuwGbxtuUiwX5oQCahqhuWG55dOBQHpyT+LES0xWkyxWQno7VNBLJIOxSudYij1jqIkKHMSqcx68uM5kMWIy6hFIStKp0Byll5lExzNdPk7nuKDZHfAuUhWF1qcPc/MTxz1594UamoFJc9NRz+Ff/4buZnG5wx5ceoNas4NyGWCTFie+egInxBBVHqEBhTIE4uetZJCA/IdIIIfCIocgkOCEAHTO0PoaXZL30QRlFSAnuRImazQ3WWtJOMfQ5so7pzePoQLH9gs0cvH+RSiNh4YFFgiQgqoS0jnaPH9v0Sed+ShnF5ohvgSwr6bQHzMw2AbDW8eLf/x2oe+zBhKdNXsxP3Pgigrplb/cwSkrSPMfPGqJFyL3ANRSiVJznBGbLBGhDoQTYAOEhnhT4FKpxC1n36LojXU3orjeoGU864bBJgtWOuFYigpLKbAHLmn6mcQsFaI9NFGFicIOIMgmo2RzyPpPTOVG1SS8T6JajXu0zs3mMqAjpdbax12dosc7SvQVaSyYqgvUVQ48QkxvG66ehu+goLkd8i/S7KVm/YHJuGJtrKyv821/9Q9qDOo3U8F3POo/v+f7vor+asXi0DZfA+lqH9rYq6v4CuSRJfZVqM2BMx1S2VVCLlpISs7VC2e9SNYKsdJiKYrWsopxASY/MSkwqcXGEKgSiXWICT1nzxGGB6WpAkHZSTBzgvKc7GeKUQpOiSoHFUxY98iyi17NUdEQZSqYnxqlHERdd0GFd9ojO7XPbHZoxHSIncpaXS3rrAvVAhg5Og8XCaYpNIcQ+oMvQJdF476/5VsY7fW1sRnzbsM7hnCfNS37iLe8lM5ZSOW68/jx+81e+Byk1rfaASi1m8UgLAGM9tUbO+OY+wjnUhrGms4bSWpIwgNygygHNVo7Q4KsCIRUEkmAsoQwkVnvKEITxVA8MKApLmWhykVAmiiwO8N0Mkbuh/4MQuLrEG4P0HhMKMgHLrQHeeRo6ZKZX54tfeYCnXrWL+niVwlh2bp/iyiu3MzfXoH20R1JIhHcIIfjsl/aevsn1j+M1YsQ3YJ3HeU8nzXjdL/8RNjPIPOffffdV/PbLX0I/dxjjCUXInvYa3ntMaVGpJTya4qWnog2qFATGkGWamQboiZS+KrC7BLbvSNOA1Me0izpFNk6ReehLOmsJnQjUU1JWWpJ8rc6grJP1BfsPNgkaLWzkaZcVikIiWx4dlcgAfF+SFrCwXKKcJ9zUwJw7xufuX+DizTNMhA2sy7lhbhtP2zzLTBTTCvtUpzzKWkQhefctXzt9kzuKzRFPEO891g4VmvWVLq+79pePL/uP73w9P/orr2T5SBulJc55lo+sU2TmhDHl8VIxgfMOk2d470mqMcb7ofijT1oXEEJCpAD3YDPrb8wuArBueIt30vY49w3rQGepDc4xu2sWoSVf//y9bN4xzcRMA2MsT33B5Vx87XnU6hUGvYyweqKk4KN/9qlveR4fllFsjniCeOfxbvjFaPfWef5P/xbpckJ5JOCdP/0j/PCLnsOBzgIVHRDqnHbZprSeuN8nrFnqYwWEBikEgzihTDO8CQnGNW62oLq9T7Vq8FqQryWU/YRB1kAFIX0vycY1NnK4wBEsemyaY/oRvU5IuxNSFhkutWhpcLMKUxNMzvYJMguFJygsrd2Kldtz9JpjZmI79z2gOPTAOpvmJohqFVSW87wrruKcS+cQDUl6pECMKUTicbnkX772wGma3Mf5GjHiZDzHH5wcWmvzil/5PbCWpunzP//jj3P9C5/K/csrTI7XyAcFvV5GjYCJxXUmNw0QY5KwlSO6Gbum24TLHZQ1VEVAsK/PfK9Fs5TUgwGhNhgRICcT/GSMmNZ0XUhqPL5w6OWcoG6o16DesATakBuP65d4L/BjiiQQbKl0kaVGxAHKCopDKZ0yR2GZn5xl7y2rBAamx+okWqMWPZdc+VzOv2gzm7asMjhssUcDZCyI10vuu/fQaZvb0xSbz/XeX/mtikUwyjB6UrBp8zif/tp9/Oc//jCdhQFjDn7h+5/Hq17zNNZXe1x5xTbGJmqMj5/Io9s00WD3IYtphwSFRxaOjJzNkxP8mx9/Hq21AX/xN59hULaR1rPezZBTEmcNKjMUaQ5JhA+HTvaiBOk8BAovJWrNUcwGFMZRW7UIa1ETAc5b0maA6hf4TOADQdHQlLnFj0fEHcfRg0e5PW/zy/Kl1OoxVz39AqYma3zlS3s4VBryToqTIKMAJzyRPk2mujC6aI54wgghOGdigr/72G389w/9A93qgMr1Ab/1+pdx4/arWC/Wedq2BmNhjRfvOB8tFUIIGhN1FlWf/rYALxyl9hS+YFOzxvde8CKWeyV/+umPAj0yL1nzHiE0Whgm9ICZYJFWZ5bF9SZj9QFJ3VPTKc4qCgXljCfYHYBUNM/PCG3Bnk6VihqweLCKnimxswLdheqKx1YtZn/IyqaU9t4jLH2ty8+95Lk0gpDnbNnFVDjBke46SyJDyR6dIkCOaeyyQ4ej2BzxnUcYBWzZPsVfv/NT/PlbPkyZGohCfvt9/5bLrt7J8uEWc9smaY5XecZNVxJXwqFBdCCgfPBYZW7YcdE8r/7xG2ktd/jjN78f2MgYKk6s5wHyDV+jY0IRnBCfvB+KSMc+kyd9diy76NiyY0bY5dBY++h9R8E5Ois9XvszN9GYbHDN8y5jZvMYe+4+hAeKgX3Q9tngG07kVDKKzRFPkKQSEicBf/aZD/EXX/oi/Rlodqq8+5f+LdsmJjm01OK85jTVKKamKyQqQQQCu16jLDxBZNDTDnJH6g1XbZvn1c94CofaLd7x8Y+zljXR1iN1BlJR1iyseIwUqJqjaEjkOgRJhkDgTIzRBi0UY5WUbj9EzQbIcUXQs9TDnGo9YymoUBqJKgTFqsROOxpbOxw8YBibX+fzey0vecFVzM43uaZ6ATPxOLdGR/CHJdlqgdxSEFmByj0r9jSZ647icsS3QLWREFYCPvTFt/O+D96DaidU4jH+/P97I9VaQu1Ii/HpOkkU8LRnXUCtFlOpRASDkOJoCbHENzWm70hzx8WXn8vTX3k99999mHf/7t9hVIzSOdZIkumCKPKoQYZbcahZhxkPKfoaihKnBBwV6HPBTxriRkH/Zo2PQ8JIYHJJHJYkY35oE+g8Rnl86iincogD9t6/j2yL4j0f+xJvfuP3MrtziqvL85itNrm1rljpJbRNn2I6oNic4HuS/a0Wp8UA5SyIzZFgdJaTG8Mr/sP/YmG5j8NT856fe+3zeekrhmLi+OSJOugoPvF08ZXXXcY71z+PGTO4PeBzyApBL8/5+w98BR0qKjsblPd0CTNJ7j2UAmXBRhobSbSC0ApsNcJKj/AC6wXaeDwgM0dUOGyk0YDICnwiUX2LyCQCh84cJjJkU5JyQuMOW1TPY3oFf/Shz/Pqp13GORdsIuvnrKx1yazHVjQogeoVCKdp2fy0ze8ohXfEEyUbFLz4DW9lTRq0sVR2wK++9rt59uahj8p4eKIuvBKc6PL3gmdexEc/cTtysU9rJsYKWOlaDgSC9935FSIXUT1Uw4gSXfVkmURXNbJZ0skTHjg0T+AdU2Ow1BlDCYtRgkGqqcQe1xPYJlS2FPTyCgLH3PgqraMxkzN9skhSugA/YamUBX6vRy4pBm1Nr6Gx3ZIP/PNtPPvpm7iwsYuVQZe1rMtaF1gNaCrJSmDx44o9nfXTNr+j2BzxROn3Mr7nql/BWwfOEzYifvG/vY5Lr9oBwPTmsePrVuvx8d9vuOlKbv3MPQxaGcdMqNNeQa894EN/8ikm55okoWSw4VckEMNOacdr1AQoj1DB0BR7o7ua9x6EBHlSFtGDBCSGLr3H3n9j57QN0Wn10Dr/8n++xDlX7uKSa3dweM8S7dU+6aAYrqOAcngs99y6/5TN5zcyis0RT5Re2eL1f/QmuntiVqMmk42Y3/je17FtYhKA+Zmx4+tWdOX475c/ZRcP9I/QK7rIgx5HSTGA1dUOf/2RL7F9dgLXCwg6JUJKRCnwPYGdjJEzOUJJ6CdMYGnHFlUImAKRDrsC91ohgQR6Mf2ax/QCVNeQZwFHuuMkMwY15ij2VYi2lExsb5GtxOQ2o70S8LXdR7jt9n00N9W5rL6L+/cdAHMIXfQgEYhuQlW0MWnE7bfvhWee+rkdxeWIb4V+tsRz3/T/YTuCLQ3BdJDw67/4r2g0h8kIm08qc65vlFVKKdg0vwPkYXplj3uCKrKEuxccumzxgT/9LNvOm0JWBKULcZFFZNArQpRXeOHQgcEsl4TjCSEpLEucDihjj9tnEErSG6sjkginHLa0OBx9F9A+VMWHJUnF0BcRpfC4RFMRFmMdtgj56mfu5OCrn8PE1gkumUj4yufvp3Owy2IzJpt3jO3r0b5bwIRnYXUBeMopn9snEJtTQogvn/T+Hd77d3zDOh74mBDCA//rIZY/LkaC0VnMh//lNv7TOz6KH1iiUrBjrs4PvuYaXvqapyLlI1cbhgXYwqLbmrw0lIGAfoHwMUGsuHPfEXbLATr1NAeWvoAg8+jCIb1HD0qiegXrLd54qtZQektZVUgPphqic4uQHl+VlKVAZSUYge6Y4dNXFYBzBD0oxhRB12OmPVJ6vHG8/ZM3829edgMT03WOLqxz9O0fH5auVSWilW+43nuw9hHPdcSIbzd/9LHP8od//WnMTAlVuOLCSX7o+dfw3LmrH3XbYlNEGhnMTIK1AtdXiEpJmSuSEu6+5yCHDg4IZh3j5xQcOapQDUNzoqBQjsqmHj4dI/WeemjYOtliLRXoNSgrIXFPQlMQdEuEkPSTgCDoEoaOZK7EFgGZU6jQk05L5D6N0o5kr8PsMFjl+eP/+Q+86nn/nslolorax55Wj/19QbUUmNoAnQeUJiK37lHPd8SIbydv/eX38bG//dqGziK44bsu4Tkvu4ZnvvCyR902jALyfMN/yG+4UXtACKQQfOWTd9Be6CCUQlUUdmCHpWiBwJcGIRQyDnGlG/6BKsCbjRzzYyKQEEOhybnh50IM93WSMIQQD77u+WHJN8Af/+r7+PPb38rWc2ZQSnH4wAqDTjYUiyzDbCXv6fazUzqvI0Z8Kzjv+cW/eRtfum8PeWsSrR0vu+BqXnDN5Vx78c5H3T5oQr+bQ19gFdhSELUcwbwns4bb889gQ4/Pa0RKk/Ys4KnZkE7oiHNFVLMQWYL1mKQoSI84fF2STQl0BL4VwxhUZUbqJaUMcFKhVhwSQ9EK6U9F2FWHX6oipgSN2QEdUaV/0PL2t3+Yd/7RTwOQ2ox9703J+iDCHFsINJCjWTp9z1lOKUKIFwO/z/B/l3d673/rYdZ7NfA3wLXe+y8/1DojvnPJyjZv+rO3c9ut6zS6BYNOyPOvfAEveNbFnH/h5kfdPqz1Wev2OHK4ieh79GqOCGL0dEK+1udDty+Qlo4g90SJI60p5LKjUpW0Qk0QQpgJ7KqHrQ3UuEEcaiOcQm0LGJ/NWDhYQ9ctZU9ikwTZh6rqUBKTBTFZx1NuEqi+I0oz6vTpNBtEoaU43ONdf/Qx3vw7rwdgdaHD37znC5SJJ6gLTE/gE4ONBcqY0zzbj5mVx1Bm9gzv/YIQYgb4uBDiHu/9Z57oDkeC0VlIlpe88S3v5wu37iXqW8KuIxSC737JRbzsdU97TGPISNLBoUpLAgil8AGUXpDlhk53QCgdwoOSGtsosUISAk5ICDVBrAikph9YJlxE/2gbqy0+U4Qdg84MhGDNsFzNS4lCUSYKlTsY5MhAQSQJopLch/hSU0QWVUKRGm7bs8AVu7Ywt2Wca595IQvvvQXdL7EKSg1OKdSx1P7TweiJzIjHQbub8mN/+Jd8feEQ8Y4cOWeYmRC89inn89y5xxabftlgjmZkSYgbD3BCY/qKXIMZt5SuSzRXMohiUhkT7ncI7VHeYb3gSLfOvIpQOfRaBr99kplgEY4W7I4UcltBXCkhE2SlxmuHUZosCljuNIl1Tm18gEkjZF8iSgexxIWeaN1DLFntFRw4vMa2zRPMxFvZHF/I3urXSCPwBnRfUA4kUXAabfJGsTnicbB4dJ2f+5E/YvlgCxAgJbXxkBtuvIJnPQaxCGDQTbG5HWYDndRxzBpHURp6rcEwEwjwXnLMiMh7D07gtadWjUkHOWVaMrapyfpSGwpxUjYS+GNGBRtiFJJv9jk65o30DQ+Hlvet017p0pyqs/OiLey6ZAt3fOGB4UOaYx3XhKA5XuG0MYrNEY+DPUsL/Mz738b6UgFOYZZi6tub3Hjt5dxw0aOLRQArRU4hMkoRoZSkUJIwFISTBYP1iIWWg0mDOCgQUiKdQJYl9kiPSGtcCJVtCf0kB1eQTFRxRR+nDa4jYVEgBx4vDDIWJInBacXUdW1WVyv0ZYJ2EmoOb0AteehpShnTdVWcgruOrpIVJXEYcMX2eabnprlv9Si2L3FO0w8DIuHZPDV9eib6FMalEEIBfwjcCBwCbhFCfMh7f9c3rFcHfgb44qnb+4hvF/cduo+fe987WbtTES5J0qWIrefO8F0vv4Zzd8w+pjFWlx1rfUV/AGHPIZAIJ3GRIA0smhZ2IiZeyrFFzOxEm7Jp6azWiNIBIg5obG7QDnNqZkAyMUErL8idpRgELHwlJE5bCAdWxBSJg1AwmG4gDDgDZaKI+pbE5QhTUGQh1hRUJyS9mufzX7gdUxp0oLn6qefQrMccXltFWIWrCpJNJf1GwM7tjy6QPSFOwzXTe7+w8XNJCPEB4DrgCQtGI9Prs4gDR9d4xuvfwjU/+hY+d/9+pAHVLwkLy4W7pmk0KnQ76WMa65lPOR/pwTc8eX1oWq3wzE03mZitYzZFFHOCSiNiIKFMPeTD0hajHLJw5Itd1kOLkQIhJNlUDecjTCyxyqFLi7QKgUfmDi+gDBw2EIi8QBceZRxuYFHLAcJ53MCDU4BEdzz37jly/Jhf8arr+K6XXY6uaZwUaIYGou50ZTH4YZrgY32N+L+Xr967j2ve+N959ht+mzvvOoxNPGJzSaXquG52M6UOMO6xZcK98gVX4qQiOWoIFwpkXNCoFWwfH6fqx+jvD1B3Qy3SDLqgKimZhtZagG57toy1mZnYD3sLikFAlmnu2buF3XYS3RaMpQPiakkqNTJ2qJoZJi2sS3xLElVzxuKSICygYjDnBvjYUjQEgdBYDemWgOX1NgCBUvzEVc/mxdt3UQ1hUISkQUBgBWV+mgJjFJsjHiMf/chXuPFpv8oPvOT3WDrcBbXhDxQqLr52F9Y/9i/Iy3/kOSdMqKUc/h5IpjaPM7N5AueHAo5IJM4MPYK8d0PvIjwCSac9oExL8GALg7Di+M2iEBJxbHw2jlOKE8LRMfFIiRPHIU56YLKxTrfdA4bldP/6Ta/goqeeA4HeKHsbrtNaPU0ZRqPYHPEYefeXPsir3/XT/OBf/T57C0/XB/S6IWJKceX27XSL4tEH2eD7nn4JY72CJC9xuScsJUmoqbgxNl1Vxy5HFIMmQaRR1RQ/7rHTkmxa4RqWIDJk/QFFmZIEOdHYALvd02+EGBsyGI/Im5JwrKBXk6y7KkWgOXLfOE4pqq4gMQXNxZx4YKAMCccyihjkWoqMPLKpyfLhOTXDCj/1AzdxwdZNRF4TzxVUZA65495DK6d+sh9nXD6G2LwO2O293+O9L4D3AC9/iPXeDPw3YJTSeBbxm//059z05jfxjn/8LxiXk0eachWSasRF1+1kebX7mMe6/LrLWKOGr4VoV6BcQS2S1G3A1PwUolUQ1x21eh+rBEvrdVpxBbYYXCjxDlrWQdNQlxl51VBOR9hqDCUIZ8iqMa0dNYQrCddzZLcgTyVFodCdkspKiZcC2wwgr9BrNBAzFfpSUhLhpmLKfJg9NDnZ4I0//3LO2TpLqCGvK7IjEW53xC237T71k33qYxMhRHVDrEUIUQVeCNzxrRzmKMPoLOBdH/s8b33358EYglygpUMkmv/wIy/gc/9wJ5GS/MIvvxytJPXGY2vHOT/dZOf2Se4/tAQRSC2oJgmH9i0xM1Ol7zLkmiVfGhqVhYGDXCBLhSghjhVT1Sp3jQlU21FmxfCedlBSTAQwLhGFQ2pIGxH1FYPIDMFgaJrtrccFCuE8VDVOQBkK7Piw3WnQlqAl//vDt/B9Lxhm3W3bNslrfuCZRI0K+xeW+eyX9iO9H948ny5GN7QjHoH/+j/+nvd87lZsIjANhap7ggB+6ftv4mNHv8pcvcobr34lQoCWj80A+ryds2yearCw1CJqe2w/YqwOewYLjJnt9NsOtilk0iNbDWhULBZBV4dEUcl4qJhIprjjUIVgm6WfOpSw6Kmc3j1VgoGl3wS3HNJblcQX9inrMDXdYmyyj1QlHV+hWjF0FyIcDt12RH2HD0E2IkDx39/3Kf7qV4dPfy8c28TrL/xu6uaL7FdrfPTOw5TKMxeexkvMKDZHPAI///N/wVe+tA/KYRdQIUGEkjf81Iv55Ie/yvYds/z0r7ycsnjsJc1XPP0Cgpqi7G1sEwiqzYQH7l4gSAIwwy+lLzZKzZTcKAMbijRhPWRytsmR+5fwwpMPiqGPkjiWVbRRemaH/kYouVGKxoOFoZPL05w7ScQaphC97U3v5jf+5hcAuPDKbfyrf/dSPvxXN3N47yL33bIfBFxxw2PL3HhCjGJzxCPwli++ka/sX0M1Db24Qba3zriq8gMvfjqf+drdXLt5Fz/+kueSPY7yj++69lre8lcfh4kB0gVUFg1RHHHrkVWunxzgegVFFmCNJQtBG0+EZ6AduuOpTwRMn6O486gC5+mFBXkgoOsJxzK8i3CJJ98hUbUcf0hDX1K0JP6IIGmkdMIZoiMGXxH4SUs45dGl49zpNitVi70v4H1/82ne8CM3AXDt+dv4oe9+Hjdvvo28cRef/do61cDxUy9+KN3lFHBqfVK2AAdPWnYIeOrJGwshrgK2eu8/LIT4xce99xHfdt7017/ARLCbT951LoNCs3igzvaxOi9/+vO4pX07z3vmxbzq9c/aKKV+bPzM62/iI//0afJxSdJLCQ8KojHH7vv3Mf7UOWxLErgeuga19gpFAXk3RjcsshISTTSpTiYsdBw+D7CJxKaCsCiQ20Dus+RCYqc1tlFF7JZYKSDz5DWF1g7VzhAthWpZxDio7VDzOZvUOgeOzpAf7nPLzffyrBdcAcANz72Y1iBn3wN3cmt7gXt3H0F4xxte/cLTM/Gn/po5C3xg4wGUBv7Se/8P38qAI8HoO5h3f/gL/M7ffRbb9wg19MNspJa61Zw7McmrXnIVNz33Mpz11JuPTSg6mZ+56Zn8/O//HVI7Xv3KK7jl0/fQ7mTsXVihV1HoukQc7uOJsOMC0Q8QLYsWUAiBm0qQrR7RkZyehyDSmFAjpcI5gQhB5Y7aYoGXw5tfaz0SDygE4CoRwklU4YhbAWnokcIhYkcaCZazB2dMbdk6wU/8+I1IKXjB97+FXrfg6kvmT82EPxSjG98RD8Hv/O0/8dfvuZkSj6jFyDhl+qKMeDzk+i2b+H+uvI5X20sQCGL1+Eo/hBD80Gufw6+/52PEoec1T7mOf166FRG1WFw9TNCxIDUmdNglTXk4wM0ENJIeY7UBeVdz11qdwWxJ/bwOpQgoayH9bgWnQtYaGpOWhA1LZTYl8wqbQ+AUXgkMCc2g4MiRSbphSBhZsq0J1fsLjDOonkCmGQvfcNznNOZ40w2vIDeGz37lD8hKwzMvO+/UTfo3MorNEQ/Br/3hh/jYR+5GpBapBN5JwkZAJdBcd905fM//83Re+NKnEEaaOAlJHselUynJjd9zAx/5s8+hYsHLfujZfOKDX6bMSxb2HAHvh95DfkP8sSd1JhNDX6K15daGLxHkhQE8GDcUe5Qalp5JxYN8jU7GbQhIxzi5g9qG4LTnrgdH50VX7uCSq3aytNDi9c98MwBXPefix37ij5dRbI54CN5682/z+YX7yTLB2r1bmb/0MKo1zVg0zksuuogfu/7ZvOaqq6ioiEgFxGHw6INuEGjFDVdcyBfuupWxiZBnX3MFn7jzXpTz7L5tlVJGBGsCEoeZKHGlxvYF8WqBswlFM+Zu28cWFpeDaxliDaav8ecbZEtTVh1qNUQdNSS+xBnIKwG+LnBLEWEGtahDFknUmCJfqpLMdugcjWBgcDXFV3bfB9x0/Lifc+E5PP/i8/j6PYf4x9v/EDOZc+GWudMw+zyRuHwsPikPiRiaqr0VeP0T2X7Etw9rDP/tQ79B397NwYVx6psDJnoF8sAEeWOCZ110Pf/mtc/ie2+6lrF6gn6cnamTMOSpl27nK3uPMLltlisv2MnnP3cfSkuO3naYXi0mXMpYzxKCeooOcgZdT7moEYEiUp5FvY5VjqJj0Z0UIgOhY2Y+I0sVnaxJcMDguxZhFV5JZF7CWAVTUQQmRGhIc4XxMaIlyCPB4pEQNV5iI81HPvrl44IRwItefAVKPYUv/su9/JfeH5CpkGql9ghn+i1wiq+Z3vs9cGobuo0Eo+9AXvgTv8fyaoYqPL6u0KbkqrFpbnrxJYSBpigtl12xHaUV1doTb1v9gkvP5y9+6XVU44jZRo3V/S3ubZccWW8RCIUvYbCjQrjqKLOQKLUoa5F9izOOvbqFTDRaCK64ahu3f+0gPgkIBhafemRpyCNJmHuU9ZTVAJME6MyiSoEozLCozHqsUuiBJWpLkBLhHDqUBNbR62fUqsNONUKI4w9Tf/c/v5Z6ErJpduxbn/SHQDBKmR/xYG742d+m5QyubkmqHlkveNpzNvHd1zwd51OUi7hsehcAiao+4f284hXXsP3caebG61SmEnZ/5iCHBxkLKx103VMoib2litkKA5XQDAaoiidp9CmQHNjfpKhECEKu3jLHZ/cfobs0RtjMcSHozOEtuDGP0iX2QIWy0HTrglJpas02eDUsVe0HZA1QMyFCOUziCZ1EhIrSWIKNmwe5Ybobac27/t/XMNZI2DY29i3P+UMxis0R38gFP/+7hGuOIHVEElSouO6p23jucy6jvdxjvBZx5VOGsdkYe+L+Pf/2117NM2+6gvlds5jScOfX9rJ6uMXigdYwS8hveA/JjWwhpTasgzxFv9zQegQiUJxz+TZ233z/cOBv6nzGCa+kk1Eb70+uxD7mYyQEMEzh994fL29TG75KU3MN3vL+n6berDC/6/T4pIxic8TJeO/55c+9hFCX3Nudw8cxyweneM71F/Cic17FgaNtZiamuX7nNgDGw/oT3tev/fAruGPhanZOzLLeTrnDHmG9VXLg1gHUNGIK3EAQ320ZBCGlDJATMdqUrLdTzCGP7imSac1UbTNr7fvJtxta6zGxsdiuAF2ifYFLNDMXdVi7c4JMa/qbE5KFEqVDpjd3qMz0uf/WTagxjRkElEFEKhWdb3BQUBuxe/6uWd75qp+guSVi29jME56Dh+M0xOUCsPWk9/Mbnx2jDlwKfGrj/6E54ENCiJeNjK+/M1hfWuLX/+GNzE91ue3QVoSfob8a46s38mPPO4/d+1c479JZrr5kmI06Nf7ExZJf+3dv4ODRvWyd384Dty5xz8FV+p0B6QN9IunIohhpPOVhjZmLCScKhAvIhWK1laLSnKQjqU80mVCae7I2eVJn4esJVoCdEpBpgsUSFwj6m2KSfsGYzkgmLaWPsZnHTA39jORRCzs1Ko2wgcZVNF374ASFY9fNS56yjbc23kRjuk4SxQ91et8SZ8s1cyQYfYdx9Y+9BdbMxrfHs01oXnvjU3nBSy5ndtP4o27/cKSmJFIaeXJaO3DZjhMGXr/+69/H7/3GB3nfHV/DKg2hQKYG6TxhNhSAAi/x0mJDiR9YXEUileCyizbz1a8dxHmHjzxxCj7WhIMSH0pkbpCpQcSgUvBy6OjgCodWniJ1pDtjhPSIVOByhVCCbuD4vfd+il/+Vy/+pnO67IItT3g+HhMexKjJ0wig38949o+/Fb/FQl8iAsN5z6zyqmc9mxdvu5KxsPmEx876OVElPMm7ZMhTLt1+/Pc/ufFH+MVP/wVf3Pt18kGMLyGYyghDjZwzNMYH5GEFSslS2iCOcvyaYkp6Lptu8tkHDhP6nDgrKSsBSmhEXFCuhYRTGZXJFL9eklQ0WalZbif4ao6OAnxhqd2uUJkjn5SYhkB1LOlSwd9+8qu85oXXPui4hRBcveM0Zv3BKDZHHOfTh+7jh//yAwRVietInBScc+E4r3jeNbzgGRcxVn/82bfHyPo5cTV60GdSSq684fzj73/vb36Wf/9Db2Nl7/JG9o8cXr+9Gz78ON7OfigmeSEQ3lMZS9iyc5rdX9q9ITCx4VH0DR5G7iSvamA4GHjlhvs7Zo7Nic1aS12++uk7ufo5lz7o2JVWXHzVjic8H4+JUWyO2OCTh9/Ne+/5IOOBIpQFg07ElL+BH3rhpdx43pVUgggueGJjp1lBEocP+izSAVdvH4rCE/Uq7/rJf82/+t0/4/DUKklaEg8MHVkhnhYkLYvrQlgNSSccUdTFr0W4McnkdMBmVWd5WWEbEHUhTBy+rnAoRE1hM0VnT5OKTPGDiJyQLJTYVkSv14Sj48TLHrMU061qfEMSlxkH01WOHlln7hvu56Mw4LJLtnLaOPVxeQtwnhBiJ0Oh6LXA9x/fnfdtYOrYeyHEp4BfGIlF3xncuvvX+ct/vh8CSeE09Shnunwmz3rBlbzghksJAsVzn+DYg7KkEjw4OzCJK5y/4xIALr2mxu/9+U/wxp/8H6yEJf6wQHULpAwQQUy4kkOgCIWmLC2ucJjIo7xkbjYh8ppwcYlQFSTVkmU5Q+hKqDnEVoU4IpB5js8dpRH4gcQZh6JEO41ILWHLE6x2GKiA5kzBxNQaC6qkMIZQP1gaqdUSLrpi2xOcjcfAWXLNHAlG32HMzzbZfnGD3/+p7/2mPyCfKL/0gb/io3t3854f/DHOn37kp4rPev7FvGv3HYiBpXooQ5UCH0pMRWNjzU5TY2/cgczhlceHGi0tH/zzz5FNhHgtoTS4foltakSgEc4j1i2iAdQFRaHwoUIOHLIEFULkJf2+wQcSnMcHEpl5Sgn3Liyeknl4QpwFqu+I008YaqbHqjz3gvP4xR9+ySmLzTe87q3su3eB//3B/8CmrVOPuO6LtlzIlwdfx06BvHiATRxBD+zugKVeje3PrrNwl8XWFJVBSWYlVki+svgvdI9uxw40E/U+YqyNaUV0e3Vc7Nk2uc7KAw3Sw4JMhjT7hgyNSQQNqXDzOcX9DisV3jiidkhRcwQtwy1f2ftNgtG3jVFsjgDOacwwvSniJ3ddzw8//fpTNu4PXvWLdJa6vOur/43xmYcXhIUQXHH9udz2j7eD2NCKEA8qGxvb3GR939owc0gOBSVn4bMf+dpGedqG6OOGrcwe9L+LPLF8wyEJPAQ1TdktwR+Tk44fEQA3/+Pt3yQYfdsYxeYIYFN4PpvGqnz31M9w9banD22STwE3/ehvUnrBh//nLxI+QnmM1ooLpqe587Z9uK6inPeEUR+7LCm8wM15dp3v2XdIIqYFJJ4od/S1457sbtS5GSIPsYOAsrTE7RKhBL1+DVKFnU2RYynlQohAorRAVBU6b5CvG4Ts471Ab7NYAbInkAcct966hxdvuvrUTMbj4RTGpffeCCF+EvhHhir2n3jv7xRC/BrwZe/9h07d3kacaprJxTztwnu5fOd/5Jz5i07ZuC94/a8wNV3lPb/zpkdcr1FPGN/U5P71JWTgMM0IH3mqRUqy1dNJFdIk+NU2ykuKaoBse9YO9VgJC9SmBF/LyPZqRKdNbcygN0Gvn6DKlEknaddCpPAUoYJCoeKMcatYXSsgc9R2lQT1CBkY0qUq6UJMa73HzPTYKZuPx8xZcM0cCUbfYXzwV//fUzre5+7cw/+56z4cgn/+7D2c/8pHFoz+z9fvpog08VJG0BfYwFOaApFrglywT/WIxjP0wNKNa4jUU0lifOKRpYXSIp0DoRC5I8gtZAZw+CBGLIOXChdJaJU4rbFhSKJARBKrJWHPIHNDM4noRYrN441TOiePi7MgiEecfoJA89E/+JlTOuY//PPXuevrC+Act+w/wsseRTD6yHtvp+gEFOd7bE0ybTKSYMDh6ix5M+LwLV12+aO0ooDl5Tl81ZFUKuw5sm1oVj2RQ+Tp5DUqomDr5mW6QtItFKmMcHMSNVCILlQncwbdGJeFVDdZcB5XCkzVU2JoziREfcvcpieeWfUtM4rNEcB8Y4xbfvBnT+mYf/rr72d5XwuA+27bz1NvvPxh1/Xe8+m//uKGiCzwSoIxgEBs+Bat711hqCaJ4+tNzTVYOrSKFXJYZub9CanomNn1MY7pT8dtjfwwISlR+MGDTbuDmsLmjtmtk6doNp4Ao9gcAVw0dS3/ZepPT+mYP/XmP2Ox1YfQc9+RBS7d+vBP/q11fPEre7GxxlpH0BMIC670FOMaN+Y4sNAlqkJ+WDC+vU93oUpdNJkd28M9+2oUucY4iRcxFJZYWMgFQheUXcPRbBzZABUY7EAi8hAZBzAhMIcjZGgJRE5RhIzVQoJqTnPraWzU8kicep+UjwAf+YbPfuVh1n3Oqd37iG+FnVtexc4trzqlY772db9BeqTD0U6XlaOrTM09/DWo2+6z7+4DKEoGzRihJTK3mL6mvSyxFY+prTE5kdK9K6EYbyC1pRk2OEwLsZZTDhLitkEh6eWC+K4M1zfEOyBOctZdhbLmKJUivDOH0rP13JJiWZJLSWepiRMBeayZMSV5NWa9PxgJRg+DPNMHMOL0ct+RRcxqiL4v4iM338nHvnLPw667+54Fbr9vAd03RAOHk6CMJSkEsnRkVUlvRtEKQlIXoApHuJZTek+JQ3oHSlItPKFzKCuxArzz2EBgcGjrEdYSpBbtQEhBvRmBFARdS2DAKYlWmvE4pJ4LrD1zuXqj9sAjThef/+dbMRMJ/Uumed9Hb+bLd+17yPWcd9y5fD972ov4CYmseJSTiDFLZdJBYgkPO8J9jrX1Bt2sSjyd09jRoTdwrFmBkQLjFbNbB4w1CsxYwKAfMqMHNFsltoR+NaIIJfmUAB1gZURjTOKMxcxoXKCQuUJHAdWZBDMVUppRbI548vHx99280dkMPvTOT3LvV/c+5HpFXvLlT9zJ0uHV4QdyQ/IREqGGfnzDG8EN56JjXkNS0uvlFGm54YctEIEGLYdiESd1QTtmoL3xXkgBShBXEzCc6KAmhsc7PtkkjAN67cHpnKJHZBSbI04XX7v5AZIlQXhQ8jfvvpn9+x+6BX1elHzyi/eyvt5HdkEPJLLl8U5jKjFxYKgfLJHLCtGGPAoQ66BqBu273HHfNFkaI5UmKMAGgqwBeaGQwPR0j7FmSlPmyJmSODBM1PvoukWOKax20DAEM450vYJKAqJL6qzXxllYPjPXzVPdunvEiJM5srhCuGTgjpL/9bv/QLvVf8j10n7Opz/yZbpHUvyKQHUNwWBAkKUEdYvrg2wrbKYxeZXuTB1nJL6iya1BHO1T5h6pQyJAKoFCIVSImHB4aWlbTdAusG1NdW+BtgKVKgamhtEB2ZhgMBNjY4jGIHrKBOMXWXLb/bbO2THOhtgcZRg9yfn4gd2QDW9aj969zm/91t/RfV2L6687j7SbIYTknAs3sbzW5Zd/829ZWFklNgJpQViHkwFeWlwI3juEgck9ln4zQK45qsahckt7kCEiTaAELgjIxgVOCZzXVAKB71mINVlVo/oWWVj8oMAHmtXWAJFolFPYzFKOSxo9zVMu3c6dC0tcc8FprB19NEYXzRGnids/vw+XaEzouVms8rOfeg+/UXsx503MU/oeWoVsqWxm/8oq//2Df0G30cY3Y+pVQxoIut2EoCiYLFJKKUmSAVMvdJR3JiSNAc0JgRMF5AVBmKBNyHI5RnFIYnNY7UeYzYpwJsfmmnBFUI6BapaEjYx8LeZI2ac+CJCxoIwhWrFMXFrl8k2buL+9wouecRo7LT0ao9gccZpYX9y4aRSCr3zibu6//QD/8Z1vYGbzBO3VPmPTNTZtn+b+2/bz2z/7LrJucVzc8WL4IORYStAxAUk1Y2y/BEDGmn47BctQVFIbPke5O75fhBiWqIljY51wMhIOOkv9k8Si4WYzOyeY3zHL6tEWz/++U1ee97gZxeaI04D3HheWBB5SrfjI5+/ha/ce5nf/0+vQUjDoZEzNNpmabfLpux7gLf/rg6TrBSL2qGCY0e4zR91m5FhC5ZAabGWM0jpa63VCH7Kc5/SNxA0kzViSBeACQxymBKEg8IrFqEKQlOg1RVFGiKIg8JAXjvzeAUpCaQMKZ6hUc+brc2xKQsaaJc+5/PxHP9nTMoFnZrcjnvxY66hU+xSNiHBW8vW7PsuP/ptD/P7/egPZ8oA8LZmZn2Rsqs7H/vHL/MV7/p6BE/hMEuQOaxWhcRjpMbMBeHCdgCjViHlPpV1SSwNWRY6PY2xN0Fx2CB1QJIIgMTRMl1VTYybuc3RTjXwFRK4orCQwliIOOXBXFyc1wjhqYzlx7Jm/bivioKM2kXD+ljP09+ZZEJsjwehJzgP3LkMMRnrEkiMtcv7Hn3yKd/3V53nJ0y5k+/wEu3cfZd/CMve6DjKSCOGRHYf0w3aLQgoEQwNr2XV475AVi+946tWI8UaFdl7gPbjS0qlKykgQWIf3glwqgorCCEcZeFSiwTqIw6G/UeFJ6x4TDVPegp6gkxVkg5Ln3HAB11y288xM3oaf6KlCCLEV+DNgdmPkd3jvf//U7WHE2YL3nuXIIXs5qmMwjYSVzPArH/4gVVnllTdcwKZqk69mS9x9eC/339xnvVZF1AXNZIBWAUIV9K1moGo0p3u4cUW+ZhAdC3OSTVWJD2E9s2TZsDS0uNdS9iRGKEyg6LsQ14Eoc/iswGQVmMwoyoBKPcXkgvRwA7VmcRp8NWRlbYC4TPCKqy9munGGykVPcWyOGHGMdJADIMSJBOzuyoBfeu3vMz7T5Hmvvp65bVPc8aXd3PKJO+kt9znWuvN4FdnxzCCOm1jb1AzfK8nUpgZ5Zmn3hvvCAdaf2HY42kaG0smlaRvLxIan0bHso41gWDq0ztXPuoSrnnMJYfhgQ+BvG6PYHHGaWOsM8Himt3fo7xkHPJ32Oj/51j+kms3w0mdcxtzOKbK79/GpPbexPsjxoQIKpLI4oZFtjxmD0lYRRQZ5iJ7NqHuLSCqcMxdj7TpyusegH9I/mBDUeqi6YmKsR7tTxRwO8bVhA5iip4iNpVAR7cMhTgm0NgQCyljjG54oK1lcbfHMZ1/KhJ3b8B37NjOKyxGnkYN7F6lPlSzHCbJlSNuG1XCdH/ye32NzLeSVL30qhx5Yohc6/unjn2dtTx89FUJREkiDqTWpdXusTI0TxJ5wtcAfdpiLBBPlANtvsG3zOEf6PXwQ4LUkW8lII4+fhlIIjqxX0M5RTkSIniaqlBSxR6yCdOB6BWWgKKcqBLJDPbAMWpIDdwx48bWXsmN+DFMCp74R2iNzlsTmSDB6EnN4vc2qLtEtqCw5BB7V9ZRBQRoqulnGX7zvZtq9jJ4sERVL0DZIFE4IfCgIixLrFd6UZBMwtiQpkwBtLKoAoz0LR9oYwFQFwlmclCjvsIkiOWxxpaUIJK4aIiXoboHsWxASaTwEDMWpWCDaBo1ESMXd9y6w0htw07OuOGNzeIpT/wzw8977rwoh6sBXhBAf997fdUr3MuI7ng/9n5vRB1P6F0b0z1UI5dATBa2BxnQkB1YK/uLzt5G6PnG3jZoyTM4WDGzMIK0RVnKC2jADYc6vUh4U2C2a9nqMC2FqIsO4kOVuh34vwR0JmKy08UaTZQIrJfq8gjAqyVZjisWIJMqIxjJcKLCRIxY5fZlQAroFZoujlIIAxVf27We9XvDdyZVnbA5HKfMjTgd/8O/+kuMijOB4do8rhiLNkf1L/ON7v0DWzyh65YaJNcNyMdi4+fuGL6dSGx3UhuOlWUF3qf9gYQlOEoQ2/jk50+hkNrKKhAN/cv80qfjiJ+/g4nbK877nqad2Yh4HpzI2hRB/ArwUWPLef5OLtxiaQv0+cBMwAF7vvf/qqTuCEd8p/Mc3/ymqUBxanaSZ5OSpohAa17I0qpI7dh/ivR/7MgNdkMucSFh6TYdvwoRK6a5JzJrHrGr8lCVNAhqppLAFetUTJQWpDFhecAxmY0RVYZCINKB6cQ83ANPWVMcz6Gr6kwGgqdRz/J0C5aDUkOzIqHUseatBKTV5HKO14ONfupNnbVGE+syY0Y+umSNOF7/0n/+AEkGjnrN2oIrvh7hYMjAeXa/zL1/by713HsKE0JvIoZYQ9B3EGhsluEzRXowIKwWyUiAKiZeKXg7JqgBvWe326a63KCc12oUgHIn1hDMC2fUUfY0MoHswZzBdIVrro5VBtv0w8cF6hA6Z2dlhutLlnv1T0MppNHM+9cV7uDG+mjAOHv1kTwNnQ2yOBKMnMR+643NIbQm6EBQOrEN4Q2AkoRB8+nP3UaxnWDw28shQYRohQc+CFqCGrQid95TjGpkbolJiByWi5VC5I88MWaAhVqAVwapBCI+pCKwX+NCj1lJEJIdZ91Ig+wbhHCCHzV9yA7lEGEE5ESCMYExqDg0KDi0scUb7DZ7arhJHgCMbv3eFEHcDW4CRYPR/GR97/y2Ax844bEUglANVUh0HGxj+aflubD2D0hLZlH43Qu8VyK0FxiuKLCIXkvFuTnk0ZjANthoxXm0zs71HUiuQUYeVzgxKOGKZ02kGWKeROJQzxHnGZTNHWLdV9qgpfCiRzRKLxzlPGFkqPqNbibChpLLfI71icirkyAOGnj+E/q4zaIN3FlxgR5x93PLJ24e/yAdnAchY0231ueWf7yLvFicWHOt8hgC3YUAt5IYYNOx6hhZgN8bzgu7S4MHf32OZRMeEIr/xyNH7E4LSyeLS8fK3Y8uHZtpTW8ZZ2rvKV/t3EFd/8FROy+Pj1Mbmu4D/wTA796F4CXDexuupwNs2fo54knH/7kV8BtHAYKuKAihjR1VVOGwHtO/u0k5T/Bg4BfH2LnHNs3pHjbavQyLxY4JIpUzEOZ0swU6ERD1LujlC3e9ZGJTMXNNB9iWJ9fTqVcxmQ6dTIV13lGGEDQKKCsSLDtfS5D2FNBajBabiGLQisqUAlzgmi3VUWzKxdZr24AG+unwLP9V4yZmZwNE1c8Rp4uCBDGmbSK9R7RJnHL7vadQsa8uHOLo3oJcbZNchlEddJBACsq9HqAKEK6BZxUclQa+EVQdBhFiH0lTRawO66wP82FC2CNs9rPSEjQiZQmIzirEQN/Csp3X0mkMsgpMFygmyuqaMFWHLsHK3plvUKJoFgXdUwoRutsTu5S8SBs89MxN4FsTmSDB6EnPLniVqaYnzIU4opBT4EM45d460KugvD8jaGSIJiENPQ4Us6BIqUOkanLC4QOAiQbSSk6xaKDzKOAgU1USQlR4TCMgt8WJBMR5iI4fEo3sCX/GoSoD0EGQeGUpMaSG3UAkRgUdpifIWqRzOhajCkwYGYz3eSVqdjK0zZ2YOH6fqOyWE+PJJ79/hvX/HQ44rxA7gKcAXn/DBjThrObLSpj+rMGU8bJAkSnpplet3bOXo0YKe6rHkLU0fkFQNwXxIa9Hh+gHMW1wuqRaOvBsxfsmA2nxJo1kS6hyjA6YTyUoRE6SeerNLOa7Yf98kE/0+2krMtKY2KDiUT5CqgLE5z6CAZr1HVC3opgkiD+m4gEBmeKUhCLCRo51arDYUbU9R2kc/2dPE2fBEZsTZR7+bn1QWdoJrnnsxe75+iLwoyTs5RAql9bAUO7dDsUcqvHMbHc/YEI4A5PEWI2EjouhkgBiKUscEJ+83Monc8ObxWOaSBsoN4QkeJCwJLx6UlbS6vAZAul7g3ZkLkFMZm977z2xcLx+OlwN/5of1eTcLIcaEEJs2HtCMeBJRCoOdk9iVEOkEXnl0x3DNs+fZ1zzC4AsOHWfMXexpHWigVEjrUIgyGh9a6hNtsrUKpqcpayA6ISYvKRpVQgeNGqw0SrqtiPRAQBY5fAJECtlTbLpwlSMNRdkLMFlArR1i8wyROmQOyBBR1+i1kmLWIoxjsBKhpEDYVQa3xuSbikc7zdPG6Jo54nQhJ0syl6BXJLYRoTNHWBquedq57Ou2Wbqtg6sFNLY4wqWMpJthCsORqI4SDtF05IMAnyl6maSapniTEwiFSirE9ZBe6DANRXV/ho0FvimJqj2EU6SxQtYNW6fatIsx8uWYntLIbgEdg/IxZn4Mei2mt6a02wlxahFIBoNl2JSzeOfyGZu/syE2R4LRk5iy1yIOc4oyRCFwkUJaWF/uEPYDbCuliBRmUhHGivOr4yy2lnBV0KWjDBRWClRhkBOKfEajj+YQKqaDgH47wwtBLEFai4gUzglE11KKiLIpCFYdOFClxwaewnlEoJBK4aQApTcy9S1Ch4jck/SG2+jAk9clKlRnbhIfXxCveO+vebSVhBA14P3Az3rvO0/wyEacxfR9gcwsQit0aSm7IToxLLslKpsERZ7h16A8rEjrTaZ3VjlYtIkGMFVtsZTVSY0maea0Hogo84TG5avEheCcczLKrOBI2WSsaajHJUFuWB0rcLFkzSTUl3OqF0lEmEJoWMkjilTiwhhZBHSLGGklBkEclPgUqISwrSQLPL6tCNahLMyZm8Sz4AI74izkJBPp43jPwu4lRKDpt3oMs4kESSVABQHtpd5wO8mG6OOHZWjWDX96QEG1mZBl+YZQxHB9KaFwJ7KJpBwaYB9LrC39xnf9pC/8yV5Jgg0RSWAHHK+mM2dQzH2csfmYH7Q8DFuAgye9P7Tx2UgwehLhvSec9ITlgMJEBBd53L1VdC5p3bOOmlGUjQHGhaytQ3OzZFAdR8ddxLQlWDBICzmSMlTUyEm2pKx2alQKxWQfykpJdczCekJVSwgEncziVj0ugE6vQtM5+mM5LCgGxiAqChOBagG+xOSevA5NP0AHgmJGUa7GtNYleioAYvLckCRnwGNsdM0ccRpIBzmDuTpmXwaxwhEgvYcoYuFwTn/VMagJpM0ppKeyc4LDBeRjLXSuUUVBORYgpSVaM7QTTTGWkIic+qTE9x2Zg1gowtyhxxPyUNJznvYKVA5b/C5BpRqTZzndlgPfRdfADSS2EWCrASxmiIkQNdOjUvYpNtUo05Bs2ZCbKtXxypmbxLMgNkeC0ZOYLx4eoHpVKl4ilEOUIAqPFSVl4ehlJVIKbC3ENiXKKVAWnXuyxrATiw01DBzhUh+hJf16TJwJBmmJAXykkEYMhaV+QeAlpqowsYBSoArQFlASW1p8BMp6hBAo47CxHj6E9QKLQBmPrGikFKTCI5DsXljm4m2z3/4JPA1GZEKIgKFY9G7v/d+e2tFHnC20V1PsdAIViwgFsmZojPWYrhZkPiHdp5iMJHZVITohcrtkNu5SaE22L6GWQysLaW7J2LbzMCjB2sEmZqtgvihZyBMGRUBtrGR4pS2pzWakD8SoriJVCf3QEEQBUjgSMUD3A3zgsVYzWe+zutJEWQm+RAvIAkcjUIjCst6WEEiyND8zE3iWmASOOAspN34eE3U2Mni6gxzvPC4HQgkWwjAibia0VwbHM4TEcaNqMRSLYEMMEpTWYUuGmUdyo2TtZM1VbqQhHduvYOh95P1wm2PrODdcdswoWwqiWkCeWjAOJATRmfFieAKx+ZgetIz4vxvnPC0RovoBIpQUi8Nrka1p+q2CvGOY3rkOOzTLCxXEoEbcKBhUzdAGbNJSH+sycMBqhfyoxtSHnUGzTNLJBJktEdUItQhJtaC/OmwQUU5BOJHTW6jjBgoqIR6PrBscDlAEm1P8UY3vxojEkRYxCI2vSJpzESLq49ctwZohOhOxObpmjjhN7D50hHpa0MoUOpN4YxHOI/sF3eUepl/ixwSVeUFROGbmQ8q7DeuRRQBdAUU3ZCruEzUM6boirBc0pwd0DlVQpcMKaFYFWejodAS6X5JYRTau6V8YoZ2gPKTIy4mNa7SjTIAJj17NkXjymkR5KNZCikJTtmICHVO5dI1wRaDK8lHO9DRxlsTmSDB6kpLmJZG3QxsG4yiFRNsSjGE9N2wej5GBIpzQpMKjDxccWj1CxVmiDNJI4aXA4dHOkUuNMJLIeGTpSPsFIlIYPKKdQigoncAZi/IeUSrClRzv5PAGNtSkUyE+VihfIPMN34XSIwJBPq3RA4HTkkIrxoOANM2pSMlVu+bPyBwe9zs9VeMNzTn/GLjbe//WUzj0iLOIhUOrEGi8FhQ1UANQkwYrHPctCy4b8/SzhLGBxpIR7OxzNOwiLh3Gh+gKCqOoYJGxo58mTIQpE9M9VN1wuPB0ehW88RQqB6vwoaTmu1gtIVBMXNAijA2VuKCT1Wnd2cTYEC8cIs4IgpKZiS7dTow9ElFOCWRHUh4N0I0QEZXUqzHbtk2dkTk81bE5YgTA5//xtpPK0R6cadRZ7jO5dQzEgOZUg/ZSl6IsaR3sD8Ufx0b3I39C+DmGHH5ji97whtTD0MdPbmQlHSstO+5ZJE4IRnYj+ygQJ4yzj3seneio5p0gqmjybsnMtnEqtW93q5chZyA2F4CtJ72f3/hsxJOId7z307hMIXCIhsYDoXW4zHE4zNiUJCyKJrPVOmGzRcvndNYKkvtqYGBu52FIJbYb4yqCQgik1wRdheo7SgWMafxiQVYGQ0NeIxENgegJOvWERmHxFYtaAuU84dY+qgfl0QQXC1ACH0PcsxTzAVG/xAeCSqwhsXQ6mvnzNiHlt//qNbpmjjhd/OXb/565eh/GE9K1mHCyi+sElBm0On0mEo2KDc1zJ3jgzoKFPbC62ie4Q6BkRnNesD4WsH4kIBgEmKZGlgZzGOzBLjYM0dsi+srSaSuCTo7WFjse0owNDMAsaXpRDe0EvmIRMxDkEpuWxxN0k5UCkQjyWY1ueip+gKVCgMQ6w6VXTZyR+TtbYvMMOpaOOJ385J/+JUlc4ldCtBfozKL7GzeeOLJegStLAiGpDyzby4BOXmKFwGaGoGvQnRI9KEBKkkIRaY1OS1S/wGlJrj3kFhsFSKnJmyF5M8RNKzQeqRTCg4kCTKKRSiMNqMKD8zjhQQpcKBFtjTfDm+HL5ifYPjtOGGg2z44xO1k/cxPpH8fr0Xk68IPA84QQt268bjoNRz3iO5g3/r9vAyCdTxCJp9hisZHDWI31kJc9Jptt4qonOgc2XRuQpzl5KrGJxW3KqG1dp1If4IzDTmuOxJOIqWHa/Fo/otf3KGk41BpjtaixZ+8EC4dn6ewL8GOG3FYIq5Zet8KRlTpGBcjIUY1TZispkRMUaUDaT+iMx+SlxgaWp1++g01TTapesmtmilCfwWcOpzY2R4zg13/ijzdKy+SJDmUnvXrtPkgQUhLWQsZmmriSE2bUUpx4iY33JwtQnPg6es/QZ+hYV7VjAtDxfbORSXRsDHHCXPvY8pPGfu73XEtzuomMYNclJ+snZ4Bvb2x+CPghMeR6oD3yL3ry8aFP/AM1k6GUIvGWoGsRVYVEoDJL2/QRocQc1filhElfReU5fianiEsO9CbYvzpNXoYUZUyR1KAiCY2D0mHyEj07IIkzpCzInERuz4i2Z1R29Kn1SsIVg/OaylSPMMlJl5sMTISXEr8aIFOJHuSkFY1c8cS6oBoUPO8ZlxNNJlTqknPP33zmJvHxxOXoujniMeC958v33cdiK4EgIKobvA9xhUAZgS8cXZmjlUMdLNjctlTaIMqCfFaRmZBiEFI5mhN2HDITjLuMqnYMshhrPbmy9KUg98PybhtrknnNlp1txrdlaAty4Kmt9HB4vIJ8HYoUVCIR9QjpNF4Istkq7b0B8azBxY6nf9dT0OMVqts8W3fsOoMT+ThfZ4BRhtGTlK8uHKDiLDIbBk9VSVwo8alFFI6WL7FIagNHryjpSyhriqwuSNqGLJaQ5uhs6EtUToQEBcNSslBQTiYYJRGrfXTusAhcqCgmNWog0H2PCxSylaKEwkuQGMrIIZyHUOCRSOtwOShl0aHEG8+er6/QrYKMNBfMzxDoM+dhdIrNOz/L2SEkjziNLKz1cHWFTSSyL6lPGmwQYEWOpmR/J0BWc5J6xp5DjvkiRx50iJ5ker7HeiehUc1JN0PqNPtW6tSmCoIi4spwnU8ubKKdxzx1635WowRlc9RaHbscQCMgHzeYnqImO3RMgKoYgvEBwQCiuKQwAd0swFtN6QU0LG5FImPPx1b2oe+FSAZcuHMO8RDmwN8uzgaTwBFnFz7nwYbX0bD0bFj6Jci7FpBY7ygygzHuRNaPAI9AHM9MEicZVIOoanw69BUS0uNPLl0Tarhfd+yhzjHESdlKG2N5cdLPje2Bj7/vS7jcElQ0uy45M1m5xziVsSmE+CvgOQy9jg4B/xkIALz3bwc+AtwE7AYGwI+cur2P+E4gzXJSJxF1i3GeUGmUVhBYytRjc4+dSVEBpK2Ssm0JhSFPJZkO0ZNQnU7pf7WOH1hE5HCxJ6hbsJ5wvobfuUSgcnqHprDTnkBlJHEOwhKknqws6WyJ0MJhWwLXh2SsT8tXkFOKSFpsYVAyJNQOE3jyPCCseD74kVsodxVMjYdcvOsM2CtsMLpmjjjVHDp6FF84ilaAa3vqzYLyqMRMJJRdA5khdwE6UxSHMtLIM6EbzG3uI5VnIW5AYrD3KEQvR0soTEBcc4R1i79kBjebYvISeyBCFODmQpaaAbHU5EclQc0SnGfw+8KhH2DuCCsepTKKyTqysNiuQwpFtcwpo4Du0Qi5FvPJD/4LjevXse1ZttW3n7F5PBti87RnGAkhXiyEuFcIsVsI8e+fyBhLqx3u2T16YPRYafdTBllI50AT5UCU0BivEko1vMkMJKL0uFBwYL7ARoI4DqBXotuGPJS4WGHjECElHkdRlZTjEfHmBvlUTB4JvHUEWiOcwMQSoQPijkW3DFJKdBxCM0FpiREO11T4JEBqSRiFCCURocRWBWVFo0tB4hWl9ejUkRWGvevrZ3Yyv8MV3yfKqYhLgMNLLXbvXzqVh/akZt/eJayW9HbWsKGAxDIZVqhIizIaIxRxNKC6Yum/v4fNFVppBuOStSjm6FqVfqrJ+5KBDPEZqMUAe6RBkc3w9d1bSNdDqrKgQ42aylEDQRpp1BZPNNenn0esFXWOtqdQOkD0JNlyg7Ib4nWA0jGE4KVE9TxyRVJOhyRRQl5ANu7JBgWdA90zO5mnMDaFEH8ihFgSQtzxMMuFEOIPNuLldiHEVafmJB5yX6ckNhfuP8KBuw+dykN7UvNPH7jlwRlFWtIYS05kDJ1Ed3kAQJFvGBB58Cdt66XEHxN+tCCoR3gDno1MIq1ADl9OStyx9Y+ZXgsBgX5w2ZmWiEg/OLNoowubihWuHLpklwNLnp65TkzAKY1N7/3rvPebvPeB937ee//H3vu3b4hF+CH/1nt/jvf+Mu/9lx9tzCfKqYrNfXcd5PCeo6fy0J7U/M6ffIx0EGMWY4Ilhxg4mlEAqUJ0JUI4fGzR65a1tKSQAV2TETcztDfkATBmEQ2HlnroG9gvcVMh1bmI9liflg5ZPdoAF2AyhZwpSdGIQBJT4G1IIBQuD7H1kFJb/CyI0FPrloSJRMYgE6glfarNjCysIZZjynZOUThW7smJ8jN4s3gWZDE8UU5VbO6+dR9LB1dP5aE9qfnVX38vlSxjot1nfCxDoajOTxBlIDKDNwZnIU1CDuVg+5J2usKKqrAyFpM7KHOJbViibSWVuECEGf7CBkE0wfpciRqzTLkUlQRo63BGUfYdczpHDyw2SDCEyEyjwoCyKehvr+GShHAhxTcizKYYnShm5Roz421iLZhIBohuj7wDh1oONXFmyriBsyI2T6tgJIRQwB8CLwEuBl4nhLj48Y7jvcf6s+x/rzPIe2/7Mr4ErzzCQ2ih7OeENc3MXBMtxbABSwB9KynLgoX7FmFgkE5glUR2HRKPVQLtIOg5VL/A9ktcCWpQEBtPUgkRscQFGpxHDBxCCXQnR3VzYimJqxG1ZoQvIGwVCOtIsxLkxsNZGYCCsh5gpwOqm2NMKJB4gugMdkiD7/gAfiKcqrgE8M5jjXv0FUcA8K63/wMEEukcSluoOxSGhIRN8Rj1AAokpufpOc2sWMWVu5mZbKMij9IOVzW0TINKmjPWyAjGcnzNoMOMtUiSNFLmGinVMKSJpTuIiSs5E2MdVKDYMrfGttkVKih0OkbFx5jIMTHfQgnDemoQUgwNfl2EHSiqC5L6YcV0WUEWw6xFdSbL0eBUx+a7gBc/wvKXAOdtvN4AvO2JHfQjcypj0xiDO4Ot1c82PvDOT50kxAwNr6UOqDRixjc3QMkT+aHeg4W1I228FHitEEIOF8uN5cdEJi2w1g8Th6RCBIpKMxn6neBBDTOFPGx8Vx0ikNQaAURqYyzAgc8dG6lMJxCSINSoij5+/MGZ7CwKo+vmo2CNxY2um4+Z24/cydi2NmLckTRLiiJARQHVJGRmKkJtEjipSYuQQeBJjWBxuYdoe0LjiF1BsRzhrCcbAxU6ZFOhWhKLJdIppq9oTEXoRkIYWXwAqmlYXWvSllVMCP5wjSSEcVunvkVSne4yMcixFvoe4nML5HiKq8N6GaMcVKue5nYBnZBym4LqGXQCOQv+KH0inPLYtGeww+RZRjZxGH1+wloxRi+tkBlNOF6hXq8wNlHDTSQoLfDjPdxkQb834MhySv+gRC/n1JSlLEIwIWKqitlSR1Vjgn5JGjmch04rYaW+nbAWUkwkKGMIlwwH72xS5hHlskTdpVDGMT5ZQU0kyMCjVgtKJWEhQyWQ1TT7s1mWwjouComSkMaukNb+BlppKsEZ6Fx4jLMgNk/3Hf91wG7v/R4AIcR7gJcDdz2eQWanmsxONU/D4T05+eOP3IKPwCce1sDnhsI5mklAoARWCFyiySIIVgqSQxlWSkSkiLvDPzL681WwJVHL4ZTABh69mJGqAKoB1UBz3eXb8E7w5fsOoVdT5Fqx8fDTD29uBYgAGjM1ltZSFIYgNRsZ/JJQK/JIYvDkE+CqBXZREUQBtSRgYrrGDzz/2jM3kf7sSBN8ApySuATYMjd+ig/tyc0nb9uLSktkHiATiYwsXQfNJCSOSwapp0gTXDcjPK9L9ZKUlTRmqdNkckcbHZf0/2UzZWWA3eJJW47psosaK+jkFdquxuYJxw3z22kPPJ9YOops9BivtNARxGZAVZZ0W00mwoJI19lXdpmu9PFNQaQKKpEkkhWWrKAUlvpeQ9jLWNlSpWIl1Thi284xXvmCp5y5iTzFsem9/4wQYscjrPJy4M/8MG3kZiHEmBBi02nwSjllsbn9ojPsY3OWsefOw8Nf/n/2/jvcsuyqz0bfOefKO++zT64cuzpVd6uVJSOBDBY5GAzGXOOEzcWfbWx/Tp8D2NeBa4x9sS/mw4ABYYMDNgiEAqAsgaTuVqfqWF25Tj5nx5XXnPP7Y5daLUFL3aK7q0rst579PKvOWTuNs397rjXWGL/xGX8gJUjjjKgRTKuBuFb9o821KiKBsOba9LLPtJjJz7aV2WtVRwVoW4GjCLs+r3nTKcbDhAc+eWG6n7HTljTn2jbgRy6Ndp1JP3vWMHtqrG2pdSLiveRzXns2Loi6AZWEfYcWecu3vuYVjNznMVs3vyhH7zz00r6yL3MunM+p5tt4lYaxg6oMcT2n2XGhqogaE3A0g502XlChQ4EXZHgTSRELwiM5VWAxgULsKcyipUSi1w34hjmhmTvQ4K67jjE83+eTj8ek6zUYG0ShGO+1qFoCGVkabkRUeGRao1NJPnRxGxqvXtHOAoZnc0wTwppGFBl5fcxSq0G8HXF8qccb77nl+gTxy1eX8BJq8+S9R1/il/bli7WWR7Y8bjkQ05jkxE85GAupMyHqORQbIKIKlEbuCvyhJrE+bqfCKTXFuRB1vELtczAPuEyugpdmCOmQrimqeRenrzgYNTh9+hgbg3V+9/ImYs/iFSVUGs+BfFRRKUOv44KZrsu1OEftlbhtiUg0tV0Y7BUUCsy8ZLxXUY0quqdXaD055PTSPHfdcp1auW8Sbb7cCaNV4PJz/n8FeO1zdxBCfB/TK7YcOHDgZX45X/4YYxjZBKeQOBOJm1S42uAimJQZukiRQGk0ppCo0iKBfFEicoGUhkoqMBUyK1E1n0IWIAQmcpFaIAUcW2izd2VEbb5Ox/exXcnW9gQyDVpjGgEEAqxgmOR4DcUk09OpaGo6qckLHYrKUIUSayyVUXgGQgOt5TZHDyyw0r2OhtdwU11leRF8UV3CTJsvNXGakY/GlC2fqi2RSwVeCFq6DIuEQqW4jsSWFUNZx48K0tigLwgazZJs6FINfBruGJoVDQlxqtnO63gTTSEtkbC8ZkWSOOvEosdtSwWe6/DpK22CZk7DKegPWlC6pK5kO8kJLSRS4AYlniMZGkkjgj1VoIce0lq0Y9B1S9RS9KoW+3odDh2Yu74BfXHa7Akhntuq8lPW2p96Eff/gzSzCrzUCaOZNq8D659pQXhu65lUWCkZjrKpsbVQYPRnPYkAlHMtQQTPfiClBGmh0M8xrDbgShaW5tjbGVFWgk43hMph7/IQHIGQ16qClKSsLIO9MSg19U+Sn/EyMgR1l3jXfnaqGoAQ+LWA+WaNQydWOHh8+WWO2Bdhtm7OtPkS8dsPPkoUlKRXJVYqjJXIukABW0WBtha/dKgGCm00mSdpqgJnriL1QxLXRQ4N4bZADh0cqylKQ29xRKNTcXa0QFHMszLusfepjHxUMS8CxFXFJVsgtcUuSrwcdKqIdwVBO6daDyh3JHnggDV4cYmkgViKqVrgiQK5JRmPaiwdaHLbMcPJ/Ut0W/XrF8wvT13CTJvXhX//zl+ldjll84pH1NbUXI2WEVLD7mRE5Tv4mSWrO2Slg8wLWmZMrWcYZW3SgaVaAyerOOyO8dolF9w2ScunamjqsSbyQxZ8n90zG6SqYKHyILdMRjlWTit0ozxHRA6JEAShoD6MqQpNFbk4icXWXcJaje1JSdWBTn1CLiEJI9prioPHFjhx5zE8171+wXwZtHmt8u4+4Kq19uv/sI933U2vrx20/xTAvffe++X7dfYK8U0//lNUSuBUoIxB1QXHeotMdmOU7xC1IvzC8MjZy+imwp1ICDXKFVSFRY01qm2hZwmelHgK4q4LUiGrCn+i6SqJtIpxHLO3M2JvN0Y4EqsNlQemHmIRBLnGXaiRbI/RGEzLh16E7yny3DLAotouWmnEEIKhxXVB+h5pUbA9nLDYu74Jo5sh6/tyMdPmS8u3fv2/wCkhrUmMFDiJyy0HOkzKBHfSoENEvbfB2cEadizJaiGdLKNShr3Yo7vap38uQO946NsdNj2H9IkGOJaiW9Ldl3Brp8QJfQZZShA8RGoyhknEcgi7I5+t7R5OPafTyZBFDxXsMCwVqQmIJnMMlKQfC/JtUMMmalySHrbozKHhWKTWZGlBkVQ0w+vY782L1uaOtfbel+mlvOLMtPnS8ue/4l98rtm147D/xDxlXiClQ3ehwdb6gM1zOyDE1HNaimvVRhLrTLfFNU8hIe21wWcWgQVXsXJgDiEt25tDhoOUdFQgqmr6eAaE0dPEkBVEdY/xTjxNFglB2PLJco3NDLubo2kb27XfAeBO+7vLvCArq+sSw+cyWzdn2nyp+Ic/+csgHCRQ+YagUiyd6lHojAUh8Jwm2xshl50Y57YJ4UTRy2JKx5BuW3ThIg9UTAoFnkerPaHKQVaC7a0OjUCyErbI8oK9IGMzSciEphHkKAEyMrhKUG1JZFBS6wp2RzFFG5w5zWrqUuUj8r2Kflygax5lqFHCUno+y4uGSQqZ5xH717fV6Y+yLmGmzZea977nt1nowGA3pKFiyl6Dzso+4qyi4zSpt0KuXthhrzPGHVWUDZ8gnRBnIZM1S7CT4B9TmBh2xyFZq0Vw6wTGljh3cTYE+5cdhraitBX9T29T5Rmloyg9i6w56EjCFY0jFLIesbeRIJY9ciHpdtpMntihKnL2vAoZgKctdQx7cZ3DrktNxGyvNSlXr2865GXS5l8HHgeaL8WDvdzNtFeB59bF77v2sxkvkifOrvPW7/jX/OV/+PNfcL/+9gh3rHBHLnXh023VMIUGJdi/2OYN9x5lL0lwAx+vsHiTCtNxqEoHfEXV8tBIZOrjCAeRVjjFtM1MIZBWMNhJePqRq+xsDtnbHAMCo8FKhQ4dylCBEpRAemk0HTKjPKSxmKQkmZQgBVZAo+2jayBDg6MtQSnptiPqysWrLO1G7ZUI7/Nzg/eUfonMdPkS8vFnznHnP/8R/vZ/+9UvuN/EtZTtEJYiGr2A1XoTo0ukgDeeVHzViQNsbGk8FUJHI5Yqqi2PQoaoUcX6VgfhWqpwOr60sALjTxca60qkA4W8ynryDHG5wagcspnW2Jw4DKWHKjVZIhilPptpgG6uk09gkNWItKEUOSU5woBFEboKf9vixQYbSsycx7zfQQQKt+4gP88I+BXnldXmK6WZmTZfQj747od4++1/n5//9+/9InuKaWuZlHgNl4X9bYw2GANHbl3h3recIp4UqMbU48AKOU3uqOlah7m2Lac/t1oA9tlhaUIq1i7ucvGpLTauDkjHxdTDyBhwFFaIz35UrWW8l03buu20tTuNS2w+Pdl0HEXQcD/HoNt1BQv7epSVodEOX7Z4vmBm6+aML8J//9jH+bb//gN85Ownv+B+ugllBNGcoH3UZ26lgzEZMpWcPnqI175qlZazSbeysObi7wiKnsOOFwICJzPIPYXMwXcMVS4otMdk5KMwUBnODnY4N95h58wIvVvijSxFUKKMRQ4FNijRCqyXM14vSaTCXU5xPM0gSdnO68RhiGhZfONhhi62EnhzBgnM9+qY7ZL93AAV8zNtzvgi/K+PvoOv/v/+Ay5unH/efXJd0GmntLoJiysOZnmV2vEV4qTEreA1txziyFKP0suZ60jU0BCtlxTdiGFVQ7ga03apOg7FoocQEjcqaXpQDAKk42NqHuevDrjy5Bbbj66hRwmyElit0S44YYnpaaplh0lN0t9NyD2YbLjobUNyaQ+bF9jAwR0YHK/CqRWkVlILBaXVzC/PUWfIvvkvL20KIfYBXwf89Ev1El/uhNGngONCiMNCCA/4TuCdL/Nzftnx6+99gO/5wZ9mnOfc/9g6Wfb8E1Di2MHm06ucJjOkk4rd3THtesQwzvnwBx4jSXMUEndHoyoLngvedNJZ2XQwSlFLHeabAcKTiEogxxaRVoiswHemx9aFq6AsMZMMXWkEhrLrIfMSkeZIQAaSKlBUbUHedAhq6to83Kl5Z7+foXKBKBVGWnRm6PgBz1zp8/D56z8ZT9gXfruJmOnyJeKH3/Vuvvc//xfyuOQ9nios/gAAw0pJREFUjzyOsc9vZGpcH6REFAZb00xI0QxYDms8vVfwW5cewJSW0nOQ+0ocaSjyECeGMnOpNgMqEzB3WLG/5UECeeSRLSo8vyDwcoQMSXSNjSzEOrDg7xIoTVlY0sst9nd36AUJjisoqpDJqIa7Lii3POpK0nE0jhFUniEuKqqOQQiFrDSl0jSWfc6tDXj4yes/fesV1uY7gf/XtWlprwOGL4N/Ecy0+ZLxg9/30/zTf/A/KDT88n/64BfeWYpnp48ZIRj0Y9KkoLvYZO3CNr/9K58Ca7HGYh1naoCt1LWKIPhMcshrunQWm9NMj5DXjLKnptZu4IAjoDJYbbHFtKJIIBBSXms549pRmZ0OkQAEAq/mgeeAlFS5IUs/r4pIKsKGz/blAWc+9fwH+a8Us3Vzxhfi237mn/Jz59/Bbu7wXx7+38+7n7EGYRSeqShUha9zyrlNKBW1uToPXN3g984+hNaCahdMrKgqDyMkTuxhfIE3n4ERtMqQ1nxAoxszHwywpaIaGWQEnnJwc5AjjaksNsvwWyXhcoLfykl2QoqWoPB9zIohWC4oRxHsOvQAXwiMo8jCjGh+hDMBecElGGa43Yr5wyl9b50Hz11fbb4YXc60+UcPay3f+MP/J//5fQ/g2Jhff+Ddz7tvUuRc3uvyRH+ZVJTodY2dDPAdhd8MePCxKzy+/jheXqA/XuJV4HgVcRYRYKi3LMGSgIGkEbep9RqUSqGzAj+KURsZQVGhKoObVVAKKiyFAFPz8GoWGxu8cwXGt3iOwDZc8rbCP1QQtDNCo3ADD3xFkuZI4RAjGV5wiNyEKIKtXHKxNuGyufDKBfoP4EvQZk8Icd9zbt/3eQ/574C/A7xk0xVe1hosa20lhPirwHsBBfystfbMy/mcX04kac5f+af/lcee3kTlGpkL/tr3vhnH+YMnoGhjmIQ5whUEY0XdV9hE008qRs9sEQUKx3VwhGDSlqStgMZOiRza6XFvE4ynEI7klqU5Qg0bOxNKx0DkYdcShOvj1gPqxpLYikS5WAeM1dAMkHY6jtiNC2gKEmXQwsOq6ahhSks5ylH1AOErEmswuUGEElVJtBQ8cHmDzLXcfevqKxzxz+Pmu8rygpjp8g/PMM34ln/102zsjXEWS2xk+ZFv+2oEf3DVzXiU4lyeIOsead0lrDlkOuVqktHPL1EXHqJ0iZoVm5/wmFQR3qGCnXmB03egnJrsVpM6X/nWJk/pjLVwzNKwwg4t+29ZI9UdxnGDXiMmdwVbcURS+YBB9puUWlBJh96+AWFfEOxl7FYHcGVFUoUE0RjcMT1jGO62SRONC4hkapKfa8MDH72MHBm+9nUnXtF4/z5eYm0KIX4JeAvTRfgK8E/gWm57Or77N4GvBc4CCfDnXrpn/ywzbf7h2d2b8N1/+t+Tjkqs5+BIyT/8se963v231vemG0KAktRqPsN+xm46YW83odb0sAb80GXST6ftaJ6cVhUJey3RBEIpXv3mW3jiwXNTayH12YlrQglqDZ/KCqosJx3k0wSUlAjXQiWm7WuOnFYblfoz+SsAitJAdS0JhQENz/2qKTPNgx9+CoTgO37gbS95TF8Us3VzxvOwNdrhB977D6CV4/RDyl2H7/+ev/q8+//ep8+SJz6hO8EPJTWnxtpORTGMcbZSgkjSWZrgzDXJKTA42EKSXPZxegViwSBjifYcXnvgFj61/TD9uA5rCjEQ6JolQLB60mHrGQezrKmKCn8kaIYZqikZtizN8RDd9ym0ohgI2ksZBZJ6WLK5WSMuS9yhgrOwmUbTqj9Rok3OxriOv/0IjZ7L933rV72C0f48vkx1CTNtvhRc6V/hr/7KP2ec1slHDdiEr/7z3/G8+//6x99NVOW0soy9coGVyGF8dcww38YrDIHnIYwm6jVYFyluUuJ7JcEoJm54CNdgcxd/7PI196zyvvsfxW5Z0jmFvayJRhmmU2Px8AL9tT5uUTHJc2ylab1K4yhNkStK5WAnHnKuQiYjhBMShTmtpZztSxG5kjRSTWE0xU5MfcmhuWyJlnP64w5pep4wrvi2r3nrKxjtz+NL0+bz2iwIIb4e2LLW3i+EeMsf6rU9h5e9ac9a+5tMD7ZnvAA+/InH+Vv/5tdwhtNSWeNJZGCpC8W///E/y8GjC8+bMMrKCuUaZCxYjRq8+cR+Hn7gIlcGBcZayqTCbzooa2A7RTYUQmt0CGqU485HyEojUkvHddnaGVA0HIQxiKTEVQGFtuhJTpqXBM0QqyTWV4hSY0YZ1aJHvuqgUhehNV5SUTmCwljkvKSalAhnWk1kKqhcAS4IM73y2muGXBUlVlj+2B03wLSCL98FdqbLF8k//ZV380sPPIBpWwhKCMGflxw7UvL/+/ofZKHWRIg/OGGUpSlIS9oTLB1p8uY757hv5ywXEofMMThBRddVLAYDNpZ9vLWARhTTGAvq8ymTQYukV2B9B78ZYdbXyLYDnHqCsprUjyioyPIxrTDG6i79uMk4DWh4Ca25PfzHHYZPu6Rv69ARE3TDJ/ISRjrCzUtSVWCMTzIJyDKFHApUAlopArekuxAxerJASHjjXcde4ej/AbyE2rTWPn9GYfp7C/zAS/eMX/C5Ztp8kXzLf/wZzpwdEG4bnJHGkxbPFew73uNH/+V3Mzf3/EazD/7eWbjWEnbwRI+jp1Z54KNPMdhNscaSZxWNVkhp9Gc/ckI824om3GllkMUStSIG/exahdG17wIlMYWhvz3BawTToQ/X8j4WPmuSbcAai5DX8kJcSyJJcS1ZNH2WZ68XTrveQAp6K212rvYBeM1bb38pQ/ulMVs3Z1zjR+/7U1xKcs72l6gyQScsCDB066f4j9/6vSx8ARPo/3H/g5hWhRx4HD20yupKm8H6Fao0o5IGIyWVbJIkmlxbojLDzEkkBnKJOuKiY42KFautkPc/6FBmApUIpGsQDUOWwDMXEsKWD2GOjEErWL84R3O+IPAnyMiQjC0yd8l0yHAtpL04ZKhrKJ0TWQdZGaoAjJXYQlNUPuWOz3yvw7mLGUjJiZXrPLnyy1SXMNPml8Lf+m9/iad3fNpFxWAjZHsyj0fF6w/fyg/+pT/BcvP5rW+eeOxJmo2K8ZUar371AVTo8vDuWUotsFmGI1MW6nWGlwvstcp7oxStuQxVGuxqQKEdTCGZW1yivPxplGcZFy6qKamki5gkXLlU0lQuo0BiaiF4LukVgd+o2Os6WARauES7BSqw1LMEW1SM9nx0XuHpCt2wuE1D6rqYbUs8duhnbZbmmuR+Qs03dMP5VyrsfzAvrTbfCHyjEOJrgQBoCiF+0Vr7Z/4wD3rdTa9nfC7/9d33Yw1YYxCFxslLbj+wwnd8+xs5def+5z0h1drwjnf9Lv5VhTeyDEzCI/FlkkkGlUHkFamuyAKNY12ML8F3aPguLpb1uoEc3LUMIyRPPrnJeq2i6DjoyqAcSe4bQiPIjcFKCGouIs0hzRGFwcEQbVYIAkDgGknRiLCFRQmHMjGUZYWKfCpXYXVFkLnEjkQ74DuCMisRqkJ5DvfcdvCVDf7nIbjpynJnvIy855EnsFYg3RxH5bjL8Pr9J/ieW17P/ubC896vKEv+w//8EHt31skXfTBjHhrHJDLB90t64Yh6mnNxYx/pnIcaAsZlrhHSqBesnfOompJ9xze4OOrw65d3iT9Rh1IxGinchiG+qqDhYIqCwNPYIqApdlnt7qGtYbWWkB/0uOK12aBJvQlPbi9CoMlDBy+tQMMgbqCtRTsFsuuhJgoygfUtRaXJawa/cLj1wPWdwjTT5ozn8vjmEKs09poBtbGCt37t7XzrN97Lykrnee+XZSXve9f9mGtG1oNBytNn1kjSaz57FtLCUPRjJAIhBFZBe6FOmVfEOylW22lLmTbc94HH0ePs2eIfK6Yta0IITKkpc03gS5AK1LRSyCqJ0FMvI6oKfBeS4ll/omfzQ1KCMVMT7WsJLsT0sbPiWpu6hEbr+noYzbQ547nE9HGkg64kceozHjT5zntexdefeD2rrdbz3i8tUorsCeompnE6phrXePw+mHQzjNaYviUpLbo+ROQOUijwHNpOA+VmbD9VYCYSHIvUgnePn2ASCGTooNoZfr0gPl9D1i1CawwaL/FwRUbRlmAk8VqdalGidyEUCV7kYloJvsqpmYp4B8rSw04UKtRkgYSOwRkodAxOS+K093CdHKXq19X3b6bLGZ/PdipJSpdabnEKjZ/Dd77tbXzNW0+zr91+3vvFyR5esE26LOgd7LN9aRu9E5HmFqsrZKtiKB3cpQFymCGTLr4jqYmI+LLDIAJvIHCzDNFw+LX3fZpyP0jp0u0aCuGQfbKABniLGdnYR1mNldNzT6lKys0Az59ar4iyxMfDLKb4sSDFx1QCaQ1SwrgToo8KqsJB7RSo3ZLWOKNakuxVIe255/8eeiV4qbVprf37wN8HuFZh9Lf/sMkimCWMbjh+8oc++zfN04I8LxkOMrQ1GGNR18rcf+cjj7GxNeCxJ9f56O+eRRtL7k//oMIIlDBsjCcUpca6Fq0NJnQQQuG4hvryiLYNaQRziI0Uay3GQN5wEFrgGEthDaq0lP70YFpZAyWI0EHXIxJhsK5ACB88jRCSVrtJOcwJlKFW5OwVFUI5DFYk0lE4lyW2tMi8omh6aMCLDVhJL/QZZAWOFnTnI47t612Xv8HnMFtgZ1zj4z/0g89u5zoj0yWDfIJSn1vx9ysPPcgwy/jo2nl+d+0CjA12Q6F7DjYwVIHg/KUKk0q8usTEknEaUBUSlVW4FzLqq4rlIsRf2uPJh+pkyuHqMx2oSVZ6E57MFbVOyXhLkjsO882SzLfsFQ12tkKaRYXXdFhLF8iFJXBHbDf2s1lVBK6h2zWoQUlDFIgPB5RBwORUTCEsdZWSpQFaQHbQYHPL4j6Prb6m6ricDDu0GtErHP0/gJk2Z1zjiR/6m89ux3lBnpYMRilB8Lljcn/lN+/DlpYPfvAMjz+8hhmXyKKc+l0biNOCUaWpzLT9yzrXTvCkwFYGC3h1jyjyGJbX6sgFWGGxQlCWeppU9tXUo+g5fWUSicSSZRU4EizTil5X4XiKItGoyEUIiRbltMzIGD5jn22ZJoeslNODS2EQCIKWz6Sfg5ScevUh5Ge8kK4nM23OuMY/ufd9z27HVU6lY2Kd4sjPJjattXzy6v9ir6zzSP/drKcbBGLA0qsLltRRCs9hbZgg3AnKryAUzDljtidNWjZjL61hXajVHRaN4HIqUCsF1oHiakTVAndfhuiXNI3LSFs8K/FaBZSQRD5+7FGWBX7hUbhgD5d4VhFowWDs4OkF4q7GcQrSkU91G4RRhnrQJ5lIyCTOnIOlIqhK3BVNPQzJswmi4fKmE9e5jRtmupzxOfzC9/7fz24nacF4EJNWFaHvP/vzvCx5aPNXmExC/tP7nsIZXSVaHLPUHbEcB0yKkK3BBH+5j9wR5JUhmq/wh5ZelXE+CPHmExr9FstC8MwGWGUxrqGSU+/cmh9T1RRhrcH6WoxIHeaCMe2wYGm14tJGxKBv8RseVVrSaObENOnUYoZC0dox5I2YA8EYZTX6NsmFx+cwrktmHeoqw16WpKXGbUHnuMDmDkmWcfvhlHtef53buOGm0OYsYXQD44cefujRbH92UtiHP/EYf+tHfwNZTk2+Kmd6EIo1VIB0JbaCNDNkEqwEz1oiq0BL4hzSvMBs+XgqpNGxIMFIgYo1RU3ijg2jtIC6wN8rKVYdZGFRaQUIpFKkTYmcaBqVwSBJAe1JiqrEuIK04SC3R4gwwIYCPzEgBK1ejdFOAqmBjg/aYH0HoyyeNVP/BmG548QSSl3/A19hbwIVz3jF8VWArwJa3mcnK/zChz7Mv/z130WHUHMUtjDYeYOUFVZzrULBkKclk8xDCYXtONR6iiIT6I0K4gRvWZLVA1xdUhiFUiX0UopnfOwiDPdinCqidzjGXvXAt2jPEpUpZe4xOu+wdLrg+OqIS0mdq7HHxrjJSBjKvo+qLM7yDnNthyubbeSqJBNwIvKI2aUKXbZ2OyjfQF/gFIIIB5lVKMfy+luPXMfIf5aZNmf8QdR8j5rv0X3OuvnDP/ub/Np7H8MpLB1PkY5LENNCH6MtUgiqpkPpKIS2CKkQrsVRYEsDhcGaa5U9xqBLS6WrqY0RdmplJAWT3ck0QeSoaRsZgJzOeBDWoCcZYa+OVoqq1GhbILWmNIDR02mj1dSgSHzGOBuIOgHJIH/2/Vgswky3ao2AbFiAkLz+q2+AdjRm2pzxB1NzfHB8nns9/x1P/F9sjj9MzcnZzG4hiWMmROwWTZqZJh5E7Ox06Bzs42YGndeodIiJu9QP5uQYbGII3BwVCWKbU25JMgK0EpRNgTcypGdzZNOipcWWimTXQWYCFViILaOypFe5uJUhXjXITYMoFbs6QDchKg3FtsWpFI3uBJlVZIM6fuRRUKLrEusY3LHC2z9GSYjKLtuDDKMVX3H85HWL+2eY6XLG8xGFHlHoPft/ay0/85v/iMfjsyy2JyTDA6xfmeNgs2J01cPXAUXhUyQutjNBG4gbEcGaQK41aPfGrJYDNlNIxh6FLxhlFX6roFnPyZWHbri4FyyjwQjbFggUrYUEuVZQqBrro4ArFz2qzNKa85GJpCwrtndD6qFgOFLEK6CGFfmmZL3XwIsq/EkBwsF1FCIpGBd1yApkYenemtD0Swb9A+TxkCr1+Jrjp65j5Ke8XNq01n4Q+OBL8VizhNFNxF/84V/kgUfXsMLiaEtVGdAlaMuhw3M8mcVURYWbV7iFQQkX31gank8USDbLglBYPHy2VEVYWtpeyLCh0aaYTqQQEqylsBapJb7rMHYtBgOugygqvNBj0BSUoSFymxzs1DlzbpPKcygrg0wNDVdA6E2nplUCVUmEmB7kCkciBITWYnPLONQYV9HfyWk0Xfq54fL68HqH+8vaJHDGS8vX/fBP8PRogsWiA00RVWijEQLuWlnkiYu7JD4EczlRMwVfE9YMvbphIajz+JMec40CWdXYWxd0ayX1VoNLFyzusABVUbkW6zqsJQFOJHFCDxKNcQQ2cVGuxasc0nXBI2kHT9ZYmvN5dJIwHlhIDGopZmFZUuom2xe7CCWoIoGqFK6vydKAce7TaQlkO2OyHqASw1ZVomoe+bDg0uXt6x3umTZnvGBe/eP/jvFTJZ4/bScbaDM98PEUtxxd4PxHzqERELgIqfB0hetJPEdQb4RsXdpFuApjDLoE1/dpzNXY64+n/kNlhb024Z5psSxhwyd+dpKZBGWhmk48y3ZiWvvbeC2fzasaUxosAiEk9ZbPZJB9tuVMiWnFkJJTLyNAORJdaiinyaRJXIAnoLScf3LjlQ/w5zPT5owXgLWWX3zsTVzJPS7uduk0Cx56usvo0jKjos49dzR5+Ow67FhcH/Jhk8mojvIFHePRXPKYv/33GJ/vcK5w2O1I9oeW/bnDqDGhKjWiViDGLpPMRxqLPxJEPZ+8mWALgdAhstDQrVCepMgzuo0ucq5ibEdUayCUxF3RBBtNJkGCrluSok4wTlCmRhx4qImBtiGqJLnQ6LGHrheMUgN1kOsll+9P4HrmjGa6nPECyZIBv/7on0RIcPw5Hp0sMFnzsVXGFRGxpFY5c/8mo0ox16yw25I0D2hVkpY2tKIA/9VX2byyD2EzVp0xntvEbTbZ7KVUiaWWVXiDnDR1SZsB1gU/E+w1faQjUUUANQjqGTigNxLaS/uoyMm9nGw0QVcCRwjqMiDtCcqGoUo9ityjG3qMPAFG4vcL/K4l9SSDtTp7EupWkQYRl64aVLwNXEfP3JtEm9e/hGPGC+aR8xtICcpKpAJXCqLQpd7w0Xe7FA4oIwgy2FeLONFrUg0yxsOUotBk2mLyinI7JdypsHXJk9WErd0JbgqEgrJmCdS19rakolEqwoHC3xMIKbDCwa8sMtFgBCMse2lBe75J5UHZVZhI0qnXELUIIR2ENggtUEawcmCOejfCKkkVl2htsLkBKxANj1E5nZh2x4nrPCHtGl+mI0hnvMRsDYYoYZAdiacEUWBRrs9Cw8d9T0w1EZSRotOznFyNeNVBhzDYw+oJ2mp2BorSVIw7Gve4wfQrHvpkzFiPKboSZw/m4pSDUYbMDNGBCWHXoPfVmKy2sK6kCBTNZU29CWFiefRijc3dgIYKKVKonvYhdYkaTR4fz9FXAVVkmAQuVUPR8lZoOxGhq+n4m1S6IgsEJYo8k+RlSZi43HZk5XqHG5hpc8YXxxjDtkopWxY60wmgQQjKFawu1EjO7aKsxQU8KVjsRix1auTjjGxSXPMesmgsuppWEVVGc/nsJmWikdipX5GxOA0XiwEpcTwH5HRiKGo6QY3AxQiBEZLR1oQg9JGhgxUShEUoSW+5A6W5VqUkmWaiYPnAHDKYPlulLRjx7DFmkZVYLcCV3PHqG2BQBDNtzvjinO1/iqGxnE/n2GWVraKFp0L8juCulSaD/ogsURgrUd2AplxgsRVRrGfkssREFRdNnSvbbQbWxd/0WZzbZGtuHeNnRM0C4VlEULJ4wMF6Ft+tCBtQFQ4yVoBAtiye1AQbFl1o+nsph9oBtYYidyUsZTSrjP2n5nDaFSZ3KYRDrZ3TWhhxOqphawGpoygnmkoLyhLSpkcRDBGypDlvOH3qOhte8yWN7p7xR5BHNn+Smj9mz3g4VUAxDKmpCG9JcWr/Io3WBaJaSRm7uFciFhpzNGsR5dqYwhOMewVndzpcOOMzvBKxvdmieWrASO2QrAnS3YBGkNPxJ/hzPZg3SCtxUjDnAsyOh8UQtEAnFmezRDclm/mA/XNdPOuiaxZWDM5EceLOeawQZOdDMu2iM5+wV3Jgvo1SHlYqMulSpRWTXOFgmKQxXmLoFpKVpTuud8hvCm3OKoxuIn70r30znlKcPrnvc7wZ/vF7381/e/9DBKXAL8Cpe2xmFflkiHUURhustdi0QghB0ZRkyy5LxqGjfa7qMTbyiOoCfSXl3qP7+dDmBray5LpCpgrtKVyrEYFgqDW67lAJSzCCvVHGG159jA/c/wSptLiOxChBlmR42sVgkQVEgWJ1X5e1nSGi5lA5UDQcqqZCWEs3DLm6PkaEHq+6zobXn0GYL77PjBk//Be+iaV6g9v2L+Orz36t/h9/6t9w35U9zNEabksRpxHPbI84QAVCkmmBR4bqOuArmv2My0+3aZ1sstw0XBoKipHHogfVYcFtp05w+VeuUs2X9DPw5kqKWNJrZRgruXpOwUTgj0uKCrbynLccPM7vXHkEVILQLn41YWINjaUxee6g6hVd1+VorcdGukdVQNyXlBsh4brAWsHiXMAzWwk1T/CmUzfAhDRm2pzxxZFS8q/f8nXc1lng+OI87nP8xr7jj/0zdnZSEAInEIi8Yu/ygEbLA2sxxjLYizFConwHKo2wllYzxAk81s7vTtvaAoUQgttedZgHf+sxEBDH2dSQGqaJHwE2niaTsBZrYHdzzOveeoqPvfdR0BKBYDxIMUIhFNMkkhREbZ/9h3usnd8hzbLnTFebVhzNr7TZvNAnqvvc88bj1yHKv5+ZNmd8MY52XsUbzT/hTx4+xWJt37S6/Rpv+Uf/jPGeRXsOkQ6xV2GUxkQdj9z38aRlLx+Tb0QoE2Fljp9kTLZXKeKQJfcp+rRIqhpyDEfuaDJeuUC+FpBvJ5iGSxVKohJ8LbFK4iyXJBNFpQxXnii56+AdvD95kmrbJ/ccrvh7OJnAGLDzBfF2QLtYpT43RzAeI9HoUmIagqoGQaWJOh128gErtzZZPnGdpzAx0+WMF8Zd+/86O3un+MqTd+H7Bz5n2NL3/LP/N5cmLfLcoVeT+CLB9mPMpAVKoa1gtJUx9FwO6oLWsSGjhTrjqz3SDUXQGOE0YdyvMUnbtHo+h4+sc+a+BUSg8EYa0XSRQhKJlImtmNQjshxCrRhvxZy66yBPPXY/ft1yyKk4+9gmbqwRNUOW1DBxxrF9bfrtGu54xKQOlAI7pwn25whR0hrUGIwKvurtPs1aDtSeNx6vBDeDNmcVRjcRb77nGK89ffj3GXnu7CWQSJzE0os8FjpN9s/VuOPOA9QEiKqivxsjiwplLfmqSxE5VJeG1DLQJ3zStqSyFq8b0t+N0YGDk2ncyJkmm6SkvVAnaoREDR9ZAVYyr1w6rRpHjs5jV0KElghHMFgfIvMcbSpkZXAqzeFWRFFWVLnBVAKFRLYdrCOROawsdXA8iWNgsdf4g4PwSmNfxG3GH1m+9vZbuefQ/s9JFgGMseiaRzEnWanXWO34HOt1ORAdou1ODTSfHFiIcrpRztbDPdxtRXf/Feb2B5hHfcRlyFIH8joPbmwiEkPdz+lmkgqD1QLfdnCqOnUbUBlJ5rjsWwHPbXPn0gLOTkChQ6pUouUOJ8M9unnOYhhTrxW87jaH7SwjyQWDyieVAf5lhVMKxLxhNZhDSTARdKPgOkX585hpc8YL4Ntvu5NbV5Y+J1kEoMvimmeeYPXQPN2FJkdOLrL/0CLSdTEGklEOwlILXYSxWAv9Sc6+o9emImoQjiJoRTzy8adASoQQhK7LNEsEjW5ErRkiQwfJ1Lg66IZ0llrcctv0RFlcm4a2N86mRttSTJNLjuDQrfsY9lO0Lq+9coHk2gQ2wbQqSQqQEAQeNwQzbc74IkihuLv3NSzVD3xOsgigvGIpMomKBUfnF+jUQ47vm2Oh1kY0JSa3JFsZ/jOW1UWNKyyltFx40mV/bT/brQiznFMGLnZ/g4vlkzR0jjQORdPHxoJGZmk0PIIoQsUBBoupKWpuRCOsc/ttc7RujRFCECvLaCMj8zRpRxJFOeUo4IC/gC5L8h2gADeyuAPwB1AzFb2lCKdh8AYQiRvg+vyL0eVMm39kcZ2Q5YVvIggO/r7J3DujGkku4Iritu4SpxYFx/YvMN8KUb7EjnPMZkL9oqZ+S4s48OFixc7vZRw/uES4P8bNSpodl6XjHk87A+5/YplUNCgSIBdEkUPU9RFFi5FtYmoQzGnqNZfe0TYnTrSZX7C85uA2eGOySY5Z1PjHLYQWdgPU2j60MeTaQKVwPEu7m7BcDKkbhbcQMbd/zC4+uW5en0A/l5tAm7OE0U2OtZb3f+Q8wUAgyul437e8/hi+4/Doo1ewxuJ6DqUwyLJCGg39irCf4zkOiSkpIoewoUiFRMSaJzf7qEyjkJi6h/UUlNCYq3HnvYfwXYnKBG6h2Lfa5tTJJR66/wJlWiFyjRxWJBLUXIQMfYS2kGU0I5+oGZHp6XQXrcAODM7I0KjAZtMpNK4nWOm1r3dopwNwbvASwRk3LsYYzqQJ42MeVcvgO5K3HDlBZSXPxBeRRiG1YpC7RFFJ4BQsLexwcP+AyLesXUoZrjcJZJ0kkXjNnIvnLFVbkXXqnOs3GOgmaVtwOOhwqn0Y6ymylk9Z97nzUItbF3s8vPUovsgRLU0+p7m6FWHSJvGgRU0WLDsjViIfJWpMtMEVFVUiGC5KxouK1qqDdkr8pKShFe3mjTEhbabNGV8qG1d2mEzM1JBaQq0Z8do33kIa51x4egNHCVASKg2VIRnlWCXAF0jf4fyTazieIGw46MygBVSZxQrw2yGTQXotoSM4dmqVoyeXUe40OYUU3HL6AMv7utz30adAgBECpMVMcpxIXUsACUDQm28iHEGZMz2xvra/BFoLdbLx1POo3qzhhzdAwmimzRl/CH7rgccYhRLjSUzh0ulF3LV6mK0q53zap1E3lG1D2YbECbi6YzGeIPB9Al9zPrlMLhuMLy9RbUj8oaXIK4rzEe1GndQmOJ0S151w5xuXWTrcwFkyjG2E9j1uv2eF5kKNT1+5QFkoxJEM0aioagmOE2IrD2EkyiiW9jehrHAGhmrbofSAdkYVCUJ1iFTkeFhqZQ3fvc4Joxepy5k2Z3w+//m/v5PJloN/QVNJQXOpi/VP8dS5knRnk7a0jBuCzHrkE49qdwORVzgCbN3hyWCXxkGX8JhmSxZsnVO4QUKOZPFUh74VZDWHkfW47U1H6c11CF0PQUG9VXD61BJOI+Sx81fZXVTcf3WBJwY9Rj2XeuhiCwe30nhFyfxCC+OCyA1hnNNQE8pEYKVgtbZClcNu0SIzC7jXe8DSTaLNWcLoJue7fvYdYA0Wg1tA1/H52IefYHttCErSXmgSa4MRgOdy262rGGsIdmFxX5c9x5DvWmxsMEZTjjKMEHjj6ajg8SgDaxFFztbakMB3Cf0AacDNC9Yu7/Kpjz/N4+c3yKxBlgZnnCPqDsL3kVmBxmBcyV33HOHwkQXCpkflCUzgIHONKjVFWbF2dZvcswxNReipL/reXxFu8IzvjBuX7/zmH0ErixACp63prCZ8eusx1sYpVVZjlC9xZeIgq5IydXjTag9jJOaEJHIPsf6MJO4IBrLCOhVutAvSUC5ZnJ0KlScEWUUrGHM538IVESgX4QnchYwnRzs80n+C+87tMi4EjSjlwNIeaRyxbRs0FmNMZqhRcu/yIe6ZX8KmDSbbEWUcUIVAaNnpW64mQ9gSDNdyarVZhdGMm5s//80/Pq0uumYoPclyHnjgPMN+gltz8EIHM0ox2iCF4PgdU089EficOLXCaHeCKS3ZRGMFZHExLSiSkiwtnv3IGeDC2Q3qrQjX8xBSIqTkqTNXeeiTZ3ni8TUMTI/EpEQ4CoFCeNPqJCTc+eoj3Hn3YRz/2ppomfa6CUEcZ+zujQHYvDogiG6AhBHMtDnjS+ZfvuvnEYFFSY0bCfrlNo9srTHezGllAUJLZDChHAnApxt1aVLgu5KDR/exXo0ZplCpEaqVkOsJRRSyU0aY+iatJKE9yhjUfJ55esgB0cP1BK4w+JnmyYtXeWJjnce2rhCPXLw+tLwMGXgI6+IWlv52g6rpcqK1xLHeKmZRYTxw0grZAbeVoe0GuTPG6pLzu1t41zthBDdFFcOMGxNrLT/x/o9jXIH0KpyG4srTa5y9us14UuCGbXaPRMTzBiMtgYGqmCNWAZg2R48vsJ1MeOpqnb0rijJxSMYTvFIg9iDYvkqjMUaoHDcZ0u9POHZwHhVr9NCl2Ak58/AVnv7Yea5evEjbFAwuuoTa0sTgb5WEF3NyK2jOe+zfP8fhU6t4qsSbTAh0ga65yG7JntlB+QXdasz46uMoeQOkQm4Cbd4A32Az/jDc/9gargZ/Ar4juXhpF5tqZKXRUlDWXGzDQ6UaURnOPHYVFj0c16EeuAw2x+SrEm+iCXZz2lHAliswEpoyQMWaWBSImsck1ly8tEcE2Jqm6mmyJyTCwCjLCYwkHFXIXkgmBXNRwGRcsD2nwFf87oefpDFXR5YgS4NfSiokpZ5eme3HJY61WBe0vv6rlWB2lWXGl4Yxhsu7Y8rlgGIJ6m3JpeQKgSrIyhp72w79vQi/XafeylkMJrz/Qs7VpMdyKZFYBmVGY8MijxZ4yylh0EbmEpQhD+cQ7ZTwUsz4SsRaw+LbAXPzmtGHBkT9kvKuGkZkbMeSfFUyfyRjvDaP1YIDNYENBzx0Xw+F4pcvPI5XtonCHCeEohEgco0z0lBz2N7LSBcUfi6w1v6+MuVXmpk2Z3ypXL20i654torHrzmcP7eLKK9NNVsvPruzFWhX8sSj61gp6HYjRklGnlRoBOpaK1h7scFoUqIB31NUgM6nFbNJnNPvJ/h1n2SQgQXlOUwmBabMsUqiEEgLCMvcQoPhICYrcoQU/Mb/+hTzc3VQAiNAOgJrwBrQOQyGGUiBVNdXk59hps0ZXyrv/sQj7MUe+CWODAkKxaO/l2CtQNcEQ7lBpzMmvdpGKIFtVGyMdqjqkuX5iLiMKYYC7UGS+fi6pBu2ObspEG2JkzoMy4ism2MKQ5xk7JgxES7DowOKzMcbNUnGe+xISSklh4IJrVsmXPlEREt6jIOcrJnglwG/+qv3s3JqiSoQ2MJgfRfjFDgjgTg0pj9qQOah/etflTvT5Yw/DD/2zl/AqWv0xOCNfGpzDk9c2cYbFri54eKyoWgLgidclACrCvYmOZNWjcMHllFmnRNyi3NpjUkhiHagGXVxqk3GnkUXITIWeL4inyjSc3tcWTKEkeTwq/rs7NTwwznYWadMLMUZSSd26N2TcfHMArVWk3E1oHZLitqq86733EfvzgNU2sdKS2upoJ8rJpMAb6IZDBIakWX/fPHF3/zLzM2izRsgrTbjS+XdDz+O1A5uBk4FQWFxqwrPahCCPFRoF7S0CKORVYWWklqqkWsxjzx6ldTXiNTgb2RYV9Fs1bESvEGB2choL7ep5kKqpqKSgq21PrvrI9xhiZCSYVlS6JKi7iCTkoUooARKV5IXJWMpsEIiK8uhW5bZd3ge4UikkhRYirqiqDvUaj6vveMgjq94zasP0WqE1zu8U6x94bcZM67x8z/zAUwjoFpQiIYhaJas1hOWmxldf0J/I8IZK9wSuv6YuVbMMA85fHJAd/Eqj21codiRZB2QqwUkitBpUSSKg4vb0B3Qc3qMtxroTY90KFjXWyRiTKtdYLRLNRxRy0YEm9BbyTiw4DEaK/yxYTcVPH6pg8zBCS0HagfZ32pRZj6yUoiJRq37qJFLr+1yz6EFGo7kW++89boni55lps0ZXwL/8d/8xrPtYjiCSoAXOsjQRWAx1jL9J7CORLjOtPondNjdnXDhiY1psgiwjgQpqLsOWgpM5JKVlrmlqQefBbLccO78BnGmMYARMO6naCzWCoQQdOZr6LKaGmZPMvJJMU1o+YoTp1Zod2rThJAUTOeyTbcXD3c4enIR6Um+6698xXWM6ucx0+aML4GfftdHMJMAlTvUWzmpcDCLiqotcJsZbq/AqSyOU9A+tkfg52AEZlzjymDA9t4zNPwYf6vClYbGYoEMLLLSqKhkbzPE60SUjsJ2NWmU89juNrupxMQeTgw7cYmUhl4wIMgdnLxNPLaoUJB5McH+HUxk0GnIydP7UHMubl2gjEWnAjUxlNLj1HyT/UGbaBn+xtu/+nqHdsqL0eVMmzOew/2ffpIqD7C5woQBqp/hM73YYVcNpglObvBDjawbTG7pdHKWRoZnHr7Kxy8WXBo2MUbSW8hZPdJHzmkuX61TXtaofEh0yKVaFVQLikFmOLe+SRz6IMGNSoY7Y6o6dPYNic9HGBGQexWLd45IRMVk3sH0HVwBt71qGbuXYBs+2YLH2lbABI+dUYt25xCLt4bo/TW+4Rv+8vUO7ZSbQJuzCqObmF/+8IO4I4ubCEIHZGJwkKjAIdOaVApIC7zcYDwX40mC1QblRCN7FSbNMTUPP9ZgXYQR5GWFCSRlU9IQAUHgIicWL4Wm5/GaOw+ztTVi2Td8YmMHJ08wSUlQGaLSctebDtO4vMfF8YjcVYjQBVPhVIL77rvAIMmpqopCCEarAUZYvMxyYmmeXq/B3HpE079Byuq5ObK+M2483vupJ4j3uWQLkjAC380RXp2mm7FeuAShxpkb016csNNvkRZdZNLkrC1Z3Y5xZYIe+pjlnMz3cfOKODG4qaW/XUN22jTrlqorKJqC3krA1x48yOP9HTZPl1yMY6KoT72dkJxS1NsOR+u3ou9aZ31NEic+RZ6gghzVtDyw9RAbY0EpK2Th0XzAYKqUYs7n7qMH8RslrVaIH904S8ZMmzO+FB4/cxUrwQqBH7lUCGqRixWCyTgDz8EAQk2TStJXuJ5Cm2kySUydq9HXppRJK5iMsqnlkDYENRfpOtOklILeapPTrznB5bPrrHsw3EgwxkwvKypwQ5d7X3ecB+87RzJMUNfMsj/z8f74hx5j1M+xpQbznF8A97zuBNs7IwZ7KdrcIIlcZtqc8eKx1rI2ilGxoV5WNBoORWhxXEk9NHj1DZI4YLBdR7UNzaTErYVc8VxUqXBjQVnVGAFSWFRRUWYOVXeAQuBXEOKCNXgTUEPFkcU2++5ZZn3cZ3c8ZCgKxqqiSHxGlwOWmw6vvfNWPv5UiDAaUwfVrLBbIdYqPrh+jsHDGWWzpPIFXmFwsIzqimP1NzIZbBDXClKVXe/wAjNdzvjSsNbQzPbo7EYU2wGOsujKEgUOURgyWohx+wHxyMVd1LRrOcpts+6F2EyhfI1UHn3r4+ymlHUYTyKybox2XLTfompr4kGG7EgcAvYf6rG0NMfO9ojH1hKSQhPFUJiA8YUFeqst3vy6W/joM4/id3M67ZJ8vmC8ETDSLr/9v58gLsfQUsw3UqoMegsZsYn46lef5uP3P4xqKPrJ5HqHF7g5tDmrMLpJycuST6xdxqkMjpU0lEtzLuLEbassHeyRK4GpStxYY6XARg7SV/SLAi0MjU6N3lIXTymQIIzBH5SMhMYqS63uI6wg3RmBhLit2d9rcPyWJapSo1MNBvKFAM9zCJKSdiugs9ghHqfYQY5woduOkBXYrKS/OaYoNaWxCGEwoQEPVAanbz3Ak8+sszlI+PCj5653eKfcBD2lM248tjeGnHvmKlVT4jUlrZpLjTmOB/tw5ArbwxphLaGzkNDfa8FuSDgSxDsJugCjWrRbPZygwvELtNW4qxXn1lzwFK5fh9BQuFt4WUnr/Ig3rATMOy0CYSg8h8xaNp+ao1Qhx+a3ua1ZEdLC9YbQGNNwBL2GS+iX4GQMypRBCoPYQTgaNciRBmzbcPuBZR7Z2mZtO+UDv/vE9Q7vlJk2Z3wJnHnoEqPhdDqaDCRB3aPXq3Pk1DILKx2sERhHgj8dc48QCFeSZRoloVH3WN7XwyoJQk6NsF1BPEkRShLVHKSU9PsJwhEIKzh9zxE67QAn8rFmWtiEBBW4CClZXGkhXUVeGJK0otGLCBoe1pFoDcO9FFvpz/0si+kUtZUj85w/u8HOxogPv/eh6xjZ5zDT5owvgf/+qQ8i3CGl5yLmI6DOgX0+ty0vsXKgw14WMRrXqWyN2lKGrUFchdjlEY05MGGdhX1LhEcmuJ7GLQWZ8InrJbYOvmkgjItjJ7S7E2jm3H3XEZoLLq6vSesOpYCFRobjCJQnmF9ukyU58dgyqQpuP6qQwsNoQcmE7UFKrjRVbBHSghaI3MEvXGrtDhe31tm9POR99z9+vcP74nU50+aMa/y7//ELXMx9JisB8s4G0VLIyvElDh7uMb+vRTryKdY8ZCVZbE3Y35pw/MgAN8xYngsRizWOr/RYWdglUob0QoOkP0fpCWTd4M+FjCc14g2JUxnEQs4dpw8iQ4UfxhwPt1gQKbU5ga8lZC4Lq136jiUbSEap4I57WsiaZrExQTslfUpYgHyk2Sl9sp6DKg29uqDKNQ/HfZ7eG3DmwU9f7/DeNNqcJYxuUr7+R/8T5BYnsxyfb3NwqUvDdbj1xCpf8/bTaAl+aqhbWJlvIBTkVlBq0KnG7KXYQlMpSxUqikhT1Qz9qsQWFrFTMspzWgsNZGFxcvgTf+I093/yHPV6QK0V4SQWU1TIRoDs+CghePzhS4ziDDUXsb/RJAwk+aKHCD3wHVSoMHUXXfNw+xonhnrdYWWxw933HKGIJBNz46xUwrzw24wZAN/9fT8BwgEfXn+oy6G5iMUA3rJ6jHs795IVinC+5JbVjNt6HpWjuFw4JJmDDFO6rR1cYShiQbbmMr87xt0xxFsJoUyozfWJsgGOalA0FKapeOPyPTy6e57Aa2OURC9YClVQxHV03CW1Ducm5xj3JfFul+OLDl4qiM/VEUVAWSk8oaj6EZO4TnJQUdThSNTCUx5v2Xcr7qZkb7P84gF4hZhpc8aL5W/85Z+bJoEcyevedJJOq0GzHfGVb72Du0+tYpQER9JcatI51AFXUk4qrLZklaUUEiGmbWTTCiGFFZKisBghyApDOimIaj6W6X4nb13mySfX6XYjbGUAg6nA8SRRKyQvKs6d3SQvCvzAZW6pPU0qIZCRh5UK4cppVZQjMVJiheDWV+0nrzSvedMtIGDtyui6xva5zLQ548XyH37p/ajc4AWKt9x9lKjrE5km3/TaO9l/JEK2ICs9FroRvXobW4c9d8xk3afYrrADS5ErBsMae4GL8aGsDHsXXWRfYNWIKkyIy5Cd3RZFHnCg2+DS2QFzbo+QlKAP49yy/0TM6qGSsSm4eH6XyqsQBFT5Kp5vqWog0gjfrwiXLLQEVR2KFmRC8cdu30ccJLz+7nmW9u2Rhk9d7/ACL06XM23OACjKit/41UdIdnzcoc+bj55CLDfpztf5pm94Hf4tiiSIkFXF4UaDY8sOV9odPrnnEe96pGdTvO2EUOUsiRi3W2CqhIwY14xp9TNGVyeU4xTT9GDPYR7DodsjhvlVlt0O49hBpAWZSDl4dELDdxklGRefWUMDYT9geGmO3PXIVwReVmG1ZOx1oB4RRzWqeYXwLK9ZvJPcCm452OC1t2yyrc9f7xADN4c2b5z+ghkviqvnE4SAxdDHKTVWOWRxwXt++1G24gQtBE7d5ehchz1Xo6whx2KswZcCr6g4evcBLp27iDesaJmQHaWxEqS2FAZkXrLxzB6mA+7IsLc9IYxCnnj0IiNbISKDLiW5Ljm40uGWA0s8fvYqepjRUJLtasBalkPPo16CwOJIyeJcxNNZipUWURr+zLe9Ac9RHD6wgAklwr1xEkazqywzXiw7rsG2PRYPhMRuzFKg2MxT/s2nz3Dp8oiiCIm0w2q0wjDzEdog9gw+JfvrFSfnd2lOTnLf0cs0ujl+u8PmlWkrjPBhMlY49YpU7hDFLVgU7FYxnhfyqf4V9qxGGIEtHVQac3ShR6t5mLObZ5hrb+EHgu2J4JkNH4THsQMj4sojci3zbo2n70txcNCu5fu/7SuYhAUn3SX8gaYmb6AlY6bNGS8Ca+30IyMF80stNrdHRK2Ara0hv/iOD7H1xA4CcBseB04ssX5hByElxgBY6i0fv+5z5OgSF5/ZRQhwaw7luAQhURKM1iAV4ySdVjF5gjit8H2XRx66xDivEMZi0RgtOHxikYOHF/jIB85QKUGtHnDl4i5JbKYtc3UH4wrCwCVQgs2LQ5DT9/H9f//rePzMGq1jiyAE9eZMmzNuTrKiJCklEsWBQ3XWdvcIw4Crl0f837/5MfbkWSzQ81yOHuxSdNYYXnVpmhLbnNDqNbHbNQ71ajz1uI9sQr7qoHdz5kiwlYfTzJhkLbQj0GOHbkuR+xp6mgeuniUHkrqEqCCehBxb2s++bo8HLn8C9iI8P+TC+BJZWeGYCkcGGKci6ihaSrJ5dox2JPiWf/QtX8XHH75Ad3GRTz3jEfa61zvEU2a6nPEi2e4PqQpJJRRHjiyysbHB3BAub6zxS+d3uRJdwXEDuvu6LOyf54JNKNYmmEjh1ksqUWcuk+i5iPNnF8i1R61RIf0RjCQmgsBLMWvgRQHUKnpFgyDvYwaWD65dYDdv0nBijF+yXrW57dgRurWAD3z0CQJjWV4t2Jg8he5C3ncJshr1VoY775NJB+OM0FcF4SHJd37D2/jkQ+c5VV/iE9su9dq+6x3iKTeBNmcVRjchf/G//jKElnAAS7U6ZWUIXYe5XsTeXkzhSkoHjnfrHDy5xNJKj30H5zC+RHqSxsEGX/+Xv4Lbv+oYclBQG1W86fVHEb7FeBBkhiIS0yymBF1zKLsuS4stDh7r0ejWGE9yTClwhMIPQk7fcYjV48uMJwWVrtDDlGI3hqTCG1b0OjU67QipJFKq6cQXTyItyLSivz3hwx99HG0qlHPjfCyFfeG3GTP+wnf8W2RWMT6uWFytUYqSKAyYDy0Ts0M5DIjSirfdWmM+OEivtsD+qIPIHUTh4O/N8/rmn6WbHWFhtc/KsRG3r+zDTSpEbql3KpK9OtoVaBMiYonYlCzWWix7+/CKiMwkaMdhaEOKWot2824CZ5HNxLCnHTqtPrYZo9qGatEw79SY8x18LLgljjbIkcZrQS5GbGd9fuOpR8FoIu/GOSmdaXPGi+Hbv+FHMUJghGB+qUGel8wtNhC+w/rmBK2mvkSnX3+Ug4d6rBzosrDaBCURStBrRvzFv/AW5hcicEE2PI4d6U0NKJUAJRAIhAIl1LQCyVra3Tr7D3RRUmK1xSqFVIr2fMjxEyv4niJJSvLCUGEZTFKMmvofdTp16jUPYyylntpdIwQyEJx/apvdzRG/856HsELS6DSuc4Q/y0ybM14M3/Bv/zmtgzHqQEa3XWcvVuzr9jDCsrWzAZlDOvC458hxep02B9oNjhxWxIlPa85w5xHB3/v2NxH4Ps5QMj+OuL3m0vAK8shF+xLlCHShyD2QrsUtfTwbsBLO4bkl4z2fAyt7BJkhCOdY6izj6YCBssQa3GHB6GzB3naDahTRnXfoOBJbVtiwouhA5VvavYgHzww5v57xK/efZ323jWtujITRi9HlTJszAP7h//ghvHs1zX0lgRuBk3Ly6DxZCI9nfRwX6tsJdx7fR2ehTqSWoN1BK4mra9z+pi4/8INvZ3VxgploVvyAzsE6o6rGbtHGaQmCJQXKo+haCkfj1ZYwukWrvUBas/iUjJo1iiJiQcyx2m4QCIdSGvJSs7blc+EZF7mpQPo0F5v4xqEaVDAnUd0Spw1dOcfFs1tcXt/jQ5+8ws79DVrZ3PUOMXBzaPPGOTOf8YJ55ME13IEljC1ZXDLcHrN2cYdLT6xTlhWyMrhJxWKvTa3XYGGpycJCE9OQlB2HbhQRZ5qfesdHsJmGwtAfpASJwk8lPg7SWqwvabYiRAE2g96+OZQQFFmFKS1Wa/JIUgjDyTsOUK+5pI4gi3x2AoW2EldKHOGwcHSee950nFYzQmcVKIuwBn9k+OSHniJJYi5d2cWJLSK/3hG+huWGd62fcWPxeBETH3LxjhRoZ8RYDNmK1wi5SDGuKHxYOrLDathiK2mw0lqmEQR4scZuSxpRg6sjyc9+6v14foqSgi0cip5P3A5RHQ8fzWivRkGdSVOSRC6hWgCRkYuCSOWEOsO/qtgeR6zWD+A7dc72Wzy2scgnn2yjdEFQrxBOSKQPc6JzJ65TI6prgrykDAT6oOBDu/dhRcWl8TZl16FQN8jnfKbNGS+Sfj/HSoH0JKMkZ3cQ8/QzW/SHMTYpsY5E1RXzvQbxpODO2w/iBC7T7jNBoxUxHKT8j1+6D43ACxWVsUghkFIgIoVVCoTE89W0rUxJlpZbpLmhNAYQU98jKamyisNHF5ibb6Iri0gqRpsThCvBlTie4tjheU7ffYioHuAFHlZe801SDh95/xmMgcuX97ACsvwGaRedaXPGi6DSmtGeJY9dyqTL3s6EzdGYB0eXGNuCspWROBKz0KDZ8+k0Yo7O38PWYJFwWeN3c+b8klG+zfsffBhjDI5vqbdT5g+OcEONaHsEzQThOFAa5FjQduociObQuqIaWnKp2PICdmVEVcGt+xdpLJSUkabV2aUKd3Bagrl6SqctOLXvIKcOrxKVAdFegA0trXoMccLvPHKWpIB106fqWMYTdb3D/OJ1OdPmH3m2d/usf8JFnasIdZ3+pQGXH8x48KGrlEmGseCkhrDWZK7TRM2HvP41J3AXPZyOxFksGMZw6fJlznzySby1HDWyIA1Rq6IxVxB4kso1OF0PoorSbdI61KY1f4C4LfHmKg4fGLK0OubkiS2qpuTYyRXyskDVciZHBLtORhRKqoGHcFvcdc9+Dh49iC89AjyUV+HM52yuVTx03zPk/YQ9u8ehlR3mnBtgWMRNos1Zwugm4+rugMleiUwsjcjBFQplYXG+wak7D6EDCRZOHuzx6q+8lTe/7VbOnttis0wQdYeg7fJ3/4+vY3h1SL/IqGoOUhs2zu9gXAfrSvbN1xHGogVM0hLdEIiGwhQlq/u6rB7o4noKR4PIDUjBu37rId77q5+k3BhQhQqtQesKHU3NOzeGMfc9cYkT++Y4eqiHu5dPM6WuZG13wic+9hSri22W2iFve9XR6x3mZ7nRM74zbhw+8IGHSX1BGQjmVAOrS+qOYH+jTtu7nY1xCxEZ7jpwnJZ4LW/qneT+9XN4O5po7LJQePzNr/kWnsnPMNoTbAwazIUZorxCf1xDlIrlVgfhGioE/biitgFzucVScqC3yIHOEoOkgV8v0aFmkLu884mHeWbv17ht4RnSoUJ5OZUUbG+3EBOHJzcynrl0lm88FLHfb6GWSlwDRSx59GLJpzbPcLg1x1y3xte9+fT1DvOzzLQ544Xy737sXdOx90rQXayRViVO6LJ4sMMtJ5enlUFW8IavuJVXv+oYx44uct+DF3E9B+EJOksNvvsvvpUPfOBR9DX/AC0FO/3JtOJHChaWWhg5NcoejQuslCwf7GKAQ8fn2XdoHqvASoUVgqIy/NZ7HuTX3/MgxWfOJ61AX5uE5oeSi2d3ePrxDU7ffZBmp4YVIKzA5paLF7a5/1NnueX2VVq9kG/+zjdcr/D+PmbanPFC+c6f+ykST7JjfA6tROgioy4Fq/UFXnMixDQVxpW8/eRRvvr0Lcw1anz6yXPkWZ3aXMrRuTZ3H/hT/KdPP0zVrLAKcs+yG2dMhEuQSZYbkpENEG6JSCzWhaPzbTCWQ8ttDvUW8UXJZFTHKJj4Y9597mHe9fGnMOsKsZTSrzkEqzH7Dm7iSniyOs9GssurF45QzyT1Mqc2n+A2J1ycXODxS1d4y7EOdyyX/Jm3vPZ6hxm4OaoYZtw4/KOf/5fUehXeiuXAyRpjW+DORRy6dZnu8YhixWWrqvO6N9/D2992O02b8ehHn+L2I3usLsTccXeTb7vtNXzwfR/CWYRUeyQbGcZm1A5kNMIKfb5JMfFwWwVzMuPY0h6LnTZSSY7V6uwTPS6c7zK6EnD2bId1nfIbn3qUT953jmIISmq0lLRWJ5x+1SX2NVI++dRVhl7FXXceJhhVzO3GLNTGXA0DHr98gY1nNjl5dD+7raO8+d7XXe8wAzeHNmcJo5uM7/qxn0chkVZw751H2L2wRb4TU6Qll3Z20U0Paw3f/m2v49WvO8a73vcQTxUjzm3tcqDW5K999ZvZv9jmmSubiMoiSoujNTvrAwrfYBXUIh8rJWEtIPAUMtWEleXxT53n4+87QyCmzy8r8EcVuhS4lSHeSejMN7GOwghLr11HOxYtNElZspFmbO6OKZKKyHdRxXQ0YxA5dHtNHnrsCjuDjP74xhhBCtzwrvUzbhz+r5/4dYySuA3F6X37MMEGzXAbV8Z84uldxK6PIuebj7yJ1+4/yXuefpid+4ecv7LN6vEuf+OvfD0Huj6X1nbAtbRrOe0gJq3WUCNBpC3prkNSecwHTZwSysCyb9nhanKGs+MHONqc0PZLaFmCWzMQBcu9EY4To/U8pvCQJfiyCaVFVCmTccHTFwxrSZ9uWBIFCmqGMhc0nJCWDPm9ncvsxQmTeKbNGTcf73znQ1glcDzBoWNLbMcpI6HJdcmZT12Yto8pwTd+y93cdvsq9913jvPn1rm80efQLUv87b/3jfTmG1xdH2AdgfYlhWPpj3M0All3ybMKISTzK3Uw04dc2Nfhvvsv8NCZy0Q1F6TEuArrSrS1qMgjK0tqvsRKCUrgBwrtSJIMxpOUnd0JV9b6tOZq4EwrlIyAVq+GF/p8+r7zjIYFaX6jlOYy0+aMF8zlncsEnZxeGNBz5tkcVhhZYKuUx4bPXPPVdPju17yOA/vmOFM+wqf7W1TOGrf3Vvmrd/95luoBsR0zIUDNa3RcsrdbUcYSnXYxMsUYONqoI9wMp5Gx2Gvxe4+d49Gndyh0RDOKEUOBih3cPRdzyZJjWWolzDsxNvPRXolsa1SQsbNesjWOuTDuE5ysUzmKvc0meWXp9ubQHcWvPjpiN8mp3Sh2sS9GlzNt/pHGGENHbbByOKYl5sGbIx4VFKVmshOzbbdoLQ5YbCm++U/cS30+YiN6kO32o1SjlDfs28/ffuv30G4GJAslKQGy7qPLCWXcpxlNaFUhppXjRIb24irD0icNLTUj+dj7H+PKE9uoyqMsHdILLnLP46Ct0JdjZKio9rskRhEUGiUqqtjjxPJVBnrCpt5j49wOdx831I3m/PkFZC9l/6l9FHMBD57dJMsmtFv16x3qKTeBNmcJo5sIYyyjjQpZGO5oN3jioYtkSYnRmprnsFlqjBB05yLuecNxHr+8xbs/9hhjWZFNSjY2B7z91af4m//4v3FmbQenhFpmaC60SHoBQkNHeKwemAMh6c5HZLZEGUElNd1ena/9k/fi+g6FLbEIjO8gOw6+thjA7wTIUuOmlu3NEWpYwqSg14h4/a2Hee2bTiJdwW4AWiq0gk4UcuLUPtrNEANcuLh7vUMNTA/4b/SM74wbgzTNGXcVRQdO3BXxePwMeZUjKVmsSS6vRUSU3L3Y5e7eSd75u0/wwfsvUmxr0jBjr+rzhrsP8b2/+Es8+mjBRPj4PY+yXObchS6ibgkWQ/woYELA4d4cup/geBZCOFQL+arlr2SYCbJEsFnWKXc95tOAejggF5LMtnCiirKtuFwk1NopYTth/0KPO0/czpHGa5jEmviyC7GHLSWtdo1bOidpRi6lgcc3N693qIGZNme8cC5c2JoaUAvBgePznHlmjUKCrgyteg2jp5VH97z+MKfvPMwvvuPDPPTwJfIcshL2ipzjJ5f4xz/4X9gbpVhX4rU92o0Am5RIF1rtCIvBOILjt+3HIrBCYhAcPtTjm77hHoajbFqBZCzGwoEDPcbjFM9VeDUfqyTGlYyuGV4bKThy6wpvetsp7n7tUTY3Blj72fL5haUOd917GOlJLHD+3Nb1C/JzmGlzxgvlNx96gLjyKMcuK/UuD69fIVOWQaZY8LvslQFZ6fDNp09z6+I+3vHQL3B5c42ttZDJqGBcGepumx9/z08zHqToliY4KJhfMuDkOGOPwx1JJS2l9tl/4gh0DGalYtsU3HJkH9/0utPsDQbIwCJ9Q6Xg9a86ycDmuB2XRHS4dGYZJoLBluTSgwvsZYq7ji3wdW+6hXtvWeXcZBdii3Qr0rZirtXhNbe1iNCM93y2dwfXO9QvWpczbf7R5pc//Bs8td7m4YvLFM4ij96/SWkNVV5wsNfkyJFNjre2+K6v+ipuP7zCz37o51kfjYlTycQdsFn47G0V/NT7f4bNwrA5bhLsdwlONkgqidoL8dU8TlgReJrTSyvkns9G0iCPC07ffpA/8fZ72boSI+sCdw6aSxO+6itPM7EWvxMxVxgW8z1EYriyHfH0sMbvXFjgdadX+PavuJPbb9nPhcEW49Kjq1LmW2OWj+bcdkzRHYzo7Q6hTK93qG8abc4SRjcRf+ZHfh5bWmqFZDTKmWxNaPgORgvOPL3OJC4Ixzl//FUnqTV8fuQn3kuCoNv0CCw02xHdTp0zT16lKDViXCEnGhEI9uYUNo45sdqh1q5hHejWQ/JxiTep6BUexpE88NBF1i7tou10rLAJJWBozjVxQ5dzaY4IPBwXnMgn7zjo0KFKC55+5DIf/egTfPqJq5hQISV0Aknb89m6vMurX30UpcG+gIsx/+zf/hp3/+l/xTd9z4+RpsXLE/CboKd0xo3Bd/y9n8JYSXDMYyIGhNEWh5qSzYHHA1fWyI1m3+EBf/KuZVzp8e8/8nHiHY/6soMrDPNuE185PDnYQYuKffv6dGsjRnHE+BGf29rn+MpTC3jGw8aSgx1JvZ3gruR0D8Ju0uCdT1xlKx8zyj2cMZR7LhSWvNjHbtLi7BMaCpcQS1V66DWfdOyS2JRL2ZO8+/zTvO/pCbovqZRiuePi12HMGm9YPor1LEp/8Vh8/y/8HHf+wP+Hb/iZHyfX1csT8Jk2Z7xAvv+v/RxWCqKOy05/wqQyRM2AIjM88vBFjJSolsutrzqI5zm88z2PoK0g6rrkC4LewRYbm30ur/ex1qJdQRUqyrxCayhdwRvedAK36aM9iRYWoyTGAcdVlHnJxz7wFP1RjJYCpEUIgcEwt9QirQzj7QzpWESgMN5nk0L9UcwDD1zgk7/7NBef2gZACEFruUasNYNxxm13HcJKgVJf2CfFWsv3feO/5mtO/F3+zvf+R3T1AsT8pTDT5owXyA9/7J04jYraQpsL7pjEM3TagiSu+NTZC6zUBizic3RlP0IIHtp9iLA+oduBu45e5t55j49f/QTb1Q65ELgazMRlR6Xs7rTprzd48+3HsXh0eiN0GhNpgx9bgsphNE758GPPkHo5hatQixnCtcQ6obPYIJtkdFcu4IUgSxdjXLLARUvN2lbBbz30DB+99AS7O0My6ZOndbocYGc9ptwMONxqYh1L9UXO8IyxfNNP/Cv+2L/9P/mx9/+nlyfYL1aXM23+kaWqNB9/+H/TXJrg+XPsXh2h+5qWUSSbKR+88gjjvTr5hUMsHZlHa81DGxeonJzx3jxbRZ3bjy7zocfew+akIvBTGuMJ3fkJxcqIdiNneAVec+8teJnEVZbNrRFzVcGiiXEdxXoS87FHLxJnJcYDebzEPZlzfiOnu7/JOB7irsZUvo+u1XEcjeoYCmHYemzIe97xGB+7/DQP7rn0VY05p+JYsZ+nPpRRSxfxGh1sTRL47heNxVd//w/xmj/3Q/zc/3zPyxPwm0Sbs4TRTcK7H3iMB9Z2EKaCYUVVFBw/MM+d9xzhttcepOz4ICVf85W38We+763UagHWGqwjKDY0txxY5JvedAe/9eFHqBxJ0nKQWlCPJHvjDOFLrOswHwRoX4HvMCk0CaCVJN0Y845f+Ajv/rX7efyRy1gLotBYaRm6hn1He4TNiEqBdQSHb13irW+/He0phLAc2T+PkIKnntphkleI0qAmmlB4DHZj8tKwfmEX3xMsrzS/YCzGw4R3/tYjoAVXkoK//A/+y8sW9xs94zvj+vP//8n3sPlkH1UrKDpjpIDXLLY5EN7G/vA27nt0lQrFX7j7DXzjgW/Fkw5ODo6uGC0I7ji8yne/5RQ//M53Uew6JGshqpVztG64uBvjTnz6SZ0Dyw5yHKByycV+woWdFnHqMVZX+FePfoDf2vokW+OzBEFJM8yxOfSfSdnf2EdaNbGZwFSSE/Xj/PGV09hM0XEL7loJCd2Cy+k5sqKkCix5r6LelQyqCYWtGJst6r5mpf2Fy3fTapePPHaRUiqeOhPzL37tZVpgmWlzxhfnb/7d/0qcTlutx5nGa3qcvGWJe27fz6tuWSHeTEHAD3z/2/iz3/lGylJjhJxW+yjB0btX+Zavv5d/9c/+NygH7DUfpG6NvfUJyk4raxvdOiLwIFDsDrPpgDQhefyxy/zkT/4OH/rQGS5ujNGBRAcSIwXnn9nm6OEeQeBhhAWleNVdh3jTm49jXEHlS1ZWOmhjePzMGoapWbaWkqgRMBgkxHFOkhUIT9HufGFtXr24y7mzu2hX8elPXea33/XAyxb3mTZnfCGstXzTz/5rKpWw0BqRpSO6JuAOf5l7bznEH7t7ke3uNuuiw9/5iq/ju151D1mVsdCasD1p4NcsbzzQ5Y1z38wvP/Vb9NuSySTEDB2WTBtHT6jVM7QLePPUFwq8TkXhj6kmCibwsTNP8R/e+34efPxhduQWWgi69ZhWO+bTz1zl1L55AkdSSU1nPucrbznE3fsOkjfAHQXs70YkleHMpXWqSuA6BbVDQxq1nJ0qYa8ssWmdWsvS2rf3BePxyaee4fxWzLCM+LUPPMXTe5delrjfDFUMM64v1hb84//8V3j83Dz0Q/y1LRbdiNMrq7zudYe55yuOEN62TRH4fN+3/Gm+6o6TXO7vEbsergur+wy3zh3hq5beym/dfx4/KhmrgMmWi3d5jn1zuxw6tosnPRaLkJ3FiCtzDUb1iskiXK1qvOvjj/Iz73gfv/uRR+kXCWXqYD1FP2/w5PgixxoRZWxItEdtv8/bX32C46cUbz54kRN9Ta2qM9Q55y9dwRuluFFBvZMySQpyB7Yv7nB8vmJpIWMv+8Jae+9vfJqhn7N8eMz7H3o3u8PJyxL3m0Gbs4TRTcK//V8fQOYGFYMoDL1mk7tffYhjt69y9MQKo7zEBQ4t9RjuxVhryS0IqxFZxd/5nj/On3zLXfzIf3wvFpBSousuq6cPomseVgO9iNe9+TYOLneY7zX4rm94DYVjsUpS5II0Lrjz9EEOn1wCDNaXyNIgStAVGCXwE4PIKtquz/mn1kFBIAXJuCCIPNy6oqortCMQSrBwqEdU9yjygiTJyCp47Jkv3PbSaEX8pa97FVQaYzRPPr3OhcvbL0/g7Yu4zfgjyf/8nQcpAkHWFvhhzmqrxenOrRxvHGG5Ps8orWg2K+bailE5YncUY63BDwu0Y/mXf+47+OOvPs37Pn0WlYK0LjvDFsPxrezutsi0ZHe4zK2d49xxZIWDCw1ee+QUciyprMPFOEC6KV9z8AQNr0PLZmjtoFxLHgo2E0MoLHPdCb6bc/tixdXtM9hE0uo6ZEzwcheVRxRDH5GDqRQrcomeUyGLlJEeU6aWB56+/AVjETpz/PE3LiOlRpUlv/TQo/QnycsT+Jk2Z3wR7nvoMkYJtJiOqd+3r8tddx7gjmOruMqhMoKgrdC+Ym+U8PCZi1OPogAqR/LTf/07ed3Jg/w/7d13mFxVwcfx77ltetmd7SW7yab3hJCEFkLvUqUpoIBgAUFEsGP3FRUrKmBBRZGu9N57EtJJ79lke50+c+95/9gAWXaT7CZb4XyeZx6Sufeee+5lfpmZM6ds3RnFMXQwdXQgDxe67Jh3KOS3mDa9goOmVzKiMpdJk0vJIpAIou1ZHF1y3InTsMIWjlvHdplIwElDPJbB1DreOx0BgUIvK1ZsxzE0ArluWmNpdEvH7TY6XsoaaBpUVnT8AJPOZmiPpbBth1dfXb3Xe1FWmUfx2DwwNRxD8PObnyAe66d5j1Q2lb2wpUODVotLy9CecJGNeZhRWsD00aVMD1eQsmoJ6e2M8hcQjWdpiSZZUPMkeGxMzcZwXJw65hby/OW4AnUgNCQamJJAvkPaEMQzOiOKcphSUs7sohLGeEaQ6yoj45HE3Sb1iQSOrnHM9FnYmoWtC1pjfoIySbY5jowK0prOtuoC6pp8GHkWW2prye50EyBEvFnidiDsduNudvCkU1gJmFWVR8bOkojbJFwJokLy72V7z+accVUEGgM4zQbtSYvrn/oV9nuz6/el3uRSZfNjKZ1NYue1kju2mZ2Oi3jCy9QpIxlZWsCcGePYmdlA23Y3B+dOpHVLE8lEirc3P4HRZLBiZTFmppCvH/V5DFMn1eigS8mY3FYiFZL8cjcrN+bwxtZySsZNZuz4cibllTA7XE7E60G2J8lLpWhtjOJJapx07HRMt4PlT1OW00IoHaepPQ5hH7kBQdoRtDdBU1qjXU+SlZKQmUvGp2MaAj0TQBaCY0jMbJY5VSPYkYqT9erU1WssIcRjKxfs9X6ceNpMsq1uNjeGWNkQ5FO3/rp/bnwfZlMI4RZCvC2EWCqEWCmE+H5fVFE1GA0DyUyGnTUJjAQYEgojPg6aWEYmnmH6jAoeeGE5UmicdcJ0gjlekqkMT760gkQmg9TgqFljmTixjLzCEBnbQRMSPQtayKBwUhEpj4WVFViGoGxELnc9vpCmHa3Ubm/G43TMuSBNScCl07ijGU3X0RyHiMdCExrSdsgkU7h8BnYmC4ZgxvRKWhNJXK1ZdGESzPXgtMQp8ruxTBMtDbYwyLVchEJeDp8/noNnj0bPgt2673eqy79wIlMq8rFSAluHa7/5n36590O9xVcZXI2tUdqQpCM66BpjQvkcFClHihjzysdx74LVoAu+cvQhCCNCezbB319/m1Qmg5MWnDN7GhFPDm4jQtoELWAj/DZmay6ZulG0NXqQEYOI20WBN48HFy+hQdQTox1/eRLDchAxKNM1Gts3YeDFLzIcVCXQTYGWlRS43KRigkZcRG0vxcHxrGs2ESNTxO0cLC1AKK+FEbkuLF0ja5m40ho5RZJ8n5v5JUcws6CKtFfQYOz7Q+wvT72cynILIz+DTNhc8p97+uXeq2wqe/PWgjU4mkBqAmkJxk0rYdTIArJZh2OPnMA7i7YgdMEVXz4Bl8uguS3OfU8uIauB5db41CcPweu2yAl6cSxB1iewTZ2yEUH8AQsQOC6TivI8coNuHn1jJVub2mhriaPrgAZZAS6/h2Ubq/EGDBwhqBiTiy7AMQSFxWHi2SwOAiEEI0cV0JZM41iCSMRHIOzBNg2KynPRtI6hakIT4DGIFAU5+eQZjB9fitQ0WqL7npD+r/ddiwhYZH0usprGb377RL/ce5VNZW/+8sajuGTH614Ki1kjysm1gpgui+NmV7GstQkpTc4bfySugIuGeJSFjW+ytTlAIpXD56bNJ+Bx4fNojA3tJNtiECqMcujEYiLlUJbXiq0ZHFqZR3GBm2Xbl9DSUk28LYZlZIhj4Vg6Pq9gcWM1oaCJ7rIpdpUQbfJj41AZycHtbSHW5CHpuJlYJXDcTWg2jI3kUxDOoG93UWoU4fh1Eh4PWsKP3eyi1BfkwnnTmDQpQFFOM+ls9V7vhxCCp356DSIpsH0GPm8jL258uc/v+3DoxaAMrr+8+XuStkZzgx/b6+LQw6oQbp3AmBzGTiikNpFBSotxJdPxBlzU72zl9Y0bKfBEKQwaXHT0fAJBD02pWkpLWmhIeWmRbuYfPIeCqVlKipJksyaHHFRJbnmYsPMy+fpiRCpDQDgUeqN4Wmx8usX6ZZuI+AQ+I8MkI0C62YUnmaSkJJdsMEl+eysji5LkVYaI1qao3Rqkclw5eTlpXGmdUTke4i1u/OEs5WMMsrakrDDCqefMIqeojCbNzTvbW/d6P3Rd4+mffwVX2iYYipHKr2N7Q9/PF9jH2UwBR0sppwHTgROFEAe8HJxqMBoGfvj0U0jDQTgOo/x+plYVMfeIceSXRbjzXy+Ttju2jS7P44SzZlFclkssmiTthXRA4+orj3u/LMejk0EihOSiYw4ioJsEvTrClmSaMlxz3T/Y2RgFXSMY8mLme0AXGI7ANjTGTilnVVMLqQIPZo6LjLujQalRZtjU0Iaja2g2+Ar9+AuDOAIKi/3ojsTjMcFlEAiaSI/AFdA5cuZIph9cxdxDxjJn7hgySLLs+0uppgn+9KOL0dIZpC7Ymoxy/2Nv9e2Nl4Aje/5QPnZu+OKfsQ2J44Hxk01ml0Q4pnwC0Vg5Vz74CMm0gbQkI3O8HFVwOGErF8Olo7c6aHXw+enz3y/LaNOQUQdHwuWnHU4gx01u2CLpFWzS4lz+xD+pbU7hZEyKLA80hsmkDYRPYGg6pZ4RLF4iaNiUh0vkIdt0DFuSaoINtTatGYugN4svalGkB3DZNlURN6Yu0IWXlO0jbLhwt0siuuDoaaOZVjCZSQWjOaJ4HDIqyKT2PSeRJjT+9qXL0bw2BoI1K2p4d9vOvr3xKpvKPnz3lw8jdcAQjBgVZszIQuYdPBY3Gl+85k4cHTAFPr+HTxw5hZyQD5/XwtEFKRvOOWnm+2VlPQa2riM1uOTCeXhzfFheDSGzrFq0hRuv/yf1PokMaRQUBdH0jjn+sHS8HpPS8lzqW9MITVBRHsFGYOsaOoKm5jhSE1h+A01ohCJehCYoyA9geUzcXgMhdKyAgdA1Qnlu5h42jkMOGcvUqSM4eM5oEJDJ7PuemKbBzX+6GNut4xgaj76wiobGtr698Sqbyj78a/Fr1Lbn0BoNUeIuZ9yIPI4ePxHN285nH/kNgdwYHsNHbtDL6QdPwuu32N5USUPCj+2GM2d3ZNPUXbRnPZjuDC7T4ZMzJ6PrIZrb8iA3w2vRl/j5a78lEqoBK0hhJIKteTHjAltAyB2kvMhLKrcOkWMzNbeAbJ5NVANX0MQOtGIUJfHmGjgpN6Y3gO0TjChwEyow0CIG2bSDKyQwXA6TAl5mjR7NmXOrmFReyFT3VJJpqE3E9nlP/H4X15w6n2zcZNX2Mv6y4Tba+3JV0t7msgfZFEKcKIRYI4RYL4T4ejfbrxNCvCuEWCaEeE4IUdF3F6T0h9jONyjIJBE1AUooZ0xFDsfOGk9LLMaFv/grjRkfBbmllJXlcOxpB2FYBi5dEnXr5JU5HDNxHAC53kIaQhYFgXZk0qJoTBUJTwlul04oN8Uvn32VX/73FvIL64lEysgfVUCNns+65jxsV4r8sgD5ZT7swlbipkVJaQX1RpDaapu8giDbsdjREiQTDTFz3E7Kch1c0kFvdePKaLgsEBkdr6aRqLew4qMZWzGDUw+aypjSEg7Jq6Q41UoivW6f9ySSF+SIylGQlkhN8Ounv4J0+nBoWh9nU3Z4r4LmrscBv9mqBqNh4IVX12JnJZ5GSXttG/U7WknbDkkcXluxHYCzj5lCcVkOhqETzvHRkk5iuw3y8vwE/Z73ywpHAqRyTbJ+jTPPnI2Z66ZOZiALoBEVksriMMceOZ6RYwqp8zmkghqO36I9ZbP49fUkLLBNHSstKfG5sH0mjdksjkdDCoHt2IwbU0pCgJHrZe6YEaxfW0NDS4KmljitLXGkDjIrKcgPM/OgStIZB8sywKWhmYJMDybkrN/ZygXzpyMdG8fS+Mlfnsdx+rgbr+zFQ/nYWR1tJ2Nk0KenqZdJ6pIx0jZUtwmWbG5DZOGGY2dQ5i9ACEHEFaBmSyuuVphQUETI7Xq/rFws9GoDq0VwQvFk7HpB+9YkeNOk4zrVq6EyEOKi6bMpd5eRXZDCW52mtjHI5tYk1akVtNe5aU+7SLvT5Bc4aAlBbX2SZJsLJ6MRj9qECkaytdGPjIWYMT6Xze011KTibE+3k2iPo2Uh6dGpCAaYljeSRDaN13BjpnUse+8T676nMdXIJcdNQMtmMJM2Z//2L31/81U2lb1oT4GjCRyfoK41TmM8juUyaW2OUVuXwNYEn/nCPKrK8hBCUJDjZ0ttM44pmDuzEp/ber8st08HTaAZGtMmjyDdliCbomN8mG1Tu7mFwhIfXzr/CCpH5CGzWYR0wJHsrGtjw/p6bClIeTRimSymX6A5kvq6VmxbgiZIpB3GjSrCsR1cXo38whCbdjbQWB+lvqGZVNRGAoZlUVoYZtL4UpKpDLkRH9LomHepJxKOZP6Zk0ntmk/p5Ktu6/ubr7Kp7EXWnUE34li2mx0707THUoTCLrZFG9kZTZNs83D++BOYXFgEQKkvRHPrDtxph7Mq5yHEBxPDC1wEvCkCumRuWTHN9TU0Rw0ybS4KrCaSzruMznXx1ennM8qfh2YkceclSQds1mmN7MxsJT/YQtAVpbHFQNMNMAXVre0YrhgBO43H1UpleAzjqrYyvSpKNGayYnuMhlQ7dbE22qKSkDeBmeMl4slnQkU5aSdNYTCIS0jC3p4tzFIRKeDIsVXItKRuZx5XPt2lDebA9CaX+8imEEIHbgVOAiYCFwghJn5ot8XALCnlVOB+4OY+uhKlHzi2zabmMG8sKCWbcNGwIknjZofcoJflq9eSzU3iLkhz/uTDGF9aDEBBcYjaaDuGN81Vh5/2flkhn4neJtAQjAwVcvZMD41r32X19hzq6nLQihJsam0kUBrmjHkXEs7xEnK34ctPoZUJ1u1oYGtNDAKSQCBK81Y/nkgaj1+nvj1BVksTLm6nZHQjZKYxcmKUMVU+2tpa2dzcRkNLnMZ4DFGSIBnWyYgixpXkMraikFQqS0lJBF3qiB5+pj1h9kwmeUYhWnV2Jr1866Gf9u3N730284QQC3d7XLF7cUIIXQixBKgDnpFSHnCPCtVgNAw0pbIYAlwOmALa6luxdIHLbeIYGo7mcMyxU5k+u+r9Y27731uYGckdX/okpvlBIAqCHvSsxJWA4kiQB59bTEYXOG4BGQdpavzy+rO58ozDiEVTWO0SLe2Q4zPwCUFjc4wc0wJDsDUeJ9IE4do4DXUtNKQzOC4dw2MxsqqQWDSBdByqyvMRjqStPkZzfYy0BD0JrphDTU0rTtqmoaaF9Wt3UF7ox3Zge13jPu9LRVUBl1xzPNedeyR6MguWzuxLb+nTe6+67yp7IqUkk0wjdIHpcygNRnFENX5D0JpoQEsL9IzNkWXTqfCPAaA1FuPxBWvIBE3+9MOL0LUP/gl2e0wyYYF/pInf5+KJxWuw23RcLWBaaXR07r72U5x5+CQa02lkRKBrUFbgxu3TWNnm4M0FTcKS1e0Uz40TnhHn3a21tG6z0VoNPI6fsTllxNMpDMemKDdMBo26ZJKdbSnslEMqDKmwpC2VxtITbIltpybRis80sbFpS+z7V88J4TFcXHU2F588HnwOWkbnsCt+2af3X2VT2ZNYPIUjBLYuQBdoLo2a5nakdFi6ZhtSgCYEx86dQFVZHgBvr9zMki11SJfgF988p9OX0qQhcQyYcVAxmUSGBW9vwZFg6wZZ00Q3dB77+mUcVDmC9vYEjqYjpSS3wIfl0qlpaCMbhnQBLGipwcpx44R0li7dgm1LHF1SVBxg7KhC2rIZhCkI5HhIW4ImO0tzexbHACGgvT2OZkA0nmTNxlriqSyYAscQxOP7npPo0CkjufaiYzj4sArSHo2MJTj+xlv79P6rbCp7srp+J2m/iRQahjuLK5imOtZC0k6zaMs6REZgSY1Tp46nyB8A4OXqp6iTdbg0+PzsE94vS0rJjgYv7W0eLhx9LNsTO2nNrsbWQQqd3JwE0jG5ZOw9ZFPF7HA14gqkMfUkBT4PFg4bt6YYqbcwyV9LHRswUi400+SdhpXk+xvR3DZTi8sZXRghGXNwGQ5ujwcnkSFlR6lJpdE0gRbO0ChqCQfc1LdlWV5TizQhVhsiWeci6+w7m8dOH8tNZ51KlasIzWuje5v53dLv9Nm97+NhL7OB9VLKjVLKNPAf4PTdd5BSviClfG8SwzeBsj67GKXPvb7lZWJ+H21uL95gGiPjkM3sIJPJsN61nUAGRnr9zDloFKGwD4DXNtyN5WomPySZVTnt/bJq4zHSHo2aWj+fnHsab1TvoJZGCkNtVJj1TAnUUJwjOXvibdhpL/H2OjQkjuVg2DmQETTVNZLrSXHQxHpq9GYyzV58OTYLtr9KsKid4rJ2Dhk3kVH5JWxrtEh7vVg5IZpas7RqWdraWjG2ZKnMjbKyoR47q5FoTbBu3U6soAchBS7Hxu5BR4Pjj5rCt6+6iFB7Dttrgizd2s7Lq/vufXM/stkgpZy12+P23cuTUtpSyul0ZG62EGLygdZRNRgNcY3t7RgZBzMtGTsiwmGHjeWgQ8eyfUMtv/3TUxhZhz/86HymzxyJtuvL54OPL4JoFq8mGFWW36m8d7fVITWHbGOSl196l+Z0FkvoFEcCWMkkOV6DsvIIhaU5LF20Ed3VsVpMbjDA+Knl5FVFOGTOaNyGxoSifNpSaZJSUL21Cc000F06l513GB6vxfaWGCkny7P3vk3NpnpsXZB16yTzDIQDUkDdpnr+8+eXWPbOFtat2EG2MQkW3Pvc0h7dn1DAw6cuOJwX7rwG4RGkpWTaJb/ou/8BQ3yZQ2XwvPH2OrJuHZmvMyk/n1PKxzMrdzQ7Y6t5cv0yCKZ58erPMTb8wWekHz/6PAmPTV7Ewu9xdSpve3UTPmJkCppYuHktmTYbb8RDmcfL7LJtzCx3yLGCRMwwixo24MpqHT0jAh7Gh0cRaCnmvCnjsFwas8aMINMUJ9vuUC1rsaWJH4MfnHQKHsNFfV2KVCrLzraHaEpvpSHqJt1sYm6VYEoMB95t28C/Nj5HdWIn69tWkcpPoUnB+uqeTTAf8Xu54ZhP8sT1X8Bqdog1J5lzWR/+KqOyqezBPx9+HTQwTJgwvogj5o9nxuRy3l65hQ1bW5Gm4IkHvkxZUc77x/zpwVewDcGI0lCnxiKAqA1pn8bSxjreWrweiSCY78IXMsjmWJRNLMLttggE3Cxcthlp6AjLJBzyMnVyOfleL5+eMQHhERw5uoJoIkMmI9lc24QmwO3RufH6U/H7XLRnoU3Ck8vWsH5HPTIrkI4ENKQmyOqCN1Zu5l+PL2BHQzMvL1pHVhNIUyB7+FrPC/v5zTfP49bvnEXW0NjcnmTiD/vwxxaVTWUPblv4LG3Nbgq8FodU5nNY5QQOrqjk2Y3vkhPZTLE/xvOX/pR8zwfZfGX7E2Rt8BrTP1SapD6pk9RNHtm0hjc2bCOZthiX68PvtaipyWd8+CBcpoHPb7Bm4wYC4QR21kU+AeaNKsalh6m0T0XPGszIn0bKlSRtpYil1uNzS6SVz+VzzyTgM0lk09RFszyzfiWbE01kLAG5aaTLIZWwyGZtntq4nH8veo3qxhae27aIpM8hbVj0VGFugHuvvJovTT+dQk8DjfHX+eFr5/bNze/90t1768VQCuy+Csb2Xc/tyWVA/0yapvSJRTueIeFIfJ58xoysYtb4cmZNqeD+R94ix4ljZDTu/8rXyM//YCXrhoZHmG7WUOWd1Kksn+YQcWLMyK3hH88tZNnGNaTTFuXF+RRVpGl8y8+8MacS9ntIZRKs3VKD4dKRLS4K8TFz5kjKqlwcObKcXG+KitAUnHialhaHhLOF4ook4YIwh48/n9ZEjGBxHZl0A4uWvkNzfTsyIwlUpRhRksQXSpDxJHjmueXc++BbbNnSwEuvv8skVx2TvHtfZGl3RcVhHvj2t7jsqEmMLa1jc/vdPLT44r65+b3PZg+LlS3AC8CJB1pF1WA0xP3kkecQCY3DR5Rx5OHjKS3KhXSWBQvXYQsdS9OYOXN0p2N++ffncTQ47sgP9w6FtCYxEg6aW+P3f3+KjAbTqwpIZFJQ6Gfi+DKWra3mb3e+xKP/W4LVkEG3JXgE846fSrzCzeNr1lHu9TGpKJ/ckI+s36Q9lURE03gRXHjeobS0xLAFSBt0n0ksnsEKWpg5bqyojZ6U6JbGEcdPoXxcEaE8L4vf2URrdTvClmzd1tDjeySEIBjws+hvX6OiwI80HeZe86sDvvfQ97+U7mvMtzJ8/OnO53BCFueeNIG5BRMocueSESme2LiaWJuL4lyNsnBhp2MeeWctRhLOnzGlS3laTKKFQLQY3Py/x9CjWU6cXUEiaZNuDDMubxRLN1Zz85uP8/CW5ViT2klrkiInwPy8KchlDo89u4zxU71UVJh43F6aWzykk3H0SJJgsc4JEydR295MMm0Ry2jUJkMIPUbAciOyXhIFFmm3jtewmJU/ibBrBC49zIs7V5PNi+OYOgs2bO7xPRJCo7w4n8V//xa+HBOn1ebCa/98oLe+o2zVi0HZg/89vxzHFFx41sEcNLmCgpCfqJPl/qfeRgooLQ/i93k6HfPO1nocA8474aBOz0spyZoCxyWIAb+7+2UcAZ84fRYZHFwegykzKlj+7nZuueMZXn1zA9IAaTtMnFzK3DmjiTVEeeqR5Rxp5+NJW2imQMtI4skstq4RKQoyc1IFq7fVYfsEaR2cjCSdlQTcAkMXSA1sISgtjTB5bBmjyvPw+jwdDUaGwBGCzdv2vnz37nRN45DJVSz/y3XIiCDuy3DZU30zQb3KprInW6JLyffEOW/iPE4Zn09+MECzjPHYmhcoCbQxr+ogdNH5q0nE8zqTIzv4yqz5nZ5P21F8vhSaIYnKOP9Z8wZpQ3B61SGkU5K2eAE5YgJbou/w81fu47lNLWypyycqDQ4bV8HcKeW0upp5uGEDTvw02pptnLiOHkiyo85Fc8LPhIhFVaSIVTsWM6awhmJ/LZYrjZ3JYtlehKMhEoJUo8no/DFMLCpkVGmErISnF26ntj6Xbc25xLI9mGRsF13XOH3cCfzssIexdRcpVyuPbPnjAd/7vu7F0OPzCvFpYBbw8wO+CKVfSClxsZSJubWcOXcWs6aPoDAnxKq18E7zy5QVtPOdk07p9GOKIx3aUo24vA4Xzv50p/KaE+0UFzSSTgoypFm+YzGBkgQzc6ey0+1GK41QHtFoaX6HX//tIbbXb8PvxDBSGY45ehIzjiunvrCeBxZl2LLlk7RsbUfTHTJ5koadLuxmN4V5IwnnBtlW8xhHFtfRlrLIxEC2Z4jkgRHO0qy7aJUWp02dQtWYQkZU5VOfifPyisW82xzh7YYSnF40wOi6xsWHXMqPT/szlmYQs7expPqhA77/fdn7TwiRL4QI7/qzBzgO2PtSjT2gGoyGuEcXrSWD5LSjp3PuhYdyxAmTOeW8uby5rgaRznDB8dNIJzuPj3bpYMSzlBneLuW99afrECEP5aW5bG2Jo0uHi045lLZolrgFG+paEFmHaVNGMHlGOeTp2B6NkhERdrRF2bamkVTMYUNzK4+u3UigLITtFmQ8OiIJ8bjNuq11NMUT2C4DaZicdu4hlBaH8RkGPpeGkDpokokj8ykeEeG8zxxBXiSIx2NimgZGe5aVq/a+qsSePPLzL3D5Jw7hilPm7NfxnchePvahh2O+lWFi7YptEEtz3vSDuWLSfKbnTeOYolN4fGkMtyfJzcdNxnY6f0jUdRuzMMZBh5d0Ke/JW69GNvupDEVobE8hfDpzJ46n3nZYsT3Cgo0xcEnmjRrBzJxijDWgJ6AoUMk7K1MsTWVolZIli5t57Ik62jKlyDZBtlWQyJHUtWRZuWkn0eYMIi3QbJ1ZefMoD4TJD9iEdJ2M1NHScOyU0RRbZVw6+ngssviFHz0gyORmefzdVft1v167/QY+eexBXHjyQfveeV/6OJvKR0t9ewYJnHX0dD53ylzmTRrFxfNmUl+XwTYE5196aJcPiSm3IOWBkw/t/E+yEILbrz0V4YbSPB/JNAifRqQ0SNIWZFOS1etrMCydKRNLmTi5FCRIQ2N0RQGbl+2gqS5KOuOwdMkOlr2zmbBmdHzwc0HGI6hpj/LulhpsHNI+cMKCww8ZQ3FpEMNn4vLpIAANTjl5GuPKCvjc2YfREk8QzvEgNIGjw1Ov9Kxn7oet++F1nDlzAuePm7Gfd3w3KpvKHkgpaSaBrmU4Z/IhHDvqVA6rGMXlkw4h7KujJeVhqvfgLj3lVrUVszYZYU55VafnXUaQ80d+itLcVsYVxxF2GjdB0iGdhMyQCbazJbMWkYkwwTWWuQUjkI4AoTFlQjHVtSuoKF+OFDHebl7O1tQWigqihDIZgkUO2xvDRJ1aqlvW4/O0kbJd5PoMpoycQG4gTDCcxec38eZEySlo57gJsxhfNILLpx1NUsYo91vk5LTg9qbYVLvvaRY+TNd1fjj7cSaGj2RS+JgDuve9zuW+s1kNlO/297Jdz3UihDgW+BbwCSnlvsflKYMinWok1ZZAxjQuPOIwzj7sUA4+dDQXXDCHaEanuTmA6QQ6HyShpd2kvt5NJDfSadPI/CpOmTmTqoPb8EVayYu3MzsvjWtEI231Llz5Tby9dSvCKGFC7hiqvNNwGw6uIIydVsE7m1ax1c7QbGdZF3+DppJqND94XTbSNGmIWuDaTG1sFZo3SX0mwLgRxcweW0K4MIgn4iHfrZFKSbJRmFk1h2kzR3HR+YeDy8BjhqhbHyThaGR7MGfuh3k9fs4/6EUivkMZGTryAO48/ZHNYuAFIcQyYAEdcxg9emCVVA1GQ57tAl2DWDSJYWjEWpO0Z9MkMjYaGi5bsuKt9Z2O8eX6cHstPnXGIV3KM3SNF3/xBYSArMtiYjiXsWV5FFkmPqlTWZxD3ZYmXn92JZGwD5/XhRGXNGxsIiQFPreFlRbYbkGzabNk604yHo2MVyDcOsWlAQI+N17dxJA6bktQVpnH1d8+HX+Oj2xGoiVspIALT51LtD1FPJ5m+sEjOeeCw5gxtRzbELRleh/g93z5E0dw6fGH7vfx7xGAkLLHjx7Y55hvZXhwHImZcnB8NjtirTikkELy1NLtxNM6BUEHl9lCY3J5p+MKfDq5Xg8zSsd1KbMoP8zTv7+KHTsliaiHw44uY1pxPjlFBr6Qm7HFAeJ6DVviS5hZYqEFfWTCOjszW5kwxsJlaSQIYDZrtK6P0rS9FU+DxKnW0OIWE3OK8PvdBCMGwgWeoMGonImcW3kpTjqANLPIYBqk5OSpU4mm0rQnUxyUN55PjZlPlScfPSZYv75lv+/bDZ8/iVOPP/Avpf2QTeUjIp5IIg2BYwo21zYRTaXxuCz+9O8XkQKCBRY17TFW1n7QFd12HNx5EM5z4Xe7upR5+NSxPP/DK2ld3Y4mBcefMInxIwpwhTR8YZ3SylzqWmJsqm6krDKC0Dt+hV25sYaysQUIQyAAqcOOuhgpQyCEIJMCXWjMmj4Cn9tFJOgnVSTRKwVHzxzH1efNx9Q0Uk7H6iwIwbRpFbS2J0gkMxwxfRTnnTabQL5J1qXxwLPv7vd9+9X8T3Bc5dj9Pv49/ZHNHqzG9BkhRL0QYsmux+UHfCFKn1tZvwrbMPH6bBoSTbTE0xT4fHxv4a/RPBl8cjRb2uvYHv+gcSWaacaWAYo8xV2GigKcO/5kbpv3MxpFNcGcBCeMPJZJeYXkewUFmpuwXsTGhiwb6lsZWxJBw8HjT/DWm5sockajOxoZodOetVht7yCDi9K8Jkx3O9KtMad4Jn63i/K8mZT5W6nwmJwybRKXzD2CrDRx60kiue0Y/gyVRUU0JhJIKZlXPolzDzqEnJDAdKf59YK79vu+XTj6m4wKHVg2e5vLHmRzATBGCDFSCGEB5wMPdzqnEDOA2+hoLOr7tciVPnP/kvvZ4eSwuHYM8XiaWEuSwrII33n6h1j5cSJyEhsb26ir/2BVzVRqPRtqKzA8U7st87CJ3+bMWX9mdWs97ySKqci/nDIxC8PrZuzoLMXuHBYtbaZ+ZyMTJhdBGDJhyWuPLWZ8OB/bceEz0pSEWtjS+C5eTZLBwh+MUzgiyuyKOQgtQHn+PCLuOJO9uZxwysGcdsbBmK1p0gkNXejEoiFyPKH3Vx08etYYzjtxLprQKErGeX7N/vcQOmnCjwn5c/f7eOj7bEopl0kpZ0gpp0opJ0spf3BAFdzF6ItCuiOE+B7wOeC9CS++KaV8vL/O91GUzWZx75C4G23ISta9uxPd1Pn5rx5DmDonHzmOcz5/DG7fBx9wG1va2BqNMnJcGI+36wdfgEDYS3UyhdQkx1aWct/tz5Fe14jM8yEdiWUZnHLOwSx8cwOvtG1H4FBd10ImK3nir1/mrgfe5InXl7OuuplUxsadcEDCly45ivPPngtAe1ucv3/3PMoqIqRTNiOqChk1sYT1r6wmnaujSUlTU5wjZneMU1+/ageWLjhi7lheXL8Np+vngsHRu0XX8oQQC3f7++0f6sLb3ZjvPugK1XMql31jy5Y62sa7iR0EGZFhc3sDXsPgNy++iZ21uP6cI5iYOweX/kEG739xMU0b0pwycwxCdN9WHzIDtFWnMQzJ/EM8PLPzP4wYsYlVm8uQXknEo3Gk90iWNG4haVeT1WFDez3zPZN560df4ZfPvcTLi9ZTvaOWWLtN20gDA8lPTjyZMydMA6A13cqTXz+PfE8+tnTQPRY5ehmL41vw1eg4wqExFmXmyI65l1Y0VxNyuzlpfDkb3lhGttns/xvcE328IOJgU9nsG0+8vYqsBkKHeCZDdX0rXo/F8+9sQmiCr112PEdMHoXH+uB1fPm999Ni2vz8+MP3WG5uwEs6CZomCFaGuP3ZN2mzwBOFjO2QE/JwwrxJbNjagJAgBWzY2sC8w8bz8ONf446/vcCrb65jR2OMtoSDo2toEv7va59g/kEdk+LvbG3jyUsvodDvBwFavuDvfg8tNQk0TSAFICVVlR1zEy5au50RRbnMOXwsjz757tCZEqgPs7lbz9zj6HjPXCCEeFhK+eHWsXuklFf13Zk71eF7qGwesFe3rcbKJMk4PrbWx8i6TXxek53tOzF1ncvmnMXk3Am4tA+y+c93ryHkauTLE/a8wJYpAtS2mvgtnUAoxp2vv0k0Bq6yNgLBOHl+H5+YNZXqTBMss5EJjTUtjZxddhzHF87j7+8+wrr6V1m5yaQp6kU6fmTMxS3nXsL03I4ehzWJeo6v/C8uEcTUdDLBYv789kvsqHeIZ/PwRgR5PpMRoVxSdpb1iUYmlBYzrnECXt+rYG3v9/u7T32YSyllVghxFfAUoAN/lVKuFEL8AFgopXyYjiFofuC+XY19W6WUn+i7Wqhs9pVmO8q6zYW0NwbYsb0Jn8eFz2uxao2BFvBw2VknMXlUSadFlJ5efj0zK5JcdNRv91iuII5GAowwNc2Cp595C01P0DA6S1LTGFke4pgTp9PUEmdLNEAybrI+2caFM8/ipFnzefi1R9jU9BY7anS0kiSFZjtOfYifnvUdSgs7eurHA14mhB7GsFx4LZNIZTt3PfUGzZv8RPLjBFq8eH0ucsM+mmMJGtoSTJlQRtk7lXhKtrBk8/2cMvW8fr/HezUMPs/2W4PRLr+SUvbhDMQfL8+v3IDuSDQJxcW5lIzI5Z2lm9hS3UJevp8bv3N2l2PO+M6fsV2Csw/vOkfKe77964dIZzIEAxalI/K5/eZHSSdTZL0G2+taOWTeeAxTJxD28eTGDdQ2pclqGrmRju6I5542i0jAwytL1vPigk2QToEmuevldxg3rZjxpcVomkYox49A4DgONdVN7NjSRDKsYbsEVjskEwmSiTRuj0VpRYRMOssD976BHs+AppPJ2phGz5Y87C+97J3QIKWc1V916UMqlwfovv+9geM30bUMpf4cSn153LVwEe2pDGPz8zih7Kgux/zszqfRY5Krzt1z1/LTfvIn0sJmwug8cqwUz9WuwDEyuLR2tm5LUBWcii4MXCIPW1+GEGmiUZ0cy4MmBJcdNptRBREeW7qS1zduw5Ntxy0y/PuBlzj4s3nkFuYjhInHDCKRONKhLtlAu16Le6OOaNWQIw1cbkg7GSzNpNJfzM5EHUua1iHCGTIxCyllt7/2DqSPaM8hlc0DdP9zi8HoaDAqzg1Smhfix395grQQHDtnJCceNL7LMS/UbQCX4KyDuv+lFGD+lb8mq8MRM0bQJlK81LCFtAvsKEQTKSaOLELXNUryQtyhPYetSzKZLB6PicdjccH5hzJyTBEPP/YOqzY3kPEBuuD2/7zMlKpivB4XLt0g5BbYjoOmCZaurab9vVUJhUDXQbdMslkbw9CZNKqQzY0tLNy2nYwpkEPkQ2cfZ/P9nrkAQoj3eubuf3eq/aOyeYBeqX4T0HGwKM0LU2QF+PbbPyODzWlln2JW/rQux0SdzeS5sxQGug7jfs/FT16Dx+0wP3I8m5qzLGzYTjaQQOpRvFiMK8hnQmEBW9pycISbpJ4lHcqQdUHQ4+XMMSeyLFhMQehhqlNriDcLdsbC/OGFl7nl1FFYJujChWG4cbKStJ3l8ZVraGkR6KE0tpQU5Qla2iXusMSlGxyUX8aCmu28VdtGaRjszOAP6Ojr98xdDTOPf+i57+7252P79IR7prJ5gF7cWU1Da5DivAy5BUH8lsln7vkFtsvh5jMuZkZVeZdj/O5t+Ao1DHPPk7p/9e7/I25aXDv7DF5dsJUldXUIn2DbOovrzjuE8oJ8yscUsb26CXMR2EkNu9AgJh1G5edy4qyT2VIbIb/qCZKJbTS1GKwLmty96AGuPeGLZGwHt8vE0nVStk1bPMmzb65BdyRVs3YSj7soFF7aGqO4i8Lk+DxMKCvg5YXr2NHchhX2kHIG93smDI/Ps4P/L5iyRz9/+gUSBVCU72fM2AK2rq/jmz/+L2Qd/viLT3d7jKZr6JrGRSd2HY4GkM5meeaVNehRyU0XHkcg5OGgw8bimppH2m+Snx/A2NWCXFQYYub4MoTtEHdsZh/WMbm2ZRmUF+Vw0UmzueC0GWhSkPUbVNdFueKH93DHv14FISgpy8Ef9JBfGCKZyuJyGaS9GtKiY4ng1iw125rYurEWj9fFhGkjOOaEaUwYVYgm4cZf/7c/bmvPSQlOLx771qMx38rQ9/Rjy/BsSDKusJBR/jxq4jv5xZNvYBsOD19xSbfHGIaG6TYpLui++2pbNM7alhbSYY0fXXEsZZ4C5kYmkl1XQKw6wNSiAnTR0cY/IpDD+LwIju7QUGMzN7/jS3Cux0txJMBXT57P8WMrMP1Zks0e1jW3ccbP/smfF71OwPBQ7MklaPrJdYWIZRPoGLjqbcxMFjPh0Go1siNRR3W8Dr/pY3SgkmNLDyIQCCJDkr8981b/3Nie6vtsKh8Rq3e0IAXMmVxOaX6YNTtreWbZZhwdbr72zG6P0cI23gKBtodG0PU7amnJOkhTcMMVJzC+LMDRU6swEWgCDptVhWHoCCEoL8klN98LmqC5JcasyRUA5OcGCIZ9fOvGT1BWlUvGpSEcWLe9idOuv53/vb6CXL+XopwAkZCPnICXrGNjWDpSA0freN98d0sNG2ub2FrfQm7Ax9TyIs6YOxUrCLYfttb0bBXDftP7bO5tJSbo+WpMZwshlgkh7hdCdP12owy6tkwrjoDTxx1MvtfP69tWY3kXURhu4dLp3bctxLI+EtkReyzzzS3Pg5lGz0gumnoiYwsjnDhtNBF3AuHJMnfEMZi6jq5pjApHcJs6ms/GMJJMKyoCINflwUqWcMWk71Lo0tjp+HCFkuyQ73DJQz9mcd1i8t1B8lwBCnx+8n1+PD4dyysIGGkskWVa2XpWV29jfV0jO5rayPf6mV8+ivOmzqDODtOk+0lmo/1yX3ukt7lU75sfK6IxRdBK8NVjj8CNxv/eeoyk3ko4mGZWVdeGXCkl7Sk/UXvPQyXvef2vGEaKETLKYeMnMj43wnFHTsVfmcYVzFJRXIhu6Bi6RkV5BD1lEPCmsLwpRhV1fE72uk2M6Egunvg96utDrKrNI8cXY/H6Vdz4t5tYsmkzRcEAuT4vxcEAJeEgbt3AtjR21BQQSDpUVjSwfk0NW6obqGuOkh8JMP/gsZx7xCykpVGekyCdzfbbvd2nYZLN/m4wumrXG/hfhRA53e0ghLjivQ8K9fWD/EFniPHZFmYbVOaFCQW9fP3mB5GOxB+wKNnDl84XbrmGJX+4bo9lHnLxLWRdguMOraI8PweP1+Jz15/M+KpSNAQ7dhufCuA3PDg+h2w2S3tL4v3n3W6Tdetrefi55UghsYWDHtTJ8Xr4z4uLOOpLv+eJ11a8v39LQztt7QlcaYEeExT7PIwaW4TLZ+EPfjA59ylnHcxXrjgR3c6yZPWW/b11faaPV3vZ55jvAbLPXILK5t7ohX6cPMHBFcWYmsUFdz0IEsq9IUyz+yFbr97+Vd7859f2WObcr9+C0Sb4zmmH4jUEHiPAuZUXk1eUC7rGmurOKyC5TbDqUwgzRUPqg9x6DZNFO3bw6ubNSENgJFP4mi08OR7++r83mfuVX7Bi5wftlNXxHWRSknREJ+XTGDEyQLG3EEt48RsdK0kJIThzxNHccNKx+PUUD7655ADuXt8Y6BUMB2ieFJXNA6RZAkcXTBldiKlrXPHrh5CaYPrYgj0es+6ib/LuRTfucftpP/4Xjkvwq6+dSkM8SlkwwLePOgZ/QGC7NRri8U77+4MuEIJkKk008cE8r8GAi7dXbGVVtBVhgZECnwCPx+Tmu19kzjW/orax5f3911c3gi7AEEghGDOmgKKCED63RcjrBsDQda48bi4XnTGTjB9uuufp/bxzfWcQVmJ6BKiUUk4FngH+3oeX8x6VzQNkCklApKn0jcTJ2vxh0x20Zn2cUth9Qy7Ad2c9yjdn/meP2+/YehvoWW46+HusaatjbE4pnx1zDPmBFBFXFJvOC0/4hIbQHdJmO6ldc2V6XRauHHh+6ToyspFcV5zmeh8eO4Rmwk1v3seFj15HMvNBlrdsbcIbSJM2NApyYhQaMwm6/Xg8JoFd00F4TYurpx/NEeVllAWbeGbzUwdy+w5YX67ENMSobB6gmDRJGxoexhNNtHDf9tcwvBl+dWb3H3OEEJw+60XOmHl3t9sdx2HZjqfxRdLcdNKPWLFxB4fOHMOn58+kOC+OvyhFbLcFm1J2lrEVrRw8uRoyAtvu6C7ry/HS4pM8++o6qrd4seI2laEGJhfbJFxpvvvCHVz93xtxdnWvFUKwbVMjImgikhrZoE556XS8OR5Ml4nP09EbKpLr54vnHMkIO0J9m4eVdeu7XsQAGg7ZPKAGIyHEs0KIFd08Tgf+CFQB04GdwC+7K0NKeft7HxTy8/MPpDofOcVBP+4mSXNdO/c9uojWeIJwxMtTD+z5S6emiT0OFVm3tZasJnEc+OF15zBmYhnTDx1LKC9AYzSBo4Pp7nzswVMqkVmDrMciEPK8v3pFaXkEb76HxmgSkQFXEmTK4fHffZERRTlk0hm+9YdHueL/7iaZzFBWmc+IKcVoWXCZgu9/5XQmz6ggFPaRm9d55v0p00fgsgXtzUNgQQUpe/7YZ1EyC7w35nsVcK+UcmVfV7kvcrmrviqbexDJ95Cu0tmeqOG3T79Ge1RSURLg5eu+sMdjdG3P/9w+/PoSRBawMnz6yKMo842mLDAHXQuyqc4hm5ZMKO+8CsXh0ycgTAvR6sHSPpgrqSonj63r6mlrdWg1TdIhHSyHl7/3ZSJuH+3pLOf/+E5+euejRDMppuRMwN+Yg+3SCfs8/PScs5gQLifH5SNkdc7myZVTMcNZtjU27N+N60t9mM1erGB4j5Ry+q7Hn3tbZZXN/hfwGTgmbKht5sf3PIsEJoyMcOfXu++VC3RZxnt333/wKaQhkJrgyGnjmFpWyiGl48nxmrQnIOuG42d3HuZWVBgCQyM/4EXu9mvg6BH53PX622QM0DIAgqC0eOwXV+L2arSmYd5P/8pdbyygLZHkqNljcGsGIMjJNfnu105nTFke+UEfIZ+70znPmTWDrAfe2FHLoOvDbNKDnrlSysbdVmD6M9DrpRhVNvtfaW4Gt5VmWf12/rXpd+RaMaYEDues8Rfs8RhN7Hm4yN9WXk97xkRImFZcxtyCkczKr6Q8EsQdakPaGcoDZZ2OKQz5CPiTTC+CrPPB4irTCkrZrP+IsKuV5vo8MlEvTmsZvz72GvL8McYVLOP61y9g4Y53iWajnH3IWPIL2nD705S7C7hw9ueoKiug2B8g8KGJ8y8aewJ5ZpRXa/+9n3euj/Qml0NoiIzKZv+rDCRxZyVvb13LX9f8E483y5mjj2dUyeQ9HiP2ks1/vvVZinxtmNJF5ehC5k0fzZgxxRRW5BI2k+Rnovg9H+TEbZik3GF2Si9TJuVg2x3ZtHSdWeNHsLztNxx88FaaGnNZu6GQgDaSH59xMQFfEpd3M9985FO0xLbT0hLnjBOnESppp2hMCzOmhDjziMsYUZlPUW4Qn7vz8LljDz2frXh5bu1NB3gHD9AwyOYBNRhJKY/dNQP3hx//k1LWSiltKaUD3EHHOHSlF15f0TFJ3rrqZn59x7PMO2wCj9/9lf0u78Jv/h1pwl9/cAGG3vG/3rYdXrz/Tba1tCLRCIb8nY6ZOr4U2xRkPLBhbQ3LF3f0+vF4LG664wl0oKTIj5SQ0mw2b2vgP7+4nHOPnYYjNLY2NePYDolEhgVrt5A2bSxTZ+L4UtweC6+v68TcmqYxeUYFWbfGaTfett/Xe8AkCKfnjx4VKeXjUsqxUsoqKeWP+6XaKpf9bs2OJjKa4KXFddz++pt8etJMXvj8F/e7vG/e/ThmQuP1H177/nOJbIxVLa8Ra7SRHog1du4ye8LMiegxib0mw4rWLaxo6Ri1EbBc/Ov1ZQgNJheWYUudTHOa+miU5757NYeNLUfLCt5dsxNHStrSCVatrsNqlXgtnQlFBfgMFx6967h0XdMxW3Kw4ya/u/v5/b7eA9b32RyQFQxVNvtffSILBjy5fCP/XbyGL502h/984+L9Lu+ud1YiTcny31/z/nPSricbfxLHSpMOSpbUdW6kGTuuCDTYWRfnrXe3snZrxwJBAa+bd+MxHBMmjcsHAxrbUgih8fqvr6Gi0oc04I1123AcSXsszebtLWQ0COb4GFGcS9DrxjK7Tj9ZFg51zBloSrY1NnXZPmD6Pps9WY2peLe/foKOH2R6V22VzX5nuBoRPpt3Wh9kTftyrpr6Ob4y/er9Kqs90U5r+nUm++u49/h/vv98faqWhY3vEEvpJAwPsXSs03HStkGXxGjmyY3r2NrSAkDQ8pA2W8kIgzJfGS22iw2intJQHncdextePYzUJIvbViClQ4w0K5scTCPFuBIPPiuPHLcHo5sfhkYFJiLRSIo0tp3psn1A9DKXPf1MOxBUNvuX40hsmQQpeWr9PWyL7+BnJ17H5+bu3/zk71avpq2tlsLcBH/81B3vP796Rz0LNu5gc9bL2mgBHR+1PtDkT5MoSrPJquG+V5YRT3VkJez10O6HNqlTmMxFb9dYv301OYHJPPDp31CsOZR729nZtpRMJksincHlqsFBMsJzEJZhkBv0omldO1PMmTqRqvwm2mLpLtsGzDDJZr8NSfvQG/iZwIo97avsQVYiBWgCdAd+dOOeu+3uy9W/vI9sWlIVCjNj7AfjwXVd4+DjpvKJ+dPQNI31Ozt302xqjiLSIHXBxMnljJvYMXVAOp0hmXEQLoP/3nkNWiKN1W7z3+cWA/CNS09iyV038OTNX+SRd1Zx6e/vpiYWR7M1ZpQV4ra6H7bznt//6FN4wybticRe9+t3Q7zFt7dULvuGxEC2acikwBOA75563H6XdeZ3/0jGghmHlBLarcHWY/gYGZjE5MoKkDqLN3We7qohk8CxBHpKMC2nknHBjklBX3l3LSIt8WdN7r3ws2jhLE6hzYJ1HY29d1z7aZb96Zv886ef4/6HFvKV791NtCZDRsDhh43Za08ogNe+/1WCWZ3nXh3oOWc/pHfZHPLzpKhs9g1p8P6cP16PxpUnHLrfZR1062/JeB0+ecQktN1yIfR8NNcsyktyyfgc7l++vNNxxUVhpABhwKwJ5Ywu6/g1+8YnH8bOyRAsMbn1hgtJmYKUS1Df2ArAUzdewdr/u45bLz6L373yBpfdcx9JA9Dg2CMm7bWuQghWfvcqpAe+9OhgjHTezQD0zBVC/EAI8d43mi8LIVYKIZYCXwY+05eXo7LZN7ZHg9gZyDomdrqMOYVH7HdZP1/2GarTYU4q6jyUtMBdRFVgNEU+ixwrwYamFzttnzUmRZ5MkKwv5aiKUZSHQgDctvxLOIaBWzuI6w/7FNKbRdhpEumOL61fO+h+bj3iIT43/lzu33w79676Dh5XG15/jNnle2+j0IXBVeNuw2dq/GP9j/b7mg/YMOjF0Fsqmwcua9sUeqKU5DRhp0zys+MYHRm93+U9uPomljWUcULVPzs9P7Y4wsSyAqyAJORJIO3OPdUTW8NsWVbC2IJKTpozAa+r43viz174HBUFbcwrP4VPnjWfJttDnaPz3uIrXz32Pr507KNMKD6Flc03sbD6BgyvTcAb5/BJe57/DMBtGnx2ylVUBNI8vuov+33NB2wYZLM/5zC6WQixXAixDDgK2P+uMR8zbyxay9wzf4ZMpLENOGLOKF574pv7XV5LSzvPr9qEE9R46NdXdtkezg9y/aePI5jnJvahpsucsA+pgW6Cz+/C5e4I8PML1oKmccNn57FubQ3elI40BHe/3fmDc7Q9wfSSQozmLBIN24KTj5/eo3rPmlBJNOmwausgdrGXvXgMDyqXB+APz7zChBv+D5wsZlTw6RkzWPK1b+x3ec+89i5bVzQRaIC/ff6SLtv9Zh5/uOoiwo5JrLHzhJkjc3MRjsSyJCHLi6l1dA9+avEaNKHx0nc+z9LG7YiQjRGH39zWeW6TlnScGQeV01KQQTPAsCWnTtvz6oq7m1hSSENDkua2+L537i+9y+ZwmCdFZfMAfOGe+xj7vVuwNYmjwdUnzmbhT67Z94F7cMs7z9Eg4+gB+NHpJ3TZrhslPH7t5ViFDmv0nZ22HTxtFFLTMD2CnMAHv2xuyjYicmzevuFqlm/cgdRAAN//zaOdjm9qi3NoeTlstkkVpEmUZDly7p4nF32PZVmUl4RZ09aI4wxiF4E+ft/srmeulPK7u5buRkr5DSnlJCnlNCnlUVLK1X18RSqbB+DOlZ/jx+8cj0ukyUiLr0z5NHcctf+LWt295juYVhv5eoCDR3RddTTfnctvj/gdzdkwjzU802nbCVWnU98aYEccIj7v+1M4ZLU0ptS5bvotrGluIN+KkxNIcOsLL3U6vi4WpZDprKl1kReMYeNiVP7Mfda5JFROvttPfWIQf2jpTS7VZ9qPhT+9+Dn+/Mon0DTQ3IKfnHopvzh3/983H1r0GXKD7Zw9xUUor/NwUEPXKQj5ufmEn1CUcvj7K7/rtP2CubmMd++gQG8m1//B3LYl7izppIejJ11O3AEjKAnlZVi9aWmn4xsbo1ipcTy+uhDTzCB1QXlkwj7rPKniCAp9Jk5sEOcYGwbZ7LcGIynlRVLKKVLKqVLKT0gpd+77KOXw02/mum8/ALaDTcf8CJ889eADKrMtncKlaXz/4qO73f7e5GJnHDWVUaWd50kRQhDJcWFK2Lb1g9bgY+eM58XbruKwMSPx6oKLTjkIPeHgdzp3+avd0cLjzy6nvT2FsAUiITl+Ttdljbvzm6vOIuNIzv/xXb253D4lpOzxYzhQudw/tm0z7gc/45cvvg4IpJFFjztccljPXst70tYaxwgYPPLLrg25UkrsXV/6Tpw4lvHFncfcW7qO12+i+QxqEo3vP/+dc0/irZ99GdNVx6Sg5Ioji6AwQ6Cwc6++Le2NPLRqMelUmoxpkAVmju1Zp5k/ff9TxLMZTv7cb3t5xX2nj7M5IPOk7I3K5v6pa26m8te/4In1m8kIiaN3fKI6esr+/0IKkCSNyw9rv9j1+4fjODiOgxCCeSNHMi2/85Lf+ZEgwhLoukVN4wcT0t954qdY8tnrWNfQSGlJmDGTc3BM8Hg7Z3PTjkYeeWEZxMDVrGM2aVSN6NmcG3efcw5Jd4KJ//zZflx131DvmwrA6q1v8JslR9CSWYktbfLcMQrMJkbmjDugcl2GF+wgN8y6p8s2KW2klJi6i6rgBEpdnf8dOKh0LIalY/maqW1tf//5Kyf8mi9PuI8tDS1MKirEI0uIZVy4cjr3cl9ZU8d/1leznSBZRyNi2BT6wj2q92klNyCo5Y8rzu79RfeB3uRSZfOj7d21f+S/rx/OivYY9XEXgWA7I7zN+HyVB1TuxGCA/IyLE6fe2mWbvWti6vJIhKLcCsLBztNEXnjIpwlU2Txfs4369g9+IL34kN/xxdl/oWZ7I7NGFCFjQeqjPpoTH7xvptNZVixby4sr2mjLusgmLcqDOQjN16N6V+ZdgKVt4/UVl+7PZR+w4ZDN/l4lTemFq3/0H7LSRhjwj59fguk1MNNQUbnn1V16YkRBHu/cfj1nzOv6/eb2u17kc1/7BwDXnD2f+276bJd9JhUXYWYFL7+wmsZdvRwMQyfgc2O5DML5AbwuE9uv05rMsPC1te8fKwydzetrSHsMpO5QWhhA1/c8UdqH+UMGcu+j1/rXEO8iqAyMI2++FVuT+Hwaz3/nswgDjFyHYl93o5Z67uyTZ/HW32+kOC/SZdt3/vc0l9x5LwDfveI0/vLdz3TZZ0RlEbZb8PSOFe9P4OkyDXxuC00z0Y0IFfk56C06NdE6dsa2v3+sJnVWJqvRGgQyK5k7qucjrAxDw+8yMO1BfN33bTYHZJ4Upe/NufvPSJGlIOLm7a9diWZoSAtGRbpmqje+OfMk1n7m692+X/3gxnv43vUdKzf9+fhz+cdJn+qyT2Gen7ST4dVlm95/zmtYBEwXlq6jaTDz0NHoCN5cs5Pm6Ae99XRDY826GoQAHJMLetgrF6AwEMJ0gene9779Rr1vKsAt639LQ9JDnmsU10/9H46ug2lRGTiwz7RnVX2D78x6oNttv3/np9y19E8AXDPxe1w6/tou+/iNIG1JeH3rByvxGpoPl+nGMnQSToYjx01E8zk8sO4NEtkP5jexhE5LdCn57laSGYPrpn+1x/UeHZmGSwezm7lUBsQwGPaiDIyH1j1Ja9LFRVUl3HjCvTy2fAqvbRxDWUn3K3D31Lgxv+OThz7U7bYfP3QL/3qlY9uXT/wBZx50UafthhGkemElW5bDwuoPZggQWhDDcmOaBi1tKab6JjK6pJm7Xrj7/Z60lmWQn59gbcNG5o1ahx61+eoJ3+5xvSdXnIfb7SDNQeqZOwyy2XXmRGXQ3PzVM3no2SVceNocAETCQY/aaHr/vbn848GFvLdORDSewuexuqyyNndSJYsWbyFU5CcS6TwpdqS4YwXLE8+dy+/fWkqyNckffv8Efz5kNJqmUVAYZPXWOqRboAuDe3/eu5WoX/vl/neNPGASGEIT/ymD574rL+HFtRu5YO4MAKQfnMIksp/+4bYdhwffXInY9cEymkzhd3edIH5kJIe1bTVMD1ViaJ2/2LqMju7AhxefQlTbguZP8dvFj/HTwzt6M5UE/Kzb2YLIuAlqBrffcGGv6vjyv/e8WmO/6+NsSimzQoj35knRgb++N08KsHDX0Jcv75ozJQs00cfzpCj759FPXsyGlno+MaZjNRdbd3A0kP3Ub3v7tkZefWUtLk9H3mLRFD5/12zm5Xipb4ly8PjO3fKFEFTkhJFScskhB/HAHQvJIPnVQy/xg4tOAiDgsWhoSiE1CPoNrr/k+F7Vcf3FPf+g3OfU+6ayy9Xjv4mjSaYVTQXAIEMy7em38721dSExnqcu1TF8sz2dImB1zWZZyKExGuPQyopOz1uGQXE4QL7jo8g/j9daHqIlBf9c+jpXHDS/Yx9NI+BpwG/GEJ7xTC7a8ypS3fnylJf37+IOlMqlspuzx32V/JwSIpHRSCkpD7dRs/3AGov25q2FD1Dgep6G5hqkPINMNo5ldu39U16VpK7F5ogRozo97/ZYuD0WgVwf48YU8+1Hn2JTNMYDCxbzyTkdnSE21PqJIWm03FSMGkfIl9OrOs6b+Nb+X+CBGCbZVD2MhhCPx/V+YxFAVjpINMx9TBC9v6LRFJYmmVCRw02/fJCjL/8NL7y5pst+BWE/jgV+f9dVk96Tmx/g0yfNwpCCbfEEyXjHLzIrV2yjTndwdMH0SSWdllEc6gRDv4ugMjCKc0PvNxYB2LZGKukB9pyJA7GptR49aHPk1GI+e+ufmf/NX7Jsa9ee1oV5QURaYOylG17YyuWoqeNJGyYrVjUipURKyYId7yLX6GgxwaePmIFp9Lzn32Drj2wOgXlSlP0wKb/w/cYiANuSOC6J2+if98033lqL9Bqceu4cLrrg15x+ys9pbGjvsl9+JICD2ON7nhCCYn+AqlG5oMOqdR35ztg2j76xClsAmuCnXzily484Q5l631TeM6VkyvuNRQAxx4thSLR+ej0vaVlCWzLC1JxPcfKvf8I5d/4QW9pd9jNEhIxmkOvt/jdzQ9Mo8Pop8+Ti1rO8W9PRaz6ZzfCP9W/Q3BKgviXET+b2vHfRYOttLlU2P9rGj55HZNfE1o7jEPJmGTOycR9H7b83qndQlypijP8CLv3RtXzrnzd0u59p+dECEper+2xapkFOyEuquYBks8W21esBiCXivLrpNUKuNLLR4Mbjr+u3a+lrwyWbqsFoCJMesH0Sr69/vpQ+9uQSbEPDbeg8+vpqsimHcVVFXfabM6kSmRX89j97/2XkixfMx3HrNLjg/hcXs3r1Dt5ZvBlpCcI+i9u+cl6/XEe/GuJdBJXB4WqzcbVkcOn900nzz6++jZEROIEYizZWk9RtRhfmddnvmJljSErBLx/d+xL3P/vsGejNgs0LsqzYspVFTWtYunUDWkJQXuDnyxcd2y/X0a9UNpVuSJ2OmaT7yctvr0XzmqzeWMuWnVHSEkJhb5f9Jo0pIgP8/b+v77W8H1x1EkII1m9qpqU9xivLN/Du1p2gwaSqCIdMO7C5mAaFyqbSjajjJim7ZqWv7EyuJakHeHLNYrZpSWrSNrro+kNInh4iY8d5Y+uCvZZ3aslZ2I7gzZrVJDNJ3m5YQl20Bs3lMDHnCHK9/dcjo18Mg2EvysCzs0mCrjhZ27/vnffTuu0tbN9ayp33PENdFJZu6P4HnYwryKqEwTubt3S7/T3Tio7DlZS8uuNlkuksLy5cja5vQ3M7HDXxE5jmYM5lsh+GQTZVg9FQpulofhNd7/v/Tdmsza///RKpVJq1rc3YXoFjORTlBrvsmxP0IpM223e0ksl0/bVmdwePLSIR0fjFPS9w979e46W1mwl7TO676bNYw6gHw/uGeICVwSHQcKf7Z5KQWDLFg6+tJp62WfRiE1qLRE9k319idHejR+QjRYYFa/c+z6Ol65T4fPjW21z50zv4z5qlvPh2C7mFHv5z8+f65Tr6ncqm0h0dQq7+ea/ZuLWehStqSDmSxcurkYaGsDSMbt7bDj14DFLAQy8v76akD4wuKULXQQo4+hu38cQ769i8s4miPA9/+/ZFez12yFLZVD5ESoccV4Ip4UC/lL9g58tsi28iY8fYXL8RgDy7+/foyqJSDF+GZ3f+d69lnjxqJgEtC6EsZ91zK89sWEN9axqRmMwPj/1MH1/BABgGX0qVgbeudhGjChqZP/Gwfin/X0/8l53tdUinFdeE9VieLJPDE7vdN1/qTAjU8+KmV/da5qUnzyc4qQU9lOXbt/yRhW+vpdhfx6zcKs466CPeOUE1GCld2JKUQ790R//DP17AQTB5TDFNjXFE0uH/Lj0Zp5sXYlNTDJF1cGybRCqzxzJbmqJMD+ShZW2kobFw+zZ2RKOcM38GxXmhPr+GfvfeuNKePpSPjTQG0X5aUf4rD/4PYQqOnVyFpA3Da3P7jZ/pdt+t0VZsjyRDkvZ0qtt9Ovar45MXFyH8kGg3effdBpqbUlx58qF4XcNnmOj7VDaVPZCmpI29/7Cxv7576yM4Pp3Tz5pGyoCMS+fPd1zW7b5NrQlsHZJpQTK95/fNBZu2ccaZ03BMQSILG3bUkkil+e5nT8Lohx+L+p3KptKNhoYGXDg4dn2/lH//5vtozQY4MnwRIVeUqkANf7v4qm73jRhBkgmT9c3xvc5D+OqGjRwcPI5UxqLR3E5t+wZGFG/njrMuGlbDRIHe51Jl82Nj/fZ/4IsJisyercTZW0+tX0Q0GeKIqRfg01MYeRm+/pnzu93XSxGmnmJz3aa9lvnKsvWMih+KW5M0e5ayzbeWde0RLj3hG/1xCf1rmGRzGH4a+fjQshq60z//ix54YSkY8IOvnoGjS7K6w4nHTcc0u/5SmpPjxe1INE0jncl2W16sPUn1xjrGTynnq8fOxTZgayZNxpRccOLMfrmGgTDUx5Qqg8Nxaf02Gf3bS7djJAVfPW4+bjuLlZXMKR/Z7b5V4Qj5hQlClrPHf8yTdpS4U8u0vLFMO7IcV8am+s0mzJjgk4eqbCofHVJKsCSa1T//z9+tbcEJCA6eXobt1si6NMaMLOx23/EjC0ET6CYYe1gZtLa9naiT5vDxlYTDAg1YU92CZRrMmVjRL9cwEFQ2lQ97veYlfEaKEv3gfinfZ65lRm4Ly5vrwC3Q/FlG5HS/UuLEwkLiMTfpbHiP5W1obMQwTY6ZPIWKcJT8QAvLos1Yuo8if9ee+MPBcJgnRRl49dFGDI/NvCnz+rxsKR2CFdsYOSnNK0sfxOcYBINuCvK7z9DY/DHUNeTic+/5R58Na2sIWy7OOP5IKotaMf0ZktkdFOcGMIbbULRdhkM2VYPRENUeT+JoAq/RP19KC8I+Qn6La35/P1pC4ovu+QUohCCS5yWj2fzrvte63WfrhloyGYdpc6vI03z4msHKaDhpm0iof7ogD4gh3kVQGXgtsQQiaRMJ989qL4GARUHIw0Xf+Qtpv4Z71J7fAHVNIy/h4GmO88TGhd3usy22FbdmMdo/juPOHklqpEbGpxE09H4Z7jpgVDaVD3lz8wbQHY6pLO2X8r05BsWlAa79zVPYPsGcOSV73NfjMhFuSNjwwuL13e6zuq6BwkCAsYWFnHfyXLBAGjBxXMHw68GwO5VN5UNq7cXY0uGE0Z/o87KldPDiYYRnBC9Vv0WLcPP1GV/Z4/7lOWHyc6JkZROvb93aZXvGSdKa2UZZbg4jcsMcV3wy0awPy7A5fUTff6keMMNg2Isy8FJ4eau2gKzd911XWmPthEydiYWjcQI11DT5uO3Tv9rj/odPLWFO0WY82hpWrq/uWl4mRmO2hcqiCAWBAP7EhWypz8OI6Zw7Y5gO4YZhkc1h/G3ho81jmeACK9g/czE4GkwaXcSaaBvpUjev/e9be90/tzQH3ZE88siibreXVuYzaVYlpmmwcv0OSt0ubAsSScmG7XX9cQkDYOgHWBl4K1dtxcoIDi4v7pfyPRIOGV2E2JRGW+Xm+a/dtNf9U+05RIMmD617odvtBZ4yKvxj0YTG29u2UzrWjRaH7Y1ttPfXuLp+p7KpdPWDt54Fl+T7R5zY52VLKSGkMWtqJY5LgKlx642f2uP+QggQkLXgf68t7nafiYUFTCosIJuxeWv9VgKWACF4bvV2Mnb/DKvrfyqbSlcpuYwCV4w8f9/3nGuJxbCyBsWu2fgsQakrwezRU/a4v9/txuVJkEjrPLJ6WZfthnAxNm8Eo3JzqIm2s6F1LRN97RT42vnrlgf6vP4Do5e5VNn82KjIq+GQsh177Al7IAx7MWNccSZFIhwyYSsz5jVjaHs+jz9cRLCgncY1Ll579t0u2wOGhzGjSykqyWF7Qwurt26nwpVkWuVO/vnO9/u8/gNjeGRTNRgNUe+s2QRIjphY2edlSympbY/z8rKtGLUZPn/KXPR9/EPxw6s/Qcan02JK7G5aoYNhL7X1bdz/7BKeXL2eLTLNoWNLwC258pZ7+/waBoRkyAdYGXi3//dlsmaGGy8/uc/LjqczNNamefLtlWTGZPj250/b5zFXn3QsmWbYsizR7RxkITPIyu11/PfNNTz3eDWb34bjppXhS9h88ad/7/NrGBAqm0o3tmeaEHqW4nDfr160s62dRifDv99cSdot+MvXP7nPY+aML0UHtjW00N1cKfl+H29s28pLmzfwzrs7iCZgRlUEW4Pr7nqkz69hQKhsKt1okw7RtIWl9f2qvzsS6xBmE/dvu5egN80fj7lln8e4tQiax8Ztdv3RRAiB38hlQ9sbJO0lrGrZRHvaplAfQaHRxH2r7uvza+h3vc2lyubHRk3MRXUigKb1fa/WN2p3UmfbPLzkv6zaMYKvHv3Pve4vhKChuZQ6GSJS1bWJQhMaOVaAjXWv0pBdhhleyvgxO3GHw9h2guXbX+zza+h3fZxNIUS5EOIFIcS7QoiVQohr+qKaqsFoiLr09w+Do/H1Tx7T52ULIYjke0h7bKSEq047Yp/HlOXnYFsGqZDBuyu7dhME8PvcLFi+iRbdxhaSb1x2MuU+H001Ue5+7q2+voyBMcQnIVMG3oplNQhHJyfU9/MYeC0Tn23jas6SNQ1OPWb6Po85ad4MZItB+xqTNY3d9+YLe9zc98qbZKs1EJIff+589JE+lja0sGZD93ke8lQ2lQ+JaimE1T89c4oCfrIuG9uTQXolM0eX7/OY31x9DpqALXVxtjS2dLtP2O3md/99BUeCFHDXly/CcMPTazYRTyb7+CoGiMqm8iEpssTpnx7z5YEKkjJDvi/OmFArfve+35u/Mv1yksLk1aa3aU41dbuPW/Pz0Ma7cFtJ4prFrUf/gLTj4Ykd/+3jKxggw2BiXWXgjQnXMzbc2C9lT4xMI0ILVTmtzBrlQ4h9NzsUlcwlGjG4b93rtGVi3e5j6S5er70X3ZNiXTSPy4++ndV1+dz+2t/6+hIGRt9mMwt8VUo5EZgLfEkI0f2ydL2gGoyGqM8cMwV0QSDQP/P/bKhrRwDXXnRkj48RukTqEikd2lu7/irj97t5fdU2yEJp0E15SS7//tFlpAI6P7r/5T6s/cAZ6pOQKQNv7qRyzEz//NPpOA4tiTRp2+DvX7i0x8dptgFoSJElaXd9gw15XGyqbsVBctDkMrwuF7defgGeHTaXXTc832BVNpUPG+/34+6nCa8Xbd1COpgm43Z47aYre3SMaeg4usDWIJVOE0unu+zT3hanLSpxNLj4qKkIIfjqsYcgNZjzsz/29WUMCJVN5cO8Wgiv6J8VOZ/b9Cs0S+LVUtw0+64eHTOrZCx2TCeekrTFBcluFnR5qXoD1SkfHjPGtVM7Mj/KM41pOZu4Y/EFfXoNA2E4TKyrDLyAcJOr9c+cnCs2fIdGPOguh/Nn3dqjYy6d9VnGOM2YiToSUZt0ums2X1z+Fk3tbnRfkhuO/jamaXDk2CijI/W8uurrfX0Z/a4vsyml3CmlfGfXn9uBVcABT+yoGoyGqOvPOY4Vt17XL2U3tLQjLYHXY3DJyXN7fFxlrgvTFtz72Fs0NbR32X72l/+EbYIwdB645YsAhAIeynL8YMO1v72/z65hwKjuu8qH3PrTz/DWf7/ZL2X/+8HXMaOCMSMKmDymrMfHmQEdKTUWrV9Oe6a5y/Zjf/RHtKjArxnc/vmOD7pTx5Ti9uukHPjPU8OwB6DKpvIhT5z+NVad0z/zGPx0xTOIQJrrjp5Drs/X4+McvWPOwHe27aChmznDLv7bf5G6JD9gcMNZRwPw2flzETrEbIel27b32TUMGJVN5UO+Pv0+vjXj0X4pu513COopLqu8B70XX3wjvnaKchtZ1rSJxnjnbEopeab23+i6Q4W/iiOK5wDwzUOuJ57xsCNTSzTW9XPwkKaGpCndmDn5FWZNeaFfyi4MVDM3fwefP/K/CNGzHoZu003K0PAVtrNxzUZamjv/CJp1smxPPMNId5TphTOoKBgBwJeO+SvpuMnKrcu6HQI+pPU+m3lCiIW7Pa7orlghRCUwAzjgD/mqwehjKC8cYPkfr+fNP1zfq+P+73PnktXh0TWbcLtNYu0fdJdva4+ztSUKEu77/sV43R+MU3/q/74AQvDMyo3YPZzI8+Rv3sqkK3/eq/r1OQk4sucPRTlAnz7ncBbc+w3+86Oe9WB4zycPnY6RdPjjPYuQWCTtD7K5YOMGYg0ppCV55NdfwGUa72977I6rEZrgltue6fG5jvrMz5h9zg97Vb8+p7KpDLAHT72CjZd8ly8felSvjivMtcCE+19+hywO6d3eA29/+VVsIZGW4PnvfbHTymj3XHYOjkty+j//0+NzTfj2LYz5zr7nb+lXKpvKALt4wuNcPvEtCnKLenVc2G2TlC7WNu/olEuAu1b/Ho9IExQOP5nzo07b5hZdT4E3yl/X92wew4yT4ca3TuZbr+17TsJ+09tcqmwqfWDa+Dc4YtrbGIax7513U1nQTNwxsbMOzofmzb3tmZ/SnnBhp31cfvgHvYmEEBw18VCmj6jh+cXze3SeaLqFh1+cx/9e6PvVG3ts/7LZIKWctdvj9g8XK4TwAw8A10op2w60mqrB6GNqfyY3m1RVDI5ASzrs2N7UqYz5N/4J2wOzxpcytrygy7HnHjoRPQWHX/ObHp1ra0McKSWTL/sJDQ39M7Z239SvMcrA259sfu2zJyFNnZTM0pBsRtttnPgXbrgPdyNcNn82kbC/03F+v58xYyI4LvjEVb/u0bna2lM4Dsz81I+6nWR7YKhsKgNP68H8Cx/24A2fxRawur6V+mgUfbdGoV88/zbSkPzxU6dgfmjhiekjyon4TaThcPa//9WjcyWxSXsyVNw2mKvFqGwqA68nc6N82KVVn6PQ287bdS/Qkuzcw2hJw9NE9CQ/PfjrnRpyAY6qOIlEJpfmpItnNu57mI0udPLMKKPCO/j54sN6Xc++0ctcqmwqfUAI0SU/PXHIyEnML97GI+88RTTaeS6/JfG1NOkmN575sy7HzRp9I/GERSKWYe32Z/d5HgOTpoQHU2/k788c1+t69o2+z6YQwqSjsehfUsoH+6KWqsFI6ZXp5UXoQmNzTQseX8d49MdfWk425SB1nTu+0f247psuORnL1GjPZPnGnQ/v8zwrbv8aelqCY/LZW/vktb5/1JurMkwUjcwh4zWpjzpYmoWUkl/e9z9EFsyM4NpPdT+B/r9+8XkMx6F+UysLFq3d53kWPfBdsi4HRwruffLtvr6MnlPZVIaBUMBL2K/jaOA3LXRNw3Yczv/Hv8gaYOgax0wa2+2xi665BqFrLKrZQbIHE2Bv+tHXIJRGaIKa+AH/oLj/VDaVYeCIkUfg0b2YrhhFQS8AKTvLL5d8llHBBrJOAXn+Kd0ee92MJ4k5Fm837nuqBU1oXD/jJVJZA7duk3W6zmU2IFSDkTJMHDX5R5g+cOVsorA4DEAymeEfT53HnIJ6ZuXl4rG671F4+Ljn2JEI88aGm/d5Hrfl4+zpf0N3SXJ9g7jIRB9mU3S00P0FWCWl7LMux6rBSOmVy885BGloPPXmagC2b2/g27c/jpaBR266CE3b80vqhVuuJOsW/G/Bvr+UAiy/80Ze/8PV3P/Nz/ZJ3feLenNVholTJo5Dtus8++4GAF5Zu5K/v7CSVC68/MBX93rszV/6BHpblq98/+4enWvJ3Tfx0p+u45zjZh1wvfebyqYyTMwcWYIUgudXrQPgVy+/xJs7doIOa77zlb0ee+rokUg9y4y7ft+jc2350ndY8OmvUuDpnwUzekRlUxkmRrryybcaWd+0BICbF/6B5dEYdZlCfnr4P/Z6bKWvgFwrxm+Wnb/P8wgh+OK0t7h04uMYmrXP/fuFajBShhGXUYwItbGiuuP75m3Pf5OtyTSeeJjLDvvdHo8zfT5CAS+5VpxH3/ziPs8TKC7h+NmvccLsYdI5Yd/ZPAy4CDhaCLFk16Nn42f3QjUYKb0yb/JojJDO6mgjsXiKM757J46uc/qRE6ks6ToUbXeBQICjR1dguyQzb/hVj84X9Lg7zbkyoCRqvLcybFx57nwCzYIFL27Edmyu/uVjOBmD68+Zj2WZez123rzp5IwPI+pSnHfOL3p0vlDQg2H0zzLJ+6SyqQwjP7vgVNAkT67s+LHk94sWgQZ3X3DWPo/9/eln4w5oxGWKS5/6d4/Ol+/xoe3HMIA+obKpDCNnVV1KntXCspa3aI+1syXzGkiN6yf+bZ/HfnHqnUTx49Y3sKL63h6dz2fkHmiV909vc6myqQyyqcUnMq9qA7gWsm7raprTmzH9cMEx+34fPO+Q+4nHLXL1t4k2Ldvn/kIILCO/L6rde32cTSnlq1JKIaWcKqWcvuvx+IFWUzUYKb0ihCAU9tCYSnL6t24npUsiRV6+f1nPGi//cPW5SF0nqklaulkxZmiRIJ2ePxRlEJmmjlfqNG9u49gv/ha91WF6KMLFPVwJ8ZE7voqWktSta8QZ8h8WVTaV4SPgcaMbgg31rVT99hegSc6cOI45lZU9On7NpTciXFlebOpZ79zBpbKpDB9VkbFEHS9bEgv47rKL8Fspziq+koA3p0fHf7b0HiQaLzT3bH7OwdPLXKpsKoNsXNk5aFqWrTX3snTjF5job+SyqTfvdSTL7k6e9E18ORmW1w3iKJUeGR7ZVA1GSq8dOq0SzXSobY1jGTrP3bzvLn+7e+Prl1AQ8OBxDVK33N5Q3XeVYSTP7SIblzRGk4T9bu76xWW9Ov7bPz2PovJIP9Wuj6lsKsOIISClZck6NuU5QX51au9WTLpuymGMDw7SL6C9pbKpDCNePUHEqiNsxRjlnsYnRvd8JcS8vHwqfWeQ557efxXsK2pImjKMCKGzcEcJi2pziCKYUnECefljenx8oOhYAu755HkP6cda9pFhkM1BGuujDGeZ9gxORoAUvHLLl3p9fCQS4dVvfaEfatbHJGCrX1mU4SNclYNT34yZcnjyzqt7ffxxJ07nuBOn933F+prKpjLMhMdYtG5Ko8c0Xvzy53p9/JdnHMuXZxzbDzXrYyqbyjDj1i0MPYZb07l6yvd6ffyJld/o+0r1NZVLZRiKZCU+I0GOu5gJY27s9fGjyoZ6zz+GTTZVDyOlV/762Ov8b9FakDpCwmPP7nts6LA2xFt8FeU93/7tAyx5bTO6pqFb8Gb1+sGuUv9S2VSGiXMevZPN8TakAUiN1Y31g12l/qWyqQwTP1v6RYTRhoEknnWoidcNdpX6zzDoxaAo7/nTgosoLWnC8qdYWpelNZ4Y7Cr1n2GQTdVgpPTYd/78CLc8+joInUd/cAlGUvLTfz8/2NXqR0M/wIoCcMnX7uDZJ9diGoK7fn4pibDgmlsGccWHfqeyqQwPB939CxY0bCfoNvj9KafhuCSf/Nddg12tfqSyqQwPP196Cq3JjaScceS7Lybf3cgj624Y7Gr1k17mUmVTGUQPvHkMqUwdMTmbTGI6Y8P1vLD2tsGuVj8ZHtlUQ9KUHmlobOZ/r60GA+7+1jlUFBdgW2CbsGT1RqaPHzXYVex7EnCGfjdB5eNt0dJ1vLOzHrcbnr/rWrxeLyIm0WIQiyfxed2DXcW+p7KpDAN3vvsazbIVoWssv+jbAFz99MPEHXCkHLyVzPqTyqYyDPx34+20Z5P4TMm3ZtwBwM/f+Scxdg5yzfqJyqUyTDy97hu0ywzFmuCsqb8lPSHDzU+cT6z+Tc6YPti16wfDJJuqh5HSI/Nu+hu2C/567dlMriwH4Ox5Y5EejU/97qFBrl0/GuItvopy9fX34GtxeOwvX8Tr9QIQERpWm+BTn/vjINeuH6lsKkOY7dj8bNXjmJZk7fnf/mCDriN1wQ1PPzp4letvKpvKENaWbObxmqdoyYT51swPeslvz0TYkQ6ytPrlQaxdPxoGvRiUj7fWhgeprl5ISgY4d+6zAFimSU3Uz5ZYkFjrR3TI6DDIpmowUnqkKORmwsgIsyd90JPou5efCVKHIb8E9wEY4gFWPt6klIRy3BwyrYKCnPD7z7/wt2+A47CzOTZ4letvKpvKEBbPpglZBheNnIFlmu8/v/nar4HpcN/GlYNYu36msqkMYVk7Ttg0uKT8qk7P/2Tyvyk02ni2/tt7OHKYGwZfSpWPN026qHI7XDKp87Dtr837PCePWcfabScMUs362TDIphqSpvTI8z/+YrfPzx1ZzNtrqjnu67/jmf/r/apMQ5v8aDeGKcOeEIIn7r2+223eYhctrVm+/6sHuekrZw1wzfqbyqYytAUsD2+e9v3uN7oAbN7Yup5DRoweyGoNAJVNZWjL9ZVy88H3dHne5/PjM9JEHYN4IorX4x+E2vUXlUtl6Avkn8K8/FO6PF9RdgirdqRY0+RnmuOgaR+l/i7DI5sfpTuuDIK/XH8BthTUNKYHuyp9T4KUTo8fijKUPPv363E0h/+9sXqwq9L3VDaVYWzx2V9AmGk+/fo/BrsqfU9lUxnGTi/+IX4zy5/XnjTYVelbvcylyqYy1MwoOpuZhU1s3HDwYFelbw2TbKoGI+WAlRV4wILm6EdwyUNH9vyhKEOIEIKcoAsjI0hnsoNdnb6nsqkMU7nhMEG/wGN+RF+bKpvKMDWh8Ahc6EjN3PfOw01vcqmyqQwxhSO+is/K4LE+gq/NYZBNNSRNOWBP/+SLxJJpfG5rsKvS99Q4bmUYe/7v1xNPprHMj+A/9SqbyjC27KzvEcukBrsa/UNlUxnGvjT5edJOcrCr0fdULpVhrqRyDcj4YFej7w2DbH4Ev0Uog+Ej21g0DJY6VJS98apsKsqQ5DNdg12FvqeyqQxzQghcumewq9G3VC6VjwAhBAjfYFejbw2TbB7QkDQhxCeFECuFEI4QYtaHtn1DCLFeCLFGCPERndZc+cgb4rPW74nKpvKRp7KpKEOTyqaiDD3DYCWmPVHZVD7ShkE2D7SH0QrgLOC23Z8UQkwEzgcmASXAs0KIsVJK+wDPpygDSg6DVt89UNlUPtJUNhVlaFLZVJShZxjnElQ2lY+w4ZDNA2owklKugl1dxDo7HfiPlDIFbBJCrAdmA28cyPkUZWANvV9ZekplU/loU9lUlKFJZVNRhp7hm0tQ2VQ+yoZHNvtrlbRSYNtuf9++67kuhBBXCCEWCiEW1tfX91N1FGU/SAZs1nohxM+FEKuFEMuEEA8JIcJ9cg1dqWwqw98AZnMAqWwqw5/KpsqmMvT0Npcqm4oyMIZJNvfZYCSEeFYIsaKbx+l9UQEp5e1SyllSyln5+fl9UaSi9B3p9PxxYJ4BJksppwJrgW/s6wCVTeVjbeCy2Wsqm8rHmsqmyqYy9PQmlyqbijJwhng2oQdD0qSUx+5HudVA+W5/L9v1nKIMGxKQA9SSK6V8ere/vgmc04NjVDaVj6WBzOb+UNlUPq5UNhVl6BnquQSVTeXjaThkE/pvSNrDwPlCCJcQYiQwBni7n86lKP1Dyt62+Oa919111+OK/TzzpcATfXchnahsKsNf77M5HKhsKsNfP2RTCHHirhWQ1gshvt7NdpcQ4p5d298SQlT28VWpbCrDW29z2YNsDoFcgsqmMtz1Qzb7wwFNei2EOBP4HZAPPCaEWCKlPEFKuVIIcS/wLpAFvqRmrFeGo162+jZIKWftaaMQ4lmgqJtN35JS/m/XPt+iIzP/6s2JuzmXyqbykTYcfpHpjsqm8lHXl9kUQujArcBxdMxPskAI8bCU8t3ddrsMaJZSjhZCnA/8DDhvP86lsql8ZA3XXO46n8qm8pE1HD7PHugqaQ8BD+1h24+BHx9I+Yoy6PqwJXdf3W2FEJ8BTgWOkfLApsxX2VQ+8oZPz6FOVDaVj7y+zeZsYL2UciOAEOI/dKyMtPsX09OB7+368/3A74UQorfvoyqbykfaMM0lqGwqH3HD4POsOMDvpX1KCFEPbNnLLnlAwwBVZ2+GSj1g6NRlONWjQkq5zxnvhBBP7iqvpxqklCf2Yv/dz3UicAtwpJRyyC3foLK5X4ZKXYZTPYZcNoc6lc1eGyr1gKFTl8HMphtI7vb326WUt+9W3jnAiVLKy3f9/SJgjpTyqt32WbFrn+27/r5h1z6Dem9VNntN1aOrPsnmfuQS9pLN4ZzLXXVR2ewdVY+uBjObA/6Z9oB6GPW1HtzUhXsb8jNQhko9YOjU5aNYjwEO4+8BF/CMEALgTSnl5wfw/Hulstl7Q6UuH8V6fFQbf/aHyubwrAcMnbqobPYPlU1VjwPVV3VRuexMZVPV40B93LI5pBqMFOXjSko5erDroCiKoihDTE9WQXpvn+1CCAMIAY0DUz1F+VhSuVSUj5H+WiVNURRFURRFUQ7EAmCMEGKkEMICzqdjZaTdPQxcsuvP5wDPH+g8gIqi7JXKpaJ8jAy3Hka373uXATFU6gFDpy6qHh9vQ+W+D5V6wNCpi6rHx9tQue+qHl0NlboMlXp0IaXMCiGuAp4CdOCvu1ZG+gGwUEr5MPAX4J9CiPVAEx1fXoeDoXLfVT06Gyr1gKFVl/d9xHMJQ+e+q3p0NlTqAUOrLv1uSE16rSiKoiiKoiiKoiiKogw+NSRNURRFURRFURRFURRF6UQ1GCmKoiiKoiiKoiiKoiidDLsGIyHE94QQ1UKIJbseJw/w+U8UQqwRQqwXQnx9IM/9oXpsFkIs33UPFg7wuf8qhKgTQqzY7blcIcQzQoh1u/6bM0j1GNTXx8fZYN97lU2VTaV7g33vP+7ZHCq53EtdVDYHyWDfe5XNoZFNlcuhZ7Dvv8qmyuZQMuwajHb5lZRy+q7H4wN1UiGEDtwKnARMBC4QQkwcqPN346hd92DWAJ/3TuDEDz33deA5KeUY4Lldfx+MesAgvT4UQGXzPSqbKptDjcpmh8HI5p0MjVzuqS6gsjmYVDY7fJyz2V09QOVysKlsdlDZ7Opjlc3h2mA0WGYD66WUG6WUaeA/wOmDXKcBJ6V8mY4VD3Z3OvD3XX/+O3DGINVD+XhS2URlUxmSPvbZHCq53EtdlI8nlc0hkk2VS+VDVDZVNoeU4dpgdJUQYtmubmID0o17l1Jg225/377rucEggaeFEIuEEFcMUh12Vyil3LnrzzVA4SDWZbBeH4rKJqhs7o3K5uBR2Rxa2RxKuQSVzcGksqmyuScql4NLZVNlc08+Vtkckg1GQohnhRArunmcDvwRqAKmAzuBXw5mXQfR4VLKmXR0V/ySEGLeYFfoPVJKScc/MINBvT76kcpmj6hsdk+9PvqRymaPDMlsDnIuQb0++pXKZo+obHalXhv9TGWzR1Q2u/rYvTaMwa5Ad6SUx/ZkPyHEHcCj/Vyd3VUD5bv9vWzXcwNOSlm96791QoiH6Oi++PJg1GWXWiFEsZRypxCiGKgbjEpIKWvf+/MgvD4+8lQ2901ls3sqm/1LZXPfhlg2h0QuQWWzv6ls7pvKZlcql/1PZXPfVDa7+jhmc0j2MNqbXS+Q95wJrNjTvv1gATBGCDFSCGEB5wMPD+D5ARBC+IQQgff+DBzPwN6H7jwMXLLrz5cA/xuMSgzy6+NjTWVTZXNvVDYHj8rmkMzmkMglDPrr42NNZVNlc09ULgeXyqbK5p58HLM5JHsY7cPNQojpdHRD2wxcOVAnllJmhRBXAU8BOvBXKeXKgTr/bgqBh4QQ0PH/8N9SyicH6uRCiLuB+UCeEGI7cBPwf8C9QojLgC3AuYNUj/mD9fpQVDZR2dxbPVQ2B4/K5iBmc6jkci91UdkcPCqbKpsql0OTyqbKpsrmLqJjCKCiKIqiKIqiKIqiKIqidBh2Q9IURVEURVEURVEURVGU/qUajBRFURRFURRFURRFUZROVIORoiiKoiiKoiiKoiiK0olqMFIURVEURVEURVEURVE6UQ1GiqIoiqIoiqIoiqIoSieqwUhRFEVRFEVRFEVRFEXpRDUYKYqiKIqiKIqiKIqiKJ38Pxs1TK4tZFc9AAAAAElFTkSuQmCC\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": "e2c68429",
   "metadata": {},
   "source": [
    "## Baseline Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "713ee16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 14 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(3,5,figsize=(30,20))\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','Delta']\n",
    "for i in range(len(axs)-1):\n",
    "    col = cols_to_plot[i]\n",
    "    if col == 'LOGIT':\n",
    "        #df = soma_df[~soma_df['LOGIT'].isna()]\n",
    "        #cutoff = np.quantile(soma_df[col], 0.999)\n",
    "        \n",
    "        axs[i].hist(soma_df[abs(soma_df[col])<10][col], bins=100)\n",
    "        #axs[i].hist(soma_df[col], bins=100)\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].hist(soma_df[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",
    "fig.delaxes(axs[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49f4119e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 10867 Cells\n"
     ]
    },
    {
     "data": {
      "image/png": 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12Qq+Q0AVPIVCkXLMng388z/zVaFQKCYSEprZ0DCx81AoFIc+RhoKPjBA5W7NGiAcTZz78Y8Pfh6q4CkUipSjvBxYuXJiSVYUCoXCZ/TtVy5chUIxhhgJg7jIpNNOA1atAk49FXjgAV475piDn4uSrCgUipTj7ruBj30M+MUvgAULJno2CoXicMb69QzP/Jd/meiZKBSKqYaBgeSN1ckyiA8MACtWAK++CuzZA1xyCSOe8vOB7dtTQwilHjyFQpFyzJ4NVFZqiKZCoZg4iDVdcu+mTZvY+SgUiqmF0dT0LS8fvn15OZU6Y4BHHwUuvphyas8eXn/uudHPWaAKnkKhSDluvx147DG+KhQKxUSgvBzo7ATOOYfHhz880TNSKBRTCaOp6ZtIKZTP8hqJALfcAvzmN8D55/Pzzp289tRTBz11VfAUCkXqMXcuUFDAV4VCoZgoRCKAtWSl+9a3Jno2CoViqmGkXALxlEJR+u6+G1i+nK9+dMGppwI//KFT8J5//uDnrQqeQqFIOSIRoKKCrwqFQjHeECt5eTk3UtOnA8uWTeycFArF4YF4SuHZZwNXXAFs2wZcfjk/r17Na//2byRa2b+fn+X1YKAkKwqFIuW4/XbgoYf4unz5RM9GoVAcThBreWcn8Mgj3Eh95jPA2rUTPTOFQnGoI0jKMjDA8PDCQubdRSLAxo1AWRnw2mvAJz8J/PWvQHa2y8F7+eWDn4d68BQKRcoxaxaTh2fNmuiZKBSKww2Se7dyJfDZzwIXXQT88Y/AtddO9MwUCsWhAj/HTkqwxMu/GxwkJ8Hy5VTuBgeBk06iYpeRAXz/++xjjOtj7cHPTxU8hUKRctxxBwXUHXdM9EwUCsXhAn9T1dAA3HgjcNddwJIlwO7danBSKBQHYjQlCfr7Y+trLlrklLxg/l0kAtx0E9DdzdIIf/oTwzQff5yy6a67aHw6+WTXRxU8hUIxKTF9euyrQqFQjCXEcu4XMy8vp6I3OMgN1Qc+MHHzUygUkw+jKYPQ3w+sWcMoAYAK3J138rW9/cDxly8nydOGDcB55zF15StfAY46CvjiF4FzzwX27gWuv971y84+6EdTBU+hUKQec+bEvioUCsVYQsIy16zhBkw2bMJWZy0Q0h2PQqHwMNIyCAMDTrmLROiRk9BLkUF+W4DK3SmnAPfcA1x1FTB/PvDd7wLf+Abwox9R6cvIiL3Pjh0H/2xKsqJQKFKO++93rwsWTOhUFArFYYKGBhKqrFrFkMyzzgLOPJPW8SuuANatA/r6JnqWCoViMmEkZRBEiWto4Ofubne+v5+smNYCl15KRXD9euDrXwcuvBB4xzvYVkif7r+fCt599wH79jEHLxWhmQK1ZykUipRj27bYV4VCoRhrDAzQQt7RAezaxZCoa64B3vIW5sBooXOF4vDBaHLrkhlzzZrYsUW5W7OG3riODnr0OjsZHn755WTwHRwErrySyl1nJ/CFLwAvvshwzHA4tcodoB48hUIxBhDhNxYCVqFQKOJBwq0AID+flOQAkJcHPPUU8MADrq2GayoUhy4kt24k4ZfJwA/plHtIfbvzzgO+9jXg738H6usZdrl7N/udey6Qns5yCG+8wWs7d9IIXlICPPts6uYoUBGnUChSDhGotbUTOw+FQnFoIp7xSOpPlZfTexeJMGRqzhwy1vk05KkoJKxQKCYnRppbF4RP1hRvbHk9+2zg4ouprM2ezTDwH/8YuOUWKn+ZmUBbG/Dgg8DixQzd7OqishcKAVu2AM88kxpSlSBUwVMoFCnFwADwv//L90ceObFzUSgUhx7iMd/195PwYGAg9vxrr1HZ++QngVdeGfepKhSKCcLBKHd+2QMffnTSwAA9d9ZSsRscpEHp8stJqPLZz1LxmzuXeXgXXcT84DvuoLFp/36+ZmQA27eP+jETQhU8hUKRUpSXO8Xu17+e2LkoFIpDC+Kl88OkBga4sXr1VWDjRrLanXYaN1wrV5LA4PHHgU9/eqJnr1AoJhPiRQJIDc2GhgONSFKKZflyypfubraNRFwO3ooVPP/FLwLnnEOlrrAQOP54lkb45jeBd7+bY1rrwjhTDVXwFApFSjEwwBh0QEM0FQpF6uB77vwcGID5dsbQov6hD5Fk5ZxzSGqwfz+waRNw3XUTO3+FQjE5IIaheDXwhKzJL2Y+MEBW3rPPpjK3ezeNRwJh17zwQjL3PvssyVaWLwceeYTnTj4ZuPpqoKoK+MUvxv4ZVcFTKBQpx6uv8vWxxyZ2HgqF4tBBMK/G/xyJMNRp+XLgy1/m9fPPBz71KZZJ2LRpbPJcFArF1IJvGPLliSh6IlcaGhxp08aNwJ/+RAUOoGwxhl689nbW29yyxZE7fepTjBzIzQWee47v166lkvjkky4fOD197J5TWTQVCkVK0N9PgTg4CFRWAs8/D3zkIxM9K4VCcSjBz6vxPXkAkJUFHH008KtfcbP1la9wY5aZyXyY731vYuasUCgmD+IRsIjSJwXMfZx2GnDDDSy1cvTR3ONI7h0AnHoqvXP79jH/btUqICeHBqXnn2ebRYsYJj44CMyYwdeMDKCszLVJNVTBUygUBw1JSr7xRoY2VFcDf/wjk4m10LlCoUg1/A3ZqlUkUlm+3IVlXn45Fb6f/ATYupXnxirXRaFQTC0ECVgkxHLVKpYuKChgHt3gINDXR0PRunU0FgmpyiOPAKefzv5f+hLZM4uKgLQ0MmOWl/N9djZw551st2gRcNddjsV3rJQ7QBU8hUKRAjQ0UIA1NNAi9cEP8nxe3sTNyRizAcC/AnjRWtsQ5/oCALcBeDJ66lZr7drotfcC+CaANADXWWsvGY85KxSK5CAbskiE4U4bNwJnnMFwzC1bSEculvjlyxkmNRZMdQqFYvJDvP1DoaGBcmP1aldDs6EBuPlmGokeeQT4j/8Abr2VMub005mOYgzw0EOUPaedBuzYwdp227cDe/ZQyVu6FLjtNu6TIhEqjmMNzcFTKBQpQUNUhYpEKOAAxppPIK4H8N5h2vzOWvu26CHKXRqA7wB4H4BjAJxijDlmTGeqUChGhIEBstYBtLTPng3U1fHzhRfSkyebqBUrmINXXe36p6WN63QVCsUEIR6ZSjz2TID7mO5u7mM+/GFGJ118MUlS/uM/gNtvJ1NmWRlw7LGUQQUFNC6FwyR3AqgQ7trF47XXgJ/9zN1jvMq1qIKnUChSivJy4GMf4/uamombh7X2twC2jqLrXACPWWufsNbuBnAzgKUpnZxCoRgR4m3IgoQr3/se8E//RHa7nTup5C1fTmt6YyNzgwX79o3LtBUKxTgjKCuCOXei8EmdO2HJ9PsJqcqWLSRUqa9nPbtTT2X+3cqV9M797ncc9wc/oJJ37LEMDc/JYTjnrFkM6czKcmPv3Tumj/8mVMFTKBQpQyKr2CTGPxtj/maM+V9jzLHRczMAPOu12RQ9dwCMMWcYY+4zxtw3OB4xFwrFYYigBd5nwRsY4KZLSJ6uvJL5eFlZfH/ppbTA5+YCra0T9wwjgTHmvcaYh40xjxljVse5XmWM6TXG3G+M+bsxZvFEzFOhmGxIVPrAr5lZXs5yB2vWUG6cdhqPU08lG2Z7Oz10c+fy9eKLmb97+um8dt55zMErLSUr5sAAcNJJrG/3+utkxrSW3rsnnmCqiuT/GuPIWcYaquApFIqUwLeK/ed/8twf/jCxcxoGfwVwpLX2OABXAfjvkQ5grb3WWnu8tfb4SJB6S6FQpASSbxevPMLgIOturlxJGdTQwPyZG27ge1mWb7xBmvLJjiRDxC8A8GNr7RwAHwWwfnxnqVBMTsRjyAQoG1asoEe/v59euFNP5bWMDHrpdu/mecntvfpqevCspbcuI4Njl5Xx+oYNDLd87TXg+uuBadOAp5+mQrdjh1Pqdu925RDGS7kDlGRFoVCkCL5gvfRSCtMzzpjoWSWGtfY17/0dxpj1xpgyAM8B8IK5MDN6TqFQjDPEEr9mzYEhmQA3Wj/+MXPwpP3q1cyjEVx5JV/f+16SrUxyvBkiDgDGGAkR/4fXxgIoiL4vBDCGXHwKxcEhGYKTkfQZbjw/FNOXF8KKCVApO+MMYM4cygcxBJ13Hl8/8hEqZk89RcP1unXAzJnAZZcBjz4KNDXx8623Mtx7+3aOvX8/8MILHCM9nYrh7t3Mzwt5LjVh0RxLqAdPoVCkDCJMf/ADvl511cTNZTgYY6YZw3Kjxpi5oDx8CcCfAdQZY2qMMRmghfxniUdSKBSjxVBh3X4opu/B868vX84Qqo0b2XZwkPl3AMOtpHTCn/7kzk9yJBMi/lUApxljNgG4A8DZ8QbSEHLFRCNRyORo+yQ7XjDPTrBqFQ1AK1c65U7q9/b3k6DplFMYFZCRQWKVdeuopD39NMeoq2M9vOuvZxhmWhrQ0UHPHgAcfzxfZ8xgiQSAeXd5eVTsxkO5A1TBUygUKcbAAOPbAfc6ETDG3ATg/wC8xRizyRizwhhzpjHmzGiTDwPoN8b8DcC3AHzUEnsBfA7AnQA2gqFQD07EMygUhzLi5db5kKiAwUF68IJECBItALDu3dln0xK/ezfZ7k4+GfjQh1jX6stfBj7/+fF5rnHAKQCut9bOBLAYwA+MMQfs5zSEXDHRSBQyOdo+Ixmvs5OKnK/kbdjAEO4bb2T+XEODkymhEEs8PfQQP69cyTw8gGQpxtDrl59PD19GBvD1r5NQJS+PXsGSEne/bdvouWtv5/nXXsO4QhU8hUKRMgjhwb338nPQejaesNaeYq2dbq1Nt9bOtNZ2W2uvsdZeE73+bWvtsdba46y186y1f/D63mGtPdpae5S19uKJewqF4tCFv1kTb1w8y/yaNdysAQda7yMRR6Zy1VXAPfdQ7px3Hq3r69ZxY7Z+PUOqpgCSCRFfAeDHAGCt/T8AWQDKxmV2CsUIMdLwzOH6+NcSeflWrGD+3AMP0IPf3+88/A0N3KecfTZDLKXtUUcBJ54IvPWtVARvvJHjHXssSyYAwHe+Q8XtjjtoSPrjH0msctFFrIm3daur//vaawzd7O7m+fGGKngKhWJEGCo0YnAQePBBFgQFJrwOnkKhmOTwN2sMmD4Q69dzUxa03vf3c6O2ejUVvbPP5maqoYHW95ISyqONG4ELLuBGbgogmRDxZwAsBABjzGxQwdMYTMUhgWTDOYcK17SWIZPHHEMClUiERqI1axi63d1Ng9CGDVTMLryQuXWPPAI88wxlR2cnQ7tffZUK3K5dwOOPAw8/DFx3HT14t9xCz9/ChbxvKERlMRRi/h3gXscbquApFIqk4QvUeEK1oQG46y6X6/Lyy+M7P4VCMTUhJAhB67zk4PntAG7SVq6kFV2IEa66irkwH/wgGTOfeorlEs44g69Tobh5ohBxY8xaY8wHos3OBfDpaHj5TQDarB1Pfj6FYmwwkpy9ocI1N2ygUpeRwRxdYczs7GQ497ZtVOK6u4Gf/ARoa+P1khKgqop9PvYxGoUef5w5dDt3Urb87nfAV77CcxkZNEz98pfsL/l18rpv3/jl3AWhCp5CoUgaIlCBxEI4EgGej3K6vetd4zc3hUIxtRFU7oLlEUTe9PcDH/0oQ6MAbsYAtr3wQubFHHMM0NXFsKt167gZq6sbt0c5KMQLEbfWXmit/Vn0/T+ste+Ihpe/zVr7y4mdsUKRGow0Zy8oM0RBHBzktRtvpAfPGBKsRCLMy929mzm6g4M8Vq2i1++3vyWZyllnsX99PWVIWhrLIHz/+zRi33orC5nv30/ZIuaVjIzY+YUmUMsa01trsU6F4tBDeXl8ISybr/JyFgAFgDvvHP/5KRSKqQt/k9bfH0uuIkalhgbgV79imNTatW5TFYmQAOGaa4AjjgCOPBIoKuJrYWHiEFCFQjF5MJqcPcm7Gxx0RcwHBvj58suZp7thA3N029tZF3PWLMqMRx4BvvtdKnAXXcSQyqOPJptmWRkjBXJzKUuOOgq4+WZg0yZX684YJ4OEQfOII/g6Ud47YAzr4HnFOk8EaX7/bIz5mbXWr+UixTqvjhbyvANA9VjNSaFQpA7xQqlE6XviCZ7fuHFi5qZQKCY/gvWsfDkikQI+CYvPyhuJkJRl925u3mScDRsod3bv5oZPig+3tzOsSqFQTF34MsM3Kkt5lHPOobIlyt6qVZQFkQg/33gjZcpNN9HrdvvtwKc/TYVuYACoraVS96c/0YuXlkZlsLwceOwxfn7+eZKnCPzg6D9EqdpefHF8vo+hMJYevDeLdVprdwOQYp0+tFinQnGIwPfo/cu/8PVf/3Xi5qNQKCYv4uXa+JEBg4Mu/25gADjtNFKTn3aa69PRwVepADAwQOXu5JOZU2Mtre5NTbz+xhvuXsFQKoVCMbkhTJji0V++nIqcePrXrQNuuIFyob2dyl5HB88BVPbOPhtYsoTK27p1LIPwve/Rw19TQ6bdiy5ijbvTT6eX7uGHgc2bqdzt2sW8uszM2OLlJSW8PpmiBMbMg4f4xTqbA22+CuCXxpizAeQCaI03kDHmDABnAEBVVVXKJ6pQKEYH2WiJ1R2gsL39dr5/LkjurVAoFDiwRELwVUojiNHohhuovEkx4dNOo8U+K4ufZcO3bRst9gA3YB0dwDvewb7hMPNlANdGoVBMfsSTCZdeSuOO5Oo2NPB8eTkLlM+aBVxxBYlUBgcpG1avZghmfz/wlrfQ07Z+PaOOamqo+G3dCvzf/wHPPksFTgxFL7/sPu/cCVRWkjFz504qgEuXAvfdx32PMbGePUGi82OBiSZZ0WKdCsUUhc9w52/UVq8G0tN5fsaMiZufQqGY3BCZIfl2Yp0X5U82bAA3aF/5Cq3yg4O0pGdlkUBBPH4bNjAnLxRivaojjwSuvprXzj3XKXcKhWJyQYzFwVdBUCaIwue/l7q7t95KWfL5z1P5GhzkvuT11xlmGQ6z2HllJfPpjj4a+PGPKR/OPJOkTenpLJcQDtOL9+yz9O7V1wMVFfTgGcNrorDddpszaidS4saT63YsFTwt1qlQHMLwLfDl5U64XnIJ8E//xPd+nLpCoVAE4W/c/DBvUf4Avp51Fr1wu3bRag7wnNSyWrSIBAplZWTLfPppbtKkwPD555OARaFQjB+SKXcQNPL4xh4fvkwIEr0Fa9x9/evMlbvgAsqWSy4h+du119IAdPvtZNMsKwNaW+nBe/pptgPo5QuHGY4pBuv3vx945RW23bWLSp+EZEqophQ5nwwYSwVPi3UqFIc4/JpUra3MkVm5khstAHjhhYmamUKhmArwSRPiEa7Ihq6wkPWq1q6lhb2/n965U0+lZ+8zn2G9u3POYejW3Xfz89//ThKFyy6jdV6hUAyPZIuNDzeGv4YTjRk08kQizJXzFTrgQMVPDMvt7exz6qlkzNy9m977RAyWjzxCZe7MM0mmkp9PxW//fhY1z811pRQ+/GHm66anAz/9KfDkkxyjuJjeOPHIZWYCBQWudMukyCaz1o7ZAYZdPgLgcQDnR8+tBfCB6PtjANwD4G8AHgDwnuHGfPvb324VCsXkwebN1p50krW9vXy/ebO1VVUUfUcfndwYAO6zYyiLxuNQ2aRQJIfNm93rSSfFfo7XTt77sqalha/W8rW11driYmt7etimr8/ahQutLSqytqPD2rQ0Hm5blvx8VT4pDicE12Uy7Ye7lsyYssYXLrR22jSua+nT12ft4sV8lbZ9fW6td3dbW1HBPj09lA/FxXzt67O2sdHauXMpJ5qaKBeMsba6mrKgutra2lprw2Frs7MpK9LTrc3NdfIiJ8e9D4U4x1DI2sxM9vFlSyqOZDCUbDK8PnVw/PHH2/vuu2+ip6FQHLYIUpsHzw0MMHn51VdZE+bPfx5+TGPMX6y1x6d+tuMHlU0KRWL4BCp+SRXfQt/eHkuWEG+MwUFnrTeGJRIaGmjJP+cckrFIm3vuoefu8cf5+amnmDMjSHb7o/JJcbgh3u98onb+eh6uLRC7V/Df+2RtUmJJSJVWr2ZYpDBiSomU88/n5/e+l2GZJ57IaKLiYtax++Mf+fkLXwBmzwa+9jUyXgIM8S4qAv72N3rvMjMZKbBli5MTWVk8NzAALFjAyACAHj0JywyF6PWT0HEfEuY5GlUrmT5DyaaJJllRKBRTCPGozYFYwV5eDlRXj+u0FArFJIYvN4K5M4Aja5I8GsnnDY6xfDk3egApzgGGhPf3U4G78koqd2edRUa79nbgi18EbrmFJAplmuGvUCSFZIuNx1vPQ8EvcxAMt/THufBC5tWefjrX/CWXOOWuvJzsuK+9xnDtsjLg2GMZQhmJsBzCww9TnnzoQ8CcOVS09uyhDPnwh4Hf/pa5dCefTDK4Xbt4PP88XwEakEIhsm9mZDjl7rjjOJb02bEjvnIHkLjFV9QKCuK3GwuogqdQKJJGssL8Pe/h6/vfP/ZzUigUkxtDyQ3/WkODU/LiGZE2bCCBgrS98kpuwlauZP7vOecAn/wk0NfHDdmqVRz7wguZa7Np07g8rkJx2GAknr4gMVtQJvi5uLfcAtx5J5kphfgEoFJ4991c0w8/7JSx9HTgH/+ggefEE2nkyc9nQfOtWykbXnyR3rQjjgC+9S2WUfjpT4EvfYkeuJoa5uJVVbGm3cyZHHv79tiyKn/7G7195eXJ19NMS+Pra68l1z4VUAVPoVCMCImEuZ9IfdVVfP/974/PnBQKxeRGMBQrWOBcEGTT9OWKbAzlcyRCq/4FF3DDdcopjgShrY21sF5+mUQr3/kOQ8cVCkVqkCiiZ6h28RS6eP3LyxkSOTjIvq2tDNs8+2zWtnvjDXrl2troPUtPJ7HKI48A//zPLF5eVQUsXgx89KNAVxeVK6lDV1EB5OQADzxA2RCJ0KP30EM0BInnrrwcqK3lkZfH6KTMTI7xyisHhlGGE1QX37cvqa80pVAFT6FQHDSCgl6KD08KJimFQjFpkMibF0/hi1cjD+DnFSsYbjU4SGv+rl3AD3/ImlZHHUXrfFkZC6Lv2+dyehQKRWqQbESPFCKP1y4R0+bdd3Odr1rFz+vXM3/u4ouB885jHt355zN8c+lSetkefZRtduygVz8nh+UQLriAciEcBj7+cebkPvMMPXvbt1M2PPcccMIJ9OBNm8YQzPR0Goyef56FzCMRegF37+axaxfb+ThcyiQoFIrDBL6gHxx0YQuFhRM7L4VCMXkguTfxlLtEub3BGnlS1Pi885hfs2ULre5r13Ijd8YZpEA/6yye/9KXSF2+Ywc3bgqFInVINjwzXti19BflT+TArbeSNOX002nIKSiggiZKVVkZ1/Qdd3B9Z2bSkLNrF/Pj9u1j6KWUQ7jiCq7/6mqGb19zDUlXnnuORuhQiArj3XeTHO7TnwaWLWPdvMxMKm2vv86x9u+nF7C6mvcKhdhGsHMnFcMTTkjRF3wQSOBMVCgUhyKSjZcfSX8/SVoEeV4eLV2PPjr6eykUikMHQpJijMujE8gmLx6Chc+l7emnM69uzhzmzmzdyrpWe/dy83XBBcC99zJUKz2d5++/f8wfU6E4bBFkxfTXbrywa3/PINf91+uvZ97tJZdQWQOoaN11F71wNTVU5K65hgrgtm1MD6muBl56iQrak09S4du2jcrYwADw7W/Tg3fUUfTq7drljNEnnMBaelLPDuBeBmDb3bspT5qbgR/9iOclDzAUooIHAH/4Q+x3k5Xlro0X1IOnUBwmSDZefiT9ZdO2YoU7v3498LnP8f3ppx/cnBUKxaGBIElKUI6sXk0rfFA++WGZIn8aGrjpmzsX+MY3aEG/+mpu9BoaqESeeSbwm98w727tWuCf/onePYXicEEqipUnO66/Pxgqz3ZgwIVX+wYbKVousuHWW6k4nXcevfSnnAK88AJz5r74Ra7/Sy9lfu1llwHvfCeZdSMRjjFzJhW8Y44BsrN5ZGWRMTMvj+3E8JOeTg9fSQnwf/8Xq9wBlCcAlbv9+zmv22478DuIV1g9PZ33Hm/lDlAFT6E4bDBSOuNk+vubNoBCfXCQ7FeAe1UoFAohSYlHkX7JJbGbIH+j2N9PBfDss91G8KqrgOuuc6UPrOUGbf16hmJecw03Zq++yg3k4CDbKxSHAw7WoDvScYdjyBQMDnK/sGGDG2/NGoZHLlrE1/JyKoBPPsmcurIyeuoLCuglmz2bxp2tW0m2csYZ9ObNnMmwy+3bGU6ZkcFcvKwsyo/0dObSffCD9AD29THKKCODzN+vvcYxKispOxYsILumKHg+JNcuEok9H/K0qsxM5uj5tTfHE6rgKRSHEQ4mPNPvH7TMyRGsYyWMdgqFQiHwN4CiyAHAY49xAyibyMFBtotEqPxdcQVly5o1LqTznHP4umQJrfwAa2QdfTRz8B59lBu3p55KzHCnUBxqOFiD7mjGjceQCcR64Rct4roGYtf4ggU02lx1FZW8G29k7Urxyl13Hb31BQXAV7/KdX/llVTAACp0O3ZQ4duzh4rWBz9I0pR9+5ivJ4rbkUfS+5eWxnMf/CDvtXcvlcZNm2gw+u1vGbop5RIAp+xJ3buXX44973vxQhOsYamCp1AoRoRgKIaPhgZa2V94gZ/Hs6inQqGYfEgUziXKnYRrRSLMrQFijUWDg/x8wQW0/AvhCkDr/LZtDKm6+GISJmzZwpp4W7aQSTM9nYrdzp1uY6lQHA5ItXKXaNyhvIT9/S4cMxKh4tbQwGudnY4lc2CA16QMwtln03O3ejULk4sh5+1vp/EGAC66CHjrW8mee+SRzLvLyGBI5L59DM3u66PiBlAZLC0FPvMZ7lH27WMu3g03kJSpuJgyo6GB5RcyMsiguW2be+6cnNjnk7H9cgklJXydKM+dQBU8hUIxIogFD4iv6M2e7cIXZswY//kpFIrJAT/EUj4Ha2JdcgnDtWTTuGgR2/tFz+++Gzj1VOfdA7jh272bm7C8PBImrFrFWlay2dq7l4rdnj3cdMULtVIoFAcimdp28pooFHRgwK3TwUEaXsQLv2IFjTB+AfH167l/2LmTpU9Wr6ZymJVFL/zFF1MRAxxT9/r1vP/3v8+c/z17WArhbW9jqPbOnbzHsmUM137qKaCoiCHcubnM1c3OBhYuZP+cHLb5wQ+cB+7ll6lsVlQ4D116euyzZmTQG3jEEfQCApQ3UuBcMJ4ySBU8hUIxIvgFh4OKHsC6M5JLI/XwJgLGmA3GmBeNMf0Jrp9qjPm7MabPGPMHY8xx3rWnoucfMMbcN36zVigOHQTDtles4PlgyQOASlxDA/N2xcIv3roFC3g+EqGs2biRStzatcAtt/A1MxP41Ke4YTvySG7IcnJYoDgnh5s7n85clT2FIj6Gy98Tz7vsBYYKBc3IYCjlli1kwjzvPK7jLVuYXwe4YuaCCy7gWj3vPHr1TjmFn4Upt72dJVJOOYXhlcuWOeUxK4vMmmvXco3v388cvZtvZri2ePR37ACmT+f427eT4OnyyxmeOXMmvXtz51JBy8ykd9AYl/NbWurmW1ZGJfLZZx3jJkAZFSxwHiyMPpZQBU+hUCSNeOQIPsX5rbcCn/0swyYmAa4H8N4hrj8J4F3W2kYAFwG4NnD93dbat1lrjx+j+SkUhzxESYtEHJ24T59+9tlU2N73PqfkCXyq9YYGJ2suvJAbqiuu4ObwqqsYrhkOM7fmjjvY55FHeK6+nnWvfBKX8dxoKRRTCcnk7/nrZ6h2GzZw7V91FZWsBQu4Zp94gorcDTfwuhiLTzuNte0Atjn7bLJm7t5Nj9s11/B9Tw+9eU89RSVu+3bO6ROfAPLz2XfzZpKv3HsvFbp9++iV27uXXjtfNsycSWX0ueeAd7+bz3T33fTKrVjBsSsrqQCGQsArr3COOTlUFCsr+Vk8dpPBgKQKnkKhSBpBcgQgluK8u5sWMcnB+/OfJ26u1trfAtg6xPU/WGujKdK4F8DMRG0VCsXoIRtACasS9Pcz9LKsDPjf/+XmTxA0JvnehI0baaG/5BIXynn00cCxx7I2leTa7d5Nq/3jj7uaVQqFYngMpbQJe3aiNhKKvXy5O7d+Pdf5wAC9d8cdx3BM33s3OMhQzEcfBVpaWHD8t7+lcnXGGVTcAJIxrVgB/PGP9NBXVZEMJRQCfvc71qj793+nIjc4yNIKL73EvgUFJGQpK6NcePFFGooefdSFZK5fHytvfvlLegZ//nMqbkcc4YxF+flUMrOyGBIqHruiovgkK0VF46f8qYKnUChGhCDNeXk5Fbsrr6T1/Oij3QarsXFi5zoCrADwv95nC+CXxpi/GGMSVs8yxpxhjLnPGHPfoDI4KA5TDJWv43v7g5tCPyTTV+6kvdTH8uVNJMKNIUDD0t1383XlSoZr/fWvwCc/yWgCyc/7whdomVcoFKnBUMqdsGMa4xS4jRuBE08EPvABhlaedRavnXOOiwBqb2c4ZG0tcNJJDN++6y4WLv/hD4HWVubh7dnD9itWcF0LQ+6555I5d80aEi+deSZQV0cv2+bN9LQNDpIbYNYsGpdeecUZnt71rthnycxkv8ceY0hmVpbLBxRs28bj0UfZVvDyy/Hr4r3yiiNhEYyVwmfsFItTOP744+1992lKjEIx0fDDpwYG6MHLzKRVvamJQvi442g9Gw7GmL+MRSikMaYawM+ttQ1DtHk3gPUA5ltrX4qem2Gtfc4YcwSAuwCcHfUIJoTKJsXhCNnQxQvp8q8BQ3sFfHni9+3spAIoRC2RCPNthGAlP59lES68kNdfeYUbsh07gK4u5uO94x3sU1ISW6A42e3PWMmn8YTKp4lD8H97qsNnwQXir3upV9nQEPs5EgHuuYcyYfdunrvuOq7ND3+YZQ0uvZTetyef5GtdHfcTGzdSabKWnrCZM+lJe+IJGnEef5zlUh56iF65b3wD+PWvSaCydi3HOOIIehD/5V+A//kfKntSFiE7m3OSc1lZvN/OnVQSjzqKYaKbN1OpBEgK9ctfsn9amvPgZWTwmZ99lu/37TswH284JCOfhpJN6sFTKBSjQlCoZ2QwKfpPf3IWNnmdrDDGvBXAdQCWinIHANba56KvLwL4KYC5EzNDhWJyY7i6WOvXOyt+MqQN8tknaLn7bnroTjyRm7w33uBmbu1aegXLyih/MjNZI0vyYP7+99haW0KvrlCMF8aq4PhEwWfGXb48dt0KfAIlKVwOuPza7m56rdraqECdfjq9b6LQPPooD1+mHHEEwzSl8PjSpcA//kFlq6wM+OY3qdzddBNz7r76VeD3v6cn8Kc/BT72MebpvfACPW633QbMm0dmzJwcKp6Sp/fCC5Qn1sbWt0tPp/F661bn9bvzTrYJh2MVuCOP5JjLljFUc6TKXSqgZT8VCsWQSMb6WF5Oq5sUIM7OprA84ojxmeNoYIypAnArgI9bax/xzucCCFlrt0XfvwfA2gmapkIxaTBaT8SaNSRLiNdXNoeyuZMN5NlnM2xTlLz2dlr5y8pYu+q733U5PGvWkLChrIybqpoabuK+/31uKm+9FfjVr1xusEIxXhirguNjjURr3X+eSy/levMjeYIESqecwjBLnzypo4OvX/sa17Cs/e99j972GTPo+ZJSBC+/TIXu4oupDK5fT8KW7GzWrnv2WXrXABI59ffT2LN4MQ1Dr7/O3Ly0NN4rM5Oeuj/8gaGX27fH1qzbs4dHVhZlzo4d9OL19TEEPDPTFVOXMMzqaub2iVH7uec47l//Gv/7zcqKJX0aC6gHT6FQJEQ862O8AueAY8traHCW8tdfH595xoMx5iYA/wfgLcaYTcaYFcaYM40xZ0abXAigFMD6QDmEcgC/N8b8DcCfANxurf3FuD+AQjGJkMgTMZyHQjxxV111IGGKEDEMDrr8PNkYnnqqC8s8+2zeQ8IwZ8+mjFmxgrXvTj2V19rbWabloYeAz3+eYVzvfS8VwHvvZeimQjHemIrK3XBeR/HQSep5sOYlwHV67LFUAgcGeO3EE4F/+zd67IyhUnfJJfTYrVvH9fvcc05hk1DNSy+ll2zhQipTu3ZRudu2jd4ygPefN4+hm3Pm0Iv3+utU6vLyWB9v1y4eRx7Jv8tLL1HZikTcvCsqeOzcyULnAK9bS6XTGBeSCVDJ27w51qAdr0RUQYF779f/i4dg/bxRwVo7pY63v/3tVqFQjB82b45939pq7eLFfO9f83HMMdYC1h53XHL3AHCfnQTy5WAOlU2KQx2J1nui8z76+tzrSSc5+dHaau2SJe66jNfXx6O8nK+9vWzb12ftwoWuf1+ftS0t1hYVWZuTY21zs7WFha7PokWURfGOZKHySXG4Yai1Luu3p8e9t5brzf8s7Tdv5p6hqYnrLhSytriY7aVNU5O1c+daW1DAo6vL2tJSaxsauK47Oni+udnaxkZ+Li21tr7e2rw8a9PTrW1rszYtza3vzExrc3PZb+5cjinXsrOtnTGD76dPd+cLCqw1hv2ys63NyHDXRJZkZblz6el8ra7mPAFrFyywNj/fzaGsjK/GJJZFo5VPQ8km9eApFIcRRpoHEC9MIyOD1jQgPo35wACTngFa1xQKxaGBRJ6I4TwUfj6OhHGLt+7KK2nBX7PGWfmXL6fFPBKJZeKVvBexjpeXkzABIBvf9u3Mefmnf+L51auB++5j3k5d3YFlGhQKRXwkCqf282rPPpsHwHV7+eVubQfH2rCBIdNNTcDb3kZildmzOeY99ziClO3b6Qn76U/pZevsZK26664je+XzzzNU8he/oHzYu5chk5/+NHNuAeCEE7jWd+8mc6YwfF97Lb1vGRl8fe65A1kxX3vNFSg/8kjHnAmQTAXgfevr6Uncv9+RqOzcyTDx3/yGcwLoLdyyha92nDktVcFTKA4TjDTZO14dKhHUDQ2OuMCnMe/vJwmCJCbHC1NQKBSHF/wwzc5OFw4lip9f6HjVKhqGBgedArhyJXN5OjrY9pJL2HbDBpIo9PdzrHCYm7KzzmK+ziOPMIzrjTfIiqdQKJJDvFDs5cu51srLXYmTsjIyaK9cyXUra7u/n4fsCwYHee0b32CY4/r17PfhD3PcV18FfvADlkh46SVXDH3dOubBnX46wxZffpm5d3v2MKzzsceA97+foZ6PPMK5/eEPVO5OPpnK1b//OwugP/ww55uVxTIJeXnAMccAF13EfqKUlZSwTXo6ldBjjqGR6OSTeT03l8Qs+flOkXz2WSp7W7fyOV9+eWShuWOyV0rk2push4YZKBSjRzKhVH5bae+HZVjL8KeKCr765xYutHbaNGtnzWKIwQknJHcvaAiUQjElkIwMiddG5Ek8WeL3kVDM1tZY+SOyZvFia2fPZphWejpDqqqrGf7U3s5Qr+Jiho+1tjIsKxRiiJSGaCoUwyO4RuWcn54h55YssXb+fK43udbXZ20k4s63tPDz3LnWzpvHEOreXte2p4fhkOEw13VjI0Ou8/LYrreX+42ODtdf2lVWMmyzpsbakhKu97Y2yoPGRoZa+uGXAM/JvBoanGwwhu/DYe5jRI50dvJ6YSFlTXMzQzALC3n/UIjhntnZDNMMhThWbS3vVV7O55F5lJVZu2zZgfJIQzQVCsWokaxFyS9WKv18JjCx3kUizsvX0ADceCPwne+4EM1XXkn5IygUignCcFEAQsAUj5ipvZ3vfVnih262t/P1qquA88+PDacUjwHAYscbN7IO1TXXkEihtpakDPfeyzAtCbk6/3ySNnziEwyRUiiCOFTKFwCpe5Z4zJ/l5cANN9Br7p877zyGVV59NcMgy8u5L5g5k5E84pm/5hqSjLzyCj3qskdYvZoes4wM9gHoHSsrc+3Kynif66+nF62+nh75PXv42tdHD9oHPsAIgN//3qlJF11Ez/60afQwtrUxTPToo1lm5amnKBvy8+kZnD6dzJqbN/P+55/PepqNjSzT8MYbfE1LoydPaui9/DLn/rWvceysLIaTRiL0IvrpKlu2AFVVfK4xRSLNb7IeaoVSKMYHPhnCUAhe7+tzycTqwVMoDi0kQ76QyIMX770QMwhhg3jqfNIVa5086uujRb2nh30zM/m5tJQW++5uHvPn09qfm0tre02NtVVVI7eQW6vy6VBFPE/VVMVwz5Jo/Y32PrJOW1ro8ZozJ5ZISbxffX308m3eTO9acTG9XEKysngx2+XkcJxQiOtYCEtycuj9Ki7mWi4tZfumJq7zjg6+nz2b7bKzSbySk8PzfX30sjU20sPf2sr3S5ZwnPp6O6wnLS2NXkHxMPb20rvotxEvoHjtZA8kJCzxjlDIvuk5VA+eQqEYU/gWQCl7kEwNPB9ShwqgtUuhUExt+HLBX+/B82KtT1Q/S/osX+68fVddxTIH3d0kbIhEXPkDaSOevlNPpafvG98AzowWOzn2WOCOO9jvoYeYs3P55bTqv/IKiQ/eeAN48cWJLduimHyYqjXq4mGoZ/G96ok87MlA8vBPPdWVSFi5kt6tzEy+Hxigh+r114HLLnM5s/fcA3zpS+xfVcV8tbIy4EMf4prdvZtESUVFzMvLzaW3LRwG3vIW4Ior6HmrrWVO31//Sk/alVeSoCU7G6is5DjPPMP5Pv008F//Re9eXx9rZz71FOXEwoXAZz/LftnZHLexkcQp+fkkZQqF6Inbt4/j7N3LmnhPPMHC6m1tvE8oxEOwYwefTerlFRXFfo/haAVyqaEn9f5SDVXwFAoFgPiCP9FmbqgQrY98hOxUgLJoKhRTHSOpfzc4ODyR0+AgQ7cGB3l0dlK527aNihnADaGEgkloZ2cnw8D27GF41CWXkAAhPZ1kC5EIN5iPP84NnIRk1tSwjRAgKBQ+DgXlTjAUy60of0FFMFnyNWl3992uTmV7Ow0txlBpef11rukFC4ALLmDY5T33kPTo6quBr3+dio+EV/7pT7zW2sox5s0jIcq3vsV9xH//N8dcvJih1mvXst+aNZxTRQWVtg0bqBB+61skT9m5k/e+7DKOmZ3NsM9jjuE8s7KAwkIWYP+3f6PiFg5TTmzbxkLrq1cz1NIYyhtRyh57jAaoM86grGlr49jCrBkO0/82OEg20KwsjinEcwDv5yuEw9XEGzUSufYm66FhBgrF2MFPng6e98Ov4oWCSFhVaakLMZg1K7n7QkOgFIpJi2TDvuLVswu2WbzYEalIjTs/LDMoa6SeXV+f69vczBBMP8Srt5dhXKEQCQwk3EtqVpWXjy4EylqVT4pDG8Fw6ETw16SsRyEz6u7m+mtuZihmRgZJTfLzGTrZ1ET50N3NkMdwmOGVEqrZ1UUSFglpTEtjGKSEZYoMaG5mSKOQoAhxS0kJxy4stLauzoVxRiL8XFzMEM30dMoEuX96OglSSko4dlsb5UZODueelsZ+hYVsl5vr6uvJ/CU8MyuLIaJy1NVxrKFqcSrJikKhGHNI+EUiT14iC6D0XbGCVqy776ZFCyDdsUKhmNoQuRDvvA9rY2nSV6xw1OqC3bvpebvhBuBXv3Ket40bXdiXhHqedhrwsY8B73kPZVJrKz0AGRlsf+GFvId4+44+mtb6l16iZX/NGs4pM5PW9IKClH81CsWkx1DeOQl/llIjQ2HjRudRX7OGZUve8Q6use98h17yhx5y3vYrr2TbykruCU46ibXurr2WHrBvfpOEJl/4Ar17e/dSFnR2AscdR4/dW97C8MmyMoZwnnEGvV87dtALFwrRy5eXxzls2wY8+SSf58EHgc99DnjhBcqq732PnseMDN7r2mvpcRsYAD76UcqN66/nnHfsAD7+cYZr/u53fLbXX+fz79rF/l1dDP+WaIF9+9hv5056AZ95hv1+//uha3Dm5cV6+FIFVfAUCkWMUpcolt/f5PnhHQJrXa0bCT+oqxv7uSsUitEjmfwbqWc1VJi21Mj02TEvuYTMelLEPNhe6mn+/e+sW3f22dzEATy/bRuZ6BobgQ9+kEx6p5/OTeBPfsJN0+WXs4bWFVcAb387Q7gWLwZuuombxl27eDz88NTJCTbGvNcY87Ax5jFjzOoEbU42xvzDGPOgMeaH4z1HxeREMqHUPqRG5erVLjc2HjZsoBJ06qlct52dXHsbN1Jpe/55rvOuLrJqAlSo5P2rrwKf+QxDJs87jwaYigqu86eeYn7eY49xzLVrOe5FFzHfbft21rjr62M+23HHcR5PP818vwcfpDI1OMic3Nmzgd/+Fvj853nv119nn337XAH14mIqlhkZVECvvZb3qq5m2GluLmVIWRnvU1pKZW3xYmDZMo41YwYNVsbwe7TRQuZiZNq1iwrk9OkuxFNq7fl4/XWGgaYciVx7k/XQMAOFYmyQDPOdH5oZr56VhFBJiEFVVXL3hoZAKRTjjqHCrYNt/Hp1ybAPxmPtkxDNYOimH6JZXk4ZIvW1pF6WtS4Us6KCrz09DP+SEK9QiOFULS28VlBg7cyZLkxT6mBN5hBNAGkAHgcwC0AGgL8BOCbQpg7A/QCKo5+PGG7cVMinQ4Ft8lBGonWZTAhmsO6tf76nh2GIwj4p61Vq0mVkcN2Vl7vQyJYWHo2Nrt5cdjbDKyWEuraWbRsbOWZDA0Mfs7O5PtvbeS493a3phQsZupmfz2PZMoZRZmRQFjQ386ir472ys7n+i4vJmNnU5ORIfT1lQnOzY78UNs/sbLJvFhbaNxkv09LYTsbt6uIYs2e7ENDZs2PTVIQJNC+P86uujr2mIZoKhWLMEfTYBS1/Qc+ez5oH0HMnYQZDhSMoFIrJgUTh1vHWfUNDbD27eP2CCHr8y8vp0QNi7xGJOI/er35FkoZLLmEI1uzZbrx3vIOfb7yRHr9LL6X1+4knyKS5Zg29gffeS69jejo9DXl57C/18SY55gJ4zFr7hLV2N4CbASwNtPk0gO9Ya18GAGvti2M9qWTJOBQTg0TRN/39zoMuR7CfoLw81ls/MMAw6e5u4Ic/JCnJeee5UOr0dOCtb+VavekmerQuu4xjZWTQE5eTQ4/7Cy8w1LK6GvjlL1nz7qmnuCYfegi49Va2aWjg2q6vB378Y45z883ALbdw/VdWMgwyFAJOOAH40Y/oCWxoYDjnF7/IPi+9xLZ79nCepaWc16OPMszzjjscGdM738n21rKt1PDbtImewvx8hn/Pns0w76wsetw6O0koFw5znGuvpedRUlPKyhyj+M6dZPV97jl6+MbEYxeAKngKhSIuJHQjWOw0CD+085JLGGIhwuuII8ZnrgqFYnSIF4odr8hx8Jofsh0M4ZSc3GDIl4RuAs44FFQcIhG+j0S4mRwc5Dh3383N5vLl3Djt2sXQsNmzgf/8T+D++8m6d9JJDOlct46hWBddxDAuYHw2VSnADADPep83Rc/5OBrA0caYe4wx9xpj3jvWkzqUSgocagimWAAup27NGv6OA25N3n13bD9Zv3ffzfIk0n5wkMrSpZdyXQEMhe7sdGtq7VoaYl5/HVi6lGGWTz/Ntbl1K/DAA2xXW8vXl15i/lp+PpXDkhLgqKO4XisqGP749a9Tkbr0UipERx9NNs4FC5gjl57OkO277uI9p0+nIgdQwVy5kvuQI4/kfc88k0agRx5hm5wc4Ne/prK5axdzBUtLGT75hS84Js2cHOAXv6Cyt3cvwzrDYbarrub+Rozacq6oiM80fTqff/NmhphKSYQ9e6jg7duXoj/+UEjk2push4ZBKRTjg2QKp8ZjyfNDNCOR5O4FDdFUKKYU/NBNP4TTvx6UDcEwsNbW2BBMObdwoWPGa25meFZTE0M2hcEvP9++GXYpIZppaQzn6upimFRXlys6PFVYNAF8GMB13uePA/h2oM3PAfwUQDqAGlAhLIoz1hkA7gNwX1Wy8fKKKYlgyHNFhQul9NtIiLNcC6ZYtLa64uSLF8cWL/dZayMRx45ZWsp1Nn8+11xnJ9fh3Ll839PDdZydzXXb2OgKls+bxzFyczleYSHDIFtbXXhoZydDMBsaeK2xkeu+stIVTk9PZ7hkWhrvISGZ6em8LsXEly2zb7J0dnYyHDMnh/OQcNLaWo5RUcG2wpLpP0tlpQvlFOZQYeEU+SKsmokKmWuIpkKhmBAMVzhVrOn+ufZ2WtdnRO3NwqinUCgOLYiHv6HBhXAGrwt8Rk3B4CDrYJ18Mt+LBy8jgzW0cnNpuc/IYK2p/n5npY9EgOZmEjr80z9R5tx+O63oM2a4cLAf/YihUbm5tP5PETwHoNL7PDN6zscmAD+z1u6x1j4J4BEwLy8G1tprrbXHW2uPj6gwPqThr7eGBuDOO/nqE6KVl7sQ50jEhVsPDLDtpZeS3bahgWGZwk7b38/18+EPk8m2u5vhk9/4Br1Vl13G8MgtWxgiefnlXLc7dtCDvmwZ1+SOHVyfX/2q8+g98gg9XFlZZMzdv58ewJYWhjt+4hP0Qs6cCUybRuKWcJh9tm6l5y07m3Pv6OCYM2YwNDIScfX4pk1j2OfJJ7u6eA0NJGZqbAQ+/WkSxQjpyxtvuJDuU06hd+7yyymLXniBxdPnz+czvfYa8JWvAH/5C4+sLEfAsmsXvXm1tZy3X/8uK2tM/hXehCp4CsVhgtHkTgwVjiOhG374lrDgPf88zxUVjfyeCoVi4pCsnPDp1RMZgST8Sxg1hWVTkJnJ87LZHBx0OXo33shNV2YmixffeSdw3XVk+xscZFjVOedwA9fQ4OjLn3qKG7HBQebaZGRQwfvRjxyT3STHnwHUGWNqjDEZAD4K4GeBNv8NYAEAGGPKwJDNJ8ZxjopJDt/g4q/FFSuY4wa4MGkJz1y92rUHGBZ54olU3OrqGEJ58cUMYdy6FTj3XL5efjlDM194geGTwhy5ciXDMC+7jOGSeXlUwi69lPlv69ZRcdqyhUpgdzcVoZUreZ9PfIKKlihrp5zCeWVksJ8xDPk8/3waeK6/nsXMy8o4D4Dni4o4t6eeoszYu5fzWbeOCml+PnMMIxH2vewyKpIdHZQd119Pls7XX6di+dJLVM7uv5/z/fSnaUgKh6nw7d7N0Mz9+xkW/sILZBHduzeWRXPnTvd+TEKfE7n2UnEAeC+AhwE8BmB1gjYnA/gHgAcB/HC4MTUMSqEYOYLFg1M1pg8J1erudiEGxx2X3FjQEE2FYsIhazhZGTFcez/0Mh5TZ08P+wur3eLFDNfKzGQ4VGYmw6wiEVfouLWVoWBLlrhQMbnW2Mj2VVUM0aqtdbLo+ON5brKHaPJ2WAx65R4HcH703FoAH4i+NwC6onunPgAfHW5MlU+HN4KhmH7ItIRktrTw93vJEoYbSghmby/XnYRJ9/a6tVVZad8Mle7sZOhlW5tjmhRmy85Oa+fM4RgFBQzJ7O7mvTs7GULd2clzixdz/LQ0+2ZYZXGxC78UFs3cXMeM2dTEEE55htZWF6rd3u7CLIWdUwqzt7S4EG8J85SQ0/R0ypSODoaOyv0aGsiGWVDA8E5h7AyFXBth7g2FeG95Zl/+lJW596FQ7LVUhGiOpYCatFS/CsXhBl+YBzdkB6vwybhLlrhNmghTVfAUiqmBYDmEodoFPw8nQ0Q++ArfwoXchHV3U17IxnHJErdB6+zkBmzuXL5KG9l0trRwoydU68XFfD97tlP0/A1TcBOVLFQ+KaYKEq3HYP5rX58zlEhu3ty5XIvd3VTKpITA4sVOWert5XW/JElXl8tNq6/nOqyqcmUFRLHJy+OYovzk57N9RgbXbGEh89q6utyanjGDytOCBW48yeMrKOD7UIhjyvXSUlfiQHLgKiqYZ9fWxucsLHTjiDIrZRvkWZctczmC8p00N/M5pAyUyBUp15CTQ7kVCjl5k5Pj8vn8Q8pBTMUcvElJ9atQHKpIFFrlU5sH8+qCDHZDjREP/f3MqxkcpEiaPZshILt28frLL4/uWRQKxfggXjmERG0SUeVL+Fewvf8+WKJA2OfmziVT3U9+QvnR3c1z999Pxj6AzH5SrPyOO8jM98QTpB03huf/8Q+y+e3Zw9ydRx5xLIACYbJTKA5FSG78aacduAaXL489f845XDfCVHv55Vx7v/gFQ6IBhjlecgnwoQ8xl273boZk/vCHDGH8yEe41v7zP9n/L39h/tyePQzRPOYY5shZy1DFjg6gqsqFWM6Ywfmcfz5lz1FHcY2uX8+i5jt2MBy0sJAhpOnpZNfcv59hnPX1zAOuqmK4Y2kpx8vJYY7cnj3MefvkJxkmeeSRwA9+QFm1bx/nsGkTwy63bWNqyd/+xucpLmZY91//ymPdOj7/F7/IfL6cHIaeFxQw7PIXv+A8du9myYiZMznPzEw+/2uvxXISVFYyvLOsbOz+H8ZSwZuUVL8KxaGIoWoUxatfF+9aojH8+P3g+XPOoWAEXO6MvAJTJudFoTikkciAE6/mXby+sv4TlVA4+2xXa0so14Wmvb3dKVwbNzqyhyuv5CFlEwBniGpoYB7Mzp0kNejudvl6N95IxW/WLObxVFSQjOXII7mpa2mh0metMzQBmg+sODQRb21bS6XNh/wuDw5y/d1wA4lSyspoQJHfcYCKW0YG67oBVGpkLV18MRUeALjqKuBTn+I6/PSnuWaFCOmMM6iYLV7Mc1/4Ao04mzZxfi++yPd79pAsafVqljzIz+ccjzqKbV57jYaczEwagBYuZF7eqlU06khNPmPY/o032NYY4LOfpcJVU0MSmJ4e3nv7dl5fuZKyQ3IFGxuZa/jss8DHP04ylr17mYe3Zw+Vv3PPBR5/3OXPnXsulcj9+5lj+JWv8PqWLVRId+2iovr667FGrmefde0yMlzOYEqRyLV3sAeU6lehGFekIrcu0Ri9vQfm8PX1McyqpcWFW0ksv4Ro5ucnd19oCJRCMSYIljOIF2I5HPy+8fovWeJo1086yeXs+O17eyknpK2EhvX0MP9G+vv07S0t7pzkDUmZhM2bHbV5U5ML1crOZk5MZWVsSGZmJsOuNERTMZURTK8Irm1ZI/5nKXfQ3By7VltauE57etxvuYQh9vS4642NLhRaQjfLyhjKWFjIkMXmZo7Z2ek+Fxe7XFjJ2autZU6blBhobGQIZ24uQzU7Onjfzk6OIddnz3a5duGwW8PZ2QyBLCx0fSQvsKOD98vIcCGWtbWcf3095UdxMftK7p6Ea8q8Cws5Tn29fTPcsqaGeXh++YXqaleipbY2NuevrCw2XDw9nbJp6VL3eaqFaCrVr0IxjkgFC1NwjIEBHlLcVIoOCzPeunXALbfw1Vpaoy65xIVfFRcf/JwUCsXo4Ydexit7MpzcEAp1n3FP5IJ4Dax17devp1fgb3+jvBBPwuzZlAvCmmmjXobublrRIxF+fve76UEYHORYt9/uKNxlrNZWvv/0p+k92L4dOPZYhmjt2EHGu82bgXe+081r1y6y3ykUUwVB71y8aJrg2i4vj/0s3q0tWxjWvHAhvV8bN9IDtnUr1+DHPkZvlxQDv/hitjnlFIYjhsMsi3DGGVzL3/0uQxjfeIPhiE88wXNdXVxrZ5wB3HorPWIvvkgGy1mzuAavu84xTr797WSkrKigbPiv/+LaXbOGzJMtLfSsbdrEuT38MD1eoZArM5Cezvu+8530GD77LIuQf+MbDLusqmLY9xlnMG1EIpbS0+lxy8igTFm1iuOdcw7bbNlC7903v8m2Ms/XXgPe9z5X3qGzk2GdEjL65JP8Lo1haOqWLTyysykbjaHH73/+h/fbs2dsPHhjqeAp1a9CMQkwEtrz4GehLpdwhFWrKLgiEffDUl7Oz7t3c2O2ZQuFIMCQBYVCMbGIF54tGEo++CGcsnn0DT2nncZN4KWXUjasWMF+kQhw3HHckC1axI2eIBLhOB0dTnET5TESYWgWAJx+OjedK1a4kgitrZQvjY18/d73uDnbvJkbrpdf5obr6KOp/N1/v8vBO/74g/sOFYrxhK/M+Wuws9OVJ5GQZiA2r14+9/dzXZ53HpWyWbOAtWup1AE8l5vLHLsvfpG/4UcfTaXrmWeAL3+ZJQKsZZjjE08AZ55JJQugAvbWt9LI+7nPAV/7GnPbLr2Ua372bN7/uOOoDN52G89/5SvA297GHLQbb+S6fuwxhonefLNT3J56irKhvp5z2L+filB9PecZDlM5ikSAb3+bJR2+/nX23baNfXbuZCjkxz7G7yESocI5cyZDM3//e4aBf/vbVARffZXP8eij7PvCC8wRXLmSiuvmzdzf/PSnvE9aGnDXXcCFFzIMPBLhd3DWWXyOffv4KrmIW7bwe05PjzWMTakQTXoOlepXoZhIjIQZL147nyHTD52KB2G1a2119L+LFiU3T4xBCBSADQBeBNCf4LoB8C2wjMvfATR51z4J4NHo8clk7qeySTHVEGTVTcS+579fvNgx5ra0xIZXBqnXTzqJIUsVFS5Es6+P7ysqHPNeSwvHldDOnh7KkJ4ehmj19rrDWp6fP58hl7W1DNOaN8+x4uXlkdEuFGJ41sGw1Fk7NvJpvA+VT5MHQ4VFx2O4ThRWHe98MDTTX1u9vVwzc+ZwnQkjbUsLwxabmlwo5axZZJvMy2N4ooQ15uU5lkuA57q7rS0pcde7u7k+hY2yudmFWc+bxzUppROMYUjkokUMa+zq4li5uSyBIqUYiop4rqvLyYn6eo5RVsb5SHh2Wppj82xr42tDg3uVsYQxMyODz93VxTbyfHPmMDyzt5cyp6GB5/1wzeZmJ9u6uhzDZl2d6y/faVYWj6oqhnTm5DhG0bEI0ZxwoTPSQ4WUQjEyJFvbKtgumG+XaAy//EJPD98ffTSlyzHHJDfHMVLw3gmgaQgFbzGA/40qevMA/DF6vgSMJCgBUBx9Xzzc/VQ2KcYLqapl6Y8Vr4RKova+XBClS64tWeLycUVu9PbyWmury8OTHB+pNdXT467LBk7yhoqLuVFsaXE5e0LPLlTjHR3ckHZ0sH1Hh8uPAbipC9KSJwtV8BSpQqJ1lqiMUbz+PhIZZcXIIoqdGFRkbclaEqVKyhKIgaSxkUqKKCZS5y0UcoqVKIL5+a4OXjhMxaWggO/z8lxZE39OnZ1U4PLyXL5aXR0VwI4OjhkOs/xAWRkVtbQ0V5uvq4t9mpupUEr9Xbm2eTNf5fn8Gn+dnRwrHOazSB07MQx1dLh6ffLcaWkuB7CpiQqi5CqKsau0lNdmzGDunpSZKC21dto0jpWT43IEhzuSgSp4CsVhjmQ3hP5mb8kSR2wQHCO4KezriyVLEDKDSCS5+47VBgpA9RAK3ncBnOJ9fhjAdACnAPhuonaJDpVNivFAsoqYtB3p2Mle8715/rVgfS1fIRQDkK8gdnVxA+fXuROZIvXyZDM6f75T7KSweU6OfdOTIApcQQHHESVPSBmmTRv5BsrasZNP43mofJo4DEds5P+ODtU2GGnT10fFzVfyNm/mOhElrrzc1a6Ttr29bq2JoUVq0GVnOwVLSESys6nk5eU5j1NTEz1R0iYnhwaWigpXy86vp1da6ow4XV1Umjo7+T4cppdLPHrigWtq4nzr6tzcOjtdrb5589h/2jR+DodZm6+iwtXSFOWyuNh5LefOpZJVWUkDU2Ul79nY6JSvnBx62QAqmYDzFjY3897iuSwtjfVahsP8bsTQJHMTZTktjXMMhdx53xilCp5CoTgojMRS70Os8v6PzOLF7gelu9v9CMyaldxcJkjB+zmA+d7nXwM4HsB5AC7wzn8FwHkJxlCGX8W4I1nlLlmP3HBIxKKZKDRTPHT+PKSguXj8fI9eTw8PCQX3ZUtPj9uUSpHhtDTnQWhs5HsJGSsvd6GfskmVQsM+s50qeIrxQLLrMF6UTLy+8RgzffT2UuERj1lvrzPWioFFFLqyMufR6+pyikx2tvO+1da6At4dHfRciQcvPd2FQGdn87wUFZfQz64uet1EYSss5P06O3m9qcndp7DQMd82NLhwbd+LJsqqhERGIpQBfX2OvbKzk3KgqckpXQUFTtlKS+M9QiEqcWlpznMXidBbmJ7OORcVuVBxkUM9PVQSxfsn3k8J38zL4/cnxc27uni+utqFcNbU8DUtjd+hKngqpBSHMFIZdpXMveL96CSTqyfx/P6PjPyYiHKXn2/ftJgng6mq4PmHyibFZEOiMDD/c7xNZbCP710I5vj09sYaeGQjGYk4ZU42mXPnxipvYmGfO5eyQiz8srErLeUmrKmJm9HZs7nRTEtzmybp09bmNkZC3x4Ou7wX8fSpgqcYb4w0gsbHUCGYEmEjR0+PC4kOevWkrJGEPYsxVvLwQiGnZNTXU9kRhSY7m2tOQjXr67ke29udsuR7zcNh53HLz6cSVFxM5VBy9Fpb3bnsbI5bW+vCO5uanOHHGI7rl28oLma72lrnQSspoXInIaAtLVS8/HIp+fnOS9fV5TyN1dUcs7GRc+vuduUa5s7ltTlzeL20NDbce8YMGxPK2t7Oo6mJ3097OxXGwkIXRSCK8cHmCKuCp1BMcowk7CqV9/QRL9wjXp9g6NXChW5zJqQKIrQmuQdPQzQVhw0ShVQOp/DJ+WCotngCfK+bKHwSpi0bSn/TKd68JUucBV5CMGXzKfW6xEvQ3e2UNvEGFBTwWl0dN4mFhVTyJHyzpcV5JABrTzhhdBsoa0cmnwBcFSVvinskO06qD5VPUwuy5mR9yTnfwOLXlJw7l94rWUdCbOQbYeV3uryca6uwkId43sRLJyGUogCWllIBCoedAiWEauLNk3s1NzsFUYiOioup5OTkOINNVxfnIh4twNqZMx3JidTQa2y0b3rqm5ocaUt3N5W14mLeT0JBxQAkiqqEcS9d6pQtuacYhSQUU/IFxTtYWMg+4mHMznahpcXFVBTr6ng9FKIc8mva1dSwX3o6v4/2dqdshkJUDHNzD5RLquApFIcYxkO5S0SSIkik3MXbEPo/Ot3dbhPX0uIEVGFhcvOaIAVvSYBk5U/R8yUAnowSrBRH35cMdy+VTYrJjkRh14naBHNtg9ekgLJ4CPzw7eDmUkiYenpoAGpspMVdSAqEYU8s5ZK/I8QPpaWumLFsOn0PQloaN0uyKZTwKLGsj3YDZe3I5BPIwPtJANcC+D2As6PHbwFck+w4qT5UPk0d+F5zCWP2QzLjrU9ZZ3K9q8t51ltbnfFl82YqGxIOmZPjFDxjXGhlZ6fL5ZMQaCFDkrUs+WaFhbwm4Y+SU1dYSCOvkK+IMpWby3tJ6GRREY0xJSVO+ZN8W/EK+gQn4skrKeG10lLe3y9MLoy6Ek0k3vzCQp6TUNPsbMqRwkLntRPldNYsKnE5OU4plOiBxkbnaayvd4yZIltEHkkopu/xk+8iPT1xkXNV8BQKRdJIFI6VDGtXsISCsHL5lOc+U56ELBx3XHJzGwsFD8BNAF4AsAfAJgArAJwJ4MzodQPgO2AZlz4Ax3t9l4PlEx4D8Klk7qeySTHZENwIJmoT3CjKeg+y6vqh2da6zaRfLkE8eb53YfHiWEa7+fO5mZIwTr9EQlOTozEXljvxHDQ2Oo+e5O/U13NzJhtEyRkCeL/iYvbJyhpfFk0A9wIIe5/TAdw70nFSdah8mnxItC793DpZQ34otDDMBtHX53LPhIhE1q149ubMoXIlRo/cXCoyvteppITGlsJCl6MmHqpIxHneRUmU33vxpre304izdKkLvZTQyJ4el4sna17yb6VMQyjkPHzNzU4hys/nes/PdwqnePDEc5eX5wxDonR1d3NOpaX0dEpEgMgJ8fZ1d3M8OS8hqxJ2Kd+HKIgFBe778Zk9JRxcvHXG8O+Rm8vvYKjSCKrgKRSKUWE4D14i+Ju9IPW5Xx5h82YKOhGMxx+f3Lw0x0WhSC18Rc3P1Qm28ZWxoJfO9xb4YV/xxvfPB5VGaVNb68LEJCxTjEK+J7Cx0XkFJFxMSBpycx15gxAhVFe7Tdjcufws+TAHu4GydnTyKRryXeJ9Lgbw8EjHSdWh8mlyIZHBNRhK7f/migFFPHLBNWata+NH1YiRQ7zhublUfGR9ybrKymK7ri5eLyjg2ps+3b4ZJtne7pSyjg5eF0UnL49rrrGR6zIjg+2Li90anTPH5ddZ6177+kisEonQWNPSwjw/Wd8AmSlFqZRQ7fx8V2cuI4PnpH6fKFjiRevs5PdSXu5CP6dPd2QrEiEwfz5fhdmzttbl9+XmUqHLzmZOsCifEkLqEzkVFcXKm2XLYj2BquCpkFIoxhWJwriC1n1/4+cXP5YfCmBi6+CN96GySTHZEMzVkZBKH2KosfZAYoZgzl48wgf/Pv6m1WfDtNYVL5e8nrQ0VyRYQsGENVPqSUmxYWGlE2+dhJHJpqyggJu/oiJHQ56b62jYCwtHv4GydnTyCcCnADwN4HoA34+Gen9ypOOk6lD5dCBSkRZxMGPE85z7aymecca/Zq0jT5EyBPJZiEI2b6bCU1vLdZKbSw9TOOw+i8InZQZE+ampiWWibW+3b3qomppc2KNfGy4U4noTI44omE1NsSUDJKQzK4uvErot92hsdHMST31JiVMYpYh6Tg49h+Kdl3Uvbevr2VZCNgsLXbF0Cen0vXVSTL2hgZ9razkH8RqGQq7EgdTfmzeP18RLGTQuyXsJiQ2Wa4lHAKUKnkKhSCl8ZW0oIoag9VHOSbFRiSuf6By88TxUNikmI/wNYWvrgZ4B8cr5oWD+JjPZnL0gq24kwg1Yb29sjp2URRCFbeFCZ/FvaWG/uXPZRogWurtdSJaQsDQ1Uc7U1rqwTtnkpadz05SfT2v9eHvwAIQAnABgGoCl0WPaSMZI9aHyKRbJpCeM9xjBfHgJyfR/Z2WN+t7z7m7+1ooiJYeEaUrJEGGxLCyk8tPV5ZSPGTP4Pj/fhR329LCdeKmE5VJy8URRlELiEh5dXx+bLyjF09PTObaUYWhuZl8JKxVWXMkLlHBJMQo1Nrr8XSmXIOUTOjocg3dxsZtLXh7lUG2t855lZ3OMSITKoOTYZWU5UpimJiprInt6e11YZ0aGC8tsaHAMn/JdxsurkxBxYRFdutTGhHFOmIIHoA7ATwD8A8ATciTTN9WHCimFYmRI9scnnnfOPx8vvCveZ6lPJSEVyUAVPIXiQBzsxlHWrp/nFlTIpITBwoXcpAirpR8CZu2B3j1rHeGCvzn1CVVEIZNiy1LSQKjZhRBBwpWEAVPmNX++qz/V2Og2k1KkWchXxJLe3OwYfJPJc0kWo/Tg3T/SPmN5qHw6EBPtwRPE84z7YcvSRvJdhZ3W/zxvniP+EA9aV5fLbZNyBdXVrkxAZWUsi2N2tmOKFEZNYxzRSFoaFSXJwxNFau5c3qO4mP2LimLz6woL2a6iguNVV7tQSgknFcZKCcfMzeX5xka2a2lxubsiU4TsRAhTABem3djoipdLzmBdnZMLRUV8vvnzHTOmePc6O914OTnOyBSJOPlSXe3mKuGXaWmOrEYMSzU1sQyi6enOKzrWIZohJIf/BHA1gL0A3g3gvwDckGRfhUIxQRgYANrb+TocysuB9euBhga+D54Pjitt+vvdffr7gQsvBGbM4LXi4oObvzHm34wxRxzcKArF1MNI1m6ivgDQ2QlcfjmwbRtwxRU856/pBQuA7m7gxhuB224D/t//47UNG2LX+KJFfO3v59i33gp87GPAqaeyXXk5r518MrBkCfs99BCwZw9w881AWRlw5pnA1q2cx2mncU7XXQcccwxQVwf86lfAli3APfdw3H/+Z47xX/8FbNoE7N8P3HADsHGje45IBDjnHKCjA1i8mFuj9nZg375Rf/Wpwq+NMR8yxpiJnogiPvzfufEYY2DArWf/dc2a2HU+OAj87W9cC7KuTjyR//eZmYAx/L8HgIIC4IILgK9/HcjJcf/3l13GY+FCID2da+fxx4GnnuJaMgZ49lmgtBQoLARqaoBPfYrrddYsjvG97wEzZ/JeoRCQnc32X/oScPHF7LdlC/DII7z3FVcA4TCwdy9w//081qwBXn8dePJJYPNmyofXXgOefx548EHKkGuvBY6I/spv2QJUV3PODz3EOT/8MPDKK0BWFueWnw984hPA6tVc7+npnE9XF+eydy/Hqqzk9TvuAC65hLJn5kwgLQ34t38DvvlN4K1v5ef8fM7zyiuBr36V301WFp975kzgppuAtjZ+j6EQ8JGPAG+8we9t2TLgl7+kDKuu5j1ffZVzeOEFPt/27ey7Zw/f794NZGSM4B9tNEik+fkHgL9EX/uC58b7UCuUQjEyJAq1HKpdvGt+krdf10rYNCXEq6fHWaAOttA5GDnwHIBHwTyWMwA0xGs70YfKJkWqkcr8nnie+aCXTs7HI3+Q/n6tu5aWA3OGCgvpVZO8FN/b0NzsvG8tLfQ2SP6NeAVKS2kx7+x0BYXDYUeHLhZ88QiKZVy8hVJzC2A/IXeYAA/eNgD7AewG8Fr082sjHSdVh8qniUUiQiNr469N8czJ2po/Pza31e/b2sr11tzsvFA9PY5QpK7OEaSIB62hga+lpVxraWmumLkU5q6pcflyckiZAmG57epydeMk3068Xo2NfI72dkfCIt6wOXO4bkMhni8ooNcsN9fVnJN1WlnJeXZ18VkbGtz4Ug9P8v1aWhwRioRACkOmhK7m5HB8mWdTE78DmU92NuWNMGXKc4vXsKuL+56uLnevUIjzklBTyWOsr2ckkzyLX4OvpMTG5OhNlAdvlzEmBOBRY8znjDEfBJCXCgVToVCMLcTCOJRHYGAAWL78wGticSwvB84+mxazVatooT/1VLa58UZa+CIRiqWnn3b9d+06uLlbaz9srZ0B4EQAdwJ4K4DvG2MGjTF3HNzoCsXkxsF4GMrL3ZofHOTa9TE4SCt+vH6dna6/jCEeg5deAr72NZ5bu5bjLF8OrFhB6/lb30rPQV4e8P3vA+vWUWYMDtJinZXFcXbtouX8e99j+2nTgJ/+FKio4LVf/Yrva2vpObj3Xt7n+utp0f/AB2hp37GD3oOvfpWW/NNPp8UcoDdhcJByabxhrc231oastRnW2oLo54Lxn4liIhD8LS0vp1e8u5tRMuvXuzV2zjmxv7/iYd+92/UvLKQH75xzgJUr6QG/+256vHbt4tpbtozepQsv5PsrrwTe9z7g0UfpLbvxRnqjtm3j2n/1VWD6dOCd7wSOO47rbedO4JprGIUzcybXc3o6PX/PPAM89hg9YbNmcS1v2wa85S3AuefyOP10tp85k/N+5RU+y1NPUYbs28e+F1zAdfu2t9H7npYGzJvHZ3niCc6rsJBes7Iyzm3NGnofN21ynsp16ygDBgfpWWxr47qvqeGYAL+bM87g3Ldv5zPu2MFnrKnhff/jP+gVDIX43axfz3ZHHMG5b9/O63fcQdl00UUce/dujnHssez32GP0qra08PrevcCcOe7vKB7Am2/m95iW5mRiypFI8/MPAP8EKnQzwXDNHgDNyfRN9aFWKIViaIzGS+cz7cnneBbHoAdPcgXE0tjbS6+dWM9SyaIJoB6sZdcN4EEAvcP1Gc9DZZNiMsJfy75XL1jb0m8frIPn58WVl7t8FMnVk3qYkYjz3Il3TzwMfmkEIY/o6aFnQgqUV1eTelws3JEILf2SX2QMreT19a7+lpC1dHc7i/yyZc5679OW+8XPx9qDx24oBjAXwDvlGM04qThUPo0fhlpfQQTZbf3XSCT28+LF9J51dzPPTLzdUi+utdWVPcjOPpCKf/p0npPC5ZI3J4y0BQWubXa2y+NraaGXTHLJAK5TP89VcvXS011OnOS2CRtmQwNz38QbmJ7OdS65e0L2kpvrPHZCtCK19vLz6VmUmnX5+Y68RHLu5DsRYhTJt5NcQWHOnDHDPU91Nb9DYeqVKAJ5DnnO2bN5XuYl3ro5c9x3LiUiJIoAsPaEE3ivI46Izb8LhRLnCyeDoWRTskLqI8mcG49DhZRCkRgjYfUKtvEVN584wVf6gn19SmY5J0yaQnSQDBIJKQBfBvA/YNHg7wM4C8AcAGnx2k/kobJJMZmRiBQp+Bo857P5CUGKtU6pE6NPX58rIpyR4ZjnFi92G8niYip00nfzZlfPTljl6uq4UZSi6MIaKOP39sZuuhYt4riySRL2zPZ2hnoZk7jmVLIYjYIH4HQAfQBeBtALYAeA/zfScVJ1qHwaX/glDhLBN6z6rLaynlpaYpXE3l7+bxcWOkVPyiO0troyAKLg+XT8Rxzh3tfWunp3ra1u7UUiVJSqqhzhSSjEtZSWRmVFipYLqUhGhmuTkUGFranJEZCEQm6ufX1cu8LWKSGjOTmOlCQ31xGjCFGTrGOAtXXFACThkEL+UlvrwinFmCTfi9TNE+Xq+OOd3JCyD6J8CkPn/PnuO1u2jG1zc/ndibGorc2x+YoS7ZOvCKGNH4bpK9KTIUTzS0meUygUEwgJ7RgutCteuGZDA0OzrrrKhWgNDroE8CDxysAAw77WrSPxgZy7/XZHrlJUdNCP9AkAFQB+AeBGAD+01t5vrZ14CgWFYgyRDLlKsgQswfULuPW6YoUjTvFDxAAX2nnppQz3ysgAZs927U4+GTjlFBK1fOITJFIoKgKuvhrIzSXpwooVDNlqaCChihAzfPSjJEX5+teBo44C/vAHzmHaNIZDff3rJGA47zwSKNx1F+fx298yDMpa3veXv2Ro1N69DHVqa2PY2PXXM1zcWoZFTQD+HYx+etpa+27QMPXKhMxEMe6IRPh/OTgY//rAAEMubTR8WMKg77yTr0Ig0t7OUEzBjh38XzcGmDuXoYvd3VyHzzxD4o+3vpXtsrIYBtnWxtBqIWjJz+ea+8//ZCjhbbcBxx/PuYZCDO/MzWV45bnnMsSxpobrPzubv+8/+hHDKNPSuL5ycxl6KSRIr77KueXkcE7f+x7X8KZNfA5jGH5tLWXGwADDOo84gs/R0UH50t/Pe9bX83nuv58hmN//PueVnk5ykxdeYGhnfz/X/wMP8LlFVu3eTZKXVas4L0kl2bmTc/jsZyl7HnqIMubLX+b8AD73X/7CtpWVwJFHUkYZA/z4x/z8pS/xO3rhBYZ05uSwf0UF0NTEMNb6eqCqit+XMY5kRf4HUo5Emh8VQ7wPwFUABgB8yzuuB/CnofqO1aFWKIUiMZKpWTVcW580Yagwk3hhX/Iq1vX09OTmjaHCDIASAP8K4D8A/D8AfwLwPQCfStRnIg6VTYpUIdHaG658SbCWlngFgjUr5VU8bL6n3h9HyifEC93u63OkJvX1LjysvJwhlUKm0trKAufi+fNr5S1cGEvYIgQuEm7W2srzYnUXL11lJa3kGRkcWyzphYWO3CBeLapx9uD9Ofr6AIDM6PsHRzpOqg6VT8Mj1WUTgmvPb9Pa6sqH+P38154eeugkVFPKhcyZw3Uh3nLxdEvoY309PeDi/SospGdOPEtSM873TEmIoxCs1NU5b6AUSZfyCVIyQeZSXe3kgJRZ6OhwoZdChOSHJDY1uXDS2trYcOqODoZzSn26piZXuD0vzz2L3LeykvOR/nl5LlxSCrmnpbm5h8POa1lW5uZeVOQIWCSENTvblVCQentC9iTeuVCIHkk/NLOtjX8jmXckwvn29LBtVpYLFQ2H45dNSAZDyabhPHjPA/gLgJ3RVzl+BmDRQWuXCoUiZRiOVj2elT5e28FBR7EuZRNkfP+1vJwWdRln+XJaGjdudBbzghRQClhrt1prfw7gQjBy4BawXMt1Bz+6QjH5IEQnPn16cH0HvfX9/Y7sRMhRVqygxdonTRGP3amn0urf0cH+q1bFlkEYHKRnYN26WC/+Pfc4mvbrrqP1/KWXgLPOAn7/e84pL88RuFx5Ja3hZWUce+NGjl9WxjZlZZQxa9dSnmzdSir2K6/ksXYt8Pa30/pdV8c+L79MD8HHPkYvAkDreShE6z1AOvKMDHot0tJo6R9nbDLGFAH4bwB3GWNuA/D0kD0UE4aDKUuSaAyfTMVHeTn/t2++mR5xOeeXGwJIivKTn7B0yZYtJFh55hl68F54gdT/b3kLSYUWLuS1HTvohRLilSef5Fo4+mh6qOrrgf/5H3qjjKEnbudOHjU1QF8f10pmJr1NDz9Mj3lhoSuDsHMn8K//SjXEGKCkhPfbtMkRq/3613ym9HS2/8QnuA4rKjj/rVvpHZw3j3NMSyNxSmYmyZZ27aLXbfp0esvOOoseQrnnwAC9cDk5vG9ZGcfNzaWHMSOD5Vfe8hb2y83lvbdv57Vnn6Xse9vbSFrz+OP0JObl8TusqyM5TUYG8L//yzE+/nES0Ozbx+9Y2h17LOclf5vp04Gf/5yey1NOoffyyCPp2bv2WrbZudOVUti7l/NKORJpfv4BIJxMu/E41AqlOJwxEg9d8HzQI5DonFjR/XN+W7H4+x4EoUAvLWVfsWLl5CT3XEicg/cBAJcA+B2ArdHXSwEsBRCJ12eiDpVNilQjUd5cvHY+8ZF/3vfq+V73YH6teMsWL3YFzIOeu95eWp57eni+pyc2B88vdt7S4qzWknNTXEwreU8P2y5cSM9DVxct6U1NtMLL9cWLnadC8vaE1lzycoSCvLGR5AtNTbSsGzN8sfNkkUg+JXsAeFdUlmUczDgHc6h8Gh7jVfjc944vWRK7RuU3VsqAyDkhN8rLi/X2RCJ8zchweXPiyRJvmZCdzJ/P9TRvHtuEQo4ERIhVpGRCbi7XU3Exc/mM4TqtrY1dV5mZLq9O8vxyclzfjg6ufSFVKSx0hCazZ7uC4uL1kny0tDR+9r1yTU0cY9kyznvZMudly89nGyFYamzks0gkQFcXnzstzZVe6e52ReLF8+jzB0ieYmMjn0nmVlLC952drsxLQQHbZGXxvXgwg4Xkk8m9GxeSFTBB+O+JjqH6jtWhQkpxuGIkBCrx+sbrFy98q7U19kfHV+Lkh8ln1pN+LS0UaM3NTkCFw8nNbwgF71YA5wL45+DmCEB5vD4TdahsUow3kgkHk/qU/lqNJwt6ex2hyUkncdMSTw50d7N9dzc3REKmUlERy6zb1+dCyDo63OfSUlezqq+P98nMdOOI0igKZXExN1rNza69sAbKxtEYR9KQnR1LeBCP1GCcQjTnAcj3PhdggtjHrcqnlGKkhtagEVXO+eQqYngR40hREf/vZZ2IkpKebu2CBfz/XbCA56SeWjgcSyiUnU1lKi3N/TYLM2Y47Oq+LV3KNZiX5xQuUfyWLXMhjE1N9k0FrqjI3cd/n5bGPsZwrlIXT5RSYfIUg4xcr6mhwiTjlJXFKrK1tZy7PI/MMTOT7WprqUyFQrFz6OpyjJs9PY6cpaCA4aWdnSRvkntNm+bCODs7+f2Hw/xuFi1y8ykvd+HgQjwjYxhDRVbq4nV38zsuLaXxKS+PobDhsAsxnwgF78ihjqH6jtWhQkpxKGO0Hrrh+gylGMaL/Q9a/YMbSWH78hn15MeptTW1OXj+AaAILJXwawDPJ9NnvA6VTYqhkArvQHC8eDl4wTbiKfDXc9Bo43sArXXKnl9oWZQr8SScdBI3QLIxlfIHnZ3ccIrHTYqdy/WeHm6oZMMp48omtqnJKYFSrLmpid6H2lqOXVrKzVVPjytqLtbwrCyXayTW/+xst+EcRwXvfgDG+xwC8NeRjpOqQ+VTapDM72mwiLnPjCn5q34JoiVL3P/7tGn8n+/udt7rhQudkiIe64KC2GLevucpLY0KRWEhlY7KSjdeYSGPtjaXn5ab69gs6+tjPXTizevtdQqOsEnK/X1PVHs712dZGc9LzprkvIkyKgaZtrbYnD6f7bOsjM+2bBmVotpaKkVSMqG42D2/KJFStDwc5rqfO9cVF5d7Vlc7RVbKO8yaxc+FhZxzYyNljpRfkaLn2dmclzCKAq44u6/gSfmEvDzOJz2dn0VB7enh31Kinfy/4bgoeDENqdS1Rt9n+5ap8TxUSCkOVQxHapLsGCM5H7x3ok2g77WTOQpBg9S+EyXPpxYOhZKb95BCivLmo2Du77MgE90CAKFEfSbiUNmkSISD8b4PN248+DJENpb+ed9rEFzbYqTxPWlLlrgQ7MZGbpQkjNP3MIg3raCAm5pIhP3mzePGTjaxTU20eBcV0YounsOGBm6w6ut5iOW8uZmbLoCv3d28JiQHshmUECi/9p0xB0dDbu3Q8inRAeCBOOcmJPLJqnxKKUbjwRNFTkoK+YeUG2pp4XqQkiJdXVQACgq4ZkIha2fOjP0fLimhgpGZyVcpPeArUxKy2NXllCkJe/RLB/gecPGqVVQ4hVG8dXl5Byok4j2sro6tB9fdzfGKizkfv01WlitzIHX4RCFta7NvKoyiyEl4pSih4sGTUgehEJ9R6ufNm+eiBmpr+VpVRTnT2MixRFYUFPC8KGMin+RZZ83iOTEeVVXxPgD/Rnl5juBG6gDK9yrKp8gw8TAG/47Bci7J4KAVPACfBvBnAI9HP9cB+HUyfVN9qJBSHMpIFGY1FOIpZSO9p/SP5w3w8/KWLHHWewnh6u2lUufn4omAys9Pbg5DhGj+MKrUdQM4EUAagCfjtZ3oQ2WTYigkuy7j5dCNBL7HQJQzWb/xPAlBpU/Cw0pL3bqWDaiEeEmOnCiDLS2OEVNCojIyuFkVY5B4IyIRyoXZs119PCncHA5zcySFi2UT5xc3l3Hy8riRldyZcJgbKAn18pW8gwmBsnboTVSiIxpe/nkA6dHj3wH890jHSdWh8il5jHTNDdc+uN7iec5FAezu5rrJzY0NR5bwTGGPBVz+ndRkq6riepoxIzaUcdo0rjXxZC1Y4JQqyS/z10V2NteRr8Tl5jq2Rwkr9K+HQk4pE6Wmo8PNISvLKZ4nnODaFRZSFjQ3u3mkpfGZJE9OautKPl9BAQ+pfSffS3a2C8eW/Dg/h1dYO8Nhl5tYW0slsq+PfZYsoSwSOeZ7N0VuSQ6fH1JqDP9uXV2cQ2Eh+0rIZ1oalcScHPdsmZnWHnfcwcmnVCh4DwDIAHC/d64vmb6pPlRIKQ51jFS5C4aDDNU2nmXR3wwmUhBFuWtu5o9Kdzf7iZLnWyWbm12IRwoUvAeiOb/nAZgZPfdEvLYTfahsUhwsgp5z38OWbH9RyPzP/nnfWyCbynjeO1nz0renx+XZivInIZWSP+eXRJg3z+XkCVmKbGBzcx3VeGEhr7W0xBKkiNXb35SJR0DIWwoK3OY2M9N58ITqXDaX8jrOIZpHALgZwItgqakfAjhipOOk6lD5lBxGaij1I1ziXZP11trqol5kLci6knIJ3d305NTU2Dc9bOKBam2lAiX/y35em4QsZme7//G6OufVknBCXyHLyOA6E4OKKB41NbFjxzv8UgBZWewvnrPcXCo+9fWukLgooaEQz4miWV3NPosXU3mT8FPxPEoJAwnDbmvjZ/HAFRa667Le5Tuqq3N5hgUFnI8UJBdFNDPTKZ3iURPFMBx2IZb19dzLNDTwb1Fd7UpISO5dbi5lV2kp79fW5sJoCwpceKh4OmfOjE1lmUgF74/R1/ujr2ElWVEoJgcShVUG2yTaLMZLAPc/b94cW69K8nL8sC/p19sbK6xyc5N7hmFCNOsBdAJ4CMDvAQxONoIVq7JJkSIM5cEbqial763zQy7F0zZ/vvMkSF5cX1+sl27xYhpoROmTtS01u8S7IBbxnh5HgiLeO9nItrY6WdHd7ebo1+7q63PkB5s3O89bW5sLZ+rq4oa4vt6xAPphXcXFDB/zN7CSc+dvaOOFaSaL0Sh4k+04nOXTwXjkkgnH9OvB+ueXLHGGECEvEiXPz1GVfK/SUqcsiFesro6KRXs7101VlcvxmjHDsWEGlbeuLrYR719LC99LHl04HMs0K7l7VVVUWoIh0Dk5nItPCtLQ4Fg9hdVWlCTJOWtudt41fyyZd2mpm5Pk0lVX0/MlymxjI59JQi1razkXCYkUhVH2HOIpE6UqM5NK1rRp9k3Dc0kJ7zd9uiONaWvjfGtreW957epy7L8ivzo7+bcTD6s836JFzutXWelyAiV03Q/pFLkkBq2JUvAuA/Dl6AbrRAA/BXBxMn1TfRzOQkoxNZHqvJuh7uGHfcT7wRlJfp+M09NDi7kw4wV//CQnT8YXZqlUKXj+AeDtAL4B4BkAf0imz3gdKpsUY4kgsZEguAZ9GSBKmjDyiWcuEnFsepI/a20sk6bQi8sYoiSKRbqx0W1chSpc6Ngl1042l6GQCzeLRBw5S0+PK07e3c1NphRaFhIE8eCJx6+5mZs78T7IBjUU4iarooLtOzrcpkmMTsG8oWQxSg/e9wEUeZ+LAWwY6TipOg5X+XQw+a9D9U1keAmeF2OJb6wRz50Uui4ocAYS8Qw1NMQW0haPlhgyRHmRMGXJU/PDLIXKX5Q7McAE87/88SUUUtadrwDKmMcfz7kJc2VaGq9LWLR4uDo7XWFvkRvioRO2Sz+sMxRy4ZtpaU4JElITCS31n1HyE9vbnec+WI5AlCrpK/OQz7W1vJcoZjJ2dTVlpzBeinGrqYnvASdTa2udV7S2lt9hRgblmEQZiAI4f/6Bzz2RHrxQNA/vFgA/ib43yfRN9XG4CinF1MRYkSsMd49EP0jBdsN578SqL4QL8TyAEufu17QSATVtWnLPMNINVDSn5cKR9BnrQ2XT4Y3R5M2OdAzxAgTbyflgPp28F/IGUd6am7mBFFICaSe5euK1FyVOPA49PdwI5eW5EKbeXhdi1tjIIy2NoV7hMO/V3e0Uy9xcR7zS0uLqS4mS19VFZa6nh4rc/PncvM6a5ejNjXFhWLKhy86Orb8pNalkcxcvDCpZjFLBuz+Zc+N1HM7yaaSkKD7iGUX9sEzfg+d7zf0juD5lLUjon6wRqZvW1cX//9JSKhdC7OF7on2FShQEn3REiD6am52XTTxkEgIqfYVAxVec8vKcopSeHls/zm9bUeEUGPFeZWU5BVQIW2T9HX+8m2t9vctjMyaWFMYvveArY763LivLkTIVFbnyBvX1sTm48t73Ph5zjBujo8N9R8J0KfU7Kyr4KmUkhO1TSk10djreAVFYJSqhvd3lCmdmUimWMNHSUvddytgTouBxDEQwCQoLH85CSjE1MZ4evKHO+z841ibOv/N/qMSS71M7+z94vb3cPAlRgljo/R+jZDBEDl4BgC8B+DaA9wAwAD4H4EkAt8XrM1GHyqbDFyMx5CQKpQ6Okeya9tdqvDXrtxMSleZmvvrKnSiCvjKXmelq2AmZiqx1ybERr5qEm0lOj+T9SHj34sXOQzFvnqMgb2nhRkg2vEKt3tND2dLdTWID2XjW1rqaXBItIN4F2Tj6uXhSC2+0FnJrh95EJToA/A1Asfe5ZKK4C6zKp7iIF/Ey1Jr0z8s6kRBmf/3JOfnfl3x1UfKEyEPyXXt6HAusMW7t5Oa6GnRBZszMTCohkiPmk5gYQ8Wxo8ORq/gKT1oaFQxRDCWHT67J/XJy3DVRGisqOFZpafw15ROoiJfdv26MU97S0/msfmkEWcPyKopdVZUjhfE9gFJQvKrKKWe9vVS8hPWyqckR0Eih9rQ0KlyiFMr3I946kSGSFywKs4RaNje7HMRlyxwz5ty5rrSE7z30iaOkEHrQ0ziaCINRK3jRzdRXAWwBsDV6DE6k5VyFlEKRHIIbSflxSUTKIptAyeMRtkw/vMQvkyBjyA9UcTF/0HwPXgoUvNsAXA/gMwB+DOBuAL8B8LZ47SfyUNl0eGOkHryhPO/xwi6DkHBK8bpJGKVP6BAkbOnt5casuTk2xNLPDfK9DZ2d3HR0dLjNqNTkKinhhqW4mEqbeAokN873TMj95s/n5kZKJZSW0pMYiVBuNDc7a/ycOS7HRQosh0KUKUI+IWGmQnwgckfqXM2axbGqq+OzaiaLUSp4n4imtVwE4GvR9x8f6TipOg53+RRPSQsaWpKNhrH2QCOoeMclrUFCnH3jim9w6e11JGULFzovkihvaWl8ra+ngaOoyBXvFk+1GDE6OpxyN2NGrNIkpQBEmYhEYsMQKyudp7usjMaXjg6noAjxSU5O7H39Wnky57Iyrntj2K+jg+u7qsp58KQ8gq/8+J7JeIqieAL99A+/wHmwPEp5ucvlFYZNUe5E6S0sdAQ21dUunFuMVDJX+W5FDkr4eGEh30u4q9y7psaFp+bmxnolxYsq4eTB729cPXgAvgDgLgA13rlZAO4EcM5QfcfqONyFlGLqYaw8eMl6C+Q1Gc+A5AuI1VGskf4PX7BIq/ygpac7unP/RyAZDKHg9Xnv06KMdFnx2k70obJJMVIE16JvOJF1Jt44v62/PsW7VlZGBUkUsWCYpqzX1lZHFFBfH8vu19vrDglB6+jg5qOpyY1dUeFye8QLJ0Ye8ew1NXE+xcXcrEr4Unq6q1PV0MAN5Zw53LyVljpCguxsnpOC6PX1LsRKlLyMjNhi6LJRqqtzG9OqKl4LhnyNtYLHbjg2GnHwOQDHjGaMVB2Hs3wayhMXr20y41kbawyR9SYhy0IsZC3Pl5Q4Ztm+Pv6vl5Q4hSMU4v+zGDFEMRAFqKOD/9fB/+FQyJUOqKlxxdJlnYgHzC9tILl92dlunUybxjUi14LMs0uXsq+fzyqhmUuXOqVNlMagwhYKxYZu+t7DrCyO5XsS/f5SCkU8ln5YtnxHQmoyYwbfL1rEZyorc946X1mUv0VnpysIL6UphLUzFHI5hmlp/DsJOVRXF793yRWuqeF9Cwv5d2pqiiVOOeIIV/zcZy5duvTA/MnxUvDuB1AW53xkomLJD2chpZh8SCa2f6wKHI8mJGyoNn7YibVOuYuXUxDML+jrc9Z2P0QzHE7ueYZQ8P461OfJdKhsUowWsjmcNs0xT8r5RKGckhcr58STIGUEfCp2aWNtLGOltOvrc4pWRoZTFH2PRE9PbPhmebkL9ZQyCRKSJl4+URbFmi3Wb6EXb2zke6EwlwLnsvmrqODmp7PTbZbKytimoYGbrMZGR9ySnx+7SZRNmoSCBa8li4Nh0QTLJVTJMdpxDvaYKPk0HikKyWA4A+dIxvGjYXw2aTG6NDVxLQshUm+vyxGT/KyWFqcU+MqHMCwGWRX9/NK0NK4/WSeVlVQA/f9x3yMkY0no44IF7rwoSRLmmZYWGyIpfX2lUJS5I46IJT1JS3O5r763b8aM2JDEePlmMh//u5BDQiClvICw6MYjU5Eac6J4yvnGRje2yKCCAld7U77Xxkb+raTAe2MjDVN+jmRdHRXwnBxX7qC9nW0lxLOoyNXvlPuGw7wu8/dz8NLSxj9Es38018by0E2UYrIgWSUrFT9wo7U0+m2H8tz53rlgKKf/AyYeBRmvtZWWSLG0t7TEFv/MyEhufkMoePsAvBY9tgHY671/LV6fQP/3AngYwGMAVse5fiVYa+8BAI8AeCVwb7n2s+HupbJJMRoE11oy61rKlQgDpvSV8ExR2vzxRAkUz514y0QJKylx5AFyzi/OXF7uQjT9ELOmJipQTU1OVnR2cvMinjx5nTvXefpycrjBysnhvdvbqTAKPblsuMRqn5/val35Fn1hrSssjA0nE49FcAM6XgoegA8AeBTAG9Gc4f0AHhzpOKk6JkI+jZWBc7QYiSdvuHGC/fz1JvUeJYRzyRIaT6RAt+S/iudIjBxSnLytLTZ3VOq5+QQnwf9l37M2axbXUzD/LSPDKX7CWCvesiBjp9xDatoJGZJfRw6IDUH05yFHQYEjd4mn1KWnO69lZaXzCoq3T+pdivIEuBDwZcvYV8I1xTsoh5CliPFZlF/J4xWPWnc3DVw1Nc7ANHs23wshzbJlzlsnn+W9H1kg8ktC02VP5IfYipIcL0dxPBW8hNbyibKk6yZKMZkwHj9cqfiRjJdvELwefB/06vl5On5O3rx5TsmTXBv/ByIZHIyFPNERDel8PBpWnhElPUgYJgXgbHg05gBeH8n9VDYpRgrfGzCavrKplFy8YKkEyQ0S5a+vjxtNKUAsStj8+Vy34TA3UxJOJoqieAd7e6lUiqIn+Xy1ta5OVG8vN215edzESn0v8Tj29bkwTqnttWwZN2mdna5tV1eslb6jg5sm3+sgngTJFfQ3nUVF3PBVVCQmWkkWo1Tw/gagFK5+8LsBdCfZd0jDlNfuQwAsgOOHG/Nw9+AJgr+Bwbz0eO2Hg3jfZf35nm/53eztdQYOCRnOy6MyNncu11s875V/+CyQokj4ylNZGddCba19U5GZMcMph7m5zqtlTGzIZzyW2aVLOVfxZskay8jg2otEOK6EbwKxNfz8sf28O/EgTpvmFB0xzviH/2x1dZQJFRWUVT7Dp/SXfNsZM1y//Hz2k+82I8Ndk/GlVp8oXxKumZ3N5xelWBg1Af7dhMV39my+1tTE5vtJyGtGBj+L3BWm06H+1uOh4PnWc//YBmDPUH3H6tBNlOJwQqrCSmSM4RS9eH1kk+fnE4g3TzaZEnIyd25s2ESq6+CN5ADwzwDu9D5/CcCXhmj/BwAnep9VwVOMGZJd2/FqT/p9hdwhnldC6tgJLbuM5Rcb9/P1xOLc3e2Kire0cOMoCpq/aZ0/n5+LilzRYql/19zsiqt3d1NeSDinlDgoK3P1ombPdiRNQlDR0eG8HAUFVAzDYSpuwpyZluYs+/4G2A9RO5g6U9aOTj5Jn6iiF5L3SfRLyjAFIB/AbwHcO5kVvMmG4G9gkDQsXttEpRLkta/P/a9LzqysE1lL4smW8gfV1U4ZmjEjvnIXVAIk9FGUDCk5IIcwX8p1v/i4FBYfytsmiqOM5d+/pCR+e/md95k7Jd8uXtusLD5rKOQUvaoqrvuCgtgUj2nTHGumn5OXkcF7Zma680JcIsqbhEyKDJFnElKWtDTn0Zs2jc/W1uYUO5n/smWUWxL66YefVlRwzjKefH8+86iwAre0xLKhSvtEsikVCl4IQ8Bam2atLYhz5Ftr04fqq1AoDg4DA0B7O1/Ly2PPjwbl5Tw6O/lZxh7q/oODwMaNbCvnVq0CzjkH2L2b11etAq6+mteXLAF27HBjvPHG6OaaIswA8Kz3eVP03AEwxhwJoAbA//NOZxlj7jPG3GuM+bcE/c6ItrlvcHAwRdNWHOpItLaDbfr7gRNPBE49Fbj7bn5esQJYvtyt3VdfBS6+mGsRYJv+fr7PyAC2bAG+9jWgvh6IRHi/5cuBm27i2GefzTXe0ADcdhuwfj2vGQOsXMnPkQhw2WXAO98JrFtHGTJ7Ntu88grQ2AhccQXwk5+w/XXXAV/8IvDww8BddwFf/jJw7LGUGQCQmQlkZXGMT3wCaG4GzjsPeP114KKLgJdeArZuBTZsAKqqOP/t2/nMCxfydedOHhkZwI9+BFRWAmVl7vvLzXX32r8/tX+/JPGKMSYPwO8A3GiM+SYYrjkc5gJ4zFr7hLV2N4CbASyN0+4iAJcC2JmqCR8OKC/n/6j8Hsr/d7zfQ/m9XLXKXRsY4LF8OdfZ8uX8PTSG160FOjqA7m6u23PPBf7+d+CRR9y4kQjwzDNAenQX/dxzwKZNQH5+7P3nzXPvFyzg6+7dvEdVFXDffe56OMzz+/bxNTOT40+fzrVWU+PuJ9i/n+tH1s3SpUBeHp/FWq4vADjmGMqZPXvif6fZ2bzfmWcCv/41kJMD7NrFa2lpsW0LCoBnnwWKiijTAMquCy8EKiqAwkLePxwGNm+mDNm6lfOR8Xbv5j5jzx6eN4bPkpYGHHEEn/fGG4Ft24CuLo4FAO96F6/t2sVzJSX8vHkz+957L8fduROorub38uMfU7699BJw2mn8vkSOPf88xwiF3HcbDgMvv8zxPvpR/k2nT3ffXUEBx923j23l/2ZMkEjzm6yHWqEUUwGpCkuJZ5UfabhmonCUoTwI/n38XBxrHalKsMaWWPyDsfbJAGPjwfswgOu8zx8H8O0EbVcBuCpwbkb0dRaApwAcNdT9VDYpRoJk8mJlnQkznoRZ+muvtZWe8yVLnHegtNT1k7xZ38sn9fB8xkw/h0i88ZGIowkXavCmJicXioocOYp47ySEc+5cx4wpeSx5eTwnYaDd3c7DWFrKcXJzGa4mRdMjETdPCekURsxly+gVaWs7kCkvmSNZjEY+AcgBEAI9cp8EQ8BLkug3rNwC0ASgJ/r+biTw4AE4A8B9AO6rqqpK/oEPMwy3FsUT7ueoy5rxfyN7e926kjUn+WNSMqSlJT7JSLAWnHh55H9dvEaS8+Z7tKQmnHjYpk2LjaSRteHn7QVz+kIhru1g0XPp75Om+B6ooAdRCEckT9a/Xl4e+xySPzeUlzEri55531vpexKlxlx2NuWA9M/Kiu8d8z2LNTV85ro67ltk75KT46IFsrLYpq3N5S375DaNjZxPR4cjTxFilYwMeieFrCYcZjt/DkcffXDyaSjZNCKBNRkO3UQpJjtSmVieDLlKsopavHCUeCGb/iZQPvs5eMKeJyGaPlW7FB2dJApe0iGaIGPwCUOMdT2ADw91P5VNinhIRomL187fRPqsmRKW6ZMfyWZyyRJuRoQCXAosywZU+jU1cbMhtaBESRTmPwn7bGpyeT2SOxSJxJZG6Onh/fLzY8O1hXilsZF5f0KY0tJCY1BnJ2WF1MDLyeHGVzaI0r+ry4W6GcM5GsPNYH5+/I2cvwH03wep25PFSOQTogRQODCtZRtYR/heAAuH6D+kghdVGu8GUB39nFDB8w+VT/Ex3O+1T14kSpycl/Ur51tbqZDNn+8YbdPTnbFi7lz+dsYLdQzWQSssdIrACSfwf1f6Bf+PfYXLD62UdosWsW+84uThsAvtDCpxMi8Jq4x373gKitR7q6hwcwmHndJUUhJLLjJ9ulNkpZ5eW1uskmoM9x5SAkHO+TX+/EOImUpL+d4P5ayqcnJDGDWl7iDg6mgGa/YB/Js2NDDUU4rVd3VxbkJIU1vrGDPFcNXYyLEkTDMYXjvlFDwcQonCCsVIkCrlLpnyBv6PzFC5AvI+OG5Q+QtaKH1Pgih0sgEUVr7GRgo92YD5Px7JYIwUvDCAJ8DQS8llOTZOu/qoh85454oBZEbfl4FseEPWsVLZpAhCSIoSGV98w0m8NSmMtVJ6QDwJLS2xObD+epR2vmfBz9Pr6+NmKjeX3gXxvInC5httpIzB/PmOjKW7m/2lLpQogmlpvCZyQwqRi0LY1UV50NXllLviYhqFpDiy5NAVFbnCxqIAdnW5TZdvpTeGlvjKSl6TDW5ZWaziFw7HeizGSsEb6oh6847D0AzlQxqmABQC2BKVWU+BIZrPD7d/Go18mmwEKWOFeCRj1sb+/sna8dezeKD99eYTCrW2UlERdticHKecDHUEDRa+UuXXtpPPQYVR2gtDZTjs+mRkxJZckD6SDyeKl18HT8hIZJwgW2Xw8I28PrtnUxPXflOTW8f+s/nPlZXllCs5v2yZU+Zyc913mZMTy2oJULmS68uWxZae8O9dUECvpfx9hJHTn1tenmsjBDk+G6rIQCliLoQ0TU3uEAbO2lrn1cvKcveaUgoeNFFYobDWHjz7ZTJtfMa8RB6+YPtE95IfM6FDj1dwWX68xBtQWupogXt7nYDKzEzuOcdCweOwWAyWP3gcwPnRc2sBfMBr81UAlwT6nQCgLyq3+gCsGO5eKpsUQYjyNZSRJZ4RJZ5noKXF1ZnzFbbFi12NJlmTcg9/YyokEH19bNvdzUPGLCtzxclLSx3DXlOTq63X28v3UguqtJTeueZmbniam2no8Wvs1de7ws8+K2Z+Ptvl5XHD1NbmvAkSCZCf76jjxdIvm1Rh3ROyFdm8+SQN0vZgLOTWpl4+AfjMENeSMkx57cfEg5fKSJSpgmCUS5BgRbznQlQkm/jmZre2xKvT2+tCMZctc8yWozn8EEbgwHBLuSaF0qurXZv6+gOVSp+MKJljqDXktxlK8cvMdHUtxTAj61Xe5+RQDixbRhkjhDK+AuvX8jOGskMUKH+evuIoxixhyhTWTd9D19bmGMBnzeJcZs+mDJKIgry8WNKcjg77pgLol3Do6qJcFAOZhHYCvG9eHolYpNTDwciniVLwkgqPArAOwBINM1AcijjYnLmRwM/NSXT/4ebj/6gJe6ac9xXDvj6X5yP0z5J70NKSWiE1VQ6VTQofspYkpEvO+UYSOSdrt6IiVkETZW7u3NgcIFH6xNAyZw43ID09HFPOS+26nh5enzfP5emJ4iV9pD6dbEykNp3cU2rmycapsZHnpk3j6/z5zkotOXtdXbEeDWGoy8nhNdn0VlUdWIi4oYEboIICt3GUIspiCc/Odpu6eFbwYI2uyaLgDXckY5jy2o7Z3mkqKHcHO8d4odGyxhIZQsWgKt4bqQHb2kplr7WV//N+ySBhVgx6kf0jXl6btJ8xg//zlZXOgx5PmRGFRjxD1dVDr4F4R3o6cwJlTcWb1wknHDh2IpZNP8wxqFzGCzeNx8BZWnogM66s76BRR3L52tqcgai9nX8n+U6kzmZHB9/39DijV3Mzv2PxNoq8bGqi/O7spFwqLHTsmr5nUULTJT+5uZn/K8K26YeLhkIHprWkau80JIvmQWJYBjtjTBOASmvt7UMNpEx1iqkKn7ErGfjseiPBwACZvITtS/oH7z/UfITRb/16fn7oIeCss8gUJoxjwX5btpA9b+9eMvEBscyZwl6lUEx1JLMmfaY9YeC76qrYvsaQNW7NGseKuWoVmfXuvJOvANn5rriCr888w3MNDWS9LCsD/vY3nrvySjLfzZkDvOMdThacfz7Xcns7mS+POorbhu5ujvnEE7zvhg2cxyuv8Lqwus2YwbZ/+hP7fvWrwB138FplJdf2t78NfPzjZPH81KfYp72djJgFBWTE3LePTHOC3bvJeLd+PfDkkxxnyxbOYetWx2pnDJns9u1jn9pasmnW1fF5s7PZRhjt9u/nPbOz3b22bk3+7zuZYK29w1p7tLX2KGvtxdFzF1prfxan7QJr7X0HjnLwSPZ3a6Iw2t/LofrLGjv1VPebJtf7+x2z9AMPuPVgLdDWxjUHAKecQkbJT30qlikT4G9lIuzbd+A5aT84CDz2GJkb9++PbUs93+Ff/9Uxx27bNtQ3EB979gAvvsgxwmHeKxTQFv70J8ol/zfeZ6s9/vj4YwdZNWXuPpvkzp3uc10d/0avvOKYOQF+r3V1wLJlZN8FyCxaU0MZkJVFRsxNmzjH668H3vMe3k9kxr59ZOANhYBPfpKyWNhC9+wB1q7lGBUVvF96Olk6167l97p3L9l9zzmH9+vo4PM9/DBZQk86id/ja6+5a+XlnENFBTBrFufqP1dKkUjzO9gDmiisUIwKw+XcJTrvFzpO1ms4lGdPvAQSqulDiBn8GldiAQta+5IB1IOnmMQYaT6s3zZYQzLoDQ96xn3SIz/UUq6Xl9Pq7DNqBlluIxG3bnt7XS6chFuWlrr3UptOrM2NjVzDPT0uV05Y4MQrUVtLq3R9Pb2LBQW8jxAISOhaQwPbC+FEYaELswyHXb5uVRXbL1vmyFjy8zmeFCqeO9flDoonQ7x36ensGwpZO3NmbKjWwdaZslbl02RGqj14AsmL9UmOSkv5P+iz0vb28n9WPDdCJpKT45gxgx6mZA/fuyVFwQsK7JtetUShk/E8bkN5DYW8RLyFfv9EXjn/KC+P9cwJC2W8Z0m0Hk84wRE/1de7guX5+ZQheXnOszdzJl8rK2MZSdPTnbcuSNAy1PwrK93fTJh/Z8zg2BJxIDl6/ndSWUlZ1NLiahz6dfh82ZaTw/ZyPTOT/0vZ2ZSz8b7nZDCUbBozYYJJlCisUBwKSCa8Mt774cZLxOJnbSzZiqC7m5Kjq4thWVIweeHCWGYsVfAUhxKGy22Vc0HlzSc5KS8/kFBF4OfcBdlp/fFk49TefiCBiuQI+YQqra1cp1JIuKnJhV+mpzMks6XFsV1KaFFnp2PM7erixqu5OTasMhQiwYlsbqSsgiiPBQXcJM2Zw02TMNdlZ3PM5mZuXCXPRcKzZEOVnc1NX00N7+vn7gQPP6wruJnMzIy/6U0WKp8OTSSTr26t+x2U/2MJOZY2s2e7NZOoyHe8c34IsbxPRO8vBELy2Q9XDJZdiKcsBNeNrAfJzxMWS3/8ZBRTY1yZgqHCT4PzHsrw4o8j4/tEKbW1lE1paZQjHR2xZSaysymvFi1i/4qK2O+rqoryZtkyKl3FxWyfnc2/r+TFSVhtdjb3Np2djhlTSrMAbD9nTmwJhbY29/eU8gmiFJaXu8LqAK9PNQVvUiQKKxRTCcMpZqnOiQiyhAXv5ZNAyPumJkoOYRBrauIGsbs7Pl1xMtANlGIqIZ6xJchGK/CJTkRZC3rvJKenuZlrac4cx04rua7SvqeH68yvDzd/vlPshDRFCB+ENGX2bG4yRGFLT+emRdpKrsncubweibiNrOTczpnDdrW1btOSlsZ59vZy7HnzuFmqq3OMf52d3ISlp9PSL2ya+flOTohnUCjGKypcjktwQ5mVxbGEJMHfXPqsfUE2wfEokzBZj0NFPqXqN3Co/PRExk4hV5k3L7bWpJQI6ujg/+9wHqOhjkTet5F45fxjKEUqnuI3VoevDAXv6TODSk05Ua58xTOoJIdCjsgk+Dx1dU6+hEKxZCZ+vcxQiH9T8aJJ1IIxVOZEQSwo4Bzz8ij7Zs9mX8kPFvmSleUUOPGw1tQ4r6Sw+ba3O+IVUfRSvXcasxw8a+1eAJ8DcCeAjQB+bK190Biz1hjzgbG6r0IxVTEwwNyYofIJRpIT4Y+TaMxIhHlCK1cy/lzy92QuK1awneTlrVrFmPNQCPjOd3guJ4e5CevWMXZeoTgc0Nnp1qOsFzm/Zo1bR2vWMGdnzRrmywHMpxkYYJ7Paafxs7XMO7GWOWZXX808jmOO4TodGOB6XL8eOO444Mc/Zi7eqlUc/6STmKP24ovARz7CnJL77wcWLwYuu4x5bbm5wCWXAB/7GNDYGJs/89xzwOc/D2RmAkceyXNPP83xt2wBfvITICMDePRRl9uSmck8mJwc3htgDu727cwXOvJI5qZcdBGf/dRTgZdeAqZP5/yrqzkGwJyWggJ3/cUXmaeSkcF2xjD378gj+X7PHubA7NnD55JcpZUr2W7/fn6XANuUlMTm5ymmHg42585Hovx0gPfo74/NvVuzBrjnHuaXPvgg/7/+9Cfm2z32GHD55Tyefpq/jxUVbn2lpbncs7S02HyzIPw8Nh/B/Dw/Jy4/f+TjCWSNCDIyDmyTleXW6VBITz8wx04g8//5zzleRUXsHPbtozzLzGTu8XPPsd2uXe57nDaN1zMyeC0ri9/3woXMF66ocM9z1lnMCQb4Hfzyl8CiRfz80Y9SFpSXu+/uqquY59vQ4PLkfvc7ypvNmymns7Mp2559lnl206YBH/oQ5fW0aeyzcydl2JFHcp7t7ZSfAwOUQbW1HOvaa/lbkZMDvP/9fE01Z8FYkqzATpJEYYViqiCe4PeJG4I/bIl+6Pwfwv5+9z6o9LW3831WFjd+gDu3YQPPyQYVAFpbmey8Zg0F65YtTDBuaOBmtLp6FA+tUEwhiKIlhEZCvCBrt6EhVvkTopXOTipqH/gAcOKJXFPGULGJRGggKSmhMpSfzwT8ggJuOgcHOV53t2tXVgacfjo3GhUVwA9/SKVo82bg0ktJ+HDUUVS8rr2W5ANHHQXMns15CSHE6tV8/Y//AL71LZK3XHwx8LnP8Rn/8Q9uYgEqSm+8wc3X669zk5mTw2sXXMANzSOPsJ21nOfPf04Fc948oKWF7V97Dfj737khzMriRmzTJm6eamo49uzZHOepp4AFC4C3vIWbu02bYhW39HRHsgDwWTMz3UYzHOa8tmzh+IqpC18JO1gIEZK8l/EBrtXVq2m06e+n0eDUU7n29u+nUePWW4ElS/h/mZXF9ZiWRkWlpYUEPyIT9u1zCs6+fQcqVT78a4WFidv5ittwRCoyj6KiodsBjoDEhxhuEymmkYgjk4lHFOPjjTdoIHrlFX7OyqKCBPA7Ky52yqS1bJuZSbmxYAEVvr17OZeKCsqAyy8HfvYzyknBVVc5cqrsbMqwX/+an2+5hf127qQxad06vj7/PGVqYaFTvvbt43f9X//F/c0ttzgSm+ef5/9jWRmNSjk5lInnnMN91Ouv0/i0cycJXnbvpny84QZg5ky2rariXgoYmoBnVEjk2pusx6ESZqA4tDHaMJJ4YZJ+4XGf8CSZnDyfqj1I/hAMFYs3B8kFmjbNETFIaGZxMc9JIeKeHuYRSP0sDdFUHKrwSxuUlzvCIbkma05KIvjhztOmxZYmKC8/MA+2p8cRp0j5BL/cgpQnKSlxBXdlLClZIKQqUoxczkkx8oYGhmgWFzO0cu5cV08qHOZ7CYWUEgnNzcyByc529N9z57ocPCFwkaLAIisiEY4hNask366ri6FOhYUMm5L5NTRwvK4u+2ZolhQQrq11MsaY2DymSIQhW/FCO+MRUiQLlU+TB/HCoEc7hqxL+V0N1sGT3z/JP21sdP/3sp79MOPRhkrGS20I/s8efXRy90n2SETQIoXQpQadP/dkcuySIWUBHHmTHy4pdfwkFFNq+3V1UW4UFzPXTgqVSzmIoiLO9YQT2K+khNckfLuhgXJS8hjDYYaBSukCwBFG+cXJhVNAxvHr2ckRDjNkt6+PYxYWcj6NjZzjnDku3FSeS+qC1te7ZxmL9JYJFzojPQ4VIaU4dDGa2nfDjWdt4qLJ/vuhiFJ8pr14bH3xxvNz74RFTAgcpNaLkDI0NPCHr6cntUJqqhwqm6YuRrpWg2sj2F+UO8m583NdfQIUYefza1j29TFPTZQ2a12ej/ST9dzZ6epPWsvPhYWxDJmy+ejr43XZpITDrnBvY6Mz2MyZ44qQZ2QwP6S4mIes9/p6VydT6oAVFsayc0ptMGHPzM/nPRsb3aa4o4NKW1aW23Q1NDhihYULHZnBggXsU13NsWfMcMQJxxzj5E11NTdzoqhKbk+8XKNkofJpYpBoXQaNlcn0SdRODJj++hNiManxGg7TsCA1zGTdiMHG3+ynUgmLd4yGkTOZQuUjOURZAqjMyPgFBe598LsQJTE93cmCyspYchRRdIL5spWVlCtyz+xsnqurc+OKTAmSuEg9vHDYEcpIfq9PvFJTQznb1MTnyM/nkZNDg5Qomj09JGmprnaGp8ZGyq5QyJGw1NfzmDeP8qq6mvsjyQUUQ5nM2Z+LKngKxSRFIkUrVWMnei+bvqCyFqRa9z0MQu4gHoggq2bQW9jT414LCymsu7ocGURTE4WY/4OXDHQDpZgojNYgk8iDIMyZvjIn623hQsdquWSJU8JKShxpSl8fz3V1cQPR08N+4iWTsXt6uClobOS5ri63qRG2TGHMbGnhukxLcxuSujq26+521O7NzTwKC9mno4PzktIEQhseDsd6MUTRlCLBwqYpcqGujnPp6eEzdnZyA9fdzTGbmqiwiaIoG7G2Nn4Obi4rKth/xgzHuimbzcJCVwR9uI1qslD5NP4YCWt0sn0StfeLmm/ezP9lob6XdSQ0+hkZjlxIlD75fxotEYpvgMjNPXCzfzDjjvQoLY313iU6xFsWb37hsFvPifpLuRRfsTOGsqG62nnySkpccXLxgInymJHBfuI1NMb1jUQc6Ym0N4bRBcKYKSQpM2fyWlMTr4n8FCKo0lK+b2lxc87JYduaGrbJyKByt3ChYyjOy3MRFSJbGxrsm0qrKHniIVQFb4oJKcXhhUTseuN1n6BC5ittfnuhau/tpdCSEBW/vR9e5j+HXJs3zwk98TbIxjSVQmqqHCqbpi7iebCHax9cH/41vyyCeM/6+vgD39vrlDvx1PX2OmVv4UKntPX0uDCxoiKnqInHrquLCqN47mQjJOGQsi6lXmVaGjdWubnclBQXcwxhwpS5SG28khIqajk53KyIAimKp8wLcF69xkbOQTbBlZXsl5/POeTnu43asmWxdOEzZ/I5Cgu5KSsrc55E2XBGIuwjNOnCYuczZkrNrOE2qclC5dPEYDS/nYkUv6F+K/3fUwlnFiOmeKTDYadISM3F7Oz4npeDPdLSYj1gvgLlh3r6/98+a+NQCuRo5hLvfLyyD8EjM/PAEg7isa+uPvB8bi7lh/T1r4dCHEuMP76iVVPD5+voiA35FG+alG7p6ODnvLzYkk7yvYmxurGRntv6ehduKTLUGN5v/nwXEdHQwLFbWjgfUXAlrFz2SfPmufmJQis1/eJ5f5OBKngKxRhjKM/aSIuOj+aewXOJCir7c5KNpnjv/HAz2aSKx87P/7HW/Qi2tFDA1dZSiBUXHyjQk4FuoBQTjZGuVQnjileYXDaKsrZaW7lhSE93CtfixbFhjpEIN5RdXdzcdHezrdxHPGTFxa4WV3k5N0pSMiAU4gYjP59WaOkva1Vq2IlCJp432dSIgidlEwoKYgv3Nje7sEoJ55TaTxKSJJu16mqOWVDA5wkWPxalrbbW0ZyLEij5c21tzNELbmwrKlyOTSgUW2Q53uZQFbzDVz75Ic3+b1s8A6bkixYXu7UmRhDfw1JWNnz5gUPlSEYxlPInw7XzlWR/XFFuysqcAuRfFyOSrHmRH1VV/NsUFVE++Xm3krdcX08ZJIXoxRsrERLSPiuLY0hun8iqigoXBbF5s5PfkrJSW8vxw2EXrltRQXkn4bySqyzREbm5lKPFxVQOZ8w4sA6hKngKxSTAaEJJRjrGcH19K2RPjxNuiZLR/TBMUepaWlwhZmvdBrKri+OJF8K38EtisWzqqqpSL6SmyqGyaepjJB68oAdAcu7E8yZrUPJVJb/DL2Yua++kk5yBpLfXFQHPyOBrXx89e83NHGP+fOf1ExKU2bNd3khBATcSsmbnzWOf7m5nORdLs9R1Sk/n67Rpbi4NDVTSOju5KRYFsKjI1fuSjZCffyOfOzudpTwUYh/JU+npcfXz+vrcM4unUbyKwY10errbmGVluff+BilRLlTQ65AsVD5NHcQzsPp5sv7vZTAtwVr+XzY3c72JR6e42IXTHXdc7P/QeNWRmyjlLhlFNt56i1frzjf+Zma63NnMTGuPP557Ccmbrag4MGxRCJVEmUxLizUayVyM4SG1OOVaXp4zctXUUJZWV3MMn7hJlD0ZTyIlfONXQwNlTn4+Za+vXObkOM9ddzfbS6SFyOHCQn5O5Ak1Jrn/d1XwFIoxRirCMIcbI56XUKyREmLph15aGz8ZPRhe5odkSo6PkETMm0fBJFYryZGRzaHkBwmrXkHBgYIqGegGSjFZMNw6jMdGK+flkFBla7lGJL+juZk/6r4BRqzCCxdybc2bx43MvHkuRFMMK1JsXLxcXV2xXj/ZUPjh0qKsCbtkQwM3JR0dHKuuzjEEioJVVsa2jY3cqHR0uMLpEkoqfTo72UbIA7KynHVaQtymT+fGSsaT76K4mGPKvSUcSmRHTQ03YKJMSniWbDqNcUplKEQilsrK+JtuXxlUBe/QRNCjbu2BUS2+wicGUX+tLVnC/8WaGv6eFRTwf9JXNlJBWjKRHkB/faSlHZjnmor5DuVFHyqn0L+PtDviCK7dzExel/y/8nJnYJa/i0QZCBGM7+EXpU4M0UKCIp4+INbDmJ3NQ3KLi4udIhgOM8JADGoSPSEEMABlr+QQLl0aS8gCHMj0G/yOkoEqeArFOGAs8+58D1/Q2ychKIk8dkEKdp+NM16IinghfMIHUfJkoyh05xKqINaqYFx9enpyz6cbKMVkgG/8iId4RERCerJ4sQtrFiu0bBpFKSsudqVGZL319DAsrKfHWXfFg9Dby6Oiwp2XtdfV5cI6ZVPT3Mzxm5tj12JursuLq6tzzLey0ZGcEyETkPsKGQrgSh9I+QMJ7RQPXX09N1CNjZQX3d3c+Eh+nR/GKYQDflhnZaXzDMoGUcJDOzq4wSsqit1AZWe7+8uGUkhg5NlljHhHslD5NPkRT3nzf/OEKdP//ZM1JRtqMWT6Roa6OufNGYsjmVy2sVLuRnIkUvBGmoMYLKMQLHXiK8/GUEZJOYHgWLLWRVaIXCwtpbwLpovU11MGS95uZSXlZ08P2woZlDDxiiwqLOS1vDwqdOL185mIJccvPd158Lq7XY4zQCNYc7MzhM+a5eYeT04lA1XwFIphMBKFLFHuW9ArNtSY8YgZErWLRwARzzoZjwHT/9ELvsb7EfRrb8l7yd9paeF72ZTKD2VPD4VaIua6ZKAbKMV4Yqj1FixFErwmpQtkDUl7P4+1qcmRnEgu3uLF/MGXNbRkiWOZlNDMpiZ+lnIj06ZxjM5OV+NOCEh8cpbCQhfSWFjIjUZRETdlFRXckIjSEwpx09HQ4DYu4vmTDU1tLcOO0tK4GZESBbIR7u6m5Vs2MsuWufzb/HznqQSo9ImilZ3NNnPmcIMUCvH+OTmUIR0dHLetzSmAwowH8FUIX7KzOTfJCQzm9iTDPJgsVD5NDQR///yccr/W3cKFjoVWckjT0lw4dF8fN//+75l4aMZaAUu2jtxYHKKsHUzYaXZ2/Hwy/wjKFP8QhVeUPilx4JdTkPUtIZcSpj53LuVPbq6TS6LMifwQGXf88Rw3HHaySvKBJcKgp8fV40xL4/cjtUMl5N7P1ywtpTGgpYWyq7CQcxKvn5Rvqa/nvaS0w9KlsfnR8jdIBqrgKQ4JpNor5o87EnKFeG392jnSLtkxEo0ZTAZPZhx5bWlx/fz6Pr7CKG2ljRCu+J9lcyp1uIQ5TyjdJReovT1x3ksy0A2UYryQTM5sIqNKczM3DJJjFyQl8mvb+YqdhFjKNWtd/lxXlzOalJVRSVu40LG2zZnD9VZVxU1EZ6dTCCUUs76eG4+enth8Oslpkc2LKGRNTVSkhC5cNkzBGlJ5eVzbmZlu05aZ6WpgpqVRgSst5ee8PJfTJyyWQjYg4UiStyfhU/n5/A7kuSR/RmpfzZrlrOMS2iSlEYS4JUgzbgznNlyphGSh8mlyIJnfVf+3q7zc/ab5h6xVCc+TcGYJPRal71DOrxvtIZ72RNd9Q1JQYQm2lbUuBc3jeQOzsmK9esJOuWyZY/KureVRX+8Invww7lDIGaE7Otx92tocS2pVlSucLqVWRHlrbY2NEJgzx8nslhZXIL221hFkiQFLlDlhFJYoCCGoEhkVrzRFMlAFTzHlMRIlLJmxkjmXqG88RUzy3pIpkRCvMHKiz76Clmg+wfcSjiIhlv6PXlBZFGum/ODJ/SRE02fga2pyG9CiIgqx0lJu0Hw69OCmKhnoBkoxnoi3jgXBNeOvRbH8S76rQHJW5XwkciCTrXj/hMRk4UKuIwln9M9LCGRREQ8/z0TYLkVBzM2lUiWeteJirsXiYm6AZHPR2eny5uQeQvNdWenC0GbOdApjbS3XvIRnZmZyfefnc+z6esoFyc0TD6JY4aUYcVparPejttaxzonFv7jYzXXGDLeZEuXTD72U/Bh/E5kMi58qeJMLI42cSWQIlVdZc75xM16Ui+/J8w2YwuY6GRW7RKGQ8YjNxuKQ9R8vbyw9nYQpMsfhvOfy/YZCyYV45uZSGWtspJyS8YN9xfvp57nV18cqfHJ/Ya4UA5TMR8JCRTEsLuZYxnAOkqYipHRtba4GsIS4S9qKyLBwmK8SXREOM0IiEmEkhe9l9L/vZDCUbApBoZgCKC8H1q/n68FgYABYsYKvwfGT6dvennhuCxbEzlHa9/e7tv39wKJFwOBg4vv397OfzHHNmgPnK+P791q+nK8NDcBddwFlZex79tlAJAJ0dwMbNhz4rFlZwHnnsR/Ae+/ezffd3cDVVwN79gC5uRxr0yaKoJtuAs4/H6iuZtvCQuDMM3lfhWKyw19jgoEBrpnOTq4Za2Ov797N9XDaacDll3Ot3norcOqpQEcH11ZZGfs98gj7yHqzFigpASoqgFWrgHe+k+vrlFOA1at5/o47gDfe4FhvvAFUVrLf4sVcZ6EQ8MwzwLx5wLXXAjk5QFUVUFMDZGfzPvv2cY3OnAls2QKEw7zW1QVs3w4UFfHen/40cNFFwPvfDzz7LPDTn7L/888Dr70G7NwJvPACUFAA3HYbxykv53ewbx/wvvex7caNwN69wBe/CDz1FHDGGUBaGuf2/vdzDsccA8ya5b7H+fM5v82bgR07gGnT+Lm0FLjwQmDtWj5rbi7bz5kD7NrFNsa473X6dN4rHAZefz32b1VT49oqJh/k9zHeb1s8xNsDyG9lfz9/11et4vlLL+VrQwPb9/e738LVq/n7OzgIXHABsHUrcOWVwLZtwNKl/L8LrvuRIhx279PSDm4swa5d8c8/80xqxh8O+/ZxDs8/754vM5Ove/YA//iHm+O+fbF9pZ1Avl9j2Cd4XRCKaij79wM//jH/juvXU14A7n7Z2cCyZbHzEXk2bx7l2bnnOhl61FGUhbt3cy5pafw/Ovpo7ocGB9kuK4sy58EHgeuuozx7y1soX1euBD7xCeD664HiYuCzn6Wcu/12/kbU1FB2vvWtwGWXAUceyTHr64Ef/Yjy/pprgIwMysDg/0nwOxwVEml+k/WYjFYoxdTBcCQK8doP9Xk4xGOxHCqM0w8zCd4z6Hnzn0PCMoNhn8LOJ/W44t1PamWJxTMSoRWqrMyRLZSWugTl+nrH1FdeTkuUJCCP1koOtZArUohkwi/99RCkUY93bvPmA2tGtra6kE2/rayNpia3hqSmZFeX83oXFHBNSTvp29zswsUk30PWl3iqCgoYpiQeuHCYYT9i0RdWS6ESlxIKEnok4ZOS35aXx76SC1JVxfZz5zrPmnjojYktFNzV5XLh8vMdwxzAe3R3x3ohpdYUwLF7evg8Ukxd5hS00C9dyrH9cKZE3oLy8tgSDaOVTdaqfBorJPoNHGr9+u/j5Z3LuvSJxKSdlBuR+q1CNiSEHEVFznt8qBypYPwMHrL2iotHP4Z4y/xi8eKxLy3l36G0lHJMiJgkpLOzMzYnLzvbhXOLTCkuprevrMz9bevr6THr7uaRn8/znZ1ORornToqjt7RQPnV387vs7GSf5mZGMdTVUUbKvmnzZsdELOH3zc2UWxUVHKOri2N3dlLuxSPa0Rw8hWIUGIlyl4qw0EQ/YvEUzaHyf4L5dn74mChmwZAUqV3l5+QJ5IdQSB38enhz5lBAzpnjQlekFlA4zPeSt+DX0lKSFcVEY6hQLmG69MOp+/pic3Ws5TlhrvQ3iMHiyLJpjJf/093NtSFFcCWsuaLClRaQPDNZn7IxkALowqwp+Wl+GKKQEEybxs1AezvHEoVHakC1tXHcuXNdDl5HB9esjCfKXGMj7ylkA37IUUODK3VQX+82jkuXso3Mq6PD5cTI5kuU06wsPv+cOU6Jk3umpblCwUItnpNz4OZHwkX9HJt4myNj3HiJjmSh8ml84K/RRKkQQxlL5fdLclj9cEwxtorBRfJQ29rc/9P06S5fa6IVs8lylJa698PltMqRDMGRHIlCq43hWk9Pd0RN2dn8e8Uz3PhMlJJPJ4a1+fNdzrAQOvlh6CKrZs50BE4Sbl5f74iw8vIcOZUQZM2d6whXRAEsL6f8lbrCDQ0uVDNY1Dwri3u37OwD/++SgSp4CsUokYqcv+B4fk5OIotkvFpbQ3kTxTIZzBsSz4LkBsXz8PkKoJBBSD6Q5PLJ0djIo7vb5RQJG9SsWW4zm0ohNVUOlU0Th2Q97f5Gz98cxsuL7e7mj6+/bqSdrLVgzUkhbCgrc544sRaLAUbGkE2FFPuWsge+16+4mB5ywJUGkCMvj5udxYupVIkhRtrV1bkNWThMBVAUrYqK2M1RRQWfNS2Nm5Jw2BEShMNc3+JdEy+gFAT2N33p6c4DIuNnZcWWbJB8QKm91djIZ/dLHsjz1tYeqODl5blrM2a4+xnD+8cjKwBYQy9YpzNZqHwaPyTjwQsqbP7vppQckVxYYa2VQtWtra68R0GBy8caCfuqHu6Ip/RNmza05zA9nZ65eH0XLYr13Gdlcc1nZrq/jS+7Skpc5EB7O6+JzJAan319zqhWX+/kjYxRUEAlsqTEEbXk51MZLChw/09i6JY85s5OZ1CQUgnCdlxe7iKdJFd6wQK+b2vj2P5zZGaqgjdlhJTi4JFq5Wqi4Vsn420orY0lQ/HZL4cib/EVQ3/z2dvrNqg9PbQ2CVW0fy8hY/G9GrKplfMirNLTKWxbW7n5E8p2UeqWLj1QYCcD3UApRotkPe3xvOPD9e3uju0r62XaNEehLSGaEq4pJQTmz+fmQupvidIm62vhQldsXEJ4pEZcUxOtvuGwC+fp7nZWalG8hJZbwjSLirgmq6s5js/Q5pOX+Ee88MWyMhfCKVZu2fhUVrqNsRQeF2+iWKaPOIJyQsJEpW6UkBfI83R00AIu8qOkJJZBUzbdZWVuczcS70oiEoaRyCZrVT6NF5L5zfdrTgoRmIQ/9/Xx/6m1lUdzsyNREaOLeLnDYff/m4gB+nA7RhPOGSxP4o9x/PGxbX0luqrKhXvHG3fRIhqNZY/hE7IsWxZrSJJaeWLAys2lN9YYZ9zKyOB+qLjYhY+LHKuvd/skKYkgyqLUR/SJeIQYKyPDEU3J/1d3d6zxvLWVY4p3MT2dc2psdCyeixa5MNSRyidV8BRTDqkKjxzuHmMxZrxNZNBbMJzCFgzFjNc/mHsg7+XHTTaSYtUsKnLeBukjeT7SRzaiviIqHr7Nm2mZikRcAeT8/FilLj8/tmhpKoTUVDlUNk0cklHufBZZv72/JnxImKbvIRADiRTTFXbZxYv5g97ZyTZ+7oasQQnhkfBnCRGS+m95eWwj9efECl1fz81AUxM3OxJ61NXFDYIxblMlFmjJP5FxRNETL92yZRxL+kl4p2x82tsd02VVFecoG5JwmKFM/oatvp739osOi7dPLNPiWRQacslD7Ow8sJD0vHn87oTSPCeH7YNsc5mZnF9dnXuWkbBpJguVT2OPZH7z/fqsfl6sGDXlsxhS5DdQ1unChc6AIv9LifIzD8UjUZ58Kvv64YfiVRfjjMi0oOdOGLjlc3q6ky2ihAMunLa6mq9lZY4FUxQoYfoV5l1JK2lvp1ysqqKskPDP2lr+T0i+s4RoSvkZST9JT3eRSyLTGxtd3U8x6onxT7gL5s93/4ednWznR1rIdxJPuU4GquAppiTGWrnzc3CGUrZGMqavGMmmUCz7iYhThvphkx8smaeUNPA3nf6mVQRJkIBFfuREgfOVNwlpWbgwtsad9Csro2CTa/JeNmelpc6KFy9ZOBmM1QYKwHsBPAzgMQCr41xvAzAI4IHocbp37ZMAHo0enxzuXiqbJjf+P3v/Hh5ZdpUH4+/W/V4qXUYa9bRQD+qhetyVsSoTSZ5I0KRlxnQHGiaOm4sdFJw4jhLy/SDow/4gmIKQkAAVkjgJyUeZWwCbIAJOCDEGZAx8GDOOsTU22NjGxjbI9xsYG9vs3x+r3lmrts6pOrpWSbXf5zlPVZ3rrqpz1l7vutpnxD77c3NaaIgTt/f6rPBYG+aVzwtJqVTUQ8DwPyb4F4vyvCwvq/JQqcgyMlKveJDsLC6qgsE8su5ueWVTbyol9DrcuCHbrIWYeSbMpRsZEQVieFjOxbGUSnIthhoxT2V4WN4vLMh3uHtXi6ew95ztbUdi2N+vIVUs4MLvaHMDq9X6sE2GhM7NaeNhNgGmEl4u14dwAppjR8s+PY+hDErzEmRFJHhng0ZzL+e6sGCYTUtgBAu9evzMQkZ8XkODQlyaL7ZFSaN90rbTC8fnlYSGzzZ1iMFBWaan5TOJEPPnFhf1PJbEWe88ZRRlDWsGFIsyjoUFlcs7O5pXbL2A29tqrMvnRR6RuLFwnSV+nDtsYbtSqb51B3vqMVzU5mNTdp+k7nQqguQ0l/MgpCLOB5IKLFjl7zBexDBHx66zHrzwGHut8FzeqzAhaaOCyf1IAG1BFevxs0ST/exYxIHHh5bPfL5eKDHJuFTSvl30SOzticeB4QXlsoRGnKSQOuoCoBvA2wHcD6APwBsAPBjsswngxQnHTgB4R+01X3ufb3S9KJvaE0nPVmj8uH1bJvvJSTV4zM7KM7Ozo8YVkrnJSVEebEEUkiMSI3rwqFgwMZ+KEr1SDMW8cUPz3OiFmpurJ0/Ly6KcTk/XW7a50Hq+uCjP4q1botQOD+s1qDSVSvUk6+pVGROb8dIKzjDLoSHJU6FHcWhIvJFXrmg4EwmfPQcr0rEC5+ionGNsTInZwID217xypb5YDKvmVasHvQhXr8p3KBY1VKtR2Fe4ZEUkeGcLzj1hZEpSfjq32313doRAMLRuZUXuN1uwIy4nv4S5c0B93uvAgOwTeuusHCLJWVzUXFtAwj17ekSWjI6K/KtURK4yXHNmRtYVi3pcpaK5cAzRHRuT45g3x2uPjqqsIqEjqWNRFUY8MWeaRoPp6frqx9WqGvZ3d2Vdd7fIexrjurpkfbmc7CHNgkjwIjoSWb1vh/HgpXnZGDbSyBOXdu1wP+tdmJ4Wlz5Jng0T4/7WM8cG5Tw3k9BtqKf1UhCML6fHjtvptVtaEkFIgcecBlbPI6mzpYtPSkgddQHwNACvMJ9fCOCFwT5pBO+rAfxn8/k/A/jqRteLsun0cVivvn0mQ4NNaGCxOafe66Tc16chOJWKPEdsI8Ky2CR41aoaOwoF9YzRg7C8LArE8LB60VZX1Zrd2ytKEAkKC0HY8EYbokQlgZU0WWGyUJDxkQzahH56BsMmwwxZs6FCtKZzPXNSqLzZkMn+flW2SADDkKzFRf0O9PB1dcn+JJj8flaG0GNJcjwwIN+VpdYrlYPH8DccHU0P28yKSPDOFpyvrOHURsGEhhqGztkWJuPj8swxpaBUkvso9AJ3+pI1RDV8hsK8RXrG0o7l82j3IanZ3DwYfj04qKHmdr0lQqz+ayMemJ+8uirHrq/rWPN5bXNAo5JtycIw3kuXNKqAlTFtg/OdnfqCWPv7WqmY1YepszFPm21kGInQ1yfyjuH8SRV/syASvIiOw2nk8KWd005GaeQu7ThbHMWu917JHCt/0XvAanze11cKY1gK8xHW1+tDOTnx3b6t+/AcfX0ivGghteOgkOrr0/wgeiOq1YPW8vBzFpwSwXsmgB8yn58TkrkawfsTAG8E8DMALtfWfzOAbzP7/TMA35xwjecBeBzA4/Pz89m+bMSRcNhn2j5f/JykIIbr6TngM8P7nx6A69fVKzc5qdZdQCbvtTVRONiPbmtLJv5KRXM8Rkdl340NVTBseNPgoBQYGBoSZYTtSBYWtJ0BSc/du/WV/6amxCBz+bIqPPRa9PQo6WIeHXNWFhdlXVeXKh8Mf+rvl3EwNy+X03DKK1dEgcnllIjy+K4u8RrSm1coqCePFeqsl/LSJe2RR6VtYKDe4j00pF5FLpcv6z5cbC5Q2pIVkeCdHazBxa5LM4ZWq2okYDXo3V15TldXNZyYVRHDwjtxybaEufVcLEEMySIr5VoymGRsYWg3oLKMnnn2j+O6lRX5TwcG5PXatXo5yBBxVi6uVFRWLiyIzLZjYOEohpJTp2HRqsVFOYY6D/UqtpNi2gpbKdioD+prxaJsGx6uj04YGZFz8PsdRT5FghdxbnEcgpYUFnnc64WWw0bHhWEjSfuHVTND74IlYWzQurKiEx4nM+vdo8Dp71dBw3PRo7eyUp+7xzBMegPtuXkMw81GRjS5eH1dJlL2ymGj0ZMUUkddMhK8SQD9tff/AMCv1t5nInh2ibLp9HFYeWCfp7Dyng1NZr8i5tkxDNM+C6urmkOxsiITMwne+rooApzc83khKJcvi2IwNibLzIySFEuIqOTYHBUWRGGFt81NOe/CgigKDJWk4hrmoFhFieFKzmmPKFrMrZeO69mAmMQM0LBLNiJnRU8SycuXNcSVihwJKwulkMzOzcm1rLLd369FEOjRs8UTACGgJLv8vj09qkgC9R7IZhU3syISvLMBjaVheGboebf7e69hdGtr8srKrAzzA5r3RIzLQSOJXWgw4bPc6Dx87pNy8gcHNRog6VjKhOHh+vB0QJ7z7W2RxcxbLhZVznK/oSGR1bmcyl/mRlMWsoH60JAY23h9trWhEY8ynbl8hYLqRsWihmXSs0cjO/Our1/XMOHVVT2O+YQ2ZNj2HMwqnyLBiziXOI4X7ijHJh1zGI9cuA89ZUnWyKTQsLDBa+hVoJBgLgJz8pgvFIZf2tLRtuoYw10YWsBQUCqvGxv1VcfYnJlEklaxvj7Zxp5YYY+pkxJSR12yhGgG+3cD+FjtfQzRPAfIYmjhet7rvL+pRO7uaogXJ+bVVVEe+Exwwuf+rMRHAwrDv5i/yrCiS5dEwWD5fz4Tw8Pq4Zqfl3DDqSlRXsKGt8PDGlZmyUtXlygpzGFh/h6g1eWo3A4OioxgaCer1t29q+0Xwn51LDpAT2RIxqjAdXfLuPJ5ORe9mWFVOIaSWkVyYUHPde2a/m4DA1qBs1CQ/Rj+/eijB+XNQw8lyx4uaWXwsyISvLNDkmHUEjnmy4fHrK9rXtXqqtxDJHn8v2MD88bLSfcAvHRJZI/1mDFiIO2Yvr76AiwMl9zcFPnI4k6VisgIyiwb8s08PIZMslLm8rKQObakYbgnC/AsLYn8rlZl/4kJuZdI4EZGtG1CpaIFYqgnra9rnh7D0XM5nS9oQF9e1t+aIaY0CFqZlQWR4EWcWxzHg5fl2EYKYiMi1ygck6B3LszNs8QuDJ/ka1gdc2NDLEmTkwebl3Pb7q4IvjAkjUSQ3gt7LV6fzUD39+X8a2uaN3T7tvZ2oSfPey3V3tvbOOwlC06J4PXUiqNcMUVWnhLsc695/5UAXlN7PwHgD2sFVvK19xONrhdl09kiNJSE7UPS9mPjYz4/09NaOZZegJ0dsVivrMhrPi8LvdhjYxoWViopgRgZ0QqQ7D1nc0vm54XIsDiJzY3b3NScP35mzgjz+vicUUGi54yNenkuevCY98HrDw5qRU1Aw5y4vVDQIgZbW2r9vnNHzsnj+vsl9JLlvlkBlASV53v44YMh2wwHZag3++sVCvWeOCqFVilPsvpPTx9UTCcnZYzNiq1kRSR4Z4e0HNndXbkX8nnNkyU4h3V1qZe7p0fuzRiSmX3JSvDsc8hnLHw2GbY9NqYFlAD5T5iDZve/dk0MTpaQA/Jf0jA1Pq6hogzLZrEmErahITUUbW+rwen6ddlnelrWs3BcoVDfrHx1VfanAa9aVQM79R0avbe3Zb5YXa2vmFypaAXkYlGuncvJdhoUR0c1/J6kM/wNsyASvIgLiaN69viaxWOXdA1LjpoRwqQcH7uPzYkjAZuZ0QaZVhFlXt3GhgocetxsYZVwfJa4sWIoz8c4cZYRth46NmBmmCYLq7A4BPPyGFIFiJU99ORlwWkpUABuAXhrrZrmt9bWfSeAL6+9/5cA3lQjf7sACubYr4e0V3gbgL/b7FpRNp09+HzZ8MtGFfZ4f1erGtLMSXx/XxPkaTjhc0KvHZuXMzSS1lkqEtev11d/vOcenewZKskwIefU02bz1EhQ2FJhclLX2wq1jzwi52EoI/eZnZWxXb1aX/ESkHXb2/XPJ4kU2yiUShpaReU4jSiFFfHSloEBUd4YpkpiWKkooSyVRNGxeXxUOqnM2ZAvK2eSlHhbij1tyYpI8E4eSfMuI1/sZ5tawLmHKQ3e6zPPvMy7d7XaYiNPUVzqlyw5q4A8742eeYZwl8saIjs+rsabMJqBCwnhyIhGGDDPl/nAc3PaXmBsTN4zKoBh41aOlkoin4eHlUQtLmqEkg3rLZVkbCyURUN3f7/KYcp6zgm2zRQNhNTbtrd13eSkFv3J5/Vc7Cs8MVHfT5i/YxZEghdxIRD2kTtsxUrrsUprT5Cl0Sq9ZM2unRZ6GR5nSRk/89jbt9VzZ/fb3dUy7lap5fgsgWO8OAu2cKKkh25iQr0Vu7sqjEdHNeTMVoqissjQKSY4H0eJigpUhPdH99gnhXV5f/BZv31bexeRzBWLQgRsjzl6p+1zxQT7lRV5lpi8v7iox1Wr8vwsLGjYIAuHsGjI5ctyjvCZYchkSFKo7PT1qeKSy2lRFuapbW1p8RJasgEhjzyO4Us2BIrbmOgf5tFevap5dbOz9UqzJVDd3fWhkDduKCGjQhgWarC5OgwxHRsT8sp9envFup/L1RNPqyQODmoLCB5Dop0WnplVNnkf5dNJI/TO2fdJ+XfWwMmm1DRyzs5qH7WBAc2nYv5WXE5vSfLO28qUXKxnnnIjiejxubZ5kyy4xOd5aEhk2PXr2tPz7l3Ny2Mv0eFhleMskDI2pga2sTEtxMJ8PUYX9PaqPkTyxrxqXpMEsFJRwsj7k21bbJVlhvMzQqRS0T54xaJcM5RVWRAJXsS5hyVWxFHy49JIVqNzhtuyFG+hMklLYxhSYvejRy4p7JNkbmLiYAPzsCiLzVMgMWRBiLW1+jC0yUn10LFPDHOMGIa2tKRevZkZjSN3ThOtbbU755JzYbIgKlAR4bPb7FnNcr7w+bMJ8Jz8WZVtclJDgdiqoFyW8zCMcXtbeyDZRtzMUysU6pt6Dw8LQQKE6PH8uZwqovfco2GIXV0HW42ERGZoSEJ9WNyE5M/uWyzK+enlsudYWEj2bNAbx8IDzknYp23JsLBQHy5KL2FYGY+5KrS229Avkj0qfay4yXFZL90jj2jjdb4Pv+/UVP33IdF84IGD3/Eossn7KJ+OimbG0nC/JKMr5z1WiZ6e1nnp9m2Zp9h02hatSKtMGJfDL2yPkuQpf/jh+s/33CP79/Yme/qs593KPUA9cJRtgEY9lEoiW2mcvnZNvf6sbpzLyXrKyc1N9QBOTmoBFcqjQkG9fDSI0fvLsM31dS2wxcIr1apsy+c1DJV5fdS7ymWNmKpWhayy797ysox5c1PkV7F48H6NOXgRHYWsVTGbKYRZFcbDevdCL4LNpwvJqd2HeXppffRIAm1OX7jd5h/ZSoE2ZIAhnbR8sioUq9DlcvXhmDzeexVupZLmDdH6H1bDO4oSFRWoCO8PWu6TnomsRho+Wzs7QhrYeNZ6A3p75b5fWpJJ9soV9ST192sPrbCsNZt1Mzl+d1eUD+c094ehQ/RaMXQJkO22KAqXvj5tDWC9Y7TsdnerUsNtlrxRYbKkKqnYSbg/Q0Sp9JAw8bnu7dXiL1tbotCw+AHbRszMyO/HsXZ1Hcyn4cJwMHohBwZUiVxclN+d5NUeR8KYpIBa41JS0+Cjyibvo3w6CppF2TRLcdjfFyXahkp7X9/Kh89x2Cg6a6P7uCQvx/n9WCHTOZEVbEJul5kZlXWAeuxnZkRGkvyMjWnO2s6OeryYt0uy1NMj669dqze8ASLDRkc1IoNFsQoFkUPMwaNXjb1OGb5P7xqNCsvLWtyO+hWLS5FE2iisalVJHCMv2AuV8i6f16JS9nfMgkjwItoCR7XEH+bcNt8tbb+sHr6kwiiNzms9dqFySqLE84RhKbRS2skraVwMubSVMm3+0f6+CCAbzsn9aY1i7DjjzVnal8LJ5h+xz9D0tFi5FhZEGG1va3gmBXjapJAFUYGKCJFE5BoRP++1SmaoDNJzzSpmfOaoPDBPAxClpFDQAieTk5oXwiIqzKmj1Xd7W/and4nVMQHNHWNZbEA9hKFhhAVDwnwzQMlOGCYZKjPOyThtyCIX2+S5v1/GMziohJYEqqfnIDFkTk1Pj3rsBgfltwubEXNhPtTEhOzb369FZ0gESfAYqsTflN66u3frid7AgHobSUqbFXpKW7IiyqejIclYmbbNruO8yIq37PMahnHu7KjSTCPBcQlKJy9H/d1YTZefbQsTPsv2GpQ5c3MH5RzlBL121arIoqUl7SU6OioyjmGPzJEjeRoZORhpZPUiEjQbrs8CKVNTqhNtbMhnkjYb/cR+xEx/Ycso9r2zFc5XVtRIzmgRzgXXr8uYS6X6sOLowYs4N8jiAct6nvCzVfrY0yqN5DUiauEYbWGURjl0lrQlhZfZ8dl8PHsdFlYJK2EmnYteOAo+euNIxvr6JKSALRpYdZMhB7u7IqByOW2FsLurwo5CisJrdlbWUYm75x6dVC9fPiig26XR+VkvUTYdH1m87/a5DF9v3hSrLIkc5QG9d8yfYwnsSkXWM6SRITvM02CeaamkpKpSEfKWy8m2gYH69+xzNzyslTaZm1cqSTI9czSSChvk8/VWbz5f9LSTbNpnjTlyJGpZFTNbGZNV3wCRQTw3Qyi3tuq9JMxXYTlwXndyUsdcKNSX/uY5L1+W8M6tLSXCly9rmXMqiPfdp/0Gw3Cuk1iyIsqn4yOrDhAaP21Uii1kQaPk4qLcQyMj8jxFcnf4xYZIZt2/q0tkWaEg+sHlyypLbM4cSR5zJGkkSjJS9fbqGBYXNU2EDcrpBSsW5f+m/nL7trZGYAj98LB8ZqP78XE1pDNXjnNApaLEjH3tWLHcksKNDS3OcvOm3psrKxr1tLWlpJHjYtXNrS2NhKpU5JUF6sL7dmgo23MVCV7EmaIRETrueUOvWlgsJc2DF3rNsow9TPwOq1PabZYAhufx/mDrgiTPAz1wfA3JIZPNeQ6StEpFQ9AYXknBs7Mjile5rKGZbIHApGMmB09M1AtIjrNSkXNMTqrieeWK7JvUyPQoSlRUoCKyeNeTChbZz1T6SqV67x0VRH5eXZUJNJ8XqylJRaEg1mGbAwKIF4meNRYhsS0Owv5ad++qp5shlX19SoJIlBjG+eijB5+lRiXLqQSFxUs2NzX0p5HCxu0DA/Ish97E+fmDeX3hsrhYT7ZC5ZpjYl6d3ZdKH/OmwnPb38Ke07n6thNp3yvrkhVRPp0MmukAnHOS5s/1dTVyFAr6DEdCd7pL2u9744b8H+yDmfYchvm5W1saDQSo3GG7FcpjhlBevSrylZ45Gsx6erTNAD1r1GeKRQ0fL5VkfaFQH+5JolUqyfkZrcHQy4kJjWS6dUu9cHQg8D5lXijbLPT1ybWqVZX7w8NaQItN2Wmcp4GelZUPK58iwYs4MzQLoTqJ8zf6nLQ+iQg2OjZpe0iwqFiGE5K9XlKoJkMlbS4d18/OqpctqfDK7m59oZS+PhFkJG4hueVnxpCXy/VNzKvV+mpRVIjZ64Ul4CmAb9yQ67BIBJuOUhilkb0siApUhPfJyp19H26nR5v3Or3YLENNSz+trvb+J2FjKCBbHXR3HyQ8tEg/+qged+dOY+WSpb3TtrNqG6tscn1ImgBRKvr7lUjOzGhhI46B1QTTnkc7FhZyYXgkFa+0whQDA/Kb8jr8vcLxUpmzVTAZkpUUxnnvvUKo7fcANOSOCp8NwUsirfZ6QLYCK1llk/dRPp0FOAcy98nOo1SCx8e1yuHcnNw78/Mn69XthCUpD9cuVvaFz9vDD6vMGBxU4tLXVx++PTsrZIYefBpm2J+T1xgakv+RPTspk9nXLpfTdjCAkCUWK2GBFX4mwcvn5Xh64yjX+V3Yk44FVmgoz+fraxbQOGhbSVWrcr/a4j8kjfv7mt/X06O5hPZ3zOeV/O3uym8ZFu3i75QFkeBFnCnSPHjHPV/W7UmegCSPWRLpa3Q9VpMkGSIJSwun5DGht49KZ0gC6XmzoSnh9ScmtNoliZ4NY2GYp/2OzFPI57VE79SUkjnr7dvYUOFYKmluES1yDGOzhRhOQomKClSE9Vgzt9QSt5DccSJmqIw1dHDiZigPIAoCK5xRYSyXtRLsyoqQGBKRS5e0Gptz9c3BbSW4oSHvH3yw/p6/e7few02idueOKKZWgbLWbpJLey6SG35mPp/11HFp1veNCwleLqfnoKctjbRSKWQ4KC3wNt+mXJaxjY/L9+zrUwUv9NTxummtE/h9qBjNzGhhp7DX5lGXrIjy6WSQ1l7IbrdFkXZ35VnmM1QuyzMc3ntxOd5iQ68btRVJWpgvvL0tstPKKubaDQzINsoHK/8uXxZ9glV1KXOXltTzxmqV8/PaIJyhmuxtt7qqXjbKf9sayobe03PG8P1SSesM7Oxo+CbPQw9hqaTHzM3peXI5JZrr6xohxT6flHPs47iyInKOVT1ZKCiUkVkQCV7EuUWjsK0whDHcFu4XnjOpcmWat4/b1tfrm5BbghaOdW9PhAY9DJa40RvovRZ/mJpSghaGnlGRJQljqBmFDPty5fPq5bPhmgxJ4JjX1zX0sloVaxYbnE9OSjUqJipvbamVnZ6MLKGZJyWkzssSZVMyshh67LPDvE/er0nPqm0dQm84J9ZKRfPdcrn6oh2bm1o9dnj4oAeLzWdZyY15E11dQhAHBg4qlY88Um8RJwFJC49M80JxoReNzcHtNhZ3AQ6GPtkG61yXRPjsdwaEyNqcv54eCUO1uXOAfCdbxILXtwUWhoe1CAot5vw+Yd5gOCZa+FkR03oM075LUrjmYXIQs6KT5dNJGWqthy4tmobzI3OiSiVR3GlwZN44Cx2dBfnphIW/ZdYcPOckmiEMZQfEK89XtlYIDV12Yag8ZdDgoOgbW1tK4spluQ/W1pR00TtHzx7DdlkRnJUxWfWSFS1ZvIXbaRi3VTHpKSaxDIt1MVKEESFsz0BCSa8hSWNfn8wbrOTJZurFooylp+dg/rRz2Z6rSPAizjXSyJ0Ne2xEAjc2lCzZsMhG5DCNWNJzR68B11kl1E5WJGx2H3sevtL1z8bjdP+T7N28KcKCBI9jsDl1tDDZXKPpaVnPois8nmEJbN68uirHbmyIYKWVanFRtlNJ6+rSvjK2zPFxlahOVqAuMprl1VnY59B6ssNndW9PJu18vj60mOHDo6NaFps97sbGpGAHFYrhYVVm+vtlYUgN9wnJw9xcvQIUGjkGBoR8MdQzbPLNJWxo/vDD9aTRnndh4SCxuXNHXq2l/ajK7uBg+nO8uChKXLg+9Bra3Dv7Pey4LTENQ1K53L0r4wl7adp977lH/tPQY2qXpOqhx5FN3vszl08AngHgLQDeBuAFCdu/CcCbAbwRwK8A+Lxm5zyKfDrM85t0bAg+r/Z5ZmTL3p7MZZOT8moLarCvGKvUDg0d7BcZl5NfbNVdQJ5hetxDQ5f1/rPKLav6ho3PKbv6+zXdg+tsaDsNRc6JR4/GbBt9xBB8kjTqQdSnmIu3uyuyY2xMlomJeoM3U2PokZuc1Lw8ErvZWb0mWymwdQJTAmiApA7FVgsrK/WVOBkZUixq/p2VwzFEM6Ij0ShHJ2lf235gbq6+6XjaOUKPW3hdhoTZUEySLpJJS8ZI1izRnJlRgcWqTgyltBUsw2IRtqqTLflri0kwBJOCiR4+VhhkMjDDA9bXRUjZhs7M0aNAZ1+ww04SWRAJ3sXFYT14BC2l9jmksYMhMGtr2oON1tlCQRSTsTHZJ5/X0Ju7dzWxnuFCrAhnPWKLi3JsX99BomLvbWtx7e2trzyZ9CyE3rOkJcmKbtcxlIlKVdo5wtLkSWTNKhPNxnbjRn1YNklWWASFClx3t+xPzx2JJH/v7m5VElngxo4nJItTU82/R6hwJv1nh5VN3p+tfALQDeDtAO4H0AfgDQAeDPb5YgBDtff/EMDLmp33LD14aWkSbCVk51A7b21syDNNL8jwsLYrsfe9JQlxab6EOXdpxYjoaUo7z+XL9eciKXnkEZGpCwtKCm1Y+733quyYn9f+oQyvLBblWHr9mWN8//0atri4qESKucYLCyr7SfAYrcSG6IuLSvryeSF7DOXk3EJDPO9HzhfM16ZuZyuds/omnQgsyEJQtxsZ0RoI9vych3Z3xfsHHJRtWRAJXsSFQWj5S9sn7dXmvoXHWPLFfazyGIZyWrLF0rvMAWIeAT/bHDxemwKF56CyavPqaCGybRa4jQJldbV+/d6eCDL7XVdWhNz19sp67zV8k7lLGxtavn1oSISiDdOiwmY9HXYCCSfgkxJS52WJsunoCEO2rDGGzwFDNpkjwdAbGjFu3dKqlVQU2OrAJrEPDdUn75dKWiWTYYGbm7J/6GlKCjEKFSj7vITrrlyp7xs5MHAwB+2oS7MKkrZ5ekhMe3u1zHnaeUZGZPx2nfXcOXewuMnoqHpeuK6/X3+DhQWJGBgelv/h8mVtvxCGWnZ11TdkP4klK86Y4D0NwCvM5xcCeGGD/ZcA/Gaz856lfEpKcQjX7+/Lc8wiFUxbYM8xVkq8fLm+b1hcjrck5fgy7WJwMN0gYsPdeVz4fHZ1qc7A8/X2au4sZbJzIqtpdAZEBiwuKkGcmVFixCiMsTHRbZaX5Tqjo3Kf3Lwp4+vqUtLEvDYawDc2lCCurNTn3NHYTr0ql5PzMtQzjM4ql1UOkeSxlgEN6489Jtv6+kTGTU2JrmbTfHivM9Q0NAxmQcsIXruEGUS0F9KEv93eCLYSpbXsc1tYuZKLLUISegas9cZWx2QfE+a6heNg0ZTdXSFozH+zBR8suC0UCLyWJV0UPpz4bNgaY9GLRQ0hIPllFcG1NTmG8eu0bjGEwIYY0MNHwQmIUKpURJBduVJfYa+vT4socF0+nzwxZEEkeJ2JpHBo+yzzuSgUhAitrspESatqtarPLYsxkNRZZWRoSCbru3e1OhurMlojBg0XtDBzfRiqFC5hPtzDDytBvHNHvUnsY2eVrbCFQKMlS+5ro3ORwDbaf2ZGfq8kT1n4PWdmVCmkYsicvNlZzTEZGqo3CjGEq6dHSJ5zmmvH7XzPJulp3ylrYZmjyibv/ZnKJwDPBPBD5vNzALy4wf4vBvBtKdueB+BxAI/Pz89n/8LHQJr3LnzPKBZbsILFJ1jBmf/7woKGWMel+dIoDzXteenuFtnE39wabG1POz673MaUDX7e3pbPfJ4pF5wTOcoWCSz6Vq2KnBgd1RYIrIBZKIjMoRxh9BG9u4DqTNWqjHNsTL1krDRudT3qWTs7WrSO/VBpYGAVT+bN0TjPc0xPS8houVzfb5gEcmZGt1Wr9Xl+1lDZ3685haEhLKt8agnBa7cwg4jWwipvSb2siKR8upAk0SVO0jMzU1+ZkkTMes12d9VSY4nSxoYoPcxvo2XFTkJh1UsSJLudDzgfXhI32w6BnxmLzbCUsLgEyZZtYbCxIYKO35k9X3p6REDYYiqcNDc2tIKTc0oI19fl+9rqTbmc7DMyIue9fFn7zCwsJCtYm5vZKmlmQSR4nQerCIaGGPvMV6syEXKi5XPGnkRsdruzI5MyG+KOjqqiMzysRI5V3UKFh012+/tlf1qom5GvNK+eVbLsPqOj2bxQYdnswyzNnsvQ+m6t9iySYNcxVy88N/Nu+vvluPC8zKeh9d42bd/eFsI9Pn7wuJ4eDQdnDk+lUl898ySKbGRFuxI8AM8G8BoA/c3Oe9YePCKtEJk1zvKVeVOcr/l/xzYIZ7PMz2uoJNfduKEhhPwvJidlH7vfww9rSDyf86kprSTJSpnlsjzz9NqTYFEuMvS+WFSCRJJnPWP06tpQX5Il6kLlsup8vMd2djQ6hKkxLIrCVJVcToyJlvjZiC3qZTs7orMx5WV0tL59A9Nt6ERglXIbRcV88lyufl4aHs72rLWK4J37MIOIoyMkaKGF3r63Aj8Mn0yyBloPnn145ubUGmjDIElobIx0mE/HhQVO7LWsgLh9Wx7gvj4lWxQu09MimPhdeG3mytG7RjLJ8Ew7Jrrsb97U5HJeh+EsHDPz7nZ2VJhQWLH4yvq6CO3hYW1ozHK8pZIqkSwoceWKWtIWFyVenaEPly5pSfm5uew5EFkQCd7FQ9bcO+/rlUBbwIgGGlYf43MwPKwePOYyFIsyWbK4SqkkBovhYVUIBgZkHZUY9k/r61PP2rVrB63c9l63Hri+PlFC00hes6WZZxCo92ylLY3Ijh374GB9VbtmZeYpH27cOHgN9g+068LPNvy0r0/JoS1YY0mb/d27uoQ0cqxh0Rt6Eubmjk72sqIdQzQBbAD4PQD3ZDlvKwge52+bjmDnept+sLws9+P162qgtH0d4yKLlT9ZlmbkmPLh0UdFhnJ9aMjhe8oMW+SGzzBbHoTHDg/rwtYH3d2iY4yPi+6ysCD6B3Mu6T3zXg3eLFoyNyekcXpawzAZgsmCcT092nOOxvPlZfXg2TQYtshZWlL9iXqdrZjOVBzqWrOzStYqFbn2+Li2bGBklfXgMTeQ24pFNVraYjSTk9metVYRvHMdZhCRjKMWTAhz3uy+jbx1dl1IApNCLe21SPpseCO3J1UGI0kLi6rYY+n1opXRVvZjSCTHMjOjpX3tPqy2ROHBsM7VVc2RW1nRnnWWEFLAsRIThQTDOTc2RNDQ4rW+LkK3u1sFLPNoxsa04h8F+9aWCKihIQ2f2trS41ntil4+Ww0wbSLJgkjwLhaSZEDSPnZfPjthGxI+L3zmGRLI0GMbIjQ8LKRkaEitwgsLGqrMPI+REfWwkaiweqOt4pil5H5//+HCBIeHNdTRhj6F+zHftdn56OnK4k0nEZ2dlee6UKgPEe3qEsXNVqJsNIbJSa10x3UTE2JU4mdLvoaGRG7kcvWhsQ8/rPsOD4uyac8RngfQ3zxs53CYJSvOmOD1AHgHgCsm+ukpwT5LtQipq1nPe1byKTTU2mgZm5Jg509uv3xZnk0+02wSHZejLza1Imnh8+2cPnN81rq6RI/hPg89JPKYHjlA828nJjT03Z6fuXnOaWQQ8+pYLIvn4vPPMHoSodVVWT85qdefntbwbpK4jQ3RzZaXVa/hfLKzI+ehscG2r7IeNvYZthFh1jhh6x/YegkzM0rymPrC84e9h5lCU6nI965UNDSUv1s+n+15a3uC165hBhH1aKS0hessKUqLyU8jWknnTgrnSus/x+vbXnM8j51UwrEyZJL7WgFA71t/v1h5VlfrvWsUDNYTQSJWLKrViO58xnOTzNGCmctpDyCSNiYVMx8OkOpS3d2ynp5Cxp0zRC2f1wlybk5LB7OZ8/a2xoBvb4tAnZqSY8bG6vN1qEytroqCbC3z998vr2lejCyIBO/ioRm5s8YRO1GGYTOTk3Kf7+1pnh1DMznZr60dbE9QKKiiMjOjzw7DBpPImzVSsGy/3c57PVSCjqOApRGo4xZf4fhu3Ej+rvfeq3lwXGc9imlesd5eIWDhb8NiCvbYiYn6nJ07d0QukaTTg2oX5lo5lx7KysbC4e/P86UVfDqKbPLen7l8AnALwFtrJO5ba+u+E8CX197/MoD3Afjd2vLyZuc8jHzKYshNgjWyJoVf2/xyS/a8l7kJkHmIrUDikr5kNSo1exbu3JE5fX6+npyFnsLubiXexaJuD595QGUOZXK5LDrF6KiQvHxe3l+9KmSIfQ0XFjT3jX3oGJpdKmlRlt5eMVSzKBx1Kuo3pZIezxD+clkjr+gxs3l5nItYr4CVMplWw1w73t9ra2q0oO41NaWeu9u31avIwipMoaHXcX9f3tPDZwsJDQ5me+baOkSzncMMOh1ZiBzXhRY7WwQlPI5C3uanNRoDz5OUi5f2nuQuzKVjfppdTy9fWOFycrLe2siCK1RCWcVvfV0fdmsJmpxUy1SlooVXLAGcnJSFAufaNRUANtyAgiqXE6HI8r9UhJmwbAUJK05tbel1qlURzMx72dyUkBjn5HVrq15oT01pc/Pubtl+2HCoLIgE7+Igq3LI54QhOnyG6OXmRL+9rcaTUknuR1qF+/tVGcjlRGno69NeczYEkdVfsyhGs7N6nVYrcie92JDItBBN65W7997k/SYmtJ1Bb6+SyL4++f3S5ERS/zIee+lSeiW/tPPxHsjiwTyKbPLed5R8yuJ9TwLn0tBzF+bcM1yOYXI2B52RIbz3wp6IcTnc0sj73t+v3lISO5t6EcpJ50SHYJVtVrtlkRTKSmu0YS4zw+hZN4BRFNQpeF5WraxU1BtHIsmUFRrzentlHckfIz1WVmS9c3Idesr6+2VOoTeNetft20rC2BqKBnXqi7aKs/dqrGftA+qb1BOpY+7u6txGQ3ypVE8E6V1kPQT+dr292Z67VhG8cx1m0Ok4rJC3FruwuEjSeS35Sru+nSCahXGG22y8tI2jZshj2I6Ax3DcJGgkfiR0DKfkg2oLq9Aqw9htkrPxcRUkljDOzGg/llu3ZN/JSXXh53KiRDHPyDZN574rK7I/85Zs/t/CglybFiuOlY1F2Z+F5+B1GT7B2PmxMbGWLSykN28+rhLVSQrURUZWuUFPOi2uTKDnQs/dtWsyQY6MyL4TE2Lg2NrSe3F2Vu5pNq9NIgMnWVr/JIp7nMViw1DTxswcu6OQI/72VAhJAtN+a5tPx/3DcfHzPfcc7EEIiPIfhpHxmgzzPMySFZ0mn47rwbPnsV467sN0As7B/Ly4eDGNKq1akqJq+vvrjSzOqey0ebV8VpOeZ6sHUGaT9E1OalhouVz/XzPFhDnSQ0PyjDNvulKp3393V2R7oVBfK+HmTRlztSp61sSE6lK8p5jHaVtxWP3MhmAWi0ou2ZScBJMEjE4LGihs6yzqm3t7sr6rSyO4mGJDssf3bNfAirHU5/gbt7UHT67b2jCDTsFRhXHW82Y9fxg6mYYwNDMkb/Ta0SUehlPa0Mwkz50lOdPT8pAyt25/X4gaCVKYN0cyaScmChQ+lHTj0zI1OanVKgsFtd7cvKmFIChIKLisxYcVAOmBs9ezYZe2gMvNmxr+0NdX3/6gWlXFbXFRE5lZiII9wAoF7RG2vCzfp6tLPjNUa2hI49wZMvXww6p4hSFeR1WiOk2Busho5Gn3Xi39nGAZkry0pGSP/Y9I9pyT16tX9b4D9N4rFNqrT1aj4iWsOnncaxyVlDVbkkjXYQlyUqgnCzQBB8NPJyflv3TuoDzZ3FT5kyRrbPjtYZesiPLp8KBxlsZVm6tULMqcxKiS3l6RCfTwtPr5Pe+Lcyoju7tF3oTPdW+v/tYM4+zvF2MuiRb1mv7+ennD1I/BQdmPRVAWFzUPbWtLo5ZoEO/rE7luo5ZIykolOcfoqBAhVhMn8aKuZD1jYW/Unh6RM/Z+o47He5IEkDrY+roexzw96mT03JHkhfUWrMHeVmCnDsj57fZtmd9oUK9W65uw8zdgxBcgY8qClhG801iiElWPo4ZTHPb8jYqkZFkXrk/KzwtjofnebudDSQIYllS3+XR88Phw2s8MA+NDyXPZ3nWWTJKYzc4q8WJOG8MDtra0UAr34fegABgfP5i7x0Rja9G0goMtDaxAYVEUxrGzGEVPjyxUeNnLamBAJk82eR4clNeuLi1NPDSkRSgoZO69VwV7lh5cR1WiogJ1sRDmytrP3sv97L0+Y+WyNi8vlcRLQ7JXrWo4crEoXpzQ+3PvvVo86MEH6xtwJ92TvKfvuSdb6f0HHzzavZ9laVbJ8rjL9HT997LPcRoxonLY13ew0mdaiGtYzXJzs77Aw+XL+t/a45K8DPY/2dysry53kktWRPnUHEnzPuc4Vi+kMk4PCNMJeI85d7y+hp2yWLJ12KqagBqYhoaSDSf0aLFQFdNC+KwuLur/xIJsc3MiT1jwqqenPoeaBmaG5Fvix7w5hmYy4qivT1srsCAdyRNTY2gw39lRAyB7z9kidd7rvWcdBCRkJJ2MorI54jSyMwVmfV0L6LFK+/XrSjjpKNjdVWMmU3zW1uRc7A1aLuv3sgQvl8v23EWCd8FxVHJ3lPDL8Pgw967RtRoVaKG1jy5x+/BZRdEWQQmVRjseW/WIBG59XXuaWAsPX5kASy+dJYa2meXt2/KQjo/LQzg8rL1hWKmPAsPm9t28qVWc1tY0bGBxUUsAc5xLS6rw8nV9Xa099MRtbakHb3VVFeBiUScB9rNjHxoSOLZE4HnYv6anR3Jhwup0ly6JAKeQj20SomxKAp85epztc8j8ioEBtYjS+js6Ks/P6KgoHSQHfL5I4NJCuLq6DnqG2BYhi6LUyUvY0yqN6IYy4e5dNSKFuXVXrx78r+bmRBEKjUVsWzE/L+cJewAmEdG08M7DLFkR5VNjWKOq9/U6gc1Xog5Qrapx9ObN5IJGcTn+8sgjMt+zQAqfEz6zlI3MdXz0UdmX1bFZ9XZ0VNeNjurzubkp/+HAgBK7oSF57hl2z1SWkNSzFRRbJNhcNVvPgPlz9BT296sBnSGXtpl4SACLRc2lm5iQ8ViHgQ37pGeNuhvvYc5dtpr5xIQWZLGpP6zRYPP1+ExQh2XxPP5G+bwYtaycm57O9uxFghdxAIepYGlfw/X2IUkLu+TntDw6vt/d1eTTpPy7pGuEHr5GVbysB8wSQZIwWlhoTZmc1EadlvTxAV1fF2WFZGt7WxuGLyxoywPrlWNoJa+Rz8tDXSiowLp2TQTk8LCQqe1tJY+5nPalY9lgtmzI5YSEAVpq+JFH9Fh6GqtVmUyXl5WQAlJNq69PjuF3mJtTax+LWjinZd5PSomKCtTFASdXm6/KUGNOnrTI5vMaujcyoj2SikWt7sowob4+eVYaNQBvpuTz+TjqwpYErV6OmgcYGmXC37Kv7yBR6+sTBW5xUUOz2B6BpBs4GCpJEsex2kbpVDC5bG6K/OExbLZulyRif9x8rayI8qkx7FwdFi6z/WeZo9TdrfdFlj6PcUn3bIYecBY54bM+N9e4VYKVJQyFTtrG0HlAyR5TQOz+i4tq5K5WZRw7O/Letn+i525+Xr19nCuYU8d7iDoO+xBb3c16yWxaj63GTH2NFcnDaDCGks7Oak0Dq2dSj7NtE5hHyPYH1lkwPa1pNnwWeP/bqptLS3r+YlHnOkBkcxZEghdRhyx5cna/pBy5JDJnX20uWximFZIvey6SHG7je26zHgE+mLT0TEzUx/uH3j1u816PWV/XAiV8+FZW9IGngLKu9OVlFQTT00LIRkdF0C4tyUPKZp0bGzqualVDjUjoKCRYxGVnRxTdoSE5LytYUgG2YaEUOlxsGfhCQZWf2VkZH3MGKZDHx2VhYntPj5anDsPGqOTx82EU3SyIClR7I4u3P5QTfMb6+rTiLA0NnMjZ8JY9kgC517e3NfdrcVEVDVqJk7xy3BYuNsRwfl7O2aw31HGXMAzxtJeQ8DUiwQw3pbyw7VCSlhs3ZF8qmCzEkEYy7f9gcw0HBrL1DBwf17xLSw4pz0ZG5POlS3It+10Pm4uXFVE+pSMpgscWurBKN/PBOU+OjZ3+s3hRFxtGnVRtlMVTko4dHNRoBz7/jJrg3N/fr0Sur08N2Neuyf/LyIrRUWkfQ92Gcnx4WGXBtWtybE+Pht8z9LKnR55phmfu7opnjHUHSM6oQ9GDFvZLpQGeNRIYPkn97tYtzYfj3MT7lvfn0lJ9qg6NlT09OmeRGO7taWSVJXDeq27GZ8E6IehZZE4i9TmSY/5Hbd3o/LSWi6xEnSXSPHKN9guJWqNzhflzvLmT2iOE57LkkGFeu7uiIFQqB5uX7++rNYiFTGjdsU3Rp6bqvQmsXsR+KN7X577xHCxfy7jxSqW+CuDUlIQjUTFZWBCBYIUTy/o6p02YaQVimBoTkHt6RLguLmrhAdvba3W1vmgKvzfz66pVOR8ngMFB7SdDxdkqYKWSCOWuLrmmrZZJAZ0Up5/Vi5AFUYFqX6SFV4fPre0jacEQlp0deQZGRjTUeHJS7jfmfPCesQaGpBy1RuHBVBjDPk12SSODF2mxRRROekl69u3vnKZwktz19ibn3lmvjiVrk5Onk5uVFZ0on7JE99BbF87nt25pPjrD2Jg3Dqih5ySKDV30hZEzSdsOG1EwOChzu61AXCioQaqrSw0xjKLgsd3dIqNJXuiJZcsDGooXF+U6Q0NqDGLvvMuX5RlnSsjOjkZBTU4KwbIVNBkayRy+qSk5luNjqgsLn9Bb19+voZoka+xtR53MtsEi8WJk1cyMOheYl2dJHD153J/nnZtTnZARLN5rOgLDSKmHMuWnVJL/wRqr7r8/23MaCV5EQ6R56tL2te/TlL+kXLnDtkfY29OiJt7rg2lJHHPn+vvlwefkwphsEjR65JjwPTwskw+TXu0Y+dCxjC0tSoD2idve1mMYarm9rd4C9ofZ2JAHt69P1hUKWtWzUhFBQEHBZuS0hDHss1JRZbVQUGFHrxwJIytBMa6dno+uLp1QgfoQrKtX1dtHoc9Qq1A5POpknAWnpUABeAaAtwB4G4AXJGz/JgBvBvBGAL8C4PPMts/FCr+CJHIXVrNlo9ew6u36uja9XVqSZ2lzUxu70jteqci9yPA9NtTmfZTVoBDLrOtyUs3S0wrB2KgBQPMneSwbKAMik+yxjYrLdHXpf98oN+uBB473/bKi0whe2ryetJ05RuF2Pvu2aXR/vyj5YeGeuPim93qaV35gQObsLOe31TTn57Wox8yMPMOMcBgaEn1hdVX2YTVt5v/ToweoQW1oSJ73sTHVQ1ikZWxMSdzCgtwXvAbbA/B61jNH/Y3bi0WdS2iAtySMOXy2BQIrV3J/kkFbI4FE0kaJ8fo8zt7bu7taH4GfvdeQTxJg5vX19cm+JHrWCbG7qwZP68F74IFsz2okeBHe+8YW+NAC1yw/L82bx3PNzOhDYhuZWld12jnDY2xFIlpm+HAwTJOltOldm53VBNaJifoqmqurGh5ge9t5rx5CVjTq66sXIgwzYX4dk3i7u1WIXb4sx7P0O8M1WYmpXBZiRfJXqSipswnO99wj12E7A1rBbJgmE6gp/C5fluux1921a3Lsyopc0zkhi4WC9rsbG9N1FC5DQ2php8IcWtyzKtJZcBoKFIDuWouW+00vzgeDfb4YwFDt/T8E8DKz7U8Pc71OkU3W223DpFkgiAofjS27u+o5vnJF87YGBuQ+nZlRTzWVHIbk5XISNsxt4T0XS6offrFhkllJc9bQR4aT8zP7Xg0N1XtNbVVSS/QYUpY0riSPX7gc9n7Iik4jeN4f3oOXlBZhQ7WvXz/cPReX7MvWlsjRZl7ugQGVocPDB6vTDg/LucbGRHdhg3Juv3JFnvGFBdGdrl4VAsd+uYWC6BIMz2T7gmJR9qdeRIM0qyTb0EUa8jmfLC/LftTTWOHSRlHNzGjaCu9HGv8Z4WTrL5DoeV+fnmNDMG/e1MIpYRQa22StrtYX9eNxjBgbG9PeetQ1OQ5b4ZP9Xe1/MTub7TmNBC/iAGkLwydtBawk0pZ0vkbWPdvvjQ+QfQisZ85eN3zoGAPNfZjgStJFCwjDJKem5HO5rJ4BVmeia5zNx1dW1MVuXfuVisZHVyr1bnxW1qTFhc3It7flnBSYJFxsHl4sisC0YSosWABok1CGZXI9LWrDw1oRsKtLBEc+r9dZXdVrb21pKASF+siIhofSi0hr0fCwEEESvqEhzcXr6pJxHSeUJgtOieA9DcArzOcXAnhhg/2XAPym+RwJXg32GbWeuzC5vFrVCpk2d+L6dVEOWE770iW5f2n9nZtLNhgwpJMhf8ctktKJSyOF+jCEyJK8tKbiltz194tMuXZN7gmbFwwc9C5SFof/Mcd4XG9k0pIVnUjwkpA059uceDaItoryzIz2RmM4H+eXuKQv9Nh1dWlRM0byWG/4zIzI2kZ5sXzuurpU5tqiSM6JTsbibQy9tJXB771XiRt71dl2CqWSeu4oB9g0nGH5TI9hrYGxMV3P3Gzmz1lyyEgnVjq3HjN6h21uHXXQqSn1pNmQyyQHgi28Ykmg3Y8k0FbK3NvTginszcfr8Po8LqwvMTGh82booY1FViIyI7Sshe0NbFuBZqEZISG024iwypFteHr7thZdsA+Trbhl46Ft2BdJFsuwUziQeDGnjn1GcjkRGEzgpVvdNsikG59VnioVLatLixOPoRWJY2Ry8Pq6Flu5elWEIr16W1tyfnryenpUKJO4sYfKzo560mhN6+rSWPuFBbkmq2HSo3f9uhBM5u4BWq4YECFIKzqFNLfdvavvk0qP2wnmtJSoUyJ4zwTwQ+bzcwC8uMH+LwbwbebzZwE8DuA1AL4i5Zjn1fZ5fH5+PtuXPWfgs2yNQ3xWbQuSjQ1t/cGejKWS3POA3NcMJWIhFat48H6JXrnWLIOD+ttn8c5PTIhc6O4W+UZPXFq/zLt364vPhF5Be5wt/JI0lpPK3cqKSPAO6g0hmFPLcDgq21evyn2Vy0nYLT3zrb7f232hwXdkRMgXPeBhS5OhIZWxztV7utmgnGGxQ0NCRLa25D8ZHJRnkjn21JlYsZtFcey4rlypjyKijrWwIOdh0+5yWQvVUb9ihcmVFTmGpI75fExbIckjkeSxto0V70kSNnrPSKJs0RUa9+ntI0mbmal3IIRzHAkcj7FRaJaoMWoqn1eCaccWFiKy26lPLy/LHMnohhs3sj2XkeB1ALKGU9p1oecsvHHT8m52dzVvjbDH8zMfKpIZm6TK7cyNIxmkp40x1zY22pJGkrOJCX3w+HDz8/KyhioODNRXXqLLfnpa2xyw/1xvrxxD4kfyZYnd3p4SMVqxBgc1oXhoSK7JSlYUdktLKqgZeslzUDG+fl2OoTePYRePPCKCjwKPIS+Dg7I/q1dZRSmflzHRE8fcBypjU1NqbbOKFAllX1+9MnUUxSoLWk3wADy7RuT6zbpLtdf7AbwTwOc3ut5FlE00/thqtlwfWjc5Ce/t6TNVKMh9ylYgvDf7+rR/Est6h0sM5Tq4nEZT9Eahj/Y/SCN9LB6Vtp2N52nYSjr/aTd7T1uyIhI8gVVwva+f75kWwaJhzC3q6hISkTVP7DwvJ5n/65yGr9v1NsSd66anNW+1t1dbyiwsyPrubokYGhmR44pF1TFyOflcLquxnCGUo6NK3BjeSR1ncVG9eGyhYKuDj49rGCbbHFBvox7HcMblZbkGDe6sbRAWXrHtClgt00Z00eAY1nJYW5N92bvO3rdhzrglbiR5toF5mHNqw5CtbmuP57n5yjGzYKD9vvxPowcvoq44SDOSF34O892s0LZKXUjkNjbqKxAlhW2xGTjdz3SXW4JE0nTrVv06Ei1aX0IvGhuG37ypMeK29DrPxRDK6WklWCyVSw8dhdTIiFbIZLPvUqneQ8eEWVqjpqdFoNHr1tUllY9ImBh2yVLCFIJbWyLMRkfVC7eyomWHBwaErBUKWvXKCvelJQ2l5HVY+Qqot+4ND8t3vP9+Offdu+nKlPXSkeCdhDclC1oZoglgA8DvAbinwbl+BMAzG13vosomS+bsOk5ezIPo69NcA1qHqQxcuaIhxFTqeY81akR+mgpUpy/0vDXah7KF+6Xl+CTJia6u+vOHve+Os5xUFdSsiAQvOfLH6h1MXWAhMpKGwUF55ltF4lvxXIXrkoxVzfJa0wxcW1ty/6fNzZcuqe5AzxrbLXEfeu6YL2frDbAJN3tf5nKyH+sI2F5tV6+qzkbytboqus3iotwD4+Nyfnq4WLiOuiQN5zQQsiIlHQN7ezKnjI/Xt5za3dU2A6H+aZ0RGxsaFkrdMIxmo6FydjaZIFpPIOtLWFiPn430skSS+q+NZrMRbPl8fXj68HC25zISvAsKe3OmhU1YhPuEVji7niQtiQBay4u9Pq0eNt6YMfi2AiZz25hwygINvCYbTvLBn55WEkcL0MyMCJxiUXu4MRysu1srSS4vax5cT49YsaamtCStc6J4ME/OJiBvbmqIwOKiNgrP5VQQbmxo7DkFJQnZ3bv1JOzyZU1EppClt7JSSRf4thfU4uLBvkFhz61QCRscTK7OZSfcmZmDDWfTeugcdsmCUyJ4PQDeAeCKKbLylGCfpVohlqvB+jy9eQCmAPxBWKAlXC6ibOJzH3rxeO9zYtzfl+eAYciFgigWvKdDYnbjRrZecTFc8+SWRhX6WNgmaRv70Y2MaN6wVT4feSS56i4XevimptQIdemS7h8qsj09sr2/v/6ch+1x12zJqkB5730keIJQZwijgHI5UcQHB7XSc6vv+/O00LjcaB9G6oTP2OCgylzqICw+xxYlXH/vvfLKnDumeZCgcD+2P2Cu3OqqetzYEHx3t17nGxvTfMCtLb0nqB+Nj9cb6icmtAIm9UGei/oe026sEZ4RWTTms2qm1UkZ+jg5qV41m3fHe5e6tO1/x223b9d7EK1jw/t6x4T9Lewzw2gxS0KtXr2zU58PCUjkQxZEgneBkdWDR8uIvfF4fPjZxjCH1yCps7HFdpvN5eMDylduY/w1LTnc17rnbd+QpSX1EPBhZ15bLqel/ksljU8vFOShZvIvrU7OiWLJviNM3N/c1LYC/f2qyAwNifcLkMmK1yEx29mRMdjQzO5uOe/Y2EGlaWFBCd7oqIyP/fMYMrG4KKSzp0cUYQpwG+JCIfDww/I73bgh52UZ5K4uuTYTp3kciyP098tY+f2tkjU+frLNZ7PgtBQoALcAvLVG4r61tu47AXx57f0vA3gfgnYIAB4BsFcjhXsAntvsWhdFNiVZN9k6hJNvf7/mgTI/gdUSqaAkeYiyKHzRS9c+C+VCT48YrKg8Dgwkh3Y3+095b1i5mOTZSfJenHQfvP7+7M9EJHiKUN+w8//KisxDV6+KPEgiI526pOWm2iWcd633HJC52TY4twVTcjnRi9jmgL3o7HP36KPyn7DwiS26NjYm/xmrhG9tqUGbFTBJzOiBI5kh6eMr9S1W+N7e1nBO1k2gjkkSZ50D9OLZZuC8JouqUA9dWdHieDyO96X1wnEhYQt1X5KuMN2I818+r2NhwRUWcCH55W9jn5XQEGrHY5+fSkUjrQApLJYFkeBdYKR54ZLAEMXQOhHm09DjFpJG3vghueNx3MZz2+pE3uuNz75xpZJ64goFbcxLtz6LozDnbG1NPXRdXRL2VS5rRUn2eiuXNRSTAo6Epr9fjrf9YKzyQC8WXeXb2zK25MczbwABAABJREFU7m6xVDOPpFCQ9+zttbio/V4YikSLTG+vbB8YqK92efeufGaeHhOaGfPe1yfbp6YONia1FThpFbcet7RcFzvJMEY/3OeklyyIClR7wCpv9tnmxEl5wfL3MzNSXIjVXJ2Te+raNQ3RPE0vTFzOdrHtKm7caBwqOTGhuUGU2XZ7Etk/y7zLrGXIvfc+yqeDBl+7bmdHFPtr1/Q/PI2qpxdlSTOKJN3/7Id7966Gs9uK2NyPhY62tjSFg3pF2A5ha0vDZ8fHtTCbrcRtK4bv7Ah5YW9gW7Rkdla8f87JOWdnZf3YmMwL7H+4sCD6TLWq9xENAzQe2iix3V3RfYpFza0jyeO9d/u2rKfeafPzwsrw4byWFPVmt4cpS/y+dFRYT2LY387qvDSAssk5iwPS8cHvwoI1/I9yuWzPZSR45xxp5C3Jc5e0r3VXhxWD+NCwmhCTSPngWauD9+qlsxYcPnzsQUIix5ue1gnmveXz8vDv7mr1p64uberN3mwsCsKqW0z8BTSUYXRUPWusAkVCuL5eHzrEilK2UMjERH0FSUuMFhZEuPT0KHmy1jO+pzXbxrqHVcIuX5bzsIpmPi/XYx7S/Lx+Xzs+a9Hr7ZVzz8wkh1sNDdWHYszPa0L29LQc88gjss+NG/Ld5uayhYUcZ8mCqEC1D8KQFSsL1ta0su3qqigCTKan8aC7W+73RiGBdomKYHstzTxy1pOWNRcurZBOK5d8Pvsz0enyyUYAUTHlOs7pPT0yd51UhdOLtmSZY8MwPUAraI6OyjmsvGSoJc89P69tDrq61DvH8925U99CCVACyLB6prswj8y2LFhZ0b50+bzqfXt7ch56tTY2ZH7g/MG2VaOjWhWdxkOSNEu8bEV1W7jE5r8l5YbzXrUOhrRwYvbLC4+1/fDCME6bRjQ3J/Pf2pr8FtZLaD2BJJ8kgCTLrOzOqJjeXtV3gejB6wgkhUOE2/maRvhu3tSb1loXwnDM0DK3vCyTOXvb8MGjFcL2BOF7PtC02tALxwpLKytClkZHtfcJQwrGxpRIzc/LjT4yIudh6d6xMdlmPXaFghZMKRblXMzXK5fr+8kA8hDRM9fVddCbx0pGDHlMEtIkTEltBey1APkNr1yRdeWy7kOlhxWqgIMFJ4aHkxXgJMUqyTti8+os0U3bv9nkcxQimAWdrkC1A0LZYj13e3tyHw4Py7N47ZpWZL17V55rhhNziaGWZ780ez7Hx+tlgv2PmpG1Rn22eO2TDqM8zSWrAuW991E+1XtVWImQyiqNha3+T9tpOao3ur9ffsvQQHbvvbKe8zYrZfb1aTEkRhsxPJMeMxZrY7QTPYJdXRpGOzOjXjiSjt1d0fcmJkR2MFKD7TCo79lWU0yrIRmkjsh5hE4D21eO62y+my3iFXrduC+dDYSNSgtz3bjd9ma2sJ46qyfb7TZNiTrutWv63ZN6xNooOXr4WKwvl9PcQ1aepRx+5JFsz2UkeOcEjTx1fE3KtQvdz3Y9SRwfAls8JXwYLLgf3c/WGmKrH9kcnelpjdVmnPTKipKb+XkRDoWCCpnJSSFg5bIIkGpV3q+vi9t/e1ubbvb2qoVpcVH24cMQJg8DGi4QNg8fHJRxstoXC6SQSLGYic3DSxLYadXBZmaSrZgUwHYCYK5c0uRILxygpcxP0tPGKnd2/KehoGVBVKBah6SJ0IaYsDgSixAxWd7ey1YZGRjQvNW4nL8lyfPaqOJmSOyB89HiolLJ/ox0snzivG/zjmz/WYaWsdddq//Xdl56e+sNto8+qnMudQYaXZjqwRQOHmPnaB7DcHi2LGB1zIUFbac0N6dtD6zeVKloEblqVXXG5WUNt2R+5fKy6HmVSr2HiqGcjMqanlbiUyqJUcDqjJYEscI6CZGt4xB64ELvHT1pNlTYegNt1VerAycROHuvhzpxGJ7MbbZmRNgknR7vsNo0j19Zkd9qY0N+K9aYiATvAiKNvCXtl7Y+yZvHB8iWh11fryd51jLhvQpuPiysaElYgkc3+8aGFjaxZXBDZcE5mQSuXJH3m5vafDyfF2HE3nQkXt3d6q1j6BfbF4yMaOjinTvqVejqkvPRytLTIyTp0iUNY7RCNiRY7PPinJzblq+1x1Fo2/VWYA8M1HvK0rxmU1NyHnudgYH6kM1w3ICGeFKhsorVYcggj3v0UbHsneSklgWdrEC1EtZqavMBODlVq/osLSyogsD8zeHh+nsuKnhnsxy15DxlAuUym+qmLYwoCK/HXlszM8kRBo166wGHMyQ1izTIGibKMfPz9nb256RT5VNoPKYSzv6zbFZdLseCKlkX50Ru3r2rxK2/X3Ud1ggYGqrvU2uf35AUMv2kWBSdZ3RUjd7lsiwTE/VtmzY3NSyQKS70ppVKGn3FHLy1NdlWqdRXOZ+c1IgqGgG3tzXdhwSI5IchnHa+4WcbBsnwTeuoYJoP04K4H/VQRo1ZQua9bBsYOJgrlyX9yZK1cL3N80uKgrOv9HLyd2NxmHJZWwxVKvpfxyqaFwxJFoU0pHnxQpe29xpWYcMseXPypqUlhJ44lrKl69jevOxTl89roQW67be25JWev95eEQbDw/KAMWH45k3v77tPhNncnE4Sk5MqKKzgSqrsyPwxTt6WALFVAknYxER6yfZQ2ejuljGSFNpt9tj+fu1bR6JlhXiSktPVpZUskwhYeAzDMOx1Q+t4Pt94QmmU4xQqPUljOO6SBZ2qQLUSdiIKG7KyqhlzAujNts8c7096oVutOMUlebHkrFHrE8oV5vg2I39py1ndC1kNWCwG09cn3397W+aeZsZUi06WT1ZO2N5iu7uaS86l1ff6WSzd3Yf7ruH83NUlz1dvrxK0nh6VtYwsss/TPffUf2b0Dyt287ysVzA0JARic1P/K9YwGBkRo/r+vjwLvb3iQcrlNJ+S5ymVNCcvnxfylM+L/kf9kWk3uZym0thWAXt72l+Onr58vp7kUR+1/ebW1uqrXVJPnZyU8VudlGTN1n0I05pYJIx1J9Jy+HiMdX7YooM8bmqq3nFCwhlG0VlCyFBOFl6xThX+BrxfNjezPZ+R4J0DNPPgpd004T6hJ4+vtr8HrXArK/KwlEr68NHzxputWpW7hLHCfMCKRREutPKvrUlYFuO/WQ2TjcQLBXk/NCQ3eblcn9NBz97AgOxvi50MDorFKa1oA1sUhBO/tfw2s3iHVuLx8WRFhVU4mykYjbYxx89ec2xMhXa4v1XKkvL+mNjOYhfh8SRsh8mhC63ix8mnyoJOVqBaAWsQ4uTDxG9OXKxYm2SsiNUwz8fC/lhZ989aIKOV+ZWUn6Ese+gheWV4MMe4va3rBga09LqNSmmGTpZPNoSbSnKlogShU4hds6VZGwQ+h5cuiTzd3BSyxfuYvXptI/Hw/JcuiZ5UKIh8Zn2DK1ck+okEq7dXjdTMm15ers+v29sTPYvFPkolbQrOAikkWyy2Qo8eCQm9dOvrSgIZ1h96tzjX0BMYFiNhYTwaEGwxH2JvT4mmPTf3DSPT6PkL92WhFBYXDEMwQ4On3cb3YcipvbYds3W0kFgm1b6g8YQRVFkjDCLBOydI8+AlEbqQxPFGsVYG3vRsR0DhvLYmN/XqqnremDzLPh+8+XZ3NUSSoZ5rayqEbJNwCnp66wYHldyNj8t5GA5Qrco6a5miZ4uEZnxcwxk2N+tL/U5O1k8s1tp848bRrMjNSOBxqv2xv164nr8LP8/NHZwwrTIVtj/g732UMXHC4fVsgZeTrISWBZ2sQJ01Qs/d+rrmbfT2yntO1mF5bbuwONJJ3SdxOd6S9MzSUwBoafVWj9MulD1HNRjMz4vHY2hI5pSREblvme/Myn57e9rXi/PaYdCp8ol6BBVipmIA8hrJXf1CHcEaZcfHRT8ZGdHicUzdCElhoSC/88CAGNoANYSzkmahIK+9vdrnd3lZiVa1KttphL96VQgeq3CyRgL/Txr42FqKpCr0dK2vS6QSnyFWZbeVNJnak1T8xPZBDp0RDPUcH69P/yEJtURoeVmrwXPc1HXpRbPeZlt5k2OxpJL3tyVptt2B1altTl+YEsUxMpTUXpMhovxNuP/srOjTNtWJ8jp68C4QQrey98neOAvL+m2SKm+WqSnNQatU6oV0qVTvuaPrfXFRjmNJ3J0dbVZprfwkg1QitrdFqI2M1OeRsdcbWyCMjmorg81NzZ0Lc3cY8kFhaLeNj9cXeDiqcpDLNZ6gGPbZ6BzH9WSEuXNsa5C2HIZ4JZHc8PucRfnyLOhUBeosYGVHOGEx34K5GJWK9rQaHEw2KMTl/C0M6wIae9/aofKp9ThyPCywNTCgYcGM7GCfUSqVbEpsGypT8eI8eVh0qnxi6BmNw+vrWqnR9mLt9CWca6emkudWytPBQVlYRI3L5cvqbe7p0RQTNjIncSsW6+sV5HKir127poXrVlZEnpO42Z52tvgHDX1bWyIjlpZkn1xO9yexsWGFjAwrleq9erZnqiVDJH+9vck9mem9s+GQNhw4rCkR5sbxPGHoZVI1TI6dMiMM9Uw7nr9VqKfT42ivbxEalbgfq45yXmbbMEB05yyIBK/NEYZLcR2rVjXKFeDNND2tN8vKilgF6HKnR47WDU6AExMaukkPHi1AKysiYNgsfGhIH2ZOulNTsn5rSytdUmCxaIgVXnNz6iU6jIctScns769ffxyiFbYm4NIKa/dh89+SvHe2DHraQtJ8FgpdFnSqAnXaCL36NAjZSalYlIkln5f34+P13upmoUdxaZ/FylxrzDlqUZbjjqHRkmQ847p77hHZVizKnMIeolQSbSEv9lhlvynvD4Z28Vk4Crnz3vtOlU+UEyR5jLxZXOwcuTAxcbiCZaHeMDGh9zWLtzE00xamsToMq1uWSvJ5dFR+e/afGx1V0kaCVyjUe/VWVkSHLJdFX2I6jfXe0etFg/3CgjYx53xgPWosukfCMjGh+Xf0RFlPFtdRn7XVNsPQSW63rXn4ntvsvmnPM48LPW18z6gVFgQMiagNHQ2j5+jlC89582Z9TqHdRk+ddcAwFJa5eN5r03jKzxs3sj2jkeCdA1gFLAy75Pa0Y7zXxE2WMC6V5OFaWZGbz8Zd7+1pPDWtO2wSvrQkN/+dO3J3MN8BkIedAolCrK+vvpITkCwMuS4L+ci6zMxk6910FpOAVVayXPM0G4tn/R3OqhhCFnSqAnVaCCegMJmdE/vmpty7hYJWqI35dXE5ycU57x94QD/fvathwMPD4nWgN4i9TNfWJPKDYWKlkswdNEja6nphEYTTQKfJJ6t3MKyOxTucu1g97yz5yrJ/mkGYy8BAfb+6zU2df9kKanlZwzV5DGUwC6vR01YsKuFhIbueHlnoTb3/fg21r1Y1lJIVL9kXmAZ+G9bIaC2GK9o6DDT6W+8Ux3L7tpyXbXTsPt7L8dPT9blsliwyzNIaHhk2aT2GHKMNe2xmqLGELSkyjgST+nCSEdT2y6MubovH2POF5+EYeA7KMUuuaTTheObm6r3iW1vZntVI8NoQaRNRaNHgjRX2odvfr7c+3LqlDzRz7CoVeV1Z0V4k7GXCqklXr4rQuHJFS58nJeX392tPunaIuz/vVfuOQjwHBtKt4w88cPakMeuSBZ2mQJ0mkgxFDGVZX9cKZAxzTmqqG5ezWUJZ2g6ytdk4G4WJ9/Zq/hEr9rEfKttq2ArNVHbY/Nfm5oQpClS67Hpus6+ngU6STzaiiArt+np91EdS78OLuMzO6r0f9rCzC/OXuR+PuXFDvWv5vNzjN2+KPsYUlEJBdKsweodeuOVljcRi2CTrGKyva1G7clm2s08de9Ztb+szw0J51tg/Pq5eNebUsZCOc3I89UySNhZKoY7K9B3r4bJhl97X59PZ97znuA8JFkkovWM2lDLNmJMWVslXK0eSCqHY9zSGrq+rp41jSYu0s+uT8vVsuCv1div/pqd1Lr5zp/mz6n1j2dRyoXPYpV2UqMMivIEa3aC2+AFd01bQTk+r271a1ZuFQqNcFgHA3hoMCaDrfmSkvoLlwoIIEpbpDfu2kUwNDCgxCWPH7XIUZfEsQ4iOsxzXIxgqcGdBylrddDoLOkmBOg2EsiTJYrm7K0rG2JiGZPb3S84s85la/XzFpb0XyquuLplXFhZEAWR5dc4729s6L+3uyn1GxdL2vApTEw5zX58lOk0+8T+h94YVdScnz89cfRL3OvWkLFENNmR1YkJIAVMo2Cyc93+lImTs/vs1tLJQEP1sa0v7/o6MiA5XKIjcpqeNZJHv6Wljm6mbN2XfxUX5DtQTczm5Fo0s9NrRK8ecNHrbCgVtdk4ysrqq3jdL1lh8xYZPrq3JmJI8b0nhjPbVFlaxHrI0eWHDQG1hFhLG6Wlt3m7zCJNAb50lhRyTJXNJxqjw+6WRPq7j+anDM0ouVtE8JwjzYLjO3hA2NpcPGRNO5+Y0v47laGdn1UPHpqOMxy4W5SHe2qov2rGwoJWYBgZUINFKw/LR3L9drclxkSVpom2HIgnhkgWdpkCdJBoZjBgeQkWN1W67ui5WmFVcTnaxCm1/f31Y3va2KoS291OoDFmEBQaS3h/mvj5rdJJ8sooow/7CImgXZQnn0J6eg+vSQjKtxw7Q/Dr2xGUhoIEB0d9KJfXmTU6KPmYri6+vy/E9PfUVNllxE1BSx5ZHbH9A4wlTdRg6yVQcGyrIfanvsU0Ct3Ff650LW+pQR7WhlCR29LQxdNqGaTYiVFZHtp9JqsJrJp2D38OGf3Khh7FR+KY9l42aS0qd4piS9rHPUHgst9t1JMiTk+odjwTvHCG8CcIbmFYFPmS0xtC6Yt26lvytrmrFJAqPoSGxDFEwsRITBQbz62yVp54eLcsbl9NbWknAkpqan8WSBZ2kQJ0GQqWZsoIW15s3ZfLf3lY50CmW+Lg0bvEyPy9KHpexMVH+CgWp1Mf5Zm9P7qUwZ+U0CVg7kDvvve80+UQlfW1N9IvLl0+2dU4rlzRvXLMcvL4+0ZFCwzdTW5aWxDNmWyTQOMLop64u2V4oaBXvsTHR99i3rljU55Ctppi3Su+ULaRCEsUG4LY6ZGh8sQRkbU09fySEJEFpYdB89qmT2txXzjm2oAiPScpRC2H1YjuG0GtnPXlp50jSt0PnSprsCj2J4TpG0zVy2CSdx3oFGdZpfyMWMeT9Wakk/04hIsFrIyTF/e7uyoPJ6pXMm2P8Mh8e5jKwNC6TbPN5zY+jEKaHbnBQXb4kF/399USDQsd+ziIoo4cvLlmWLOg0BeqkkETsWA5+cVHboWxttf4+iMvJL6EMHhgQCzCNdYuL2iKHYVo7O7KOFv8kK3OaApSk/HQCOkk+2ZzdSqU9o0JOeknLKSTpu/9+NZRzIWHr7xcZy9DIalW8cs6Jjnb3rkZUMVeO+7FASbWqOav0jo+Pi57HnsMDAxoeyVw+hjvT4E9HgSUg1tNu17PKJeszMFzThiHyfvBevVr0opFIhuHWJIk2VHN2tr6hOJEWdZKUv2bfZyGLfA0NUrdva6RcknwLSWF4fYZ5ZvUE8pW/X+gJJfnlM0cHTCyy0qZoNCmGoZosNcuyuYyzZhloFkhZXJR/a3RUHvjr1+VzsSgFU8K8tyQrU6uFaFw6c8mCTlKgmiGr8hxOGAzh3tsTpQI4/8WIOnlJktl374r8LxblPZW7rS31svFeqFSyE7JOI2yHRafJJ1ZQbFYx8jwttpIrc0lZjyDJO8n+isPD8syxNy+337ihn3l8uay5ziRn9JBVKnIuescZnbW6Knre+LhWP6e3nMVPtrZkPDwPwz6Z52aJHg18XMd6DvT+cY4g+SFh5H4kaSQhJDOUKzyOOXlJnioSSKvnJvWPS/LEhaQsbf80A1RSpFx4TKPIA0vGQiJI8ptUSTPpPGGYpg0x5XxtQ1u9l/8XkP8kCyLBOwMkkTjv62OZ+Zn7sGhKqSRWgXJZK2FOTsprd7cIoFzO+3vvFaHS1aXhVffe2zjUKhK7uLR6yYJOU6DS0MgaGILeOlpMKxWZ2NkstRMs7+d9YY5OuaxRGI88olXqNjflv93eTs9jS7uPIk4OnSafdnY0tLDVz8hJLWHUkiV1/Oyc6lPc3t2txS+6u8XIfveuVr3k7zQ4qP3g2FeYOXZ8z7w8QM7HNgelkvzm9MLRq8OedNQTSyWtuEiCOD0t623xDx5vozpIAEkq+HlyUlODbCVMvrLqsvfNwxK5PokIJXnkQiJn57+kEMwwXSkt780eawuqJHntksbPsYWEkHp7Ftjv0IhshpyAzpxyOdt1IsE7BaRZFsIHgFYRWkZsudmlJbX0FIsSDtDfr43Fr16V/Iju7oPhAeHSiqbccYlLliULOk2BaoQsyjllC/N1SyWxSs/MyELDzkXJmzlvC3Nu7t6V/JxCQd6vr4s1niH5oSLTrLpbRGvQafJpZ0fkCcPF2nmZn1f9x6aacGEOIdNWKBtZJRPQcPaFBfnMQimAPLfLy6KrjYxoaOXKilx7bEyWkRF5ZUGVYlHIXXe35syxRgLP3dUlxy0vayEs67khIWPbK5br39uT8dhwzZA4sNgIvXzUPdlygKGKy8vqYWNxEpsatL5+sO9kSNIIzkvst8ewQxt2GZKyZl44rmMYI0NDkzxptiCLPSb0QKYR0ySvW0g6mxlgrQcwbIqexBEsKb19Wwle9OC1CGk3ub3prGuaVpKdHREKq6uaTMkQgNALF/Pb4nJRlizoNAXqsAjDSvb2RJawMmajtiVxObmlu1sqE9+5I5b4rS3NnWZemy2GZf87vrevEecDnSSf9vfVENHq563Rc2g/p0UrPPKItirgMjur76l3FQryDJOUjYzIuv7++gbibE/ASIm+PnllBAVbhXR3a8hlsahtC/iZ5y+VVEdcW5N9pqZUhrC/cakk5M42xp6c1DnAth6hd46F+hi2Sb007KlsWx7QaDg9XZ+Pl1Q7gsfYlgRcH4Zm8lz2+GZkKSnHza5LOz4kcEkes7Rw0SRZHZ4zTYaHZDAchw37DMNX7fdjteKYg9dCJP35e3tappYFUubmtOfJ4qJYk4aHvb/nHvn1k6oyNRJYcYnLeVuyoJMUqMPAWgNtC5WVFSEYd+5EY9Bxloce8v7hh0UZGxmR35N5buxHxR5WzHmxCor9nyIuLjpJPu3uCkFp16igZtV/GQLpnJK1ri7RywYH63WroSElWmwHwQqXm5uyz927cgx7hy4va9XJYlEbi8/MyOd8Xo5nOwJ62VZWZBtDLW2Rko0NzZMrlZS8kPhZgmZbBlhjks3tIgkksdrYSPZesQAKjVHMv7MFUZJCJa1HjvumFS1pRKiSzm23NzOIJV2v0fmScgebHZs03qTxWWLX6HvZ38MScn5eXxdPc9o5QkSCd0qwfxAFxOSkKAeMad7eFuWBQuksGlvHJS7ttGRBJylQIdImGmtt5QRQLssEEEMvGy+jo0rYmN/GanWbm9oSwP7mWSfUiM5DJ8mn3d3WP79Zl+5urVYJCIFaWhJCNjUln+/cEXmQz0vai3PyHtAqs/m87DM2pukyPDebh29syP70qFHHY/gmDUC22NHNm6L/TUxo9Bb7ndkG42trItevXROvYBgimeQh29/XCA5b5yEkDuEx4XxDIsjrWY9dUrQa1/F72ny9pHDNRiGRjXBYedzMI5j0+2U1zJEwW5KXVByG6xkW2uja/MzfzaZ5Ze2B531j2dRyoXPYpdW9pvjHVKv6h5TLImBsv5O5ueiFi0tcgGzPVicpUBZplkBaZm2lNZbyDivmXvRlcvJgufKHHxbL+uamhklyYXJ69KpFnBQ6ST6dJ4Jnl5kZMepsb2v1YBK5y5e1zRTl59SUEDU2HWf1cnraaBSiLC4WRccrFMRrx4If1aoY8BkuSe8aSQELJlnvDong1JQeV61q43Hue/Nm/bVColUqyXdhPq/dZite2nWWdNkwy5DA2HOF721KkiU84VzWKOct6fx2XdZiY3b/wxRAsb9Fs7HY9KukXMIQIbmzJDjtmvSesi1G7IN3RGSxDNgHgJbfxcX6viSPPqohlg89pFWYWi3o4hKXdlqyoJMUqBDhJEGrbaEgMqddQ6WOutD7OD8vpcsfeECI2+amyFY2ZLeW8Ohhi2glOkk+VautlxFpC4uf2PYNrEbJAilWBxsb8/7SJfHOTU6qdw7QpuKLi3KM9cAVCrKdOXK3bomRrVisz6+lfLIeNxI3Ng634ZU2hJLheDZ/jt46m5NlK0cmeYKYl5dEAPf25Dez17e5ekkkMA1JxshmYY7cp1GfuKRxp52/EbIWQLHnTvJM2n0IW1ej2W+V9Buw7UQjzyH/q6UluR+zktWWETwAzwDwFgBvA/CChO39AF5W2/7bABaanfOwShQfvp0d+cweJcyr4APJpp5cCgXv77vvYvWBiUtcWrFkwWkpUMeRQQBeWFv/FgCPnoZsCoX9zs75zavr7xclanRUyNujj4o37cYNeWVOivfZJ6+IiHbAWRO8VupOly61Rn7YwilpqSwLCxLuyH17eyUEslKR10uXJHLKOSFyrFBOnW99Xatdbm+L9+zKFTmmUhHCc/26yLCuLsnhW13VXLqbN9WLw9BG26zarrfkj3UZeA5LItLCKK3Hx5IyVsBkcRQijTRMTmoIKJubW6LXDGFIYbgt7Zgs+yWd35LbsApl0jmb/QZ2WzPvW9o4GHKZlmsYXiPJm5rk6Uwy7tLAQs7SDC0heAC6AbwdwP0A+gC8AcCDwT5bAH6w9v6rALys2XkPo0RVq/WKUi7XeeFNcYlLq5fjCqlWyCAAD9b27wdwpXae7pOSTfv7OukWCpLo3qx4QKsW52R8ly5JaCQLj4QW4YiIi4qzJHit1p1OWw4lGa8mJ4VQDQ2JYciGUXI8uZzu/8gjmnvHFgejo0LmlpbklXUR8nnZh96u8XEtqDIwIOeZmxMZVy7L9YpFIXxjY3I8C6JYckeStbNTX/gkzGHzXh0JNi+O223xD5uL9dhj6p3jviSUbFqexVDGa7N1wfS0ENuQqKTljFmSaZHmmcviSWt0HhKf27fryXDSMbYCZaPzJ32vpO1px9mcyqQCLWnH2ePt/8rzJZHB/X25d7P+fq0ieE8D8Arz+YUAXhjs8woAT6u97wHwQQCu0XmzCqmdndMVUnGJS1yyLccVUq2QQeG+dr/jyibvRcC3+n8ZGJDJfntblJlyORK1iIgknDHBa6nudNZyqKtLFNp8XusWkAQODcm6hx6qr2nQ3S1yi+1KhofllUXuLLm7fFnOV6mIgl4qiawbHZXzzM2JV29zU4xuw8NK3HI5CR9nJcz9fc2Rq1TUK8ZcPdss3JI2S7C813U0klkvD5V8ev6sEc0uaUU/Qo+YJZHe13sYuQ+9VEmtFLg9jeSFn5N6Qof7JJHCZvslHUNSnQb7XcLz2N8mSzho6IlL82o2+y7hWBhJSEPB3p4Q8HYneM8E8EPm83MAvDjY5wkA95nPbwcwlXCu5wF4HMDj8/PzGb90XOISl3ZYjiukWiGDALwYwLPN+iqAZ56EbDpN+TQ5qblsMWctIuJkcMYEryN0JxZCAcSrlrTPwIB688LKwfZ4bmNrg+5uIWfXr2uD8vV1CbfM5cRLd+2akDyb47y4qD2LNzY0RYe9itmEemFBSGOxKMr49LQQx74+rbDJ1gdra5pbNTsr556clPOtrtZXSg69PMzfm52tz+MKG2+zJReJpPUGJhElS+C4T9hqITzetlpoBO6f1OjbegSTwiEbnTPJ29VsTLxeeK3wu2UZA49PO4/36RU00869uyv3zNiY3A/Ly3J/9PefTA6ek+0nD+fcMwE8w3v/92qfnwNgxXv/j80+T9T2eU/t89tr+3ww7bwPP/ywf/zxxzOO4RhfIKKt0NMDfPazwPAw0NUFfOITB/fp7wc+/Wn93NsL9PXJcd3dwNQU8J73AKOjwF/+pZznL/5C7pPPfU726+mR/T7yEXn98z8HpqeBd74TuHpVrvvudwOTk8DEBPCHfwgMDcm47rsP+LM/A973Pln3yU8CMzNyzMAA8KlPyevly7LuXe/Sa4yNyZi/+IuBX/olIJ8Hcjng9a8H7rkHGBmRz4Cs47UKBWBvT67X1we84x2yz8qKrP9rf01ei0UZ14MPyljf+la5xqtfLfuPjsr60VH5/IlPyG/V0wPMzgJPfSrwy78MXLkCjI8DH/2o/A69vTL+j3xEfp+9PTnm4x+X3zSreHHOvc57//Dh7oqm5zyyDALwHQBe473/r7X1VQC/6L3/mbTrHUY2yTmb73P//cDb3575lBEREaeA05BPDa7VNrpTfz/wmc/IXPHZz8q6z3xG5oMPfhD40IdkLsrnZd9PfELWX7kCPOtZwMc+BrzxjTJHzM8DH/gA8GVfJvPH8rLs+/rXA1/zNcD3fz9w65Zc4/Wvl9enP11eX/ta2f+tbwWeeELmqac/HfjZn9X3b30r8Nhjsm5iQubW6Wm5JiDvAfn8wQ8C167ptte+Vl5v35Z116/LfPmBD8h5H3hAj/mFX5D9fvM3gb/+1/Wc09PA7/2e7BOC23nuJ57Q8wGiJwByzZmZg69PPCHHh/vZ9088oePmOe1+FknHN7o+t9mxNkPa/knr08aZNuZm12h0XNJ3yzqGpPOGx/F/aDZ24lWvknuV98PMjKy7cSPbOBrJptMkeE8D8B3e+0drn18IAN77f2n2eUVtn99yzvUA2Acw7RsM6rBKVERERPvjlAjekWUQgBfYfe1+adeLsiki4mLijAle1J0iIiIyoZFs6jrF6/4OgKvOuSvOuT5IIvDLg31eDuDrau+fCeBXGwmoiIiIiEPgODLo5QC+yjnX75y7AuAqgNee0bgjIiI6F1F3ioiIODZ6TuvE3vvPOuf+MSQZuBvAS7z3b3LOfSckZvTlkLyWH3fOvQ3AhyGCLCIiIuLYOI4Mqu330wDeDOCzAP6R9/5zLfkiERERHYOoO0VERJwETi1E87QQwwwiIi4ezjIE6rQQZVNExMVElE8RERHtiFaFaEZEREREREREREREREScISLBi4iIiIiIiIiIiIiIuCCIBC8iIiIiIiIiIiIiIuKCIBK8iIiIiIiIiIiIiIiIC4JI8CIiIiIiIiIiIiIiIi4Izl0VTefcBwC8K+PuUwA+eIrDOQ3EMZ8NztuYz9t4gcON+fO899OnOZjTxiFlE3D+/tPzNl4gjvmscN7GfNjxdpp8Om//JxDHfBY4b+MFLv6YU2XTuSN4h4Fz7vHzVto4jvlscN7GfN7GC5zPMZ8lztvvc97GC8QxnxXO25jP23jPGufx94ljPn2ct/ECnT3mGKIZERERERERERERERFxQRAJXkRERERERERERERExAXBRSd4/6XVAzgC4pjPBudtzOdtvMD5HPNZ4rz9PudtvEAc81nhvI35vI33rHEef5845tPHeRsv0MFjvtA5eBEREREREREREREREZ2Ei+7Bi4iIiIiIiIiIiIiI6BhEghcREREREREREREREXFBcCEInnPuGc65tzjn3uace0HC9n7n3Mtq23/bObfQgmGGY2o25m9yzr3ZOfdG59yvOOc+rxXjDMbUcMxmv7/lnPPOuZaWps0yXufcs2q/85uccz951mNMGE+z+2LeObfrnHt97d641YpxmvG8xDn3fufcEynbnXPu39W+zxudc6WzHmMrEWXT2eC8yabaWM6VfDpvsqk2piifGiDKp9NHlE1ng/Mmn85ENnnvz/UCoBvA2wHcD6APwBsAPBjsswXgB2vvvwrAy87BmL8YwFDt/T88D2Ou7TcK4NUAXgPg4XYeL4CrAF4PIF/7fE+7/8aQ5Nt/WHv/IIB3tnjMXwigBOCJlO23APwiAAdgFcBvt3K8bfh/Rtl0BmOu7dcWsukQv3PbyKfzKJtq44jy6Xj/aZRPpzze2n5RNp3+mNtKPp2FbLoIHrxlAG/z3r/De/8XAF4K4E6wzx0AP1p7/zMAbjrn3BmOMUTTMXvvd733n6x9fA2A+854jCGy/M4A8F0A/hWAT53l4BKQZbx/H8B/8N5/BAC89+8/4zGGyDJmD2Cs9j4H4I/PcHwH4L1/NYAPN9jlDoAf84LXABh3zt17NqNrOaJsOhucN9kEnD/5dO5kExDlUxNE+XT6iLLpbHDu5NNZyKaLQPAuAXi3+fye2rrEfbz3nwXwMQCTZzK6ZGQZs8VzIUy+lWg65poL+bL3/hfOcmApyPIbPwDgAefcbzrnXuOce8aZjS4ZWcb8HQCe7Zx7D4D/BeAbzmZoR8Zh7/WLhCibzgbnTTYB508+XUTZBET5FOXT6SLKprPBRZRPx5ZNPSc6nIgTh3Pu2QAeBvBFrR5LIzjnugBUAGy2eCiHQQ8k1OAGxMr3audc0Xv/0VYOqgm+GsCPeO+/3zn3NAA/7py77r3/y1YPLKKzEGXTqeO8yacomyLaBudBPkXZdKboOPl0ETx47wVw2Xy+r7YucR/nXA/EPfuhMxldMrKMGc65DQDfCuDLvfefPqOxpaHZmEcBXAfwKufcOyExwy9vYcJwlt/4PQBe7r3/jPf+DwG8FSK0WoUsY34ugJ8GAO/9bwEYADB1JqM7GjLd6xcUUTadDc6bbALOn3y6iLIJiPIpyqfTRZRNZ4OLKJ+OL5tOI3nwLBeIJeEdAK5AkyufEuzzj1CfKPzT52DMS5Ck0aut/o2zjjnY/1VobZGVLL/xMwD8aO39FMQdPtnmY/5FAJu199cgceSuxffGAtIThW+jPlH4ta0caxv+n1E2ncGYg/1bKpsO8Tu3jXw6r7KpNpYon47+n0b5dMrjDfaPsun0xtx28um0ZVPLvtgJ/0i3IBaEtwP41tq674RYbwBh6v8NwNsAvBbA/edgzL8M4H0Afre2vLzdxxzs2w6Cqtlv7CDhEW8GsAfgq9r9N4ZUf/rNmgD7XQBf0uLx/hSAPwHwGYhV77kAng/g+eY3/g+177PX6nuiDf/PKJvOYMzBvi2XTRl/57aST+dNNtXGFOXT8f7TKJ9OebzBvlE2nd6Y20o+nYVscrUTRURERERERERERERERJxzXIQcvIiIiIiIiIiIiIiIiAhEghcREREREREREREREXFhEAleRERERERERERERETEBUEkeBERERERERERERERERcEkeBFRERERERERERERERcEESCFxERERERERERERERcUEQCV5EZjjn7nPO/bxz7g+cc293zv1b51zfCZx3wTn3xEmM0Zzzrzrn9pxzb3PO/TvnnEvYp+Cc+y3n3Kedc998ktePiIg4O0TZFBER0Y6IsimiVYgELyITag/6zwL4Oe/9VQAPABgB8N3HPG/PCQwvCf8JwN8HcLW2PCNhnw8D+CcAvu+UxhAREXHKiLIpIiKiHRFlU0QrEQleRFb8DQCf8t7/MAB47z8H4BsBfL1z7rXOuadwR+fcq5xzDzvnhp1zL6ltf71z7k5t+6Zz7uXOuV8F8Cv2IjWr1K875/5PbXmktv6Gc+7VzrlfcM69xTn3g865xPvXOXcvgDHv/Wu89x7AjwH4inA/7/37vfe/A+AzJ/D7REREtAZRNkVERLQjomyKaBkiwYvIiqcAeJ1d4b3/OIA/AvALAJ4FPCkk7vXePw7gWwH8qvd+GcAXA/he59xw7fASgGd6778ouM77ATzde18CcBfAvzPblgF8A4AHAXw+gMdSxnoJwHvM5/fU1kVERFw8RNkUERHRjoiyKaJliAQv4iTwKgDPrL1/FoCfqb3/EgAvcM79bm2fAQDztW2v9N5/OOFcvQD+X+fcHoD/BhFKxGu99++oWcF+CsDaCX6HiIiIi4dXIcqmiIiI9sOrEGVTxCnitOJ4Iy4e3gwVRgAA59wYRPD8DoAPOef+CsR69HzuAuBvee/fEhy3AuDPUq7zjQDeB+AhiAHiU2abD/YNPxPvBXCf+XxfbV1ERMTFQ5RNERER7YgomyJahujBi8iKXwEw5Jz7OwDgnOsG8P0AfsR7/0kALwPwfwPIee/fWDvmFQC+gZWYnHNLGa6TA/An3vu/BPAcAN1m27Jz7kothvwugN9IOoH3/k8AfNw5t1q79t8B8POH+7oRERHnBFE2RUREtCOibIpoGSLBi8iEWtLtVwL42865PwDwVoiV6P+p7fIzAL4KwE+bw74LEjrwRufcm2qfm+E/Avg659wbABRQb7H6HQAvBvB7AP4QwH9vcJ4tAD8E4G0A3g7gFwHAOfd859zza+9nnXPvAfBNAL7NOfeemnUtIiLinCDKpoiIiHZElE0RrYST+y8ior3hnLsB4Ju993+zxUOJiIiIeBJRNkVERLQjomzqbEQPXkRERERERERERERExAVB9OBFnGs4534bQH+w+jne+71WjCciIiICiLIpIiKiPRFlU2cgEryIiIiIiIiIiIiIiIgLghii2YFwznnn3GKrxxERERFxVnDO3agVB4iIiIg494gyLaIRIsFrAzjnNp1ze865Tzrn9p1z/8k5N97qcR0HzrlvrH2XjzvnXuKcC8MBuF+fc+5nnHPvrBHPG2cwtk3n3Oecc39aG9/vOuf+ptk+5pz7AefcH9X2eXvt81Rt+z92zj3unPu0c+5HUq7xQufcv6gJ4L+snecTzrm3OOf+rtmvzzn3Hc65P3DO/Vntd3iJc26htv37ats+4Zz7fZZbjohoRzjnXuWc+3utHker4Jz7EefcX9Sedy5vqG1bqMm4/xUc819rMuBrzTF/buTGnzrn/rS27ztr2/60Jl9/xDk3EpzvEefcr9Zkxsecc//DOfeg2Z4ok5xzT6nt/0Bwvl9xzn3P6f1qERHtiyjT6mTah51zr3TOFRL2e5Vz7iNW16vJlrvm81+vycBw3Secc7Ev9wkjErwWwzn3TwH8KwDbkF4mqwA+D8ArnXN9J3ytM3mAnHOPAngBgJuQ73I/gHKDQ34DwLMB7J/+6J7Eb3nvRwCMA6gC+GnnXL72m/8KgKcAeAaAMQBPA/AhAMu1Y/8YwD8H8JIG578NgIrcH9euNQbgWwD8v0bh+hkAXw7gayD//0MAXgf57QApd/xltW1fB+DfOuceOfrXjoi4+GixsvCvvfcjZnko2L6S9Ax773+CxwD4UtTkhllHfFnt81MBLAF4ITc4554G4Jcg/avmAFwB8AYAv+mcu9+c44BMgjRA/j4AVeee7MH1XACXAHzHUX+MiIiI46MdZBpEFrwXojM9iZpBeh0iQ77cbHo1gC80n78QwO8nrPst7/1nT37YnY1I8FoIJ71DygC+wXv/v733n/HevxPAswAsAHi2c26uZrGdMMctOec+6JzrrX3+eufc79WsJ69wzn2e2dc75/6Rkx4sf5AwhtvOudc78WS92zn3HWYbLc7Pc879sXPuT5xz35zhq30dgKr3/k3e+49A+rhsJu3ovf8L7/0PeO9/A8Dnmvxed51zjwfrvtE59/La+1vOuTfXrEHvzTLWWmPQlwAYBPD5kOae8wC+0nv/Zu/9X3rv3++9/y7v/f+qHfOz3vufg5C+pHHmATwA4LeCa/nacR8B8KBzbgPA0wHc8d7/jvf+s977j3nv/4P3vlo75kXe+9+vjeO3Afw6hHBGRLQtasaS/+mc+0BNLv1P59x9ZvuEc+6Ha3LlI865nzPb7jjxqn/ciff8GbX1f7cm5z7hnHuHc+4fmGNuOOnH9C3OuX0AP+ycG6xZnz/inHszgL/WYLz/yTn3fcG6n3fOfVPt/bfUZAo9XjeTz5QJ/xrAdx/jeACA934f0hT5qcG5f8x7/2+995/w3n/Ye/9tAF6DBJIWyiQA3wNgFMCWc24GYnz8eu/9p4473oiI84wo0wDv/Z9DevY9Ndj0dyAy5kcg+h8RErx1iEwJ17262bUjDo9I8FqLRwAMAPhZu9J7/6cQ78/Tvfd/DCEKf8vs8jUAfsZ7/xnn3B1I08zHAExDCMBPBdf5CgArkAk8xJ9BHs5xiNfpHzrnviLY54sBXAXwJQC+pUZMGuEpEKsx8QYAM865ySbHNcP/APAFzrmrZt3XAPjJ2vsqgH/gvR8FcB3ArzY7oROr2N8D8KcQArwB4H/X/oOj4lEAv+K9ryOszrku59xXQn7rvdq1Xuu9f3eWkzrnBiEC/U3HGFtExFmgC8APQzz48wD+HNJsl/hxAEMQWXEPgH8DAM65ZQA/BoloGIcoAu+sHfN+AH8T4nX6uwD+jXOuZM45C2Cids3nAXgRxGjz+ZBn0ioeIX4KwF3nnvRc5SHy7qXOuS8A8I8B/LWabHnUjOko+I8AHsggRxuiplx+KaQpMZxzQ5A55b8l7P7TEGNSeI46meS9/wzkt/0uAP8VwH/13v9/xxlnRMQFQcfLNOfcMICvRk3mGPwdAD9RWx6tGYcAIW5PqZHfLgAPA3gZgHGz7q8jErxTQSR4rcUUgA+muKb/pLYdEALz1QBQe1i/Ckpqng/gX3rvf692nn8B4KnOePFq2z9cs77UwXv/Ku/9Xs1D9EaIUPiiYLey9/7PaiV0f5hjaYARAB8zn/l+tMlxDeG9/yQk9Ii/xVUABQAvr+3yGYhnbMx7/xHv/f9pcLpV59xHIWGhXw3x2H0MwCTktz8ObHgmAMzVrvVBiIB+jvf+LUe41g9CyPIrjjm+iIhThff+Q977He/9J733n4B4rL4IAJxz90KIyfNrz+lnvPe/Vjv0uQBe4r1/ZU0mvdd7//u1c/6C9/7tNa/Tr0FCEdfNZf8SwIu895+uybpnAfjumux7N4B/12DIvw4JL+L5ngkJG/pjSGRBP0S29Hrv3+m9f3uDc32zc+6jZvnRYPuf136Pf97gHI3wc865TwB4N0RBfFFt/QRkTk+SKXY+AdJlErz3r4cYy65BjIcRER2PKNPcRwF8AsAagOdwg3NuDUJAf9p7/zoAb4cY3uG9fxeAP6qN4SEAf1D7Hr9p1vUB+O0G1444IiLBay0+CGDKJcdW31vbDgA7AJ5WEyJfCHnof7227fMgeVkfrT2AHwbgILHSRKqHyDm34pzbrYUdfAxCGKeC3ezx74LkdjTCn0IsUgTff6LJcVnwJNmFCJGfqxE/QLyctwC8yzn3a07yUdLwGu/9uPd+ynu/6r3/5dr6D0F++yOhZpF6OoD/bVb/ce1aE977p3rvX3rYaznnvhfilXyW97G3SUR7wzk35Jz7z865dznnPg6x0I4757oBXAbwYS/h2yEuQxSEpHN+qXPuNU4S/T8KedatrPpAEEo4h4OyKxG1Z+qlqJctP1Hb9jYA/z9IiOP7nXMvdc41koHfV3veuSRZ2X8IEtXwZQ3Ok4avqFndb0AMXPwNPgKZG5Jkip1PgHSZRLwJwDuNbI2I6GhEmebHIalDfw7gC8y2rwPwS957ypefRHKY5hdC9dbfMOte673/dINrRxwRkeC1Fr8F4NOQ8Mon4aQq2pdCin2gJjR+CcBdyEP6UqPkvxsSlmgVisEgrKYRIfhJiAfssvc+B/ESuWCfy+b9PKTISCO8CWKZIR4C8D7vfWLO2iHxSgDTzrmnQgQXPZnwksd2BxIe8XOQsKTD4pchIQbDRxzfXwPwLu/9BzJea9mZOP4kOOfKkPvhS7z3Hz/iuCIizhL/FKIErHjvx6A5Fw4isyZccqXgd0PCj+rgpDLbDqQIyExN2fhfqJdVoZz7ExyUXY3wUwCeWYt+WKldT07s/U9672mp9pA8kiPDe/8XkPzr78JBeZv1HL8GyXn5vtrnP4PMKX87YfdnoTafREREHAkdL9O8938E4P+COBUGa2kjzwLwRU6q+u4D+EYADznnqAOS4K1DCd6vm3UxPPOUEAleC1ELCSwD+PfOuWc453qdVCP6aQDvgcR0Ez8JiXN+JgypgRCyFzrnngIAzrmccy5pgk/DKMTy9KlarPjXJOzzz2rWq6dA4sRf1uScPwbguc65B2sC79sgikginHP9zrmB2sc+59wA48ZDeMkR+W8AvhcSkvTK2jn6nJQZz9X2+TjEmn1Y/DhEIO845wq1HJVJ59z/45y7VbtWT2283QC6a+OlF/YWgF/IcqGa1/CVAP67c+6v1s476px7vnPu62vXeiHkP9k4IYIcEXEWGIVYej/qpEAUwwjhvf8TAL8I4D86KVzQ65yjslQF8Hedczdrz94lJyW5+yAhRR8A8Fnn3JdC8kka4achsjFfM6J8Q6Oda6GJH4R4117hvf8oADjnvsA59zdqCtmnat/rKLIlxI9DcrCfcYxz/ACApxtl6gUAvs45909qsiTvnPvnkMJMjSoZR0RENEaUaXLOV0KM/M+D1Hf4HKS+w1NryzUIgWNLp1dDqv1+ISQ0E5AaBFcg9R0iwTslRILXYnjv/zUkz+H7IKTktyEE42bgtn45pNDJvvf+Deb4/w6xvLy0FjbwBMTbkxVbAL7TSU7HtyPZ6/VrkKTaX4G46n+pyXf635BqbruQ+Ot3wQhD59ybnHNfaw55C0TAXILkl/05xKqUhp+EFCj5b74+f/E5AN5Z+x2eD+Brkw5uMvZP1879+xDy9XEAr4WETTBO/NtqY3wBpL3Dn9fWAQfz75rhmbX9XwbJVXwCkojMkNF/AbHSvc1pT6yYFxPRzvAQ4jEIUS5eg/qQZUCe1c9AnrP3Q8KF4L1/LWrFBiDPw68B+Lxazss/gcinj0CMHi9HY5QhsucPIREQP954dwAqW6wRrR9SXfKDkJzde2BaEyTg/3b1ffA+mLSTlyJM3w4xVB0JtUiBH6udB16qET8KiQr5E8j3XwKw5r0/UEU5IiIiEzpdpoX4XgD/N4Tk/bD3/o+89/tcIMVnvtY51+O9fyuExO6TYHqpXv5aSPpOLOJ0SnAxnSciDTVv4h8C6PWxR0lTOKkc9XoAl2KeXEQnwjn3fwB8p5fS+xERERHnGlGmRZxXRA9eRMTJIQfgn0ZyF9GJqIVwX4MYOSIiIiLONaJMizjPiAQv4khwzv1iEIbU8eGD3vu3eu/DHoQRERcezrl/BQkZ+pZaaeyIiIiIc4so0yLOO2KIZkRERERERERERERExAVB9OBFRER0FGpVT1/rnHtDreDPgeqCtcquL3POvc0599u1fNSIiIiIiIiIiLZHUoPttsbU1JRfWFho9TAiIiJOEK973es+6L2fPqPLfRrA3/De/6lzrhfAbzjnftF7/xqzz3MBfMR7v+ic+ypIpdq7jU4aZVNExMXEGcunU0GUTxERFw+NZNO5I3gLCwt4/PHHWz2MiIiIE4Rz7sxyHGpFcP609rG3toSx6ncAfEft/c8AeLFzzjUqoBNlU0TExcRZyqfTQpRPEREXD41kUwzRjIiI6Dg457qdc78L6Vf0Su/9bwe7XIL0o0StRcjHAEwmnOd5zrnHnXOPf+ADHzjlUUdERERERERENEckeBERER0H7/3nvPdPBXAfgGXn3PUjnue/eO8f9t4/PD19riO4IiIiIiIiIi4IIsGLiIjoWHjvPwpgF8Azgk3vBXAZAJxzPZAehx8608FFRERERERERBwBkeBFRER0FJxz08658dr7QQBPB/D7wW4vB/B1tffPBPCrsYF9RERERERExHnAuSuyEhEREXFM3AvgR51z3RAj10977/+nc+47ATzuvX85gCqAH3fOvQ3AhwF8VeuGGxERcdHgnHsJgL8J4P3e+wMh4s65bQBfW/vYA+AagGnv/Yedc+8E8AkAnwPwWe/9w2cz6oiIiPOCMyF4zrkBAK8G0F+75s9471/knLsC4KWQ4gWvA/Ac7/1fnMWYIiIiOhPe+zcCWEpY/+3m/acA/O2zHFdERERH4UcAvBjAjyVt9N5/L4DvBQDn3JcB+Ebv/YfNLl/svf/gaQ8yIiLifOKsQjTZd+ohAE8F8Azn3Cqkt9S/8d4vAvgIpPdURERERERERMSFhff+1ZDogCz4agA/dYrDiYiIuGA4E4LnBUl9p/4GpMcUAPwogK84i/FERERERERERLQ7nHNDkCJQO2a1B/BLzrnXOeee1+DY2MYlIqJDcWY5eLV8l9cBWATwHwC8HcBHaz2mAOA9kN5TScc+D8DzAGB+fv70BxtxYfC+9wEzM60eRUREaxDv/4iIc48vA/CbQXjmmvf+vc65ewC80jn3+zWPYB289/8FwH8BgIcffjgWiYpIRda54rD72f3DY8NtwMFz2/PQRjE9Le+vX9f1H/wgcOMG8LM/C0xMAO94B7C8DLz2tcD99wMf/jDwxBPAF34h8PrXy3lyOeBjHwM+7/NkGwC8+c3Al3wJMD4O/I//AXzd1wHXrsk1bCek8HM7zrNnRvC8958D8NRa9br/DqBwiGOjkIo4NN73PmBrC/iP//H4D19UlCPOEzhZntT9HxER0TJ8FYLwTO/9e2uv73fO/XcAy5A6BxERTZFEtLLMFYfdr1wGXvACoFqV9Vx3/boQqhe9SM71e78HfPd3A319wEteoud+1auA7/1e4F/9K+Dv/33grW8FvAfuuw94//uBl74U+PZvB/b2gE9+Evh7f0/OR/T3A5/+dP3YnJNzNMLLXqbvf/RH5Xqf/CRw9aoc+8lPAu98p3yemAA+/nHg5S+X/UNSy9+jFXPwmbdJMH2nngZgvNZjCpCGw+896/FEXFzMzJwcudvaUqU5IqJd8b736f0KtI7cXZRn5aJ8j4jzCedcDsAXAfh5s27YOTfK9wC+BMATrRlhxFngJOVQkj6TRVfi/lnmFJ5velrJ1MyMkLsXvUjI3QteAHzDN4gn7FnPAj71KWB7W8/9xBPAV3818IlPiBfuD/9QyN7Vq8B73gP8i38hnjUAuHwZ+MqvBH7iJ4QkAvI6MADMzQFdXbLce6/sS0xM6P5p8B5497uFLHoPvPGNQig/8Qng7W8HlpaA170O+IVfkN/1Va+q/31bqT+eCcFL6Tv1exCi98zabl8HI8QiIk4CJ6HcnhRRjIg4TSQRu8Pcsyc1AZ3lhHaa10j6HpHwRZwUnHM/BeC3AHyBc+49zrnnOuee75x7vtntKwH8kvf+z8y6GQC/4Zx7A4DXAvgF7/3/PruRRxwGx5UZJy1P0/SZRnPFE08AX//1wLOf3fz8NtRyZkY9cu97n3juSPw+9Sng+75P9v0rfwX4ru8C/v2/1+Onp4FXvhL4zu8E/tk/E0L39V8PfMu3yPaf/Ekhh3/2ZzK+l71MSNf99wsJXFoCKhVgbAx44AHgn/5TYHFRQjmJD38Y+Mxn5H1PT/0r4Zx+r2vXgD//c2BwUM79kpcAP/iDwOc+J+TzG75BvkO5XH8O+3uH/6MlgieNs/Lg3Qtg1zn3RgC/A+CV3vv/CeBbAHxTrdfUJKT3VERE2yGSu4h2RJoVNu1+TZtETlKJSFMgTnoCazbm414v/B7Rkx9xkvDef7X3/l7vfa/3/j7vfdV7/4Pe+x80+/yI9/6rguPe4b1/qLY8xXv/3Wc/+ogsOAmZcRoG5mbnCo1aL3qReNcaebts9Eg4LyWt/4mfAL7ne4T0/cRPSP5cuSz7P/GE7D89LaTq3/974Id/WNb/p/8kBO4HfkDJ2tiYeOZe9CI5ZnQU6O0V0vX7vy/LD/wAsLEhnjwAuOceYGQE+KIvks+frVUD6euTMZDY0QP5uc+J925wUMb09KfL+gcflLF+5CPy+Wu/Vsb19V8v432u6Q0Q/g78zO974nOL9/5cLX/1r/5VHxERcbEAaTDecvlynOWsZdP+vvePPSavh91/by95+2nhsGO1xx1l+1Gv1+wap/kbRbQvonyKOCrOm8zY2zsoO/l+fz/5++zve3/79sHt4fv9fe9v3vR+Y8P73V29DpfHHpP1t297v7Mj69bXvV9d9b631/vtbe/X1vT46WnvR0dlfU+P9/m890tL3k9OyvGlkveLi96Pj8vr0pL3hYL3w8OyOOc94H13t7wC3g8Ned/fr5/v3JFjcznvy2W5Xqkk5+nulnPfvKnX6+mRfVdXZYw3b9bPufa3THo9LBrJpjPPwYuIiIiIOHkPU9r5aVkFxEL6gQ8Ajz6qVcPs+U5qbFnH2ug6Wazfad/dXu843yUcQ/TkR0REHAbnRWZwnnjRi9STxvX2O6TJZO+1yiWPs/tym3NS+OR7v1dDGbe2ZHu5LGGbH/gA8PznA7/5m1JYZXsb+PzPl22f/CTwrd8q4ZXf8z0S6vnrvy5etR/6IeDbvk08fO96F/CmN8m5v//7gQ99SDxwf/zHwBd8gSzf8R1y/c99TjyAxSIwOVlfmOXnfx7Y35d8vn/zbyRE85OfBN72NvHmff/3iwdyYkKuefWqjNV7KRzjnBSRefrTJT+Pc9Kzn13/257KfZLG/Np1iVaoiCw4b1azTgc6zEJ+0h6m8Dw8/96eWENpNaWV1Xrw7Hue69atbGNL8gQeZtzNfoOj/D6h1fi4v3O7ypJ2HddFRKfJp4jOgpWTafKzmRdvb0/mDc4x3Nd78WTNzMg+e3sHPVp7ezJH2W3Ly/X77++Ll2x31/uVFe+7uuQz101MiActl/N+ZES8e1tb4s1bX/e+UhFvW7ksHr/paXnt7vZ+bk48btWq99eueQ94PzUlr11d6tHb2pJx7e7KdYtF+b6cZ5eX5Vrr6/J9dnd1/Gtr9fMvf4/jopFsarnQOewShVREM5yk8nzY60YcDZ2oQB0mtDJtHUNjNjYah37YxWJvz/vZWZ2ELClMIo3hsXNzBwniYZB0zqMey3WNwotOAu0QstkqGdep6ET5dJ4Rn4vDo1G4uw3bDEM4k8Iw7XrOUevrGpZJMmjDMaenleTt7QkxYygm56PdXT2OxI7nW10V4pfPC9Erl4XQDQ3J5+VlCavs6fEe8P7yZQn7HByUsMv+ftm/WPR+YEDWA3LM1pYQwZ0dGdPNm0IG+/rkld/bEtSdnXrC28iwehxEghfRcWgFuYsK19ERFahkpBEWu46Tpc1jSLOyJp2fEy8/21e7X5JlNiR3x/HIkTByLM3GzYm92TWaKSVZx2eva3/rk5qoD4soa84OUT6dH8S5uDGOYoSjnOM8YL1vacbAUN5SBpPAbWzIsr6upI3Gxb09nZdu3VKiNzUlhGtjQ4jV3JwSPZIuevjyef9kTl2xKJ9XV4Ws9fQIASyVZJmfl337+jS3zjl5XViQ/QsFHWOpJNe4evVgTuHurnyemdE8QmtAPen7MhK8iIgzQJxQjo6oQKWjkQeP75PCLBtNJnbiXVtTL16jMdy61XyiakQQs0xwOzvpRCwcX1IxgKTx3L6tY290XNYJOBxTVCYvPqJ8Ol+Iz2MykohXo1d7HBcbvm+Ni0nXCs/F+YOELows2d/X0Mb19YPRJyxiUiyKh69aFTI1OSmhlX19Eoq5sSGvuZx45Uol2V4siqewUFBvXakk57l7Vzx6pZKQutFR2S+fVy/g5KQQw54e8fJ1d8t11tflmvTa7e0p4dvZkfmV3zf8fY6LSPAiIiLaGp2uQB3Ho0QCE3rtQk+YnUQtWaNFtdE1GB6TVCktaX9aMpO8iuGkbq/B4+x5OMYwHDTL72PPbc+Z9n2PMukedaKOSuj5QafLp4iLgyRZmPSaFjWSVZZyHztncP+NjWRiuL8v21ZWhExxjuK2alWJHbfv7oqRkhUzp6c1JNPm7PX0CGmrVGT92JgsXV1C7BYXZb9KRT5fuyb7VSpyrUpFjp+clPDOsTH5XCwKwWN1T5suwTlzaelgBMxhIm0aIRK8iIiItkYnK1BpnqPDeJRCIhUSonBCYWgMw0vCa1hvF0Nkwn2TyJad2GZm5Bo2SZ4hOOvr9QnnNscizYOXRlgPi6Tf+TjnO+oYsoSMRrQHOlk+RVxcZPXgheusPE/abtdZImdf02QeC6xw7iApXFsT8sact7U1Dc3c2RFPWy4nhVNKJSFpPMfenhw3PKzeuNFROa5a9X5zU0Iyy2U5Hz13w8Pi6ZubE/I3MSHn7+vz/vp1IX39/XIOXmdnR7+jbfuQNH9lneMbIRK8iIhDIipcZ4tOV6CO6sFL239/Xyca7w9aQsMkdztZs/AKt1mCZglcSCCtJ5H7sHKYJYo7O/UV01jl007mSQpBqCg0CxNt9vuFVuU0kn0cNLs+X9s1zLMdx9QKdLp8imgvnNRzmVVWhsdQlofeuST5mcWIZaM9dnd1/rHnWF8XT9jGhuboLS+rR69YlOqZvb1CvpiPd+uW5N5VKuKtAyQEs7dX9snnJdTy/vtlHlpZkWuwtx6Lstx/v+z/2GNCBDmOSkXnsFJJyF84dlYJbTZfHQWR4HUQ4oR8fLSzwnVR0ckK1GEm1izb9/dlMmFenfXmWW+ZJWJMWLchL7SOpuX3pVkkvddrrazofpzkwjCW8Die3+bjcYJvppCkFZxJ+q2SQo7S9ml0zTQcRo6chqzJQi6bHR/loKCT5VNEe+G4z2WSYemwsi40+CXtH8rvtDYM1ap4wUisbH6e3W9lRVsQcI5aX9d8vNFRIXLlsnj6lpc1F6+3V7ZfvizeOnruqlVtYr60JOtv31biNzgoBG9mRvZhuwU2VF9dlW2VihZXWVo6SOyYh3caBbkiwesQxAn55BB/w7NFpypQjSyfWfYjkqpEhn2G9veTcwQmJzU5nZXKVlc1TMbm6/FY62WzY2SIyuysTHQ9PfXEjBbaJNLI3Ax6H21FzaS+QSFBtF7CLFbjw+zD94eVr62SI1nJbZbznMQ+5x2dKp8iTg9ZDXZJ+x6H3DVqccDXrPNSGmHZ2RHSZqNIwvOytcHMjIY42rkljAaZndU2BSRv9NCRdFmCxry427eF9A0NeS/tx8VjNzKin9kvjwVebt6U8xWLEq7JqpzXr8srC73cvCnn7u8X7x2vv7OjBDSX0/NmmXMOi0jwOgidMNlGXDx0sgLViMylTcDh/gwXseuSJlTrkbOesmpVJkpaSKtVmVA58XI/TsY3b9Z7Aulhm5wU6ykb0DIMhtfb3ZXtJH30KHIy5LG8nkVSSwaSQo7HhpPa3yqtnUIjT14aOQqPz7JvVhyWPB7VS3dS80SnGBU7WT5FnDwaEa1w+0k/Y83kQlrxDxtCz89pha/W14XwJMldayycmNBCKDaUn0SOcwZz2Gj8I5nzXskUQzUZKknPGSttMoSzXJbx3b3rPeD9nTuy/fr1epJZrSqxY5QLyeLOjmzjXGW9hjs7YiTN57VAC8duewDGPnhRSEVEdASiAlWPsB2BVQTCdfRu2TwGFjLhBEmLaOhNYy6DzcdjoZNKpT5s87HHZJKzxVPCxrU7O/UTFydAhq/YHIdKRa/LsBvbS8h6Di1Cb6Btjmt/I9sUN6khe1hJtFkluXAMtv1Cs16F4djD942OSbq2vX6ryVWrr38WiPIpwuIkjCdJMj3tPCfxjGX1Ajby+tsiWHZusdjbE7KzsnKwcqQlOZwT6J1jWgDbEZBAzczIe85VLKDC7RMTapRkG4P+fvXq9fRIvzrOMfv72qR8YECOv3pVm5azx93kpIZ/7uzI69aW5PGVyzKG5WWdNysV9TJyTqMXMfwN0qqIHgWR4EVEGHSCQnLe0KkKVKPJNMkLx+3hup0drYrJyXNqqr4ZLCdIkhqGjZAc2nNbwmYnd16L17DH2oIt9ruRaNmKZpWKVC0rlXSstu+R9czRkmuJGN9bshk2f799Wz2bSVZmFgkILdONvHLhOez1khSoRuS8kZe20TXDeyPi9NGp8qnTkOV5amSIOaq3LSv5OiqS5pBmRVGSzmGXsNCKlZ8kUmGrHBrxbt2qj7igjKZXr1TS+YxeOZKs5WVZt7YmRCyXU69ad7ccW6loDvjioqwvFnWeJCGbmpJjJye1+iYgxywuqkduZ0eIYj4vBVr29nSMNDIyNNTmuyd56tJk91H/80jwIiJqOKoAjjhddKIC1UxRsO+TSIddx5wBJqIzB+HWLfW6VatKpBiCyXw4S1Ssx41WRobMkNSVStpqwVpjWSnMlq8OvWm3b8s4i0W9tt1uCZedJFmpzBZ3Saq4yffWw5j2u4VLeJ5w/7T/MckiSyUoifTa67dbXt9pK5vnEZ0onzoNh3kWG+1z3OclyziOco1mz3WSvLJIyvO2hjfmTFs5PTtbb3RMap1jSRBlvp1TNjY05JLRHzRWrq5KKwPnJFdubEwIHz2Ay8ty7NCQrB8b05BLO6+xv921axLGyfy8kRHNRa9UtCBMmONn56FwfrK/ddJvyNej6qWR4EVEGESFpf3QqQpUFnIXen2SCA5DQqpVmch6e9WKacMwmTswPV0fIsnz0mJqCRYLsYyNyXuGopCMra/LpMmcC+4/Pi6E0nr8GOY5NiZFWEjcWJzFKgDh78FrJYX92H2pGNgCMWm/Jz+nhWZaBSSpn1O4T/h/phHQ8P9utk/SNbOg2f2VtC2Lpb/T0KnyqdPQLvf5YZ7Ro5wjbXuYwxxes1HemCVpNsfaytQw9J7GMRoPbbg/0wU4L9h5hN4+bmeeHfdhKx4bblkuyzy1ulrvdSPJK5flOuWyNjVnpcx8XuYy5ubRmGk9lXauDecWm18YphJkkf2NEAlem6FdhEhExGnhsPd4VKAESULf/pY2n2xvTyadlRW1bt68KZ6xq1dlUltbq58I9/fFIklP3+qqEr/1dbVkMpSG56V1dHlZC6F4r8fRasqeQGtrMhHu7GivIuYrMAxmfFzDb4rFg8n14cRnJ0hr/bX737yp17KFZ0JrM/dNuo59tZNzo55PSes5vmaeupBkNtony7nS7qW0dY3OkfS5ExHlU8Rp4LAGmKz7NCNk+/sHPXWN5BBlYXiOJHLCHLwkI533IuOtp5DzDOW1JYbMM2Z1yrU1bVewvq7Gyt1dnf84/p0d3cZrsNIlr8f3lYqQu60tCcUcG5O5kfl0JHAs6ML8PxLE3l4ZF/MDbWRMGIVyFENdI0SC10aIFtGIi46j3ONRgVKEeQvhRMnPNpGbkyYrjI2Oan4brZhMPGd1TOY48DzFokxSCwsa1lkqyfmGhiRcpVLR3na7u5Kkvr0t6ysVuQbzISYn5fhcTggiQ2N4LYaMlssSYlOpKCmyIaFW6QirkdGSa38T20Q9JIK02rKaZ6P8CCoUSVbtRt44+z7J29hIqWtkzQ2VqKTtzXpZpV0/ojGifIo4aRzVAJPlvN43NxglFfmwBrDQUGUNXJZ82etYQ1uakY6hnDxPWFTLVkSmx877+srMnG8YJrmzo8TPhnla0ri+rvl9JHc0ao6MyJw0MSHvczk5/8SEzmPMwxse1v15/mJR52GSP37PpPzuk0QkeG2GOMFGXHRED97hYCdA5i3YCZCTlCU29KRZ6ycTy8fGdMJhqwKGZJIM8phqVSe5hQWZFcbGhHhxe3e3rA/zGLa2/JO9hK5c0Wpk167JORcXtZ/Q6KgSN46N3kLmWOzvK/EbGaknazafzv4uNsQn9PCFiodVGng9/u77+5rjwevNzqpXMikEk8fa/9D+p6Hy1ignzx6TtedhuE8jRSIaF4+OTpdP5xntcr9n8dYd1wATPuONZEjSNkaIhLnLoeErDEm3MtnK2iSPIAkbPVthgS/ODex3NzurlTFZ5TKX07w7Gg05z1ljHucEnrta1dYMjBxhfzzOdyR+zBOvVLTCJo2VXV1yPYaEstLmxobOIYxyOWmPXYhI8CIiItoanahA2ckxrABpPTSWuFiL58SETDpMbCfZYR8ehpeMj8ukyP5zljBtb8tE1dUlJK5SkUltcVHWr63JuuvXJWSlUpFzDQ5qOeuhIe0pxAlxfFzW9/bKxFkqyflXVjSHgaEuVAY49rk5GZdtvs4Ju1o9SMrojQy9mVbRsNdYW9McQp5nbk7G2denY+K52cTWVt0kGCZr/6Ok/zRUlMJ9Qo9tWmiVVZqS8gVtzkeoWLSL964dxnBYdKJ8ughoF6NGlnGc1FjTCEXoRQsjQ3h9KxfTzp8k16whMpRlXGflJItthYa26Wnti2rbHxQKKn+ZC07Zy88cC3PhbIg9vYPMJbfRKc7VpwxUKrIfSSPTC2hI5OK9RoOQNHLcYdugRh7V4yASvIiIiLZGpylQSQQgVPT5nh6vMEehVBJCsryslseZGX3PCZQL8/AYoshKYZubaskkQcrnJcGc7QyGhzVP4dIl/2Qp6XJZPHPLy0LKOKkVi+INXFjQPD6GjU5NyblWV7VHEL1nnNDZPJYNY1dW5NieHi3mcuuW5jwsLsr7pSVRBmwPQPZR4vnt5MzJt1wWpYcEMiwMEFqlQ4WEk39as/VGoVJ2exj6lHTPhN7cpPOFBWHCEKssSsZpKMXhvX1e0Gny6SLhuB6xsxpHlmcibXszj32SEdHuZ2VDeGwarLGJx4Ryc29PC6RsbNS3w7HRF5YE2fBNOx8x5YBzIvPuWOTLetT29jTKhNdha4SeHpmrZmflNZ+X19FRWcbHZW7M58Vwaat30pgaztWcVy2hZfQMxxEaB08KkeBFZMZ5mnQjLg46UYEKratsTh5aPW2lL5vzsLtbXz1seloJCokW1/F1Zkb7AQ0NCXFjr6CeHiFqoWV0e7u+v1A+L16tQkEmw+5uOQ+LrORy4hFk6CZz8jhZ8ziGblpPI/Mo+H0rFW2AW63KeUdHdWwbGzp2EkjmQNhke9s811ZPYz7iwIC8el9v9U37r+x7q5yQeGZJpm92ziQFK21M4bpwm1VIGpVCt/tnUfDSvk8jWIJ6XuabTpRPnY6zvEeP4+ELDTrhtqT3dl3a8Y2OtcTGkkiGXLIfHMMnbQqBPRcJnY1K4RzIeahU0hB/Guimp8WYZys3sy8dK2H29Oj+9MCtrAh56+uT9IJ8XuYc5p+Pj8v63l7v79yR+atQqC9Wxpw/mxNuPZPWWMf5iPPCadxLkeCdIs5ygjrta523STfi4qBTFSirkO/uqoWTZI4WSOu58V4nFMb8cwIkGWLTVTZqXVqSdQzJnJ8Xz9z2tmxzTour9PRoBcyeHpngBgZkYuRkOzQkkyM9eMWibFtYELI3PCzHdHXJ+3xezlMsyjmHhnQS7uqSY+2yuirXY3gpw17GxuR8rP5JAkjv3fq6egk5IdOKXK3We0BtERXuR2uvLRJgLdwhwQo9atzH/k9J/3kzr559Da+XZOlvdM60fRvtk7Yfwd80LSQ1DdGDd77k00VAqzx4hz02i3cu7TnNYrAJYUO5j0IsQw8eZefqqobXc30Yvk4Pl214bkMxi0Wdn6anZV7hPLG/X98OiMY85txVKnIMjYfFoqYKMHWhUpFr5fO6P4u4TE+L0ZCtFSjT6VFky4RKRUgsDYU2KmR/v76ydaPf+LhyMBK8U0K7WXhO6joREWeNs1SgAFwGsAvgzQDeBOD/StjnBoCPAfjd2vLtzc57WNmUlIQe5ivMzNSHCHK9DQdZWlLSxFAShqKQJOVyMlnmct5fvizbLl1SUkZv2/a2vLJCWVeXJqF3dQnBosesWJTrMfF8fl72GxiQiXFtTYqujI/rNYeHZV1Pj+xDMlgqyf6rqzKxXr8u1xocrA+P2dnRdg02Z47ePJaxpleQlmMWVQlDKPlbsuALq7KFYZD0iNIq7b3+XyTiVFpsiNLAQD3JC4kg1yVtT0JocQ+JXRopTAoNskSykQKXplSyGE14vvB92nc4TzgN+QTgJQDeD+CJlO2pMgjAMwC8BcDbALwgy/XaSXc6K7TKaH1S1z2M4cS+NjsHSaHNfyOSogSakRPKA85JtlgVz2ll6cqKFi6xsnJzU8hYqSRzCskc88TpDaNhs1qV78E0hqUlDeek4bO3V+a48XHtgcdoGRr2WMyLJI5VnzkP2CgaFiyzHjxbDIyeTduUPe23PIn7JBK8U8RR/pij/pnnbVLsJMT/5ng4Y4J3L4BS7f0ogLcCeDDY5waA/3mY8x42B8+GAaZNzDa8heSDfd44IRWLstAaubwsn4eHpZolG4szbHJ42Pv775f33d0y+fX2+ifz8Xp66sMmq1WZ8IaG5Lzj40LA2DLh8mU5z8iITNJDQxquOTWlvYUGB+XYclnONzKiHrliUSfb3V2ZiBcWtH0Crc3Wq8gwHZuXsbYmvwN7/YUhn8yVuHmzvlIbe/kxlNRapfl665aM3YbiUJlh4/iBAa2e5r1al8NJ3f7/aT31stw7jYgdwRAoq2CExO8wxDDpGvZzFpJ63iJFTongfSGAUhOCd0AGAegG8HYA9wPoA/CGUH4lLe2mO50VWnWfHeW6jQwxzc4bGnoaGVyS9uH12T4mNCTZY0O5QY++zZ8LQxLtNRm+aPexrXmYVmANZwyz5FxIcsp96AHknEVjIQuD5fOa323DRW0O3a1bGuVSLOp46bnjefr6tK8sv7817DF33BoDG/UYPA4iwWsjnMfJ7Tyglb9n/E+TcZjfo5UhUAB+HsDTg3WnTvDCyTPp99rb07DLyUktE80Jrlj0T3rclpdlUhkbE8I1NKRkbWlJyMngoIbAAJJL19UlXrWBAcmBYLGUalX71TG0knkLly7JcQMDch4SMeYyLC7KuItFGffVqzJuev8GB+X17l0heKywybBL9vJbXKxv7WC/P0NqOHHayX9lRSZ45vlNTMg6evdWV0WBILljawYqFvTi2UmbRW0YJmQnf449nPQ3NjSvJFRw+P8nWXibEbesFvuQqNnvZL2Nja6RBaEMzOpxSPvcbjgt+QRg4QgE72kAXmE+vxDAC5td67zrThcde3sHq1uGaKRrWPnSKA84PIe93t6eyCuSHxoXw6JfJHO2XQI9Y7bic1JIu5V/dh+up9yknOd+rGS8uqoeM0ZV2HxzpiawAMvOjnynYrF+vPTAMU+Q52N+uW1VZCtl8rM1tHE//qY2eiTp9z9JRILXZmj3yey8oR0I1llfu93vIUtgsqBVBK+mYP0RgLFg/Q0AH6pZx38RwFNSjn8egMcBPD4/P3/o3ygpTJPbvJfJioVAWE3S5hEw58yGxtALVS7LxMZQxqtXReIPDso6hl+OjWlvn/l5JVbsfUeyOD8vpGxmxj9J6rq6ZN9CQc7JRum9vdIXj+NjAZSxMQ2XmZvzT4aGOiffkeNaXNRxMXyS36NQ0Jw8Nkrn78LJlxM7E+zX1zXXwvYpohePLRhsKI4lY6zEWS4f7GNnlYbw/6Q30pbsZt9CKhe2n59VupIs8lnz1+z2ME+ORJjhpqGCl1TBs9F1khSX45DDdkQLCd4BGQTgmQB+yOz3HAAvTjnHkeVTxNkjy3PXzKjD84TPY5IXPySVJHChESoMM+f5bUuA0HjFgichybHEcW9P89esbGObBObV7e4K8bIFwHZ2tLfq3p7MKQytZLVlynL2WbV5ckyBoOGOkReUifzeJJChQWx/X2U5o0fsvGFle9J/m1XGNkMkeBcY7TwpNsJJj/u8/g5HwXlQiGjdameCB2AEwOsAPJawbQzASO39LQB/0Ox8R6miafsO2cmZRVKYz3b7tk5YJA3OaWgjvVuzszp5sbcbE8JXVtTjdvmyvL9+XcIySyX/ZJ4dSSD3ve+++ly8oSEN19zelmuQDPLYrS0ZX7ks1+7ulnUM5Vxc1HBOehSXl/U8ly5pG4ftbbn+0pJ6BCsV+S3oERwb01y8tTX5TI8nwydJBtkmggoIiTLDNUm2rHXZ7h8WTrGV1EgQ+X9aZcVag20TX1sIJgwbCr1wvFZW40m4b+iV3NtTYsx9rcJmz5N27qSKo4eVT+0sy7w/PfnUhOAlyqDDEDy7RN3p9HES93HWc1jvvz0u7fkLjTFhqGK4n5U5JDkWlJP0piWNzeaPW6LD8EWSJGvE4vzHsfG7MFR/dVXGMjjo6xqUk6hxbgiJl604ze/D707jHmUi86ofe0zONztb/z1DIs3IEc4blIu22iaJdBqxPioiwTshtNskdB4U/SSc13G3E87Db3eYMZ41wQPQC+AVAL4p4/7vBDDVaJ+jyKYkzx1z7Ujy+Do5Wd8KgcSEeXisFMkJhA3AczktEV0o+CeJmCV0U1MyYbLginO6nURvbk4JWLGo52KbhO1tJYHb21JmemhIJl3m/jH3jjl/ly4JyVxZke+ztSXXYx5FuazhpAsLso8tKsPwUoZeslALQzMLBU24Hx+XxSbWs7cfwzRnZ7Xy2vXr9fkoVkmxEzy9e8wJ5DH2/6RywImfhNF60KyStLNTX6CF4yUhzFI1j+MO97XhVww9suMNPYSNPIdWIUtSEpvhPMgx709PPjUieAn7vhPAVAzRbE+chF7T7Byh94gGvaT9kp7V0MsXRo+EJJDyg20PrPEqNIKF1yLBYgEUGrB2d7WFgZWtNBiFoY304DE8npWVORcVCkrgSOzCsEnOn/m8bC+X5bM1mLH6MkPuWcQlDL235NcWWqFc53Xt72vHklRk6zj3TSR4J4B2JSXtNp6saPdxt2J87f6bnCbOkuABcAB+DMAPNNhnFoCrvV+uhXG6Ruc9ah88+8qwEXrqSBA4yVSrEqrCMMzpaZmsenuV8K2siAcrlxPyw+RyEkB64Hp6hMhNTPgnCd/UlK8jgCRiJH2PPirvt7bEezY4qF44TsS2KqddRkZkjCMjSiwXFjQnz+579apWRBse1rDQrq560sfKodWqKDy0DO/uakjq4mJ9iCYtuczJYLgPrbXWm2nDfCzZsmGM1oNH4mYJIa/BY6lw8P+gJTdUlJIs5lbBCu8lex+tranyl0TWWAggTUELr2Gt+knjCj1+oeEi6byNiGO7oUUevEQZBKAHwDsAXIEWWUkMIbdLJHinj6Pcx+ExjZ7H0BiY5DlL2jftWpZ4kOikhaCzHQBlBqMZwmfYyj3rIbSvjLYIe+nZvD7v671tNIitripR3N7Wolu9vdq43OYO2lDMalXz2W3bBRpAuZ055yyskpYvz/HxezIyI2yGHv6m4Wv04LVISIWTXMTFRyvIfLsaEM4KZ0zw1gB4AG+EliC/BeD5AJ5f2+cfQ1oovAHAawA80uy8hy2yElpTmZ+1vS0TBauIkdxxslpeVpLCgiTDw7LP+LhW0BwelklsYUH23d7WPnf9/UIAmThOIkfPXbj09opHiWGei4u6bXBQjmOFzGJRrvvoo/UNz4eGpKrn4KCs7++XMS4uSsjoyIj3996r++Zy2q9veFjHNjQk39s5OWZhQQukTEyoJZfeRPboo+U2n5drMu+CkzRDca5fV4LJ/D+GfDK80rZTsGE5LN9tP9PjtruruX4DA1pqm6FCtgVDEom0xQ1CZcMWI+C9FIb+2nuPFvZGnoIkecSxJ4WPhsemWfbTKnq2s+w7DfkE4KcA/AmAzwB4D4DnZpVBNXn1Vkg1zW/Ncr1I8E4OJ3WvJj0PjYoVNbpuUjRIs+Ns6CD3SyKctmIwt1v5Yp/5sIerLSZFmWiLmlCmWbn12GP1RjDb05Tz4u6uzCXd3SJLR0dlbhsdPWhQsyH2JIesPM1IFxI6u35np76FQlLrB8pv/j6TkwfD9JNC3tOqah4WLSd4SOk7BeA7ALzXKlnNznWWQuo8TDwRp4NO8uC1w/191iGap7EcpQ+e9ypnWK2SXio7sTBMhj16GK5SLB6c5IpFJT5DQ7KNBU2Gh9Vb9tBD8vrII/5JEtbdLcQrjejduSMTGENCbSVOkkSGdC4uyv7d3XJNkkLnhNCR/DGEc2FBSB3DLtncdmBAm9bevSvHs53DzEx9LiItryMjWhzGNtAlURwbk9+YBM6Gwubz2l6BjdStF48kjeSYSgMVBxYBsCSNuSCTk5oH4r2ej0oIc09sLpxVgrj/xkZ983V6fbneKl/N+uAd5jPvU1str5GXrtF9n3X/dkAnyqeIZJy0Xpj23B2G7NFL1MiokuTts5Ukm42RciYMNbTbKetsOxoawCiXaKziEhrEKAsZ+r6zo6GT7C9386amCGxtyRj29tQ4Sllsc4R5XuuxGx5WQra7qwZTRptwTAz/t2GdJHfLyzIX0OhHj58lb41k3nHvo3YgeIl9p2oE75sPc65WevAiIs4rDmupP63rpaHTFKgwbIPhL6VSfRNVThTs78YJieSkp0cWVrLs6dEQSZIuFkiZmhICtLnpnyRsExOy3RK6vr50gkeSxybk9KjZIis2rBMQT14+Xx8Keu+9Qtz6+jT3b2dHJujRUZlg7RjGxrR5+5072ryWuXiViraNYBXOy5eFqLG1Qi6n51xYUCLX1aXkmQrE0pJ6CQuFeoWBZbppheY2KlH0Fob5JVbB8F7z6sJckDCciRZv+9mem+fi8RyLtWI3a2HQTA4khRUdxfqc9Trthk6TTxHJOCml/LDXSQrzC/PmrJcoyQuX5EWi7GK+rz1fEqxBx+YOk8zZqpo2PNMWPWGo5f6+eufC61JWWi/Z+rrISoZ37uyIHOc1KhWR5UxVYDSHDZNnCsP0tOaDM0eboZuLi5onTuLH6ss2XJOymEW8rJfSGthCz99Jo+UE78BFa32nzgPBi4g47zhrpeoopLHTFKj9/frqYcz/shbS/f16aySbrG5va68itksgsbl8WUMTSWaYN8d1Q0PidbtyRY5lTt7AgOxLEsljRkf9AZLHFgnOqQcuiRTOzclEubgoE+GDD6qHzy5DQ3Jdev7m52V8g4PynVj0xO7LgivXrgl5XVmR/ei5ozdxclIIMvM1FhbktywUlDSSANLTl88LiV5c1ONv31arLi3RNseDCsnkpJDAsJeetaTb/JD19XpvXHgMm7LzfmCYVJJlnuttbgjX87VZH60QWfLpDoNQybTvT/I6J4lOk0+ngVb/h8fFaRlDm13HErikZz0pBy6pmXZIEOmFt4YneqySWqWEMscakyi3GHVi961WZY5gfhv7gjI14Pr1+mJTlHPW80bZyJY37PlK2b26KnPA1pbOlWNjGmrJnOt8XuRypaL9YtnYvFyWuS6XU9LH8EuSUttqwcpa2w4i9NxtbGhro9NAWxE8mL5TNYL3zlouzEsA5FOOib1cTgnnXehGZMNZ/8/Rg9cYdsLlBGsLedh1bNq6siKTYS4nExstlfm85rXl8+KhYwsCNm0NPWtdXfUVMrktLHYSkjqSvUuXNASUnrgkglcuS7jlyoqEV9qQzEcfleuOjQlhI9GcnT143eFhuR7DPAcHte/R6KhchxM7vaCFgvxWrM45PS3HF4v6vZmTx8qZlYocw+1DQzrJs8ANvYXWk2ZJF0N6bJGT0Gtnw6lCBct6BnluhmCy95/NzQuVQVrTbR5IqBiG9yJfQy9ASEqTjj/MPR9+TlJkk45rdapEp8mnk0Y7/IcngdMcf5Lhw/5uNhcuPC7JY9fo/OFx4TNovU4hkaRcomHKyriQRJIUjY8r+VtdVfLEYmC5XH3RJ4bFUxbye5MYsmom50VWTO7tlWuNjsp8MTqq7XH6+jSUf2NDrl0syjI6qhEd167JPpT3vAZz7GwIamiItb+tHTcNbs1CYY+CtiF4CPpOAZgB0A2gC8B3A3hJs3O02gp1kXBRhG7E+UcnKlB2UmRyNhV9KvUkHPm8NsxmaEk+L4SPFSWZV+dcPVGbnvaJnrwkEsd9L13S/Xt6dBvfh15CW0wFkLEB4n0jIevulgk4jUQyV7C7W887Pl4fAsrCLczTo9ePoZQMV83l5PPYWH2bh3y+fuylkvyG29vymZVBSX67u1XRYB8+hiHZsEnm3VkFKSyYkuSV5XZWiqtW5TtXKrqOCkUS0bIKnyWRttCBLc5i7z3uaz0AVmmx6+0Ys3jZQgUzzQsYKrJJiml47lagE+XTSaPV/2E7I6v3OincOslQktT2IDxfkmHHeqEo02ypf57bpg9YI5M93uYD2+NplKRXzBo3edzkZH0Dcso928aAhrxSSStiVqs6Z/b0aO72Y48JybOhoySJ/f3qXWRF6tVV7S1rqy9PT8s5me7A3zDN0GZlbiPZeRy0BcFDk75TyNgPptVCirgowuqifI+I4+E07oPDnLMTFahQIWd8vy38wcRuTkAsGEKL5eSkeOwswWK56EJBJi+StJCENSJ6diGp6+7WENBLl/TYMOfOkskbN/yT5IpEK/TQcRkfr8/lCxd+F4aislk61y0s1JPbsTElw7TeMgynq0sbvHd3y0KvJ8NEKxX97akwsOeSVXBoWWZuHkN2aH1m7olVYKhYkBSyAurYmFyfnkcWZ0nzwoUKhVXqdnaSlTAqdEkegaQQMMKSzKRx2FLnYXhomufB4ig5fWeFTpRPEWeLrPd9klHEziXctr+v0QJJx1ijE8mIDTWnB485diGZ4X62si/nL0YiJBFEGsysh5Dbw6qU9JTZqpzMqXZO5DYNewzVZOXNUkllJwu1VCr1TdS910rKzKdbWtLtq6syd1y/rnJ7bU3mESu/k+QiozwsMU4zYB0HLSd4SOk7BeBe8/4bAby02bnaQUidpefrtK/RjpNpxNniNO5naxXMgk5ToMLfnFZMTn4MG1xd1cmHOQUsqJLPiyLtnJCThQXJb6DHi94tVrkkiSIhmpqSczJUMiR0aa0Tws+5nJ6zGVlM2s85zelj7t/0tIZyApKTZ4keiaAdy8iI/Db0AG5tyWTM36G7W8jdlStyre5u+c2uXlUP4MqKTN60PpfLajGuVOT3paLB/4xKzcSEkEhW2STZYzjS7Kx6AldWJPSWihZJPXMqSyW9rm3DkBaqFZI2Kmv0Mnpfr/xZMhjek9YKbytlhtdMslpbCz/X2W1E6Dm0Y2j23LQCnSafIpoj7V486j2a5G1rdk4rC+hlD5/zJMMKCZzdl4YqG37Oa4RyYHdXSaGtfMlWBCR5+/sqCxn1wHw0kiNbkZdyMoxYYIij7flKYsZr8DvxvPweHNf4uCyswsnfZmVF5pRyWeU6/wu2IuJ3KJUOVkLmb5RksAqNbqchv9qB4KX1nfpxAHu19S+3hC9taRchdVbk7jSJ5FkS1Yj2xmHvgSyKmLX6NUMnKlBW6HMC5CtDUFjJi/3rFheFnLBYCPvJDQ3pZ3rJWC2TXi7ntLXB4GDjfLupqWTPXH+/etKGhoQcZvECphFAjsEStt5ebYZ+966GeXI/Flm55x712C0saGlrnrOrS5UAevOGh4UYb23pORcXZeJnuCstz6xSmssJ0WYJ7WpVft+1tfqKcKurQvJYRXNlRT1/JIU7O3IOW5qbXjrmeTBEqVhURYvWZCp0NtzHkqiwSmdoHbdKW3gc13M/3othk1/ul5RTEo4n9BqGHsTDzEGtnK86UT5FpCPtXjzKPWqfiaQ5s9k5rbEkKVw76Xr0sIVeOhu+aI1JodeO5Md61ug5s8YdFlKhwYoGTFaypLwiiaQBjHLVtlNg9IpzKj9v39aIibAipzXC0ehmCSC/BwtwMVSTYaAkvENDYmxkETR6GK2cswVrQjlpZe9pFLtrOcE7yaXThNRpTmanZVGIOJ/Iei9kmcQOe291ogJlBT8nFDv5sPIXJyjmGPT2SqsAVs60nq6kpa9PvXKWKFli1t+v5I8er7SFxO/RR2VMafvZapkjI/XbmEcXHkOix1BFFl4BhMRtbck1OVbnJFyUxV9sf73hYVU+6IEj0dvZ0SbrVB5IxnI52Z+kulyWa7K/3tqaNsHlf8OJnKGbzC1hk3WrxK2vq7JBa3YYssQmvxz/2pr8j/xslS2+39nRnBXrGaZV34aVshDL3p54Enlu7/V4W0Ah6d5N8gwkVe/z/qA125YNP6wy3Ap0onyKaIxGinoSSUvbNy1fLotn2xpx7DrOK6EHLzTqUB5QLoXrKVu4fmamvriUvYb1btnnnDnF1qO2tiay146PTcVLJSV/zINjxWnKSvbipDeuWNRqxzyuVFIjlSWoJIV8ZesFGtBYBIbhoM4JAbTzs5X79j+njLfeSRsOmibzjmO8igQv4gCi9y7C4rD3QzNyd9h4805VoOzEacstW5JHssB+bPTMMfSS4Y025JFhk+Fii6JY0tfXp565tFDMuTm1dB7FWzc7K4RmcFBeQxKadJ7BQb0ePXPhvs5p03ZASCCra5JgjY4KUeT3LhTks030Z/+77W2Z9JeXNY+DldYKBVFMJiZkn0pFjmPVN/6fDMPs6ZH3XE/rL71w/H/D3nbcbnNYbEgQQ6AYKjQ9LeNlie8kBY37U1Gh8sFKrVw/MKDX9v6gEtlIKU0ifaE8oOKTVQluB3SqfIo4PMK5tNncmmZAaTYf02sWetkpO/b361uwhM9t6NGnLLKeeRofbXh4GF5NWcMQTOvBo8yzBaNo+CJxooyam6sPbd/bE+Pa1JTINEYt8JUkj6SPFTIZWk+vW0hICwVtq8PcdxspwRYM9DiygAvbO+zuyhhZ9Cwk1wxT5X5h24m08PSj5udFgheRiHaeUCMOh5Nw+5/k/dAsHCFEpypQoRLOiYYT4fKyvGcO2uKiEKW7d8Xbtbjo/f33i1JOwmaLhfT1pRcuCQkcG6OnkbXe3oMFXRqFZ/JcWXPzwsV6FUkwQ88goJU/redwaEgrd9ILyIT8clm9lNvbWsG0UNCw2OvXZaJmj8FcTl75u5bLss/kpHj1hoflOJbs5j7j42px7unRkFtavmm1ZqNdKmPMG2EluzCEyipMvF+q1XrPn/f1JMx68azl3HoC6T203sWZGfldwrYP1pIdKo3cz4ZiNiOC7Wxw7FT5dNFxWvfbYYwXaYp90jnCdfZZIyxJS+udZ89lPXF8bz3vYQXecMycu0iI6MG6eVNkBg1oJD8MSd/eVjlm5Q5lKKM+pqcl8oKVLEnybK5fsaiEbnlZQ9yZ6sB8waUlmbNsY3IbqUCSSu8f5TBf6TG0jddtBIctEmN/b+tVtUVnwvfRg3eGQqpdJ5vziPhbnh7ShEOrlabDXLcTFahQwFsLaT4vk9HkpBAFkjQmfG9sCOlg5UiGJw4OCsGhp4ler6xet4GBbISsWd5dX596ug5D6qwnjuOmx465eH190quIn9n3b3BQSSAgpGtrq77PHj16rJrJHkysxEaP3M6OjJ/VR5nfxwqlOzty3Pa2/FfFovxflYooLwzLtDktDBMlobc9DvN5JfPcxlwUG0JlWzNwPUOX+vuVTNp77NYtsTaTjNkiAlTgeD5LPHmtnR0NfQrvWebHWIXQtmWw1uvQexAqrO08R3SifLroaPX86L0aa8IQPgs+JzbPy26zRY24Li0kkOexuWg0+Fi5wGc/JHz2HDREkaQxioAtBhh1cPu2yA8rf2zFYconetUoi60hj2H1VvbYfqE3b4osZqErkk22E6JncHVVIyrs9yMpm5qS8/D3Cb2cNi/ZysywgrDNhbb/Yfgfp3nzDoNI8I6Adnj4TwOt+D4X9bc8TRz2tzoJD95Rr3ES6FQFyoa52KR15gUsL6vSXq3KREdL5vZ2fXESS26GhrSi5COP6HpW0eRCK2mj9gQkV+GxJFv2/HZ9GhFMulZ3t+bdJXnyAG25wNCesEALCRyLzVy9Kt9vdFQIIcMs2WKCTc/Z4JylttfWZH+ec2tLvVm0Lo+Oaigsm6uz0W6pdDBsh3l7JE+snDk9La8sQkAv3vJyfREChkyyimfoHfNeLeVUGO366WkNV6IyxzAnW9DAFvuhd3BtTY5PCzFKsu5bBTNJebVW7UZypV3mjE6VTxcdrYpwsUgKfebn0BiS5O1jCGRS+CTPY4khw9FphKF3jcYaGovoLbPePY4hDAHnOOjl4nkpY2wI5O6uyNPVVZUfJHuUiTS00YiWlOtGWUbPYC6njdN3d+W6jNrY2FDvoTVO0VvX1SX7M3TUkkArz2yRmfA3tr8v53L7H2b11B4WkeAdEe0yuZwUWkm02uG3bIcxZEE7EuLTHlOnK1CcNOxkScvi6KiWawZkchwZUfL0wAP+ACmam8vmtWNDcpuPFxZkoXfvMJ64cOE5Z2frSZ9zycTRLvfc03g8k5NaMGVkRMioc+rhu3RJ1tMyPDiooZb9/bKO4Zi3b2vBlfvvF5JIhYMJ/+ypV6moIsGG7MWiJuvzf715U37niQk5HxUNFhvg9Wh1plJhC8T092tfPhvOaa3M1otmlbD9fVFYSBipmFUq+l1oYbchX1xWV/U4e796f5BQJimkaaXak9ophPscphLvaaLT5dNFRdZ766TnvzSPjSUMIVGzRCscV5LRJwz957loxKFnnm0JbM9NkrvwvLalQWi8YURCPq9h72wOvrxcnzdMeUo5tbqqxjVb7IWeN9swPYnw0ijK/Gled3RUPX3WG8nxrq3JOoZs8rvz+jSO0cDHCpsM2bSeTkvobAEWju8wsuww91kkeBFPoh0my1agHUlTI5zmOI967ujBOx3ZZCdjJprPzsqkWChozkC57OuIUV9fPVmylSWPQ8aOSt7s2Jrt08ird+OG5tWNjCgZGxjQapmAeMt6ejScJ2z7YNexSfv8vOxvvZ1Xr8pvy/yJ1VUhkENDsu/161plbX+/vrBKqSQKjc3NY6gRQ4qoKFQq8ttcv65WXub3MfSTBU5s3yiST5Iy5n2Q3FE5swoGrdNUoqwy470Sz1JJQ0OTckF2d+U+q1brvQQ2z4akNCy8QmWOxQxC+ZGUlxI+F0m9NFshwztZPl1UHFYnCInVca6b1hLBwob58Rjb15Lr7T5hQ3KGC9r8OlsBkgYtW2GSz3fYisWSqvA7UJ6Q+FQqGp3AFjBsZcBm5CRVjIxg9APl5dSUGrTo2aMM4zUtwWL1TRaZst5JG8oZtoPgfGsjGapVDXln9IQldSSyk5PJFTNtXh+3MVc5y/1xUvULWi50DrscR4k6CZzW5HJeiMd5RvyN25fodroCZSdlmwM1Pi6K//q6kBR6s+6/3x8gSfTY9fQkV8A8Cnlr1Csv62JbOVivIr2HgObqhQVULDnkdxgb0+9nQ1P5nrmHzmmYKhuod3dLgZreXiF2ly5pHiN/5+vXNeySfZ04mdNbl8tJGCdDNMtlmfRpdZ6c1F5NVqlgE3UW0GG1Tls8wCoSVI6s8mSJGhUSW83NKlthmJFV2GxfqtADaJXGlRW1aFtFk0UGaPVPUjypRIXV5uz5+T3S8ohDJbYV8qvT5dNFxVHuo5O4B9M8eARzYy2h47PUyMsXnjf0xluvnPcHIwCsbCBJsteyMsQSLXreSLpmZlTW2dBMyg42LC8WtViKNWzZkE167yhzuB8LqzDaxeYJW9K1tqbFrkgWbWgnySavnc/LezZTtzmCJIyco8OG7CRy/K5ho3P+llnuj6zoeIJ3UpOCvclPEu2qdEdczP/kpL9Ts8kqCzpRgUoS+ja0jj161tY0R6xalR54gHqNALFA2s9ZiNnDDzffp1mPvWbELgzttB64Rh6/cBka0gqeXV3y/Uj8+Do1pTkXY2OyT7UqRKy7W77L4KBM9sPDWpmUYxwY0GqklYqW56ZiRWv02JgQx9FRIXhXrghx3d4WC/LGhuTtWWWCSsnkpFZo4zmtQsJEf1qyqYRYSzNDiEi2bN8m7s9rcOzWu2fvLXograJnq2za+zGsiEmlLVRiwnmSypfdz3rm7Lnts8D3obcjevDOTneKSMZRvH6HQfhs8VxpVTGTChgleeRDgpZUxdF65sP2JnylYYn72h50DHmcnhbDnY1kYIQE5WippNEJ9tr0+nPu4/ErKyofGZJJ0pjLKemy7YZYHKtSEdnPqAX+Xlb2MSeZHkMa6UgO+RuQ+FG22/EnkTr7fyVFJRwHHU/wvD+5HzRLYvhRcNGIxEX4PmdFvE/aonOWoJXsuEKr0xQoCnpOkszbYlL6ykq9J29xUYjIwoJMVPSE9fZqKGJXV32hkqGhxnl4tldeV1fzQiths3KSoqN6+cbHhawm5ff198t2Ei6bV2d749ErB4hHDhDLLnsFLi7KhM42CUNDQgALBTn/zMzB32lwUH53KgbVqioLq6vyGw8MCDEql+XYzU0NzdzakvMMD8s6FjdhKBLJGK28/D9tzh0bBtvGvjMzWvCFeXHWE0iv4+iojI3kNMxBYehToaCFDKy3j/uFRC+pcMD+vpJUuz30Fto8PO5rt1F5SpK5J2FAOi46TT5FHB/hfW/XJ322JIyhlaFnzXq607x2fCbDYkvclhZGyG3T0zr/2DBwgvlu9IpRlpFY2fw7FpaqVoXsjY/LK+c4RkbYnp+Ui6yCSS8d5Qblsc0r3tmReYKyzxZwYZsX5jqPjtaHVlqjV3+/yk16Dff25HwkhfSu8rrWKxrmQ4dIyqM8LiLBO2G00pJ4HnAaxKhVv/VpW+qy/Fbt7uGNHryjySbrbQGUVIyPCxmpVrUPW0iAwt5vNgySJKWrK1uhleMuzfLrDrOQrNlxb29L2CQgoanMj3NOrk3PJatsXrsmE/jcnJCsq1dFmdjeFnI2Nqa98Zi/NzKibSIKBe2jNDIi75mfUSoJyQaE3NFCPDOjhVfYEJ4KABUbEjCG+rCwAMmYVXC4TE7K9uvX5fz0UFKRYWgUc/NyOfle5bKGPlEZsZ5hWqgZcmrLgoeWesofG9pltw8M1FfKs9vte3veMFeI/bKSjmsHdKJ8ijge9vc1PNve+0lN0EPHAT1BzJejNyvJsx7qBiSIzHHjdfjc8Vx85mloJDGxLRS8F1kyMKCEkx6zalWJmDVWMqSd3raVFZ3nWFSK1yOJKpVUTnGs1ap63vi9GFIZVuz0Xr1vNpqBY7L9PJeWdAzMP+bvXK3K9jAXMZ+XxRr6mOPI75/mNbX3w2lEAEaCdwpoB6W7XSa/JJw0uTuNB8Oe/7jHH+deOM8evJNCJypQ9OJR6d7dlQmN1R/5evWqKPiPPuoTyRU9aEmhjoftQ2cJY7iOzcHPYsnn9fvce2+9x3J4WHLnwvBRehO3tmSSth7HS5fUejs+Lt+vUBDPW0+PvFYqQv6uX5dtrIrJsKIrV7QQwNaWKifLy3JsLqc9l9bX1QLO1gq5nIZt7uzI+BcW6sOYymU9liGhrHa5taXFWOiB4/mpyISFXEIPW6ggWSUoqe0C2yMwJCvJW0BFz/bNS/JaNAqzTMrBO4pcPS052YnyqZNwWvdNkvGzmQcvPDb0uJF48HlMetZ2dsRowrBJ6k827Nu2a7F5wiSXDLUcGBCZw+fR5p7xOqx2yfEwPNzKDsq+lZX6tiv0slUqSlhJyIrFes8+c5wpb5aX1Thke4gyEiKfP1jsisVaOA56BClz+/tVhvF8NqyTURAkjtZTGxalCe+BZob8oyASvFNCq8ldo8nvohGC0wyNPan8zIh6HOY36UQFisqx90rycjkhE+WyTJKbm9r81YZQsjWCc5pLFi7OHb+9QaPlLKt12u+ehbQuLopSYtcNDtaTvv7++t9nZkb78dneejs76iV89FEhjz098l/xHFeuqFeQ+SfVqigSxaKGQrLf3fq6ELb+fvmv9/bk1Tm5PkMyqYgVi3JO5vmVy0L6WLXTtmaoVFTpuXlTy6BTYaGCxDAmLmFz37U1DbVikQFboICKDYsxsI2Ec0KkbUgmldIkmdBI+TlMtMxpGl07UT51CpLum9NQxLOcI4mshW0BrDfdbiehsqHTtljKxoYSKYZh2pYtljjSE9bfL3KKIaMkSLbgCKMIbOVg++xTLjCMnCGa9neoVlWOkXhSTlHW2O9AecbvTZJoi7KwGFZY4MnmO7M6ML8Lt9MLSVm9ulpv4OJvZ38Hew5+b+shPa0orUjwLigakbtWexdPA6dpZYs4Po5jfe80BWp/X5T2ri6ZQJnTcO2akLlSSbYzBHFwUPPPQuKTVHlycFCJX2/vwe3NlrDpeNI1WrEwHDSXO5gzODCghK2/X8jcwsJBT5/tG8hWE+zFd/eu5tCx0uburngN6UVkXh/z/MJlZET2WVmR35FFWxiSubKieSDlsihMzEVhcRYqGQylZL5csagEkGFNrABnLfy0LO/vyzmZGzM+LgsJpFVIrDJIZYq5M/RMsgk7lShLELnwO1EBs0pk0nNwlBD1Rl6P00CnyadOQ9a56zi6lb1nkwpxMOQvLWfPerHCypfMC2MoOQmGJW8kfZQZzP+1febs+bg/+9bx2la+0NvGis+TkxoaSlm0uqphmFxscRJ6DFk4hd415iDzGH4mibNeQtvuoVrVIlK2317oCaRxyoZrcp/VVZXb9EhSfvFYRnAwr7qvrz7fmWGfJHnN7pnowYtCKvNN0CmevYuK8/Y/2UnLrsuKTlOg9vZkAtrc1FC9UkkbmDsn5IJ5YiHBsRUzm5GhoyxHzd2zXr0sxDKpcEu4WHLJoitJ35EtD+yxg4NKinkeEr6uruTxXrsm59nclO1bW/U5joODQrimp2Uf5u9tbmohl54eJWVLS6oETE2JF445KjY8l2GcIyPyWiiIwkCiXy7r92VhAio7VGpIHsfGRLlg70R6CUslGaNV+rjwPOvr8t2orKysyHG0olM5o8WcSpP1AFjPNFtBpFmxDxu+ZK93VobMTpNPnY6T9OCFnrbQu2PzvZKMIEnnskWQLEFkeHcYjmmrStKQw+JObAVj+1xar+DkpMgteruY32vJFfOT2bqAEQf02NGwRa8YDVf01lmvGgkVI1co55kbzfw/5s4xQoJyp7dXrsNcOvs7Wy8of6Nbt+QczPdj2PrCgkQ/sEgWyTJ71VIe2oIrtogKZSWveVqIBO+C4LieuXb07LXTWNoF7fg/NQMnrdOwQp2X5bAEj9W+CgWZXKiwl8vaY43k5MEHk71o9DxxsSGH4f48V1ig5TSWRv3sGpHCpMW2VrDnYM87Hs8QTBKxmRnxvA0PC0lbXDx47qkp+f3p7XROSWFfn3j0cjkhTAsLsp4FTa5f1/yM0VF5LRRk8i8WNXzHKgGbm6JUFQpynrU1VXRI7oaG9DvwfCwBvrUlBG1rS0OHSiUNGWW10K4utV4vLtZb5NkWgc+s9RhSGbMhUCR8Yc+nlRVRgKgo8b5mb6uwaIP1WiQ9D1llnj3HWcnITpNPEcdD6HUL73tLMqyBpRkRsMaN0PtnSSJDGUlgLHG0BLFYrPeEWa8c97Whh6wsTIOP9W7R6MTQSls9mESL++7tqUyx1Xft+FgdmP3o1tdlXqShjOHmlJPsD8pQ0dDDye9gcwNJNjc2ZD7p6xOZykbn29uyrlJRgxfDSfk5/N1s8ZuQMJ8GIsHzh/tx21mxPq6bt52+23kkMmeF8/ibhGM+jNWq0xSo/X2ZwFZWVIEvl1XJHx0VxZxeJJKPkJzNzsprUuPzMIRxaurgPu22DA0d9PwlhaEyn4MNzfv75dWSo7Ex+S27u+X3ZHGWzU35PDOjluGhISFFly8rQdrakv+jp0faOfA8lUp9CBHDiOi5s+FE3quXjjmBm5v1Xj5W0BweFoWLRHx+XshYsajjZz8//ia5nNw/o6PqGSyXRUlhLyvrQdvYEGJKslWtyjioSOXzYjRYWalviRA2Yqfyws8kdLu76hUgwly8tGIqVlni+nC/Vs0ZpyGfALwEwPsBPJGy/WsBvBHAHoD/D8BDZts7a+t/N+vYIsE7XdicsLBSZlJkS/hqSUHSdnsd62HjM8fy/jwmPI8FtzPfzlbAZZ4diRkJC72BNDbReEMDzsSEehBZWZiFl3i9sGecfe753Xg9FmbhsdevK9liNADzk5krx+OWlzVclVVISTLZRy+fF9m3uqrFqba2VO6RJDJ3enJSIzHYe5SFZfi7hdWAbUhoJHinJKQOMymcZ9JxHsd+nsYakR3MCchK8jqR4K2uyiSzvKxhfs6pEp/k5Zqb02bdWbxjWYus2FDJtKItrVrScgxZZZRVMQcG6n+3zU0hO0ND9eR2ZESUgmvX5DyLi0Iqt7ZEEeB5L10SksjPvGZPjxzDMKNcTsMo+/rk/6QVnZbt0VFtycBiKpcuKZmnglEuy+diUXtFsWXD2Jgcw9+kp0dIX6WiHrzRUbk+Q5hopaYCZ0OjqLCtrGh7B4ZoWmWRzzLz8ezxVOIYbuq9egP5Pik0is8AX224E8dqixPYCIFGYZ6nhVMieF8IoNSA4D0CIF97/6UAfttseyeAqcNcLxK8oyHLvWWfhaRm5EmNyMNtPI8NPw6fGXvOvT0Nq6xW1Zse7mfPba9B0kEZweeLMmt5WQkk2yssL4ssYpi2za0lwQ3DGL2Xfdl/0/bUsx4uS8bo5aN3cX1djVDW0NTXpwYy/u4s6MI+fbYFxP6+7M+WCJSTNNJZb6YtNsVWNDaagee2vy8Jn71n+Pvx3CeNjid43l8cD14znOexR1wc0LqX9X7sRILHCZNl+IeGRGGfm/NPEor5eSU4zM07Komz5KjVpC3LkuTJs5/7++vXscgKq76F5yMJXlwUcmdDOxcWtErl+Lj399yj57RksKdHx1Uuq9erUtH8v0JB/lMWM2FuyuBgfZXUfF7Gz5w4egN7e2U7eywxv258XAjc1auiXJTLmpOytKQ9E9lMmF5G61lYW1NFJklp8l5bOFBB471Khc5a4akM0UNIhZC98ZiHkxSSlqTI2nAwa81nCFsauTttw+ZpyScAC2kEL9gvD+C95nMkeGeApHsr7T4LwybtvmF4ZniNpM+WOKTd+/aaYf4enxmGRVKekJyxeqT1fvEYkiR62UioVlflmWYlThZcSiI1VkbQQMT+e9zftpNhMRZb2dIaqejNY4Go3V2tZkm5s7ZWXzXYejK919xhW32Y4fS2QrAtWLW3J/vxmryWLbwSeupCQm/zI7PcS4dBJHjnACc5OUWSd/FwHv/Tw4y50wie92qZvH1bwzPn57WC5eamkICREW0NYFsEhIQny3Lctgmn2XYhiZBxOQwhvHNH3vP3oVePLSXm5oQoWQI5NCStDpwTkk1Szby+cln2Z988hkkOD8s+k5Oa41csiuLT3a0euoUFOf7uXVEmqDiQ3Nu8tpER7X9XKslxy8tK6EggqRhROcrltNce14fhVSxyQCWFHkJWyOS+S0uy3jYKptIVKpO7u1o0xnoB9vZk/NPT9UUUwn56oScvyaOR5rkjzqMHT06bmeB9M4AfMp//EMD/AfA6AM/Lcq2LqjsdFVnvp/B9o1zzZvtaw0ajcYTPR9o1rac76VqWHJKU0ONF+WPzyUi6GMpIOTEzo2GJNPTQax8abKyBxhqIuM32y7N5yr29mtfMEFB69BgOybEwB5CeNBqsWPHTkl47LlYQnZoSeU05Z38TyijKPNvQnd+LbSZsL8IkI5b3Kvtsexl7LxxXdkWC1+YIGf9JnSviYqAT/tNOJHiccJnITSJC71NPj5AFhuUxCdwSraNWu2zlEnrmsrZxePDBxj3w+vvVq8em8D098vsODsp6Xovkj+TROd02MyOkbHBQid7cnFaC296W4+67T45jhcvr1/V67J03MiLnIbkDhKDZSZ45HevrQhgZopnPy7K4WN8HzzktKsDwH1bgJPmjUsikf1rpWYiAiiGLrjAMyebUsCodwyapAG1saBuE/z97/xrc2tal52GDBMAbSILg5XBv7kOae5v8CvxE+DPhUwR1TMpH3pSPvHdKW/kiF9OpuJq6pNWm7XKcNJJuyVYZrsTRpYy42q0fHQuKrLgiO2WmrEtFpZJaUKyUJEvtvlEtu9XdkiLLaUaqRLKt/JJVyI/Bp8dYk3MBCyDAyyFn1SoSwFpzzXWba7xjvOMdnj7lC5xj1NGHp3T5Isdhi70Dx0FrGrQ9JMATkV8tIv+FiKy4797c/P1MRH5aRH5VyrbfJyI/LiI/vrW1Nd6T9IRa2js15mQIf++lBuu38/Rk30KnRZijBRjwgM1HisL9xSKHHth5wFWv6xy1vGxqmETnmA/8s03DGUS5FOYLX46FuYFonacw+gh9GMFj/ETjQuYB0S9PASe/GFoshdZRBiUvj7EADpnHoNSTA1+rGdA9OrLIJLmEzGEcC/356CjzY8hIgEbrzwPH/xLBC5ZvIsDrdvtPLIP2NcxvL+12eyzn67GMY5AWUkZ6tecG8K6ulEpSqegLCYl9D/CQ6gfArKzY/wATQEmWsgmPaYEmOkwpB0RQRG5HNDl3lYrlKc7P634QqIGa+dVXt8sqcC4B1j7/j2tUqSiYQ/Hy4kINhFLJ9r+zkxRtaTQMwHlj5epKr2upZMCxUlEDo1ZTwwKgR2TOFyrGKCOyRy6ezynxBhEUp/fvLXJIvz6q5w02T3dif/QLaKRPb5gRYaR2ns/dizW2iRmxDz3/PRTAE5F/TER+UUS+1WOdf0NEfqDfvr6pttOwLQbu+hnfWSN45MelvQM9mEOcwwt28Pzxl77S6KJhRBDnigca/D0+VnDnhVm8gwcnUNg/dT3LZVMMRlHT19EjKojjyAMzasNxbETeOA84gYjK4UCCQumPD3XQRsOicBwbYifMVbARLi/tWFELPj21mqSdjr5TKLj+8aNFD5m3fUSQ+crT1kNmgj+XHpCPal57AXhPqI3zRfZYXpZPpb2cr94tjTLS7VoeTlaQ99wAXrerLxQoJbxwoA+K6PmbmNCoXT6voGhyUg34ECiFACgNHKG6+VQWn3MY5iBCZZ2YMPEZAJmPCp6dJYFySDMlKlooaB+Li0lRFv6nADqADaBJf/PzVpahWlXwR+kC1NfK5WSuB8YBhtPcnBX2XVqyewMak/cUA8gweC4vjRJK6YRaTe+zet3oSZOTSkddXraSDwA1KKEYR/zGeH29qeNjA554vr1h5g0cjEg86FmN51F5ue/aHgLgiciWiPyCiHwZfF8UkQX3/58TkV/bb1/fdNtp0JYWveP/XlG8LP2GdLzYemEuq//N0y75PQ1AkF/nqd4+AkbZgm43Ga2jb/LWfBSK9XDSwGCgvie09Gr1NoWbsXiK5MePNpd52ihRxLW1JBPAq2KSl+fH3OlYnc+5OXW6AT79cZDHHFLSeed2OnpMU1NWS5R5zju+PIgN6ZghPdW3NDruqOa1F4D3hNq4X2aP4WX5lNo37XqMan+xFyAvGiY0L5Xer90nwBORTRHpiMhfEZGfFZF/JbLOhIj88I2B9TMiUuvX7yBz0+Wlzr77+yaMAYVvY+O2mAoggqjUUyh50G+JqWP2W7xK5uSklTfwQjQe3HHeYgXN83kVLOHlDjh788bWn5626Ojbt/Ydoik7O1aYvNHQ7T1AazT0N8R0MBowkgBmUKYQYdnfN0XVvb1k7psHWxQgBgAirtJuax/kBXrqZqulY9ve1u23t21sOzsWAVxft5wb+l9b07wVxFMwkPCK46X3uX8YP+22HtvqapK6RQsN4nCO6Rc1GWcbk4rmHxKRXxKRfyAif0tEfrOIfL+IfP/N779PRP6uaCmEn2IMIvLuhpb50zfz12/Psr9vuu3kWxYQ1kvNku8GNchD0OZrosWcFbH9s/j6dWEUz0fzAFRrawZwcjl7pikGPjlp+WQhzZQoP0qcUBJx2NA/ir2ApmJR5xeEoKCDk3cbFjEn8n98bGNnDiCfzrMS2m2dO/0x1mpWrgHmAmqYUC89LbxU0vNBvt3Ojs11KBd7YEgkkHI3OLhC8RdyGLl+sahtv0juKNoLwHsi7SlFjAYd41M4pvtu9329R72/XhG8EOz1a/cM8F4D2ERkQUT+qoh8O1jng4j88RugdyROojxtGZSiiXgH0TlABWCF/LqpKTXCffTtLrl3WXPeHusCuO1XIL3XwrYULxdR5cy5Of1tdVWNF2rkFYtqKGxvW909EQWIlBkgoths6nbk0VFHCSpjpaLrEdmjWPrxsfaBGmY+r89QPq9ROTzax8dGS9rd1d8RT8HQWlmxGnqNhm5zdGTGEyIzRDb39y2/r1pNRgihkALgMGwoRIwhCcWMdT58MK/38bHu7+AgafB5ZTqfqxIau2lz133Noc+RYfBUW697Je1zGvjypTl63WexvjxNMmsffp2QkunZMaFiI88q0a5CweiJning9xNzqNDP9LRF0jiG42P9DoBXr+s8Qj6wV9H1ebxQyaF0+5xdH/kCDLMdolEwCq6vkxE4/jJGnFBzc0Y3Jcd4cVF/a7f1fdpsKlikNI6P6uXzlnvn6egcA7R6StvgOOt2k1RNWoxa2+u+G7S9ALwn1J4CEBr0pfqUgOt9t3Gck159Dru/fi/LWBtEHOEhDSgR+cMi8muC735URL7Hff45EXndq59B5qZOx0RU5uf1ZbGzo9GjyUn1OH7nO91bgOQuoOabthCdA7B66iV1/XyuH/XsWPL52zl44fl9987q3tVqqtDJbzMzajhUKmoYLC4qYNrb09/5nsLjCwtqsCwuKpjyuW8k9APmSiXdJ8Iq5LN0OuY13962KJoHXNAlUf6kmPrqqhVpp7jv9bWCOwr/7u4mI41XV9qvF3ChiDCULW/4eI+1NyAp8wBI7Hb1N19/y+eqhJEN31fYRmEk9WsvAO9ptUEcAb0Aoc8JTYskp0X/eC5itR97jTsEbrF9+X5jwh1+XokBOaiRgLIwL9jnxeEU8s6aTkefZ08j93m5rZZF0LxICfXncGxRtNwLqxAxBOx5IRYPNKtVA7SwAtptncMBft/9ru7D17djXzARKE/jRac8JdbnEnunV6djecyAU18DsV/0l3skrQRG1vYC8O6xPRcQ8xARvOdwbu96jOMA02Gf49jHQxlQNzkwf1NEFoPv/5iIHLvPPyYiX0S2H0ql7vraoj9LS/qyAjjMzmajYBIxeip17YZZeuUT5nJ6DtJAbyjgsrp6u4B8DNz5daanFWgRxaMfiqXPzZly5uKilVCYnFTDot1O9vH2rdXqI/Ef7/qHD0ZJ8sYL+S++FtXFhUUIPTUScQDq0u3tJdXi5ud1/5VKUkEPARjKLJTLSWGCSkXPD4qgUDm9IelLJLx/f7vAuc8j9MZkmKMSo373i3w81Tp497k8dtupVxu3/ZAlMsfnLNFB/10sghMDi0Si1teT+XOxfnmufITRP2v+mQNYAdIQQ/KU7akpm0cAajyrPv8NwMScUamY+iQCJrmczTlE+xkLqrz1ujqWmEugdF5d6dxDiQbOny9YDv09n9c+Xr0y5kKnY44r9s0x+JqAzLlQyxGdgurpnUYhyPVqxKzn6+f1itbGPr9E8J7IJDXOF803Adzc5Ri+CVHALN67rMc4jijdIPsb9T4ewoASkXnROlLfjfyWCeD5ZZgcPCIqlYoa3uR89QNtw6hPDgOiHuMSgrTFxWRkza+HyqanpQKMOe7lZSt2zjqfPmkZBE/LPDvTa7S9rUCp0dBt9/b0b7VqKprT07ofVN087dbTQzGkVlYs361WS1KGyCNZWjIRmFLJFDebTRMTODlRxwFiLldX5mVutez4dnaShdJR6yQPhby+VsvoV3jbiSgWCvp/t5uMPHhal48ieIONbbyQRMzojVHIehnT42ovAO/h2mN792cFdyw+l47fPAjzgCB8TkKhshDEeScKThbW938RWQJEkVuGA6rVssgdipS+DALPXbOp81e5rM++dxIRnSPCBhOASB5qlh4g7u3ZXMfxESHsdq2PpSWbH9fWLHeduQpqZS6nc2O1mnRQMT9CJQe0AvzabaOtM29yjj980DmUvjgeH2GNRe3Soq/3aTs9+KQz6HIfk9Rdgcio22Ob4IZpWY/hvoHLfbVRHH/Wvh7LeRpkHPdtQIlIQUT+hIj8L1J+HytFs93W2bfRsBpmgDqfXwfVMC3a9E1bYmDz669N4CRtgZ755Zf9z9fW1u0cxlxO6ZV+PYyIclm3WV5W44IxEqUDWFWrCr6++MKifdWq5dwRAfPlEygePD+vlKFSyfJaqlU1OMg3gWK6uanfQe/lPvr40URVpqYsAuiL9p6fmzDN4qLuq15Xo4ryDyhwIkKwsJD0emPUNRq6L4zDlRU9Hh8hQHTBq+xBY8L4DWlmzB1+nru+1n7X1tLrkLHdONoLwHvYNqrrOur7I+2+9TL+5OL1i+DF/g/BXRiFCsGhzxUD0PnalvTT6VjkjaLhPI+tls51sAM89bJcVpAHMIQKeXysIMyrYCKI4umPi4u6zuGhqVUeHCSdQJ7VAPgkdw8wBqW8VrMIHoAVcAcADFkECD0hOAM4XVzUsXgxGLYlagdd87vfNXDoI7T+umaN9N61vQC8AdpjBVOPbTxprZd3dZQRrKfYRnlcaRPHYzmHg47jnkVWJkTkD4rIv9NjnY+ByMpf7NfvoDl4uZwCAWgqXlVydlaBzfKyFvh+aOD1UAuRSkpDoCKaBuKg/fjvt7b0r89p9PXzpqeNeri+rv0UiyYBXqno+oDMtTUrnL65aTkmHoxPTenv0Dipn0c+XLttyfyHhwq4Gg09voUFNTbI74Nq9P69Rv0of3B4aCIweMZPTxXEISRQKOg5wTDC8FlctBw6jKJcTpf9fT1+8vgQhwGsERHsdKzUB1FIvO94yFst3Y9X0wujdt4oDeeO0Ojtl4s3rrnvBeA9/TZKJ6vvz0dv+Mwz4csNDNov64dKswC2WN08KIQ+r45n0oNBr4zraYV+zMw77At1TmrnQen88EG/299P5tIBwmAAeGEUABslVjwQZXvEpwBRAECijBQzb7eT0U/KwXjFTQ/WOBafa9hq6VjZD1FMgGdY3gUA7ee2GNi/j/YC8AZsD20cP9UWm0AHfemO4tw/p+sXerr5Luu242yx/IG0ds8A71hEujflD5Ag/xBIlE+IyO8VLTJ81Y+e2R1ibiLfCiN4aUkNcoBeLqdAohddM6zpNsplnH3fdZ8TE/F137yx/3M5PZ8TE/oyJroFqPN9zc8rMELRdHfXfi+V9Bq1Whbl84XlLy5MHZMxAc5aLVOrJNcllOOmvt7CgnqQKXEALXNnx8RaEAEgulgqmdonRgn5Np2OCZvwGalzKJd43qnXd3BgKpwYUJWKedIphFyv63FtbycNI4ymtTWjgCJb7gEcQK3dNkMszCdiXZrPeUlrLxG8F4DXq93VyRx+7xUU+Z1IWNp9nGUsHtyFatQAMS+g4kEIUXE+r62ZGJKPjFUqyQg6QM7TI33tOYRNyJfzkayVFYve+cglIJPIP84hIvzkGvtxEwEEmPKbXw+QCPvAA1jOCXPl3JxG7DyNlBqgqBZTJodtazU7zutrE4PyDiaOkWsRUnGzXuu7tgcHeJJSd0pElkXkT4rIz9/8Lffr6zlMUk8ZoPSjHtzH/h9DBOs+Wpqne9Btx9GgaGRV0nxuBtT1tRrpGOUrK1YHb5j8urW1wbd5jIsv4u6XLGUh8vn4uTs/V7Cxv6+fP//cqJiFgkb4iLSR49ZoWF7f2poBOvL/zs91HZ8vOTdnipk+p40cvoUF/R3DpFazGk1v3ugY8GgTkYPeyX6rVTOoSiXdrlRSYMb2ROXwfGPokD+3saHjmJ42EMbfdts82mtrGrVkLHj+McKIWBaLSTn2w0PzfrfbpggKpQuqGSDRG3n8FjqumE+gSd0H9cm35zY/PacW3ju98jz9O7PTMXGSWLS53z57vX/DqJ3vkyi4r2nnSw/wHAE4AEO+H0CNLzOA4IgvLN5qJSmfOItCCioOHB8F9Pl7UDQ5t+/fm3ATOcSAuakpc1hB32SfvlC6n0OPjixS6MHi/r45mMjfq9d1jmfePjjQ40QZ00cnOR5fKsHPT14B2FM1B7nWd22PAeBF606JyO8WkR+8+f4HReR39evrmz5JPSaAMuwY/I3/EGN4DOcuS3voaOUwwDBrewF4vdvlpQl31OuabJ5W+DtWrPu5L1mVQ2dmbqtjzsyoseIjgOvr9htgslhUQ2ByMlmDkIUIq0i3+/q1FdMFYBEZq1QU4FCXbn9f+0VOHCXQxUUTEiDfpNOxvLhKJUmPhDIKlZR9oeYJDalWM0EUQB20KmhQFP4lclcqmcPh3Tvtp93W84Ah1WzqMTebRg+Dqsm+r68tl/D8PFnrjrwer7TnlQG73WTUzufF+HYf78znNj/dtT2Gd3CWMcTYL6F0vY8qhxFlL+E/iLM1bT2iaRQc98+Bd3b4iBnzAVRyBJZ4FsmN49kh6lavm9quF0Wq1y3SD+ADfDFuX+i7XLZ8PS+qQgmCjx91bOTmsh1RfuYNQFmlYuVh6APACB3eUy1R/sVx5vfDd+G2zImwI7zyMCVlOh2d69bWdN5lzvJCOFyjsOZhlms9qvbgAO/WTm/qTnnhghsQ+HP9tv2mA7xu9/FMjsO+NL0XdphtRzGGx94ew7GNewyPlaI5rmUYFU2iU2ny/Y9V4fIh6JsepKUtqDuKWA04XxjdR/guLgxUF4v6/5s3us6nT8m6eZRk8MCxWFQjZHbWQBxUW6JiOztW77DRMAoogAvqJWUXDg8t8oUx0unob5eXeiwIFyws2HGsrOh45uZ0LLu7RgVdXTW6JWPEUEGEBY/+3t5tlVHybtbWzBN+cqJgFvoTBhIGIrWkMJIqFT0vaV59hFu63SSVE0892/SqZTbO9tzmp7u0x/ZuyxJNCz+H0TJf9qPbTYp2+G1ixx6CQ69+6Rt0TB8x8mqbPgLX6STBDICl2UwW9iYqRySOZx7KNQIlfmwnJ1brktqZ1Mzzz66fW3j2eeavrgz0tVr6vmg2dbwIt/gcPh81A/itrup85M9fq6Vzry/CTskC72Tid8YJs8EDWcZZrSowZqzMRVx7LxJDBBD6ps+5hMXg77vQYTWO9qgAnq87JSJ/z30/4T8H2wxVaypL+yaCh1G1u0aHhtkmrV7MN7E9hmMb1xiYHLP2/9wMqE5HDed+gEWk2/3qq/7rPKXl3bvR9JNGyfSLp2KGv6EoKZKsO0i/09NqmHz2mfXz+rVSLoneeWEVRFVQwKTm3PZ2MppXLmt0rFDQbebnTfSEvD3AVaFgkbp224qhVyoKsObn1dg4P7exELXM5czzTvkFzpdX6YQi2Wzq84roys6Oia6gXFet6rmqVPR4ymUr9QHlq9PR36CpeiVRig5jlGIo+YglRm63a/lCgL+HmjOf2/x013Yf1yltHx6c9XIKZN1HrFg4ESXASVhoPIwI+vw2lDBj1Evv5PBFs0Onh48eAghxhJD36x1E6+vm5AHsQN+kXAAAyee+MQ4AD+Pnc62WLCFAHT+eaQCVV6zEQeWBK2ARZ9b79zY/+Tw4HGI+msk58zU9USjmGAoFi0givMJ+ccBdXirgbbWS55rjpiA759uzC3x0k4XrPogdNEx7NAAvrDsVAjoR+bv9+hjlJHXXh/+ljb49xWvxFMc8qpZ27NBIXiia6ecHACBi9dNiy2ON4o1y+da3htsuzM3b3NQXdgzQiSjYAlRTtgB65uSkASBUNun/669N5dJTPBsN7a9QMOVNqD9+TIC8y0v9bWpKtwUE7ewYyNzc1L4BldwjjYYaJ/PzlldydaXfAyS3tgy0VirmvV5bMw81+SaAL0o8TE1Z7h3lG1D8JEKYz2vE0EugY7A2m/r9xYXes7u7egzkyFQqyTwavO+hgRTmsmBI95pnH5IG9VSWbxL7KS1KGIItH/ny2/q/sb79/zEmCpEZXxTb74+//p6GvgjgCQuSv3+vAINnykeuPbUZoAiQwjHCvgBHq6vWB9F/SghAt37/Xp/56WlTpAQE0T+lT9gnvzEvHB5aAXOAmz93XvSl1bI5gcihB2+XlzoP0Q/5jZ2OOsGWlgwIAhZRDBYxkSocV/RZLuu5qFZ1DltcNFA6P2/A7fDQ6LFcZw98OZ9h9C6M6HH8YeR2HO1RALxY3amHpGimPfwv7aUN0h4DHeWhWq9jh6LxAvDiDe8ncvvkOy0vm0EPfTCff8m/iy2cE39uOHdpAO/sTM8n683O3j7PhYLVq6O4Odfjs8+S+6IEg4gaGktLCrgomu7LOszOGk1za8uMHK++WS6bouXiokZviY7h0abQOABrbU3HubRk5RioAYWiZaul+8I4wnuNAXl8bAbR4aFF/Hy9KiKMPj8HAwbaJuDSq7+i/kkujzeSMKAwkHzhZv+sYBinPUth3tSo23Obn55Ci1ErQ5uu1zppghheLZGIVK/cqhjjyAtyeBDHfT8zYwAHYABo8TlsPl/Vgw2eEUCQF/tgn144hD54xv323a4VJgcEUieOyBq18nxfHz7os3x0pL+jhOuBG1RMgNDpqfa/t6egEnbB8rI5rKB7cj6Zx3xUkjmjXDbWA04topj1us6JtVoyn5Bi7O22jn1x0fLvGE+3a+cWhxQA19NdWQ9HVYySGQPpo2wPDvDS6k6JyO8JRFZ+d7++RjlJPUejPGt7OTfZ23M+V2nH3um8ALxe7fraBDFENJ8KUOLFQB4aRD3FpVxOUl99vuD09G1aJ2I3PlJKvl4ul4wSck3W19Xz6yN1c3Ma5Vpa0n3mcpZzNzdnBcbJlaO/uTkFdefnZrDs7FiNxPNzXZf8uY8fjapJHhw0I2rb4ZE+Pjaxgvl5XXzuC4YjhgzL4aGBRXLvjo50H0dHSdU+jK1aTaN2CK9QnB0qJt50hF7I6yHvJsw98pGTfmyblwjeaOenp9qy3ANhBC8EhL5OI2AiLeUAQBga9L4uG/sIo9IADp5DLzLkKZgAqbW129ROIkuMkb54pkolA4Dtts4/Hlj6kgPv39uzTdT840d9VpeXk0JInBtAI5TIiwvdBwXDPUXRn0/2C9OHAujMXR7EQtn20cFuV787P9ftYBAQAX31SuewvT0dG/mAnu5ZLut7t1w2CjoOMRxS5bKVmrm+tvxFnFQsfrxeZdM7wTn2UQcEHgPAS6s7tSIiP3ZTJuFPichyv76e6iT1lEDAc45KvbS7Nzx3LxTN9IaK5pdfdn8ZMExOKjABUKyvZysP4JdicbD1n+sS0l49wPNiKh7Ura4maZMLC0mAt7WlRgLCKyL6f7OphsHCglEcMYqo59domHAKhdG3tkx8ZXvb6EcYZ6WSUkd9NA+RE9QwAYmNhvZVKlmtPDz11apF/DDeLi91rNvbVv+O8ePZPzgwuhcRPkoq4EUvFCxSSf+tlgmz+IgHRi0RDCIn3e74AVy/9tzmp+fSfGSF5o1zXyfO3480H3X2LTTuw/uX/gAcbMNvHiiEeW/huh5AkesHmAFEsT7zBwIqsAIAOV6YxD+rqP56YIqAC/U6oas3GkZ/ZPwcJwDR5y8SVSPKRnTNRy0vLxVAegEUWBJv3yYjkX6f0Cr57IE34+eYEKmhZiAAtt1OAms/rpCK6z9TiN7PYeNgDT44wBvl8hQnqTTANI4X1qj6HNfL9JsAGh/iGLLuc5xj6+U9j33Xbmfv+7kZUJ1ONwE0fIHuh1KofMpLLwqrz2fLss3kpC7r6wruAHhe7XRz04Aa12ty0sDZzo4unjpUr1v9ub09K3C+umrbzs4qKGMM29sKqmZmtN/JSaMfMa6traQMOLkuRBIBnvm81eFrtdQrT3RQxMQN8JYDUufnTXiFUg4YTiiGoqRHngulFBjr8bHl/R0dmZR7t5ukcmFw+YjJY8iVf27z01NtIQAK/8aaB0BhJA4ww33ogZqPLsXGwP9phn3M0Cca5IEl0WyeAZ/XBojxUbXLS3O0kFvno0s+cgf9kXw6gNDSks5Z5LfxvFM2gDxBon+odxaLBsYQXVpZMbEUchFRreR4WAeqJ0JXAE/o70TpyLM7O0uq8ZITyHXzUTsfKQ2js0QiV1ctcnp1pfMS7ARfq5PoJuchBPFXVzrOMJ84y/04aHsBeI+gxS7wqF9a44q8jRI0PvSL+q4t6zE8xHUd5PwOOr5eTorY952OTepZ2nMzoPBAFotJgZXNzWR0CSrfN2WZnLQC4uNeEC0pFBTM+TIJMeGafN6ol3NzFj397DP7H1DFX+iXHBP7hJJ5daW/YXBcXqqRAr0SEOVBYqVihg7rMDb6LpVMXAVAROQOMZNm0yK6BwcK9CoVPb6FhWS0d2bGKJ4AsUrF7lHqTVFMnWNBMILadwBaD/K8Yh1GkhczIC+Pulx872lnw3i978uIeirLU7WdsjYPpGJ/ezko09QwUXz1EbJQ3TKMzBG58c1H2thnCAj8umEEkX2RB4Z6JXXvDg+NXlmv67OKg8WDO8AP5QD8s5nLmYOFPGScPj7ixvgQVqEoOXVD6QPgSYQMsOM/0zg21vd5c+wLMEqOMMfYbuv81WoZQ4GoHRHNdjtJowRIMy9znWPROeiafm7ykVVfHiG8jlzr2H06qvnpBeA9khZe2MccwfP9jfJmfOzgbhTgaJTnbNB7Jev4hxnfIBG8F4DXu1EHj6gNhjbqjSzfRMrloLRTln5CM9AdP/9cPb4AIgAMRstXX1muWKyfzU3bln0SPYMWNDWlv6F4mctZ2YNCwfIpoeBubppHvVpVgIWwQa1mNfNErEj45aXVlMOI2tkxgHZ0pP9jlHQ6WtIAFc9y2ZwHvtTG7KwaYtWq0psmJmx9jCf2S6kExgzAI2KI0VWtmmff0728MecpoPyGiAP79pQyIhe9BFbS2n0aUU9lecq2U5bmAdMgEROMfQ+6QupdGAHjd6Jt/I6ios+9AwxA8WQ7DyqI9vj71kexiXLhWMEB1GrZM8YzhHPFP6OMm/WI8PFs0T/v62ZT5wXo2YAYFnJwAYpQGw8OLOeP/flzxne+hIJ/1k9ONFKH6iU1O2EnwADw4ic4mY6P9ff9/WQdznrdSq1AQb2+NtDKeffAz3/v6bGcH65hmGfp6br97rlRtReA94jaQwOcYfbvJ86n2AYBR/08faPeZ69172qk3NcEk9b/4WH2/Tw3A+rqSg3tVsuUHH20bm4unVYYgsBvyuKB7iDLsFFOACEFw0UU+DCOsIZeLme18yYnDWyJaC4cwE/EiqXz/+Skec5rNQNoeIYRGKAsAYqXPrcOQES0DDBVrapRdHBgBdURELi8VHBZrepYzs5s+709OxYUMj1tqlazc9toaH9LS3qO8GqjvMexXV/r33JZx0PR5aUlPQ8YZ4eHSeGVTkd/Q1mv2zVRBy/uMEh7ieB9s2ynXu2uzvNwuzCaR7QnjMrEQKG/f3k2iIL5qBxg4+pKnxUfKWQMHih5CiVzSj5vOWvHx0YB9zXhiCp6sIniJc8xzyGt00lG/4iUUUKFcgvkqL16ZSIlPgqGeBL7BXQS4eOYAVtEJhsNUw8G0OJ0IhoHsIXuCdW0VjNVTuYPT62kfAz1/wBqPuex07FzdnKSpL9zPohQAhjp398P92E3vwC8l9btdu8WuXmq1MpBxz4OsNUPaI0KVA7S77jb9bUakC8AL97a7e4vG9yDgJJ+hb2/Kctd8hCzFI8PF1+Hj0LhfN7c1L9E9ai1d36uL/+LCwUwm5sWhTs7M7ri9LSCHIwPclU8eEMavN22/BOiagcHatx4SuPRkSlb+tIEIgrENjd1H9S1q1bV+CNKdnlpIi8iegxTU+YxJ5cul9PfyLejxh2iLCh6tlq2falk46/XDbBi/FGUGfGCsNxCt2sGGODOR036tXEwZJ7b/PQUm7/uw4C9cDsP5nwELm1bwBD36+qqPp9EwQBrPiLto2dHR7ePgeeF55L98BwTqSNPjKgaSpIULSdiBnhBGZd6e6hM4kjK53UeAdgBMEOgRS1Aov6IpACEQkYAUf6rK6uvB9UTimWlYtFDwNnams6DCERdXibzdjlG5p9mU+ePVsv+drs2J0P5ZC7ifwDo0ZFFYjkna2umJoo6KDTQGB3zvmyvF4DXoz1F0HKXNo5o0DjaKKiSg643jn6yAK3Yb6MY80Pe20ykTKz92nMzoMh58OURhqUuPpclpFT6PDi+60XjzOUsRw4g+J3v6P/z82rAQFcsFnV9Sh2I2HV688b+Z10Ri+ptblo/ExMGiOp1U7TM53WfGAy+wHGtpn1UKrosLJiB1+3qetBCLy7Mm4/Ay9u3dozlsu4vl1NDZ2JCx4toSqXS7b57p8ZRo6HGWa2m46RWX6Wi4G521ur6UZOKdefnlRpFdABq5+6ujgHVTO79paWkQYUh53OmvAhLt3s7lypmwPtIygtFc7y20yBtlO+iLO/SNLCXtW8PsLwoR6x56ibg7fraxEdmZpL3t7/PAUOAHb8P9onYCYAMUOGjhxQsh9oIYCLCRH7r2po9456eiUMHkMS7qdnUfZ+fG1hExMULkXgaJRHBZlOf81LJ6KOA1NNTq7W3tqbO4Hpd57JWS+cSqKE+WofyJbmGPifw5EQdaeQSNpu6rc9V7nZNSRSQ6YVZOL9cL59bBzj2n7l2YamM2D01rvYC8FJa7OF/boDvMbasoOipRBWHjZgOIyzwUC08xutrNfJeInjxdn2t+VzUPHto8PSUl0EidjFFzfDz1pblrn3xhdFi5+ctmsdyfq5GydmZibhQdmBvr/vLIJB8OyJfh4cKxDBSfD5Is2n9U3C9VDLjqF7XsWCE0X+7reO8uNDfUdzc3bX+vv5ax1gqWS5fLpespXd8rH1MTOj6hYKpeRLNu7gw6lmjYQp4eLr393X/8/N6j2OgEQ0pFJIG18qK0bm63SRlys8h3lMeArmYcf8C8MZjOw3SRvmu7tWX/y12P4Tr9htPTDgj1g+16AAk9H18rI4bqI6+LwAGjh0f/fPRQGiKjYbtByDGs0LkG7q2p0ICSnwR704nmWNGhNCrWJZKOj+dnNj8urNjOXa+sLcXQsExU6uZMwnnlM9pZFw4hNbW9DwdHSUjgP5ceHVLIors058vIngwChCc8vm/AEScTB6YdzrqbAV0c77CMXnRmvAeiTkYxmWrvgC8Hi3m/XsKoCFLe8rHMcoI3jjbuMYwag/0XVq/McSeG/j3WUHqczOgoGj6yAtG+Eskb7AFAZQQyIlYvuJ3vqNgwyuWiqgRBMXR01+JDtIP0S+AHNcLkRyKfENL7HQMuO/uGkAk363R0N82N82goZD54aECqkZDPdLz80llOIzGalV/w+tdq1npBWrvQX/69En3jRHYbKoHvdm0KJ5X7zw50f0AeI+PdUy5nEUoSyUTakDxDsOx3daxzc6q4QiFE3oWRhGe99Bgxcvv8+/8dqzn1485a0c1hz63+WnU7b4ieNxTyNjHDOvw9/C3LA7/cB1v+PvvfQQwXA9Q5KM/HpBBQWw27Rlot40dgDDL6amVFkBFkqgcQi4800T4/HNF/p3P28PB02wqkMKxk8+r88ZHsIjOEzX0Y8WxQ6Tv6MjomgcHOn8eHhqVkggg4JNzQmkFnv1OR7cPnUIcNwAOeujKim5fr+sYqN0XsgT8dSHXzkcq/T3jr1N43WOKqeOy514A3gDtMRjUtLuMZdgb6jEd/7jaqI5x3A6Bx3Atsh5j7EVZqbxE8NIaKpq8rFFnfK5LP4XMrAvAjMWDtvV1BTeAvIkJBTYbG0Z5JC8tS67j1JSJlJC3l8tZCYO1NQWEAB5KJ8zPK2D69En7qFSsHl0uZ2URAI31uilwNhpmBNVqejw7O1acHefA5qbRfwFfRN4QSCDnBRCLuubcnBpx+bx68ZeWdJ9QURcWrIA74jBQuzCyVlf1OMtlo4ohJIFBCRj0FE1PYyMC4gUYCgWLHoStn7F+l/bc5qdxtHG/z2IRvF603ljUJQR+vSKFYTQ57fh8ZOjVK3sW63XL0+N3FGXJsUWaHxDT7VpOHyDo8NBy5Xwurc+pW101Cme5nAQuy8v6jFPLcmFBn2tPuaZuXqlkkTMAGcdXr1tuH3RVooHkDS8v63xXLts7b2fHSjpQbw8hJr8fryiK6M3RkUUHEW0iCkhkDqAHeKVeJ840H9lDPIVz7kFdGBVGICe8V66u4vmaLxG8JzBJjavFXk53BQ/DgLuHihoNss+HAL7jGMtTacNEU4lQZS12PqgBJSK5Qda/j2UYgId6JhGdcMFwfy7L7KxFx9LKGPRb0raD7hgDb6urll8moutR4iDs880bvW6ViqluTk7q9/PzGvGamLAI2NycqVhOTWlf29v6P6IlKHRSMLhaTRpAHz+aIA/5bktLVnuq0dD/FxYUOBYKul/okSsryaLm5OGtrFhEbm5Oxy6i/e3vm0hLq6XrNpsG7KB2Ej3M55PqmtCfAKMYoaurRjPDYCM/EfEGQJ43jr1ceb826nn+BeDdrfW7HsNep5jdFO4zRutNY8h4Iz62TvgbfXlj3v9PlCcUOVlZSTo6oCRD4/Yqsz73logb+XpQosmHnZy0aBs5czx/OFS8Oib9Lizo87u3ZwDu6iqpSElhc57PsDg6OW2UYaCsAXRs1D7JvYOOTpRvcVHXh0VQKCRpmIzdC7V4eifnivP94YN+v7qanJM479Wq1SMF+PmSLuH94JunprLESi7cR3sBeHds9+l5Gsd+B+nnPm9M2iAv4+vrOLVi0P09tfaYxxy7fuQRNZvZ+hgC4P2UiPzKQbYZ9zJMmYTXr7sPDqqe0+JLIgDavPCKjwBOT5uYyuSkFUyfnrZ8vIMDU33L59XbXC7rb1A/5+fVoDg/1/W2t9WYAXwBEn39p1bLgOjCgnmyEULZ3TWK0+SkqXiWStrfxob+hmd+YUH79vXwoIqurmp/ExM6DpZcziJwvjQDUcF37yxiiVceA86DM0QioG81m9pHtWrGEbl83hD1whVegCI0uEcNGmJtHABPRH6/iPxtEfnLKb9PiMgPi8gviMjPiEjN/fa9IvLzN8v3ZtnfQzvHe12ncal7h1EUREf61Vb0aph+f55OiUjKyorR9K6u9NkD9K2vW7TOr+PzyTyt9PLSqMyAGHLnJiYs+g4oWlmxvNnTU5sffNkVonn0CfUSldrra6vFyRgAm+Tlnp5aLU5AF7m01WqyzhwRSo6V8QPMEJxaWzN1YMBis5kEwt7BU68n8/qITO7t6TxEPjNzQqdjx0j/KPdWq1ZfFNoocxwKmTFb2F8/jseXsgBMZk2xGcUc9QLw7tDuK6o1TprfoOCplyJQlj6G2SbrduEk+5jaY7iG4x7LIPv79Cn79kMAvLqI/EUR+fdEpDzItuNahs3BW1oavpbbc1sKhSQgy7JAQQSgpf3Osr5+O3JKYXNKJQAO37yxenifPuk6W1umOkkUrVo1+iOqlnNzahSglLm9raANj/XFhRoz1aoZStfXlmd3fq772dkxxc6zs+TYLy4MgC0sWN0+FDJzOaN2cS4oA0E+4eWl7gcjMZezelsXF2oUQeNEVQ6VvnbbIpW+mDElIDBwj470+kDngi6F8eRVAz1lCvGDGAVvlG1MAO9XiUitB8D7ICJ//AboHYnIf3bz/bKI/LWbv+Wb//vOgQ8N8Hq1LM7tXtG2tD5jAhf9nNhs5/Pn2C6k65E/6p0PHhT6XDUP5AAxAAKAA+Ch01EAsb1tEbCDAwN20AcrFY2082z6yCDOGPLyyFcDsPnyBPm8OWh8NKpcNor59LQJoeDE8TU6iWiGBcShYVerSTEXH+3y0TjmDn73AieXlzoG3wd5iUQZ/RzAXFIu6zGS0zg1pXMQwJh+ALZewdOPkeigZyf4ou0xymaW+/Mu7QXgDdjGFUl7qDYoMLgLuBsGiDx28JKljdsRMO5zOsp2fT1+iqZuIhMi8i+IyC+KyI/ceLt/WER+eNC+RrEMA/AAK9RhKxTuVgPuZbm9EIWbmLgtshJbPID09fFmZy1aNz19W70zn9fvFhaMejkzYyUXoOOifJnLqbGyvGz5bXNzRjkCRC4sWMQOCXMRBWuIvFQq2j+Ard020RS88LWablMsqtFHiYVGQ49nc1ONRQAhEYCdHd0eTzj9XVzo2MmzwbDzxuDpqa6LEAPGVqlk22BMNxpmKBFhwWDyn8MoSlhceBwCVeOiaIrIdg+A96Mi8j3u88+JyGsR+R4R+dG09dKWxwzwfAvfX/7a+u+yRkhCYNZLhZMWE/Lh3vL/Q69kHV+8m+9CJ4R31gAu6Avwdn2tzyJRdXLYoBiSs1Yo6DNfqxklEuEWABHghQgXAIWoGMqc+/tJeiYRyk7HnutuV/v0ape+QDiRLcofsC9q6cEaCM8RUXqopoiuEMnzhdopVo5I1MGBATwfGfXAsF43gLyxYTRzzs/pqeUC++vMcb16ZbmBnDsfdU0T9OnXHk0ET0Q2ReQ/FJE/KyK/TUQK7rf/JGs/d13uQ2TlsagXPpbmz0UWr9kg/WXd5i7rD7vNU97HQ4I7pJInJpLezF5tSIC3ckNz+gsi8htvaEvfm5WyNOplUIrmzEwScExOPl4FzWHz4e5rySrSMjdnOWr+mNh+eVnBVKmUvC4Ab18mAXotv5HDNzGRFF3Z2UlGaAFVGBiozXkqFRTnQsGihiK6Dnlyc3M2npkZHQP1nq6ukpG7atVEFi4u9DuijGtrOh727fNg/LihfFJ3MJdTwNhoWJQA4wpvuFf0w5jFi04kAdoW1DEfQfDRjTRqXQgE/Hejag8E8P6YiBy7zz8mIl+IyA+IyL/mvv/XReQHUvr4PhH5cRH58a2trdGelDG2GJDLajeE9gpGea97w0eGfX6d/z3sN+zLC3140OcX1BgR8QA8ETkiogfIqlatViZlDwBpHz7o81IqaaRvZUWf65MT3e7oyBw7FC7nmfJCTcw71ao6fxB4gsroo1QAN5w/AB3Aqi8b4AuBA+6o2XlwkKRg+iieiAFV6vexfwAX9e2grfpcY2rldTpWaJ0x+H0R7fSUUH7n2nEv+FIKPrLr7xUf+U1r47DLRgXw/qSIfL+I/OMi8u+KyJ8TkZWb334yaz93XR4igvecWzjBjoLCOUzh0UG3Dyfjpwjahz32u+xj2HX8etfX4y90fjMX/eLN34lBth3XMkwELwQmWdQbn9vyrW/FAZwHaGF9ujTARzQvtvj8OwDgmzfJQurv3ul6b98qUJqcTAIwEYvsUY/u8FANsd1d3XZ726J0UIcAR0TBqlWlYALaXr3Sdc/PzQuOUYKyZrWq69brFmmbm1MnArly5NYViwo8l5fNSMSQxBgjF3B11cRpKL4uYhTUyUkrh4BnnIgCuXfQMjEkKSDPfgDWq6tmRL1/nyy1kmZk8/84a4c+VYDnl6cSwQvbIA7h8B1JHlwohx/7n6hXSM/sBTLDvniGoRj67ep1AwkALCJAXkil3TZHDSACYOPBVKul21PnEicNzz7zGWJQ5PIi4FKtqsNmcVH7WlrSvhYWLIrmc+ugZjK/TE0pmOJ59VRFzgV/KfECHZSc3aUlc/4AXFsto39S+gGQxjnlc7V6G3R61dBCQecWzpWnUPqcQRZfWB1b15/3tNxMGnl/sXloXHboqADeTwWf/6ci8rMi8o+KyE9k7eeuy1OdpO6zjTrKM0gEb9T7HUZ8JrbdY4quZe0rduyjNGCyTDiDAGq/Xqcz3gieiPwHIrI2yDbjXoZR0ewFOF6W7Au5ZeGyuHg7Widymwaby2n+2t7e7Rw/D+CIzsVyJqem9DcMK2iaW1s6Br4vFhXkNZsKeiYn1ajCS+9z7CYmrNC5V8FcXFRDjXpO0JVQ4Dw50X0AdHd29Pd8Xr9nO6+AicABUTwAIaU8mk2L8hG5A+wVixoZLZf13AD2KhU1sADgvtYeRh/GsBef8JTMWIvNS+OgZtJeKJqPo/lIW+w3/5cGKPD3R3j/xKK/MbsnBvQ8OLy6UmfMwYHd14wBsOQpf4iPeFojIM+Duo8fLZpGHh00aCJtiK202zqfLCzo8wZFkxIHgKDTU51TEEqZn9d1yI+lLx9BBxR1u8nSA2trCgABpf5cAQaPjiwqibAJtEc/buiUJyf2udUyoRqinoAyxoVQCmwI5jJomTjPAHBQQIk8sn4ozuKpsx78h/l23vbpdR+Nuo0K4P2siMwE352KKjz9UtZ+7rp8EyapcbZhDfLH2gYZ330BurSXy7ijaw8RwfOUhX7b+wntHsokHInIT4vI3xeRPy8i3x5k+3Esg1I0p6fVeM+SF/aclhhYI7KZlp8YE14JQVhYfiEW5UNBMvx+eloB4KdP+tlfs+lpU9acnVWwQ8078u+2t9XYIqLmgebsrBpUzaYaJnt7NhbKJ1Cv6c0by7lptRTooYC3tpaMoJXLVlqhUjHFz9NT21e9rkbU7GyyPt7JiYnBUKsPlc9m0yJwlYoahFCLp6et3h75iLu7Vv+P+oCenuW94xjKRAZi81OM/hb+1qsNM38+EMD7KEmRlb948/2yiPx1UYGV8s3/y/329U2wna6v44qq/ZzBsb++j34O4dCQ93mfRNSggXqnhS/3AVgjAuXzxQBcqN3ybALiYMQAjlZX9VkC8DSbOp/hUKF+Jgs0TF/awJdVODoyoSeUOYmseXr18XEywoViJgAMYMh5uLrSsZ6fWzRsbU3nIq8iSpkEVIYLBYtytlr6PfRTrl1I7zw+ThZ05/z6a+1FbwBi7P/yMkmdPTkxBsLy8u1tiPZ5mq2nbI4rHzhsowJ4/6qI/FOR7w9E5E9m7eeuyzdhkoq1+zTax7HPh273BVh77ee+o4RZXkh33VcvSq5/0fnvqJEjYtSYfm0IgPfjIvJrRGRaRP45EfkTg2w/jmUYiqaI1U3DqH9ogPXQ4G55Ofv6vSKgADlf+86DOoRNWIjOQbH0YDIEg2/e6HdE2ADqFAfH4Go0bnvHfb29r77S4z07MwnwUsly6kSSCnerq2rEQasU0f2trCiIoogvvy8u6v21v280TQBasWigE0C8u2vADdXPfN7WffvWoox4+qFczs5a/xsbahgdHFhpCkBftWoUTC+qggHY6RhI9blT3a6uCwUKwzoUWRkFIyFs4wB4IvKHROSXROQfiMjfEpHffEM5//6b3ydE5PfeUNGvROQLt+1vunGu/4KI/MYs+/sm2E5cc29MewdBuG5aLl34Xou9w9IAINv7/QMaQqcFdEFPNfY5YAifAJqgR6NeCaVxe9sElHw9ScqtkH8HKyCfN8XLUknnEkobAPCgin74oOOYn9ftLi4sZ439EG0n0kiE6/jYov2ILbEdBcqLRRNB6XaNIYCgiY9WIswEZZ1xEu3kOr9/b+eOeQTmArl30DJDBV5f4qDbNZVPjofrSO6wB4A+cgfgixU8946ncdHGaSMBeI9l+SZMUmELQ/8PBR4eYxvk4bgvgPUYrkV4z6QpjA061hhQTOuDCTO2DRTEMQK8nww+3xtNPG0ZZG7CI/vll/pSDPO4ntuyspJ9XQ+8iIbFRGCmp5NiKf2Wr782IOdLDYT78zXupqdNVROlTBE1tObnrWA5oGllxXL3AI5bWwp8yKUjykUOX7NppRPoN5+3qFkup9t4tUz2Nzenv3OeUMQETELlurjQsVGAfWdHz4EHizgfoHVNTiogJAKBAh71rK6vzdjb2TFDDS87NC1f7wuD7ODAjMW0KB2Rkl5iGLH2mCJ497k8tO00Kqejv8ZpgI2WRssMI3jhNqF4Rgw8+ujRhw9GC8Tg9+UQfOSHqB65cOTFse7lpd77ALBcThdy6DhOaJcAP8Dg5KSVYDk+1rkBGibgiYgjY7u60jFAr263DewAlMKoowexqG/yvHNc0Bs91ZPyDeTbTU7q348fDbweH1utPMYJ8EL1c2rKAC+lDwCii4v6Gdq5nzM4d4jWUBCd6+KVStk30Tr251kGXhGUeyPNNhtHG1UE7/eIyG+NfP9bReR3Zu3nrstDT1Ljaj70HxrN9xWdGqTdx1i8t/YxtFFfh7v20y+CN+h4swDD8Le09e4hgvfXROS7bkl8HqSvUS2DzE2dTjcBIJ5THbxRKoWS9xZTxAzLGMTy7mZnb9e8i23L9ltbyWv16pVFub7+2qTNd3ZM7VJE14HGhEedunr1uuWmMcbzc4t8zc9bwfGlJRNIKRb1XM7OmhIlRhPGYLlsIiuMC6DGPhcW1Fgh94+lVDK65tu3Ora3b9XYOT/X/VGj6/hY19/f13FRDH152QBlq2XGIOp4i4s6tqMjy4HBm4+XnXnJ57bwbghzfGPOrlG0F4B3tzYK5kusjxDopW0X/p9GofM2h48yh/XVsNUAAuTPcr92u0lQwv378aOBCcDO6qoV3YYSSU4sdeqgYa+uWiTuwwdz7FC6gEhfsWgOF6jbiKkAuIjuLS7aWKCHErWq13UMUMDrdYu0eyVJwCOlXJaXb1MoEU3i2ceuazTs/BwcWC08omaMjfy6tTXdvtHQ80l5h2bT9sUxs+7RUTK3DqALKCWiyD0AkPVRu07HrhXj8BG88J5Liw6Puo0K4P3nElGrE5HJNA75OJZhJqlhTupdLsRdtx13BC/rDddrwhykePpdWq8k+0HbqDyIoxrLfVFKB10/bWxpDohYHzs7Ors0Gtn2OwTA+z/2WH7/IH2NahlGZCUNfPhlkOjWy6JLPm9A77PP9O+bN90EAGQ9vx0UTdaJ5elBC0UFcmfHIrCx/MGVFTXevLEGKAR0Qd+kVEapZEByc1ONsN1dXX9+/nYJg2rV6JKlkvaNgZfPK6jb3LTxzcxYvTzokAsL2udnnxnVcnFRt+E8EJHk2cbQqlSMSgZt7P17E3TAWCW3CDEDaF147VdWzEjzOUwYZxhUsahKt3vbsBrVHPsC8O7e0t6dIcDqtX2vSEm/fYf3RSyCx33Fu87TLhmfFw8hygMY8PXQ2B6QAo0QQOb3Ry1KngsviEJEqlQyqvfSkubq8swTvVtctGg8ObVHR5bHRi4sv8MG8FRNolxe8ROnDM+izy08PLRo2cqKgrtGw47NRzUBVN6RQ54btFVompQ7wCm2sGDzQq1m25XLOr/5PDmvkOnVQLlm1PukZE0ul4xOrq8bzZTzwb4Bpz7nMkYFjjkjHrOKZiqIE5GfzdrPXZdBJ6lhPHp3uRDDbHufkbl+4eOsL8csY+6XYBoDEFnasOd4HDLad71XHmu7awQPxb+Li2z7e24GFBRNlqx13L5py6DHDSDrlavoc+82N3WhYPDGhsn+i6ine2NDjQWojNAs19c1YlUsaqTOj3dqSo0llCoRzBHRvLrJyWQUcG5ODYm5OQU/jB86ZLFoOYEApP197ZdcuFxO/+KNrlZtTEQH63WjaUKrQvBlclL3wbEuLJhXHEnyzU39C5Uzn9dIJGIqrZYaRoghAPYAviiCHhzYup7qSaRiddWKJR8dJQUgfM0vqFRepKKXiuK41JOf2/x0ny0WPYutk+Z07Af0PNgK7UC/TUjnDEGhj/pdXuo84QtlE+3z2/j1AHjUe4TGeXmpz3mzaaCJfVDrDoplrabzQq2mIG9pSb9fWzPnCmUBymWjfUNjPDqy3yl3UCwaFfHkJAlSr6+tjMnlpR2j/5+6luTTUcOTaJ7PaVtZ0f15wFgqWY7x0lIy/w4wtrio63BuajXL5cWJ1WpZziA17qCZcuyrqzqPrayojVIqmThMu52krQJir6+1L0Af+ZT+3mOsscjdU4ng/SUR2Y18v3ufk99dInijjDwNE/lKW/e+6ZdpN9wwYLjXPnoBqti+7lIfL0sbl6LROK7dYwZ/tLTzeXXV7X772zq7fOc72foaZg4RkX0R+fdvBFd+/Ob/6qD9jGoZFuA99iLiD7UAmLKWklhdNdVIwFM/Oujbt/Z/mrjLzIxF6AAx/FYsJvMnv/xSDYadHVPDnJnR6Bugbm7OKFcInszPW04cVCyi4K9eKSgqlbRPvNPHx7rd4qIaU0ilz88bHZKaWOQBescLFE0KrQMkZ2ctUuCLqgMw9/bsvFYq1hf3cqNhtFKAKxFJ+sWTj7cdhbzjYzM0T070miB04MUMQgOcNq55M21+EpFfLyKfxX57bMtjBXi+9bIXejkX+zmsuX/C32Lqh7EIH3RMH1nmOfSRPk/R5K8XB+l0DLBBQwSw7O2ZoBKAkOgTgPDkxOrC+TIFrZbVtvN1MHkmycljjCcnOgby85gDiK756+Gppz6HD6dMraZzFDUzAZ/NpkXOcMzAMCDa1+nodkQryQHk+D3DwIMtopUwBKCWwpLAOcQ5K5cN1E1NmWOuWrXz+P69jg3qpc+bRASH/LxQVIXoo6eu8ttTycH7Z28Um85FpHqz/EYR+asi8iFrP3dd7jpJDXqi01D4Y8rFGmVL84IN21fs/7Tv7iPqOe5zPYr+h72/ep3PLNd1GMdEmAPDCwFa3Pl5tv6GoGh+EpGfv1GT+8dult90892nDNv/fhH522nMBBH5SkT+GxH5qZvld/Tr8y4RvJfl9uKpgVmWYlENlljJhHChZtvXX+tncuLCaFy4zM4aoAPkzM8riPOlHMihIW8NA+TiQr/HYFhaUgPMAy4oVScntg8igJOTVjzdG4FLSxalFNH/cznd786OUTA7Hd0PlC2ijIuLBv5mZowGurdnhY8BcWEe3/v3JsRC7Tue/VJJjSjy+1ARJYeFiKOIgVIMbrziRDU81evjRxM+iHnNR916ALz/WET+65t5598Xke8Tkf3Yug+9PAWAF2secPUTrwgN734Oa4zz8HfuMS/OQU6op+0xLoDT9LQ5LrhXT08tP+3VKwMk5My22/ocUE4FkAaAqVaNDkikHWozkTrKpng1yVZL/9/ft/IFnEMcOvPz+oyz/4UFnT/9efTveJ5bcvhWVsxh8/Gj9nN8bHmGrZZt2+lYOYaDAzsH5MsRSbu4SCrpcvw+pw8Ah9AK5xtAWKlYsXgPgmdmdF+Xl7oO15Prd3xsQHd19XYkj7IIiPqE94c/b/4eG2cbmYqm85j/5zfLvXvM73OSik0s/rdvYuMmHuXxjQMQPzbRmW53tOMaBtz1UtLsVe4gtn2WlhbBIxlZRA3oLG0IgPfTIrId+X5bRH46w/a/SkRqfQDeHxtkTIPMTY1G95dBQ6/8u+e4DHo+soq2zM8boJmYMColn0WszIEXSGFZX7fC336cq6sKLicn1bGxv69GBApxxaIZKh7M4I33NEfy7ABBh4fd7uvXVqaAkgyzs+ZtR8EPStbGhuX0eVrk0pIp72Fs5nL6d2HB8vMmJ42OiVjK9bWBNM7hzIzV1isUzDglIk3UbnHRBGMaDculgSZWqeg2ePehaGGIoq4H0AsjeF7E4qEKnd/MO/8TEfmRG9vo74jI/63XNve9PFWA1+32prv5v73KH8RaTGQFIOUpl0SvuN98/9fX5mzw+XZEeqBhAu66XaP6oQaJOm6pZE4R5iyKgQMkqUXJ79TL5PtCwWjRnu7I2Bk/ebPMVyL6jAOKOB6fb0cZB0999GUXoI4TSYNqDWg6PLQadTimyBUkvxf69+6ulWDwJQ+os0fkk31wbCsrug5RT67F9bWpapbLlnvohVk4z34bPl9f3xZ68vjA/3aftukoAd6aiHwhIkuDbDfK5b4nqdgkEvv9odqoI1nQEUZ9XKMGx/d93rPu765Rt7u0Xvcmk1iv1u/3XlHY8DcKQo8xgpea9ysifyVjH9sPBfB8HbyXJfvi6ZpZgaAXUonRYZeXb/c1M2P7YpvdXVsv7MfTQcl3EzGVTtTudnb0dzzI1apRMT//3BL+5+b0HvHUx1JJDbC5OTWC3r414xHqF8f66ZOOv1jUMSwsGHjy5Rwo70DOHXmCIrrvYtGMG/KJdnft/G9uar+bmzbuYlH7ajZ1vxQ+Zz8TE1ZqYXbW6FyMjVwbvPKeGnVyctuAR9UvLJlAG8Ucm2V+EpGKaC27toj8rIh0+m1zn8tTBng0D+y73dsAbRDjmiidp+IhBDQ9bblrYd+APe9sIJ+s2zUhDi9Ywvf0g/MC9dtq1Rww0LeZS87PDUBeXWkUrN22XF0cO6hTEglH5RYBFaJviLJQU7NcVkcN5RiWlnROhHqJQBJCTlA0UbZEOOn9ex0bgPXjR/2tVtNxQD31EXnKKxCRXFqynOlczoSe2NYXIydfjuPzkX/yeIkownQoFCzXkZqeXEeihNwLXDfy+ny0z99D4b0xqJPhrm1UFM3fckNp+vMici0ivy7rtqNc7jJJ3fVE94uSjGN/vdqwOWX9ojXjvCF9VHSc4GaUfY0rYnhf1wEjqFdkNstY0n6PvWSpu3V2lm2MQ0bwtiLf/yMi8jMZ++gH8P4/N/v54yLyK1LW+z65yQHc2trKdrA35yyMPL1E8oZfPJWzV+075P5Zb25OPdgAnhC0+esChVFEDQUiXl98YSUXpqfV2PKF67e3FehAoQToiVitPPLTUNEkJw/hlfNzNd6gH21smLGytmbrz85aHsv5uRpH+bzJg19eGqXUHxPjQrCAY8zlzGAsl3VbRGI4RsYLkEQMAvrp2Zmey0pFAdrFhSl7ct7Yhvyi62sDdFC+fP4L84w3tmOOyVHN3T0omr9NRP6oiPyFG0bTvyAiByKSi63/kMtTB3i8x0KqbgzUx2iXYV9EgzzN16s7+qgdC8BsddWcDe22PisAupUVfR4QMfJ5ffQDbRDREURULi/1WYESjZOJWpg8h9AboVFDxabkDsqTOzumRHl6ag4jas8RWWu1TBgJZzCqlxsbFqmrVi1PljIsRCOJXiK4gmOmVtM5EScMdFPA2MePum9EogDI5NIR1azVklFSmAg+Z44yE14IBsDHfA0lHrEb7ofwOnOdpqZM0CWce9JqLMYwQq/78S5tVADvL4vI2s3/70Tkz2fddpTLsJPUqCb62Ask1vdd95fV4B5UFdJ7vu4zChZGk/pRBgfte1TXNq2vcZ2rUV/fXvvpdwzD/B7SE2hffaWzy6dP2cY3BMD79aL5v+eSzAn+ORH59Rn76AXwFkVk/ub/DyLy8/36G7QO3ou4ynDL5KSCKg/q+H9+Xg2SYvF2/h4Uwrk5U6+cnDQAhgEwMZEsTQG9kCUsW0HuHoAQeifABxGVszP9H9ECaJAYI4xHxCjOFDvH0723Z9LmW1tqzJydmcebfJi9PfPCA+J2dtRwonZepWKR5IUFU9zL528Lv6CGSUSS4uiUnqAWIXmEADnOD/kuGHJQs4jsTU7qOoeHOs/gNYfGhSQ67ww85bxLes1v44zgich/eUPJ/DdE5J8RkVJsvcewPDWAl2Zr+SVmQ3Q6licXOpP931C05/TUIjqs4+vY+aiypzyS9wbwob7c9nYyssd+Dg+Nrkj+HYW+r6/NmUEUD0fMxITl5QEAETJCIEnE8n8BRtSBI3q2s2OOGCJ6OKyWl5N0TA+UiNSxFArmkKGkgS9fAPhlfry8NGq6P6cAROZCwCPzF0JQzJtEI/N5m2+4LvW6UTR9/mO1qnPMu3fKQCB6ybV8/z5Z887byfW6Udq9yE445/Sbf9Jwwl3bqADeT/T6fF/LQ0bwBu17HBG8u9wcHjTgBRumjyzfxfYbjr3XdsMC11EA6nFE6oZZZ1yqn6NoHgz7Fym/YaB+9VW2/gYFeLqJfEdE/qBYTvAfFJHvDLB9KsCLrPs3RGS11zp3qYP3smRbYuURAFaFwu1I3PKyGkGIk7A9nu6pKRNF8QAyDXyjUDk1ZRGumRkzms7O1LjCGGs2TVXS72N9XdcpFq2WXD5vUV3UKKEWYdDl82qsQGPy+YDn5+ZBX162iBlGXa2WzPfb3jYql1f2ozA6ZRCI2n36ZDRLVDVR5jw5UcOJHJtKxaKH5+cWsaCoOeIQ5bJdv/l5E52YnjaxiP19/S6mbIiRndZGNXf2NKJElkXkfyAi/5aI/GkR+Ysi8u+JyG9M2+YhlscE8LLYDF7EIovjNXwndbtmvEPF7BVNieV4AgA8WPBRvasr7Z88OWo8EqHf3U2qUBKhg+5IDUyfYwroqtVsziMiDxDZ3zcVTMARgkWAxcNDE1RaWTF6JaVayPP1AiOULqlUrIbe5aXl80F3RKEXcRVPo97etvNERLPVMmExQCFzAjU+V1YUSFH+AQcThdjJl+NaEvEvl5PnLxSI6XR0PoQtQdSR64GgDUXkvUIq//v+oWsOEwQYZRCINiqA97dF5IfdkvictZ+7LvcxST1Gg7rbHc0N4SeuQXOu7hKpHGTMV1fxejjDAMlB2jiue5Yx9VpnVBG8Xq3f2GLfMV6vbOUNsC+/7I4d4KUtMepmynq9InivRGTi5v9DEfmbfE5bBo3gvVAyB1uWluLfedACpTFcJieTuXYYTfzm16Pfyclkzh9gyn93dqYg5vPP7XdfTmBiQtevVJLjZPF5acWiGhgXF1YsuFxW46NY1P3s7lppBRHdlkLjgDwRjaxBa6JsQ7msHuyZGQVrFD6mFtTCQnyMExNJMRqidrOzJuoCQITqBSiEjnl0ZIIvePErFQO2FxcGRlHfw4hDKS+cC2PUN99GZUB1u91M85OI5EWkLiINUcXxf9hvm/tcHgvA63VdPPgKndC9nNux+wDaLsZ5jBHjc642NgwcIeTho4G+nhs2FGDHUzXJpysWrUbc+rr+//693dvQJXnuGR9RqDdv9DkiR80DOSKEXj2TaBe5ZETnydE7OTHg+e6dziG+ODvgCGB5fq7nAaETn0O3tqbHCGUcIEzEDgAFeIRaiuNqZUUBKKJMi4sW0SRyf3BgbAGcPkREAfG+bl0owEJOJWPwNHBfgw92QqNh1xE1VM4PwLrTMZEY5qt+9zS/x+7fxxTB+95eS9Z+7rqMe5Ia5qVwH4DQe5VG1V+/37NSFbOOaVCQl2U84TqjfHBG1YaN4N3HvRi+GMNz2OtFfH2dlAv2Hi8omuNS0dRN5FeKyG+QmzpUoqUS/s8i8l9l2PYPicgvicg/EJG/JSqO8P0i8v03v/9LokIJPy2aX/Nlvz5fInjjXYYpBr+8nK2OXqxvX6hcRF/6U1MKSACb29u2Hn9nZ9VAgaJ4fm4ROCKMr15pFI46Thh9yIefnSXpo4ChhQUDYogQYJQgVDIxofshvxDqqS/WTh9QvhB0KZVsjJOT+j90TMbkwSlGUL2uhurRkdUWRO2TaGChYEWGiaQWi/q7VwrsdEzsAqrayooZ2N1ukt3gIzeDOgWzth4UzV8nIr9TRP6siPx/b/7+LtEyLmuxbR5qeSwAr9vt/75Ls3f8tQ7f9THHsxcX88CPv96RjGHfahkdj/V8NA/A1+nofeujfFA1ea4R5kDKv1YzcHN6qs/04mIyt/TqyuYOEZsbcNKQO4yTB4Vdn5tHX8xD5AZDA8d55PPU/Pmi1iXFv3nGQwDFOfORPIAjQHBx0cRYcDihFAqtk7IvHz/aPokalssGHr2QCteU6CrAGMonEUKuky8WTxSPPOaFBWMw0L8HkpRkIMJJ/iElJWL3of/rU5JG6XjqdtPnpq7eHpmNqd8mIgdZ1w+2vVVz6oaz/l+L1ZnKVEtvFJPUKKNNo75YafvoJ5Ixyn3F/h9Fv3dNNs0aBbuPa9KvjQqUDXI9hj3HPqrby2uaNiZvbFELCCPxiy+yjWFQgCciv0dE/osboPaXROR/Iyr+9K+IyMwgfY1qGTSC99CA6akvvXIY08Cg/75XBLVQsAgc30HxIUH/yy/t960tNaaaTTVWoGRC3yQKtr2t0T76hKKFl50yCFAbazUDaAi1TE6a+iZ0LhEz/PyxepXOmRmjdRJhIxduYUH3ubdnIiqofAIMSyWNIPoiwZ2O5cnNz+vvuZwVhAfclUpGGSVqNztrVC9yfph7kFiHHoUEfC/FzJCCN8rWA+D9X0Xkf3njbJoKfluPbfNQy2MCeLEWgvVY1CP8PosT0m8Tgjp/D11f6/NG5I7v+AvQ4/47Plbwgtrm8bHViFtZSYqwUCgdmiTAA4oinz0V9NMnnWsozM18Ab15f98A4sqKPis8WzheSiV9Vjc3jSI+O2sACyDlxwnA8VG462ud13ytN34DvJKDiMomuXLQromkUROQMRO1RFCJaCTHcHSk3wFEDw+t5ATni/p1bAf99ODAIqdcJyir1PKDZksklKgszmqu3eGhAVvy/bhPaJ7J1EvddZRz1KgA3pmI/AER+cmbv2ciUs647a2aUzcA7wey7p9lFIXOx1HnbdxtVNG7rCCp37p32fcoAVg4Qcf+H2Rso2iDHF+a53nYPu9yjtO8pr3GEbtXWi2jyn3nO9n2PQTA+ysAOREpi8jfl0hdvPtcXgqd398yNWVOhDDS5pf1dStMHgNxPmeP6Bzgj+Ljr1+b15vCwGFfFDrf2zOhktlZNSYBgYCzublkSQU+E/GanFR6Vrmsvy0v67pnZ2rAoDrpjTp+93l+r1+bCMHmpho9qGx+/rkJq5BvA/1rYcFETwCXm5tGrULWnagf6qAogW5v6zgaDSvavLCgxlStpsdDjStPycJgIoLXbuuzAr0KdUGMvXDuD3P0Rtmyzk8isnTDBvgxEfl/ZdnmvpbHDPBi75XYOyz2f6/vwhQC/o817jsfufGaBScnSTojdESAki8BANghKsX3XlgFgEde79qa1YUE0OVyVopkclKfo+Nj3ScOE/L+yO9bXEzSNim5Qo4eNEXy63w9u6MjA1+AVCLnROiur/U7VH2XlozmCEjz5/34WNfZ3zdgxbEDLNfWLHoGQCZi6KmSRM4AaQi+HB3p3+VlA5UHBzoPca4Bkji1ANuA9u9+V/vnuDiHAF+oudxXoYbFuOegtDYSgJfYSGWAf0hEOiLyn4rI7xCRwz7bbD8WgDeOOm9PoWUx+u8KEMaxblqDLjFoLmHs91F7fgfpK+btuWufsfVHdXy97hE8pGtrOrt861vZ+hwC4P1E8PknB9l+HMtLHbz7W7y4SK9IHtQkX5LCq2IitMK6afsqFk0dE/ojpUBEFLAQKWu1TIiE36F0fv65GkMIlqCSyXrb2wrUCgXNk+H7L79Uo4McO4y7qys1bkSS4/GRRwRhKJvghWpYDxl2DEdKOSCTvrlpBdDJCQzHPTdnynZQP1dWtG9qfL1/b6APOhvlIaBqotiHcYUkvS9KHDPYx+FApPU0okRmReR/LCJ/RET+KxH5e6JlVibTtnmI5TEDvG437lxMu4Zp199HT1BeTIv40o8HgBjxIWXRR5DYD9FrIj7k2QG6KIMAHZO6cdzv9bqVVNnaUgBEjcm5OXPQ8AxNTOjvrZbNYdAc2QelFKg/2WpZ+ROeH8BVpWIgEEXMiwurS+mjVVdX2pcXHeGYvCgLv3mAjsNrakrnDYAjET1yHymOvrpq4+V8cmwA3XY76TTifPuC8KiZAg5rNT1fqIySQ8i15hrWatp3uWx/oYeGdRFj93Ds7zjbyAFeogOVE/8ficj/oc96MYD3N0TkZ24onKnRQBmy1lRae4rgLgsou2s/d133PmmRfpLNsl7WyNegv42qjdM46devf7FmjTrGtuWzzy979Srb+IYAeH/vxqBiSXwepK9RLcNE8IbJK/umLt/+dv91fIQqXHoBvVidu5mZZHQvn7cxrK9brbe9PRMD2Ny06PTengmNrKwYxbJS0XUXFiwXlbH5IuMiauwBBj0g3NxUo4Lo3uKi9o0cOnk519emAlet6niazWQOj4gVPn/7VsfJcc/MGI3U75s8OkCtB3KMwUftyKXb3dXjh95ZqdhxEfVbWjJ6KFFA8ovwtGMkk7vT6aihWaslc3495W5cFKhut5s6P8lNzq9ocfNfIyI5EfnrsXUfenkKAC9rnj3vfu/k9e8iCmITvQlLIgDq1tYMHPlcO/+ZPojkAAAppH1yYt8BnsjTojwIEUAolRQpX1nRPHVA3dyczjk4myht4p9nomE8T+S6QbWkvALRexSEyUGDjujFUMgD5n2Uz5ttBViamNBxAJo5PxRQhzLpc824HjimqMfpqdbsB+opDiCAIPRPIo9EKH0dUKJ80C0B5UToLi+TOXh+fe4F/nINoYei0sm4s7KtQsf9uNpYAZ72L78mwzohwFu/mQwnReR/KyK/P8u+HvskNY7WK+o4LCDoR8Ubpt03cM66v7uM6yEivvcJ2K+vLQEY3nk/D1W4ji8KysSGofzll9nGNgTA+6d6LYP0NaplkLnp0yd7YXvD/mWJLwC7fjl0hYKez88+S34/P59Ug5ycVCAT64ccOCS6Wy2jQSJYgLEyMWEADc88uXuUQtjc1KiciP5988YKgJMPR5FhomJEyyjjQG4cFM183mrekdOCAVkuq1EIZfPiQr+LgeNXr7T/UsnOB0COunaccyiSgEHoplBZWy2LQHBOfZQP8It4y+SkAUloaj5qQD4PXnnKUxAJ9HS5cTsYewC8n7pxUv+AiHx+891fi6370Mt92k7D2hf9oiI+and6agDB/xZG2LziIkALEHB4aIWx2Q+A0Dsrd3asRAn749mjL9QWASg4gihdQE1MolPUmFxb0+eWnLnZWXMCUdcznzeqJiCF+QFgyTv49FSfQwRUcAa1Wkph9AqSABrO0cGBjqVev+34ZW6pVJK0VYBUCKQ9GLq6UpCH+AlOHM771ZXNUZxjaK6AbCJpXD+il9BT19cV8L16ZfRYD0YRbYIC6umX3AOATk/rDM9Rr3JaaRG8cdqP9wHw/maGdRIAL+tv4TLqSWrchvso+scIH5S61+v7URYav882DlDX62UUO/eP5ZyNyqjhxYA6WL++Y1E7POnQITAKV1ayjWEIgPcHBln/PpYXimbvJWu0Mkv5iDRlzPV1o1AWCsn8PJ8Lh4iI3/aLL/TvzIyClnZbAQj0wc8/T9IbkdYulYyaSG0ocummp+MA/tMnNe4oqUAkoNk0uqMHYXivGw3dhiLqvkAw9CNydZBq98Bxc1MB5sSEUbeQWgfEvnljYPLy0qKICKb42nX+mgLuME6hoJJfSE4e1/ezz6zMBXLopVKSvrW6aobc2poaoBjTPmoXRvBG3fpQNCsi0hQtev7/EJG/I49MYKXbvT+Ah3E/Cq2DmEORJRaV89u123qvtVr6GQPeOw0AGQCJEJhcX1vOKc8VuVu+vh1gALXFVkujPxcXVjZkctKiZkTZGo1k8W+cRThBEDhCmRcaNTUmK5UkBfX9e1PUpK4dxc5RrCQ6iTAJ+a5eiRJw5SOl1BPN560Y+8mJAUf6xh4gx67T0eeYguGcbyiZnHdURYkGQgOlr+Xl2zRTSsEcHel5QQUVMI/6J3MI54o5j2tFBNQDQB9BDHPrPHU1dq+yTuz7UbeRALyADuWXPyoi/78M24cRvNfu/39VRP7DLOMY5SSVlRIwzv4H6WuQ3/rte1w3211bv+PMSrccROSkH9gNXxz9zut9tlHuL6Qe9DrG2DnwlA4M4W9/O9u+hwB4PzHI+vexDDI3XV11hwZKvWiKj32JFSofZimV7P9eZRB87bvpaZPon5+3iFW53P1lQAJdKhzr6mpSmlzEwBOFwvFuA87W1w0UiagxMTur3x8d6W/r6wpy8I7ncjpeqI4zM2rM7ezobwC1XE5pWtCaMNDIm8MQnJ/XY7640HE1mwY8RayWFkXb37yx8ZPvw/EiQuPPETXxcjmjYRHR87TTmRkd7+Ki/g9A3N62fCBy+zCqyD3CC48R3u2aAYUBirofbdQOuazzk4j8EyLyb4vWzfxzWba5r+W+I3j9qGx+3ay/h8ZyGphkvVB5FSESBD2IANIHkSkPHInSESnCGeOLYiOhD4gCfEAp9E6lw0OL7pHvOjFhDhaeoVLJ5pt373Q91G5R361Wdb+UGYBKXq0a7ZKo4cGBRaaury13DfBDRIu/iJqQl3ZwYE4mnLmUPNjbSwIwH9lE3ZJ8OV+nb2oqWR4FsRXyHT0du9XSeQtKLXPfwoLNFdAuj47smnEOfekKHEelks0tPirJel5kxt9bgNtQcdzfM9Tg87+Ng2UwKoD3d0XkY4QO9ZWI/L/7bBurOfV/EpGrG3rDH/GAr9dynxG8GId20IuTFWQM0l9WwPFYQEjWNkrAPaoIXqyvtBfXuB7gh27h8cTOV/is4HnMmjI7BMD7L0XFnmqxZZC+RrUMMjeFOVIvS/eXQcSg28Ry73xhbmqw+d+ozwZAhLJZqagx5RU2fQHz83MFTUTa6ANAMzGh362t6XrUmyMnDe/8+bmBrJkZ3R8gbGLCjDe89RT77XRMPAGlOBGLFnC8Cwu2f0oX5HIq3AB43djQ891san+lkm4zN2cUSs4BSp4zM9pPPm80U4oXY7QuLmrfi4v6/E9PG92V884+UMQrl9VoXVgwOlmhYGAP+rh3Ivn6Y576FYKAUczJQ8xPBRH5HYNsM+7lvgHeqJ2xfptuN0n/C20iomreEPc5Y14IJFyQ6ie3LuyvVtN1vDom9ylAi//JkwPooZLJu7FeN8cIQkwiSYAGTRxRFuYGIk1zc6ZkiUNle1t/Yx5AlGRxUceO+MriohX+RlCJvDbyXXFeocTpxVxqtSQTgJxZchDJfWPMq6sGjImm+lw8QJkHfR8/moIpcyC5cUTrEKthziSf9/ra6ulRs4+xra4qMPWloXAsQQPleEOg5qnBsXmG+yLt3h2ljTgqgPfHReRXp/z2n2bt567LfdIMwrDssC+LUUSlWDcWbRp0cnzMIOQhI4tZIqH9wu2PaeyjWjdWUDjmZPDPCi+q2dls+xnCgPrvRORPiyr5hsufHqSvUS2DzE281F+W5DJsdDIEeVtbVj8KNUpqzG1vK+CYm1MDh20AclA1WTwdEYByfm7rv3qlVE7AmQep5FrOzlqR4RCIvn1rtamo94Z0+OGhGoSFguXSeNpRs2lAa2pKI2o+Ykm+zu5uMsdORPcF0NzasrwgD/CmpvSYODZAcC5nThwifRQyX11NnrdcTr+v1UyhjzzCSkWPZX7ehCCaTaNaYZDPzCQjByjm+cLH3mueFs0btvXIwVsUVRT/ERH5Z0RkQkT+JRH56yLyh/vNGfe53Ld+wagieLH1SAnwOVGsg0iKV1X0kRUPJEL1TF8OwBexRkgEEAPQ8OqanY6BJgSGCgWLqIelEYpFoxjyLMEmQIUWlVlqUObzui7FxHnuGg0FK0tLlnPrI1zNpuUVV6t6LPv7FumHOtlsqjOnUkkW9KbcAOJH5bLuF2fT9bX+5fxQ1uTgwCJnnFdKI7x/b3UDuVZ8Xy7b8802/PV0dPL1KAexs6PjWlxMppswJ3DOut1krU3WIbIJQIPGCqDzOaA+J8+zC7zwU9p9/OgKnT+WZRRlEgZdt59R36+PfhG2UUabsvYxiu3GAWbucvPf5bh4MWQBbb3A/kMAvEGdBMPWx0t7BrwCFetjRBaL2fYxBMD7yUHWv4/lJYL3eBYPFEPwt7enRlOYR7a5qXlhRKgAOmwHcGGbmZnbpRVmZ42Gyf0/NaVGFhTP6Wk1QlChJBdve9uMMgxRDEIoml6gRCQpYgL48uPZ3DQQS+7d118bkFxfVyEkEf3eRy49FRMDVMTuXY59c1ONup0do5mRkzM7ayUViDjg3ICeSWF4DE5EDxBd+fjRxorHnoidz4/xhtyoa932AHh/WLQu8G8Vkf+LiPwZEfm/i8g/3m++uO/lISJ4WUFeuG0sXSB8b3lQxjr89eDLG97UToOWDB1vbc0id9xXRG6urkycBGVM8vAODzWaBAApldS4LxT0np6dtXxYQMf1td7PpZI+A6jKfv55MgIHHRLV2rDEyc6O7nt/3+jde3t2bETCKQeAM8VHyVi3WDQlWwAlVEmonP4ZI3rnn8vJSR0LwNkLorDN9bV+R94fFNBq1cbJdv5Z5npwbQ4PLR8ZkPvuXbKep4/S1mo6f5MnTJTSRxM3NpK0USijgFqAPM07A0Il334OpscYwdsRkX8y8v0/KSL/aNZ+7rrcZZLqZcRn2XaY39J+j0WGBu33Lm3QvkMK3l1Ab7+xDHt9ho2u8iIa1CiIjXvYl1rWfaStc9dxD7JdzJMaTnK8hCYns/X93ADeSwTv9jI9Pb6yET5nb2vLIlJ+fxhPfD87q9E1REIAOQAiX2Lhq690H1AgqZOHct7FhRpo0ChF9PejIzVI2DdFwsll+fjRqF65XDI6SEmEalVB2fy89kfZgelpo4iyjQez9bqeC+iWKG6ibAmAm5y0cgnkHTJOwODmpn5mG3KKCgUDitQHZCwIppAnQ82qy8uk1DvGVaVyu0QCxh9zN1Eab9iPovUAeFfu/5yI/G0RmUmbIx5yeYgI3jDOxPfv9VnrlRPuI3XcEz664il0fjyAOAAO6wAefHkO8tA6HQU+0COhGEMrBjDV6/qZe58oE5GlYlHv4WbTHCn+mWHeOD+3550IP88mokci+rwvLelzRf4doO3gwOruQfckb42INyCo2UyKtkC3BNB4iiLUaAqGU3rAAzrEUUKhExRwAZ6Ap1bLgBL5dR7gEfEDVB8c6H5R+GU+ArwDAD3Nc3paj/PkRM9vp6OAz9O6OUbmXo6Xc1evJ21ERG1wKPh5yN+n49T56HbT56au3iaZDao/JiLVyPdVEfmjWfu563JXgJdW8PIufQ5i1IcX3/cx6I0x6P6G7Zv1vfeC74fp6y5jGaTvQbcbxUMYo4wM27JODoNE7/ptnwbiaJ3ObSeJnwzZlhdWodB/XN1u70kqtkiG0iz3vQwyN/mE+pdl8MV7sn3uCgtROyJda2v2WyzyRp9Eu1ZXLd/My5YvL1uRYSJpy8smsFIoKMiDjrm+bvRDAIunN+7sWB27N2/USNneNgOJXDuK9FYqptAJdRKFP0oNUKgXURa28wqfRAVEkrROZNqhsnIO2dfFhR4j6pcUV/fnlGjCwYH9TomElRWjwJ6d6VzRalntPOr9Vau6LQY3nnWvWki9MeTgPRMjfFfdtfUAeD/R63OvRUR+rYj8nIj8goj8YOT3/71oGYafEpG/KiJ/z/32D91vfyTL/oaxncZhf/Ral6VX/lJsfT4DCLwEP79xX3haXah6iCEPRdDn0EFxfPtWn/lPn/S5aDYtEkZUC2BEBM2r4hKFJ1fVzyXMXaWSzh3Fovb5+rXta29Pn+9KxRwtb99a2RSA0MmJUb8vLzW6Rp5auaz7h/ZIjlq9rs8U4Jf3PWCL80YeHtsAcn2uI9vwnBKl8xHFSsVUM4m0QaGlDAYRVkA1VM7ra9s/QO/qyq6fF3RBaRjHHrmSoZOAfF7mE+/ADtVVuc8YL1HgQRzoo7B/RwXw/lKP366y9nPX5a5eqGG8SsP2OQiYGSSCNygwyzJZ9uqj2+193rL2lfU83aVl7XsQQJT1IQzP9yDbpbUsjoOsx5ImGBQep6c9XV2ZN5XJz3vX0sbCyyyf7z82XX9ggNe5ycGLLT82SF+jWoYpdP6yjG/xNM3NTQOFYbkDAGLW6GEaOLy4MPECT+08PzcRFnJeiGShrEdO2vm55dvVagpqKhU10gBA5MwQVWs0TBxlacmKBVcqatQVi7rgjZ6fV+ORAu6obVKva2LCVDw5ho0NK2NQqeix5vM6bmoPsi5UMURoZmYM3PloIteFcz8/b9HSmRkzaKlrhXT7yYn2tbSk+1ldNUGMbve2k20UrQfA+4ci8t/eLP+diPz37v//NrbNzXY5EflFEXknIlMi8tMi8u0e6//L4moFi8jfT1s3bRnUdrpP56uPyKXZGP0YNtgXvJsw7q+vk8XGDw9v0zqJ+nlwQhQZ4NRu6/O0tqbP6eSkReFRcTw81OevXNbv2219VqFYE+mfnTVqJQCxUtH7mdxYwCDPyNmZ3vOoaM7N6f8ISCF2xL5xIs7O6mcvjkKuGiBuedmAFJE4IpmAIyJ5RMgoLcB8A1Dz55NcRoRnEIxpNvV5J2oIGJ6YuF16guggY/OF0rtdo4uyD+jhgErmC8AvOYP+3vAiTr5sRrebzOUL7zcv4BOK/WRtjyWC9/M9fvuFrP3cdRkFzWCUE39an+MCM4NOuncBtDHAMmwbNNJ5l330A2bDRMbuEqEcZMyjGEPafkJwF9tvLHnYe8fx1B0fG/ALnQAUhxUZq4rmPxFZ/kUR+X/2ckaNc3mpgzf8QtHsce4DMBL77c0bow1hLIkYWEOoZWJCDZL1daOUktsG2IISBTURMInYCBE0yigUixYxK5f1PBC1Oz83KfJiUfezt2dRBYw3cn2aTQVfRPH29mzciJv448ZoRJCF/DrKQvhzhyLm4qKOe2XFog7b2yZiAwD02zabem4AjZwbgG+tloxqz89rH95wIpoA7cvX8PJRmHG8Zwadn/otIvIrReRPuM8/JCI/1GP9P+dZC/cB8Lrd0YG7LDaIjxbF+oA54iN8/t0InZLPp6dWC256Wu8TnCY+wsu2oZOAqBE5aMfHydIdiJlQ382XSYBWzfxBBH9iQkEezwDAgyhfvW70UZ6XclmfL8oM7O+bYBHgDxGi6ekkzXJ2Vp010C3JJ8SJBB1xZcWcKD7iVioZzZOx4eSlv3rdFp5NIqGnp1ayoN3W8wFtHWdVva7HRK4t55/cPKJ2zab2R4kDcv/W1ozSyjgQU/F17i4v9ZyRI0gN0akp3QcgD1EV7kUPJsNoMU4Efy/ddxsVwPtDIvI/i3z/W0TkP8raz12X++aRD9PGDWYGvYlC436QvrMA1awRqmFv/kG2G0cEL+s2sXOXZb+9vhs0ipg2rqwRZv8XTrk/DiY8vFzeoGKyu7pKGndZ2l0MKNFyLX9KtMjwPztsP3ddXiJ4d1uyRNB8ZKzfEoK5WAF1ivcCNubnrbabX8itobxA+BtlEw4OtD885J9/bsf12We2zdSUCa6wX9QuEVdgO/oH/NE/YgiAKU/zon8oXJ8+qREkYhRT8hK92AxROsYbuyYYqCLa/+6uqXGyz0Ihrog6MWGF49++tQgHRm2lot/v72sf5D5Bh8JLj7HfapkjCqMszeN+lzYGgPcbROT3uc//vIj8SMq6/4homamc++6/F5EfF5G/ICK/vsd+vu9mvR/fyuptu0Mb9n3LOwojO+2dyHsGkR2ieoAKnz5AhIWoE+8uoky+hRE/8r4OD82ZAh0QEAfIohYmUTAohFAkeYZxYvio286OrgcrgGd9asoifJQcQfjk5ERBE2Ix1KfsdIwhwHNMgXXERHxEjHcQETp/Hr3CJLl8pVJSmZRzBZDm/Kyt6fiOj+3cQ5mFvXB0ZH0fHZl6LtG+737XVH0XFqyIOZHUDx8MuB0eWl4jAiulkl6PnR27bwCYRCWXly2q6ccaqqqenFi+Hr8BWsMSCg9RI3lUAG/9xov0Z0QLef7bompRf15EXmXt567LUwB43e7DIPm0NijFMAsQyFpnKGu0LA0w3vdDM+xxhKDei45k7aPfev3OYb8+e7WYiE5MrIBrxeLpCZ7K6Y3sLG0YA0pEvhaRP3sD7n71oNuPehl3BG9cAiSjWIapX5cGukSyR/NevUoqSYp0u9/61u31QiVNX+ybc4s3/N07+436c6jnEaXyfeGhL5dNoGRuToFPraaAx0fOEDVZXtb97uyooYgXmoLsAM1CwYwSCpUj3oCHv9FI7mN62nJWEINBMIYxsF6tppE8+uR6AsY+fbLjPjvTOeHiQscbigV9+qRjXVmxchI+z6jZVAPs7Vur05fLGQ2VGoTNpi5ck4sLo2VWqxYpwOjD8MKIHzWD5oEB3v9aRP7d4Ls3N3/ficjfkAxCd+O2nfqxUfq1MPqa5uQk0gQoa7f13oLa5/uj1IG/L2ge6PG+82IrJyd6f0KnrlYtMlerGTURgRLy2Ih68YwsLur9jaAIlOnZWf3sI9c8ZwiCsG9f8oRi5Air8HxcXSnwELF8XGimzGEc3+qqOYmIaBHJg4bqqZIAVv/d+noyikcOGv159Uu+r9UsX86raRL9JCIKFRPGBHl0x8dG1aROHfRNfq9WTa2XaC32CcDWHx8gDhDqU1SgmRLdZV3ygbmv+gUxfAR61G0kAO+XNxD51Td88H9ZRP7pQbe/6/JUAN5ja8NG8LKsdxdwAT3C0y9CoJEWCQ2B5l1bP2CVFSR1OpaYG9tH1rFkXT827qzHgBcujDh78OYBXQwEeo8p63mjL0sbgqL5l24Mm39RniZRQt0AANDlSURBVGCh86ccwRsWzGVdfOQnDQAOsiBZDnih/xAwz82ZoAqFwUU0YgUQov4d4Gd11URJqHnHghEHTYsImy+/cHFhnvhmM5l75nPg1tetWDLACcVMcm/8MSFUcnFhanxzczqmrS3LjcM7vrSkY2i1dO6qVg0wAgCRUl9auq3OOTOj5ymft6LoU1O6TS6nYHVhQaN11OSiKDsiNAggbG2ZsAvjhHa1u6vXE+Pq6MgMzlANuZdjctD2kBRNEflJEfmyR19/QER+Q7993oftlAbusoJt/24hGhsa0NgMgAeM8FhfHrARbSFSNTFh9N5uN0lR9JRCSpygTlsoKAgC8Cwu6n0J1ZIcVxH9jVqVsABQ1eS5zOeNGo5Tp9UyIRCooPv7um6ppP+fnup+ebaJgPPsITRSrepzksvpsVxemgLl9naSckn+GnmKfjsEUTifqEqiNopCpo+qfvxotEmuWbNpwMg7ZzodKzXBM44IFaCUvijf4OvykWOHw8srfF5fWzR2ZUXnJYAfAJS+vb3jab7eviHCx73YLwDRb727tFFF8GZE5H8uWtDzt4pIPuu2o1xeAN7o2zjz4rK0tAgek3y/B+guL+5Yf74NmsMIIB3FOR1k32lRT39+wv5QiEpLDvZROp+A7n/n5csx41m8B4D3ZyRe5LwjT6DQ+UsdvMGXGOUvDWwinLK8bIWz+a3RUOqkB3YiCpooJry9bZ75iQmTLkdYBGEUDxLJx5uetpy1XE6jUHjmoWRiNGJwUEAdsIbgAH2jiFmrqdGTz5uAikgyx61SMfGUfN4EZhYXzVCj8Do5MMWiCR+IWDH4iQmr/8d41tYs6obYBOUhwuvFuSCHMJfTa3N2puvMzFheIjWqiNq9eWN5Onjtp6Z0fYActDpPJb8L0yGtjQHg5UXkr4nIWzGRlV8RWa9y48iacN+VRWT65v9VEfl56SHQwvKQtlM/sB2zAWCQED0Jo3Nh/cOQXdLtGggk182/y4gE+3IAiHOEAhwHB8nnk/ccwiG5nG47P2/lDy4uLPqFkwbny/a23ufkmq6uavSaCD3U6vl5Eycir4wIFUJHUKmJ0rEt0f1OR7dbWLBSKuT4odQLSEZIBmBcq1lZiKUlnSvIQWR9ShCQM4cY0qtXljdHBI9yCUTeoOUSmQNk42zK5Sw/zhet9/m35PCheorDbHExuQ+UQysVmz99gXXEWLyzoNPR/paXrfB6aHPG7KfQ7oo9A6NqowJ4/5GI/Ac34O4/EZF/J+u2o1zGMUmN68SPq99R9u2N/HG3YcacRZloVBE83x9/B82lHOR89orSpUXVwn316juchMI+/Pcx8Ob3EwOHvg+fEO1rjo0L4D3GZZC5qdPp3jKIX5b0xUvypy3QHVdWDLTNzVkeCoAwFA8RMUojqnXVqu5zYUENqVLJFB7zeQUonvaJiib0K6J1GGtLS1YaYXvbjK+jIwVJ5OX4vLmZGe1re1vHhpFXq1mtvEbDiqWjqofXnlIN0EcxDjnmyUk9LsDW9LTVzwvXJVJJhIFzARgrlfQ4KIEQLs2mFUInlwjACPgm2kgdPKIoPoeq3U6KOzE3jbruXdjGMT+JyAfR8ge/KCK//ea7f1NEfp1b598Qkd8ZbPeliFzdgMIrEfnNWfb3GJzjaY7ckMXD9154JVbQ3hv7ROlCqp3PmfKRl5CO6Q157qlazaJTRLvrdaNeEjVDgZb8VujZBwf6d3pan82trWQtSBF9Ns/O7Hlk7tje1n55PnmO5+YUDOJAAUxS8BvHDblzfP/2rVGcUchE3bbRSM6NPu+VXERAIRTSjx8tr+742Pogv25/3yjUCK7wTHuqJfshGg8NEuDMvogq4tBBadgzCqBqIgwDEKeOZj5v25VKRrsF2NVqyfnl/Xs7ntCO4rMvsxCzu0at6Bu2UQG8K/d/Xgao9zLKZdST1CijP/TH3zSqSLhulv7C70Y55vsCd4MqQmY5zqznIisIHGac4fZZxFXSompp24TrQQPtd+3SIpx+//DTvVpmLMfST2p+3/4l22zerkuWpQ0Rwftfuf//ueC3f2uQvka1DAvwwvywl+X2EjtHGxvJ+nYiVseOJVbSIEb7pB/omawD1ZHPeNsBPWz/7p0ZbgDR2VlTl/z0ySIAROMuLsygwru/vq7GGKqYgEb2s7Vlggbb25Y7uLBguWtQy6CF8d3paTLq+OaNgUD6R/xhfl7PJeeF6On6um7vi6BjXEKr/Pa37Zrl81YPkPOLMIw/doQgDg4sv6ZW03OMeuDaWtKwv742xb9YlGfYOTzWnpsDapiW5R0cvkv8b7H3jc+bg9Z3cmIS/T6Pi3fT+/dJ4x4wV6tZSgHGO5Eh/seR0OkkAcH79wquoEty75K7Cvi6uLCIP/PI4qL+znzVaOh3Fxf6vQd8X3xhrATo2pOT+ty225bzurCgz32xqGCS8gnMX8WiFQ6vVs0JBKAjfw/HELRGInZQS4l2AnJRsOSd3+mY2ubysp4r8vN8Lh0CNFxLgDU0XEofcA07HQOgHz7o2JaW7HkH2PmSERSY5/rj6PIAnnuAcjGI5yCi4u8ZxlguG73X22zcazgK0pwU/Wy7u7ZRAbyf6PX5vpZxTFKjADi9JqdRSPXHto+tP6o2ir7Sxpx27LEX8l1AsP/fP3z9+s0KBmPbDXKts4buwxcd/Q2iFhdbx9+bvSJ4sevmX7p81+nohOcN0jECvJ+I/R/7fF/Lc8nBG3YZRT6dV9HEyAH4hDl1/URpvv7aIlQYbDs7Rn0ExHz6pPuambH9+WVz0zzbHvhtbVmtOr/kcma4sQ1eeqJcUETJQ3v7VsEXUUrun+VlM/5QkIPGOTlpxqI3PisVA5z+fAKil5aMRloq2bmZn7dzur2tY9reNk84lM5aLam0SdSzUDBP+8SE5SwCDMkFJAoJrevkxOr0eWMQyXaiM34uyuJAG6S9ALzerZ89w/e8Z3qlXvj3XLgO1xZjHDDgr/vlpd5DUHtZhxwunARHRxaxo14beVilkj0P9bo+k1CIKxV7rqEq12p6/9frOh+Uy+YIaTTs2fj0yfL6oFAT3Q7nCRxFW1vaH/l1sAVwFlHvstlUUMR2RBahlUPPRLUWpgML0T7o4bmczivdbrL0wdWVnWMo7e/e6b5CVVPO5dKSgk3y8LxzmcgpdFicx+TwNZuW9wyV0rMUALELC/Zbuayf83nL4QNwkm/IuDxDgEildxx4dfCQChyqaIbgLnaPc/+Pyl4fFcAbqqDnqJfHGMGLGb587/+G/2el8Q0KBsP99Ou/X1/DtF59pAHWXoB40H2HD1AaKBl07Fn23Qtw8vug+XWxF91dx+r7iY2/38vXG1H8xovzHgDeT8b+j32+r2WQuSnMU3zuC0BnEKVQokxs+/p1PGKXtszN2fp44ms1fdnv7yfBGZGsuTl98QOMyGWL5QeyTE/bfqam1PB4/VqNPiiWS0vmsWc7asVtbxst84svzCj04Gxz04xSatEBGjm+ctmMQ38OAIG5XLf75Zd2XJWK5daFxzc1ZYXMRSwi4QGhv67r63qMW1u6HTS0iQmjhh0emriEVwoEzJLLVygkqXMYZ91u3HM+CkPqBeD1b/0cur0ciqxHJCQtpYC/4Tvev1MvLw2cYIBjxEMVRKDDS/7jHKnXDRyR08qzMjdnkvybm/obOW65nAKdXC757CGqQukS1DPJk0WNE8dHpWIqsiL6udHQceFsgmq5s6PzSD5vz0+5rMALkRXo5OTZ+rpyAMdSSR02U1PJIuSAXyJynE8f6SPXeGnJBFTI6SMXjlIMACyuL8Ioi4u6LtHF62tz8Hz8mKzDd3RkcxlCKj4nEucPOX/kQeJgKpWS1NyrK2MBoOLpi59zf6yv6759pNFTM/29DhU4ZoemAb9h20gA3mNZxlGsc1TRqixerKzlBbKOsZfRn4W+N0iEkBb2229s4cQcG5u/6Xvlnd0FEIVc/ixjH7T1AtqMy3O2++0ri5Ey6FjT1mdiZmyhsIqfvNJom1A0vDGYpT23CN4LwBt+CUsOeCDlP6+uKvV4dlaBSqFggMODLpbdXfVuQzcUUSBydmZFxRErwdjb2bEcGhTytrdNCXJy0sAMoAlv/eSkFST3wisiOlYi4eT3+PEuLOjv5L7V65Z7g7Kmp6vOzGjf0EMRdSEv8OuvtT9+I/8OEOapsIzR02YRlkDoBdAIRXNnJ5lDGYJZQClFixGWKBT0mkAtg/JFlID6XD4HL4zkjaK9ALxk63deQwemvzaxd+T1dZJ66dWZWScEid5WuL62SEunk1RapV+idBcXJgzC+oAIwFEup/cr6oyVioGTet2ep3zeSn6gZpnLWc4ulOew7Atg7uxMn2UvgNRq6T1fKimoIK/PPzsLC7re/Lw+twCg42MTJfFRdQRdUOIlulmp6Hbb2/bMrawYWATE8MwBwqGaEjXc2zMgDDg+Pra50qtd5vNJgRaUMgFuROmnprQf8nC5ficnVgIB4Eukn9w88v2gaJKbR+6kp+JeXVk+4NGRHvvamuVs0ohKevAGoPT2pk+fid3Do5yXut3ec9ODTzqDLoNOUlmB1KhOeJb9xIDPIKIc/nPasWWNEPUDXbF+ff5V1mhY6L2L3egh8I29CIZVlfSgz/c3Duna2HjDl1mWY8pyvGlAstf6abQB7626vrZJMiyF4Gvf+TFCpTg4SL6QsrQhAN4/jLAJ+PwPBulrVMu46+A9x2WQiFyvBe859ElfKuHdO/1/ff02bdGDI/7u7ZlBxneFgqliQgGiHMDsrBpaFCtGRKRWM3U3vPnttqlubm4aiARQIsQCDQzAw5jps163mlscB0CxXNY+KXPAthinvn7W2ZmVMZiZMYMV49Sfn4kJMyg9HRdg57/7+msDiGdnOmccHanBRi0rcoqog0VEBsPr40eNtK6sJOnqXoxjUJGstPYC8Kz1cmL632P2ROx96B2KGNwxCltM4IJyRBjpXHeMcQAekRoETwAIBwfmOADwlMs27+DsgCKMyiT3PZRtakQeHemzENaH/OIL7Ytc1GpVo3FEsFGthQbtGQ0XFwoiAYmIL+Eomp83IRMiUzg/AHmAV+YFot/lsoHNRkO3YQ7BYQs449x+/GjRexSHoT9WKjrPkQ+MUArRNdgRROsaDct/I5+PZ5yi8NAsibLhgGOuQFimVDJBmaMjU931NfxgfCCA48s+0D8A01NJoQTDHPAUYU/bjN2r4TOQ9kwN2541wOt2s4GcQZD1XS5GbHJ8/96Ue9Kaj5r4vkYV2Yl53HqBx17AMLZ+OJ4YmIuNO22Sz9rCF374W1j8NGufg6wbO5cxoN9r+7TfYl5ND+Bif9P43yFg4yXqE4fpP/RS8YKmztW4Ad5jXF7KJMSXXvTFtCUEWb3WQ/6b+msxUDgxYePwkSdyvkTUc4+wwtdfW38ARPqhaDEAZ3vb6E7eUDw9NaMJqmGppM8IktwIixBRoz6Wr5nnj7VU0ucun1egSYkDETWU8GSTSwK4Oj834xkAyTkG9J6f67k8OkqWKpidjRutCMigSieiY6H/lRX97quvLIrnaa9ffqnrFwrJqN/WlvZ5cWERRGTYazXLz6P+lveyhw60lxy8u9lOaa2fnZDVkOW90csG8PvBoOb/dlvvcV/EG5omhbgx7tmW+6ZSMQcLBj2RK54NnCx+vuDepDSCp2OSV0pdR1RnWci/3dy8Xe9zcfF2ju/qqkUHvbKvnxtQ/S0UrLg6IkU+6uhz3DheKIvktxLx4jxR1qVQMMokFNhcTvcH2wBK6OGhPq8fPphAFeq3lIWBys3zTP4bLIDTU6NqA1yhYV5d6TW4vjYxHMAmoJWII5R7XzqBqCWRSuwXHAEofKL4icAOgBDnAWqtoQCLv2djFM3YM3GXOerZA7wsLc0Qj6131xdGzLOVBjK8p4rQ8KjGEdtXls+DAOJ+4wzPey/a4rDHHFIKfUt7waS1YccQPuSxJPNhjjkNwIUe7F600XA/4Xox4Mf3rAfnvtW6bdBnac/NgLq+Ho3oyEMuw4w/S44dEaF+65HzkmW/gLUvv9T7E/oiJQ2gVHrjLpcz2f+NDRtTuXw7OueLhTcayZINeMQXFoziSSFzPOgieiysQ8Tu8ND6Kxat9hXGEXWpGPPGhqnPQZnC47+wkBRzAFxRK48SJ+T1iCTrDJJ/yGconO22FXMmEsE61NUTMUVPf11ev9ZzH4vyAXhbLSuKfHKSBPSIQPBMYWyFTil+H7Y9t/lpkDbsee3lfI016HAANv5vNg20AO6gz11d6fPA/cIz02pZvtnCgjlbcETwDsNZAZVwY0OfGUqf8Lu/Z9++tVqTnk4O/ZI+iIaLaP9nZzbHTE/rc0ukjNy/tTWL/rfbNlcUi0a3FtE5Y2HBnhGidx8/mlomdE4PgqifB2vHK0peXiZLqAC8OKdHR6ZwibPJA2gULMlZzOcNGJJbxzk8O9N9+WuJU2xx0Y4HkEVqyPW1UTU7HT1+VD4pQk+ULhRMof4hdFCc2NBQiTJ6G+vqyujhMTvLp7n0suXGNTc9+KQz6PLQUr+xddKiIWnbDkqd5OYJPQIxUHJXwJe2feylmXVf/UAM33sgG4t4Ze0r9KKElJ2wD++BGeb6++8HOSfhOIZ5+NMAWUztMrZdmmw1fXiaQtp9d31tlIty+bYXMkt7bgYUYhYvy+1lZSUZPRt2AcyFQIXlq68MgPA7hbunp9VQIxcnn7cI1vm5GgmlkgmhzM8b6Ds4sNwQXzcrBDB44X2hdShEyJ1DkWI96JLIgC8u6uKB29dfq/GI+AERBgDTzIxug9Ll9LSOeXvbAKQv5D4zYxFGH1n1pSUA7t6gDc81Igtv3iQjfCyff24AFwrn/HzSY47BiAohtLxu16I2CDOEzIZxecmfyjIOgTr/N/w/7btwfQxh/g+pmN2uOWKJkBPNWVqy/Klu10AgQKLb1edyaspKHBB5w/Cfm9P/cT7Mz1skjmg/zgsRKyz+9ddJh9W7d1a7DieHLymCKAvPaLms96t/ZgB9RM6mpmxeKJUsF5X6leT5Tk7quQC8QXEmMreyYmqgjBl6N4Dn9NScTkTVwjIVUD1hKxwf27kGGBExpPwEKqMrKxZ5Pz1VBgLgkDy/3V2jezebul//PPs8OdRziUpSKsOP5ejIaolCT8cRcH1thdCJMDLXrK/b/hCYgRYe2ly9RIHCHL1ROJvC9gLwerRRAKIwNBvmqcW26TfppVEXQ/pdmB91V0XFNCoh4iD+oR90X1lATOxBie0rra/YuY8dj/8c1i4Z5Hj8/4OUYsgytizbMnnEvEfe0+R/Cw2f8B72NBdAnleOev8+OakhV95uJ2uGsWRpz82AAhg85mUYauVjWRYWjA7Fd9RnYwHwfPWVrcdfX8T71Ss1ntptEzXY3zdq48yMRfUAYeTczc2ZAAr0Trz+ExMKwsjlIzLgi7NXqwq+5uYsYrazY0bc3p5FCxFcQeFvYsLKGKDMB3UMYxWvtqdOzs2pMeSjnNXq7XvW1wqcmdElpMd+/rnlDYZqo2GE4+JCj2tvz8476oAYWnjRMfDJDcLbDv0uzeAatj23+alf8++MXiA6/C60WfiOKA3pKt4wvroyyh6RGYxnCnHznmJ9ImXkokHxhRKNswFxICT2t7aM8jc3Z+Ik+/sKjkTU8cNztrFhtex45jz7gM84kHzOHrm11LODJcC6lD5BfAlxFKiTIpZH2GopeLq+tmeXKD9Apl63MiXNpn7//n0SeNVqevzUtCPqR4SvXrfi7YiosB2ROwAj9esQfGq1LC8OdoOPmp2cGM3SgzWEdzodK2hPgXTAXLOp54QSCdWqibBwLQDHgD9y+S4uTISF+wcqKPfV1ZWOwYs59bK7Q9sstMtGyb57AXgpbZAT3Wud2AXuldcVm/TSIla9xnF1ZZ6GGNAc5Bhi68QiPKG3Les+eoGeXmPv9VvasQ5y7nuNuVdL68cbFjHA5dfNch/0G0Po5Qx/D5VJ086nT3LHC+5zHnzBUyZwJj9yY6BrvAC8/i2kqr0stngvdr91oSXlcknKkwdx/vPMjHnnwzw/7ltol76eGwqV5E76fWG4UFyc6Bt0RqKCIknJ8tVV7Wd3N1mQuFzWPhEgIDrnhV6KRYsqYIweHZmyHsIJq6tWK4vogYj2zfGjYMk+qC9IFI+/Imr4Ud6BXCDOGdfAn3Ofu0h9Pn9dNza0f5QLl5b0fM3Pa//QSj98sLweQB5R1KMji+55h11aG2a+f27zU5YWe6f3swl4X4QOWJ/D5B2N3qnoa45h+9RqFv05PTWgMjOj9wvy+D6Xi3QCnitUaAEfRLEAcQCl01O9X1HEJRJOuRVy63CkrK7qep9/bvf80pJF3DzTZXranmmemTC3FdERclOZwzh2wF+hYNRNgNX6uj3jRPsAMe22ngsEUZg/yK2DGk2tSlROeYfhHFpYMMVLgCbnD4Xc42N14JCXTJQVCijjIhcOYRNfi847lMmPw8EGHRWgCpCDCcB+pqasuPvamo798NAcR4yVfXY6RhlNs91iQZsQ3L2oaI54kup3IrOCqqxqhlnGk3YzDNPCBPO0NmjErRcg6wWs0hKu++07lqQajsW3GI2zXxskstZr32m/xR56/oZh+rQHPevLshc4zAIkY/0S7fMFZENAiPccrxbfkaC8t2d0tBeAl96+aSqapVLyMwCoV57eoAqZgAiAiIgaTzMzSpuKATpqrPF5ddXoTXiXw/0gWgCoef3ajEEMO8ZPhA7nRqOh/W9t6Tmg7h3rI04AbfLTJ4sOUu/t/FyNj0LBqJ4YSZzPV6/0WeP7hQUTPBBRgwaAOTVloNWfS1T7oC+J6P8YnvS1vZ0Ehzs7JkdOJHB11YCziNIwp6ftHJJ/hFKnL4RORAEBGbb3+YxnZyabTv7N4aEed62mwJPIUK93yLDe8+c2P8XaoOcs7f0Y2iyxPDwAHJGaWJ6ez88C3J2cWOQIWuPOjuVv4Qh4/96oe42G5ao1m/obdOv1dX2WUW/0EWcAnXdIQaUOc403NqxYN/TO42PdBkD3+nUywg3dk8h/vW4Km9DIczkdP8/szo5GGwGeIlbA/OrKaupxbup1o6lTJ48cN+aTel3/n5jQvhFs4TyvrOj+oWVCu1xZ0fXJ6cMxgwCMV+qktqUXQGEdaJjcJ5RbAIzVanpdymUr2XBwoOcScE+uHmCR6+xpn/Tn6yReXpoAIuDO36/9gjSh1sIogR3t2QK8QSbzfuveNeLDdj5se5fGzZXmrYxF4rLus9e5SPut3z6ynLO0UHfYT0x0ZpC+BwG5g4LirLTcfg982oTRT546bbuQshQbq/emHR0ZJcp7rVDKRGUKD5cHdl5V7AXgxdtzUtEc1RLmd62s3M6t29zUXJgwDxQQ4j97mtTsrEXacjkFGPm83udQKT14wbgLDSXETIhEAU4oTHx+robbyYneA4uLug7CKxhpUDAXFzW6VSqpIbK4aFFLAJxXmwScLizomDAsRZK5ctSegsLUaOh2iCNQ+w8j8uuvLeqABPn8fBL4+XNODiUy8xiO/EZ0wM8V29smEy+i69fr2gd5gajwYfwhfkA0Yph5M0t7bvNT2Aa1pXrZCWFLy7knb+vkJPm+wvnoDW2UH6+v9bdy2XJcybsCLKBu65/rqSldn/sMmrAHaD7iTwkPD+S4l8lbxenEvT07m5xHEHCanEyKF/n8bFQ4KavAdwgWeREj6nwSrSSKf3FhTqRCIVncHBomBds7Hctv/fhR91GtGguAKBuUzNNTHQtlEWo1U8VFzKZQMIBJ9HRtzRRLT06MfprPax+tVnI/RGk7HcuVIwcQ59vFhUUrmRNaLUs3ubzUsbx9a84DFDMBrd5RcHJi+X6e/uupmyEdPGZnjRPcdbu956YHn3QGXYaJ4A0atYp91w+pDzKeUeXKEW3JEjEbBNz5v73WyfL9Xbx+sbwyT+OIvbhj4w+9LlkEXPqNP3bOY8qYvY6zFygeFBCmjY1JNSzy7hOJWQ/QfHqqhh6er6kpi1KwLVQM5KYxEv0LjCVLu28DSkR+v4j8bRH5yym/T4jID4vIL4jIz4hIrV+fg8xNIQh+7EvokU4T04itO64ljL7l82oUNJsGeHwE8d07NY7wsi8uJqN40BYRF4GOBSDy9eKIsgHG8HAj810q6XOCFzw0xhB2yOVsf/6cVqsGviYn1ejC8UKNqFotKf7CAu2o2dTzAXAC4G5uGuAkAjc5aUZTvW5y4uE5pywFz7mXkX/3TreZmTGxBKhvhUJSOOfTJ6Ns+nPiqWXkFlH0GWoY+XgUNOYzzINw3hzEBkhrzx3gdbvx89jL9sj6rgpLXXS7RssEuJFj2WrpfUz9MtYlt6rbtXyxxUVzlBDBubxUg31pSe85cvU8oIIejBgQNS9j89zFhQG5XnMkv8VUf6l16Z/9YlG3R3TIK2TS1/y8PfvMH+SwIcDk6+WhNAmoWljQOWx/3/LaKFUAOIO+SKQQKnqxaJHBclnH4qmRRNlrNaNeA5aIhiHKdnCQpGhS825pyfbTbluUHiA6NaX7PT7W68B9QQmFkxMDcuTnkpvXbGpfvki6pwET2UN51VNGseHZHpAXprf0cjKNsj17gDfsifUGeC+Z+WH6j1EOBh3boBG8tIiP/5wFoIwrAhbbNpYr5r/31wSAFXoE08BSuJ+sUc60sWV5qYXeTZ/fFvYfHhufB43Ehjxw/sbyIOjfn18mQA/6vJfs5EQn9eNjM/D8CyxLewCA96tEpNYD4H0QkT9+A/SOROQ/69fnIHMT3tvnvviCvhsb+lK/C0Ak1yxGDSWf7ODAPNzLy2ocAPxE9HNYw2p31zzoW1umvufryc3O6rYYTxhauZwaFICpN29M6KHRUANrd9cEUMj3Q3yF3BfoatCj2H562sRZJid1bMi/T04mwR3ACuGGfN6cDe22bksuzqdPFmGE5imi+yPSRiTw0yeLfiAig4Jfp5M0dqGKcmwbGzZ+kaRwg1cC5Livr9WIQ7IdIy427w/idOvVXgBe/L2UNUoXNt5JV1f63ghpbCgb+velr6V2eWl9IcTBewtjvtm8HXFCKKRSMXEUonFE/HhmPLX7O99JziXk0nFf53K3Kec8V5RY8Oq36+s2zyG0giMGUSYcMLAKNjdN9OXszIDnxYVF3XhuSiX9rtlMUrl5bniPI0pCFA7FS0+fbLeTz2a1aqJN799b7i8FzAFEnY4+fwDLw0MFa0dHZlccHVk+3ocPpt4JEAWwdToWref+IZqPyiZzxPJyvG4eojsA3KMji+76db2TG+fA8bEdD/euL4zuKaA+V3TQ52KY9qwBXrc7eIh00MjJMFGqUHFxkP6yRg97rXd9bWqIHjhkSVDPSmXtN75hxsw4014sMQCYNX8y5nlJawCzQa9V6N2MXQf699t5sRQibWn78Z/TwF24rlcSjY0xpG0yqZXLVtgUeob3Rj5WgKe7lO0eAO9HReR73OefE5HXvfobZG7y6l7fxMVHq/wyaN5duCDNz5LPW0kDahaF25TL+pfcFaiS4XgQMIFa6fuA7vTmjdW8w2jzAi/5vBmNOzu6Hsbj/LwahBhh7bZu68sloHJXKpmnHNA6NWURLehK5MJhMBHtopAyYJWIJmMnb29vz+5FSiRcXNh5OTszdT9PcX371sRk3r41OXhvFFerZgyK6PiJlkJj29w0kZhiUf/i0YfyPT9vXniMMIypqyszwGKRoFFE77o6ATx7gNft3v393u0aUAujJr5dXur9wfXkOtZq9h7i3QaIYD2ozCJ2n0NJLJUsuoUqrc+r3dnR9an1xrzBPe2fc+5jEc0pPTqynDyed56Rd++0v5UVe95w8tEXarwAQ2ijOKbYjjw+xowziIgaKpU+D495k/nEC5bUavo9VHAPwNbW9DNziGcrMJcyp6BGCujC6UJUizzG1dVkLj/qqO22/ka9P45nfd2if1AtoWmSJ4cgihfVOT62fD1UQU9OdKyIz0ABhg58eGjRYT9GaMJeWfz0VO9ff4zcz6MKkmRpzx7gdbvZo0l3jToNsu4wEcGYF63femmANeQPhwnQaa0XpfAuqqSx7dO8hb1e3LHIWr+xpAGnXtsMAobDiGNs/7SYJ7rf72lU1hg9M5bo7l+ynFsMJrzgPs8Fw4okdl6OjcZt+l6W9ggB3h8TkWP3+cdE5IvIet8nIj8uIj++tbWV7WC7Lzl4fvGRsnD51rfsf/LtQkEXvwAgKIgb/j47GweZ09O6MJavvtL5APU6VPeIfr19a8ptAMLZWavthAodRcPJX8MTL2LFwSliPjlpUb5m04xMjE6vhFks6vNHPiy0NGpJcUxIvpMvhwre7q4VDF9YMCEH6JGhQevPE0AMELu1ZdRWDMDdXTNIKdqMceqBIqUQZmZ0LNDqjo8NHC4tmXGHiu/BgdH3KI3AXMX8Nahjt1d7AXh3b0RHZmYsJ8uzRbpdcyZ2u3Ha5uqqpQuwfq1mUSki28yv0JRLJYvcE6nmeUP4hwg8IIi8uTRGATRPonthju/Ghu6HyP/urj4vCBoxF+zumoBQ6FwCLELZ9GCT+YhyIQAeqIhe/db/LRZNEAUKN/2R98p7vl7Xc1guW/mU6Wk9JsqnwOzpdOzv/r722WopOEMIif1SsxInFeBod1fnA+jX3A/v31uO3tKSMSAAp0Rpy+Xk8Z2cGAilzh/gnqhdp6Pbkmt3eGjzBwAPmqgX/gEEcm+G846/p8PPL2USxjBJDRPBG6TvYS7aIMAlHF8WMBgCvV7gsJfIib95Y/l9aVTAQcfdC+yGv8UAVhqwDVsswtcLhMVaViBL/kAatdKfd4AmFBSiqmGELw24h59DQ8fnM/jjDWvbMbkhpNJqWX0iQCMvloMDe/l4pT6WLO2pAjy/PGcVzXDJSrOMrddLeTO24KUmahjm5mGIERHzv83OqmHja9qFhhogKJ+3fB2vYkcEDrn/hQU1Ii4u1FgolQzAoAS6va3PEpRRvMl47wF1/lxQE29jQw2XRsOMGO6nSkX3SS09auC12/q3ULDoJYqiCDxMT5vBt7ho6y0uKvCdndXjw0iqVG7n3O7s2Pn3Ajg+/4/rBXj89MnoYXjeMeYQgYCChxFLrat63Yx7T3c/Pb3t2LpLewF4d2u817iGvFu4Tryn6nW9bxD2ClNZuP7e0YgqoldVRGSF+wdQiKFPyQGe10LhdmpBeA+HC/c+JRL8XObVfn0kPewTcMVcBGXaj+XTJ9v21StzUDGH4GilzAOOFeY6ar0BaBEyaTRsToJuzTwG9XBhwfokMuZr3VarBsYoP0CkDGo1QibNZtIR0+nYNfz4UccE9RtWAvcO/QDWAGb7+1bvs143CiwgkrIJRAOnp21eY5zMi4BlT/clx7dUskgfuXe1mq3r7dHQ9sqSHnWX9gLwxtT6IfVB+/GRF996fc4CBnsBgV5j8eMJZf79+qgUhUAyjWaYNo7YTZ/2Yqa/QaKJ/B8DOH5/o4jg+pdTLDKads0BhGzTK4euXwQxbFdXRm2J5TAyofE/E+/lpU6M1Wryxfrhg050UDgQuAgjJFnaIwR4Y6VoXl6mGw7PbVle1hdwlnU94AkLsWNgTU8nI0+5nNE0v/pKo28YVFAj6c9/FlFDq1RKCpL4BZB3cWGRRXLLcjkTK4CGyD7LZaNRQjtD2Q+qIzk7m5tGLSsWdR3KDKBQyT6ooUeeEfk4797pufMGrYgCLx9VOzuz2lWNRlIJkGgjVNJ3724DZtQ4iVJ6+ursbDK3CcNzY8PyiJhPDg4URHql3k7HqFlIqWPEeboUDisvujHoXBm2F4A3fPOGOp+9IiHOTK4bUvaUNuCdxO8IePg6aeWy5YdBg+R7noNm04AMFOvFRXuuefZic0ps8Y6kmFPqzRsdB/f59LQ5hlgH5V4UcIliixgQhO65sXHbeXV+riAHcRWojDiYTk4MWAF+EELx/fD8sT/YCQCzy0srBk+6Bvm3KytGgcSphNPJlyJAGAn2j8+pJYoG4ATkX13pGE5ODMDlcia6xPFRkmFlxY5zedmEXzodu9fqdT1fRCapA0oeHRFMFMN9nT2ALPfmwYHZRNzTOJawd2OssFFG8V4AXkob5OSOO8xKCwGG93z5CE8WkJcGGLOAvFhUKwRC4Zh7fRfzcIT7jNUM6eV97QfGYkCUbXySdmydWB9pfftt/YvIe3fS1uU4PBjkvMfG4vcRnqte15U+4ZLH+ri6MqPJ5zhcX9tk7bf3icy8VGPKe1naIwR4HwORlb/Yr79B5iZoOs9t8RLgIXDzgI2IWWw9b5QUi/oihwJJvhtG1OefmyEX5s1A54GWmcupVx5QMjubTMqvVLQ/D/4WF3W/ACtkxSsVM7quriwXiJw1vOwzM6bkeXRkUUAAUbNpKpzk7hExAJBxTOTpYOQQDfCAlULrGI9Qv3Z31dD19DaWMIqRyyUjnsWijWlnxwoI+1qIFJhfXjbwNzurcwiGloiO+/Aw6YVnvsHIQ0zB53Bh7Hnjivnr9DRbXkyv9gLwBmvhOzwtgnFyovdOjOrG+8rbEHznqX5HR3rflEpJwZDzc6M+T0woqPv6axM+ItoMVVrEVC6Zj2JOHeagr77SZ465JqQzM495kOjpz+QEi6gjhTERUZ+dNWolEf3NTd2nV9WkNAvlBU5P9XmemNBzQG4yxcZZ5907y++7urLcRKJy09M2n5TLuo+jIwNKvr4e4iao7EKNhdpIRG9qyoA3QJ0oJDmDOIqJ8kE/PTy0uRUaN/cHAK3dthIMzJfk9JInR84mIjKIt3gnN4Dw9NRyB6enrWQDDqfT0+Rc44EeDqe01J+XCN4YJ6lBAFqvSarfdsOOzbeY8d9vbH7btHytfoAgre+sIDO27xBUxPbpP3tPSNo4w/33ivr5cfmHMnYcva57LDoYjuH9+ySYiq3LSw4+etpY0sbH3zQjxlMNqPHi8/BCdU4mOzx1PnoLfYEXBP1BfcHzHxqEWdoDqGj+IRH5JRH5ByLyt0TkN4vI94vI99/8PiEiv1dEflFErvrRM7sDzk1PiaIZoy71WmKGzqBL2AeRoq+/tu8qFatJR902qMLks1AkfHZWDZrVVTPmiL5hbKGqCQCZmzNZcL4jCkZ+G0Yi6pUzM0YtPDxMqrVdXFh0r9UykRbAbLOp23gnCXlxPmcI9T/q5JFXwzMImMI4nJzU48aY9OCsUrFzS7+lkin1oSaKkp8XofnsM4syzs/bOuQNcQw+j/L83Ixc9o8hSb4P3ndKUWDIr60ZgCTvkOiAp3wxl3n2yQtF837SW1g3q07A9XUyghdu52Xrvf0DXY4Itaft1Wo2Z3mHD++lzU29j8gdbzbNyYDwCc4YKNlpc1qMnvn6tT0D5N/BUJictOi7iDmdEBny705En3gu2cfmpokxnZ3pOxiQCh2UIuTQK5nnzs4sklYum0qtV5LkXY+ThecPx8vxseUiT03ZcwczAobC0pLue3XV1CpRRoVyeXRkgJ1jbTbtO8Bgva7XmegdzAXEWXAukRfH/VEsWukaxJiw+7hfwvy5kxOdX/zv3W7S2eTnmVATIYzohb+Puj0KgBerOyUiyyLyJ0Xk52/+lvv1M8pJatBJa9B9DSshnLZuFpDTDxT49UL1qjSQFts+6wsz3P76Ogkqeh0TkTb/gk47vjCHrd/4ePH7HIC0cfT7Pu2Fxnn2xe1DgM1EkEXBsxdwDdWbOBfr6zZJ43HitzDCen2tE6DPY/C1pa6vzfsJ7cHTGnhpeFrXYwV441gGmZvC2mUviy4YKf47L1GO4ba0ZJ5zJPwxjubnLdfEFxCm/+1tM/xQjtzeNiGPqyuN5B0eWqSNPJW9PYtwzc7qOC4u1EvsKZanp8nIH2qbIjo/YSQVi+Z5X1kxsQAMPgCNB7ZQiqj3hxGFtDnrkbuDqmelos8mgjDv3hmVc25OowIIsiDAglFZKJgQRbWqxhzAttEwAQnO9+vXFjHw57/VsvwfqGCNho6bc831YxvyXjBYDw4sMnp8rN+123HxKP/3Lu25zU++DeIM99vwN80pCShPswe8YzKM5GHwLyzoc+LfSUtLdl9VqyaiMjNjkXnuc1/b0tdpFInTM/0zgRODHF+/eFGhkDkQ6/vtW7vnGSP7804YnkO/D4SdSiV97kRM7ARqoadfEu26vDR1UYRLEEpZX7d8Rd7/KFKiOAmrAFC1smJ5tPS9vGzRQ7YDNGE/HB/rvACAW1qy46ZeJ+szVxKV9fuamNA+cDhzf3AuqQ+KaMvSklFBvf0EjXNpyWrveac4tjNgOLRPveL5IDUhh22PBeDdqjslIr9bRH7w5v8fFJHf1a+fUU5S/aIld229Ij+99sfvg0yqTJhpoC0W2VlbS0aZYucnLOcQo0X2AkGxchAe3PQ6Rh9W71WPMOZVSRuP/9svghf7LgvIjgmihA977F7oBej7CbWE/XjA673YXO8wD4/zC6ceuounsl5fmwwxPPX3760eTatlkQ6/ZGnPzYB6aoXOB1kGieARzUFJ0tMPMUh6ia54A4rlO9+xfDEMAYDd5qZ5+Ccn9d5ttcwoxFEhYoBHJBllggZGXl4+b+pz5+fmMS4UdP7K5bQvjiuXM0VMcvBQzDw+TlKaGPfUlHr+yRH0BdIBezybePZPT4066kEitedEdN1y2cAyCocIMACaOQa2R32QiCTXyhvMGIFcV653Lqf7gEb34YNFHRBLoNzF5KRFNl+90u2I5qF6B4vA06FG/T5/bvNT2PrZK/yNOXO9c9OnaXihlfD9zTuJ64q0Pv0SvSPfanFR31lQNlstddKsrOizur+fpCpvbycjfX6ZnEyfw5iLoF4WCvoMAyKIWtXr+jemEByCO2iN0EOnpuz5urgwWiL5r9vbpmQrouusr1sUb2bG8lKhq0JnB0wiyrK2pgCRqD3v/YsLBTe+NhzRtsvLJGNncjJZLH152Sjis7M2x/M/eW0+r25tzSiVzD3QRZeWLPqHYufhoSmSFgommkMN0EJB53r63NoysR1sH46F/3HwQWklisd9SQ5wu61jAizGHPqjdC71a48C4Ok4kjkvXrhARF6LyM/162NUk1RoYA+ahN0PCPTabwwE0GJAIOsYMLi9GEoIjMIIT2xsvu8YcAgBZL8I2DBA178E0sYVUnD65ePxoomBxXBcaWPtlRPY717qByb77Tcm1BKLbPrvw317qiUJ0P5e43derP6FDFjHa0ceDC9PDOnQIM/SnpsB9U0FeGk5diy9Shz4ZWrqNtUXg2Z52WhOXvHSixd89lnyt5kZU70D3BBto3Zco6FGha/jeHZm/SI0hBFXLptBValYngr15sJyIYA/qJnFohpStZqNxxf3BWhubxuQnJzU7aFlnp0ZwMXrXa+bwYcBzLEXCmpIUiuPYykUdL8IqHjBidlZMyq9cYpxvLGRBHdECDGSPOWVgugY4BjFnJ9CQa9HrWY5RLAQ8JgfH9v5xvjE+cWc1U/Qa9D23OansPV6r/qSPDgQ/e/+nRvmfHs7hIiev5a894gmXV+bWmO1qnMBRc0p2VMu27349ddGU/TP9fy83oM4S7797cHmOR+1g9bp56n5+WQtUD/v+e+pqwmNPG1/RMKJQgLCdnb0t0ZDj4d3MGqYi4uWp0yOPHmJsHXISQMMk6+8uKigB7ol5/royKJuvgA6NM5yWdfhmrTbum+EonAiN5v2P7l40EY5Z0T2lpb0uPf2LCpI3b3JSZszOA7mePIMOe8+txf7BzYFDj4vCBPanuT9wYjy93JaCtW422MGeH/P/T/hPwfbDVVrqtvtDZR6RV16tazgIO37NBAQo82lAYpe+4uBthAo9BprL5AZ2ybrufPr9KJQZgGA/tg8jSBtf/6ch8fV6/jC79LOnf8+zbDod82yRJj939NTy90LxVfSSkgAiJlQ/br87ksmhAnEJGOXyxYtIFoyNXW7jpbI7fMZa8/NgHpshc4HLU2QtoQqdN5rHi6FQlIpLtw2rSi6BxnkuLDk82oAQEnCKDo7M8OPouH0s7ZmtKDwXBSLCkQwOra2dFxe+hyBlMtLo1kheIJQAoIirZb+9cqdUD7J+VtcNLnxYlE/k2tHfS5vLEItw+jtdMyLTQSTPillQiRwclLnAoAXAgfUApuZsULKRAyhYBNhQJUPIOcVPukjNHLDyMnUlCkAQuMkCkiUtds1+lS5rP3hjQ+NqtAR2OvdkKU9t/nJt7Rz6H/vdu190s/W8hE6T33z6QP8fnmpxnuhoPcG7ySfbwcg4f7EsTEzo/9TkxI5fO45csZYiKD5OTGkX8ZKIYS/z85qpGdvzwqdLy4q6Jievl1aBDAyNaVjmpjQdRgr+4F+KWKgEpEn5hJfu29hwUSLYAvs7RlFFXYO+XeAI0+7JLq1v28FxSkp0Ono3EmUkc8wEI6OLB+6XNa5CkoloiV7e7fz3Pb3LQrry8CwlMu6DkqhzLeAR2ppQnOHBYJADdFeDyoR6oFCWqvpta/Xk/Yl9pHPCw01HbIGBEbVngTAu/n8d/v1McgkhbHbL8+KdWlZwEcYTQn76Pd9GkAI992Lx5vlc3iThSAy3Nb/9WCh3znp972/8bNE2wbJ8fNRSz/2NMDkQWmWsYTHEBoT5P35eyLtuvUCfWlA3v8f8r3TIni9wHsYmfPnwYM6jsMDvstLneCJMMCbJ3IzM3M7lyFLe24GVKdz2zj4piwApDRxFkBH1v6gUwJQROweI2dvbi4JCD99up0r8/q10apELIqKp9jTi2dndb+hEYg32JcLgBqGsbC9bQbV9rZRLcmFazatjtzOTrIY7/Gx0UQxcHd2zDhlPJOTagSinre8rEZPoWAGVbOpY0QVD5U6ootEK+lzc9MElBYXzeBtNCxC/+6dOXSo7UfEAEMUZw+RifC+YIF6Sl4fwgnkIEHR8mIYy8vmgUftzqtM+zaoo61Xe27zU9j6nau0dyPNv09CGm0s+udpgZQ6AHRQmwwhDZ6lSsUcKKiy+jnk3Tvd98yM5eFyX/q5pdfiAWKstEtYnoX/maP4HEbCcTih/OmjecxZ1KlbXbW8w/NzBSLFYjJij1OEuYTadTyT8/N2jpeWjDpJ1I5o+fW1RUwRSIH+eX1tNgD5cDyT1MmlXmWlYkCXY2k0dM5mX52O7pvo3sqKKXIi1AL9k7IYOJfqdf0NtWKAJCVXcIrBNJiftyicF47b2TEnUqlkTicP9EImmL+/Y/f9OAVWut3ec9O9TjAPQdGMRaF6tSwiHWm5YGn779c6HYvEpPWRxYuWppbZK3eNv71qymW5SWMAIwQ4odfDb+c/e95+2j7YJlTDjO03bJ7WGRb3TjuGtOPk9zBXzSfkxmgrsSU8llgkdVBnRfgi9QWAw3sl7TzgMf3wwV4QeNZ8zSsRnbCfSh28US+DOp/CSOdTXGKlDMIlLRIXLqEHPVxiuTG+bwQ+Yp7116/1vvXbI3Szvq6GgTfwUJMkv21hQbf3CngTE0aN+vDB6jgheEDh4vNzE3Ug13Bx0cAMBhnlDQ4P7ZkjF217W/f1/r2BRl9wHHonAkk+MrizYyUYVlbs+zdvbJws0J+8IunOjilkUi8LyiiG8d6eAkVPPd7ctHMKldXTOjk28hlFdL/U+2JuWVnR/r2kOc9Qt2t5MlnZL37brG0c85OI/NobG+gX0CIIfj8Xkb8jIj91s/wW99v33ojT/byIfG+W/Y27TELMGcl7jLI73a7lTfpr4yMk2EMwRfb3DSjwjBQKRvnDOTAxoZ8/fjTlyFgB8+np2/UbwyVUxowBs7RlczOpFLu0ZJE91vnqq+S4Pv9cnyVYDW/f2nMR1tsjAvfpk/5dWEg6alCiLBSMsokYDXNesWj5aYuLFu1DXKlcVmCE3VGpmI3q89WoBdds6nr8jq3Xaik4o4j41ZWNgbn16kr3DV2TeQp2EA40H61vt+2+oIwBTiy2OTkx4ZjTU70uFJ5nPN2u5SIyL+I0I3J4cqLXkBJRoZ3kAwax52Gc4K7b7T03jXTC6rdEAN7vCURWfne/Pu46SWU52VmiRzFAEVsnC9j4+DFZXyzr2PvdUINEjuAWx/Yf846G+wlD1H7C9hQML2ftgVBYeDwGSnu9wMPj8WOMgTImCg92YqH1XoDajzX0Ivv8gVevknV+8FYeHd0WsEE1LARiPq8ydp/EgH2noy8LriueNc6/P7+xuoCMCQMW3jv1vU5OkipVYc4US5b23ADecyp07nNO+i1QjwCO6+tJEEcUDmPH05tyuWQUiXVEkvl43sCZnjaDScQiXJeXRqWCGpTPGzXQG1yAKTzhjYYaFlAgEVVBOZJi37Waqk8iP35wYAbs5aX2R+0uxGSaTT1WIoQLC6Yih2R4rab7gdYJVQkvP4qUFHvO5w24UtMPlUzOD+eaaB0CM0QdOFesi5ccDzxCL82mnqv5eVPwrNcVGNIX0Q4MwUZDt6M0hadk+ohQSCkfpfd81POTiORuSrC8E5EpEflpEfl2sM65iPxIZNtlEflrN3/LN/+X++3zPurgxYxeHJP+3VgoGMjzNsL6uikUIurR6Zjxv7+frI0GGCBaxbNaKtn7yDuhVlf1uUD8hHmCv70o5cMsuZzly336lHQiIShFRE4k6UCq1ZKfWRA2Yjk7s37Pziz/z4O+iwujsjJ/lEqmrlutmsMWpxHOFOriolwLnd2raiIyBfgmAuejcbAc1tb02OfnjTo7MaHXtt3W+wIVzN3deJ06avMR9RexiD+g//zcIo8wj5iL2m2rg/f+ve7r+FjPEyCV4+beitm33a7ZkX6eic09/ez7YdujAHgSrzu1IiI/duOF+lMistyvn7vSDEbBxw/76gfKwv2mAaN+44tFfNKAW1YKJp62Wk0nmhhFNEaBCffvb2g/YfMXUEMkyE/+bB87d/7/LJFI9knfx8e388h8X748QTietDzJ2Ln3oCmMUvrz4GmSYdSWa+Hrrfjvw5enN3I85dQfi/embWwkFUk9uJuZuT0W6BsTE/qiQMGK9TBCSRCPKWiK3L6usfbcAB4vpae8oFI5qv4o9ht6yTGAqOVG+QBKIwDufHI+ht3MTDyCSG4Zzom3bxWkLC4apQmDjwgbdKdSSceA0t3urlEwyX/Z2rISChQSRywAxTcA4O6uCY+ghEkeXEjl8rWZyBvBAw34q9WsnhzUKIxdauohyEBhdZQ1qUdFBA8jVSSp3Dczo/2/eWMAHnrowoLV8NrfN+ORfCQiF1BZfT5irWa1BX0e1fS0Gq8IMvCuur42Oiu5M6HQ2CMFeL9SRP6E+/xDIvJDwTppAO97RORH3ecfFZHv6bfP+4jg+fSV0OD166EW6x2Z3W7SkF9eTkrqIw7GdeYeJsrbapm6LFGrr74ywSCiX7Ozes/G2AfhfObX8Xm9IfsCkaIQ3IkkS7XwXT5vbIKJCdv21St9VotFS3v49rf1eVxbM2q4j/75OcLXy6PcCLTLkxOdj6gZx3x2cqLPE/8DrIn8894/OjLBm1zOgBPvMkAhQi37+xYJozYeNfsoD0PuHnl3bEP0lujchw+muomiJvnK5NtR/oH7QMTuDZQvAbkAz+lpA5W1mgnyACqJGq+sJB1HpC4RuIgFJ8J7Pk0bIXyGBm2PAuCNarnrJBUDPoPQD7N832u/aWAv1mcYTQrrtoXr+n3FwFfsL78DLNL6i52n2NjDffrJ3n+Xdu5iUTMejth2flwhv//qyqT+/bkLo4p+jKFYiT8mIo1+HOE1igFWPymEtJTwWMJopv/N/48RE4JnykqEoNbvzwNCwCMiBvRPFLHVMspGrZakSbx/n1QAS6OvZGnPDeB1OvFz9RSWQfLn/IJRFJMgRxQAJ0G4jv+MDHmMihkz1HZ29AXtx72+btEtjAO2A8hQ4w3DjET+xUU1nKAIIU+OgYPneHc3Gb0sFi3Xw+eWAQzxcheL5iWnjh3Km2trSfqTiHmyDw6UykgNJyimvvQE1FP2jcpdq2VG99SU1a5CXZRonI+EYpTOzRmAbDSS52xzU/dNiQak1aemrDj69LT1Ozen44eGSvkK8vUwCDGqTk7MSKvXra5V6ES9K9AbA8D7DSLy+9znfz4EczcA75dE5GdE5D8Wkc2b739ARP41t96/LiI/kLKfoQXqhmmx91fM5uFddXRkAM6/I4n0kMfFswGwA1BsbNi97muL4lRJm7+mp5MlPtLmtTRWCgtzkFeP/ta39O/qqm4fAkmvNJzLqWOrUEgKurDNF18YIFtZ0WMigk7ZEiiZOKJ4vt+8MRo57/LvftciaoA+5pK1NYuYetBGlGxiQucYSiAgRLK0lMx7Yy4lmtZsWn1PInPMFdQrPD21PDnmgMvLZBSNPGQf+UPobXHR5kGYDCyeFcE9hMOA/WCjkauIKqiPXHpHO/cwRdE9Iwy7zN///O2HM4ZhHDxrgNcr6hRbJ/w+LdQ66BjScpzSxutBWCzqFus/NsYQzMUibuHnsL8s444dZyzi5iNssWjh2lqSzsH3x8dJqmPsnLGuj4QVCklKqI/gsT+fIxdGzkJQHEb8YnmOsXOZFhEM98OLzh9P7FjDsV1dWZ2X01OdvH2BznA7X/8Q+ou/zog9MFkhK0zSOwnuGIoYy7FoSZb23ADeN6nQeZY8vHAJhXhE1CCJgbbPP08W8K7Vkp/T+kdMIFzX54mSY0bxXEQYcjnNCcSY5B73lGSoTJQ5QOThu99Vo4VaTUTQ5udNYrxeN481BhFFgnGmkPtG1A+6I/MW46DQOrknrZauu7dn4igzMyYgQK7g3Jx5qzEg8cjXahZJPDlR0Okj9OT/oFqIfDvCM9TrAuDm86YCyLlnnxjSRA6JMgA+mcvw5F9fm+AD7xQcXTGqedp7fJD2QABvRUSmb/7/rSLyp2/+zwzw/DLKCF4vGyoEd/zl3XxyYpR/3tPT0wY4qI3HuxBhjVrNHClbWxbF2tqy99DZmf6/vX1bIMnfe34Z1mElkpyvAGj95iaW73zH/v/iC3N2LC/bs0WxdiL3zBVeSbfRsKgWziOe1ZkZKysCSwBbAfBDORMAIqUOvDAUEVKeM0DP0ZGB7uVlm1uYF0jpmJuzdRCDYc4SMacYasdE8XAiF4tWX7TdtlzliwsDewBXH9Hf2NBtYCucnCSjdzjAfD5eoWBU4E4nWTsYm+rqSu1RanB6O8/beoM6mIaZn54twMsSdQrX5y8RHCRRe/WZpfUy8MN9hhTHNFCQ9jn0Xsboj7FtfKTKg7s00ZnYuGKRO/5SwwgPXczTFxZqZ0Jhm7S+wzGwTb2efl15gGPRuBBA+f34yFg4/tg57+XBAYxinECjZD9hbmJaLl6nYxMVE1GshuDpqV4DL0kNDYHjgoPO5MX5JLGZYqIUR0W+PU1MI0t7bgDPA4enuuRy8UK+AKde22KcecpS2jbQq/znXE6jQrOzdt9BhfKgAZU5IoDT0waut7fNQPAGE32hgsl3GxtmeJDL8eqVRfCgIUGVXFhQEIjwiPdMY8iiBglQhIKIR90XROd84ZleXdXxt9tJkIg8Oc9qo2HedmibKJmWSkaRJP8unzcaLMYh4BHjFQN7fv52qYrZWSuevLBg3vd22+pVERmYmDDDGGMW1UQ868yN3tkkkmQUhCrGvvV7D2ZpD0HRDNbPich/c/P/vVA0e9kqaYJufh3ebbxTeLcRkYFy3O0mC2pjVPtUAJ6RsJQJz4WvVennmDdv9BkJ0wc8vTyNCTCM4ypt2dgwh8z6uuXmpr0zPWNhdtYcNzhmECAhesVxeOVa6nbiNOIZhv7IugCkvT2df3CsEOFiPiJy3m7rs0g5k5UVE5aqVJJlYdptA4mwH5jjyNlbXU2KQHnVzWZTgZwvME/JBXKLqX3IXLa8bIwknnlfAqJS0XmbY2o0zK6EzXRyYmwo6JrYr2E5j9AO9XaafxbGpab5bAFet5sN0PG/j9QwKcWiSVkAV+z3tOKrTIREqmI3jW+x6F7sOFi31xjDm6/TuZ2PFQKctBvWg40YKOx0LAqXxtX3wOXqyorcso6nIYYRtdi5iXlSADArK8mHNQRPaRHU9+91+9PTZLHw8BjC8xKLpoULhpu/fn57L9UbUnVjYDW81qenpg51fa3/ewlkEqeRBkYts143LxpFlmdn9eWApy/t5ZalPTeA9xQomv1A2tqaAYXJyXhUzhtU/rOvf5dm2PTrp1g06p/fNtwXETmAH+tdXOi9Pj1tQgjNpqngzczoPnZ3zcNMVAowh7FzcWH0ThTaEGYpFnUblOLI92i3dT6AXsj3OHoARDyvtZoaYRTkvbw0w5XcQQwzqE/kvWCAsXz6pMdAtI7oGYqhgDeUOjlfgDJ/brkXNjf1XC4tmbIhgFfE+keFj9wbii+zL1TscM7hqPOsBERpcOT1A2+x+XCQNgaAl78RR3nrRFZ+RbDOa/f//1BE/sLN/8si8tdvBFbKN/8v99vnXZ3j4e/hZ28n8O4MHdZ+PT5jWPNORmRjddWAPYJI5+f2bPo5AcdDTCSlH80ytpBvx70e1qRj6QcCfX5poaB/cUSdnyfZAJRr8NsjHAWtVMTUaclP3dkxRwrRULanHADgkMhXtaqfcQDt7CRzfxEZIbcXB9bCgild0j+ACIcSczrOHIAf7AGikScnSZEs5sdazcSamD9gS9Tr+hl6t4hFEKGU7+zoOeD+wnlAhBGxqtNTPR4ipUQjcSRdXVl+89WV9ePtxF42XficjAPcdbu956aRTVj3tYyKZhDzQsUMeR8FSYtm9ZoMYxc4tm9uqlev+udfeW9DTJ41pBXGbsRwHP57gFcscukjkWm/h9uGY+NB8WMNz4XfR/gbD2GrlfTc+nPj98dvLJxrX8DSK5myfhh699tBIQHkheA9jaLpx3N6eltpjJec9w6RzOtrBaUB0bT70J/vWs3yVvxxdjq6b4odn55akjR0i40NM+h5+ZGzA7UqfMllac8R4PXKIXtMi5f3zrJ4IytLiYQ0IOmjejMzvctKzMyYUcB+Mb5evzb1Sp9DhrdZRD39+XzynvZRrnrdxFEwLhcW9DpCaSKfD8/z9bVF8wBS9bqCNJ/ojyIcAi7Qn6ALvX1rIAaDaWHBZL0/fdJnFCoSwib5vFGNiIiQ48c5KBYNjJVK+hdFzIkJy2HGAz47awYqojRff63rIQpzfm7y9nt7On+dnSnldXJSQTLHW6/rOMnVozbfzo6et05H10PABQco70oAdj9Dqh9g6dfGMT+JyAcR+auiapq//ea7f1NEft3N//87EfnZG/DXEZGK2/Y3iZZX+AUR+Y1Z9jdO53hoG/E+8u+x8L2IgxHhFIC8l9av1+3e5T7Y2LB7mIje1JQ5exYXb887fq7FMdnPmdRvrlpeNkCWRvNMU+VEDAlqM/0SmaPPXM7qxbGtZ02w37dvdR7a27NzwP69aAlMAVgBgCL6aLct548yA/W6je/tW3NeIZAEKPNAcmfHgNj8vF6v/X3dJzR1Sq2Qk0jpGMCjiIlTFYsW9aM+HrmBnY6Jp2xvmwprp6PjmpgwmxNhOAAsVHhYBMxLHz6YUwFhuZkZ+5+UFdgF3rYkrzFtzhk10HsBeEGLGf1hFMgDAf97Frqj/25QimUaeAzH7MfZazwxEJBl3DEwy/feK+ejRyGlNDwmvCmTk/qgsn5sH74P/xljxdMZ/THE+M8h0A2LfIfbESmjdg/f+YLfaS8wP2Ya91AIAn0ZBBwKvm8vBONFU8Lr5o8vPCb+enGY9+91YvTfffxoeS7ttk5mRAAwKr0Rvb6uE7ePoJDvE77MsrTnBvCurobzLD+GhbywLOumgdis6ptff6199MtrCc/l5qa+8FdXTeyDyBv3KFLdxaLe50j1exCIMEqpZKUPKKZLPhwFcj1FaWLChEIwhijwS54IHnKMroMDO2fttj5jRB19jomvn7exoQYFBtvsrBnJ5Cv5ouAYNRMTaij6iODenqkPQos9O9P7tdOxaCEiNO22AeTtbZtToEphSIcUTtQM8aQT+aTcBHk75BZBoSWChwMvdHp1u+lOS34btj23+alXizkw/TvPv09iiqb+vQxLBNsAiiBOhdVVvQ8Qu0Aw6PzcREZYADWUWkkDWV746C5ONq+OKRKP5jGXhPuZmbHtPU35s89u94Ejx4+Z4ufMkaRL7OyocmippI4twNfamp7HQsHqcO7vG00ReiSUSqiSsAfId5ua0me8UDARm3fvjP59cKDPM46uXE7Xo8g6VFNqnlInkOLzuZyBLvIuEVryDmnymJeXjYK6taXjIs/QU0e9A6FWs7JgS0v6HTRS8hEBk54Gzn5KpWT90tXVZCmqtbUkEy90hoyaqvkC8FyD9hcibA8gfF2d2Msiy/9+f74Nc2FjEbkQ+KWNJQSDsehbOD5PZfTH4EEHn1lvetpAG/37RFcP/vBQx8ZPArb38EGlvLoyaiT7iIGbGBU2ds7Ch80DWK5/eP5DT41PwPWROsbgjz8GBL0jISa3GwI7P57wO/7iwQqLyzK+kxPzPPFdq6UTPqI0JGAvLur/3uu/vm5G8uef2/dpHtEs7bkZUO12/Fw9tWUYgQKMHa/umLYAIKan48aPiL6kAYx4ww8OjDYM/RDaIXl3CKxcXloe6s6Oqb35Yrf5vAmm1Gqm6kjkjN+RGffGH/WvLi6ShtPVlRpWS0u36+TxTHLsnAeihgA1wE+5bNEvKNWUb8D4QhqeaDxADcEFDB7yawHNROigaWEU4u0XsYgn/xNxOD83hUBEbPDsIx4zOWmAl0gpxj15VxQuvrxMslxi7+bYe+Cu7bnNT/1aP7snBHShrcX7DaVM7DKezVZL76OtLVOkxcGA02di4rbzYBxz2SB1PNMWwF8Y8fP7/OorfSYRWxGx/F0W/x5uNNRJg6MlNn7yb6n3RtR8YcHqWF5cWMkV1Cip61mtGrXx48dk/jJlCsjng/qZz+v1wrl0eWnMBKihHDvOLUCeiI0RyiQqv5TN6Ha1z4sLY1cwzt1dK+tAjVBU1I+OLN+QCB/AcWVFxwV1lBxohH+4TwGUlFaA5VSrJe1hz/zLoj5/l/YC8Fy7vk7m1cUmoVApMdze/xbL8QrXTQMUfj3/N/w+to4HFfwfUg69Uc9fPKAxqmlIr+BchNSLmPeu3TZKKwW8Q7pmuI0Hiqzna8MB8ACO4e8h8IpFKfs9TH59zg+188J7IgRUUCpDsOqBINuyXlgLhfvx/Xs9Tl/GgX14QM49F0YI2Q9FQL1nlO0Amb7Y+dWVTmIo7U1O2m+VilEWeHkQMcAw8zkGIhZx8EuW9twMqMdY6LxcHnybiYmkGly49PKQx4r4hkuvSN/kpAE1JMGp2dhoGNVnfd2k+0UMLAFK8Nr6vDEMAF74iIJQOmF62oRWcGwgrY1C28WFUQ0vLsy5Uq2aRz2fN2Mul9MoGlLlROEAltCRfHkUvPKIrTAuZND39szIATwCEBEyIEKCYUXuzOWl0cGgxW1vqzF2cKDHsLOjRnapdFtwJ5dLGqhTU0YDhWIKLZb6WKyL0AIGGdLmXA/mqG739vub+TfMn2fdYdpzm598u4sB6t+tITPl+lqvK3YL9DtKYSDKgUNAxIRDeIZ7RfbDCNtjWojAkcO+vq7vUk8BR6zEz4M+GkkEjfWZ/3wkkWcSinutps87ecGFgm4DVTuf1/kzl0vmDfPMIXbD90RaUbsEHBHlajb12SY3mRzbzU0DZ76W3sSElXpgfqIMQy5n6SK8V3zuXqul8xLAEADpVT9xbhGtLJdtPsQ2RjjG1xzFfjs6Mg0GzsnBgR6rd5j7uSctmMLzcdeI3gvAC1oIALxaZhhl6bV9WrSI/0MQFuvXR7pCw78XvTPs8/RUvQtMlp5OyH48II3RIok0+aLZMelpf+OGL1ZC1CF4C8+N358HqSEYBEQxbrx9PrpFCwVXwnMYe9n780SidwgSOx31CoX5mP6a+X2EFNDLS8snSbsH6Cs8fsBYSKf0IHdjwxT9MAxD+qennHKNvUMAY7Natc8YV0dH+mJ980YnxJ0d7cNL0PeKxGRpz82AIjozykLhsWWYPJO7LD5/pN+6oXKlN168CAvnyFOyXr82L7RIsuwB63jRF/K6oD9eXJgBUSyagAj5bYgNUYuN5H3ADYbLq1dWpmBpycQKzs/NGKlUbP4ATAISKQoMkAPEYCDt7lqRXg9wvDHCeWs2TZQCrz3qde22RSwxwqj1hyFHBBPjDsNrY8NKTpAXRP/QsFD68wY39LT1dbs+29tWiHpx0aiagMyLC/OII9zgr8XKioFLqPo+fzpkUsQcrsMYU89tfqKFtkbWbbpduxZQkX3+JCJCiKpcX5tQBsYzVGTKfgBU5uYs18xHtMKlV87uQy3ku4UL8935uUXW5uasXufmpoEkgOv2tq7P84cTZm5OI58TE8n8acSm+MxzODNjwA/RFvonwgWYQeSEeaBaNWcWebIURce5Re4tdUFxHCMuBU2d+eLTJ6OzA/APD23OYT/n58ZEWFgwhgJ5cji6fCkF2BnsG8Vf7CzmGur6YXdhT5Gb5wX2CEJ4e8s70fvNOXcBd91u77npwSedQZdRJgrT8Ez46EkasAr7TFsvVDn0IMiv58Fd6HFMAybdbpz66WmFIegIVaxiIMiDQEAU3ooYIPRUGX9zeyEQH0kElNAHwNEDvjTaJce3tqYPradHsv+YVHbsOEOA7o8hBNl8j0EFWOLFFO7PA0+O+eNHU4QL98/5DscRjh/wipHno6rttvYB1YpkYH/P+YVr4sEtCdV45HgBHB0lDXFoMqgGZjHms7TnZkD5iFIITO5zuUsNqEHHHO6LXBlvhORyViA49Eb7fU5Oqqc5FID58sskyATkzc+bCAkUxDA3xxczx4vsa9IRKQMAUS6B561WS46P/5eWLLcVUOcL7Hpj5OjICgN7Sicedi8xjje60VDjiSLfeKZ3dkztE4EDavn5a0cUD8EFCkD785PLGR1ue9toTiKWb8f2u7v6+d077QtmAOIN0MKopdlsGh2TXCvyFHEwEt3xOTHQNcmDYS5Oc6R1u8MbU89tfvLN2y/9zp9/7333u3oNT0/13eLZKNhcUKA9S6TRMMXGSkXfbeR6E606P9fffAmSQeepu8x9d5lbidqHDIZC4fZcKGLgj3xgIpiUYREx5xfPGgIktZrNs9vbloMMaKTUAKkWExPab7FoOfg819QhLBatRAPXbmlJQSUgy0fVvAAVziQiZ14cipIy0M4XFnS+gF2B+u7SkoE42AbQSyntQl4xUVHuA0pFoNzLuSbCt7am9xppQdhbzCnYXkT3sHd9oKLbNRGoMFjA8zTK9mwBXr9JKQReHhyFzXujskx0aZGafsqdaePr54kEKIT0FZq/MX0xcSbccJwAVHjHXrIaUOCBnM8788cEbdNTPa+uzPvtgaGn1sQALOffbxOOO9wm7XzGrrV/GD0oxVOMx6bTsSRdP9aQqsm2fryc17TILjTUmCMgPIceaEMxoI4L1/fy0mgFjMffIxzP6qpNvIitMImtrSWl0ufmdGKN0TFjS5b23AyoTifbuRulYREzNNKEXtIif2HfRGHuajz1onKmjXFuzlQcRfQ+RemVF3q5bLXweKETucJIQZ4f4wkj5ODADJi1NRNNQUCgVEoqtl1d6e9EwZB0bzb1mQbMkM9GLtvJiQEcDFUULJFAJ+KF0VQqmZrt3JxRGvFA0w9iKCcnZjxRqgDjeG3NqGLb26ZuR50ropAYpqhsko9DTu7Ojgk9eDVT/iJKg0Ivxh9iBuyr0zE5dpxXzFPMY8yl1FWF5UHusaenj6I9t/kp1vpFIUJwhwos4I73FnYIkeRCQa83AkZQq6HYeZpvLzp4v7nrsS69cpKJPL15Y5Eq8n2hoheLOh9sbtr8E87LU1OWX+xrAlIKhtIoCNzA4OFZZh4rFvX5qlSMZrq7axE95gCf3nF4aNF/nMY4o1dXLe8Wu4SSCMzfXmSHeQ5WBcAP2jeOoeVlK4PDX+ZHmA2bm3b8zN9QWs/Pbc4pFMwu8vMQjClvS3s7jsh0+JwMGhHv154twOt206Ng4YlOO/F+4sLAjgGofvv3n9Miev53DwD6eQGYODsdvcGXl2/nX8UAFP/HqCx+IYxOpJG+Y9ukgUV+I0qGd4TfeUg6nWSeXdhXWsJqCIiyXM/wHPLi4X8MIYwQIoc+wkm+XgjuMDqooeK/jwFUf77DCcNv76mZ7M8DTz6vrZknHJoWXPrdXZswAeZQFijqCdDsdEzFa3paJ1HoEFlfYFnaczOgHtqoyLL08oqPesG48AYPi1dqZSFihDdZxCLNePkpcoyICcYB9CHyUAAynnKIMVIq6fMDOOK8NJvmRfZy2qurSvlZWDBlO8BMoWDGDPshL4bnj+eq2TQwiJeafa2s2LF67z6KcEQHcdgghsA5oljxyorRthoN874D9Jh3EBLAyz87a6UZAJhzcwZs+Tw7q2MAcFPfDgoUYyb3kSifjxhA18PDfnJihhVzXrd7+z09SoEVfV6f1/w0SAsdl/69tbyczF8n8kpKCYa5V2xEEGhz055FEfsbU6sctJTLY1m8GrE/PlRs/bEBuObnkxT2t2+Nbs6z5+sE5vMGXIigi+g+eE6xD1otm6vI+11Y0H14eql3XvkIHXl2lDaAYotjDAplva7PcaNhpZkOD5MgjhzeV690f9WqnY+FBf2LKJSfR9+/t+OcmzPQB4AERPLe8LTg1VWd27BRqSmKXehtSnBATLU9ZOTFbL1RqWk+e4CXdjLTwJc3sMnP84Y1FzA01GP9xfKqPHALI3pEhTyI8ADI90Mfq6tJIOE5w/zvX3h+3OFxhGPudpNRMzwWscibp1n68+Ffwl4V0kemfFQrLUoaAki295RF9sFYfcTRP4Cxawbn+urKjDYKDdNX6KUJI3j+OIn8efBIvkh4PUPlzetri7aRb+KjinC8/TgAZFx3vKjeWwZ/vtHQfokiMK5228YE9aFU0okW44xcH/8S6RWBydKemwH17W+nn68syzhKLPSjXMaicrHr3q/o+Xe+o/dOKMoRLsvL+oJG/cwft49aUXwcR8Tc3G1Q6EUGyGkj1wTlOiJ3m5vJ3LC5OaNncg6Q9UfhEWEIL+3uBVuurgxYLi3ZmFkHYMbYkCSfn0/mwCwtJaW9oVuSE9jpGMC7ulJqJqIDW1vm5MHrzXFg2LXbug2CBp2OeeBRweQ+QWQG7/jiokmw8xk6GWUZGAtRQq+E12rpefPUPcQbJiZMbIt30PFxsmZot5v+967tuc1PvVpoh/j/w3c87yFPqT08tHcskWvyojD2iUyFYMWDloeI1MX2mcuZQNXyctL5GZtTfR+InJTLyeOcmTFKtAevULzpt1w2YRTOC+v5Z5Xl/Ny+82kWzaY+kxRjJye5XtexEDWE4kl9z8tLdRhTjoD8NKjiROaph4d40/GxMSNwwjEnUKoFqjg5h7OzRin1TmeKxNdqOl/gGOK+on4mzrKjI1PCZN5mzJTqQHCqXk+WPPA2XigW2O3etvUHwR/DtmcL8NKiOPwW+wwP1xvtPsrFBEYOGMmWGMkhUo/l9oUgheajJqxHvlS4f4x/D2Toj219GNlPxLGC2RzL1ZWpOUI9mp42cHJ8rA+IV36Erxw+AESGiHp64OnH6fPzYg8I38cKu/vf+Uwi7NqajtWLtITXge9D0NTtJsFSmE/JtYEiFD7EvND8JMC4/LlmzP6F6MVufGSOa0IUNcwXpG8fMcVYQu736kpfnldXOolSz4VJF9CH1x+DlgLFeMZIcOZl1gscZGnPzYC6K8DzhsB9b9sLzEP7LBaThcdjUbjYd+Gys2NULyS/X70y+Xzuz5kZvb+pn5TP6z1KFHJy0kDe27cG3jBmRHRd8kGgi52cmMCKLxcCXdPnx1EjaXdXxwvww/Ag73Jy0n4j0kauSbttxtr5uUmUo+oGhQr2AEYJ7wPoR74cgpdEp54e6wOSMQyhSSJGc3ys3yHIwPqzswaYEUAg1w+jjhyqXM6A+fa2nh/2B10WQDg1pfsFDPDu4ph9/jC5hr0cgvfhJX8qy6gE6gBsoSI0v/N+8zVjYZVg1ENN9s+UN8j5bnraSib4OaYXgBrlMo48vbQaoquryf0hJENNT89U8PU8qSHHOfIgjzl+ft7yFnG+eeor3331lT3LS0sWfT8/V1sP1gOACPVLInGwA9ApwEGMIwxQv7io8wZRPY4RlVQAVrNpkTtYDeRK7+yYc6lS0fus1TJq98mJ5SnSD+8MHIQoH1MrkJI40P8Bez744m0ub6/FAjZZ6Jh3nZ+eLcDrdm9TBrvd2xN/GNnx4Ic+QiCGAY0RDeAJL6ZH+DE0H17cWBQo5inzVEZ/E/lJN6Sw+OPxoh4A0aMj67fVMtoRntxQPQgePYnv/niur/UBXluznAvqkwCM/AvAl3OA0uFz3Dww5KUfgjsaeYYYVmGEMoyQUVQYJSR/7RgrExehdyim1JSLRfaYMNgn9CIMmLBunge7IVDnXPvoqR8rjgmuBdt/+GDKfe22erCQIsaYg9KLR4sXx96eeQ/xog1TGDZLe24GVJYSAVmW+/JiZ1GkCz3NXlGR+2Z2Vp8BDBbKHHijzRf+3dkxb367bUbM1JQZL2x7fm6GP2DE55swLowmAM7lpQkAYMRAGeMFXihYfhmGRbVq4iXQKKEhQt+kVh0CKW/f6joYIoBRjKSlJSsO7BXbDg91nPm8Od4wqJgb6nU1ojz4hG5JLkqxqOfXF1rvdIyi+fatrovIwsqKHiMUTAAehhkUKcYKuKPgOZ79rS29XuQTsj8MRtRKyR8mQhe+v/iNSGS5bNcp1l4ieMPbTmnNv+di9pV/P3kHKu9X7mXA/OKi3h9Q/fb2kuIYrZbNP0ST7mPOy7rwvhzFXOyFy87ObL7y8ygqmeE2zLEbGya6Qm4sFEnfT6lk+XV+H42GzmfMe+fnVh5hZUWfd64v9eNg9ZADC3OoUtHrTa06RNxWVqzUElR45nNYDswfALiTk2SOMsXXy2VzAJLPib2G+irCWL4kzfy8gUXEq66vk+rD3g5G5MkHIzwl3DP9eE7S7Hy/zkuZhCEnqV4nLwRMgB4PBDzoCMGgpx74bTCq/STohTH8vj04C8fF/yH9JFTqiW3jucEAhzAK6WmR3W6yMDb7rNUsarSyYiF5xoW8LXlbIVAmd4MikfW6PXjeE+sjjFD/8PT4+nGMGy+PLyEQRvBC2VpeNogQkAdQr9uE5EG6P/+An5kZ8yR72dwwEsc5A+Du75shdXlplFofqfXXxZ/H01NT8+P8xRwF3gvlabSXlzrp7e/rd9Wq1caamjKDjDxLJOSZMKHJzM3dpgWGL5+0JUt7bgZUlujVQy0xr3hWQR2/IHvtjSAS2/335+dWk2l93YyXcBy+PwwdcvcwhlCPm5iwnLKNDX3R7+3pM0mSPmIEGD+lkm4LrfH42KJuX3xhkTGoy+QQUcKACBtFuXHq+Jy4w0OLxkGVpH5Tp2MKb97xRTTfA1eeZXJEAJUAZ2rtocRZLOpxQZ/EMQc1slAw42lz02pqVSomuY64DIp3Pl8HD3kulwTRnU6yxhV0LBT6FhasFAI0PT93MTd7JgRzLKwQz1oYV3tu81O/FjqT/fdhbd5Ox9ghGMq8r6jPuLRk8vhEdXnWLy70Xpqezv7OeQzLMM7QtGVmJgkgAUack/DdPDtrJUlqNT3/RNNZ5+uvTe2XvnEuwczZ2LAi6DhmpqaSAlEwICgv4+cn6JywCQCWRM9mZ5PsiLdvbX4lP1PE5rq1NYs8np/bnALLgXmQeQSACAuDc8P4yRnEEc89DHWTexdm28KCKYB64SDu9VjQoh+Ie4ng3WGSSqND8tlLxWctU+BfvLGcrvBlw43i88QAfiF9MhTq8C+vMEoUC/9C8wyBJy9EjhP1Il80N+QS+9IAcObDCB0PFZGycFwcA/1Vq8lcsXLZKLE+wsYY/Lng/DSbphwZXmPOtweOgNvLSzWk8vlkvTsfGQwLnHc6pvYJuGMf/hiJOGLQcU8Bjo+OkscXA+H1erJeHscW1lnxx9ntGuilzEGnY1G5TscmMAxEX9zz6Mg8qtApVlZ08kTtD+nlYV9OWdpzM6CGAUyjXMLr6b3inkJE1MULHcSonaEXOO1+iXnf83kT6wipWDGv+MyMCaP4ortffpksAYD0P6CGqFqjYZEkEQMu29sWTcTxAT0IIOopkUT0Wy0DHYCm3V2j8ZPgDxDy8y1Om5MTA2s4zLrd5PxLzTvyYDG08Foz3+zumqecfEA84kQPiSxWKiZDTt4M8zRUKXJ2AWpIrLPe5KTl8mGMbWxYlO34WI1JqFoASSi3gE0PTnlvVCpG1YRdEhpQ/t0xrvbc5qcsLRah8O9U7muYJzw3S0tmkHN/4bz56iszxIkePTVVzHCZmIiDMOZJlEHT6KArK/oc+O2np/UZZK7b3DRAxzowKJj7/LxNGRqeZ0AaS7Op8wjlZFC1bLctJxkWw+Gh5tdiTyws2Dy5vW1CVeQMA8JyORs/YA1FT+4RaiNiz8Eog2ExO6u2Dt9D26ZWJ/PLxYXNXzA0Oh0rCQV77framCJ7e3pf4nSnbwIEUI69vc3cjr0dBoHG0Z4twPPRNI+iQzAGldEXJ4yBwdhnXuxeWTJG8/NgyQughOje0yd9MeoQOAE+YhHGGFBlcvXqlSyh4g/7PjxMgiNojD6CSB+12m2qIvvmnHQ6+sC0WrYflJO8omMIeHlo/HlIU0rjxXJ0ZN5fD2KZBHhoPQjl3Phxc+6urqy0A+eCcD2U1rU1M2r8Q+3POefa58n5SGEI5Jg0fOSOc48nFJU+IpLkxDApMikdHJhxyAt3ZSVJpSKCgMFLFG9iQo9vGKCXpT03A8pTbEa9xNQv+3mTs+aaxKJy3ljh/1ieCd5YkW73W9+ybbjXMEDCsYb7RF0Nio0/3kLBaq8RXfM16TCAdnYsqZ8acuRokINK5ByqZz6fpPNQRoFIeLOpzzXGEp5pqGiAoVYryYzwJU4AMgiKoNxLHiJKdaenaoBgeBBFxGAh5wUJdS/gAhWVOQK1YE/tx4C6ujIAC4jn+pyfmxrm/r4p6gEooWhxXT990m0/+6z7y4YnOYWMnchop6NzuFdh5D3rnWz9ys6Myqh6bvNTvxaL2uHExpbCacG93O3aOx9Qf3hoUWHmDE/DfPdufPPkfS1TU3FwB2tBRHOye0Un00S1NjasvML2tomVUEtza0ufUQStvvMdc95UKhoxA0iL6HrUuDw5MbVeUjSIvFM+BiVekWS+3Pa2qWju7Bg7AtG6y0uzM87P9TguLmyehF2B7cK++AwllNp32E7HxxaBJFrsUwWKRdMRYN4iykg5sPV1Pc7VVQOIBCawBXHIU17K2+XefvOOqFHlA4ft2QI8T+3rdm8DPS4UExO5WB6ssF0v9UWMfCa2EGARUfIGfxiZ8+MLBVDCyB5j9y83PyYPHNgfHhGiSz4HzINSD0hCYAKI4bwRkex09EFGgdEfC30w8fMAdTo6ufuCwxgS4TWk5hxADdAViowApsndwLviveEoI01NGRjywAhAzblBRIYx0JdXkarVDExyD/h7L7xW/tp4Kia5dl6Y58MHqxvDb1ALiDSsrNi20J78WNttPbajI6uhhScNegM8+U7HJvs0sBAu/dbJ0p6bAUXx2YdeQsAOGEoD8iibffmlvfz9EoI+ipbHVDUx4hAS4cWLMcO+0rbDSKII+Xe+Y/WZRCy5vtm0KF21qi9vHB/NpuXpUQ8O4IEoAM8V6m61mlE3fZRQRJ+n9XWd70j2B8SxDRF1lN28SjGAjTw59ocqHE4lKNZ4ypnfT07M+CEiD4jCqEI1E4/41JQpG15fm6eb/QGyqE8mogAbBb9Xr/T/w0NT4kNinajc+bmOzRux09Pm2SdySRkLqFEe0PnIkHf0he+7MKc5fKcM057b/BRroT3kbRwfweh2k/RkyoN4mwAAcXWVLBvC+yRrnt1TomxmnY9xUjGfLi8bSMnlzDkWzrdEQicmjH6Jc9bP5+Q7QlXnmX3zRp93nED7+5anBzDypVeoMZrLaZ+Adt4fExPq1IFp4fOYcZIRnYP6CT0XRgd19LBRmNdETDiKeX1/30AjjrSDA6ObQlftdMwZRSoNonywGHzN5f19s299kAEbk7QY/4ykPTdp69ylPUuA543ncILnhbC2ZmIfHjj4/CgaxraPrsXocqwXRtRC1cQQVPmbxr/U/Jj9cfmoGN97D+yrVwYU8QSjfgmQ9SDPn6vVVQOlfjJGlCRGbaTobxhh9IAwjIitrakXmgkCz7SnZV5dGWXp8tLq05GnF0Y3PTUk9K74Om+AMSJyyHcTieXcUqcJAQGOF+8OkTcikP78+PuA8ftzHUYQUbLEw8V23hNPvRi8WoyH/vxv0MjIFUKmGEVBJn2iHUQCMZz5bXJSJ/9hk9uztOdmQHkQPcolJo2dtniD6q779VE3b3TMzMS9z+vr+vIF6OLl9uuGeYoI0/h1AA5IinuDb3VVX8zUytvc1OecBH8cJEtL+r+PPiH8AYWS55fIl1ePZbv5+aS3GUchn5eXDVziVNrbs/xa5hKoj0Tt8LajCMcc44VGZmdt/xhaodw3qrcoYOK4Im+x01Hja2pK7yPyUwCZGFY7O+YsIxK9uWm0TzzxgEKMKtT5Gg0TfEEYoVIxtUTyhY+ObueKX12Z0w7Hqs8f73aTTtNRec6f2/wUnq8Q1IUsJWwc79zkuYFdwruO+5H7E/XZiwu9twZR973PWp33saytJanq4fExJ8bexTAd/PzrlTL9OYOJUKnoHAQ9fXvb3k08k7AXfPFzX4yedX09Ty/qgpAJYjlewIT8YebklRXdHw45HH9sg0NAxOje796Z2NXcnB4DqSjkX799q+uTT8jxQ2snpxiNhlbLQB73J/OMF9hDKMuLFobPTRiE8KzCUbRnCfC63SRgC6NvRIa8oc1FImcslkAcM97TJj+/bqdzO3IYyg0Dqsrl22If4XFdXxtlEDDqAVYIbD0oI1fEAy/Gh/Hhw9AYDTwMnk5EXx7Y+HPlVR+9KAovaAAMXjsEACivQL02wGizafXckDAnWhXuG6EW9rOyogDIR1w5ppUVHQN9IbKwtGTFvUVMYvzoyM5RuWxA2Htz/DkvlXRypH8iuFBnKeeAZ2111fovlSyP6PDQDEaOxTsQ/DUrFvXv/LzR0c7OdFLe3dXzc3ZmBZd9NIOJexRiIFnaczOgwpyHrEu/+nfkc2RZ0vLpwiWLgma/MU5NJamWOzvJfJEwUkcRXo5pZ8fqsdVqlnPiGQAiOmdA99zZUWMFIDE1ZZTjQsFEQFCyBNDNzuo6ABLv9AHgLS7qHAL1iNIN5Lf6+RKKDzkhGEHQoikMTCTOO9QqlaTxAtjiWb+60s+5nG4LsCIqSlkbABb7xIi5vDSwCjj+9EnPlRcnKJV0ffZBiRxop4yfcwl9lvPKfqtVHQ/3CPUCUeeD5tpsGj0V9kb4rgIQ855h3g1b6Kgdpj2n+SkWoQsd0qG949NbuC+90Bg5nkStMdzJtzo7sxpsaWUEnvoSc7z5ea+fA/Xrr5O5roWCPrPQJz04Xl622p7s18+/lEuhHIVnS0xO6vPJPCiicw+1N7EpJiasNh1RWOa/qyudL6DTky+MsA7iT8yfi4tWFiGfN2Encv+oc4ejirmiVNJ97OwkqfHVqoFb6olOTiYdq42GzmXkfO7tWW428+PHj0mKsXemU3PP1ygOHR+oa/oAjmcXjALkPVuAd3WlNzo3nfcuxWqRhbXOYhTKWFTQT35cQICMB5Cst75uLyb/O9t7ep2/ccKxAIS83L7/G8s59KAXT6mnB3r6Ji9s/9C+emUUSGibXl3Tq51RaJu+ON9si+HEyxwO+cJCUka30bCclbU1A3UYPkT+wlqBqCghuc1LxnvKiQp6daiTkyT9CQMJYAUY85Fgzo83RvD+4wXCS874oAVQiJMJELqVjzxiBEGZ8gYQANuXs+C6NRrmNXvzxtTxmBRRvZuZsSjM0lIyEuIpI8MsWdpzMqC63dGraHrKZ0yUwIMpFl7eWRZfvHeQxUf2ikUz3oiahWBwclINkVLJ6uihhglQQFhkedlUXr0SXKVyuzxCs6n3P0Ifa2umwIYwwMWFeaaLRXseYQpQWJsyASJqiBD59oqZROHX1iz6jiHhleT29swRxVyKYeEVMvE6AzqJDjKfQrnEMcW74egoychgfVgce3s6j3pjjnw9vN3MH+wfhkS1qlFYlEgx6up1K83QbBogxojz1wbACt0X442cSA8aeGeHzAeOyasph431e63Tqz23+SkEd7H8+JCtw/v+1Su9L1ZW9Jp7479W099wDOBcnJjQCL3Pl3rKS2wOzsqA+eyzZJ3ILNvMzOizXCzqs0PuHfls9EX5BD6jRMx+KI3AOq9fWxmEkxMre0UtzN1dnT98LUPmQqiZ5bIBQ+qTiuj8s7mpc+H0tO6HfiloDvhjHgYAXlyYw46xQ8+nBisRPJzmCwtWssWfWwAwQi2kraCtwJyDM93rQXgMEQN54I0wqBM+Y3dpzxbgEdUCfPicqF4nPIwEeeDABfOiKkx+UP6Oj9M9Xv5G4aXk1SB93TVCwtBUwkjN2lqy9gb9+3U8KEU4BWluCph7WiOUVcAML3bvrQPMUJ/J148DQHrhEfJQDg6S6pLkrU1N6YPrJbPhWyNI4MGhf7i8KEkMdAOkyD0hOopaHMCR7zAYUAxFcRSKFqpKvig6Y/I5dp2O0augdAFc+f39ewNiFEfGS+XvIQAlkchy2QoQY6jBiUfhCgMSgxTxFDjx5CUB+BBRYcKjFg0T/V1evFnaczOgoBQ+pmWQor7UufNKbizeU+wX6DN8/vJLA4AYNGdn9gLHmGg0dF88Kzs78WgggHBvzwAixhZGjpdaJ0/Fe75RiFxYMC81zzH09uVlfZbwppPrRq6IL5UA5bNSsZwTHEdERnH8fPe7ur5P5sfZg4oeqnLMQ+RXo9BJPgqG9vGxzWXMvYeHFiXDMMI7L2KUSpxPePuZn8hT4dh9yQi85AgXUOZlb8/U/HykYHY2WSeLsULj450NJdQDPOZ/r+DsW4xh8xLBG67F3q+hLgHMGBwUvp4Y4kVcZ2T3eY59VOqh58HHtPj5ihIysdxn8te8Y3Z7W8/97q5+nplRG+zVK5t3Z2Z0HsNRRjSQeZx0Dpz8zabNi4jj7O5aCQVo3FNTNu9S1Jw6vB8/6vuP94YfM9+Rw4dYFJR6XyewWNT+oJleXtq8T/2/01MDp0T8YBns7FgB+EIhmd/HXOSjz69emV3KXOrrM3tHhw+sxIIto2zPFuB1uxZR8x4nX+8sDJWGF8PnVTGhMZF5UOMnudhF5AUEMPB13aAs+ugg3kqfnEx0z6twxkAq0amQ6wtN0YMMPz6v4gbwDIVV8DZj7JDD5pNSEaqBcugpQzwcRAyhRxJe52Ein8MDG6JU/lpyXrwB488//WOM0JfP4wCY43EHyNEXlCoML/bJZ3+tiE5yDPW65fQUi+bhJ5/x6ChZn4rx0NfxsU5KRE2JEjL5eAcG3rKzM7sW5F16SuDsrAG2ublu9/PPky+LN29uR3fuksyepd23ASUiv1ZEfk5EfkFEfjDy+7mI/B0R+amb5bf063OQuandHv58DrMsLvaPwvbyMMe29RRP72leW+tPJfXjErEIIQYGcv6lUnJ83Lc7OzqnYGDwwkd6PzQazs9vR/aqVfPWLi4aBdLXjBPR54lnnWeTZ5CFJHzmPC8sgPQ3tel4xg8PdezT08l5CTCJEAD5IkQUAXrM4QBLqJqILDG/8n5hfVQ9McCZa3D8sW6rZU4gzuv+vuWdYCwdH9t84yN4zJOLiyb2gPf+/NxYE17V1zNA/Ny/tmZiL92uMSR4J/Be6HaTkaXQaTtsewF4t88ntodXFsT24B46OjJnQK1mOd3QM8eVi/xUF8RF0n6fmjJHbGw+DsskEKX77DPLsSuV1N5BrdTn1JGrvLVlYIf5meiZiNoIGxv6bDNXopJJHd3zc6PQw0IAaC0vKyjb2jIqJWDzzRudEwGHu7s6D25t6TiIwKHUiaMIevrJiR4D+8rndT3yicmlRneB3GiErxhvp2NgkHxggiyewQWw456P4YowIh4+W3dpzxbgAdC8qiXc2bAsACAhDLFisPuIUBido/mQbRimBVQSjfFAJKRf0i8RpRCY+hvNA0f6AWCELzyOJ6SNhpFFjo9jB5SsrJgXF0AKSGy3zVABCPISJvpF9IyHkBzCRkO3pXYUDxCAh89LS/qQ+n2zX86Vf/g82EIljnPAMZE0y/nyBYcx6JDn5Zh4qZEnw0vMG3ZIk3c69h15JogqMC4830iLo9rEBATViTIV1apFbgGL0NYwkOfmDJiyTyJyeEm3tqzAqIgVoZ6fTxrId63ZlqXdpwElIjkR+UUReSciUyLy0yLy7WCdcxH5kUH6HWRuQsxmnEsYkeuVS1coqPHgo7beW5oWlfPUTf//zEzcKYCBNz1tVEz26SN6sAtgGngDR8ToxSJJ2hFzBM/B9rZ5vMnzwUCandU+KBsA6BExOhDUz2bT8ol2dgwUIlTCs868ybxBnhu5fMzdq6tJQEXDqUORYKhNCJswh1CDFHDDsw7lkfw7GAw4KpmPAIrNZpJqDrWO+SKXM9oWVCwiqdTZQ+EOJxdGIAYX56FU0jltZ8fm3osL84zzXgTcUcvTOx696rAX3yIXfVze8ucG8GK2g7d5vIOaOoxefALwja1QqZiTEccQz/Vdaqx+05csNHo/z/dyrMHUAQSSp4ywiYgpVyKWxBy7uanzgWf5ML8C3NkPAiulkqV+EEXc2jKnF/Py4qKJ53ml3nfvzOE2Pa39kBu8sKB98i5hTPW61Vbc3NR5SkTXRVwKx9fxsUUkOU6ifMzX2GfYW6G93e1aQIAawmGwJnyuwnIud83Fe7YAr9u9HanyER8+I9/MBfTrxvL0+By+THjp1OsWEfLgjpe3Fw0J0b6/aaanLYLHTYWBQJ4FLz9yv5h0Kc4NKA0TQQFD9MP/3sOM19rTNjkmzgeg58MHy8O4vNSHDU8NcuGrq1aXDq8vIAxqDsvlpXmaOec+3w+qFHWqiN4R2UKhCaPGU32Wl/VB91ExBFwwevJ5U6zb3zeDicgl5wnj7ejIeOMiSc/40pKBNOTWoamENbVQZkLlk0Re6GqAP6Kh5bKJNECnevPGcpegUZDvgHLpzIxRNcOXiX9JkKx9l5dUlnbPAO9XisifcJ9/SER+KFhnrADPn/esy6D0pbAUwyACLLElzMOjBh0vRx9tC+mYfhtvlCCpPz1t9x/H6fPCwnOwu2uFfamjhxcZ+tf2ts1HROe8hxuPNg4a8n4ROiGa5iNzFxdmPAD82Mfamj5/GLqIL5HHwXvIsyEoHsxv0BPpE+MY44NyBMx7ADYUg8lRXF1NsgAQcAEQIvLi8715tzD/ezC4uGjHfnFhlC2YCl55GmB+emo0Kpxx5DF6wReAIFRz1K09W4J3IO8H70zFQedVrkfdnhPAC9M6eP969oyP0nEduV48KzhhNjbsHZTmKHrOy+RkNifq5KTmKeZyNpeHc7JnYfz/23v74MjXq77z+0it93eN5s5YMyM0g8aluYy4GfmWJI9nYMhMMq57EyY4hmGz4EzMxgsysAuLtkxBLRYpEqgChUB4KbICJywQA9oNrrV5d9+49oJtTMDI9gXHNrAxy+BrmzdjG7D97B9Pf3xO/9St6dZbq9XPqXpKUuvXv366+/c7z/M953u+59Yto67PzBjdEjVi3+sW372+bkFfmFW0SfCvQzAYaneplB4bGLDgN5k2L4AyNJTomdTkUvfM9bGwkM772GPW+oAaax/8wwfhA9fXrbQHv0q9H1nK7m4LYI+O2tpBLz2SBwTi+N3v+TzzD/9DEA2/ip8ql6t1OGKs9mEHFYjqWICHswFV84VBOfTH4ax8IbFX4MQ8SKqlmsMXCMgCFHhQCODgd4/26cdHwbLPHFKP5qkrnhbBaz/9tCmtUaxPcSjPw0ETafPRZ8RJUIrkAqafDfVh0MyoBfOAam3N6j+onfCCK/PzVp+3vGxA1X9O/nFuCGrnTp1KNyUCKFtbprBJBg1pX78YIQ6AGp/flPT2pnN6FTd+shmamzOJZxzZ5KRJAKMGurZmnxH1b0Q0izU1OAc+Ix/p9xstqE5E8C9fTq83M2NzJeo/P29iEERPoVsRbaNBaiMLy34WsEbsiAHeyyX97+7vryyCuQrA+2NJvyPpZyVdqHOuV0l6h6R3zMzMNPZmo0k9H8ZgMd7tNYrfO9dErSwf339vb1rER0fr04Tqnf/WrZ3RZXrjnTmTFlsisaVSms/ISHo9BIH85nBgwNTWkLgeHrbmvgRRymXrt4mstpTuHeroCLD4zQsAjPqu5WWrJSEbPztrWbQ7d6yPH697544JRSG7jQ/yII++nESWCWzhe3lN6m7x2/jPqSlrQDw+bv4IcMn5OJ65ESCCRu9rmv1coYBS4zs6av4TcEnLFjY+y8u2qUNwhddhLvwfKjsgzf9eLluGjyydZ7VQElCLBXOQ1kkAL8adGbxyuToIzr6KtYpAJccC+sgaXb9uNLnivZxH48NnymoJX9Vbz+fnq4W9enuNGgkQojUMgbvZWasbxqdeuJCOGRnZ2ToBpW7ucQAZfhGwif/2lHjm5WvTu7tTsJoAII/xPj2VEhYTezXUO8fHU4sEgCPPJbNI7R5Uc85FwoOMYFFNk2veH08Ay9fn+b0rgM8H82rdc81axwM8qG+nT9tiUqRYAoy8yAZqUB59e1Dnf9bi2vLlshD555ON83VdMVrNBNQ7FnnqSgAAZNto5k1tAufgf14tlIX99GlbRPlMfI+7a9fSjXrtms0bKoavyXv40DJJN25YtHZ83CIo8K6h4NBPjhsfmXCi09w0HvAV6+62ty2ThUAAm9CZGQOtZOTIxLHo0AxzaMhqA1mUfENxgOLVq+n5OMTLl+2m5z3R3oKeUtPTRnVgYUOFjs8QtSmvEIgoCoqDcNcXF80RXrq007kDFFHEYxMmWf2LP363bNCj1L5qKTLuNhqxYwjwTknqq/z+P0p686PO24xvKpeb+wz3MiYna1N26LVUfNxfE/VA/cBAuv7YoNF0118Td+9W17r5TLIU4+OPV1N0as2FCC5RV2p1zp5Nz0Vtrbs7/aRe5Ny59PPMGasVg064smL3/tycbTyoL+vurlbinJy0IBctWfAPFPQD5vCbtAHAv589a8wGH0haWDDAQt87goFXr1bX1/J/1jBez/8PkZOtLSsD4PirVy3YhIIza8LiotXsFWsEWT/95oVAHnUxMBP82nrtWvosqcWjLg9/Bs3cZw0AuHzWqHX6ufC+Yadgfv2tZQcB+joV4Hnje/BUTMA2exkfICaQy73b15euNS/ykcfOUc/3ekEav0bTAgA6Yj2/ze+3bpkvHBqy74NgMD4VEAW4gsJPLTB7k5mZ5GM4P2wn7nEvukTGDlG94eH0GOBrYSH547k5W0MePLBednNzaU6IwA0OJl9E8HthwYSoSqVqWirCLZIJt6AEH4IFxdjzzs+n8/r6at9beGKiNqXcJ1qK9GYSOrXA3V6pmh0L8GKszqr5jBXZrCIFc3s7gRGyK74mzatG8UWVy9XND72gixdlYcEGvXMsC1kxc0hNBIsaCzo0UQAJtBZPQXz6aSvwJ+sHUPQXJBsIsnxnz1ZL3ELjQS2TKK0vMAXcLCwYZYnNwtyc0VEXFiy6DNCirxKbPOpH2LhQr8fiz7zZBADA1tctOlQUJOF5bExQuyMT+OCBbbiWliwYcO2a9ViBThmC9b6CRslNz6YPWhafC5z2kRFzMGwU2XQB8NggAsjW1qz/3tWr1qfu1KnqSBfiBV1dBkJxkFAnqKtjAxyCbf6hbDDqAby9Cq00YseNolk4vlvSnz/qvM34pq2txj+/R33ue8mw1qrteJQISzFI8MQTtlGj51lxM8JzpqbSvcD9zr3qWxMMDCRZbs5RbFtw65YVxyPfT2QZ8ZWhIavzI2g0OpruLTYl0Epp+g04YkFnkwLzAJ9JJouMValkaplbWxbRrqWw63uM4nu8YIj3x/hWfLNXPPaMCE+xhybpfSfn8DR15sMaBWWSQCFAjrUH9oZvxo4wAWuFD555wSyyimQQ6c85NmabQ7J6bApZf30wke9gt7qWYs9a/7/c6Lw5/1TvM6uXMeU74dqivhNK79qaBWPwC3ttu5LH7qPYQ5BsFYIqPA5t1gfiCJwRKNrYsCDYrVvp+ZcupXu2KJBz/76Bw9nZlBnb2DD1Xd+vr78/zZNrAkBJ3R5sC9Y19jE+oLe4WC0cx16GzNzCgjU3922hyDjC9vLKruzT2S+GYNk/H7igJKdWwMNjiXp1dvWYBjmDF/cG8PhZBE1Q/fwX4ykmflH0Co137lRnzQBkvrdbjJaJ4nwAMcAWFw3z4fWhabJ4E9n1c/DAjAWOrCOLKZsPHLAXiymqXT18aBknH33r6bE+NmTXeC8jI9VZtNlZ22TQJJz6CqTGicjymQLOaMewsGCfLQv73FzaoNFHZW3NaEs0AKYpJtk46gConyO7RubN18Dcu2dR/vFxi5azsSFyvbZmqpcECijChf7ETyTPiVyR6meDymeAWl6xNgU59aWl9P7IZuBAJyYSWMNBA+SWltL5iJohLIFz9RkdFgM29kWgV280I6cvNXafHjHAK0n6gKSLTmTl8wrHvMD9/iWS3vqo87ZCRbPRurxGjuM68NeI/64HBtJ1WQsIFqPHNM7t7k7XHtfuzIxl76jzIIhx/bo9//p164lW3IRAmx4aSgs49XcEOGhWS3uUq1et7oJsOL6CjDr1aZwberz34YCZK1eMLrm+bgprpZJFe4u9SfFnULs9dX9722rOvOLlqVPW0mBtzer6fO1fsSwAIOnPiw8rUr9jNHq7F+by4hjQU7u7jfZfzLD52vCNDRNMYC4EBn2QleGpoag7b26m76O4aQLk+Y1TjNXRcr9JqsWq2asdhn9qQMn3GyW9p0IT/1VJn+P+92mn8PuGRl6v2QxevU0qYC5G21MRAOA+8b1tp6ZSlsVL8Oexv9HXVx3YYw+023N8HSRCKJublt0CMEF9vHHDgDhKlaOjCfTg2wmwSen5xWAkffb8XGdnLbtHGQHlMQiteDDK4yiRozQ6Pp7eC++LNaq7O82xuzvtg5aX07oAo4N2LYBJFF7L5bTPxM+vrppoF0kDdBP89e9bmeGLCfLV60V9UE3OY4y7+qZHOoWjGJL+QNJ2xVnt6kibdVLFejlfBMmC4xUpi00J+b/vtUPmhS+RQkrAof8SWUBXVkxxzS+KUCh9SwMWSSKkHoghBtLXZxRM3geS2mTeNjYMnPjWCgBRIqReZptedIuL1c1tSyUDa6WSNR8GbFy5Ysd7SidAiOwUVA5q9AAZ0KQAhevraZ6ApNHR9Di9msgAIDxC2p8N1Py8RW9ofDk2ZkIvbBwowsUpEMUfGUn/h/oI5XRw0OrdoDkAHqm5ednLjA/OhpPNJWCOmp7BweraGP+/qSkrJvb1VIi5QMPs6jIJYS8lL6X3NDiYznHrlhVH83+oc82CtmZGI3bUEXJJT0l6b0VN81sqj327pC+u/P6vJL27Av7KkuYfdc6jBHhQI/c7PG2l1oag+Nhjj9X+P/0X/aKM1PaVK+k647nnzpkPoS6kvz8t8hcvVvfW6+szmk9fn6nJrq+bLyAjTlCDRZ/i/bk52xRA44EuyE/uac7t6WYeMHnJdwJgXV0GZHzQD7AEVXJryzYQ+Gjudw9ubt9O9zv9N/E/i4vp+dS6ANioa+anrx8hU0bAkDo9QCyUTRqks0Z6ujz0SjKBnnLP+uYDoR48lsvGkGB9gxFC9o51FMAHNdYzYmKsBht+zfT/8xuqYo38fuyg/ZMaU/L9IkmDld+/RtLr3f8+1uxrNrt3evrp2u2WtrbS/UoGg++OvQ3ZbyjM1IYfBfDplFHr8/S0156e5O/u37f1fnzcnocqJfuUhYVqUazBQavLAwBOT1fXSp87V80IgmrvA4SwJShrobH5/LzRPeutSezbpPTapVI6np54qCezf+nqMqaTz2QSIISK2t1te2VKhmCdEWRH3fnKFfN9vj66WIrlAR3rhhdk5J46aoXfhhzDYY8KwJtq5Ni9qGj6qBM1CV5ti4iqB2g81395CLRQYLmyYg6O46G0xGhftFdKpNje19qxefBZRVLILKJcLIidINWN4lu5bLQjItjUt9HHg/cP8JuYMOoSNWnQZsh6kUWijoTnkY1is7K0lG4GInkAVUALTh7qppRuVNQEoVMBGqXknBB2YPFfXEw3Mhs2NjvU+cDPDiE5n5GRRCvgNaCLknns6UnnILoIVYDCY3r20MgTChKNRVF+IjLkVea80/W95vjuJGvqjOoqPevoZ8fm11NayIZ6p0yk7AUvqKZf4sx9Uft+1RSbHY1YJ1GgYjzcPni+kXS9Y/hfUVSFuijfKqN4HoAcUdbe3jSI8j54YBRQJPfv37d7YXzcrnEvi/3gQbovAIJeNY3NA0X+RHK9H5maMopnf396HL9BkT10aLJup06Zgi1NbYnglsvWBoAsEz6X4Bg9n6gjA0z19xsVkw1vuWybKfwrm4ulJXs93uvaml0v9L2DmQDFncbS/ESkhE0KawHy4L49i1eLo+0CoIrgo//JGghI9RshAqf8LJeN8om/JZMHGKZtjl+bAbtkQQERxexdkQ6F+d/9c/ZrhwDwmqWJX5P0rPv7UAFejNUUTL8nYv/iMxX0W2V/srBg1DrabBzlmnOSB/69t9cESGoBPk+T9wrHfly4YPuip55KATfvk6UEzvz+gfOxJ7l+Pf2NWubYmAXpNjbSY1C6JQu0+cA1bXqK77G/364dGrUjsEeLBJIZBLNu3LBavaEh6+c5Pp7+5nj8Nf6/r8+YEiQ9qLlD9Mrvx4sBp2Ji6LDYBEXrWID38KGJh1Ar5aXwic5OTVnmx39xnlLz8GH60v1CDSj0ksIUbJI1owZia6u6YBNKja/1wEFCraTAHwfKZmB42DJNAB1omSha+uiqX3SpSUEWnGPYuFBDB/D0Smss8D4DRtTa964DoLKZGh+3jBrnXl83EZvBQcs0Mh8yh3Nz6aakqHZuLgH0vj6L7uCgLl2yrBgUhunpBIKIgK+sGO2UjCUFxY89lv4GONKsE4rS1pbVFa6uWl3e6qoBU/pWAWTJPiwtJQc1MGAgEQolzUJppi4Z1QAwS7SKTe3Zs2mezN1TGrq6TKHQj8cf3/tisp/RiHUawCuXDz+qDWWw+Lin8xQFTvx1U+u5SE5zjfpgAvUens7jz0Mk+d49ExPiHr53L90rHkz29tp9PD+f7pUQLKhD9oyAzuxsmj/Ka/gGWrhIRu/Gj29sGK2drJentXvGAbUnMCdWVky97emnbV70nkT1FwES6pMIgi0v2waC1wRI8XwAERlF1g3o/p7S7dkJDAAW0W/OA5Dy79nXVsdoIlxsmhCaQRKfzB7/39xM3xmAGOr61lb6bPgfNFP6nJbLBiKL9e0wXMgM1qoD2y1TdxQ0qL2MRoSeCsf/W0nf6v7+lJJ671sl/aNdnrcnld8Y7bOmVQLg3wfNuW7YwHMf4CMeJdiVx+4Df+yZE/UCbgSyi8Mff/58uu992wHJ9ikelOHT69WAnzlTnc1jTaAFgWRsooGB5NNQUSVJMDKSmB6+/pk1hD0WvUhR7oUCzHujBObMGUt+sAaNjJgCsa99LpcNjPb0pL8fPLASJf+ckZH0ORSVfgF67PF9C4WD7nW3m7UDwPt9Sf9F0m9KetVuxzaziSKiSJYJIDU3l740aDHQYLysqQdEXBT052FR8rUB1FsAlnB+9DErl62Yn0X1ypX0DUBLhLoCQGLRGx+3ejcaMQLyuJE8aCMqSoTt5k1rFwCwQGofmg43G6CMRZwIdjEC3d1tTgCZcm5gxE/YfAFW5uZM1ptNzOKi3bhkDBF7QS6dG5lNKbQubnqKgXEoUCm9MyLSQzQf4Ompj11d1t+KzxYBAtTieA1AIkDUN/4EiE5MJOdVKiXqGe8HCti9e9WNm/kuz51Lx/jidGSIa9UxFDMqxTE+nuZcS62w0bGfCGwj1mkAb3u7uc/UL9J379YG8MXB/TI6WnuRhlYDBcdfP729du8VBVTYbJw9ay05urpsg+BrIliwpeo6vYEB65+ESlp/f/U1ioIm9GrqVW/ftvu9tzcFiZD4JrMG6CNTh2jA9rbRFMmEra5an0kCawA7glF9fQbgADAs7DAUUHKbnzefCsjzdcWoEwNmTp+2dg2IUPnMFtlDApKnTlWLV5VKJlDFmsOaRDaOdYwAGvP3PT1Z9/xmHlVkAobQWXnvZAIXF61WkEAltD2AXzGDB7OFAGJR3bpYMkGfO8+a4V46zA1UjAfvn5oBeJK+ogLk+txj5yo/L1UC5J/7qNdsNjhO0Jm9jlfkppct14UP7LLe+ns/j/2PRsW0iqCMGmfKMWgy72nwMH9mZ9MeCDbS2bO2jqBKef26MZ3IILJ/IeCNoE5XV3VmbmYmzQdfzePDw6b1QJmRZ2wxz5UVK4OBkg+TSkq9X8lAIuzCnPBLJEYk0zpA9Iz6cKj9y8smkEfJFMwPxFlgBXr2n/elPgPu76+DsnYAeDirxypc9C8o/L/pKBTZOxZCL50PFdCrQnogAxWTBZ/Fzjdk9TQeuOosWhTMc0GhgMaCXaTZ+IwbWUAvXoKwCBcYAAs6EPVd0CF5rRgtesqmamIibfaJxI6Npf8DVObnDfTwehsbRiFaWrLzXb1qIMdvAOnBRmNNlI+6u23DcOFCdU82T3XitbixcFr37hkNFOEGH9manjZwRfYP5+Lpm9C7yLR67vn4uGXcJibS/0j9X7wYP7to8X4QQAkhOcmBAaOmePlh+tXx+fho2+io9cvq77dsHU6d8xGZ4n99fen9oWhVKyPULKg7jIhrI9ZpAK9c3vvnWatXXSOLfq3Hd6PuFK8daJf+XCGk+wMa9fCwqZWVSskfEiUF+K2umugJtXrco4C+1dXkdyiCn521LBsghYDI2prRAJeWbMEmqDc6ml6bqDGBno0NA699fUbXBmQRdGLjSqsSMmP0qBsetmDT8LAFgxBMoWYbahsAifUA+jpzA1ARsCvS9G/fNpBEzSAbD471VEY2G1D8+/qMispGhbnQ+gWQB8PFMzV8ixieR1AOsQQANM9hI0VtlwcHnJNGwsyfLF6M1XRBLxiGHSa4i/Hg/VOjFE1JdyQ9J+mxXc71Okkvf9RrNqvy69lPXhjOaxdwL5VK6T4jyCOle4J7u9nWOnnsHLUYOMX6ee+jQ0jfwepqus+7u+sLaNVaWwBHfJf+OeyBEFnp6rLgM/13Od6DfK8kXi7bngpgB8Ci9QrsK16DjOD9+/YarDl+7Sq+LzJ6lNsA+rimCYIhrMJnCdBkf0wNHoy6qalqYa4YqxmAxcwe/z/IWrxjD/CqJiS9VtI31ft/s1Eov0gtLBjPFlEMT60E6PGFSpaNozE3ixKRVCJcqDyyiUC8hIvHF8ADFFkAAZxQN7e306Z9YsIACDVyY2PpsYsXjTbpG3yTfWMeqM1dvGiqcpwLbjI3AdQmQJ8Hk6WSiZnQV4QaGvjX3PyTk7axIqqDVP/GhgEqinZZAACDvMdy2ahROA9oj0U1P+pAaLdAnQ4FvnyGOCt6p5BBpIbu3DmrFaQVATTYmzfTAobSEw6MWjnev3e6g4Mp4gV47e83kLq6agAVB1Qr0zI5md6TF085e9Yc0fCwSRajpumfXxTk2C2qehi0wUas0wAeEcODHsXvr55yXb3vmesPQLbba0GRJKOOgE93d7pXuH+uX7eFfmTEIrUEPpgPGX4iw4AchJbwFdSAQYOmlpiAGFkhMlTUoZFFp0cbEVhqccnMFzN6BP4mJtK9RkaOdQB6GlmrhQULCJbL1f3rPOUdYIjCLiwO3renxse4UzETehJ0UQApm4py2aiMBBZ9/01AcLmcjoeqyuuwPlJXRdARMAcw9HRW+geSTYRFA82Kz8y3n/B1K9Sm8z8vksbzEB47bFDn7RAAXiNKvtcqQiyXC49PyHp0Tkn6r0WBllqjUf9ULle35aDvLaykpSUTFGM/1dNj66Aftfpw5pHGXtrbSNW1zFLjQdm7d63X5+XL1XsNX0fNOsC+59y5dPzQkH2f7DEAVxcvVrc18OsILBEEtDwjDP/i2WiMc+fS67G+0AN1ZsZeq6vL6uZonbC2ZmsL1H7OGUKa8+XLJmY1OWllN319aY6sTyglQ72ndRrsBAIhMAK9aJRnQpTL1U3OG6GXN2rHGuBJGpI04n7/NUkvrXd8swAPiWsipjgs+qX5Gge4uTgyCtNZTFdXqzNkvkidTQYgjouYxZAFe2jI6vCQgqYOzTvSlRUr9CR6QaEo0QqACbViUjo/6kPQOBECuXbN+kR1dxt3eWYmPUb0BmVMotik7YeHq29whBOI5Pibc3XVsovciNxcUno9Moazs9VUMaJCZAbX1qypJU0sAZf37ll0CQdGpIYePKgjXbhg1DaynoDNtTWba19ftSgKTnRtzTIexZo3yQCYd9w44akp+39/v4H1hQVzYJOTae6+oNmfp6cn/c/3wCuV0t84fegZ3nlPTbW2sWwj1mkA7yhEVlhg6x03PZ38hgeBnpbpr7OpqXRt9vZWBxf88Av0+ropa9Kmg2a1pVJavFk8eR6BGwSTNjYMLCCTLVkga3g4BZ0I6ExMWPsUaikmJ1NAZW7OqOq0X/DZK9YFatnm541WCDWNjSwAibWC1idQ8KmxJXuJLyXQ6AVLaMC+uGhZPEAhQTpf8+TbKBBdpgSB9w8Q8hkv1OGYNyJhrDmAK9bB7e30fjwQQy2TzCJ0VcRUenstOFcEfv69sEFClY7Mnhfw8KAuRsvaAe4OSh2zUTsM/6RHK/n+iqQ/UaEdgqTrSqrj76z8/KpGXq+ZPnhswM+csd/Z57D20uuOLDJ1rsVN+l6BTKeN3T6nRwXbvD9GJZdzslfxtfn++MlJ0zTo67P6/tnZap/O3o8+dC94gekYUNft+5gy+vqqywRoN4VPWl83VlV/v+1/aHOAmvrly/GzawvqzNTdXb1aLbzV15f+RpSLADpMuJERK5Gana1mQ0xMWLnA0FD6/AimwdrwILVctvZZvp2OL+GKcWfAqlivx73XrB13gHep4qjeqSRL/i27Hd+sEpRXDIPW4YVHYrQF1UcHfUNZxEROnTJVSpTQKKCneSsLNxFUOOlErwFNXnKV5tVET2nLQBQAKVcomWxEPEcZTvPycnUGh+h3sW3AxYtWkArwWl21ZpNQHOtR/HwExTsg76SWly3jR5rd0w8vX06fwb17BmB93xTeB5m+uTlTwqOo1zs1uOae9000pijHjgOBQ07z5LNnba7Xr6fPamTEVP/gZ0tWU0T/Ll5jYCA5i0uX7LXYaEOzmJ6upqD60dtbOyq3W93Vo+q5Wqli1ohlgNfYOGgKbS06i7+fue66uqxOo94cfNS2CA5ZvAGJkrUcuXy5mmYMxQeGAXR6lNnoeydZM1v8I/WygAwYDfRxI/jm67KLaoEAMNSW+fvsWQOACwvpc/Eqwvh2aEeXLu0s7qfu2wMdMoDMCwo/oJW2Ab4mj3pvlJaJLEOh87UfADroraghA9qoLycKTiSaOVP35s/ns2+so1CXoF9CASXbSr0i4iwIirFRAtzdvm1Rbx/hLio68thudlxFVloxmgF4vvaO64NrnesZIQo2u/X2CrlFwuGPosAKCpu7PadUSj69yPLw7KMQTHWbvcrFixYYv3u3WlRlYMD2QWTtpbQecF5KaEg+cCzrB4ykuTlTZPWUfIJnrDHnz1v5Cnskz4ZC8fzKlXQ+aKywyegtzP6aPSsZQPadaE3AlCBrB8uDcjDPnPC+mHur+NP/vpda4mMN8JodzQoZTE1VRzZZ3FhgtrfTwn3jRspIIYoCcKMhLQuP/4J9o1YWRuhDnk7CJury5eooqK//unXLqKKcl+jAw4fVEWVPsbl50+rByG6xmZqfNyEU6u4uXzY6I5FwbgJ/A/I8elORfaJOxdMFucGLUf27d60dAH8Xa8/8RpBoDH/PzVWn13FYXua/VLLMX5F6yHvaLTJGpMvTRXkujgeBku7udFMTpSoWkvuFzGfhHntsJ/2AmsKREXM4jxrQKPiceb16QPG4jEaskzZQMTYO8PZCp22EWrnb3/5aJQBS71gG9aB+DqOjOyO6XuJ7YCDd35OT1ZtDNgj0apqetuxdqWQ1er7xLeCPANzamgmqkLWDMolv3tqyjJMX8yBr4XvF4Y8BONQncS786chIOicUdvw96wiAz1P2CSwxD87ps3y+NxxBR2rsNjbSdUJtnKdY+mAhAcObN61ZOxuV0VGjpCJow/wAgzQw9z3QAGCUOqDk7J8PQCBgCtAF+HkRF87B+X29HZulRjdEB6le10n+iQweG1bfxoPgAqyalRWjJec6u4MdRT9eL7BGeUstv+8fq7VH4DuDbs/jZ85UB9F7emwtqlXzViwtoRbXr189PdV+GpBHYB/9A/r5wuiguXpxveF3GGmsPyinA1DJ3kH3J6OHiqb/fIp1ioh/+Ubq1FizB/dK+b5dgq+D9v08/X1W9E8nLoPX7NhLHzwvHc2CypcJyAPQ3L9fXURPtg81Ra8g5uW06fsGyPMSwmTyiBijZEl0mQ07QBCwUirZxQQdgqwTNXpQM/2Fzt8XLxrAgvt85Uq6yKHZrK4aQORmL24qoRR6BxOCRX5221BSYzY5aaDQ16iFkCIwtXq59PenheXChZ2USf+6/ob0dAQv4DI+ngCVB6a1BpkBfvqsmacPNDO8iAvp/uJ7YK71BDTaWY2sEeukDRR+6TBos2Sv/TXm69ympqrFh/z11deX6NG1amkmJ+28RGZ91p7FtHgPA/SK95cfLLTcWwRs8EmlktXHQuUeH09z9RsNaOeIRN2/b2wLBsDCS22z2SCbR3YKlgfBO08NBIBw/OKiCU34bJxfH8iY9fXZ+uKzex648ZNaD+imtFbwAA+REoAm80KF2WfDyNJJFkz0NdSch/nAfmEdnZ62NbMoo7+1ZX2qeN1an5enjt64kdaypSX73KemEnAg8s3mCIEWf56cwTt4/0QGD7Duqcywmaippafu2Fg1OyePoxu1fGq9QBxBfsRL2D/eu2f9SWFa8Bx6c/pAOqVGxdfyYiwXLhirinH+fHr+1avWU3NuLu0lEWcaGkq+e3vbRHs8yJKSD2Wdok8qAX1/HUI99TXcPph//nx17SCg89Sp9Jzi/ppa8bExC25AxSdw5ZkPBOmgmte61/ZrHQ/woNQQiVxZscUfNR9SwCx4Pmu3uGibAB8JvnbNFiJPnYQi5Bsv3rxp0WY2RNR9EcEocp6Rq6VWi43Lgwfp/LUyP+Pj1RtHf4GSreLC95u82dnqbBo3PbV83GDQEm/dqs25Byz296fPwzcr5hgamHPTXLhQvxgb8MvnUiql1wYA40x2c4D37lU7QSI69aiQfnjaWfF/9bJm9+4luV7+9vWJzWzq/bHtTHNpxDppAxVjtUR0vdHod95Iy4Ra5/T3nPc7RDdPn975/JkZC4b5YMWDB1a35+s+/NwQayLizONra9V/I0+9tZX+R40dtcijoxY0mZ83hUdok5ub1puSFgvQfebnLeA3OmrMBxTSqAe7c8f8uM9i+VYFPpsGCFtZqRYz8XRDsnQAphs3LLNFlgtgxaagKOxF+QDgB7AK9ZEaP94Dhf60a4jRAKmnfJbL6fMr9jfj3F4y39dm+d511AFy7np9oXzU2kuO8zmRaWRTVASHAD3OfxTWaf6pXK5uoUQmGNDP/To2ZrWprV5nOmk8irFTK0jnfb3vSQoN/tw5A2R9fWmf5WvnfMsqfud7R9VcSue7dSud0wfE2U/i42kD5efmGR+3bqWfCGNNT1e/b0qIfLLBD4Ao4ite3ZPPYGTEsooc19VlvZdZpx88sCwirbQQuEJUEbywuWk9nqGj1xOFygCvMJoVWUHZa3zcmoATaZ6eTgvS1au2yHh1S+gi0EgQDaHwH8VHCjOpwRgbs+aIFKJ7xUoEVdiA+JuHtDh1cfT4gL54756d02eG/Dh71gBZo1G1Yi0dEY/BQcss0gsK0OVvqscf30mp5MajH54HPVI6T1FCt9bcQjDaFgOOeT3AtB9A1Ne3O/Dr7jbKmJcSLr63Wp9xrfPSX6Y4B35v98Vzv06qXcZe2iQcRBbvUTScu3fT9TQ5adcllBtAHkCMLBQsA85BHZ5UnQWfnKzuu0QWbWYmPTY8nKKktF2Yn0+LpQ+coEh78WJ6LkCC+mGUNwcHTYmXtiLUdpXLCYgg0LKwkN7HyEjy77AdJiZsMYb+g/IZ1EkAy+ioZaN4jGbivb2mXkyU1ouFbGxY6x1EQxAkAbgAsG7fTusM9P6VFTsvWROybyg9AxKL0WJUKIugiDnzOIANILW9bXV40FQ9TdKD2aeeshYWgGIAsc9ccrzPTBYzeLwvXqOooAmY9DQnnzncjzhBM9ZJ/gmBHYIUXuF0aytdn+w9EL6Q9tdnNY/GRzFL5QfBdQ+sihm+ogrn5KTt8XwSwK8L09PpGICPB2q02SIryJoCPRIW19yc7Zt87bcvo6EX37171S0f0Ibw6yUtqTzgRKgOFVB69LH3un+/WoGdNaXWXpmkDg3TOSetxKiTJugGIwM2Qy3BlRjNVxUFVvZqHQnwoK9w8czMpIuXRZ0viAjxykr6ckDjRBJRCWKhhr6Jsub589arCXUgnCAqltTs4QC5kKip29qyRrnUOHR3p4WtyGEmTU4hKP/zgILm2kWnwO+1AMVuo9YmtN7GtL/f6vTqvQaAhUxavdfdC+WjKL5y2KMROfl630OnjP06qXYZe2mTcBDXQwjNyZF3ddl94rPYBJOKNGYWXyiavb2m1EogyS/QsAZ8YILIrH9sctIEn6QExAAI+I/paesLSq0dm4SBAWNM4JdhWRDMg74IpYh+ofTu8u1yYqwGEKgFQtV/6imrD6NPE6ITp04ZGCTIB/sCWijRXV4HFWfYH8yF+fkMFZsBr04JDROQ58Gpz5hBtxsfT8cV3yfnXVoyWqUHiNBAoYYCDougtphd9PV3PM77o+cdz/eqcszFq9B5cFjsIeVrAQ/LOsk/8Z1z/yA4VypZGyLuYQKvpVLtoGYeBzd22yvtZR2pVdoiJUB261baf5ZKaT/pFcAXF21f6imOPNbfb79PT1sygv02Cpi8Hj1EyQIW3+fMTPK3no124UL1cbOz1SUE/vkEE3mPHsSy/vhzM1ZXTcSQdQtF8pGRdL6REastXloyxoX3ZcUaO6+keRA+qyMBXowmZNDTk74Ummmj0NbVZcqSUIz4YimiR8CC+g2yfuvrdjwcYlLJUDjJ6F27Zq0GUOzh9Sjy9yqSw8M7U+w06+VvsmE8RjaNC9Fny0KwXmjj47V7rTUzPIiql3UrZuvqDS+YUmu+jFqAsh2LujtxEdyvk2qXcZiNzhsV0SFaW0vVdmDAFF/rFeVLu2eM2RRMTVVHTf0xRPdRvp2ZScdQT+EL9j1TgD5ELLa+L93SkvlvaDUXLljWyIuiLC+bajIUQIRZeIw6NJrd+poyQAhiW0ho87zJyXROmqrTCufGDavvZs3wgiFkqAgeoqKMeAmZRYRQoIFiHtB4MZUbN6rfA8f49wMQ5X148xkx3+Ca7B7AlbnUoksW35/PEpK53NiwcwLuyNj58y0vp+uiOE8/32LU+6Ci4fWsk/wT38HKimkELC4aHe7u3Z0q2nkcr+FBl/fztGFCLOTWrd2DgwMD1Uwx/LNfXy5fNjVjAvz4cIL41MiVStay5sED01aAkVXUK+A9sFbwusX9JfRNX8vNuHDBkhrF5/F3qWSflaea4ruYI9k+37fUrx34PjJ4tWjkB8066FiA9/ChbT76+pLTIo0MbRNlHS56yVS9qM2gB4Yv6KeVgG/cfeaMSVvD9+XiLt441LEMDNjvbL7YHAwM7NxsedWfvYxmwF0jUaGjzkj5nm/7eW95HN3Yr5Nql9Gsb2rkXm50A9UsPYpaWql+XZ4fHHPqlBWzA866u1PQB1/lfe7wsFFdCHBJyZf6qDHnefDAQCoCUFLaQExNJT+8sWHnuXDBKJEoGxM59RRD6iIAa8vLaQ59faZKTFaKjCDZixs3jJkBQIJ2SSbuzh2j6g8Pp3lDH/ULeblsYIz/QX/0oizQMX27A8zXtwGwOMargd65Y6ANyuXiotE/i1k8Pw+vvEm/qqmpnZnOhw+rlS09IPWfP2COTGMxO1dUyySzx2sVf9YCc4dJ0+wk/0T2Go0BMrVQqf0o9r/N4/DHbvucoqhJ0X/DuBgeNs2FWrX++F0YE9PT1a/LcYh20TOuqyudd2rK/KCfB0CO64YyJoBgUTX9+nUTSfHCKo1+RoODtQX6pJ0st/Hx6rUQDQwAKwkbAne+ThifDE2zr8/UhBsRg9qPdSzA80IGqN+wEervT18UFB9ULldXkzPjS0LYZHzcLq7Z2bSh2doy+tDkpNE0oVGyOfO1aqdPp+N9JqdYIEoDyfn5nbKt/ubqxLEXFcvjMDr5O9uvk2qX0Yxv8gGeVn43XV3pnmKhLdIzqZ/w6mgAu5ERa1ILbdI30vUCUkR0+b9fqAcGrH0KYlOcgz5Fc3OJ1kPwbX7eWBNra8lHzs5aFu/2baNQUuO1uGi1dh54cCygDWBHpok2O1AOqQtj8Saz19trvfh6ehKApz7QUy09kInRREWYC8DMC474jQKZMZ/5irGasok4ihcq8dk86v6ow+NcXkgFIRPfCsgDSQ8yeX1PEyWb57ONHIfxmrdvJzDBcbWoTLVq7rLIysH7p+3tak0C7p2bN21j7rP2+w0657G/QWKg+H1MTu5U3/a11pLVlUmmpMyxPT3VtdULCwn0XL6cfhb3NJ7myD6a/suzsxbwoz77UfXnXkSwmM1rdvT1pcyzfz7vd2rK2vbwv6mp9JpDQ9bvkQAiYlaeju7p536NKdLKD8M6FuBtb6eLFICEROz0dHU62AuaSCbbTB0HxZx8+VyYpJypD/FSrGTzZmbSgj88vFMoxA+Ai++34VU1G+2Vlkcex23s10m1y2jGNyHRv5fRaLDgURuvvr6dwSW/APri+/v3DeTdv2/KvktLFiAbGKheJMfHzadSmD82lh6DDTE0lPzt5ctGkaHn5NxcAl6o9NEraWMj/aQmaHzc6JoLCwbW1tfTRpWFmVY3ZOG8aAcLsxckIXMBvRHwROTWi5P4ZuNk4MgcUoPnhUMwwCLUTP982g8ANAE+POaBX4zV74Xifp7HuXzLBi+k4sEjlE+AHSDWvw+fifSv7bOKZOFqNfr1zwHcnjljmdYiKAa81qpnOQqw12n+aXU1XQdnz6b7KATLUNdS183jYEazlNd6bZWK5+zpMX/pfbQPuI2NGRjzzcyl5J9htBHMw8efOWPtt3icNgmw5eq14HrUKIJAH+B/1Lk88PWAlvUT8IpISwjpfQ8Npew1PU2h4vsed7UCb96HelZDjDmD1/DYaw3elSsmnU1R/8KCRaCHhlJWjkzf/Hx1Hw/UIP0FNDdXXY8GzRNASbRrcLA6MuKLNvcyDqN/Vh55HNbYr5Nql3EYNXi1MnzUphUfb9an0MuqKMAkpYyaVzcbGrI6uVOn0sZibi4t+HNz1sag+BqPPZbOMzRk/pY6BilFfWEqQPEZHDR648ZGWhxpVTM/b8Drxg1rgUJtx+ZmWpwXFqrpl2xOqJOjv5ynEQJYnnrKxFju3EnnR3iLRdzXsyFIwTk9TfLUqfR+fZNvLyhCGx4PvHzWiuOga3IM1xBCLYAd5gzFsrgB8ebP7wVciESTpeMzgaKEQArn56efn//b0zL5WRSC4XlPP209svz7elQGr5VR8nYZjfonmE9raxbgmJ21YHer15M8mh+1gCN1Z2hBsK6gWgyNHR8NQJyf31nLhqJ5reBjsf3WQbGZmhW3C2GngubMjGUZh4etjY0P0BWp8kW/FWPtwFU9O2gf1bEAz0fJPcVoYSE5LOg0XtqVC5/fz56t3jj548jaFX+nRqVWC4DdLspawgh55NHuY79Oql1GszV4s7M7KZHFEcJO6WqpPpgrivgU6wygNdZ6br0WC8PDFsian08DeXT83uCg1cf5YBYRY2icnoLZ25vAU39/emx93XrZbWyk35Hh395OrzsyYuIjRIwvXEibkFIpvcbYWDovNMulJet5iqIxGcjTp009EuDka9YAV8yB//f1meol/d96euw439aADQPiJb52b2XFaKflclpvyBhynQC0yJxBfbx92z4L38rAb1CKdFDOySiqcPr6Pl/7599LsW0EtKVyubrWjg0S8/W1hV7sxYO4cjl9lpOTtbOD/vhagPWwrJP8E/um2dlqNezBwRQcr+WP8ji80Swg8sfDKkNoxB9XT/DN72MPovXF6dM7X6tey6hmR1dX7cCiPzfMucFBY1SsraW1YHw8+S7WBajtvna5XpsDrBmBp8MIRHUkwNvern1xhmB1JVK6OGoVjxKpakZ+fLeBAlWrnUUeeRz12K+TapexF3bBQUbEd6Pr0HMT2kytvote5Kmovsk8YTl4yvoTTyQ/OjGRgBmAbnbW6JX1aDp+Ie7uTgstLWOgXN68mR6jrm19PS2StJqgEN7XBZZKaS5QzBDUAnRAwYdWCTjyapK8xo0bBkqI6HIuPwAxMdr5vFjI1lZ1f7vTp632m1q3zU0Di16RDbDmqY+0E1heTueiTmppqTrq7DcgUIjIMvp6PY7lfz7TiLKnz1C+7GWWIUXNlM+IHrJkUaGEQhv1mcwYq0ExYBartSkqbrhyBu9g9070D5PS/d6J6s8nYXjlSWmn4nGt4ZXND2r/y0BpXtoZpGyGvlkMRlLCRD0dtYUDA8nneEXjlRULtnlhK89o8P7oUdm4Ztsd5AzeLqMZqd9aPdH8JqheBGGvhZy7XYB55NGpY79Oql3GXgAe/eFqjVqL624L7r17tpgXA1VS/Wwh/YP6+qyemP91d1umrKi2GUIChWTwfG+i6WnL2kHvQjiKGg5omfSzo0fSxIQBNBpqI6biqZHb2+n4zc00eC4g6ebNRNdE8IUFHeDY3W2P+5o0gJEHJWSnTp1KnxF0T8ASGS3qx27etB54PnOF0Mv2diob6OmxjF1Pj2XI2HjcuJG+F581e/ppmxOAydcW+p51fgPC8wFQACPeF3WJfrNDBtH/7UHs9LSBXoByjNXnBpj6LJ6nmmKewunNv6Y/rgjscg3ewfin7W2rdV1by0qZ7T7aIbGwWzbPr0el0s6edaxBUnXg8f796uAb4lTeN3qK+l79yWH6nUasYwFes8XAjYCyvTi7XDOXRyeP/TqpdhnNUjTn5kxNs5HhF2r6Dfn/1/MzLJ63btX+v/d7RHj9a7GgFqlZQ0MJxMzN7ZSvRo6bv6FOAvQ8AAWcUcOxvp6OuXo1AcmzZ9Oxo6MJDM3PG60G9c+FBaNgFsEEoA9QQeNt+uX5OrwYd9aKQdPkGFonFJUsEShhM0GWkNccHa3OlNF+gNeYmKgGXlwn6+vV9YFe8MTXywEC/Xvx9EaEZpC+55y3b6fPztcl+n59Xi2zSEeilo73VHxNPnNfm0e28exZ+4z984r3CQIxxZo8f8xhWyf5p3LZsvijo9bqpNXrSB4HP45LIqIYuDx3zgTAimvJ1lYKPIyPp6Bmf3/ykZubybcg6AW9vygwVaSrF/1JMfB03K0jAd729v44vsWeGHnkkcfexn6dVLuMZkVWenrqi5PsZxT9Xj0/ODlpbRJYYKG2AB67ulIGrqi+huDK2NhOhd8QUo0ex6OISaZua8v6fAL6Tp2yXnhzc2lMTFQX6KO+2d2dNp0LC4mSOT9vap6AEqiGm5vpGFofbG+nzAT0G099LEZ1ifaiqglQIYPma9R43AMeX8u3uZnWFA9myNYB0gB/XnWyXLYerp4+Wmywi1gLNCTO4WmWSN/zf2rgeG0A48OH1a0mmCf1ekVFTJ8p9I3aoUGRWeSz8IB0aytt0Ir98bwVN2b1AF3O4B2MfyqXTUPAZ0TyyOMwhmeQSCmJQjCKFgX4Cq/M6ynhmPeJuwWOvBWp39PTh1vPe9DWkQCP+oy9jt2oU3nkkUfjY79Oql3GXjJ4Bw3wUKgs1l7UG/61ixnB3l57rL/f2Av0nOvtrRZdYYGmQezoaJrLgwfVktsoYa6vpywdm/2rV6tfnwa3UO0HB9M5L1+2mi/qKagFI1u2vGygL4QERqGSQv/0CpO+zgwQ45t+A7A8oKqldhljdQ0HEeSbN42uWcz4+Wwcr+tltn0dH8ImbF4AlBsb6X2VSpbdLAJWLxYA/dQDK78x8iIzgDOUQDnOA1romAis8D8yd7wv//oAazZotcQK/Ge8G3Wq+NyjrHNpl9Gsiqa0k/KdRx67DTK9xebp1G4TSKRlDbXWm5tWg+x9xm73e5G6XbS9+oB2AncxxtiRAG97u/UXex55dPro6dm/k2qX0QzA29pKC970dPObp0ZoNcUFtpFzFmlYRcBHNL+ry8RO5uZMRKXYboHavWINxeJiAralUnrOxoY1CwckXr5s/bfu37fM3sREqqvb2LBaMjJsS0vpb8AdVB7JBFUWF01BEwVKDBBCRo2m5j6jB4gk4wdQgd5YzAjGWA2cijUexeJ+jvdApShGQssGos0AqqUlq0fs7bVG1R64euqkF2LhtVDs9GqZ9N0rguHl5ep6RY6LsTqT6Wvw/GNFAZh6770YiS+CwCLNqtYx+7VO8k9FWfs88qg38OFdXWl9WFhIj7NWXLq0M9CztmYBLC9wQubsUVm0Ru7vw/ABx9U6EuAhYpBHHnm0duzXSbXL2EsfvFJpp7KZV/WtNR6V8aNROMfuBgjHx+3Yxx+v/p+nzUjVAO7s2bSYh2D9stbXU6ZnbCw9Vi4n0DgwkAAbrzM7m0Aa5yar5hvP0sJmcDAByNlZU69EvIRM1c2bCWBSG0c93tBQAoQ0bQZYbGykz3xhobrZeYzVdXulUnXW7ebN9DzonjS63dpKx09M2LG+BQAZv+Jr+ewXGbHihqSWipvPukF5ZANFvQnCJ4ineGEBX6NYzHiRsaMVRLEhvAddtcRoOKYIHIubvFo97TzV0z9WPLbWpq1W7d5BWif5J99eKo/OHIOD1WqatBqQUi13T0/yq/g/7u+trfS/jY3ErqglmLQbFbvW77Wskfu7E8BdjDF2JMDzNIM88sijNWNoaP9Oql1GMwAP/9SsEFSt4YERj9ETTqrfM88Dxakp+3t42MAcAicM/xo0Q6currs7vS70zcVFi+R6EZb5eaOmXrqUPosrV9LfZ86YsAPiKahjLi6mDQQiKShP8vpjY2kMD5vE++CgAUJoQNRvrKxYlgpxEYAJjdU9UAOg0NttY8PA0o0bCXCyyQEcUcfn6YkxWpSajRFUUL8p2dqy2j+eQ8YLBU2ynh70eRokdYm+Zu/GDautKwqX0NoAJU+fueMz8lnNhw93qnICyvw8zp5NoP706Z3tI3hv0EKLj+8msFK0w9rUdZJ/akb4KY+TMQYHjT5J7TQ+9Pz56hpf/GCMte+33epp6z0n296tIwFeUUo1jzzyOPoxMbF/J9Uuo1mKppTq2oaH6wO9ZuibAwM7wdwTT6RFuR7IY/jeoH74GjzEWmilsLCQ5u5bI6ytmez/4mJ6zsyMUSWl9JzFxbSpmJ9P4BJqD9ROgB2qlYuLCaTduZM2oDdvWgaPeWxtJdBFw/GNDWvCvbGRNjBktJaX7XNfX08AcWXF6Jlk6wAcAD+yYuvrad6bm2kzs7lpIOrmTYteU1/oaZj87oGSr6uL0dQrvXDM1FR13yYvgBLjTgoj9YgATa8kh4hKkRZaFF8BhBKZHxtLc0KUBfDGeXkdPhc2e3w+NJ8/e7Ya0HrKaDHDWQTG9ahXh0nL6iT/RD/MPNprFHuq9vVZ8G9gIAXZlpeT70LcamMjBeHwKfgqAjBembde9ryWZRB3dJYBXh555LFjHHQT01rj1q39O6l2GXvpgydZr7ju7v01FK5FxSyVqtsj9PRYpq6/P4Es/lekftK2oK/PeuhNTibfOjGRNgrz89XPAZCuribgxfsiOzc8nFQt19bSsRMTRu8cGkqbjbEx61G3sWEqkuWyZRd6e9NxGxsJcHGeO3fS65KpW1lJtWmeHri9nR4DqNFnj82Npz/yO2Dm2jXLJo6OpvOsr6dzUQPoM1GALLJtExNGLwUMASi9SAsZQoDYnTv2HN/ovB7Q8SIvZPy8euWpU+lzKmYNi5s4nuOpWDQqP3PGNoM8F4BIfzx+kjUFDNaau6e2+gxncW6Pqr05DOsk/5QB3vEbrNXFgN+FCwm4jYxYCxlES4qgLcZq6jOWwVh7W0cCPM8fziOPThi7tQUpLgyAgWb64DyqlqvWa12/vn8n1S5jLxTNvr5Un9bVlaKsUBprfZf9/Y1/J8XaO1+T538Wrx8UMoutEvr7q7/j6en0f+ZbL9NI2wXOPzubgBGtFR48qG6kXiqlTcqVK+n1VlYSgAJc9PQYbXR42DJYa2tGuyTLtLFhdESfuaJ2jpq98XFTciyCCJ8tm5839bfl5fQ+RkbSnAcHjfLI5gpgheALwG593YAdAIpMo59nb296H4BbHu/rS++zKEjif4c6ynvgPS8tpefSwxC1TX+OololAjOepgqIJMtXFIrxWUrmQnbUgzZv5XJ6b16oBeM9+QzfUVon+adiT8s8DmdcuhQ/69cJ8q2vJ1+2upr8zfq6tQnwbV6ok/ViJZ4ina1zrCMBXo5C5dFpo9GMHBtTsio87jfjxb+hePC/qSmj40GpQ03Lv9bGxv6dVLuMZtskTE+nDTYtBHw/OS+UstexW3Pi3aifPuNXaw5376bv+8qV+ucAoHZ1Wb+89XVrRLu8nMAh1wuiLevrRr2khx1gdWPDsnZDQ0bd9FnE1VWjf0J7hBbINeozY8vL6ZzQEH1DcS+CAsVzdjad/9o1a/UAfZRz9PVZxg9KJwCHjCSURcAfNFqUKQFHZMyYF3RH3+fOi6t4qiSAkeuNurrl5QRqr15NABJqqQd2nMMraQL0yEj6LCHHeEGWomIm56PvnTdfi1hLbKVe9u6owF4n+adHiTzlsfvo6kr7z74+SzScP5/8HYEqH8Tw94g3H2DZjZacrbOtIwFeUREujzzaddRacNl4k12ZmUmbWrI8HuzxfC+YAc1uYSFtioeGquukzp9Pm2sodRsbaWM3O2sUOGTj2eAiRT82ZpQ+34R0r06qXcZe2iT4RsIAq7t3E2ghi8Z3uRvga2ZT1kzWtvi8EOw6un69diNk5nvuXAKZCwsmWLK6mt7T5qYB23PnLAs2MpKuxVIpHTM8nB6n5m9oyADd2ppl/GjE3t2d/u7pscwfjdHHx03BExGYra1EO11cTI9du1ZdswZ4ednLrFavq8syhP39BiA94CG7dfOmgT2AGRm5pSVrG+HbKHjw4zNmHuB4KqPPjAEOyQBSC+hr9splm0OxcbnP+o2PV1NX/TEATr/59MqctYafm9+wlsvpcyy+B/+aRVVO5vgo0ZWDsk7yTxng2WA99YwKhJzu3k3r7sZGGtwP/h6Ksfr+rWePAmoZyGWrZx0J8MbGWu8c8sij1kAMw6tWra7Wpk0ODKRN78hIyqBB5fMZHrIj9CNjwWEh2tgwXj4ZjzNn7DmLiyYjT0Zuaio97jdtN2+m4zY3rV/W1lba8BebMVNjVozU17Oj3kBJeqmk35P0PkmvqfH/Pkmvr/z/bZJmD8o3xZg+09FRy1TRiJtrwl8bewVkzYxTp2pTQH2N3sxMbepoqZTmWFRhm59P2ToyiTw+OGi9tohqA9KGh2O8eNEydt3dRleam0uPX7qUznn1avo/tEfUNBEPIOjQ3W11fzRBp9E5ypqIudy8ma5nVDqpA2N0dydKLZs4aFK+no57hdff3LS2CxsbBvw2NxNonZqy+8fTFAFgNCTn/IA3znXqVLovfQ8/Mn03bhhFElEVFCu94maM9trcs1tbyYfcvFkN6DjWZ+uoPaTfns/w+Sycz0LyGK8FkAN8+oyin0Mxe3mYDc6xw/JP+/FDkr658vjvSbr7qNdq1D+d5Kbm+NNbt2J84QtTkOr+/TRWV5MfIRPvgxMevBWDD9mytdI6EuDtl96UR+eNnp60wZmaSn/Xu4Z6etIieOtW2hTPz6eNIDLx9A8bHbXNO1kI+PNTU2kxGRkx8EVGDRpHMSqIQAIbPzakUMuuXjXVPBog+w0Z0W7P2V9crI7i+yxEsd4FAFiUMy8WbPM6jYK7GGM8SoAnqVvS+yVdktQr6Z2SHi8csyrphyu/f7mk1z/qvM1SNJeXbXP/spfZ942QCCqTc3MWVb9wIf0dQrreyFpJ1haBa5HrdXa2utfebtRNKV3XvlXCpUvpNZkX1zj3QvH509Pp//S+8zRgqXouAwPpGuT1BgetRg+Ax+uRPQMoAd7W1+3/g4Ppc11ZMSEYghq0V6ApORlEatG4vzY3U/ZqdDQ934OsxUVrNO6/S+4pWgbcuWOiKjdvps9vcDCBsfl5a+uwsJCA56lTBvquXTNxlmKdHvWF9AdcWDBAVKR78TwyZn7OHoj6+dNXkHNB8wQgnj1r78/TKb1vwr95H8FrMB8ynwSWbtwwoIhaqs+EAO44R7HmkJ+1mqAfhB2Gf9qPH5L0eOX4PkkXK+fp3u31GvVPrV6Hmxn4RVqhXLqU/NStWybKtL6errUMxrKdROtIgNdqx5PHwQ3fENk//sQTaRNEsfLkZMoySGkjzCZwdTX9znF9fUbPgvZ15YptZNicbW6m1wSAUdhMtB8QxWbD17j4zNfTT9tmrxgBp4CaDVi9zYqnOPF38WdR5KBotepXduP+1zu+nkgCtlvNQD07YoD3Ykm/6P7+ZknfXDjmFyW9uPJ7SdKHJYXdztsMwIvRNrS1MhFcbyhEIjbC98W1RluAS5fsurxzJ4EEKI9bW+k6GxqyLNrsbAId8/Pp8dnZBLBo1zA3Zw3DaZT99NMJjNDzbW0tAaHBwXS/lUrpXgMoAEhogdDdnY7r6Um0zIGBFJRAsKSrK82XvnfcS1Ag/fB1Zltb6bUHBtLrraxYw/Pe3vQTgZNyOYEPaJw0SD9zJv08ezZRJwHL4+P2mkV1zeL1DQ3SZ8oIxNBigMyi7wVYKqXvYGEhHYci6NTUTmokAZxy2ajRy8vpsyLwQsbOK1n63lT4Ft+vjyzk8nK10icg0/fYe/jQ6hcBXrdvW9aS761ePZ73lWQiyVpybv+anoZZpLrtVpNXC/Dt1Q4J4O3ZDxWP9cfVG+2yd+rtTdfd448nGiRBUvxbq+ows2U7jrabbwrp/+1jTz75ZHzHO97xyONe/WrpB3/wCCbURjY2Jv3N30if+IT9/fGPS7290ic/KX360+nxyUnpYx+TRkbS30NDUn+/9KlPpf9J6XmS9Lu/K/3Df5j+/2d/Jn3u50of+Uj6/fM/Px3zV38l3b4tvetd0vnz0gc/aD9/7dek17xGestb0utduyZ94APS614n/emfSt///dKVK9Ib3yg9/bT07LM2x1u3pD/5E+nMGelHfzT9v/g7/5fS7889J01NSadP2/+ff97+5jgp/f2ud6Xfr17d+Xm+6107H/fPLT72/PPVx/tj/Tz9/4vnKR6zX2v2nI0e3+x5Qwi/GWN8svFn7N1CCC+X9NIY4/9Q+fsrJS3HGL/WHfOuyjEfrPz9/soxHy6c61WSXiVJMzMzL/rDP/zDpufDZ1XrM+P689erv07PnJGeeSbdI8Xr9/nn08+rV+06l6T3vld6yUvsNbj+n302Pc69xv94vmRz4Hc/v+ees3vyla+Uvuu7qv8npXk+95z9/O7vlr7zO9P/PvxhuzeZ0/PPS9/2bcmX17o/mMczz6Tn8noYr+Xv8WeeSa/7ylem98v7uHo1/eR13/526Sd/UvqJn6j/2ReN59f6jnhvX/qlyed+7/em9/zRj0o/9EPSv/7X9hkwl6J/8Y9531X8rorviZ98lv5z4zvz1wOfY/Gcfh6veU367vz19Q3fkN4Hcyp+DrX88a1bNo9ax/qfq6t2LRT/rmUH5TMPwz/txw9Jeq2kt8YY/4/K45uSfj7G+LOF12jaP4Ww//fm7YknpPl56Vu/tfa1lC1btr3bbr7pxAI8KYG8H/uxBD4+8xlpeDgBl098Ii36f/mX0sKC9OY3SxcuSH/+5+l53d3SY4+lRRh7yUukX/qlBEA+8QlpYCA9fvq0tLIi/ciPpE3DL/+y9L73pf+96EXSpUvSm94kveAF6fXOn0+PffSjCQRhbKiefVZ64QvTef7JP0mPvfGN6TlsYFis3/vedGxxEecYv9Hzm47iJsQ/5jcMjTjhWpuQg7BaQCnbybV2BXjemvFNJ90OMhDQysDGYb22dPjBm3qvfVCv00hQ6qDtKIJetaxdAZ63ZvxTEeRtblrQNFu2bMfHOhbgZcuWrT3siAHeiyW9NsZ4t/L3N0tSjPFfuWN+sXLMr4cQSpIeSjodd3GY2Tdly3Yy7ZAA3p79kKTX+GP9cfVeL/unbNlOnu3mm7qOejLZsmXL1mL7DUmXQwgXQwi9SuIFbygc8wZJ/7Ty+8slvXk3cJctW7ZsTdp+/NAbJH15CKEvhHBR0mVJbz+ieWfLlq0NrOUAL4Tw0hDC74UQ3hdCeE2r55MtW7aTbTHGT0n6WiVhguck/XSM8d0hhG8PIXxx5bBNSadCCO+T9I2qRMyzZcuW7SBsP34oxvhuST8t6T2SfkHSq2OMnz7q95AtW7bja6VWvngIoVvSD0j6e5I+KOk3QghviDG+p5XzypYt28m2GOObJL2p8Nj/5n7/pKQvPep5ZcuWrXNsP34oxvgdkr7jUCeYLVu2trVWZ/CWJL0vxviBGOPfSPqPku61eE7ZsmXLli1btmzZsmXL1pbWaoB3TtJ/c39/sPJYlYUQXhVCeEcI4R3Po+GcLVu2bNmyZcuWLVu2bNmqrNUAryGLMf5IjPHJGOOTp32To2zZsmXLli1btmzZsmXL9llrNcD7I0kX3N/nK49ly5YtW7Zs2bJly5YtW7YmrdUArxGZ4GzZsmXLli1btmzZsmXL1oC1vNF5COEpSd8rqVvSj1aUoXY7/nlJf9jg6ackfXhfEzx6y3M+Gmu3ObfbfKXm5vw5Mca25l836Zuk9vxOsXaeu5Tn30prx7l3mn9qx+/IWzvPv53nLuX5H7XV9U0tB3iHaSGEd9Tr8H5cLc/5aKzd5txu85Xac85Hae38+bTz3KU8/1ZaO8+9U6zdv6N2nn87z13K8z9O1mqKZrZs2bJly5YtW7Zs2bJlOyDLAC9btmzZsmXLli1btmzZToiddID3I62ewB4sz/lorN3m3G7zldpzzkdp7fz5tPPcpTz/Vlo7z71TrN2/o3aefzvPXcrzPzZ2omvwsmXLli1btmzZsmXLlq2T7KRn8LJly5YtW7Zs2bJly5atYywDvGzZsmXLli1btmzZsmU7IXYiAF4I4aUhhN8LIbwvhPCaGv/vCyG8vvL/t4UQZlswzeKcHjXnbwwhvCeE8DshhF8NIXxOK+ZZmNOuc3bH/eMQQgwhtFRqtpH5hhC+rPI5vzuE8JNHPcca83nUdTETQiiHEH6rcm081Yp5uvn8aAjhQyGEd9X5fwghfF/l/fxOCGHxqOfYSmtH3+StHf2Ut3bzWd7a0X95azdf1onWzv4p+6bWWvZPbWAxxrYeSg3S3y/pkqReSe+U9HjhmFVJP1z5/cslvb4N5vxFkgYrv39NO8y5ctyIpLdIequkJ4/zfCVdlvRbkiYqfz923D9jpQLgr6n8/rikP2jxnL9A0qKkd9X5/1OSfl5SkLQi6W2tnO8x/D6PlW/aw/yPlZ9qdv6V446Fz9rDZ3+s/Nce5n+sfFmnjXb2T9k3Hf/5Z//U+nESMnhLkt4XY/xAjPFvJP1HSfcKx9yT9O8rv/+spNshhHCEcyzaI+ccYyzHGD9e+fOtks4f8RyL1sjnLEn/QtJ3SfrkUU6uhjUy338u6QdijH8qSTHGDx3xHIvWyJyjpNHK72OS/r8jnN8OizG+RdJHdznknqT/EJO9VdJ4COEFRzO7lls7+iZv7einvLWbz/LWjv7LW9v5sg60dvZP2Te11rJ/agM7CQDvnKT/5v7+YOWxmsfEGD8l6c8lnTqS2dW2Rubs7auUsiCttEfOuUK/uxBjfONRTqyONfIZv1DSC0MIz4YQ3hpCeOmRza62NTLn10r6ihDCByW9SdLXHc3U9mzNXusnydrRN3lrRz/lrd18lrd29F/eTqIvO2nWzv4p+6bWWvZPbWClVk8g2+4WQvgKSU9K+sJWz2U3CyF0SdqQ9KDFU2nGSko0gltK0b23hBAWYox/1spJPcL+O0mvizF+TwjhxZJ+PIRwNcb4mVZPLFvnWrv4KW9t6rO8taP/8pZ9WbZDt+ybWmbZP7XYTkIG748kXXB/n688VvOYEEJJKd36kSOZXW1rZM4KIdyR9C2SvjjG+NdHNLd69qg5j0i6KumZEMIfKNVbvaGFhcGNfMYflPSGGOPfxhh/X9J7lRxSq6yROX+VpJ+WpBjjr0vqlzR1JLPbmzV0rZ9Qa0ff5K0d/ZS3dvNZ3trRf3k7ib7spFk7+6fsm1pr2T+1g7W6CHC/QylK8AFJF2XFkp9XOObVqi4U/uk2mPM1pSLQy63+jBudc+H4Z9RakZVGPuOXSvr3ld+nlFL2p475nH9e0oPK71eUeOGhxdfGrOqLrDytapGVt7dyrsfw+zxWvmkP8z9WfqrZ+ReOb6nP2sNnf6z81x7mf+x8WSeNdvZP2Tcd//ln/9T60fIJHNCX9ZRSdOD9kr6l8ti3K0VtpIS8f0bS+yS9XdKlNpjzr0j6E0m/XRlvOO5zLhzbcofUwGcclGgQ75G0LenLj/tnrKTm9GzFIf22pL/f4vn+lKQ/lvS3ShG7r5L01ZK+2n3GP1B5P9utviaO4fd57HxTk/M/dn6qmfkXjm25z2rysz92/qvJ+R8rX9aJo539U/ZNx3v+2T+1foTKG8mWLVu2bNmyZcuWLVu2bG1uJ6EGL1u2bNmyZcuWLVu2bNmyKQO8bNmyZcuWLVu2bNmyZTsxlgFetmzZsmXLli1btmzZsp0QywAvW7Zs2bJly5YtW7Zs2U6IZYCXLVu2bNmyZcuWLVu2bCfEMsDLli1btmwdZSGEj7X49R+EEP5tK+eQLVu242fZN2U7KMsAL1vDFkI4H0L4uRDCfw0hvD+E8G9CCL0HcN7ZEMK7DmKO7pwvCiFshxDeF0L4vhBCqHHMfAjh10MIfx1C+KaDfP1s2bJlw0II3a2eQ7Zs2bIVLfumk2sZ4GVryCoA6f+U9J9ijJclvVDSsKTv2Od5SwcwvVr2Q5L+uaTLlfHSGsd8VNLXS/ruQ5pDtmzZjqmFEIZDCL8aQvgvlWDQPfe/V4QQfieE8M4Qwo9XHjsTQvi/Ko+9M4RwvfL4fwoh/GYI4d0hhFe5c3wshPA9IYR3SnpxCOGfhRDeG0J4u6SXHPX7zZYtW3tY9k3ZDsIOa3Od7eTZ35X0yRjjj0lSjPHTIYRvkPT7IYQvlPTPYozvlqQQwjOSvknSc5K+X9JVST2SXhtj/LkQwgNJL1MCiN2S/ikvEkKYlfTjkoYqD31tjPHXQgi3JH27pL+UNCepLGk1xviZ4kRDCC+QNBpjfGvl7/8g6R9J+nl/XIzxQ5I+FEJ4eh+fS7Zs2drTPinpS2KMfxFCmJL01hDCGyQ9LulbJV2PMX44hDBZOf77JP3nGOOXVKLew5XHXxlj/GgIYUDSb4QQtmKMH1HyYW+LMf4vFZ/0k5JeJOnPlfzXbx3ZO82WLVs7WfZN2fZtGeBla9Q+T9Jv+gcqzuf/lfRGSV8m6dsqzuIFMcZ3hBD+paQ3xxhfGUIYl/T2EMKvVJ6+KOnzK85n1p32Q5L+XozxkyGEy5J+StKTlf8tKTm4P5T0C0og8WdrzPWcpA+6vz9YeSxbtmzZsCDpX4YQvkDSZ5R8xBmlYNbPxBg/LEkxxo9Wjv+7kl5ReezTSpshSfr6EMKXVH6/oMQY+IikT0vaqjy+LOmZGOPzkhRCeL0SCyJbtmzZipZ9U7Z9W6ZoZjsIe0bSyyu/f5kMdP19Sa8JIfx25Zh+STOV//2yc07eeiT9uxDCtqSfUQJ02NtjjB+oOLCfknTjAN9DtmzZOsv+e0mnJb0oxvh3JP2Jko9q2CrMgjuSXhxjfEIp8s05PlnxVdmyZcvWjGXflG3flgFetkbtPUop/M9aCGFUCbD9hqSPhBA+X9J9Sa/nEEn/OMb4dypjJsb4XOV/f1Xndb5ByZk9oZS58yIusXBs8W/sjySdd3+frzyWLVu2bNiYpA/FGP82hPBFkj6n8vibJX1pCOGUJDka1K9K+prKY90hhLHKOf40xvjxEMK8pJU6r/U2SV8YQjgVQuiR9KWH85ayZct2Aiz7pmz7tgzwsjVqvyppMITwCumzykvfI+l1McaPK4G6/1XSWIzxdyrP+UVJX1cRaFEI4VoDrzMm6Y8rtXVfqVSjhy2FEC6GELqUgOT/U+sEMcY/lvQXIYSVymu/QtLPNfd2s2XLdhItJGGnv5b0E5KerLAFXiHpdyWpUkv8HZL+c0WEYKPy1P9J0hdVjv9NJXbBL0gqhRCek/Sdkt5a6zUrPum1kn5d0rNK9cnZsmXL9lnLvinbQVqIsV4SJFu2agshXJD0g5LmlYIDb5L0TTHGvw4hnFHKkv2LGON65fgBSd8r6Xrl+N+PMf6DisjKkzHGr60cNyvp/44xXq3U3W0pZed+QdKrY4zDzYisVM75pKTXSRpQElf5uhhjDCF8tSTFGH84hHBW0jskjSrx3D8m6fEY418cyAeWLVu2Y2chhCck/bsY41Kr55ItW7ZsWPZN2Q7SMsDL1hZWAXjfFGP8By2eSrZs2drUKgGer5f0P8cYf6nV88mWLVs2KfumbAdvGeBlawvLAC9btmzZsmXLli1btkdbBnjZ2tpCCG+T1Fd4+CtjjNutmE+2bNmyZcuWLVu2bK20DPCyZcuWLVu2bNmyZcuW7YRYVtHMli1btmzZsmXLli1bthNiGeBly5YtW7Zs2bJly5Yt2wmxDPCyZcuWLVu2bNmyZcuW7YRYBnjZsmXLli1btmzZsmXLdkLs/wfYwF4s9QdZKgAAAABJRU5ErkJggg==\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",
    "print(f\"Removing {len(soma_df) - len(df_to_plot)} Cells\")\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\n",
    "        axs[i].scatter(df1['Overlap_0.1'], df1[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\n",
    "        axs[i].scatter(df1['Jaccard'], df1[col_to_plot2[i-4]], s=0.1, color='b')\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": 26,
   "id": "ce984bc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x2a67f7fa0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MZv5Q+INkefJVYOM8nLhxhQn0Ri5Z538Qnn529vS6LB+Keh8kh701w1eBjfFw4sYVplO0kY7LhSYgQf3RBQt9kPIYfjY8NsWmWw+wdte9bLr1gIeumVXhjuTGFeYMvZFL1iwHvJlJSMenZ1o+a2jXZaSvGiw17khuXGECHbJfstb6IMzfptGfX9Hf1/JZQzsuI11rtBS5I7lxhSm5NGKhCUhQ/0OxUHmn1UlI7biM9KQVS1Ezc0q6XaHO0LOaX55pdJRLvfJOK2cN7biMdK3RUuWO5MYkGejQ+geh1s83UsuvVtdux2Wka41mBqCIpp4z0bLBwcEYHR1t+OeK0vlXbWZrX29pZivkOx55ofdain83KSnK59HSIelwRAxWW1eoM/Qidf4tVNf+6q7X59peT1rpjCJ9Hq07FCrQl+JEg1pnaHnUtYfHprhp31GmZ0oToy68oJcP/tJrapaCHCKLayl+Hq27FSrQl1Ln3/DYFB/6x6M1Z6G2WtceHpti5xceZPaHZ0piTz87y857HgR8BrgULKXPoxkUbNjiUrlv+dyldmWYz5k7Q2v1lrxDI+Nnhfmc2VOR+3BEz1xtzlL5PJrNKVSgL5X7lte7tcDx6ZmWx9A2+zi9RvlJOs1bKp9HszmFKrlk6fz7wPAR7nrgGKci6JHYfvkqPrxtQ67tqBeoc2dordS1F5rtmucZoOvAzXNntC01hQr0ej4wfIS/PvjE6denIk6/zjPUFwrbvM7Qdm5ef04NHaC3R7meAboOfLZGhyG6M9qWkkKVXOqVB+564FjVn6u1vFm1bi3Q39eb29jvbRsHGPqVS+jv6z297MILehl62yW5BojrwGe4/GRFV6gz9HrlgVM1JknVWt6sxbrUXoyzP98A6QyXn6zoChXo9coDPVLV8O6Rznqdx+y+VC61XQc+w+UnK7pCBXq9sd3bL191Vg19zvbLV53+3rP7zpXKf06t8j1xrOgy1dAlbZE0LmlC0q4q68+X9Hfl9Q9IWpN7S6k/TOzD2zZw3RWrT5+R90hcd8XqszpEfatZq8XDEK3o6p6hS+oBbgPeBEwChyTti4hHKja7Hng6Il4l6VrgI8Cv5d3YLOWBD2/bsOCIFl9WWy0uP1m7tftmbllKLpcBExHxOICku4GtQGWgbwVuKn9/D/DnkhRtuJVjq+UBX1bbQlx+snZZjHJvlpLLAFA57m+yvKzqNhFxEngGeGkeDcybL6vNrBMWo9y7qJ2iknYAOwBWr169mG99mi+rzawTFqPcmyXQp4BVFa9XlpdV22ZS0nnAi4Hvzf9FEbEH2AOlB1w00+A8+LLazBbbYpR7s5RcDgHrJK2V9HzgWmDfvG32Ab9R/v5twIF21M/NzIpqMcq9dc/QI+KkpBuAEaAHuCMijkq6GRiNiH3AXwGfkzQBfJ9S6JuZWdlilHsL90xRM7NuttAzRQt1cy4zM6vNgW5mlggHuplZIhzoZmaJcKCbmSWiY6NcJJ0Avt2RN2/cMuC7nW5EG6W+f5D+Pnr/iq2R/XtlRCyvtqJjgV4kkkZrDRNKQer7B+nvo/ev2PLaP5dczMwS4UA3M0uEAz2bPZ1uQJulvn+Q/j56/4otl/1zDd3MLBE+QzczS4QDvQpJPZLGJH2x/Hpt+eHXE+WHYT+/021shaR+SfdI+rqkRyX9jKSXSPqypG+U/7yw0+1slqT3Sjoq6WFJd0l6QZGPoaQ7JD0l6eGKZVWPl0o+Xt7PhyRd2rmWZ1djH4fKn9GHJP29pP6KdbvL+zguaXNHGt2AavtXse59kkLSsvLrpo+hA7269wCPVrz+CPDRiHgV8DSlh2IX2ceAf4qInwQuobSvu4D7ImIdcF/5deFIGgB+DxiMiIsp3fJ57sHlRT2GdwJb5i2rdbyuAtaVv3YAty9SG1t1J+fu45eBiyPitcBjwG4ASRdROqavKf/MJ8oPs1/K7uTc/UPSKuDNwBMVi5s+hg70eSStBH4R+FT5tYDXU3r4NcBngG0daVwOJL0YeB2le9gTEc9FxDSlB31/prxZofeR0n3++8pPz7oAeJICH8OIuJ/ScwYq1TpeW4HPRslBoF/SKxaloS2oto8R8c/lZxQDHKT0tDQo7ePdEfGDiPgmMEHpYfZLVo1jCPBR4A+Ays7Mpo+hA/1cf0bpL/iH5dcvBaYrPljVHpJdJGuBE8Cny2WlT0l6IfDyiHiyvM13gJd3rIUtiIgp4E8onfE8SemB5YdJ6xhC7eOV5aHuRfRbwJfK3yexj5K2AlMR8eC8VU3vnwO9gqS3Ak9FxOFOt6WNzgMuBW6PiI3A/zKvvFJ+fGAhhz+Va8lbKf3HtQJ4IVUudVNS5OOVhaT3AyeBv+l0W/Ii6QLgD4Eb8/y9DvSzbQKulvQt4G5Kl+kfo3TJM/e4vmoPyS6SSWAyIh4ov76HUsD/99xlXfnPpzrUvla9EfhmRJyIiFlgL6XjmtIxhNrHK8tD3QtD0ruAtwLvqHhOcQr7+BOUTjoeLOfNSuA/JP0YLeyfA71CROyOiJURsYZSp8uBiHgH8BVKD7+G0sOw/6FDTWxZRHwHOCZp7sm0bwAe4ewHfRd5H58ArpB0Qbn/Y27/kjmGZbWO1z7gneWRElcAz1SUZgpF0hZK5c+rI+LZilX7gGslnS9pLaXOw3/vRBubFRFHIuJlEbGmnDeTwKXlf5/NH8OI8FeVL+BK4Ivl73+c0gdmAvgCcH6n29fivv00MAo8BAwDF1LqK7gP+AbwL8BLOt3OFvbvQ8DXgYeBzwHnF/kYAndR6g+YLf/Dv77W8QIE3Ab8F3CE0mifju9Dk/s4QamW/J/lr09WbP/+8j6OA1d1uv3N7N+89d8ClrV6DD1T1MwsES65mJklwoFuZpYIB7qZWSIc6GZmiXCgm5klwoFuZpYIB7qZWSIc6GZmifh/6C31GDZG664AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = soma_df[(soma_df['PC1/PC2'] > 30)&(soma_df['PC1/PC2']<1000)]\n",
    "plt.scatter(df['PC1/PC2'], df['RAW'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df9ba22",
   "metadata": {},
   "source": [
    "## Load Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b59a77a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5639\n",
      "5228\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bkang/mambaforge/envs/imlab/lib/python3.9/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\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>InfectedCells</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.191720</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.182375</td>\n",
       "      <td>0.951255</td>\n",
       "      <td>2.971174</td>\n",
       "      <td>0.263777</td>\n",
       "      <td>0.100066</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058255</td>\n",
       "      <td>0.012449</td>\n",
       "      <td>0.038651</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>3.086518e-03</td>\n",
       "      <td>0.068215</td>\n",
       "      <td>0.026202</td>\n",
       "      <td>9.317850</td>\n",
       "      <td>4.936615</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.450634</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.299142</td>\n",
       "      <td>0.663825</td>\n",
       "      <td>0.680385</td>\n",
       "      <td>1.279714</td>\n",
       "      <td>0.250318</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006242</td>\n",
       "      <td>0.026956</td>\n",
       "      <td>0.076686</td>\n",
       "      <td>0.025459</td>\n",
       "      <td>1.807716e-01</td>\n",
       "      <td>0.109060</td>\n",
       "      <td>0.049730</td>\n",
       "      <td>9.702929</td>\n",
       "      <td>-1.126068</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.123755</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.098491</td>\n",
       "      <td>0.795852</td>\n",
       "      <td>1.360571</td>\n",
       "      <td>0.814115</td>\n",
       "      <td>0.084115</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006916</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.020191</td>\n",
       "      <td>0.010946</td>\n",
       "      <td>5.893018e-02</td>\n",
       "      <td>0.032417</td>\n",
       "      <td>0.009245</td>\n",
       "      <td>7.678627</td>\n",
       "      <td>6.474439</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.026527</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.007423</td>\n",
       "      <td>0.279833</td>\n",
       "      <td>-0.945291</td>\n",
       "      <td>1.770039</td>\n",
       "      <td>0.029651</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009310</td>\n",
       "      <td>0.017912</td>\n",
       "      <td>0.008027</td>\n",
       "      <td>0.002127</td>\n",
       "      <td>3.666942e-03</td>\n",
       "      <td>0.010847</td>\n",
       "      <td>0.001283</td>\n",
       "      <td>10.278169</td>\n",
       "      <td>11.612705</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>intensity_mean-3</td>\n",
       "      <td>0.069560</td>\n",
       "      <td>C12</td>\n",
       "      <td>0.034919</td>\n",
       "      <td>0.501994</td>\n",
       "      <td>0.007974</td>\n",
       "      <td>1.526264</td>\n",
       "      <td>0.055174</td>\n",
       "      <td>HSF1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.023954</td>\n",
       "      <td>0.009854</td>\n",
       "      <td>0.009225</td>\n",
       "      <td>0.005172</td>\n",
       "      <td>2.236398e-02</td>\n",
       "      <td>0.018912</td>\n",
       "      <td>0.009059</td>\n",
       "      <td>9.708878</td>\n",
       "      <td>9.509619</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>128036</th>\n",
       "      <td>376</td>\n",
       "      <td>intensity_mean-16</td>\n",
       "      <td>0.076190</td>\n",
       "      <td>F5</td>\n",
       "      <td>0.064743</td>\n",
       "      <td>0.849768</td>\n",
       "      <td>1.732784</td>\n",
       "      <td>0.636280</td>\n",
       "      <td>0.041614</td>\n",
       "      <td>ARPC3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031545</td>\n",
       "      <td>0.009325</td>\n",
       "      <td>0.022673</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.469804e-06</td>\n",
       "      <td>0.000155</td>\n",
       "      <td>0.007196</td>\n",
       "      <td>11.826623</td>\n",
       "      <td>9.977252</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128037</th>\n",
       "      <td>377</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.128333</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.075780</td>\n",
       "      <td>0.590499</td>\n",
       "      <td>0.366030</td>\n",
       "      <td>1.466102</td>\n",
       "      <td>0.051011</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031354</td>\n",
       "      <td>0.007039</td>\n",
       "      <td>0.004849</td>\n",
       "      <td>0.032047</td>\n",
       "      <td>5.027296e-03</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.006829</td>\n",
       "      <td>13.872565</td>\n",
       "      <td>8.909060</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128038</th>\n",
       "      <td>378</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>1.126980</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.716923</td>\n",
       "      <td>0.636146</td>\n",
       "      <td>0.558675</td>\n",
       "      <td>1.369687</td>\n",
       "      <td>0.487330</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.324126</td>\n",
       "      <td>0.014652</td>\n",
       "      <td>0.030898</td>\n",
       "      <td>0.329987</td>\n",
       "      <td>9.035076e-04</td>\n",
       "      <td>0.016441</td>\n",
       "      <td>0.098017</td>\n",
       "      <td>4.925003</td>\n",
       "      <td>-2.935362</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128040</th>\n",
       "      <td>380</td>\n",
       "      <td>intensity_mean-2</td>\n",
       "      <td>0.069445</td>\n",
       "      <td>C10</td>\n",
       "      <td>0.062363</td>\n",
       "      <td>0.898025</td>\n",
       "      <td>2.175467</td>\n",
       "      <td>0.468172</td>\n",
       "      <td>0.048991</td>\n",
       "      <td>NCK1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033787</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.002761</td>\n",
       "      <td>0.028677</td>\n",
       "      <td>1.301575e-07</td>\n",
       "      <td>0.002292</td>\n",
       "      <td>0.008940</td>\n",
       "      <td>14.045950</td>\n",
       "      <td>9.117386</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128041</th>\n",
       "      <td>381</td>\n",
       "      <td>intensity_mean-10</td>\n",
       "      <td>0.116183</td>\n",
       "      <td>D7</td>\n",
       "      <td>0.115965</td>\n",
       "      <td>0.998118</td>\n",
       "      <td>6.273389</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>0.066863</td>\n",
       "      <td>MLH1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043921</td>\n",
       "      <td>0.006482</td>\n",
       "      <td>0.033927</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>1.666852e-03</td>\n",
       "      <td>0.031659</td>\n",
       "      <td>0.025177</td>\n",
       "      <td>10.418358</td>\n",
       "      <td>7.281187</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>117175 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        label        Barcode_Idx       SUM Barcode       RAW         P  \\\n",
       "0           1  intensity_mean-10  0.191720      D7  0.182375  0.951255   \n",
       "1           2   intensity_mean-3  0.450634     C12  0.299142  0.663825   \n",
       "2           3   intensity_mean-3  0.123755     C12  0.098491  0.795852   \n",
       "3           4  intensity_mean-10  0.026527      D7  0.007423  0.279833   \n",
       "4           5   intensity_mean-3  0.069560     C12  0.034919  0.501994   \n",
       "...       ...                ...       ...     ...       ...       ...   \n",
       "128036    376  intensity_mean-16  0.076190      F5  0.064743  0.849768   \n",
       "128037    377   intensity_mean-2  0.128333     C10  0.075780  0.590499   \n",
       "128038    378   intensity_mean-2  1.126980     C10  0.716923  0.636146   \n",
       "128040    380   intensity_mean-2  0.069445     C10  0.062363  0.898025   \n",
       "128041    381  intensity_mean-10  0.116183      D7  0.115965  0.998118   \n",
       "\n",
       "           LOGIT   ENTROPY    X_NORM   Gene  ...  intensity_mean-1  \\\n",
       "0       2.971174  0.263777  0.100066   MLH1  ...          0.058255   \n",
       "1       0.680385  1.279714  0.250318   HSF1  ...          0.006242   \n",
       "2       1.360571  0.814115  0.084115   HSF1  ...          0.006916   \n",
       "3      -0.945291  1.770039  0.029651   MLH1  ...          0.009310   \n",
       "4       0.007974  1.526264  0.055174   HSF1  ...          0.023954   \n",
       "...          ...       ...       ...    ...  ...               ...   \n",
       "128036  1.732784  0.636280  0.041614  ARPC3  ...          0.031545   \n",
       "128037  0.366030  1.466102  0.051011   NCK1  ...          0.031354   \n",
       "128038  0.558675  1.369687  0.487330   NCK1  ...          0.324126   \n",
       "128040  2.175467  0.468172  0.048991   NCK1  ...          0.033787   \n",
       "128041  6.273389  0.013692  0.066863   MLH1  ...          0.043921   \n",
       "\n",
       "        intensity_mean-2  intensity_mean-3  intensity_mean-4  \\\n",
       "0               0.012449          0.038651          0.000044   \n",
       "1               0.026956          0.076686          0.025459   \n",
       "2               0.001170          0.020191          0.010946   \n",
       "3               0.017912          0.008027          0.002127   \n",
       "4               0.009854          0.009225          0.005172   \n",
       "...                  ...               ...               ...   \n",
       "128036          0.009325          0.022673          0.000000   \n",
       "128037          0.007039          0.004849          0.032047   \n",
       "128038          0.014652          0.030898          0.329987   \n",
       "128040          0.000291          0.002761          0.028677   \n",
       "128041          0.006482          0.033927          0.002052   \n",
       "\n",
       "        intensity_mean-5  intensity_mean-6     Delta  embedding1  embedding2  \\\n",
       "0           3.086518e-03          0.068215  0.026202    9.317850    4.936615   \n",
       "1           1.807716e-01          0.109060  0.049730    9.702929   -1.126068   \n",
       "2           5.893018e-02          0.032417  0.009245    7.678627    6.474439   \n",
       "3           3.666942e-03          0.010847  0.001283   10.278169   11.612705   \n",
       "4           2.236398e-02          0.018912  0.009059    9.708878    9.509619   \n",
       "...                  ...               ...       ...         ...         ...   \n",
       "128036      4.469804e-06          0.000155  0.007196   11.826623    9.977252   \n",
       "128037      5.027296e-03          0.000053  0.006829   13.872565    8.909060   \n",
       "128038      9.035076e-04          0.016441  0.098017    4.925003   -2.935362   \n",
       "128040      1.301575e-07          0.002292  0.008940   14.045950    9.117386   \n",
       "128041      1.666852e-03          0.031659  0.025177   10.418358    7.281187   \n",
       "\n",
       "        InfectedCells  \n",
       "0                   1  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  \n",
       "...               ...  \n",
       "128036              0  \n",
       "128037              0  \n",
       "128038              1  \n",
       "128040              0  \n",
       "128041              0  \n",
       "\n",
       "[117175 rows x 30 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 28,
   "id": "700d4604",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removing 0 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": 29,
   "id": "6292eb0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='embedding1', ylabel='embedding2'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2880x720 with 4 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(1,4,figsize=(40,10))\n",
    "sns.scatterplot(data = soma_df, x='embedding1', y='embedding2', hue = 'Gene', ax=ax[0], s=1)\n",
    "ax[0].legend(bbox_to_anchor=(-0.3, 1), loc='upper left', borderaxespad=0)\n",
    "sns.scatterplot(data = soma_final, \n",
    "                x='embedding1', y='embedding2', hue = 'InfectedCells', ax=ax[1], s=1, palette = ['blue','red'])\n",
    "sns.scatterplot(data = soma_final[soma_final.InfectedCells==1],\n",
    "                x='embedding1', y='embedding2', hue = 'InfectedCells', ax=ax[2], s=1, palette = ['red'])\n",
    "sns.scatterplot(data = soma_final[soma_final.InfectedCells==0],\n",
    "                x='embedding1', y='embedding2', hue = 'InfectedCells', ax=ax[3], s=1, palette = ['blue'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d3318361",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infection Rate 0.33\n",
      "Total Infected Cells 38325\n"
     ]
    }
   ],
   "source": [
    "# infectiviity\n",
    "print(\"Infection Rate\", round(sum(soma_final.InfectedCells)/len(soma_final),2))\n",
    "print(\"Total Infected Cells\", sum(soma_final.InfectedCells))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4a28dd9a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38325\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": [
    "# plot number of cells\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "208afb0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save data\n",
    "soma_final.to_csv('Coverslip2_soma_final.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79575143",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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