3b. Descriptive Statistics - Heart Disease Data.ipynb

{"cells":[{"cell_type":"markdown","id":"93e08862","metadata":{"id":"93e08862"},"source":["## Unit 3, Part II: Descriptive Statistics - Heart Disease Data\n","\n","Kyle P. Rasku MS BSN RN\n","\n","In lecture this week, we discussed the importance of descriptive statistics.  These summaries and explorations help us to understand the forces driving a dataset, and help us to understand the people behind the numbers.  \n","\n","Good descriptive statistics help us assess the direction, amount and strength of associations between data points, and allow us to reflect on what we know about the real-world sources of these data points.  Do we agree with the mathematical measures we see, or do we detect possible confounding?\n","\n","Every analysis starts with a good descriptive analysis!\n"]},{"cell_type":"code","execution_count":null,"id":"28547f82","metadata":{"id":"28547f82"},"outputs":[],"source":["%matplotlib inline\n","import matplotlib.pylab\n","from matplotlib import pyplot\n","import matplotlib.pyplot as plt\n","plt.rc(\"figure\", figsize=(12,10))\n","import pandas as pd\n","import numpy as np\n","import datetime\n","import math"]},{"cell_type":"code","execution_count":null,"id":"7e36b3f8","metadata":{"id":"7e36b3f8"},"outputs":[],"source":["from ucimlrepo import fetch_ucirepo\n","\n","# fetch dataset\n","heart_disease = fetch_ucirepo(id=45)\n","\n","# data (as pandas dataframes)\n","X = heart_disease.data.features\n","y = heart_disease.data.targets\n"]},{"cell_type":"code","execution_count":null,"id":"01e2014d","metadata":{"id":"01e2014d","outputId":"d6364727-d94d-4245-e522-f73dbf236c9a"},"outputs":[{"name":"stdout","output_type":"stream","text":["uci_id: 45\n","name: Heart Disease\n","repository_url: https://archive.ics.uci.edu/dataset/45/heart+disease\n","data_url: https://archive.ics.uci.edu/static/public/45/data.csv\n","abstract: 4 databases: Cleveland, Hungary, Switzerland, and the VA Long Beach\n","area: Health and Medicine\n","tasks: ['Classification']\n","characteristics: ['Multivariate']\n","num_instances: 303\n","num_features: 13\n","feature_types: ['Categorical', 'Integer', 'Real']\n","demographics: ['Age', 'Sex']\n","target_col: ['num']\n","index_col: None\n","has_missing_values: yes\n","missing_values_symbol: NaN\n","year_of_dataset_creation: 1989\n","last_updated: Fri Nov 03 2023\n","dataset_doi: 10.24432/C52P4X\n","creators: ['Andras Janosi', 'William Steinbrunn', 'Matthias Pfisterer', 'Robert Detrano']\n","intro_paper: {'title': 'International application of a new probability algorithm for the diagnosis of coronary artery disease.', 'authors': 'R. Detrano, A. Jánosi, W. Steinbrunn, M. Pfisterer, J. Schmid, S. Sandhu, K. Guppy, S. Lee, V. Froelicher', 'published_in': 'American Journal of Cardiology', 'year': 1989, 'url': 'https://www.semanticscholar.org/paper/a7d714f8f87bfc41351eb5ae1e5472f0ebbe0574', 'doi': None}\n","additional_info: {'summary': 'This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them.  In particular, the Cleveland database is the only one that has been used by ML researchers to date.  The \"goal\" field refers to the presence of heart disease in the patient.  It is integer valued from 0 (no presence) to 4. Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).  \\n   \\nThe names and social security numbers of the patients were recently removed from the database, replaced with dummy values.\\n\\nOne file has been \"processed\", that one containing the Cleveland database.  All four unprocessed files also exist in this directory.\\n\\nTo see Test Costs (donated by Peter Turney), please see the folder \"Costs\" ', 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': 'Only 14 attributes used:\\r\\n      1. #3  (age)       \\r\\n      2. #4  (sex)       \\r\\n      3. #9  (cp)        \\r\\n      4. #10 (trestbps)  \\r\\n      5. #12 (chol)      \\r\\n      6. #16 (fbs)       \\r\\n      7. #19 (restecg)   \\r\\n      8. #32 (thalach)   \\r\\n      9. #38 (exang)     \\r\\n      10. #40 (oldpeak)   \\r\\n      11. #41 (slope)     \\r\\n      12. #44 (ca)        \\r\\n      13. #51 (thal)      \\r\\n      14. #58 (num)       (the predicted attribute)\\r\\n\\r\\nComplete attribute documentation:\\r\\n      1 id: patient identification number\\r\\n      2 ccf: social security number (I replaced this with a dummy value of 0)\\r\\n      3 age: age in years\\r\\n      4 sex: sex (1 = male; 0 = female)\\r\\n      5 painloc: chest pain location (1 = substernal; 0 = otherwise)\\r\\n      6 painexer (1 = provoked by exertion; 0 = otherwise)\\r\\n      7 relrest (1 = relieved after rest; 0 = otherwise)\\r\\n      8 pncaden (sum of 5, 6, and 7)\\r\\n      9 cp: chest pain type\\r\\n        -- Value 1: typical angina\\r\\n        -- Value 2: atypical angina\\r\\n        -- Value 3: non-anginal pain\\r\\n        -- Value 4: asymptomatic\\r\\n     10 trestbps: resting blood pressure (in mm Hg on admission to the hospital)\\r\\n     11 htn\\r\\n     12 chol: serum cholestoral in mg/dl\\r\\n     13 smoke: I believe this is 1 = yes; 0 = no (is or is not a smoker)\\r\\n     14 cigs (cigarettes per day)\\r\\n     15 years (number of years as a smoker)\\r\\n     16 fbs: (fasting blood sugar > 120 mg/dl)  (1 = true; 0 = false)\\r\\n     17 dm (1 = history of diabetes; 0 = no such history)\\r\\n     18 famhist: family history of coronary artery disease (1 = yes; 0 = no)\\r\\n     19 restecg: resting electrocardiographic results\\r\\n        -- Value 0: normal\\r\\n        -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV)\\r\\n        -- Value 2: showing probable or definite left ventricular hypertrophy by Estes\\' criteria\\r\\n     20 ekgmo (month of exercise ECG reading)\\r\\n     21 ekgday(day of exercise ECG reading)\\r\\n     22 ekgyr (year of exercise ECG reading)\\r\\n     23 dig (digitalis used furing exercise ECG: 1 = yes; 0 = no)\\r\\n     24 prop (Beta blocker used during exercise ECG: 1 = yes; 0 = no)\\r\\n     25 nitr (nitrates used during exercise ECG: 1 = yes; 0 = no)\\r\\n     26 pro (calcium channel blocker used during exercise ECG: 1 = yes; 0 = no)\\r\\n     27 diuretic (diuretic used used during exercise ECG: 1 = yes; 0 = no)\\r\\n     28 proto: exercise protocol\\r\\n          1 = Bruce     \\r\\n          2 = Kottus\\r\\n          3 = McHenry\\r\\n          4 = fast Balke\\r\\n          5 = Balke\\r\\n          6 = Noughton \\r\\n          7 = bike 150 kpa min/min  (Not sure if \"kpa min/min\" is what was written!)\\r\\n          8 = bike 125 kpa min/min  \\r\\n          9 = bike 100 kpa min/min\\r\\n         10 = bike 75 kpa min/min\\r\\n         11 = bike 50 kpa min/min\\r\\n         12 = arm ergometer\\r\\n     29 thaldur: duration of exercise test in minutes\\r\\n     30 thaltime: time when ST measure depression was noted\\r\\n     31 met: mets achieved\\r\\n     32 thalach: maximum heart rate achieved\\r\\n     33 thalrest: resting heart rate\\r\\n     34 tpeakbps: peak exercise blood pressure (first of 2 parts)\\r\\n     35 tpeakbpd: peak exercise blood pressure (second of 2 parts)\\r\\n     36 dummy\\r\\n     37 trestbpd: resting blood pressure\\r\\n     38 exang: exercise induced angina (1 = yes; 0 = no)\\r\\n     39 xhypo: (1 = yes; 0 = no)\\r\\n     40 oldpeak = ST depression induced by exercise relative to rest\\r\\n     41 slope: the slope of the peak exercise ST segment\\r\\n        -- Value 1: upsloping\\r\\n        -- Value 2: flat\\r\\n        -- Value 3: downsloping\\r\\n     42 rldv5: height at rest\\r\\n     43 rldv5e: height at peak exercise\\r\\n     44 ca: number of major vessels (0-3) colored by flourosopy\\r\\n     45 restckm: irrelevant\\r\\n     46 exerckm: irrelevant\\r\\n     47 restef: rest raidonuclid (sp?) ejection fraction\\r\\n     48 restwm: rest wall (sp?) motion abnormality\\r\\n        0 = none\\r\\n        1 = mild or moderate\\r\\n        2 = moderate or severe\\r\\n        3 = akinesis or dyskmem (sp?)\\r\\n     49 exeref: exercise radinalid (sp?) ejection fraction\\r\\n     50 exerwm: exercise wall (sp?) motion \\r\\n     51 thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\\r\\n     52 thalsev: not used\\r\\n     53 thalpul: not used\\r\\n     54 earlobe: not used\\r\\n     55 cmo: month of cardiac cath (sp?)  (perhaps \"call\")\\r\\n     56 cday: day of cardiac cath (sp?)\\r\\n     57 cyr: year of cardiac cath (sp?)\\r\\n     58 num: diagnosis of heart disease (angiographic disease status)\\r\\n        -- Value 0: < 50% diameter narrowing\\r\\n        -- Value 1: > 50% diameter narrowing\\r\\n        (in any major vessel: attributes 59 through 68 are vessels)\\r\\n     59 lmt\\r\\n     60 ladprox\\r\\n     61 laddist\\r\\n     62 diag\\r\\n     63 cxmain\\r\\n     64 ramus\\r\\n     65 om1\\r\\n     66 om2\\r\\n     67 rcaprox\\r\\n     68 rcadist\\r\\n     69 lvx1: not used\\r\\n     70 lvx2: not used\\r\\n     71 lvx3: not used\\r\\n     72 lvx4: not used\\r\\n     73 lvf: not used\\r\\n     74 cathef: not used\\r\\n     75 junk: not used\\r\\n     76 name: last name of patient  (I replaced this with the dummy string \"name\")', 'citation': None}\n"]}],"source":["# metadata\n","# print(heart_disease.metadata)\n","\n","for key, value in heart_disease.metadata.items():\n","    print(f\"{key}: {value}\")"]},{"cell_type":"code","execution_count":null,"id":"e46cea9f","metadata":{"id":"e46cea9f","outputId":"fd5d1ac8-f27c-428f-c592-01fdc56a8d3a"},"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>name</th>\n","      <th>role</th>\n","      <th>type</th>\n","      <th>demographic</th>\n","      <th>description</th>\n","      <th>units</th>\n","      <th>missing_values</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>age</td>\n","      <td>Feature</td>\n","      <td>Integer</td>\n","      <td>Age</td>\n","      <td>None</td>\n","      <td>years</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>sex</td>\n","      <td>Feature</td>\n","      <td>Categorical</td>\n","      <td>Sex</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>cp</td>\n","      <td>Feature</td>\n","      <td>Categorical</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>trestbps</td>\n","      <td>Feature</td>\n","      <td>Integer</td>\n","      <td>None</td>\n","      <td>resting blood pressure (on admission to the ho...</td>\n","      <td>mm Hg</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>chol</td>\n","      <td>Feature</td>\n","      <td>Integer</td>\n","      <td>None</td>\n","      <td>serum cholestoral</td>\n","      <td>mg/dl</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>fbs</td>\n","      <td>Feature</td>\n","      <td>Categorical</td>\n","      <td>None</td>\n","      <td>fasting blood sugar &gt; 120 mg/dl</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>restecg</td>\n","      <td>Feature</td>\n","      <td>Categorical</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>thalach</td>\n","      <td>Feature</td>\n","      <td>Integer</td>\n","      <td>None</td>\n","      <td>maximum heart rate achieved</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>exang</td>\n","      <td>Feature</td>\n","      <td>Categorical</td>\n","      <td>None</td>\n","      <td>exercise induced angina</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>oldpeak</td>\n","      <td>Feature</td>\n","      <td>Integer</td>\n","      <td>None</td>\n","      <td>ST depression induced by exercise relative to ...</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>slope</td>\n","      <td>Feature</td>\n","      <td>Categorical</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>ca</td>\n","      <td>Feature</td>\n","      <td>Integer</td>\n","      <td>None</td>\n","      <td>number of major vessels (0-3) colored by flour...</td>\n","      <td>None</td>\n","      <td>yes</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>thal</td>\n","      <td>Feature</td>\n","      <td>Categorical</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>None</td>\n","      <td>yes</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>num</td>\n","      <td>Target</td>\n","      <td>Integer</td>\n","      <td>None</td>\n","      <td>diagnosis of heart disease</td>\n","      <td>None</td>\n","      <td>no</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        name     role         type demographic  \\\n","0        age  Feature      Integer         Age   \n","1        sex  Feature  Categorical         Sex   \n","2         cp  Feature  Categorical        None   \n","3   trestbps  Feature      Integer        None   \n","4       chol  Feature      Integer        None   \n","5        fbs  Feature  Categorical        None   \n","6    restecg  Feature  Categorical        None   \n","7    thalach  Feature      Integer        None   \n","8      exang  Feature  Categorical        None   \n","9    oldpeak  Feature      Integer        None   \n","10     slope  Feature  Categorical        None   \n","11        ca  Feature      Integer        None   \n","12      thal  Feature  Categorical        None   \n","13       num   Target      Integer        None   \n","\n","                                          description  units missing_values  \n","0                                                None  years             no  \n","1                                                None   None             no  \n","2                                                None   None             no  \n","3   resting blood pressure (on admission to the ho...  mm Hg             no  \n","4                                   serum cholestoral  mg/dl             no  \n","5                     fasting blood sugar > 120 mg/dl   None             no  \n","6                                                None   None             no  \n","7                         maximum heart rate achieved   None             no  \n","8                             exercise induced angina   None             no  \n","9   ST depression induced by exercise relative to ...   None             no  \n","10                                               None   None             no  \n","11  number of major vessels (0-3) colored by flour...   None            yes  \n","12                                               None   None            yes  \n","13                         diagnosis of heart disease   None             no  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["# variable information\n","vdesc = pd.DataFrame(heart_disease.variables)\n","vdesc"]},{"cell_type":"code","execution_count":null,"id":"39123a81","metadata":{"scrolled":true,"id":"39123a81","outputId":"b12107a3-906a-42ea-9ee2-4f6bf8f31122"},"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>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>63</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>145</td>\n","      <td>233</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>150</td>\n","      <td>0</td>\n","      <td>2.3</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>6.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>67</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>160</td>\n","      <td>286</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>108</td>\n","      <td>1</td>\n","      <td>1.5</td>\n","      <td>2</td>\n","      <td>3.0</td>\n","      <td>3.0</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>67</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>120</td>\n","      <td>229</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>129</td>\n","      <td>1</td>\n","      <td>2.6</td>\n","      <td>2</td>\n","      <td>2.0</td>\n","      <td>7.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>37</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>130</td>\n","      <td>250</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>187</td>\n","      <td>0</td>\n","      <td>3.5</td>\n","      <td>3</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>41</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>130</td>\n","      <td>204</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>172</td>\n","      <td>0</td>\n","      <td>1.4</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>3.0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n","0   63    1   1       145   233    1        2      150      0      2.3      3   \n","1   67    1   4       160   286    0        2      108      1      1.5      2   \n","2   67    1   4       120   229    0        2      129      1      2.6      2   \n","3   37    1   3       130   250    0        0      187      0      3.5      3   \n","4   41    0   2       130   204    0        2      172      0      1.4      1   \n","\n","    ca  thal  num  \n","0  0.0   6.0    0  \n","1  3.0   3.0    2  \n","2  2.0   7.0    1  \n","3  0.0   3.0    0  \n","4  0.0   3.0    0  "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["# Load some data\n","\n","heart = pd.read_csv(\"https://archive.ics.uci.edu/static/public/45/data.csv\")\n","heart_backup = heart.copy()\n","heart.head()"]},{"cell_type":"markdown","id":"6b3b7d93","metadata":{"id":"6b3b7d93"},"source":["Typically, we first look at the shape of the data - how many rows / instances (n), and how many columns / data points?"]},{"cell_type":"code","execution_count":null,"id":"2df6d82d","metadata":{"id":"2df6d82d","outputId":"10982d4e-4b36-4b58-e35e-e7b4e3d6835b"},"outputs":[{"data":{"text/plain":["(303, 14)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["heart.shape"]},{"cell_type":"markdown","id":"3b766cdb","metadata":{"id":"3b766cdb"},"source":["In this small data set about heart health, there are 303 rows (n), and 14 columns / data points.\n","\n","Now, we need to see how many missing values the data has.  Sometimes, when there are a lot of missing values, it is important to make judgment calls about how to handle the missing data."]},{"cell_type":"code","execution_count":null,"id":"40484d3a","metadata":{"id":"40484d3a","outputId":"82f0487c-04cb-4ae4-b206-31b07103176c"},"outputs":[{"name":"stdout","output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 303 entries, 0 to 302\n","Data columns (total 14 columns):\n"," #   Column    Non-Null Count  Dtype  \n","---  ------    --------------  -----  \n"," 0   age       303 non-null    int64  \n"," 1   sex       303 non-null    int64  \n"," 2   cp        303 non-null    int64  \n"," 3   trestbps  303 non-null    int64  \n"," 4   chol      303 non-null    int64  \n"," 5   fbs       303 non-null    int64  \n"," 6   restecg   303 non-null    int64  \n"," 7   thalach   303 non-null    int64  \n"," 8   exang     303 non-null    int64  \n"," 9   oldpeak   303 non-null    float64\n"," 10  slope     303 non-null    int64  \n"," 11  ca        299 non-null    float64\n"," 12  thal      301 non-null    float64\n"," 13  num       303 non-null    int64  \n","dtypes: float64(3), int64(11)\n","memory usage: 33.3 KB\n"]}],"source":["heart.info()"]},{"cell_type":"markdown","id":"1f45086c","metadata":{"id":"1f45086c"},"source":["The data as loaded has few missing values.  There are 4 values missing from 'ca', 2 values missing from 'thal'.\n","\n","Let's see those rows."]},{"cell_type":"code","execution_count":null,"id":"6eb75494","metadata":{"id":"6eb75494","outputId":"a6498829-d7db-4d8f-f9df-a8be94bbcbd1"},"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>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>166</th>\n","      <td>52</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>138</td>\n","      <td>223</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>169</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>3.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>192</th>\n","      <td>43</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>132</td>\n","      <td>247</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>143</td>\n","      <td>1</td>\n","      <td>0.1</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>7.0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>287</th>\n","      <td>58</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>125</td>\n","      <td>220</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>144</td>\n","      <td>0</td>\n","      <td>0.4</td>\n","      <td>2</td>\n","      <td>NaN</td>\n","      <td>7.0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>302</th>\n","      <td>38</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>138</td>\n","      <td>175</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>173</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>3.0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","166   52    1   3       138   223    0        0      169      0      0.0   \n","192   43    1   4       132   247    1        2      143      1      0.1   \n","287   58    1   2       125   220    0        0      144      0      0.4   \n","302   38    1   3       138   175    0        0      173      0      0.0   \n","\n","     slope  ca  thal  num  \n","166      1 NaN   3.0    0  \n","192      2 NaN   7.0    1  \n","287      2 NaN   7.0    0  \n","302      1 NaN   3.0    0  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["heart[heart['ca'].isnull()]"]},{"cell_type":"code","execution_count":null,"id":"a0c3d685","metadata":{"id":"a0c3d685","outputId":"2019a261-13cf-4507-e8ee-00b9f3ca74c7"},"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>age</th>\n","      <th>sex</th>\n","      <th>cp</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>thalach</th>\n","      <th>exang</th>\n","      <th>oldpeak</th>\n","      <th>slope</th>\n","      <th>ca</th>\n","      <th>thal</th>\n","      <th>num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>87</th>\n","      <td>53</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>128</td>\n","      <td>216</td>\n","      <td>0</td>\n","      <td>2</td>\n","      <td>115</td>\n","      <td>0</td>\n","      <td>0.0</td>\n","      <td>1</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>266</th>\n","      <td>52</td>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>128</td>\n","      <td>204</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>156</td>\n","      <td>1</td>\n","      <td>1.0</td>\n","      <td>2</td>\n","      <td>0.0</td>\n","      <td>NaN</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["     age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n","87    53    0   3       128   216    0        2      115      0      0.0   \n","266   52    1   4       128   204    1        0      156      1      1.0   \n","\n","     slope   ca  thal  num  \n","87       1  0.0   NaN    0  \n","266      2  0.0   NaN    2  "]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["heart[heart['thal'].isnull()]"]},{"cell_type":"markdown","id":"40e84a7e","metadata":{"id":"40e84a7e"},"source":["Since we're just looking at descriptive measures and there are few NULL values, we don't have to do anything other than identify the rows.\n","\n","Now, let's examine what the data points are.  Below is a summary of the data points / columns and their meanings, taken from the data dictionary for this data set.  A data dictionary tells us what the data points are, what type they are, and may also tell us how they should - or should not - be used.  Data dictionaries for large, multi-year data collections and surveys are very important to pay attention to, because they specify analysis and comparison limitations in the data."]},{"cell_type":"markdown","id":"68750267","metadata":{"id":"68750267"},"source":["### Data Points Summary: Heart Disease Data\n","\n","- **age**: age in years\n","- **sex**: sex (1 = male; 0 = female)\n","- **cp**: chest pain type: 1 typical angina, 2 atypical angina, 3 non-anginal pain, 4 asymptomatic\n","- **trestbps**: resting blood pressure (in mmHg on admission to the hospital)\n","- **chol**: serum cholestoral in mg/dl\n","- **fbs**: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n","- **restecg**: resting electrocardiographic results: 0 normal, 1 ST-T wave abnormality (T inversion and/or ST elevation or depression of > 0.05 mV), 2 probable or definite LVH by Estes' criteria\n","- **thalach**: maximum heart rate achieved\n","- **exang**: exercise induced angina (1 = yes; 0 = no)\n","- **oldpeak**: ST depression induced by exercise relative to rest\n","- **slope**: the slope of the peak exercise ST segment: 1 upsloping, 2 flat, 3 downsloping\n","- **ca**: number of major vessels (0-3) colored by flourosopy\n","- **thal**: 3 = normal; 6 = fixed defect; 7 = reversable defect\n","- **num**: diagnosis of heart disease (angiographic disease status): 0 < 50% narrowing, 1 > 50% narrowing in any major vessel"]},{"cell_type":"markdown","id":"5290a9c0","metadata":{"id":"5290a9c0"},"source":["### Data types\n","\n","- **age**: continuous, ratio\n","- **sex**: categorical, nominal\n","- **cp**: categorical, nominal\n","- **trestbps**: continuous, interval\n","- **chol**: continuous, interval\n","- **fbs**: categorical, ordinal\n","- **restecg**: categorical, ordinal\n","- **thalach**: continuous, ratio\n","- **exang**: categorical, ordinal\n","- **oldpeak**: continuous, ratio\n","- **slope**: categorical, nominal\n","- **ca**: continuous, ratio (count)\n","- **thal**: categorical, nominal\n","- **num**: categorical, nominal (outcome)"]},{"cell_type":"markdown","id":"07ef6076","metadata":{"id":"07ef6076"},"source":["Looking at the meanings and types of each of the data points in the data set provides us with a baseline understanding of what types of descriptive analyses we can do with the data.\n","\n","Categorical data points cannot be summarized using summary measures designed for interval or ratio data.  However, they *can* be summarized in other ways, for example, by using counts or by stratifying counts by another category.\n","\n","#### Below, the describe() function is applied to the pandas dataframe of all the heart disease data, to produce a summary dataframe.\n","\n","To gain meaningful insights, the dataframe must be divided between categorical and continuous variables before the function is called.  Since the columns specified as categorical in the data dictionary were not yet marked categorical in our dataframe, I've fixed that by first calling the pd.Categorical() function on each categorical column."]},{"cell_type":"code","execution_count":null,"id":"3dbed14d","metadata":{"id":"3dbed14d"},"outputs":[],"source":["categorical_columns = [\"sex\", \"cp\", \"fbs\", \"restecg\", \"exang\", \"slope\", \"thal\"]\n","for c in categorical_columns:\n","    heart[c] = pd.Categorical(heart[c])"]},{"cell_type":"code","execution_count":null,"id":"356f30cb","metadata":{"id":"356f30cb"},"outputs":[],"source":["heart_categorical = heart[[\"sex\", \"cp\", \"fbs\", \"restecg\", \"exang\", \"slope\", \"thal\"]].copy()\n","heart_continuous = heart[[\"age\", \"trestbps\", \"chol\", \"thalach\", \"oldpeak\", \"ca\", \"num\"]].copy()"]},{"cell_type":"code","execution_count":null,"id":"9217821b","metadata":{"scrolled":true,"id":"9217821b","outputId":"1baa66f4-b2b6-4f58-c338-9bf2d68befe5"},"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>sex</th>\n","      <th>cp</th>\n","      <th>fbs</th>\n","      <th>restecg</th>\n","      <th>exang</th>\n","      <th>slope</th>\n","      <th>thal</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303</td>\n","      <td>303</td>\n","      <td>303</td>\n","      <td>303</td>\n","      <td>303</td>\n","      <td>303</td>\n","      <td>301.0</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>2</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>1</td>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>3.0</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>206</td>\n","      <td>144</td>\n","      <td>258</td>\n","      <td>151</td>\n","      <td>204</td>\n","      <td>142</td>\n","      <td>166.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        sex   cp  fbs  restecg  exang  slope   thal\n","count   303  303  303      303    303    303  301.0\n","unique    2    4    2        3      2      3    3.0\n","top       1    4    0        0      0      1    3.0\n","freq    206  144  258      151    204    142  166.0"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["heart_categorical.describe()"]},{"cell_type":"code","execution_count":null,"id":"1757632d","metadata":{"id":"1757632d","outputId":"65b6883c-7be1-46a7-dd0b-cf8b980afe26"},"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>age</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>thalach</th>\n","      <th>oldpeak</th>\n","      <th>ca</th>\n","      <th>num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>299.000000</td>\n","      <td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>54.438944</td>\n","      <td>131.689769</td>\n","      <td>246.693069</td>\n","      <td>149.607261</td>\n","      <td>1.039604</td>\n","      <td>0.672241</td>\n","      <td>0.937294</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>9.038662</td>\n","      <td>17.599748</td>\n","      <td>51.776918</td>\n","      <td>22.875003</td>\n","      <td>1.161075</td>\n","      <td>0.937438</td>\n","      <td>1.228536</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>29.000000</td>\n","      <td>94.000000</td>\n","      <td>126.000000</td>\n","      <td>71.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>48.000000</td>\n","      <td>120.000000</td>\n","      <td>211.000000</td>\n","      <td>133.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>56.000000</td>\n","      <td>130.000000</td>\n","      <td>241.000000</td>\n","      <td>153.000000</td>\n","      <td>0.800000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>61.000000</td>\n","      <td>140.000000</td>\n","      <td>275.000000</td>\n","      <td>166.000000</td>\n","      <td>1.600000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>77.000000</td>\n","      <td>200.000000</td>\n","      <td>564.000000</td>\n","      <td>202.000000</td>\n","      <td>6.200000</td>\n","      <td>3.000000</td>\n","      <td>4.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              age    trestbps        chol     thalach     oldpeak          ca  \\\n","count  303.000000  303.000000  303.000000  303.000000  303.000000  299.000000   \n","mean    54.438944  131.689769  246.693069  149.607261    1.039604    0.672241   \n","std      9.038662   17.599748   51.776918   22.875003    1.161075    0.937438   \n","min     29.000000   94.000000  126.000000   71.000000    0.000000    0.000000   \n","25%     48.000000  120.000000  211.000000  133.500000    0.000000    0.000000   \n","50%     56.000000  130.000000  241.000000  153.000000    0.800000    0.000000   \n","75%     61.000000  140.000000  275.000000  166.000000    1.600000    1.000000   \n","max     77.000000  200.000000  564.000000  202.000000    6.200000    3.000000   \n","\n","              num  \n","count  303.000000  \n","mean     0.937294  \n","std      1.228536  \n","min      0.000000  \n","25%      0.000000  \n","50%      0.000000  \n","75%      2.000000  \n","max      4.000000  "]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["heart_continuous.describe()"]},{"cell_type":"markdown","id":"a238bab1","metadata":{"id":"a238bab1"},"source":["You can see that, depending on the type of data (categorical vs. continuous), the describe function provides the correct kind of summary description of the data. (Although, min, max and quartile data can all apply to categorical data as well).\n","\n","Below, let's use the continuous data points, and add the IQR, range, and variance to the summary dataframe.\n","\n","The transpose() function, below, rotates the table so the rows become columns, and the columns become rows."]},{"cell_type":"code","execution_count":null,"id":"e23cad07","metadata":{"id":"e23cad07","outputId":"a7b58347-3446-40b7-b7b3-bae1b8e46513"},"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>age</th>\n","      <th>trestbps</th>\n","      <th>chol</th>\n","      <th>thalach</th>\n","      <th>oldpeak</th>\n","      <th>ca</th>\n","      <th>num</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>303.000000</td>\n","      <td>299.000000</td>\n","      <td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>54.438944</td>\n","      <td>131.689769</td>\n","      <td>246.693069</td>\n","      <td>149.607261</td>\n","      <td>1.039604</td>\n","      <td>0.672241</td>\n","      <td>0.937294</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>9.038662</td>\n","      <td>17.599748</td>\n","      <td>51.776918</td>\n","      <td>22.875003</td>\n","      <td>1.161075</td>\n","      <td>0.937438</td>\n","      <td>1.228536</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>29.000000</td>\n","      <td>94.000000</td>\n","      <td>126.000000</td>\n","      <td>71.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>48.000000</td>\n","      <td>120.000000</td>\n","      <td>211.000000</td>\n","      <td>133.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>56.000000</td>\n","      <td>130.000000</td>\n","      <td>241.000000</td>\n","      <td>153.000000</td>\n","      <td>0.800000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>61.000000</td>\n","      <td>140.000000</td>\n","      <td>275.000000</td>\n","      <td>166.000000</td>\n","      <td>1.600000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>77.000000</td>\n","      <td>200.000000</td>\n","      <td>564.000000</td>\n","      <td>202.000000</td>\n","      <td>6.200000</td>\n","      <td>3.000000</td>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>IQR</th>\n","      <td>13.000000</td>\n","      <td>20.000000</td>\n","      <td>64.000000</td>\n","      <td>32.500000</td>\n","      <td>1.600000</td>\n","      <td>1.000000</td>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>range</th>\n","      <td>48.000000</td>\n","      <td>106.000000</td>\n","      <td>438.000000</td>\n","      <td>131.000000</td>\n","      <td>6.200000</td>\n","      <td>3.000000</td>\n","      <td>4.000000</td>\n","    </tr>\n","    <tr>\n","      <th>var</th>\n","      <td>81.697419</td>\n","      <td>309.751120</td>\n","      <td>2680.849190</td>\n","      <td>523.265775</td>\n","      <td>1.348095</td>\n","      <td>0.878791</td>\n","      <td>1.509300</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              age    trestbps         chol     thalach     oldpeak  \\\n","count  303.000000  303.000000   303.000000  303.000000  303.000000   \n","mean    54.438944  131.689769   246.693069  149.607261    1.039604   \n","std      9.038662   17.599748    51.776918   22.875003    1.161075   \n","min     29.000000   94.000000   126.000000   71.000000    0.000000   \n","25%     48.000000  120.000000   211.000000  133.500000    0.000000   \n","50%     56.000000  130.000000   241.000000  153.000000    0.800000   \n","75%     61.000000  140.000000   275.000000  166.000000    1.600000   \n","max     77.000000  200.000000   564.000000  202.000000    6.200000   \n","IQR     13.000000   20.000000    64.000000   32.500000    1.600000   \n","range   48.000000  106.000000   438.000000  131.000000    6.200000   \n","var     81.697419  309.751120  2680.849190  523.265775    1.348095   \n","\n","               ca         num  \n","count  299.000000  303.000000  \n","mean     0.672241    0.937294  \n","std      0.937438    1.228536  \n","min      0.000000    0.000000  \n","25%      0.000000    0.000000  \n","50%      0.000000    0.000000  \n","75%      1.000000    2.000000  \n","max      3.000000    4.000000  \n","IQR      1.000000    2.000000  \n","range    3.000000    4.000000  \n","var      0.878791    1.509300  "]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["heart_summary = heart_continuous.describe().transpose()\n","\n","heart_summary[\"IQR\"] = heart_summary[\"75%\"] - heart_summary[\"25%\"]\n","heart_summary[\"range\"] = heart_summary[\"max\"] - heart_summary[\"min\"]\n","heart_summary[\"var\"] = heart_summary[\"std\"]*heart_summary[\"std\"]\n","\n","heart_summary = heart_summary.transpose()\n","heart_summary"]},{"cell_type":"markdown","id":"d7b109ef","metadata":{"id":"d7b109ef"},"source":["## Measures of Central Tendency\n","\n","By taking a closer look at these measures, we can tell quite a bit about the data set already!\n","\n","For example, variance in cholesterol levels is much larger than variance in ST depression or vessels colored by flouroscopy (this count was limited to between 0 and 3).  "]},{"cell_type":"markdown","id":"7b7bf5b8","metadata":{"id":"7b7bf5b8"},"source":["#### Examining the Five Number Summary\n","\n","The 5 number summary provides insight into the distribution of values for one variable by describing the order that values fall in. The five statistics in this summary are the following, from highest to lowest data values:\n","\n","- Highest value in the dataset (MAX)\n","- Third quartile; greater than 75% of the values in the dataset (Q3)\n","- Median or second quartile; splits the dataset in half (Q2)\n","- First quartile; greater than 25% of the values (Q1)\n","- Lowest value in the dataset (MIN)\n","\n","Because if focuses on order, the five number summary works to describe either categorical or continuous data points.\n","\n","The summary is also less sensitive to skewed distributions and outliers, and more **robust** than the mean and standard deviation.\n","\n","#### Robustness\n","\n","\"Robust statistics\" provide valid results across wider range of conditions, including assumption violations and the presence of outliers. The term \"robust\" can be applied to both a statistic (such as the median) and statistical analyses (such as hypothesis testing or regression).\n","\n","Since they are not sensitive to outliers or skewness, the median & IQR may be favored over mean and standard deviation in real-world contexts where data fails tests of normality (more on that next week).\n","\n","References:\n","\n","Frost, J. (2021). \"5 Number Summary: Definition, Finding & Using\", Statistics By Jim; https://statisticsbyjim.com/basics/five-number-summary/\n","\n","Frost, J. (2023). \"What are Robust Statistics?\", Statistics By Jim; https://statisticsbyjim.com/basics/robust-statistics/"]},{"cell_type":"markdown","id":"ee401ad7","metadata":{"id":"ee401ad7"},"source":["## Box and Whisker Plots\n","\n","Box and Whisker or Box Plots are great for getting a quick overview of the Five Number Summary for each data point.\n","\n","Prior to plotting them, let's standardize the data, to get it to a more comparable scale. Standardizing the data scales each feature to have a mean of 0 and a standard deviation of 1, making the data points easier to compare side by side.\n","\n","Note: Standardization is often used in machine learning algorithms where it is more important to be able to compare the data points to one another using a distance measure than it is to retain their absolute values."]},{"cell_type":"code","execution_count":null,"id":"94caa2ae","metadata":{"id":"94caa2ae"},"outputs":[],"source":["from sklearn.preprocessing import StandardScaler\n","\n","# Standardize the data\n","scaler = StandardScaler()\n","heart_standardized = pd.DataFrame(scaler.fit_transform(heart_continuous), columns=heart_continuous.columns)\n"]},{"cell_type":"code","execution_count":null,"id":"1d3349cb","metadata":{"id":"1d3349cb","outputId":"6ac5ed72-dda4-4f82-ee1b-80f1429872a1"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA9QAAAMtCAYAAAB+STlVAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAABSIElEQVR4nO39e3hV5Z0//H/IgZBwUBQPqCjRaMGCUtEiKqdpg8XiRZriWO102o7ttNWWsYAHqG21WplaQDv2Uqvtz05/rdrvF9PYJ6KVTjnEQ6aK2ooVFASxFcdDVQRiCMl6/uBJhgREWCHsvZPX67q8cK9177U+e+07a+/3Xmvdq0eSJEkAAAAAeyUv0wUAAABALhKoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUijIdAG709zcHK+88kr07ds3evTokelyAAAA6OKSJIl33303jjjiiMjL2/0x6KwO1K+88koMGjQo02UAAADQzbz88stx1FFH7bZNVgfqvn37RsT2F9KvX78MV5NZjY2N8dBDD8XEiROjsLAw0+WQBfQJdqQ/0J4+QXv6BO3pE7SnT2y3cePGGDRoUGse3Z2sDtQtp3n369dPoG5sjJKSkujXr1+37tz8L32CHekPtKdP0J4+QXv6BO3pE23tyWXHBiUDAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEaoAc19TUFEuXLo1ly5bF0qVLo6mpKdMlAQB0CwI1QA6rqqqKsrKyKC8vj/nz50d5eXmUlZVFVVVVpksDAOjyBGqAHFVVVRVTp06N4cOHR21tbdx9991RW1sbw4cPj6lTpwrVAACdTKAGyEFNTU0xY8aMmDx5clRXV8eoUaOiuLg4Ro0aFdXV1TF58uSYOXOm078BADqRQA2Qg2pra2PdunUxe/bsyMtruyvPy8uLWbNmxdq1a6O2tjZDFQIAdH0CNUAO2rBhQ0REDBs2bJfzW6a3tAMAYN8TqAFy0MCBAyMiYsWKFbuc3zK9pR0AAPueQA2Qg8aMGRODBw+O66+/Ppqbm9vMa25ujjlz5kRpaWmMGTMmQxUCAHR9AjVADsrPz4958+ZFTU1NVFRURF1dXdTX10ddXV1UVFRETU1NzJ07N/Lz8zNdKgBAl1WQ6QIASKeysjIWLFgQM2bMiLFjx7ZOLy0tjQULFkRlZWUGqwMA6PoEaoAcVllZGVOmTInFixfHAw88EJMmTYoJEyY4Mg0AsB8I1AA5Lj8/P8aNGxebN2+OcePGCdMAAPtJp19D/be//S3+6Z/+KQ4++OAoKSmJESNGxPLlyzt7tQAAANCpOvUI9VtvvRVnnnlmTJgwIR544IE49NBDY82aNXHggQd25moBAACg03VqoP7BD34QgwYNijvvvLN12uDBg9+3fUNDQzQ0NLQ+3rhxY0RENDY2RmNjY6fVmQtaXn933w78L32CHekPtKdP0J4+QXv6BO3pE9vtzevvkSRJ0lmFnHjiiXH22WfHX//611i6dGkceeSRcfHFF8eXv/zlXba/+uqr45prrtlp+l133RUlJSWdVSYAAABERMSWLVviwgsvjHfeeSf69eu327adGqh79eoVERHTp0+P8847L/74xz/GpZdeGj/5yU/in//5n3dqv6sj1IMGDYo33njjA19IV9fY2BiLFi2K8vLyKCwszHQ5ZAF9gh3pD7SnT9CePkF7+gTt6RPbbdy4MQYMGLBHgbpTT/lubm6OU089Na6//vqIiPjIRz4Szz77bNx66627DNRFRUVRVFS00/TCwsJu/YbuyLagPX2CHekPtKdP0J4+QXv6BO119z6xN6+9U0f5HjhwYJx44oltpg0dOjTWr1/fmasFAACATtepgfrMM8+MVatWtZn2/PPPxzHHHNOZqwUAAIBO16mB+pvf/GbU1dXF9ddfH6tXr4677rorbr/99rjkkks6c7UAAADQ6To1UJ922mnxm9/8Ju6+++4YNmxYXHvttXHTTTfFZz/72c5cLQAAAHS6Th2ULCJi8uTJMXny5M5eDQAAAOxXnXqEGgAAALoqgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgCgi2tqaoqlS5fGsmXLYunSpdHU1JTpkgC6BIEaAKALq6qqirKysigvL4/58+dHeXl5lJWVRVVVVaZLA8h5AjUAQBdVVVUVU6dOjeHDh0dtbW3cfffdUVtbG8OHD4+pU6cK1QAdJFADAHRBTU1NMWPGjJg8eXJUV1fHqFGjori4OEaNGhXV1dUxefLkmDlzptO/ATpAoAYA6IJqa2tj3bp1MXv27MjLa/uVLy8vL2bNmhVr166N2traDFUIkPsEagCALmjDhg0RETFs2LBdzm+Z3tIOgL0nUAMAdEEDBw6MiIgVK1bscn7L9JZ2AOw9gRoAoAsaM2ZMDB48OK6//vpobm5uM6+5uTnmzJkTpaWlMWbMmAxVCJD7BGoAgC4oPz8/5s2bFzU1NVFRURF1dXVRX18fdXV1UVFRETU1NTF37tzIz8/PdKkAOasg0wUAANA5KisrY8GCBTFjxowYO3Zs6/TS0tJYsGBBVFZWZrA6gNwnUAMAdGGVlZUxZcqUWLx4cTzwwAMxadKkmDBhgiPTAPuAQA0A0MXl5+fHuHHjYvPmzTFu3DhhGmAfcQ01AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAK+y1Qz5kzJ3r06BGXXnrp/lolAAAAdJr9Eqgff/zxuP322+Okk07aH6sDAACATtfpgXrTpk3x2c9+Nu64447o379/Z68OAAAA9ouCzl7BJZdcEp/85Cfj4x//eFx33XW7bdvQ0BANDQ2tjzdu3BgREY2NjdHY2NipdWa7ltff3bcD/0ufYEf6A+3pE+yoqakplixZEsuWLYuioqIYP3585OfnZ7osMsx+gvb0ie325vX3SJIk6axC7rnnnvj+978fjz/+ePTq1SvGjx8fI0aMiJtuummX7a+++uq45pprdpp+1113RUlJSWeVCQDQZT322GNx5513xmuvvdY67dBDD40vfvGLMXr06AxWBpCdtmzZEhdeeGG888470a9fv9227bRA/fLLL8epp54aDz30UJx88skRER8YqHd1hHrQoEHxxhtvfOAL6eoaGxtj0aJFUV5eHoWFhZkuhyygT7Aj/YH29AkiIn7zm9/EZz7zmTjnnHNi5syZ8eqrr8bhhx8ec+fOjYULF8Y999wTn/rUpzJdJhliP0F7+sR2GzdujAEDBuxRoO60U76XL18er732WowcObJ1WlNTUyxbtix+/OMfR0NDw06nGhUVFUVRUdFOyyosLOzWb+iObAva0yfYkf5Ae/pE99XU1BRXXHFFTJ48Oaqrq6OpqSkWLlwYZ555ZowdOzYqKiriyiuvjE9/+tNO/+7m7Cdor7v3ib157Z0WqD/2sY/FM88802baF7/4xRgyZEhcccUVdtzQAU1NTbF06dJYtmxZ9O7dOyZMmOBvCoA2amtrY926dXH33XdHXl5eNDU1tc7Ly8uLWbNmxRlnnBG1tbUxfvz4zBUKkMM6LVD37ds3hg0b1mZa79694+CDD95pOrDnqqqqYsaMGbFu3bqIiJg/f34MHjw45s2bF5WVlZktDoCssWHDhoiI9/3e1TK9pR0Ae2+/3Ica2Deqqqpi6tSpMXz48KitrY277747amtrY/jw4TF16tSoqqrKdIkAZImBAwdGRMSKFSt2Ob9leks7APbefg3US5Ysed8ByYDda2pqihkzZrReCzdq1KgoLi6OUaNGRXV1dUyePDlmzpzZ5pQ+ALqvMWPGxODBg+P666+P5ubmNvOam5tjzpw5UVpaGmPGjMlQhQC5zxFqyBEt18LNnj078vLa/um2XAu3du3aqK2tzVCFAGST/Pz8mDdvXtTU1ERFRUXU1dVFfX191NXVRUVFRdTU1MTcuXONwQHQAZ12DTWwb7kWDoC9VVlZGQsWLIgZM2bE2LFjW6eXlpbGggULjL0B0EGOUEOOcC0cAGlUVlbG6tWrY9GiRTF9+vRYtGhRvPDCC8I0wD4gUEOOcC0cAGnl5+fHuHHjYuzYsTFu3DineQPsIwI15AjXwgEAQHZxDTXkENfCAQBA9hCoIcdUVlbGlClTYvHixfHAAw/EpEmTYsKECY5MAwDAfiZQQw5quRZu8+bNroUDAIAMcQ01AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUEMOampqiqVLl8ayZcti6dKl0dTUlOmSAACg2xGoIcdUVVVFWVlZlJeXx/z586O8vDzKysqiqqoq06UBAEC3IlBDDqmqqoqpU6fG8OHDo7a2Nu6+++6ora2N4cOHx9SpU4VqAADYjwRqyBFNTU0xY8aMmDx5clRXV8eoUaOiuLg4Ro0aFdXV1TF58uSYOXOm078BAGA/EaghR9TW1sa6deti9uzZkZfX9k83Ly8vZs2aFWvXro3a2toMVQgAAN2LQA05YsOGDRERMWzYsF3Ob5ne0g4AAOhcAjXkiIEDB0ZExIoVK3Y5v2V6SzsAAKBzCdSQI8aMGRODBw+O66+/Ppqbm9vMa25ujjlz5kRpaWmMGTMmQxUCAED3IlBDjsjPz4958+ZFTU1NVFRURF1dXdTX10ddXV1UVFRETU1NzJ07N/Lz8zNdKgAAdAsFmS4A2HOVlZWxYMGCmDFjRowdO7Z1emlpaSxYsCAqKyszWB0AAHQvAjXkmMrKypgyZUosXrw4HnjggZg0aVJMmDDBkelurKmpKZYuXRrLli2L3r176w8AAPuJU74hB+Xn58e4ceNi7NixMW7cOOGpG6uqqoqysrIoLy+P+fPnR3l5eZSVlUVVVVWmSwMA6PIEaoAcVVVVFVOnTo3hw4dHbW1t3H333VFbWxvDhw+PqVOnCtUAAJ1MoAbIQU1NTTFjxoyYPHlyVFdXx6hRo6K4uDhGjRoV1dXVMXny5Jg5c2Y0NTVlulQAgC5LoAbIQbW1tbFu3bqYPXt25OW13ZXn5eXFrFmzYu3atVFbW5uhCgEAuj6BGiAHbdiwISIihg0btsv5LdNb2gEAsO8J1AA5aODAgRERsWLFil3Ob5ne0g4AgH1PoAbIQWPGjInBgwfH9ddfH83NzW3mNTc3x5w5c6K0tDTGjBmToQoBALo+gRogB+Xn58e8efOipqYmKioqoq6uLurr66Ouri4qKiqipqYm5s6d65ZqAACdqCDTBQCQTmVlZSxYsCBmzJgRY8eObZ1eWloaCxYsiMrKygxWBwDQ9QnUADmssrIypkyZEosXL44HHnggJk2aFBMmTHBkGgBgP3DKNwAAAKQgUAPksKqqqigrK4vy8vKYP39+lJeXR1lZWVRVVWW6NACALk+gBshRVVVVMXXq1Bg+fHjU1tbG3XffHbW1tTF8+PCYOnWqUA0A0MkEaoAc1NTUFDNmzIjJkyfHvffeG++99148/vjj8d5778W9994bkydPjpkzZ0ZTU1OmSwUA6LIEaoAcVFtbG+vWrYszzjgjTjjhhDanfJ9wwgkxevToWLt2bdTW1ma6VACALkugBshBGzZsiIiIWbNm7fKU79mzZ7dpBwDAvidQA+SgQw89NCIizjrrrKiuro5Ro0ZFcXFxjBo1Kqqrq+PMM89s0w4AgH1PoAbognr06JHpEgAAujyBGiAHvfbaaxER8cgjj0RFRUXU1dVFfX191NXVRUVFRTzyyCNt2gEAsO8J1AA5aODAgRERcf3118czzzwTY8eOjQsuuCDGjh0bK1asiO9///tt2gEAsO8J1AA5aMyYMTF48OB49NFH4/nnn49FixbF9OnTY9GiRbFq1ap47LHHorS0NMaMGZPpUgEAuiyBGiAH5efnx7x586KmpiY+/elPR1FRUZx22mlRVFQUn/70p6Ompibmzp0b+fn5mS4VAKDLKsh0AQCkU1lZGQsWLIgZM2bE2LFjW6eXlpbGggULorKyMoPVAQB0fQI1QA6rrKyMKVOmxOLFi+OBBx6ISZMmxYQJExyZBgDYDwRqgByXn58f48aNi82bN8e4ceOEaQCA/cQ11AAAAJCCQA0AAAApCNQAAACQgkANANDFNTU1xdKlS2PZsmWxdOnSaGpqynRJAF2CQA0A0IVVVVVFWVlZlJeXx/z586O8vDzKysqiqqoq06UB5DyBGgCgi6qqqoqpU6fG8OHDo7a2Nu6+++6ora2N4cOHx9SpU4VqgA4SqAEAuqCmpqaYMWNGTJ48Oaqrq2PUqFFRXFwco0aNiurq6pg8eXLMnDnT6d8AHSBQA+Q410YCu1JbWxvr1q2L2bNnR15e2698eXl5MWvWrFi7dm3U1tZmqEKA3CdQA+Qw10YC72fDhg0RETFs2LBdzm+Z3tIOgL0nUAPkKNdGArszcODAiIhYsWLFLue3TG9pB8DeE6gBcpBrI4EPMmbMmBg8eHBcf/310dzc3GZec3NzzJkzJ0pLS2PMmDEZqhAg9wnUADnItZHAB8nPz4958+ZFTU1NVFRURF1dXdTX10ddXV1UVFRETU1NzJ07N/Lz8zNdKkDOKsh0AQDsPddGAnuisrIyFixYEDNmzIixY8e2Ti8tLY0FCxZEZWVlBqsDyH2OUAPkINdGAnuqsrIyVq9eHYsWLYrp06fHokWL4oUXXhCmAfYBgRpyUH19fUybNi2uvvrqmDZtWtTX12e6JPYz10YCeyM/Pz/GjRsXY8eOjXHjxjnNG2AfEaghx1RUVERJSUncdttt8fTTT8dtt90WJSUlUVFRkenS2I9cGwkAkHmuoYYcUlFREffdd1/07NkzLr300igtLY21a9fGTTfdFPfdd19UVFREdXV1pstkP3FtJLCntm7dGjfffHP84Q9/iNWrV8c3vvGN6NmzZ6bLAsh5jlBDjqivr28N0++++25cd911MXDgwLjuuuvi3XffjZ49e8Z9993n9O9uxrWRwAe5/PLLo3fv3jFz5sxYuHBhzJw5M3r37h2XX355pksDyHkCNeSIyy67LCIipk+fvtNRhZYj1ju2o/twbSTwfi6//PL44Q9/GAcffHDcdtttceedd8Ztt90WBx98cPzwhz8UqgE6SKCGHPHCCy9ERMSXvvSlXc6/6KKL2rQDoHvbunVr3HjjjXHYYYfFX//61/iXf/mX6N+/f/zLv/xL/PWvf43DDjssbrzxxti6dWumSwXIWQI15Ijjjz8+IiJ++tOf7nL+z372szbtAOjebrnllti2bVtcd911UVDQdticgoKC+N73vhfbtm2LW265JUMVAuQ+gRpyxA9/+MOIiJg/f/5ORxO2bt0aN910U5t2AHRva9asiYiIyZMn73J+y/SWdgDsPYEackRxcXFMmTIltm7dGn379o2JEyfG/PnzY+LEidG3b9/YunVrTJkyJYqLizNdKgBZ4LjjjouIiJqaml3Ob5ne0g6AvSdQQw6prq6O4447LrZu3RpLliyJZcuWxZIlS2Lr1q1x3HHHuWUWAK0uvvjiKCgoiKuuuiq2bdvWZt62bdviO9/5ThQUFMTFF1+coQoBcp9ADTnk8ssvjzVr1sQhhxwSY8eOjRNPPDHGjh0bhxxySKxZs8ZorQC06tmzZ3zzm9+M//mf/4mjjjoqfvrTn8bf//73+OlPfxpHHXVU/M///E9885vfdD9qgA4o+OAmQDZoP1prkiSxcOHCOOecc6JHjx5x1FFHxY033hjXXXedL0cARETEDTfcEBERN954Y5sj0QUFBXHZZZe1zgcgHUeoIUcYrRWANG644YbYvHlzzJ07N84555yYO3dubN68WZgG2AccoYYcYbRWANLq2bNnTJs2LcrKyuKcc86JwsLCTJcE0CU4Qg05wmitAKRVX18f06ZNi6uvvjqmTZsW9fX1mS4JoEsQqCFHGK0VgDQqKiqipKQkbrvttnj66afjtttui5KSkqioqMh0aQA5T6CGHGG0VgD2VkVFRdx3333Rs2fPuPzyy+PWW2+Nyy+/PHr27Bn33XefUA3QQa6hhhxitFYA9lR9fX1rmH733XejR48esXDhwrjooovi2muvjb59+8Z9990X9fX1UVxcnOlyAXKSI9SQY4zWCsCeuOyyyyIiYvr06TudvdSzZ8+49NJL27QDYO8J1JCDWkZr/dd//deYNm2a07wB2MkLL7wQERFf+tKXdjn/oosuatOO7uXhhx+Onj17RkVFRfTs2TMefvjhTJcEOUmgBgDogo4//viIiPjpT3+6y/k/+9nP2rSj++jRo0eMGTOmzbQxY8ZEjx49MlQR5C6BGgCgC/rhD38YERHz58+PrVu3tpm3devWuOmmm9q0o3toH5rPOuus3c4Hds+gZPvBli1bYuXKlR1axqb6hnj0mTXRf8AT0ae4KPVyhgwZEiUlJR2qBQDIfsXFxTFlypS47777om/fvjFt2rQoLS2N2bNnx3/8x3/E1q1bY8qUKQYk60Z2PK37ueeei+OOOy4WLlwYf/jDH2LNmjUxdOjQ1nbtgzbZT+bIDIF6P1i5cmWMHDlynyyro8NOLV++PE455ZR9UgsAkN2qq6tbb501d+7cNvOmTJkS1dXVmSmMjNjxNO8hQ4ZEY2Njm8c7tkuSZL/WRsfJHJkhUO8HQ4YMieXLl3doGas2vB3T/+8zMf+84fGhgQd2qBYAoPuorq6O+vr6mD59etTV1cXpp58e8+fPd2S6G/vsZz+7y+mf/vSn4957793P1bCvyByZIVDvByUlJR3+hSbvpTejqLY+hg47OUYcc/A+qoxc1dTUFEuXLo1ly5ZF7969Y8KECZGfn5/psgDIUsXFxfEf//EfsXDhwjjnnHOisLAw0yWRQb/61a/il7/85U7ThencJnNkhkHJIMdUVVVFWVlZlJeXx/z586O8vDzKysqiqqoq06UBAFmstra29f/bX2u74+Md2wG7J1BDDqmqqoqpU6fG8OHDo7a2Nu6+++6ora2N4cOHx9SpU4VqAOB97TjQ2NChQ6Nnz54xZ86c6NmzZ+uAZO3bAbsnUEOOaGpqihkzZsTkyZOjuro6Ro0aFcXFxTFq1Kiorq6OyZMnx8yZM6OpqSnTpQIAWar9YGP//d//vdv5wO4J1JAjamtrY926dTF79uzIy2v7p5uXlxezZs2KtWvXOk0LANitJEl2+r5QW1srTEMKAjXkiA0bNkRExLBhw3Y5v2V6SzsAgPdz1llnxdatW6O6ujq2bt3qNG9IySjfkCMGDhwYERErVqyI008/faf5K1asaNMOgK5jy5YtOw0itbc21TfEo8+sif4Dnog+xUUdWtaQIUOipKSkQ8sA6AoEasgRY8aMicGDB8f1118f1dXVbeY1NzfHnDlzorS0NMaMGZOZAgHoNCtXroyRI0fuk2XdsA+WsXz58g7fngegKxCoIUfk5+fHvHnzYurUqTFlypQoLy+PF154IV566aVYtGhR3H///bFgwQL3owbogoYMGRLLly/v0DJWbXg7pv/fZ2L+ecPjQwMP7HA9AAjUkFMqKytj5syZceONN0ZNTU3r9IKCgpg5c2ZUVlZmsDoAOktJSUmHjwjnvfRmFNXWx9BhJ8eIYw7eR5UBdG8CNeSQqqqqmDt3bnzyk5+MiRMnxvPPPx8nnHBCPPTQQzF37tw4/fTThWoAANhPBGrIEe3vQ93U1BQLFy6Mc845Jy655JKoqKiImTNnxpQpU5z2DQAA+4FADTmi5T7Ud999d+Tl5UVTU1PrvJb7UJ9xxhlRW1sb48ePz1yhwD7R0VGdjegMAJ1PoIYc4T7U0L3sq1GdjegMAJ1HoIYc4T7U0L10dFRnIzoDQOcTqCFHuA81dC8dHdXZiM4A0PnyMl0AsGda7kNdU1MTFRUVUVdXF/X19VFXVxcVFRVRU1MTc+fONSAZAADsJ45QQw6prKyMBQsWxIwZM2Ls2LGt00tLS2PBggVumQUAAPuRQA05prKyMqZMmRKLFy+OBx54ICZNmhQTJkxwZBoAAPYzgRpyUH5+fowbNy42b94c48aNE6YBACADBGrIgI7eXzZi391j1v1ls4N7DgMA5B6BGjJgX91fNqLj95h1f9ns4J7DAAC5R6CGDOjo/WUj9t09Zt1fNju45zAAQO4RqCEDOnp/2Qj3mO1q3HMYACD3uA81AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApNCpgXrOnDlx2mmnRd++fePQQw+NioqKWLVqVWeuEgAAAPaLTg3US5cujUsuuSTq6upi0aJFsW3btpg4cWJs3ry5M1cLAAAAna6gMxf+4IMPtnl85513xqGHHhrLly+PsWPHduaqAQAAoFN1aqBu75133omIiIMOOmiX8xsaGqKhoaH18caNGyMiorGxMRobGzu/wCy2bdu21n+7+7ZgO32CHekPtKdP0J4+QXst/UB/oIX9xHZ789r3W6BOkiSmT58eZ511VgwbNmyXbebMmRPXXHPNTtMfeuihKCkp6ewSs9rLmyIiCqKuri7+tiLT1ZAN9Al2pD/Qnj5Be/oE72fRokWZLoEsYT+x3ZYtW/a47X4L1F//+tfjz3/+czz88MPv22bWrFkxffr01scbN26MQYMGxcSJE6Nfv377o8ys9af1f4945ok4/fTT4+Sjd32En+5Fn2BH+gPt6RO0p0/QXmNjYyxatCjKy8ujsLAw0+WQBewntms5U3pP7JdA/Y1vfCN++9vfxrJly+Koo45633ZFRUVRVFS00/TCwsJu/0deUFDQ+m933xZsp0+wI/2B9vQJ2tMneD++a9PCfmK7vXntnRqokySJb3zjG/Gb3/wmlixZEqWlpZ25OgAAANhvOjVQX3LJJXHXXXfFfffdF3379o1XX301IiIOOOCAKC4u7sxVAwAAQKfq1PtQ33rrrfHOO+/E+PHjY+DAga3//frXv+7M1QIAAECn6/RTvgEAAKAr6tQj1AAAANBVCdQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkEJBpgsAAAD2zpYtW2LlypUdWsam+oZ49Jk10X/AE9GnuKhDyxoyZEiUlJR0aBmQiwRqAADIMStXroyRI0fuk2XdsA+WsXz58jjllFP2wZIgtwjUAACQY4YMGRLLly/v0DJWbXg7pv/fZ2L+ecPjQwMP7HA90B0J1AAAkGNKSko6fEQ476U3o6i2PoYOOzlGHHPwPqoMuheDkgEAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKRQkOkCAAAAiFj7xubY3LAtY+tf8/rm1n8LCjIXFXsXFUTpgN4ZW//eEKgBAAAybO0bm2PC3CWZLiMiImYseCbTJcTimeNzIlQL1AAAABnWcmT6pvNHRNmhfTJTQ31D1Cx5LCaPHx29i4syUsPq1zbFpb9+OqNH6veGQA0AAJAlyg7tE8OOPCAj625sbIxXD4k45Zj+UVhYmJEaco1ByQAAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAF96HeA2vf2JzxG4uveX1z678FBZl723oXFUTpgN4ZWz9Arsj0Z4fPDQDofAL1B1j7xuaYMHdJpstoNWPBM5kuIRbPHO/LEcBuZNNnh88NAOg8AvUHaDm6cNP5I6Ls0D6Zq6O+IWqWPBaTx4+O3sVFGalh9Wub4tJfP53xo/UA2S4bPjt8bgBA5xOo91DZoX1i2JEHZGz9jY2N8eohEacc0z8KCwszVgcAey6Tnx0+NwCg8xmUDAAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSKMh0AQBdwdo3Nsfmhm0ZW/+a1ze3/ltQkNlde++igigd0DujNQAA7A8CNUAHrX1jc0yYuyTTZURExIwFz2S6hIiIWDxzvFANAHR5AjVAB7Ucmb7p/BFRdmifzNRQ3xA1Sx6LyeNHR+/ioozUEBGx+rVNcemvn87o0XoAgP1FoAbYR8oO7RPDjjwgI+tubGyMVw+JOOWY/lFYWJiRGgAAuhuBGgBgPzDWwnbGWQC6EoEaUsj0l6IIX4wAcomxFtoyzgLQVQjUsJey6UtRhC9GALnAWAvbGWcB6GoEathL2fClKMIXI4BcZKwFgK5FoIaUMvmlKMIXIwAAyLS8TBcAAAAAuUigBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIwSjfAACQAWvf2JzRW0+ueX1z678FBZmLBb2LCqJ0QO+MrR86QqAGAID9bO0bm2PC3CWZLiMiImYseCbTJcTimeOFanKSQA0AAPtZy5Hpm84fEWWH9slMDfUNUbPksZg8fnT0Li7KSA2rX9sUl/766YweqYeOEKgBACBDyg7tE8OOPCAj625sbIxXD4k45Zj+UVhYmJEaINcZlAwAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUijIdAHZrqHpvcjr9bdYu3FV5PXqk7E6tm3bFq9seyWe+/tzUVCQmbdt7cZNkdfrb9HQ9F5EHJCRGgAAALKFQP0BXtn8UvQuvTlm/zHTlWx3y4O3ZHT9vUsjXtk8IkbGYRmtAwAAINME6g9wRO9jYvPab8SPzh8Rxx2a2SPUjzz8SJx51pkZO0K95rVN8W+/fjqOmHBMRtYPkCuy4ewmZzYBQOcTqD9AUX6vaH7vyCjt96E48eDMfRlobGyMtQVrY+hBQ6OwsDAjNTS/9040v/d6FOX3ysj6AXJFNp3d5MwmAOg8AjUA7GPZcHaTM5sAoPMJ1ACwj2XD2U3ObAKAzue2WQAAAJCCQA0AAAApCNQAAACQgkANAAAAKeyXQH3LLbdEaWlp9OrVK0aOHBm1tbX7Y7UAAADQaTo9UP/617+OSy+9NL71rW/FU089FWPGjIlJkybF+vXrO3vVAAAA0Gk6PVDPnz8/LrroovjSl74UQ4cOjZtuuikGDRoUt956a2evGgAAADpNp96HeuvWrbF8+fK48sor20yfOHFiPProozu1b2hoiIaGhtbHGzdujIjt99JsbGzszFLf17Zt21r/zVQNEdG67kzWkC3bItOyZTvoE9kjG7ZDNvSHiOzYFtkgG7ZDNvSJbNgO2SIbtoU+kV2yYVvoE9klG7aFPrHd3qy3UwP1G2+8EU1NTXHYYYe1mX7YYYfFq6++ulP7OXPmxDXXXLPT9IceeihKSko6rc7deXlTRERBPPzww/FSn4yU0MaiRYsytu5s2xaZkm3bQZ/IvGzaDpnsDxHZtS0yKZu2g31EdsimbaFPZIds2hb6RHbIpm3R3fvEli1b9rhtpwbqFj169GjzOEmSnaZFRMyaNSumT5/e+njjxo0xaNCgmDhxYvTr16/T69yVZ1/ZGHOfqYuzzjorPnxEZmqI2P4ryaJFi6K8vDwKCwszUkO2bItMy5btoE9kj2zYDtnQHyKyY1tkg2zYDtnQJ7JhO2SLbNgW+kR2yYZtoU9kl2zYFvrEdi1nSu+JTg3UAwYMiPz8/J2ORr/22ms7HbWOiCgqKoqioqKdphcWFmbsDS0oKGj9N5NfUlvYFpmXbdtBn8i8bNoOmewPEdm1LTIpm7aDfUR2yKZtoU9kh2zaFvpEdsimbdHd+8TerLdTByXr2bNnjBw5cqdTBhYtWhRnnHFGZ64aAAAAOlWnn/I9ffr0+NznPhennnpqjB49Om6//fZYv359fPWrX+3sVQMAAECn6fRAff7558ebb74Z3/ve92LDhg0xbNiwWLhwYRxzzDGdvWoAAADoNPtlULKLL744Lr744v2xKuh0DU3vRV6vv8Xajasir1fmhmDctm1bvLLtlXju78+1Xmuyv63duCnyev0tGprei4gDMlIDAABkSma+hUMOe2XzS9G79OaY/cdMV7LdLQ/ektH19y6NeGXziBgZOw802F1kw48s2fADS4QfWQCA7kWghr10RO9jYvPab8SPzh8Rxx2a2SPUjzz8SJx51pkZC1BrXtsU//brp+OICd37Eo5s+pEl0z+wRPiRBQDoPgRq2EtF+b2i+b0jo7Tfh+LEgzN3BK6xsTHWFqyNoQcNzdgtBZrfeyea33s9ivJ7ZWT92SIbfmTJhh9YIvzIAgB0LwI1QAdlw48s2fADS4QfWQCA7qVT70MNAAAAXZVADQAAACkI1AAAAJCCa6gBADqZ2+tt59Z6QFcjUAMAdDK31/tfbq0HdCUCNQBAJ3N7ve3cWg/oagRqAIBO5vZ627m1HtDVGJQMAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSKMh0AZBr6hubIiJixd/eyWgdm+sb4onXIw5/6a3oXVyUkRpWv7YpI+sFAIBsIFDDXlrz/4XIK6ueyXAlEREF8f9f/Ximi4jeRXYlAAB0P74Fw16a+OHDIyLiuEP7RHFhfsbqWLXhnZix4JmYN3V4fGjgARmro3dRQZQO6J2x9WeDbDhrIRvOWIhw1gIA0L0I1LCXDurdMz7z0aMzXUZs27YtIiKOO6R3DDsyc4GabDprITvOWIhw1oIfWbbzAwsAXV33/sazB7LhS1GEL0aQzbLhrIVsOWMhwlkLEX5kaa+7/8ACu/L2e5sjr9ffYtHq5bF2Y5+9fn5Dw3vxt5fXd6iG5uamWLVyVbwYb0deXsc+v44cdHQUFfXa6+e9/Pctkdfrb9HQ9F5EOEBA7vEJ9wGy50tRhC9GkJ2y4awFZyxkFz+y/C8/sMCuPf7XldG79Ob4/62LiHUZLOTwiEfe/n86vpy/p39q79KItxpHRsRhHa8D9jPJ6ANkw5eiCF+MAHKJH1mAD3L+iJER8aMYdFBJFBXs/Z1s9+UR6g8N+VDGjlBHRBT3zI8zjzmxQ+uHTBGoP0A2fCmK8MUIAKArOeKAA+Kb4/6hYwsZ0bGnNzY2xsJYGOecc04UFhZ2bGHQTQnUAAAAGdaVrqvvyBkLuXZdvUANAACQYV3quvoOXFMfkVvX1QvUAAAAGdaVrqvvyBHqiNy6rl6gBgAAyDDX1eemvf/pAwAAABCoAQAAIA2BGgAAAFIQqAEAACAFgRoAAABSMMo3AEAnq29sioiIFX97J93z67fE2tXPd6iGpqamePqZNbGloDby89PfDiciorTshCguLtnr561+bVOH1guQbQRqAIBOtub/C5JXVj2T6vkNr66OV//z0n1YUccc/vmboujwstTP713kKyjQNdibAQB0sokfPjwiIo47tE8UF+790eH6+lNi7WdGdKiGpqamePqpp2PER0Zk7Ah1xPYwXTqgd4fWD5AtBGoAgE52UO+e8ZmPHt2BJRwQp5UN7FANjY2NUbLt3Thn4pgoLCzs0LIA2M6gZAAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKQjUAAAAkIJADQAAACkI1AAAAJCCQA0AAAApdFqgXrduXVx00UVRWloaxcXFcdxxx8V3v/vd2Lp1a2etEgAAAPabgs5a8MqVK6O5uTl+8pOfRFlZWaxYsSK+/OUvx+bNm2Pu3LmdtVoAAADYLzotUH/iE5+IT3ziE62Pjz322Fi1alXceuutAjUAAAA5r9MC9a688847cdBBB73v/IaGhmhoaGh9vHHjxoiIaGxsjMbGxk6vL5tt27at9d/uvi3YTp9gR/oD7ekTtNfSD/QHWugTtKdPbLc3r3+/Beo1a9bEzTffHPPmzXvfNnPmzIlrrrlmp+kPPfRQlJSUdGZ5We/lTRERBVFXVxd/W5HpasgG+gQ70h9oT5/g/SxatCjTJZBl9Ana6+59YsuWLXvcdq8D9dVXX73L0Lujxx9/PE499dTWx6+88kp84hOfiPPOOy++9KUvve/zZs2aFdOnT299vHHjxhg0aFBMnDgx+vXrt7eldil/Wv/3iGeeiNNPPz1OPvr9j/LTfegT7Eh/oD19gvYaGxtj0aJFUV5eHoWFhZkuhyygT9CePrFdy5nSe2KvA/XXv/71+MxnPrPbNoMHD279/1deeSUmTJgQo0ePjttvv323zysqKoqioqKdphcWFnbrNzQioqCgoPXf7r4t2E6fYEf6A+3pE7wf36toT5+gve7eJ/bmte91oB4wYEAMGDBgj9r+7W9/iwkTJsTIkSPjzjvvjLw8t70GAACga+i0a6hfeeWVGD9+fBx99NExd+7ceP3111vnHX744Z21WgAAANgvOi1QP/TQQ7F69epYvXp1HHXUUW3mJUnSWasFAACA/aLTzsH+whe+EEmS7PI/AAAAyHUuagYAAIAUBGoAAABIQaAGAACAFDptUDLg/W3ZsiVWrlzZoWWs2vB2NLy6Op5bURzNbx6YejlDhgyJkpKSDtUCAADdkUANGbBy5coYOXLkPlnWhf/ZsecvX748TjnllH1SCwAAdCcCNWTAkCFDYvny5R1axqb6hrh/8WPxyQmjo09xUYdqAQAA9p5ADRlQUlLS4aPCjY2N8dYbr8Xoj54ahYWF+6gyAABgTxmUDAAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSKMh0AQBEbNmyJVauXJn6+as2vB0Nr66O51YUR/ObB3aoliFDhkRJSUmHlgEA0B0I1ABZYOXKlTFy5MgOL+fC/+x4LcuXL49TTjml4wsCAOjiBGqALDBkyJBYvnx56udvqm+I+xc/Fp+cMDr6FBd1uBYAAD6YQA2QBUpKSjp0VLixsTHeeuO1GP3RU6OwsHAfVgYAwPsxKBkAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKQgUAMAAEAKAjUAAACkIFADAABACgI1AAAApCBQAwAAQAoCNQAAAKRQkOkCAICdbdmyJVauXJn6+as2vB0Nr66O51YUR/ObB3aoliFDhkRJSUmHlgEAXZFADQBZaOXKlTFy5MgOL+fC/+x4LcuXL49TTjml4wsCgC5GoAaALDRkyJBYvnx56udvqm+I+xc/Fp+cMDr6FBd1uBYAYGcCNQBkoZKSkg4dFW5sbIy33ngtRn/01CgsLNyHlQEALQxKBgAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANAAAAKRjlez/YsmVLrFy5skPLWLXh7Wh4dXU8t6I4mt88MPVyhgwZEiUlJR2qBQAAAIF6v1i5cmWMHDlynyzrwv/s2POXL1/eoduwAAAAsJ1AvR8MGTIkli9f3qFlbKpviPsXPxafnDA6+hQXdagWAAAAOk6g3g9KSko6fFS4sbEx3nrjtRj90VOjsLBwH1UGAABAWgYlAwAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagAAAEhBoAYAAIAUBGoAAABIQaAGAACAFARqAAAASEGgBgAAgBQEagDoYpqammLp0qWxbNmyWLp0aTQ1NWW6JADokgRqAOhCqqqqoqysLMrLy2P+/PlRXl4eZWVlUVVVlenSAKDLEagBoIuoqqqKqVOnxvDhw6O2tjbuvvvuqK2tjeHDh8fUqVOFagDYxwRqAOgCmpqaYsaMGTF58uSorq6OUaNGRXFxcYwaNSqqq6tj8uTJMXPmTKd/A8A+JFADQBdQW1sb69ati9mzZ0deXtuP97y8vJg1a1asXbs2amtrM1QhAHQ9AjUAdAEbNmyIiIhhw4btcn7L9JZ2AEDHCdQA0AUMHDgwIiJWrFixy/kt01vaAQAdJ1ADQBcwZsyYGDx4cFx//fXR3NzcZl5zc3PMmTMnSktLY8yYMRmqEAC6HoEaALqA/Pz8mDdvXtTU1ERFRUXU1dVFfX191NXVRUVFRdTU1MTcuXMjPz8/06UCQJdRkOkCAIB9o7KyMhYsWBAzZsyIsWPHtk4vLS2NBQsWRGVlZQarA4CuR6AGgC6ksrIypkyZEosXL44HHnggJk2aFBMmTHBkGgA6gUANAF1Mfn5+jBs3LjZv3hzjxo0TpgGgk7iGGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSKMh0AbuTJElERGzcuDHDlWReY2NjbNmyJTZu3BiFhYWZLocsoE+wI/2B9vQJ2tMnaE+foD19YruW/NmSR3cnqwP1u+++GxERgwYNynAlAAAAdCfvvvtuHHDAAbtt0yPZk9idIc3NzfHKK69E3759o0ePHpkuJ6M2btwYgwYNipdffjn69euX6XLIAvoEO9IfaE+foD19gvb0CdrTJ7ZLkiTefffdOOKIIyIvb/dXSWf1Eeq8vLw46qijMl1GVunXr1+37tzsTJ9gR/oD7ekTtKdP0J4+QXv6RHzgkekWBiUDAACAFARqAAAASEGgzhFFRUXx3e9+N4qKijJdCllCn2BH+gPt6RO0p0/Qnj5Be/rE3svqQckAAAAgWzlCDQAAACkI1AAAAJCCQA0AAAApCNQAAACQgkANXcQXvvCFqKioyHQZ7Afr1q2LHj16xNNPP92h5QwePDhuuummfVIT+8aSJUuiR48e8fbbb3doOfv6vR0/fnxceuml+2x57J096Rc///nP48ADD9xvNbW4+uqrY8SIEft9vQDZQqCGfWh/fOncV2EKyDxBFQBym0AN+1GSJLFt27ZMlwEAAOwDAnWGPfjgg3HWWWfFgQceGAcffHBMnjw51qxZ0zr/0UcfjREjRkSvXr3i1FNPjerq6p2OTv7lL3+Jc845J/r06ROHHXZYfO5zn4s33ngjA6+me/vCF74QS5cujR/96EfRo0eP6NGjR/z85z+PHj16xO9+97s49dRTo6ioKGprayNJkrjhhhvi2GOPjeLi4jj55JNjwYIFrct666234rOf/WwccsghUVxcHMcff3zceeedERFRWloaEREf+chHokePHjF+/Pg2dVxzzTVx6KGHRr9+/eIrX/lKbN26tXXe+PHj4+tf/3p8/etfb+1zV111Vex4O/pbbrkljj/++OjVq1ccdthhMXXq1E7cauxOc3Nz/OAHP4iysrIoKiqKo48+Or7//e+3zn/xxRdjwoQJUVJSEieffHI89thjbZ5/7733xoc//OEoKiqKwYMHx7x58/b3S2A3drXPWLduXURELF++PE499dQoKSmJM844I1atWtX6vDVr1sSUKVPisMMOiz59+sRpp50Wv//973e7rvnz58fw4cOjd+/eMWjQoLj44otj06ZNbdo88sgjMW7cuCgpKYn+/fvH2WefHW+99Vbr/Obm5rj88svjoIMOisMPPzyuvvrqfbYtiGhoaIhp06bFoYceGr169YqzzjorHn/88fdt//Of/zyOPvroKCkpiU996lPx5ptvtpnfcir2T37ykxg0aFCUlJTEeeedt9Np43feeWcMHTo0evXqFUOGDIlbbrmlzfwrrrgiTjjhhCgpKYljjz02vv3tb0djY+P71rV27dooKyuLr33ta9Hc3Lz3G4JOs7vPlL19n8kN48ePj2nTpu1y372rMx7ffvvt6NGjRyxZsiQi/vdyk9/97nfxkY98JIqLi+Mf/uEf4rXXXosHHngghg4dGv369YsLLrggtmzZsv9fYLZIyKgFCxYk9957b/L8888nTz31VHLuuecmw4cPT5qampKNGzcmBx10UPJP//RPybPPPpssXLgwOeGEE5KISJ566qkkSZLklVdeSQYMGJDMmjUree6555Inn3wyKS8vTyZMmJDZF9YNvf3228no0aOTL3/5y8mGDRuSDRs2JL///e+TiEhOOumk5KGHHkpWr16dvPHGG8ns2bOTIUOGJA8++GCyZs2a5M4770yKioqSJUuWJEmSJJdcckkyYsSI5PHHH0/Wrl2bLFq0KPntb3+bJEmS/PGPf0wiIvn973+fbNiwIXnzzTeTJEmSz3/+80mfPn2S888/P1mxYkVSU1OTHHLIIcns2bNbaxw3blzSp0+f5N/+7d+SlStXJr/85S+TkpKS5Pbbb0+SJEkef/zxJD8/P7nrrruSdevWJU8++WTyox/9aD9vSVpcfvnlSf/+/ZOf//znyerVq5Pa2trkjjvuSNauXZtERDJkyJCkpqYmWbVqVTJ16tTkmGOOSRobG5MkSZInnngiycvLS773ve8lq1atSu68886kuLg4ufPOO1uXf8wxxyQ33nhjZl4cu91njBo1KlmyZEny7LPPJmPGjEnOOOOM1uc9/fTTyW233Zb8+c9/Tp5//vnkW9/6VtKrV6/kpZdeam3T/r298cYbkz/84Q/Jiy++mPzXf/1X8qEPfSj52te+1jr/qaeeSoqKipKvfe1rydNPP52sWLEiufnmm5PXX389SZLt+45+/folV199dfL8888n//mf/5n06NEjeeihhzp/Q3UT06ZNS4444ohk4cKFybPPPpt8/vOfT/r375+8+eabyeLFi5OISN56660kSZKkrq4u6dGjRzJnzpxk1apVyY9+9KPkwAMPTA444IDW5X33u99NevfunfzDP/xD8tRTTyVLly5NysrKkgsvvLC1ze23354MHDgwuffee5MXX3wxuffee5ODDjoo+fnPf97a5tprr00eeeSRZO3atclvf/vb5LDDDkt+8IMftFnPySefnCRJkjzzzDPJwIEDkyuvvLJTtxXpvN9nSpJ88PtMbtrdvrvlu0RLpkiSJHnrrbeSiEgWL16cJEnSuu85/fTTk4cffjh58sknk7KysmTcuHHJxIkTkyeffDJZtmxZcvDBByf//u//npkXmQUE6izz2muvJRGRPPPMM8mtt96aHHzwwUl9fX3r/DvuuKNN5//2t7+dTJw4sc0yXn755SQiklWrVu3P0km277j+7d/+rfVxy46ourq6ddqmTZuSXr16JY8++mib51500UXJBRdckCRJkpx77rnJF7/4xV2uY1c7wCTZHqgPOuigZPPmza3Tbr311qRPnz5JU1NTa31Dhw5NmpubW9tcccUVydChQ5MkSZJ777036devX7Jx48a9f/HsUxs3bkyKiopav+zsqKUP/PSnP22d9uyzzyYRkTz33HNJkiTJhRdemJSXl7d53mWXXZaceOKJrY8F6sx7v33G73//+9Zp999/fxIRbT4L2jvxxBOTm2++ufXxB723/+f//J/k4IMPbn18wQUXJGeeeeZu6zzrrLPaTDvttNOSK6644n2fw57btGlTUlhYmPzqV79qnbZ169bkiCOOSG644YadAvUFF1yQfOITn2izjPPPP3+nQJ2fn5+8/PLLrdMeeOCBJC8vL9mwYUOSJEkyaNCg5K677mqznGuvvTYZPXr0+9Z6ww03JCNHjmyznpNPPjl59NFHk4MOOij54Q9/uNevn863u8+UXWn/PpObdrfv3ptAveNn0pw5c5KISNasWdM67Stf+Upy9tlnd+pryWZO+c6wNWvWxIUXXhjHHnts9OvXr/V03vXr18eqVavipJNOil69erW2/+hHP9rm+cuXL4/FixdHnz59Wv8bMmRI67LJDqeeemrr///lL3+J9957L8rLy9u8b7/4xS9a37Ovfe1rcc8998SIESPi8ssvj0cffXSP1nPyySdHSUlJ6+PRo0fHpk2b4uWXX26ddvrpp0ePHj3atHnhhReiqakpysvL45hjjoljjz02Pve5z8WvfvWr7n0KTwY999xz0dDQEB/72Mfet81JJ53U+v8DBw6MiIjXXnut9flnnnlmm/Znnnlm63tNdtvde7t58+a4/PLL48QTT4wDDzww+vTpEytXroz169e/7/IWL14c5eXlceSRR0bfvn3jn//5n+PNN9+MzZs3R0TE008/vdu+1r6mlrpaaqJj1qxZE42NjW3+ZgsLC+OjH/1oPPfcczu1f+6552L06NFtprV/HBFx9NFHx1FHHdWmTXNzc6xatSpef/31ePnll+Oiiy5q81l03XXXtfn+sGDBgjjrrLPi8MMPjz59+sS3v/3tnfra+vXr4+Mf/3hcddVVMXPmzNTbgc7zQZ8pe/I+k5v2xb57x2UcdthhrZcG7DitO38eFGS6gO7u3HPPjUGDBsUdd9wRRxxxRDQ3N8ewYcNi69atkSRJm+ATEW2udY3Yfj3MueeeGz/4wQ92WnbLlzAyr3fv3q3/33JN2f333x9HHnlkm3ZFRUURETFp0qR46aWX4v7774/f//738bGPfSwuueSSmDt3bqr1t+9H76dv377x5JNPxpIlS+Khhx6K73znO3H11VfH448/npHbsXRnxcXFH9imsLCw9f9b3uOW/rUn+w+y1+7e28suuyx+97vfxdy5c6OsrCyKi4tj6tSpbcZL2NFLL70U55xzTnz1q1+Na6+9Ng466KB4+OGH46KLLmq9RnJv+1tLXa6R3Tda/jZ39Te7q/132r/llmXt+N7dcccdMWrUqDbt8vPzIyKirq4uPvOZz8Q111wTZ599dhxwwAFxzz337DQewyGHHBJHHHFE3HPPPXHRRRdFv379UtVH59nd3/ievs/kpvfbd+flbT+uuuP+5P2um2//meTzoC1HqDPozTffjOeeey6uuuqq+NjHPhZDhw5tMwDMkCFD4s9//nM0NDS0TnviiSfaLOOUU06JZ599NgYPHhxlZWVt/tsxxLF/9OzZ8wOP/p144olRVFQU69ev3+k9GzRoUGu7Qw45JL7whS/EL3/5y7jpppvi9ttvb11HROxyPX/605+ivr6+9XFdXV306dOnzRGKurq6Ns+pq6uL448/vvULVEFBQXz84x+PG264If785z/HunXr4g9/+MNebgk66vjjj4/i4uL4r//6r1TPP/HEE+Phhx9uM+3RRx+NE044ofW9JvP2ZJ/RXm1tbXzhC1+IT33qUzF8+PA4/PDDWwcz25Unnngitm3bFvPmzYvTTz89TjjhhHjllVfatDnppJNS9zU6rqysLHr27Nnmb7axsTGeeOKJGDp06E7tTzzxxF3uy9tbv359m/f6sccei7y8vDjhhBPisMMOiyOPPDJefPHFnT6LWs6We+SRR+KYY46Jb33rW3HqqafG8ccfHy+99NJO6ykuLo6ampro1atXnH322fHuu++m3hZ0jt19puzp+0zXcsghh0RExIYNG1qnuSVrOo5QZ1D//v3j4IMPjttvvz0GDhwY69evjyuvvLJ1/oUXXhjf+ta34l//9V/jyiuvjPXr17ceoWz5lfmSSy6JO+64Iy644IK47LLLYsCAAbF69eq455574o477vDFeT8bPHhw/Pd//3esW7cu+vTps8tf6/r27RszZ86Mb37zm9Hc3BxnnXVWbNy4MR599NHo06dPfP7zn4/vfOc7MXLkyPjwhz8cDQ0NUVNT0/ql6tBDD43i4uJ48MEH46ijjopevXrFAQccEBERW7dujYsuuiiuuuqqeOmll+K73/1ufP3rX2/9FTIi4uWXX47p06fHV77ylXjyySfj5ptvbv0VuqamJl588cUYO3Zs9O/fPxYuXBjNzc3xoQ99aD9sPXbUq1evuOKKK+Lyyy+Pnj17xplnnhmvv/56PPvssx94am5ExIwZM+K0006La6+9Ns4///x47LHH4sc//vFOI/iSWXuyz2ivrKwsqqqq4txzz40ePXrEt7/97d0+77jjjott27bFzTffHOeee2488sgjcdttt7VpM2vWrBg+fHhcfPHF8dWvfjV69uwZixcvjvPOOy8GDBjQ4dfJ7vXu3Tu+9rWvxWWXXRYHHXRQHH300XHDDTfEli1b4qKLLoo//elPbdpPmzYtzjjjjLjhhhuioqIiHnrooXjwwQd3Wm6vXr3i85//fMydOzc2btwY06ZNi3/8x3+Mww8/PCK2jwQ+bdq06NevX0yaNCkaGhriiSeeiLfeeiumT58eZWVlsX79+rjnnnvitNNOi/vvvz9+85vfvO9ruP/++2PSpEkxadKkePDBB6NPnz77fmORyu4+U/bmfabrKC4ujtNPPz3+/d//PQYPHhxvvPFGXHXVVZkuKzdl7vJtkiRJFi1alAwdOjQpKipKTjrppGTJkiVJRCS/+c1vkiRJkkceeSQ56aSTkp49eyYjR45M7rrrriQikpUrV7Yu4/nnn08+9alPJQceeGBSXFycDBkyJLn00kvbDDzF/rFq1ark9NNPT4qLi5OISO688842A8m0aG5uTn70ox8lH/rQh5LCwsLkkEMOSc4+++xk6dKlSZJsHxRm6NChSXFxcXLQQQclU6ZMSV588cXW599xxx3JoEGDkry8vGTcuHFJkmwflGzKlCnJd77zneTggw9O+vTpk3zpS19K3nvvvdbnjRs3Lrn44ouTr371q0m/fv2S/v37J1deeWVrX6mtrU3GjRuX9O/fPykuLk5OOumk5Ne//nXnbjTeV1NTU3LdddclxxxzTFJYWJgcffTRyfXXX79HA4kkyfa7CJx44omtz20/WJBByTJvT/YZTz31VBIRydq1a5Mk2T4o3YQJE5Li4uJk0KBByY9//OOdBjdr/97Onz8/GThwYFJcXJycffbZyS9+8Yud1rNkyZLkjDPOSIqKipIDDzwwOfvss1vnt19+kiTJlClTks9//vP7dHt0Z/X19ck3vvGNZMCAAUlRUVFy5plnJn/84x+TJEl2GpQsSZLkZz/7WXLUUUclxcXFybnnnpvMnTt3p0HJTj755OSWW25JjjjiiKRXr15JZWVl8ve//73Nen/1q18lI0aMSHr27Jn0798/GTt2bFJVVdU6/7LLLmv9TDn//POTG2+8cZfrafHuu+8mZ5xxRjJmzJhk06ZN+3Qb0THv95mSJB/8PpObPmjf/Ze//KX1M2jEiBHJQw89tMtByXbc99x555079Y32+4HupkeSuKgul/zqV7+KL37xi/HOO+/s0TVvsKPx48fHiBEj4qabbsp0KQB0oquvvjqqq6udwgnQyZzyneV+8YtfxLHHHhtHHnlk/OlPf4orrrgi/vEf/1GYBgAAyDCBOsu9+uqr8Z3vfCdeffXVGDhwYJx33nnx/e9/P9NlAQAAdHtO+QYAAIAU3DYLAAAAUhCoAQAAIAWBGgAAAFIQqAEAACAFgRoAAABSEKgBAAAgBYEaAAAAUhCoAQAAIIX/F/9oxbBZ4fAWAAAAAElFTkSuQmCC","text/plain":["<Figure size 1200x1000 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["heart_standardized.boxplot()\n","plt.show()"]},{"cell_type":"markdown","id":"fbee7dd9","metadata":{"id":"fbee7dd9"},"source":["While we can't see specific values in a standardized, frame-wide plot like the one above, we *can* see things like the spread, outliers, skew and range of each data point, overall.\n","\n","We can always go back to examine a particular point of interest more closely:"]},{"cell_type":"code","execution_count":null,"id":"20bf48fb","metadata":{"id":"20bf48fb","outputId":"1420bda5-9b4a-4f95-e4a1-ea75cbf730ed"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABHkAAAB4CAYAAABmfy5uAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAbRElEQVR4nO3de3CU5fn/8U+OmxhIKkEkSzhEQJFwUkARQ9HiiC0HbcYaaFUYpzOlFiWKyEnFMzgFBbVgpQ5Th7bYSmQQ2yragGHAMgOkEkQECSIQGrVKAkKIyf39w1/2lw2b3SeQfe7Nw/s1w4xk1903y+Wd9UqyG2eMMQIAAAAAAECbFm87AAAAAAAAAOeOJQ8AAAAAAIAHsOQBAAAAAADwAJY8AAAAAAAAHsCSBwAAAAAAwANY8gAAAAAAAHgASx4AAAAAAAAPSLQdcC7q6+t15MgRtW/fXnFxcbZzAAAAAAAAWoUxRtXV1fL7/YqPd/Y9Om16yXPkyBF17drVdgYAAAAAAEBUfP7558rOznZ03Ta95Gnfvr2k7//A6enplmsAAAAAAABaR1VVlbp27RrYfTjRppc8DT+ilZ6ezpIHAAAAAAB4TktenoYXXgYAAAAAAPAAljwAAAAAAAAewJIHAAAAAADAA1jyAAAAAAAAeABLHgAAAAAAAA9gyQMAAAAAAOABLHkAAAAAAAA8gCUPAAAAAACAB7DkAQAAAAAA8ACWPAAAAAAAAB7AkgcAAAAAAMADWPIAAAAAAAB4AEseAAAAAAAAD2DJAwAAAAAA4AEseQAAAAAAADyAJQ8AAAAAAIAHsOQBAAAAAADwAJY8AAAAAAAAHsCSBwAAAAAAwANY8gAAAAAAAHgASx4AAAAAAAAPYMkDAAAAAADgASx5AAAAAAAAPIAlDwAAAAAAgAew5AEAAAAAAPAAljwAAAAAAAAewJIHAAAAAADAA1jyAAAAAAAAeABLHgAAAAAAAA9ItB0AAEBrK//yhE7UfGc7Iypq6k7pyInP5E/rLl9Ciu0c16X5EpXTMc12BgAAQExiyQMA8JTyL0/o+oUbbGdETXzKYaXlvKAT5feo/lQX2zlWFD9wHYseAACAEFptyXPgwAHl5ORox44dGjRo0FnfTo8ePVRYWKjCwsLWSgMAnEcavoNnccEg9erUznJN6yuv2qM5W6UlBYOUk36Z7RxX7as8rsLXSj37XVpArKirq1NJSYkqKiqUlZWlESNGKCEhIWq3P3z4cG3evPms7i/arQC8y6vnB9/JAyCqTp6u06dfHFfPi9opNbntH5poO3p1aqd+XTJsZ7S6+JTvF1c9O7VT30zv/fmAWHI+fg4rKirS9OnTdeDAgcDHevTooUWLFik/Pz8qt5+YmKjvvvv/y1un9xftVgDe5eXzgxdeBhBVn35xXGNf2KRPvzhuOwUAgBY53z6HFRUV6dZbb1X//v21ZcsWVVdXa8uWLerfv79uvfVWFRUVtertr1y5UnFxccrMzJQkrVy50vH9RbsVgHd5/fxo8ZKnvr5ezzzzjHr16iWfz6du3brpqaeeCly+f/9+XX/99brgggs0cOBAbdmyJejfX716tXJzc+Xz+QKbMgAAAAD21NXVafr06Ro7dqzWrFmjYcOGqV27dho2bJjWrFmjsWPH6oEHHlBdXV2r3P7QoUP10EMPaezYsTp06JDGjRunhx9+WEOHDo14f9FuBeBd58P50eIf15o9e7aWL1+u5557Tnl5eaqoqNDHH38cuHzu3LlauHChevfurblz52rixInat2+fEhMTtW3bNt1222169NFHVVBQoM2bN+vuu+9WZmamJk+eHPG+a2pqVFNTE/h9VVVVS/MBuOxU7fcH5L7K8+OroLCvYdYaZg/ewXkCt51P50lJSYkOHDigv/zlL4qPD/46cHx8vGbPnq3hw4erpKRE11133Tnf/oYNGwK/T0xMPOP2w91ftFsBeNf5cH60aMlTXV2tJUuW6MUXX9SkSZMkST179lReXl7gZ9keeOABjRkzRpL02GOPKTc3V/v27VOfPn307LPPatSoUXr44YclSZdeeqk++ugj/fa3v3W05Jk/f74ee+yxliQDsOzQ1yclSYWvldoNwXnn0NcnNaSH7Qq0Js4T2HI+nCcVFRWSpH79+oW8vOHjDdc719tv+vvmPh7q/qLdCsC7zofzo0VLnt27d6umpkajRo1q9joDBgwI/HNWVpYkqbKyUn369NHu3bt18803B13/2muv1eLFi1VXVxfxlaxnz56t+++/P/D7qqoqde3atSV/BAAuy74wVZJ33+kIsafhHZgaZg/ewXkCt51P50nD8/aysjINGzbsjMvLysqCrneut9/0901vP9z9RbsVgHedD+dHi5Y8qamRP8ElJSUF/jkuLk7S96/jI0nGmMDHGhhjHN+/z+eTz+dzfH0A9qUkfb+89eo7HSF2NcwevIPzBLacD+fJiBEj1KNHDz399NNas2ZN0I8x1NfXa/78+crJydGIESNa5fYb/76oqCjo9iPdX7RbAXjX+XB+tOiFl3v37q3U1FS99957Z3Vnffv21aZNm4I+tnnzZl166aWeeD96AAAAoC1KSEjQokWLtG7dOt1yyy1B7zhzyy23aN26dVq4cOFZP2dvevtbt27Vk08+qXXr1ik7O1tvvvmmnnjiCW3dujXi/UW7FYB3nQ/nR4u+kyclJUUzZ87Ugw8+qOTkZF177bX64osvtGvXrrA/wtVg+vTpGjp0qJ544gkVFBRoy5YtevHFF7V06dKz/gMAiG09L2qndffkqedF/GgFAKBtOd8+h+Xn5+v111/X9OnTNXz48MDHc3Jy9Prrrys/Pz8qt//VV19Jkm6//XbH9xftVgDe5fXzo8XvrvXwww8rMTFRjzzyiI4cOaKsrCxNmTLF0b975ZVX6q9//aseeeQRPfHEE8rKytLjjz/u6EWXAbRNqckJ/FgFXHXy/70LTtnhY5ZLoqO86vt3+/m08rjqT3nzz9gc3lULbjsfP4fl5+fr5ptvVklJiSoqKpSVlaURI0a02le1Q93+8OHDtXnz5hbfX7RbAXiXl8+PONOSF8WJMVVVVcrIyNCxY8eUnp5uOwcAEANWbT2oWUU7bWdETXzKYaXlvKAT5feo/lQX2zlWFD9wnXI6ptnOAAAAiKqz2Xm0+Dt5AACIZTfmdpYk9ezUTqkefLHUmrpTOnJikPzXd5cvIcV2juvSfIkseAAAAJrBkgcA4Ckd0pI14aputjOiKEODdbHtCAAAAMSgFr27FgAAAAAAAGITSx4AAAAAAAAPYMkDAAAAAADgASx5AAAAAAAAPIAlDwAAAAAAgAew5AEAAAAAAPAAljwAAAAAAAAewJIHAAAAAADAA1jyAAAAAAAAeABLHgAAAAAAAA9gyQMAAAAAAOABLHkAAAAAAAA8gCUPAAAAAACAB7DkAQAAAAAA8ACWPAAAAAAAAB7AkgcAAAAAAMADWPIAAAAAAAB4AEseAAAAAAAAD2DJAwAAAAAA4AEseQAAAAAAADyAJQ8AAAAAAIAHsOQBAAAAAADwAJY8AAAAAAAAHsCSBwAAAAAAwANY8gAAAAAAAHgASx4AAAAAAAAPYMkDAAAAAADgASx5AAAAAAAAPCDRdsC5MMZIkqqqqiyXAAAAAAAAtJ6GXUfD7sOJNr3k+eqrryRJXbt2tVwCAAAAAADQ+qqrq5WRkeHoum16ydOhQwdJ0sGDBx3/gd1QVVWlrl276vPPP1d6errtHEk0OUWTMzQ5Q5MzNDlDkzM0OUOTMzQ5Q5MzNDlDkzM0OReLXU6bjDGqrq6W3+93fNtteskTH//9SwplZGTEzF9WY+np6THXRZMzNDlDkzM0OUOTMzQ5Q5MzNDlDkzM0OUOTMzQ5Q5NzsdjlpKml39DCCy8DAAAAAAB4AEseAAAAAAAAD2jTSx6fz6d58+bJ5/PZTgkSi100OUOTMzQ5Q5MzNDlDkzM0OUOTMzQ5Q5MzNDlDkzM0OReLXdFsijMteS8uAAAAAAAAxKQ2/Z08AAAAAAAA+B5LHgAAAAAAAA9gyQMAAAAAAOABLHkAAAAAAAA8gCUPAAAAAACAB7TpJc/SpUuVk5OjlJQUDR48WCUlJa7d9/z58zV06FC1b99enTp10i233KI9e/YEXccYo0cffVR+v1+pqam67rrrtGvXLtf64uLiVFhYaL3n8OHDuv3225WZmakLLrhAgwYN0rZt26x1fffdd3rooYeUk5Oj1NRUXXLJJXr88cdVX1/vWtP777+vcePGye/3Ky4uTmvWrAm63Mn919TU6J577lHHjh2Vlpam8ePH69ChQ1Fpqq2t1cyZM9W/f3+lpaXJ7/frzjvv1JEjR6w1NfWrX/1KcXFxWrx4sfWm3bt3a/z48crIyFD79u01bNgwHTx40FrT8ePHNXXqVGVnZys1NVWXX365li1bFnSd1m5qrTOyNbsiNdmYcyePU2NuzLnTJjfn3EmT23O+bNkyDRgwQOnp6UpPT9c111yjf/zjH4HLbZzj4ZpsneORHqfG3DrHnTS5fY5HarJxjjd1ts8to9nVtMnWnIdrasqtOXfS5PacR2pye84fffRRxcXFBf3q3Llz4HIb8x2uydZ8R3qcGnNrvp00uT3fkZpcnW/TRq1atcokJSWZ5cuXm48++shMmzbNpKWlmc8++8yV+x89erRZsWKFKSsrM6WlpWbMmDGmW7du5vjx44HrLFiwwLRv396sXr3a7Ny50xQUFJisrCxTVVUV1batW7eaHj16mAEDBphp06ZZ7fnf//5nunfvbiZPnmz+/e9/m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size 1400x100 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig, ax = plt.subplots(figsize=(14, 1))\n","heart_continuous['chol'].plot(kind='box', vert=False, ax=ax)\n","ax.set_xticks(range(0, 600, 20))\n","plt.show()"]},{"cell_type":"code","execution_count":null,"id":"7a437825","metadata":{"id":"7a437825","outputId":"e89267c5-123f-4f47-af2e-f2fe2c37b9ca"},"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>Serum Cholestoral in mg/dL</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>246.693069</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>51.776918</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>126.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>211.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>241.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>275.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>564.000000</td>\n","    </tr>\n","    <tr>\n","      <th>IQR</th>\n","      <td>64.000000</td>\n","    </tr>\n","    <tr>\n","      <th>range</th>\n","      <td>438.000000</td>\n","    </tr>\n","    <tr>\n","      <th>var</th>\n","      <td>2680.849190</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Serum Cholestoral in mg/dL\n","count                  303.000000\n","mean                   246.693069\n","std                     51.776918\n","min                    126.000000\n","25%                    211.000000\n","50%                    241.000000\n","75%                    275.000000\n","max                    564.000000\n","IQR                     64.000000\n","range                  438.000000\n","var                   2680.849190"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["heart_summary['chol'].to_frame(name='Serum Cholestoral in mg/dL')"]},{"cell_type":"markdown","id":"a7831e8b","metadata":{"id":"a7831e8b"},"source":["## Serum Cholesterol mg/dL\n","\n","Here we can see the median of 241, Q1 at the left end of the box (211), and Q3 at the right end (275).  \n","\n","The range is 438, which is quite wide.  The min value of 126 is a good value for a cholesterol reading, while the median value in this data set is not only above the recommended maximum of 200, but falls within the 'High' category:\n","\n","https://www.healthline.com/health/high-cholesterol/levels-by-age#adults\n","\n","There are some extreme outliers as well.  First, a cluster around 410, then an extreme value at 564.  These outliers pull on the value of the mean (246.7), making it higher than the median (241.0).\n","\n","The IQR is 64; for this data, this would be the more reasonable measure of spread.  Can you think of why this would be?\n","\n","HINT: Think about the changes in variance related to the ultimate spread of the data, and how this relates to the standard deviation.\n"]},{"cell_type":"markdown","id":"304ac6c1","metadata":{"id":"304ac6c1"},"source":["## 'Robustifying' the Mean\n","\n","One way statisticians sometimes improve the robustness of the mean is to trim it.  Calculating a 'trimmed mean' involves removing some percentage of the outliers, and then recalculating.\n","\n","First, we have to sort the data, to facilitate trimming.\n","\n","Then, determine the % of values to remove from the end(s) of the data set.  This has to be considered carefully, because by trimming, data is being excluded from the analysis.\n","\n","Next, calculate the number of observations to trim using the % cutoff, rounding to the nearest integer.\n","\n","Discard the designated # of observations from either end of the sorted data set, then calculate the trimmed mean."]},{"cell_type":"code","execution_count":null,"id":"39de74de","metadata":{"id":"39de74de","outputId":"1116bb7b-3bc4-49ab-e3b7-757543873539"},"outputs":[{"data":{"text/plain":["6"]},"execution_count":19,"metadata":{},"output_type":"execute_result"}],"source":["sorted_chol = heart_continuous['chol'].sort_values()\n","# A common trim is 10% of values, but because this is a small dataset, I'm going with 2%\n","to_trim = round(.02 * len(sorted_chol))\n","to_trim"]},{"cell_type":"markdown","id":"6360eb4f","metadata":{"id":"6360eb4f"},"source":["Removing 2% of values has us removing 6 values.  These could be removed from either end, or just one end.  In this case, I'm going to remove the six highest values because they are more anomalous.  "]},{"cell_type":"code","execution_count":null,"id":"fbcd9208","metadata":{"id":"fbcd9208","outputId":"bf308886-e8e4-4535-e369-4e0f532f34b7"},"outputs":[{"data":{"text/plain":["243.0875420875421"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["sorted_chol = sorted_chol.iloc[:-6]\n","sorted_chol.mean()"]},{"cell_type":"markdown","id":"c3d31029","metadata":{"id":"c3d31029"},"source":["#### 'Robustification' of the mean has had the predicted impact:\n","\n","By removing outliers, the trimmed mean value, 243.1, lies in between the mean (247) and the median (241).\n","\n","Reference:\n","Frost, J. (2023). \"Trimmed Mean: Definition, Calculating & Benefits\", Statistics By Jim; https://statisticsbyjim.com/basics/trimmed-mean/"]},{"cell_type":"markdown","id":"e859e3d6","metadata":{"id":"e859e3d6"},"source":["# Graphical Descriptions\n","\n","## Histograms\n","\n","To get an idea / overview of the distribution of our continuous data points, we look at the histograms (below).\n","\n","The histograms show that the first four data points: age, resting blood pressure ('trestbps'), cholesterol, and max heart rate ('thalach'), have a nearly normal / Gaussian distribution.  You'll learn more about distributions next week, but this is the one that looks like a 'bell curve'.  \n","\n","### Skewness\n","\n","Skewness is the statistical measure that captures the degree of asymmetry of a distribution around its mean.\n","\n","Positive skewness indicates a distribution with a longer tail on the right side, while negative skewness indicates a longer tail on the left side.\n","\n","It quantifies the extent to which a distribution deviates from symmetry.\n","\n","Looking at the first four histograms below, are the data positively skewed or negatively skewed?\n","\n","The last two plots show data that has no 'bell curve'.  We'll learn more about how this data is distributed next week; for now, we can just see that these two, while similar to each other, are not like the others:\n","\n","ST depression induced by exercise relative to rest ('oldpeak') and number of major vessels (0-3) colored by flourosopy ('ca') - both clustered around 'zero'."]},{"cell_type":"code","execution_count":null,"id":"c56e7cea","metadata":{"id":"c56e7cea","outputId":"7f37042c-05c6-428d-a327-7c53e4c08f85"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x800 with 9 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# Plotting histograms for each numerical column\n","heart_continuous.hist(bins=10, figsize=(10, 8))\n","plt.show()"]},{"cell_type":"markdown","id":"12311bfb","metadata":{"id":"12311bfb"},"source":["Descriptive Summary - Normally-distributed Data, Resting Blood Pressure mmHg ('trestbps')"]},{"cell_type":"code","execution_count":null,"id":"f9459cfb","metadata":{"id":"f9459cfb","outputId":"9e501a5b-2a4e-47a1-e084-1b685839aec8"},"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>Resting Blood Pressure mmHg</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>131.689769</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>17.599748</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>94.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>120.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>130.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>140.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>200.000000</td>\n","    </tr>\n","    <tr>\n","      <th>IQR</th>\n","      <td>20.000000</td>\n","    </tr>\n","    <tr>\n","      <th>range</th>\n","      <td>106.000000</td>\n","    </tr>\n","    <tr>\n","      <th>var</th>\n","      <td>309.751120</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Resting Blood Pressure mmHg\n","count                   303.000000\n","mean                    131.689769\n","std                      17.599748\n","min                      94.000000\n","25%                     120.000000\n","50%                     130.000000\n","75%                     140.000000\n","max                     200.000000\n","IQR                      20.000000\n","range                   106.000000\n","var                     309.751120"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["heart_summary['trestbps'].to_frame(name='Resting Blood Pressure mmHg')"]},{"cell_type":"markdown","id":"e57f4450","metadata":{"id":"e57f4450"},"source":["Descriptive Summary - Non-normally-distributed Data, Exercise-induced ST Depression (mm)"]},{"cell_type":"code","execution_count":null,"id":"bbf015e1","metadata":{"id":"bbf015e1","outputId":"40651db3-32b4-44c6-9f8e-70175723425e"},"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>Exercise-induced ST Depression (mm)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>303.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>1.039604</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.161075</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.800000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.600000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>6.200000</td>\n","    </tr>\n","    <tr>\n","      <th>IQR</th>\n","      <td>1.600000</td>\n","    </tr>\n","    <tr>\n","      <th>range</th>\n","      <td>6.200000</td>\n","    </tr>\n","    <tr>\n","      <th>var</th>\n","      <td>1.348095</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       Exercise-induced ST Depression (mm)\n","count                           303.000000\n","mean                              1.039604\n","std                               1.161075\n","min                               0.000000\n","25%                               0.000000\n","50%                               0.800000\n","75%                               1.600000\n","max                               6.200000\n","IQR                               1.600000\n","range                             6.200000\n","var                               1.348095"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["heart_summary['oldpeak'].to_frame(name='Exercise-induced ST Depression (mm)')"]},{"cell_type":"markdown","id":"7032c8f1","metadata":{"id":"7032c8f1"},"source":["## Pie Charts\n","\n","Pie charts represent proportions well. This is good for looking at relative prevalence, such as males vs. females, or the proportion of types of chest pain among the participants studied.\n","\n","sex: sex (1 = male; 0 = female)\n","\n","cp: chest pain type: 1 typical angina, 2 atypical angina, 3 non-anginal pain, 4 asymptomatic"]},{"cell_type":"code","execution_count":null,"id":"c0fb9135","metadata":{"id":"c0fb9135","outputId":"18eddb2d-4c28-46c9-b79b-c82a417a050a"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAy4AAAGICAYAAACnabRxAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB1zklEQVR4nO3dd3hTVQMG8PdmNG2T7r1bVssqG2TIFkRliIqKCAo4Eff2w4V+oH4OHKDiwL1FFBUFBATZe+8u6N4rO/f7o1ApFOhIejLe3/P00abpzZsCTd57zj1HkmVZBhERERERkRNTiA5ARERERER0MSwuRERERETk9FhciIiIiIjI6bG4EBERERGR02NxISIiIiIip8fiQkRERERETo/FhYiIiIiInB6LCxEREREROT0WFyIiIiIicnosLuTxNm3ahKuvvhrx8fHQaDSIiIhA37598dBDD4mORkTksSRJatDH6tWrRUdtkv/85z+Ij4+HSqVCYGDgRe+/du1aTJgwATExMfDy8kJAQAD69euHBQsWoKqqqvZ+kiThnnvucWDyc61fvx7PPvssSktLG3T/Z599ts6foZeXF5KSknDfffc1+BhnSkxMxC233NLo7yPXoxIdgEikX3/9FWPGjMHgwYPx8ssvIyoqCjk5Odi6dSu+/vprvPrqq6IjEhF5pA0bNtT5fPbs2Vi1ahX++uuvOrd36NChJWPZxZIlS/Diiy/iqaeewqhRo6DRaC54/2eeeQbPP/88+vXrh9mzZ6N169aorq6uLQyHDx/G66+/3kLpz7V+/Xo899xzuOWWWxpUwk5btmwZAgICUFFRgd9++w3z5s3D5s2bsX79ekiS1ODjLF68GP7+/k1ITq6GxYU82ssvv4ykpCT88ccfUKn+/edwww034OWXXxaYjIjIs11yySV1Pg8LC4NCoTjndle0d+9eAMC9996L8PDwC973u+++w/PPP49p06Zh4cKFdd7Qjxo1Co8++ug5Jc9V9OjRA6GhoQCAyy67DEVFRfjss8+wfv169O/fv8HH6datm6MikpPhVDHyaEVFRQgNDa1TWk5TKOr+8/jmm2/Qt29faLVa6HQ6jBw5Ejt27Kj9+rp166BWq/Hwww/X+b5FixZBkiR8+OGHjnkSREQeaNq0aQgODkZ1dfU5Xxs6dCg6duxY+/np6VPvvfce2rVrB41Ggw4dOuDrr78+53tzc3Nxxx13IDY2tnYK03PPPQeLxXLRTDabDS+//DJSUlKg0WgQHh6OyZMn48SJE7X3SUxMxH/+8x8AQEREBCRJwrPPPnveYz7//PMICgrCm2++We8ohJ+fH0aMGHHO7Z999hnat28PX19fdOnSBUuXLj3nPkeOHMHEiRMRHh4OjUaD9u3b45133jnnOb3wwgtITk6Gj48PAgMDkZqainnz5gGomfb1yCOPAACSkpKaNYXvdCnNyMiAwWDAQw89hK5duyIgIADBwcHo27cvlixZcs73nT1VbPXq1ZAkCV999RWeeuopREdHw9/fH8OHD8ehQ4canYuciEzkwaZPny4DkGfOnClv3LhRNplM9d7vxRdflCVJkqdOnSovXbpU/vHHH+W+ffvKWq1W3rdvX+395s6dKwOQlyxZIsuyLO/du1f29fWVJ02a1CLPh4jIXU2ZMkXWarW1n+/atUsGIC9cuLDO/fbt2ycDkN95553a2wDIcXFxcocOHeSvvvpK/vnnn+XLL79cBiB/9913tffLycmR4+Li5ISEBPm9996TV6xYIc+ePVvWaDTyLbfcctGMt99+uwxAvueee+Rly5bJ7777rhwWFibHxcXJBQUFsizL8vbt2+Vp06bJAORly5bJGzZskLOysuo9XnZ2tgxAvv766xv8cwIgJyYmyr1795a//fZb+bfffpMHDx4sq1Qq+dixY3V+TgEBAXLnzp3lTz/9VP7zzz/lhx56SFYoFPKzzz5be785c+bISqVSfuaZZ+SVK1fKy5Ytk994443a+2RlZckzZ86UAcg//vijvGHDBnnDhg1yWVnZeTM+88wzMoDan8lpDzzwgAxA/vPPP+XS0lL5lltukT/77DP5r7/+kpctWyY//PDDskKhkD/55JM635eQkCBPmTKl9vNVq1bV/hxuuukm+ddff5W/+uorOT4+Xm7btq1ssVga/PMk58LiQh6tsLBQHjBggAxABiCr1Wq5X79+8pw5c+SKigpZlmU5MzNTVqlU8syZM+t8b0VFhRwZGSlPmDCh9jabzSZfccUVcmBgoLx37165Q4cOckpKilxZWdmiz4uIyN2cXVxkWZYHDRokd+3atc5td911l+zv71/7O1yWa97M+/j4yLm5ubW3WSwWOSUlRW7Tpk3tbXfccYes0+nkjIyMOsf83//+JwOoc6LqbAcOHJAByHfffXed2zdt2iQDkJ988sna2873xv1sGzdulAHIjz/++AXvdyYAckREhFxeXl57W25urqxQKOQ5c+bU3jZy5Eg5Njb2nIJxzz33yN7e3nJxcbEsy7J81VVXnfMzPtsrr7wiA5DT0tIalPH088/NzZXNZrNcUlIif/7557KPj48cFxcn6/X6c77HYrHIZrNZnjZtmtytW7c6Xztfcbniiivq3O/bb7+VAcgbNmxoUE5yPpwqRh4tJCQEa9euxZYtWzB37lyMHTsWhw8fxhNPPIHOnTujsLAQf/zxBywWCyZPngyLxVL74e3tjUGDBtUZDpckCZ9++in8/PzQs2dPpKWl4dtvv4VWqxX3JImI3NR9992HnTt34p9//gEAlJeX47PPPsOUKVOg0+nq3HfYsGGIiIio/VypVOL666/H0aNHa6dyLV26FEOGDEF0dHSd3/ejRo0CAKxZs+a8WVatWgUA56xu1bt3b7Rv3x4rV65s9vNtqCFDhsDPz6/284iICISHhyMjIwMAYDAYsHLlSlx99dXw9fWt81yvuOIKGAwGbNy4sTb/rl27cPfdd+OPP/5AeXm53XJGRkZCrVYjKCgIkyZNQvfu3bFs2TJ4e3sDqLm+p3///tDpdFCpVFCr1fjwww9x4MCBBh1/zJgxdT5PTU0FgNqfA7keXpxPBKBnz57o2bMnAMBsNuOxxx7D66+/jpdffhkBAQEAgF69etX7vWdfCxMSEoIxY8bgnXfewdVXX43OnTs7NjwRkYcaO3YsEhMT8c4776B///5YtGgRqqqqMGPGjHPuGxkZed7bioqKEBsbi7y8PPzyyy9Qq9X1Pl5hYeF5sxQVFQEAoqKizvladHR0k94sx8fHAwDS0tIa9X0hISHn3KbRaKDX6wHUZLVYLHjrrbfw1ltv1XuM08/1iSeegFarxeeff453330XSqUSAwcOxEsvvVT7utlUK1asQEBAANRqNWJjY+vk/vHHHzFhwgRcd911eOSRRxAZGQmVSoUFCxbgo48+atDxz/45nF697fTPgVwPiwvRWdRqNZ555hm8/vrr2Lt3L8aOHQsA+P7775GQkHDR71++fDkWLFiA3r17Y/Hixfjhhx9wzTXXODo2EZHHUSgUmDFjBp588km8+uqrmD9/PoYNG4bk5ORz7pubm3ve206/wQ0NDUVqaipefPHFeh8vOjr6vFlOHyMnJwexsbF1vpadnV27elZjREVFoXPnzvjzzz9RXV0NX1/fRh+jPkFBQVAqlbj55pvrLXlAzYX2AKBSqfDggw/iwQcfRGlpKVasWIEnn3wSI0eORFZWVrMydenS5bw/l88//xxJSUn45ptv6ixKYDQam/x45PpYXMij5eTk1Ht27PQwdHR0NEaOHAmVSoVjx45dtIDk5ORg0qRJGDRoEJYvX47x48dj2rRp6N69e+2LABER2c/06dPx7LPP4qabbsKhQ4fw0ksv1Xu/lStXIi8vr3a6mNVqxTfffIPWrVvXFo2rrroKv/32G1q3bo2goKBG5Rg6dCiAmjfcZ47Qb9myBQcOHMBTTz3VlKeHWbNmYcKECbj33nvPWQ4ZACorK7F+/fp6VxY7H19fXwwZMgQ7duxAamoqvLy8GvR9gYGBuPbaa3Hy5Encf//9SE9PR4cOHRwyknF6Y8ozn29ubm69q4qR52BxIY82cuRIxMbGYvTo0UhJSYHNZsPOnTvx6quvQqfT4b777kNiYiKef/55PPXUUzh+/Dguv/xyBAUFIS8vD5s3b4ZWq8Vzzz0Hq9WKG2+8EZIk4csvv4RSqcSiRYvQtWtXXH/99Vi3bl2DXxyIiKhhAgMDMXnyZCxYsAAJCQkYPXp0vfcLDQ3F0KFDMWvWLGi1WsyfPx8HDx6ssyTy888/j+XLl6Nfv3649957kZycDIPBgPT0dPz222949913zxlNOS05ORm333473nrrLSgUCowaNQrp6emYNWsW4uLi8MADDzTp+V133XWYNWsWZs+ejYMHD2LatGm1G1Bu2rQJ7733Hq6//vpGFRcAmDdvHgYMGIBLL70Ud911FxITE1FRUYGjR4/il19+qd3oc/To0ejUqRN69uyJsLAwZGRk4I033kBCQgLatm0LALVToufNm4cpU6ZArVYjOTm5znU2jXXVVVfhxx9/xN13341rr70WWVlZmD17NqKionDkyJEmH5dcnOjVAYhE+uabb+SJEyfKbdu2lXU6naxWq+X4+Hj55ptvlvfv31/nvj/99JM8ZMgQ2d/fX9ZoNHJCQoJ87bXXyitWrJBlWZafeuopWaFQyCtXrqzzfevXr5dVKpV83333tdTTIiJyO/WtKnba6tWrZQDy3Llz6/06AHnGjBny/Pnz5datW8tqtVpOSUmRv/jii3PuW1BQIN97771yUlKSrFar5eDgYLlHjx7yU089ddEVIq1Wq/zSSy/J7dq1k9VqtRwaGipPmjTpnOWOG7qq2JnWrFkjX3vttXJUVJSsVqtlf39/uW/fvvIrr7xSZwWx08/1bGevvCXLspyWliZPnTpVjomJkdVqtRwWFib369dPfuGFF2rv8+qrr8r9+vWTQ0NDZS8vLzk+Pl6eNm2anJ6eXudYTzzxhBwdHS0rFAoZgLxq1arzPpeGPv+5c+fKiYmJskajkdu3by8vXLiw9nsv9NxOryp25lLXp58vAPnjjz++4OOS85JkWZaFtSYiIiKiZnrooYewYMECZGVl1XthuiRJmDFjBt5++20B6YjIXjhVjIiIiFzSxo0bcfjwYcyfPx933HFHvaWFiNwHiwsRERG5pL59+8LX1xdXXXUVXnjhBdFxiMjBOFWMiIiIiIicnuLidyEiIiIiIhKLxYWIiIiIiJweiwsRERERETk9FhciIiIiInJ6LC5EREREROT0WFyIiIiIiMjpsbgQEREREZHTY3EhIiIiIiKnx+JCREREREROj8WFiIiIiIicHosLERERERE5PRYXIiIiIiJyeiwuRERERETk9FhciIiIiIjI6bG4EBERERGR02NxISIiIiIip8fiQkRERERETo/FhYiIiIiInB6LCxEREREROT0WFyIiIiIicnosLkRERESEOXPmQJIk3H///aKjENWLxYWIiIjIw23ZsgXvv/8+UlNTRUchOi8WFyIiIiIPVllZiZtuugkLFy5EUFCQ6DhE58XiQkREROTBZsyYgSuvvBLDhw8XHYXoglSiAxARERGRGF9//TW2b9+OLVu2iI5CdFEsLkREREQeKCsrC/fddx/+/PNPeHt7i45DdFGSLMuy6BBERERE1LJ++uknXH311VAqlbW3Wa1WSJIEhUIBo9FY52tEorG4EBEREXmgiooKZGRk1Lnt1ltvRUpKCh577DF06tRJUDKi+nGqGBEREZEH8vPzO6ecaLVahISEsLSQU+KqYkRERERE5PQ4VYyIiIiIiJweR1yIiIiIiMjpsbgQEREREZHTY3EhIiIiIiKnx+JCREREREROj8WFiIiIiIicHosLERERERE5PRYXIiIiIiJyeirRAYhchdlqg8Uqw2yzQQLgrVZCrWT3JyIi52K1ybDaZHip+BpF7oXFhTyKLMsorjIhv8JY81FuqPPfwkojKgwW6M1WVJusMJisMFisMFvr36dVpZDgrVbCW62ARlXz35rPlfD1UiLMT4NIf29E1H5oEBngjTCdBiqWHiIiOkVvsiK7TI/cMgNyygzIKdUjp9yA3DID8isMMJptsNhkWGw2WK0yLKfKicUmw2K11X5ulWWc3lpcpZDg562Cn7ca/j4q+Hur4e+tRqCvGqE6DcL8zvjQaRDur4GvF98akvOSZFmu/x0ZkQsrrTbhcF4lDudV4Gh+zX/TC6tQUGk8bwlpSQoJCNZqEBmgQVKoDskROrSL8ENypB/ignyhUEiiIxIRkR1ZbTKO5Fdg38lynCjRI7dcj5yymmKSXapHucEiOiIAIFTnVft6lHzqv+0i/KDVsNCQeCwu5NIsVhv2ZZdjz8kyHMmrwJH8ShzOq0RhpVF0tCbzUSvRJvx0kan5b+eYAIToNKKjERFRA8iyjLTCKuw5WYZdWWXYfaIU+3PKUW2yio7WJJIExAb51CkyKZH+aBWm5ZRpalEsLuRSqowWbM8swZb0EmxNL8aOzFLoza75QtBYrcO06J0Ugt5JQeidFIKYQB/RkYiICMCJkmrsOVGGXSdqSsrek2VOM4LiSGqlhFahOnRPCMSANmEY0CYUAb5q0bHIjbG4kFMrqTJh4/EibE4vxtb0EhzIKYfFxr+yABAT6IPeScHolRiM3knBaBOuEx2JiMgjFFQY8dfBPKw8kI/tmSUorDSJjuQUFBLQOTYQl7YJxaVtQ9E9IYgjMmRXLC5u5O+//8Yrr7yCbdu2IScnB4sXL8a4ceNEx2q0tMIqLN+fi+X787A9sxRWFpUGiQrwxvD2ERjRMQKXtArhiwURkR0dyq3AigN5WL4/D7tOlILvni5O66XEJa1CMKBtKC5tG8YTbNRsLC5u5Pfff8c///yD7t2745prrnGZ4iLLMrZnlta+IBzNrxQdyeX5e6swJCUcl3WIwODkcOh4USURUaNYrDZsTivG8gM1IyuZxdWiI7m86ABvDGgbioHtwjC8fQS81UrRkcjFsLi4KUmSnLq42Gwy1h0txG97crDiQL5LX0zv7LxUCvRrHYIRHSJxeadIBGu9REciInJK5QYzVh8qwIr9eVh9KN8jrlMRxU+jwhWdo3Btz1j0SgwWHYdcBIuLm3LW4pJZVI3vtmXhh20nkF1mEB3H46iVEoa3j8CEXnEY1DaMyy4TEQFYf7QQX2zKxJ/7c51iyXxPkxjii/HdY3FNj1guPEMXxOLippypuOhNVvy2Jwffbs3C5vRizgt2ElEB3ri2Rywm9IxDXLCv6DhERC2qrNqM77efwBebMnC8oEp0HELNsst9W4Xg2h6xGNUpCj5enEpGdbG4uClnKC7bM0vw7ZYs/Lo7BxVGDrc7q9MvFNf3isPIjpGcc0xEbm1HZgm+2JSJpbuzYTDbRMeh89BpVLiicySu7RGH3kmcSkY1WFzclKjiYrXJ+H1vDt7/+zh2nyhr0cem5gvwUWNin3jc2j8R4X7eouMQEdlFtcmCJTuz8cWmDOw9WS46DjVSQogvbu2XiBt6x/PkmodjcXFTLV1cDGYrvtuahQ/WpSGjiCuvuDovlQLXdI/FHQNbITFUKzoOEVGTHM6rwOcbM7B4+0mO/LuBMD8Npg9IwqRLEqDlapkeicXFjVRWVuLo0aMAgG7duuG1117DkCFDEBwcjPj4eIc8ZkmVCZ9sSMdnGzJQVMUNuNyNQgJGdYrCnYNao3NsgOg4REQNciCnHP/74xBWHswXHYUcIMhXjVv7J+GW/onw91aLjkMtiMXFjaxevRpDhgw55/YpU6Zg0aJFdn2sEyXVWPj3cXy79QT0Zqtdj03OqX+bENw1qA0GtA0VHYWIqF5phVV49c9D+HVPDheC8QB+3ipM7puAaQNacal/D8HiQo1SXGXCmyuP4MtNmTBZeVGjJ+oSG4BHL09B/zYsMETkHLJL9Zi34gh+2H4CFhvf1ngaXy8lJvaOx+0DWyHcn9dnujMWF2oQvcmKj/5Jw7urj3GeMAEALm0biscuT0GnGE4hIyIxCiuNePuvo/hycyZMFp5M83ReKgUm9IzFXYPbcD8YN8XiQhdktcn4bmsWXl9xGHnl3N2e6pIk4KrUaDw6Mpl7wRBRiynTm/H+38fw8T/pqDZxujLV5aVS4PZLW+GeoW24CpmbYXGh81q+Pw8vLTuIo/mVoqOQk9OoFLi1fxJmDGkNP14oSUQOUm2y4ON/0vHemmMoN3D0ny4sNsgHz4zuiMs6RIiOQnbC4kLnOJBTjmeW7MPm9GLRUcjFhOq88NCIZNzQKw6SJImOQ0RuZNneXDzz816O/lOjDUsJx7NjOnJmgBtgcaFa1SYL3lhxBB+tS+PFjdQsPROCMPeazmgT7ic6ChG5uIIKI55eshe/780VHYVcmLdagbsGtcGdg1tBo+L0MVfF4kIAgBX78/DMz/twslQvOgq5CS+lAncPaY27B7eBl0ohOg4RuaDvtmbhxd8OoLTaLDoKuYnEEF88O6YjBieHi45CTcDi4uEKK414Zsk+/LonR3QUclPtInSYe00quscHiY5CRC4iq7gaTy7eg7VHCkVHITd1ecdIPD26A6K5+phLYXHxYD9sO4HZv+7nmSxyOIUETO6biEdGJkOrUYmOQ0ROymaTsWh9Ov735yGuFkYO5+ulxMyhbXH7wFZQKnhdpitgcfFABRVGPPr9Lqw6VCA6CnmYmEAfvHB1JwzhED0RneVofgUe/X43tmeWio5CHqZXYhDevLEbogI4+uLsWFw8zN+HC/Dgt7tQWMlVWUicW/sn4olR7XntCxHBbLVhwepjeHvVUW4iScIE+qrxv2u7YDiXTnZqLC4ewmy14X9/HML7a4+Df+LkDDrHBODtid2QEKIVHYWIBDleUIl7vtyB/TnloqMQAeCJNWfH4uIBsoqrMfOrHdiZVSo6ClEdfhoVXhzfGWO6RIuOQkQt7NfdOXjsh92oNHIjSXIuPLHmvFhc3NzS3dl44sc9qOAOw+TEbugVh2fHdIS3mmvrE7k7s9WGF389gEXr00VHITovnlhzTiwubspgtuLZn/fh6y1ZoqMQNUi7CB3entgd7SK4aSWRu8ou1WPGl9uxgxfgk4vgiTXnwuLihnLK9Jj+yVbsy+acYXItPmol5ozvjHHdYkRHISI7W3+sEPd8uQPFVSbRUYgaJTnCD29P7Ia2PLEmHIuLm9mVVYrbPt2K/AquGkau696hbfDAZe0gSVxXn8gdLPonDS/8egAWG99ykGvyUSvx4tWdML57rOgoHo3FxY38sisbj3y/CwYzl5Mk13dVahT+d10XDs8TuTCTxYanl+zltGVyGw+PaId7hrYVHcNjsbi4iTdWHMa8lUe41DG5lW7xgVg4uSdCdRrRUYiokQoqjLjr823YmlEiOgqRXd18SQKeG9MRCgVnBbQ0FhcXZzBb8ej3u/HzrmzRUYgcIibQBx/d0gvJkZxbTOQqDuVW4NaPNyO7zCA6CpFDXN4xEm/c0JWzAloYi4sLK6gw4vbPtnJ1FnJ7fhoV3prYDYOTw0VHIaKL2H2iFJM/2ozSarPoKEQO1SsxCB9M7oUAX7XoKB6DxcVFZRVXY+IHG5FVrBcdhahFKBUSZo/thIl94kVHIaLz2JpejFs/3oIKbipJHqJtuA6fTO2N6EAf0VE8AouLCzpeUImbPtiEHA7BkweadVUHTBuQJDoGEZ1l3ZFC3PbpVujNVtFRiFpUVIA3Ft3am1OaWwCLi4s5lFuBmz7YhMJKLndMnuuxy1Nw1+DWomMQ0SkrD+Thri+2w2Thqpbkmfy9VVg4uSf6tAoRHcWtsbi4kL0ny3Dzh5tQwnnDRLh/eFvcP7yd6BhEHm/p7mw88M1OmK18O0GezUulwLzru2JU5yjRUdwWi4uL2J5Zgls+2oxyA+cNE5129+DWePTyFNExiDzWd1uz8PiPe2DlxpJEAACFBMwZ3xnX9+L1mI7A4uICNh4vwrRFW1Bl4rxhorNNG5CEWVd1EB2DyON8tiEdT/+8j/uHEZ1FIQGvTeiKcd1iREdxOywuTu6fo4WY9skWGMycN0x0PpMuicfssZ0gSdwMjKglvLfmGOb8flB0DCKnpVJIeHtid1zeKVJ0FLeiEB2Azm/3iVLc/ulWlhaii/h8YyZe+PWA6BhEHuHNlUdYWoguwmKTce9XO7DmcIHoKG6FxcVJHS+oxK0fc3oYUUN9uC4N7645JjoGkVv7anMmXlt+WHQMIpdgstpwx2dbsfF4kegoboPFxQnllxsw+aPNKKoyiY5C5FJeWnYQP2w7IToGkVtac7gAs37aKzoGkUsxmG2Y/slW7D1ZJjqKW2BxcTJlejMmf7QZJ0r0oqMQuRxZBh77YTdWHcoXHYXIrRzIKceML7bDwtXDiBqt0mjBLR9vQWZRtegoLo/FxYkYzFZM/2QLDuZWiI5C5LIsNhkzvtiOnVmloqMQuYXcMgOmLtqCSiOX4ydqqsJKI6Z8vBlF3EC8WVhcnITVJuOeL7djS3qJ6ChELq/aZMXURVtwvKBSdBQil1ZptODWRVuQU2YQHYXI5aUVVmHqJ1tRbeJJgKZicXES//lpD1Yc4PQWInsprjLh5g83I6+cb7iImsJitWHGF9txIKdcdBQit7Erq7Rm2qWVK8Y2BYuLE/hsYwa+2pwlOgaR2zlZqsftn22D0cLV+Ygaa9aSfVzKlcgBVh0q4BL+TcQNKAXbml6MGxduhNnKPwYiR7mhVxzmXpMqOgaRy1iw+hheWsa9WpxB2YZvUfr3p/DrMQbBw28HAGS8dFW99w0cfCsC+lxT79cq96xA0W9vnHN7/EM/QlJ51dxn3yqUrvkEstkAXeoIBA2ZWns/S1ke8r6Zhagpb0Ch8W3msyIAeHtiN1yVGi06hktRiQ7gyfLKDbjri+0sLUQO9vWWLHSJC8SNveNFRyFyekt3Z+PlP1hanIEx5zAqdv0BdVhindtjZ3xW53P98a0o+v1N+Cb3v+DxJC9fxNz2Xt3bTpUWa3UZipe9hZAr7ocqMBL53z8HTXxn+LbuBQAo+mM+ggbdwtJiR4//sAfto/zROkwnOorL4FQxQUwWG+76fBsKKri6BFFLeObnfVxpjOgidmaV4qFvd4FzMcSzmfQo/OV/CLl8JhTedd/YKnVBdT6qj26Cd0JnqAMjL3xQSTrne0+zlOZC0vhC234gNFHt4B2fCnNhJgCgav9qSEoVfJP72f15erJKowV3f74dem423mAsLoI88/M+bM8sFR2DyGOcPllQyKUoiepVYTBj5lfbYbTwomFnULx8AXxa94JPYtcL3s9aVQL9sS3QpY646DFlkx4nFtyKE+9MQf73z8GUd6z2a6rgGMhmI0x5x2DVV8CUcxheYYmw6itQuvYLBF92Z3OfEtXjUF4Fnvppj+gYLoPFRYCvNmfiq82ZomMQeZycMgPu+XI7rNxEj+gcTy/Zh6xibn7sDKr2r4Ep9xiCBk256H0r966EwssHvu0uPBqiDo5FyJUPIPyaWQgd8wgkpRq5nz8Kc/FJAIDSW4fQKx9A4dLXkPvpg9B2GgqfVj1QsupD+PW4CpayPGR/fC+yP7wbVQfX2eV5Uo0ft5/k+8IG4jUuLWxHZgmeWbJPdAwij7XxeDHm/n4AT13ZQXQUIqexZOdJLN5xUnQMAmApL0DxyoWIuP752utPLqRy9wpoOwy+6H01MSnQxKT8+3lsB+Qsug8V25ciePgdAADfdv3qFCBD5m6YCzIQfNmdyH7/doSOfgRKbRByPn0Q3nGdoNQGNu1J0jme/XkfOscEoFNMgOgoTo0jLi2oymjBfV/vhIlrdxMJtXBtGpbtzRUdg8gpZBVX4z+L94qOQaeYco/CVl2KnEX3I+PlMch4eQyMWXtRse0XZLw8BrLt3+shDFl7YSk+AV2Xi08TO5skKaCJbAtzcXa9X5ctZhT/uQDBI2fAUpID2WaFd3xnqENioQ6OgTHnUJOfI53LaLHh7i+2o0xvFh3FqXHEpQXNXrofmcXVomMQEYAnF+9Bz8QghOo0oqMQCWO1ybj/m52oMHInb2fhndAFUVPfrnNb0W/zoA6JhX+fayAplLW3V+5eDq/INvAKb9Xox5FlGab8NKjDEur9eun6r+Hdqgc0kW1qroU5ozDJNgtg40lYe8ssrsYj3+3C+5N7io7itDji0kJW7M/D11u4ySSRsyiuMuHxH3hBJHm2N1cewbaMEtEx6AwKjS+8whLrfEhqDRTefvA6Y1lkm7Ea1YfWnfei/MKlr6JkzaLaz0vXfQn98W0wl+bClHccRb/Pgyn/OPy6jjrne00FGag++DcCB0wCAKiCYwFJgYpdf6L62BaYi07AK6qtXZ831fhzfx7e//vYxe/ooTji0gKKKo14/MfdomMQ0VlWHMjDd1uzcF3PONFRiFrc1vRivL3qqOgY1ERVB/4GZEDbYVC9X7eUFwDSv+enbcYqFP3xNqxVJVBotPAKb4XIiXOhiU6u832yLKP4j7cRNPQ2KLy8AQAKtQYhV9yP4uULIFvNCL7sTqj8Qh335Dzcy8sOoUdCMHokBF38zh5GkmWu1u5ot3+6FX/uzxMdg4jq4adRYdkDAxET6CM6ClGLKTeYMeqNtThZylXEiJxRm3Adfrv3UnipODnqTPxpONi3W7NYWoicWIXRgoe/3QWewyFP8p/Fe1laiJzY0fxKvLeGU8bOxuLiQFnF1Xj+l/2iYxDRRWw4XoSP/0kXHYOoRfyw7QR+3lX/SlJE5DzeXnUU6YVVomM4FU4Vc6Ab3t+AjceLRcewK0tFIUpXL4L++DbIFhNUwdEIGXUfNJFtAAA2kx6laxah+vBG2AwVUPqHw7/nGPh1u+KCx6069A/K1n4Oc2kO1IFRCBx4c5215Cv3rULpmk8gmw3QpY5A0JCp/2Yqy0PeN7MQNeUNKDS+jnni5Pa81Qr8eu+laB2mEx2FyGHyKwwY9r81XEWMyEUMaBOKz6f3ER3DaXDExUF+3H7C7UqL1VCJ3M8fBRQqhF/3LKKnz0fQkGlQaLS19ylZuRD649sROvohRE9fAP9e41C8/F1UH9l43uMaTx5A4ZKXoO04BNG3vgVtxyEoWPISjNk1a8Rbq8tQvOwtBA2ZivAJz6Ny70pUH9tS+/1Ff8xH0KBbWFqoWQxmGx77fjenjJFbe+n3QywtRC5k3dFCLN5xQnQMp8Hi4gAVBjPm/H5QdAy7K9/4PVT+oQi98n5oopOhCoiAT2JXqIOiau9jzD4Ibaeh8I5PhSogAn5dL4dXeBJMOUfOf9ytP8M7sRsC+k6AOiQOAX0nwDuhC8q3LgEAWEpzIWl8oW0/EJqodvCOT4W5MBMAULV/NSSlCr7J/c57fKKG2ppRgu+38QWC3NOOzBL8yDdARC7nhaUHUFptEh3DKbC4OMDry4+goMIoOobd6Y9ugldkWxT8NAdZb92E7I/vRcXOZXXuo4ntAP3RzbBUFEKWZRgydsNckg3vVt3Pe1zjyYPwSepW5zafpO4wnjwAAFAFx0A2G2HKOwarvgKmnMPwCkuEVV+B0rVfIPiyO+3/ZMljzf39IMqquXMxuRdZlvHsL/vBAUUi11NUZcKc39zvhHhTcB8XOzuUW4FPN6SLjuEQ5tJcmHf8Bv9e4xDRdwKMOYdRsvJ9SCo1dJ2GAQCCh9+BomVv4eT8WwCFEpAkhFx+L7xjO573uNaqEii1gXVuU2oDYa2q2RRN6a1D6JUPoHDpa5AtJmg7DYVPqx4o/O0N+PW4CpayPOT/MBuwWRDQfyK0KQMc9SMgD1BUZcIrfx7EC+M6i45CZDffbTuBXVmlomMQURN9uy0L1/SIRe+kYNFRhGJxsbNZS/bCYnPTU1qyDE1kGwQNmgIA8IpoDXNhJip2/FZbXMq3/gJj9iGEXTMLKv9wGLL2onj5Aih1wfBJ7HqBg0tnPZRc5zbfdv3qXKxvyNwNc0EGgi+7E9nv347Q0Y9AqQ1CzqcPwjuu0zlFiKgxvtyUiRt6xaNTTIDoKETNVmEw4+Vlh0THIKJmkGXgycV7PH5vF8995g6wZOdJbE5zrwvyz6TUBUEdGl/nNnVIHKzlBQAAm9mI0r8/RdDQ6fBt0wde4Unw7zEa2pRLUb75x/MfVxtUO7pymq267LzlQ7aYUfznAgSPnAFLSQ5kmxXe8Z2hDomFOjgGxhy+QFPz2GRwKXNyG2+uPILCSvebvkzkabi3C4uL3VQaLXjx1wOiYziUJqYDzMV1L+w0F5+Eyj+85hObFbBZIJ01egJJgQtNrNbEpECfvqPObfq0HdDEtK/3/qXrv4Z3qx41SzDLtprHPUW2WQCbrRHPiqh+m9OL8Qv3uiAXd6ygEovWp4uOQUR28vaqo8go8ty9XVhc7OStlUeQ74YX5J/Jv9dYGLMPoWzDtzCXZKNq/2pU7loGXfcrAQAKjS80cZ1Qsvqjmqlcpbmo3LMCVfv+gm+7vrXHKVz6KkrWLKr93K/HGBjSdqBs4/cwF2WhbOP3MGTshH/PsedkMBVkoPrg3wgcMAkAoAqOBSQFKnb9iepjW2AuOgGvqLaO/UGQx5j7+0EYzNaL35HIST3/y36YrW46fZnIAxktNry+/LDoGMJwA0o7yC7VY/D/VsNkcf8z/dVHN6N0zScwl2RDFRAB/17j4Nf18tqvWytLULLmExjSt8NmqITSPxx+XUbCr9c4SFLNSEzul49DFRCB0CsfqP2+qoPrULr2c1hKc6EKjETQwMnnLHEsyzLyvngU/pdcB982vetkKl6+ALLVjMBLb4Zfl5EO/imQJ3lkZDJmDGkjOgZRo63Yn4fpn24VHYOI7EwhAX8+MAhtwj1vw2QWFzt4/Ifd+HpLlugYROQAgb5qrHtsKHQarmVCrsNksWHE62uQXlQtOgoROcCYLtF488ZuF7+jm+FUsWZKK6zihnVEbqy02oxF/6SJjkHUKJ+sT2dpIXJjS3dn42h+hegYLY7FpZleX37YfZc/JiIAwMK1aagwcFNKcg1GixXvrz0uOgYROZBNBuatPCo6RotjcWmGI3kVWLqbqw4RubsyvRkfrUsXHYOoQX7cfhIFbr5YDBEBv+7OxpE8zxp1YXFphjf/OgoOthB5hg/XHUeZnqMu5NxsNhnv/83RFiJPYJOBN1YeER2jRbG4NNHR/Ar8ytEWIo9RbrDgo3W81oWc27J9uUgr9Nw9Hog8zW97cnAo13NGXVhcmujNlRxtIfI0H/2TxlEXcmrveviu2kSeRpaBeSs9Z18XFpcmyCquxq97ckTHIKIWVmGw4EOOupCT+udoIXafKBMdg4ha2O97c3Ewt1x0jBbB4tIEn23MgJXDLUQe6YuNGTBarKJjEJ2Doy1EnkmWgXkrPONaFxaXRtKbrPiGm00SeayiKhOW7uKIKzmXvSfLsPZIoegYRCTIsn25HrHCGItLIy3ecZJz3Ik83Kcb0kVHIKqDoy1Enk2WgS83Z4qO4XAsLo30yfp00RGISLBdJ8qwM6tUdAwiAEBGURV+35srOgYRCfbTjpNuP5WZxaUR1h8txCEPGIYjoovjSQxyFu//fZzXXRIRSqrN+GNfnugYDsXi0ggf840KEZ3y654cFFZyd3ISq6zajO+3nRAdg4icxNduPl2MxaWBsoqrsfKAe7dYImo4k8Xm9i8Q5Px+25sDo8UmOgYROYkNx4uQWVQtOobDsLg00OebMrjhJBHV8cWmTFisfNNI4izecVJ0BCJyIrIMfLPVfU+qsbg0gM0mY/F2vjgQUV05ZQb8dTBfdAzyUCdL9diSXiw6BhE5me+3nXDb695YXBpgw/Ei5FdwLjsRnevnXdmiI5CHWrLzJGT3fG9CRM2QV25025NqLC4NsGQnR1uIqH4rD+Sj2mQRHYM80JIdLM1EVL9vtrjndDEWl4swWqxYxvXxieg89GYrlu/nwh3UsvZnl3N5fiI6r1WHCpBXbhAdw+5YXC5i1cEClBt4NpWIzu8XThejFsaZAER0IVabjO+2ZomOYXcsLhfx8y6+OBDRhf19uBBl1WbRMchD2Gwyr60ioov6zg33eGJxuYBKowUrD7jnxU1EZD8mqw2/780RHYM8xMa0IuSUud8UECKyr4yiahx2symlLC4XsGxvLjf2IqIG+WU3z4BTy/iJe7cQUQOtcrPVxVhcLoDz1omooTYcK0J+Bc+Ck2MZLVb8zgVjiKiBVh1icfEIepMVG44XiY5BRC7CJoOri5HDrTqYjwouGENEDbQtowSVRvf5ncHich4b04pg4jQxImqEvw8XiI5Abm4N/44RUSOYrTLWHXGf3xssLufBNyBE1FjrjxXBauNW5uQ4G45xJgARNc6qg+7znpbF5Tx4VouIGqvCYMHOrBLRMchN5ZTpkV5ULToGEbkYd3pPy+JSjxMl1TheUCU6BhG5oL8PF4qOQG5q/VGOthBR4+WWG7A/u1x0DLtgcakH33gQUVOtdaO5xORcuGAMETWVu6wuxuJSD17fQkRNtetEGcoNZtExyA3x+hYiaqrVLC7uyWK14Z9jHHEhoqax2mSsP8rfIWRfWcXVOFmqFx2DiFzU9sxSlOld/6Qai8tZdp0o5Rr5RNQsazjdlOxsPU+oEVEzWG2yW0xlZnE5y/aMUtERiMjFbeK1CGRnnCZGRM3lDr9HWFzOsutEqegIROTi0oqqeJ0L2RUvzCei5trnBiuLsbichcWFiJpLloG9J8pExyA3cbygEnnlRtExiMjFHcwtd/lNkllczlBcZUJWMS9+JKLm232SxYXsg6MtRGQPBrMNaYWVomM0C4vLGXZztIWI7GQPR1zITjanFYuOQERuwtWni7G4nGE332gQkZ3sPlkqOgK5CXfZ8ZqIxGNxcSMccSEie8kq1qO02iQ6Brk4k8WGtMIq0TGIyE24+okQFpcz7OKICxHZEUdxqbmOFVTC4uIX0xKR89ifw+LiFnLK9Cio4KotRGQ/e3iBPjXT4bwK0RGIyI0UV5mQU+a6C1GxuJxyJM+1V1kgIufj6kPyJN6hXBYXIrKvfSdd97WJxeWUjOJq0RGIyM2kF/HaBGoeFhcisjdXvkBfJTqAs8jgxY9EZGeZRTwhQs3znvEx6OMl5HvFI02KwR5DBDZVhGBLmR+sMs89ElHj7c9x3WnMLC6npPMNBhHZWYXRgpIqE4K0XqKjkCuymqHK3Qk/2Qo/bEVrAMNPfUn21cAYkIgSn0RkKuJw0BKJbVWh+Kc0CEUmtcjUROTkOOLiBjKLOeJCRPaXUVzN4kJNU5IOyNZ6vyRZjfAuPoQoHEIUgD4ApgCQFRKsYdGo0CYiRx2Po3I0durDsb4sBAcrfVswPBE5qxMlepgsNnipXG/UlsUFgCzLyOQ1LkTkAJnF1egaFyg6BrmiomON/hYJMlQVJxFUcRJB+AcdAIw59TU5wB/Vfkko8E5AhhSDfaZIbK4IxYbSABhtrvcGhoiarqDSiJhAH9ExGo3FBUBuuQEGs010DCJyQ5m8QJ+aqjTTroeTjOXQGndBi11IBDAIwN0AZG81zP7xKPFNxEllLA5bo7GtOgzrSoORY+BoIZE7KqhgcXFZGby+hYgchKO51GSVuS3yMJLNDK/SY4goPYYIAN0B3HDqa9aQcFTqkpDrFY/jcjT2GCKwsSIEO8p1kGWpRfIRkf256t6FLC4AMnhGlIgchCdGqMkqWqa4XIiyKh8BVfkIwCYkAxh16nZZp4XBPwlFPgnIlGJwwByJrVWhWFcSiAoL31oQOTsWFxeWU2YQHYGI3NSJEtfdoZgEc4Licj6SuQo+RXsRi72IBdAPwDQAsloBS3AcyrSJyFbF4YgtCjv14finNBjHq11vWgqRu2JxcWGl1WbREYjITRVWuuaLAzkBJy4u5yPJNqjLMxBanoFQAKkArjn1NVtQMKr8WtXsSYNo7DNFntqTxh9mG6edEbWkgkrXPGnP4gKgtNokOgIRuSmjxQaD2QpvtVJ0FHI1LXSNS0tR6Ivhpy8+d08aHw1M/oko9k1EliK2Zk+a6jCsK+GeNESOwhEXF1bCERcicqByvZnFhRrHYgKqi0WnaBGS1QhNySFEldTsSdMbwGTU7EljC41CuS7p1J40Uditj8D6smDsr9QKTk3k2lhcXFipnsWFiBynVG9GuL+36BjkSirzAMiiUwglQYayMhtBldn17Enjh2q/Vij0TkD6qT1ptlSEYGNZAPRWniQguph8FpcasixDklxrrmoZp4oRkQOV8eQINZaxXHQCpyYZK2r3pElAzZ40ACBrVDD7x6PUNxEnlXE4ZI3G9uow/FMahJMGjcjIRE7FVa+/bFJxufnmm7FgwQLodLo6t6enp+Pmm2/G2rVr7RKupXCqGBE5Uhl/x1BjmbhMf1NINgu8So8jvPQ4wgF0wxl70gSHodKvFfJO70ljjMCGcu5JQ57JYLah3GCGv7drXUfWpOKyf/9+dO7cGZ9//jn69+8PAPjkk09w77334rLLLrNrQEez2WRUGPimgogchyMu1GgsLnanrC5AQHUBArAJ7QBcfup2WedbsyeNdwIyFbE4YInA1sow7klDbq+gwugZxWXTpk34z3/+g6FDh+Khhx7CkSNHsGzZMsybNw9Tp061d0aHKjeYYfPsacRE5GAsLtRoZm5c2lIkczV8ivYhFvvO2ZPGGhyLMm0SslWxOGKLxi59ONaVBuMY96QhN+CK24E0qbioVCrMnTsXGo0Gs2fPhkqlwpo1a9C3b19753O4cr1FdAQicnMsLtRoJhYX0STZBlV5JkLKMxECoDOA8ae+ZgsKRrVfUs2eNFIM9hkjsKkiBJvLArgnDbkMi9UmOkKjNam4mM1mPP7443jnnXfwxBNPYN26dbj66qvx0Ucf4YorrrB3RoeyyhxuISLHqjbxBElLKCkpwYcffogDBw5AkiSkpKRg6tSpCA4OFh2t8UyVohPQBSj0xdDpi6HDNrQCMOzU7f/uSZOAE6f2pNleHY51JUEo4J405GSsLjjlqEnFpWfPnqiursbq1atxySWXQJZlvPzyyxg/fjymTp2K+fPn2zunw9hYXIjIwVzwpJbLWbNmDcaOHQt/f3/07NkTAPDWW29h9uzZ+PnnnzFo0KCLHMHJcKqYSzp7T5peAG4GAAVgDY1GuS4Jueo4HLVFY7chHBvKg7G3QnfhgxI5iMWTisubb74JrbZmAyhJkvDYY49h5MiRmDRpkl0DOhp7CxE5Gk+QON6MGTMwYcIELFiwAEplzT4eVqsVd999N2bMmIG9e/cKTthIZr3oBGRn/+5JA7QHMPrU7bK/Dnr/1ijwTkCGFIP9pghsrgzFhlLuSUOO5TEjLh9++GG9t3ft2hXbtm1rVqCW53p/aETkWlzxxcHVHDt2DD/88ENtaQEApVKJBx98EJ9++qnAZEQXJpkq4Vu4Cwmn9qQZCOBOnNqTxi8OpdpWOKmMxWFrFHbow7C2JJh70pBdeMyICwB89tlnePfdd5GWloYNGzYgISEBb7zxBpKSkjB27Fh7ZnQoF/wzI8FCvMxo68tpHNRwUWoubeto3bt3x4EDB5CcnFzn9gMHDqBr165iQjWHgmfaPZ1ks8CrLA3hZWm1e9Jcf+prtuDQM/akicFuYwS2lIbAolcITEyuRmNxvU0om1RcFixYgKeffhr3338/XnzxRVitVgBAYGAg3njjDZcqLpzBQY31XcIStMr6UXQMciXyVAB9RKdwa/feey/uu+8+HD16FJdccgkAYOPGjXjnnXcwd+5c7N69u/a+qampomI2nMQ3oHR+iupC+FcXwh+b0RbASABvpV6F0P1KdF92HLbCItERyQXEDJwHpMaLjtEoTSoub731FhYuXIhx48Zh7ty5tbf37NkTDz/8sN3CtQSZU8WoETr7VSEpe6noGORq+CbU4W688UYAwKOPPlrv1yRJgizLkCSp9mSbU5M44kKNo5Zs+G/cbvhN0+DhzN7osOww5JJS0bHIiUlK1/s906TikpaWhm7dup1zu0ajQVWVa02J4IgLNcZ/o9ZAyjSJjkGuhm9CHS4tLU10BPviVDFqJJ2tppBXKIx4JnE7Qm73xcPHe6PNsv2QK7i8NtVD4Xon1ZpUXJKSkrBz504kJCTUuf33339Hhw4d7BKspbC4UEPF+xjQKXex6Bjkivgm1OG+/PJLREREYOrUqXVu/+ijj1BQUIDHHntMULImYtmlRtKdNZJYpKjGE222I/IuHR453Bvxf+yBrOdqdfQvyQWLS5MSP/LII5gxYwa++eYbyLKMzZs348UXX8STTz6JRx55xN4ZHcpL5Xp/aCTGS7EbIZlca0SRnISSG8852nvvvYeUlJRzbu/YsSPeffddAYmayQXfUJBYfub6L7TOVVbiofbb8dBMLXKv6gVJwxXJqIak8RYdodGaNOJy6623wmKx4NFHH0V1dTUmTpyI2NhYzJs3DzfccIO9MzqUTtPkhdXIg4R4mdGn8HvRMchVafxFJ3B7ubm5iIqKOuf2sLAw5OTkCEjUTEov0QnIxWgthgt+PVNZins770Db9iF4cE8cQlbsBCyWlglHTknh53qbnzbplI5er8dNN92EjIwM5OfnY+PGjXjggQcQGxtr73wOp9VwOJ4u7r/x26HQF4uOQa7KO0B0ArcXFxeHf/7555zb//nnH0RHRwtI1Ewsu9RIfqYLF5fTjqiKcFe3nXj+/giUDe8BuOAF2mQfSj8/0REarUnDDWPHjsX48eNx5513QqVSYcyYMVCr1SgsLMRrr72Gu+66y945HUbrxREXujCt0obhpd+JjkGuzDtQdAK3N336dNx///0wm80YOnQoAGDlypV49NFH8dBDDwlO1wQ+gaITkIvRGquARgzU7VXn4bZeeeiVGoO7twZDu3YXL/z1MApPKS7bt2/H66+/DgD4/vvvERERgR07duCHH37A008/7VLFRaGQoPVSosrkAstjkhDPJO6B8mS26Bjkyjji4nCPPvooiouLcffdd8Nkqln5z9vbG4899hieeOIJwemawCdIdAJyMTpDJeDX+GsWtmiycWv/bAzokYjbN/rBe8Pui38TuQWPGXGprq6G36kn++eff2L8+PFQKBS45JJLkJGRYdeALSHAR83iQvVSSjaMq+K1LdRMLC4OJ0kSXnrpJcyaNQsHDhyAj48P2rZtC42rXojMUTpqJD9DOYCmX2y9zjsL6wYDI3q1w5T1aqi37rNbNnI+ko8PJJXrzTpq0jUubdq0wU8//YSsrCz88ccfGDFiBAAgPz8f/v6uNy830JcXQVL9Hks4Cq/SY6JjkKtjcWkxOp0OvXr1QqdOnVy3tACcKkaNprEYoFY0fwXDP7XHcdNlh/DFPSmwpibbIRk5I6XO9S7MB5pYXJ5++mk8/PDDSExMRJ8+fdC3b18ANaMv9W1M6eyCtFyqlOo3yfKD6AjkDvgmlBpL48+9XKjRdCpfux1rid9R3HjlMfxwVyfI7dvY7bjkHFzx+hagicXl2muvRWZmJrZu3Yply5bV3j5s2LDaa19cCUdcqD53xmbAt3CP6BjkDjjiQo0lSfx7Q42mVfnY/ZjfBB7E9ePS8dudXYA2iXY/PomhDHDN3y9NntwWGRmJyMjIOrf17t272YFECNWyuNC57lT9LDoCuQMvHaC2/5sJ8gDaUIDLsFMj+CkdNz1yUdA+fHItcEdhNwxbXgA544TDHoscTx0VefE7OSHXuyrHAWKD7De0Su7h2sg8BOZuEB2D3EFgvOgE5KoC4oDCw6JTkAvRKRx7IlaWgHfD9uCDGxW4J68H+v9xEnJ2rkMfs7GGHzuK7Ho21rwxMBCzIup/s26y2TC/qAi/lJeh0GpFpEqF20NCcE1AIABgfVUVZufloshqxTCdDs9FRsFLkgAAFVYrJmSk48O4eESrXefSA7Ur7m8FFhcAQFwwiwvV9Yjvr0Cp6BTkFlhcqKn4d4caSSu1zNs6i2TDG5G7sGCyEvdn90TPZemQ8wtb5LEv5tuERJy5TuwRoxHTT2Rh5AWu6XggJxtFFgtmR0YhwUuNIosVVtTsaWOTZTyak43pwSHor9XigeyT+L60FBODapYsf7WgANcHBrlUaQEAVVSU6AhNwuICIC6Y0zjoX0NDShCevVJ0DHIXgQmiE5CrCowTnYBcjF8LFZfTjJIVL8XshG6qFx7M7IXOy45CLi5p0QxnCz5rid8PiosQp1ajl0/9J6nXVlVia3U1/mjVGoHKmgUxYs7oICVWK4qtVtwYGAiNQoEhOh2OmYwAgO3V1dhnMGBWRIRjnowDueqIS5Muznc3HHGhMz0d9CckcPdgshOeNaemYumlRtLJkpDHrZRMeD5hB6bfZsbhCb0hOcnWGCZZxi/l5RgfEABJqv9ns6qyEh29vfFRcREGHzuKUceP4eX8fBhsNgBAsFKJMKUK/1RXwWCzYVu1Hu00GphkGc/n5eGZyAgoz3NsZ6aOjhEdoUlYXAD4e6sR6OtaQ3zkGN0DKpGQ/ZvoGOROgvjmk5qIpZcaSSeLPelWpjDgP623Y8adCqRf0xuSVis0z8qKClRYrbj6AitonTCZsV2vxxGjEW9Gx+Dx8Aj8WVGO2Xl5AGo2t30tOhrvFhVhdHoa2ntrMD4gEB8UFeESrS80kgI3ZWTgiuPH8UWJ2NGmxlDHuOaIC6eKnRIX5IvS6jLRMUiwF8JXQcoyi45B7oRvPqmp+HeHGkl3apRAtHxlJR5ttx0xrf3xyKGOiPljN2SDocVz/FhWhku1WoSrzn9y2gYZEoCXo6Lhd2qqmEmOwP3ZJzErIgLeCgV6+Pri24TE2u9JN5nwc3kZfkhMwuTMDEwOCsYArRZj09PQ08cHyd7eDn5mzaPw8/OsDSjdEa9zoVa+BrTPXSI6BrkbTvehptJFAA5c3pbcj8567mpaIp1UluP+Dtvx2Ew/5F/RE2jBC9hPms3YUF1VuzLY+YSpVAhXqWpLCwC08vKCDCC3ntXJZFnGM7k5eDQ8HLIs44DRiBF+fghRqdDTxxdb9NV2fib256rXtwAsLrXiuCSyx5sb8w8ks/P/wiEXogkAfAJFpyBXJUlACHcsp4ZztuJy2nFVCe7pshPP3heKkhE9AJXjJ/wsLitFsFKJQRcZWejm44sCiwVVZ4xWpZtMUACIrCfnD2VlCFQqMVTnV7t6meXUFD0LZNhc4BJZTetWoiM0GYvLKfEhLC6eLFxjRq/870XHIHcTliw6Abm6iA6iE5AL0ZmNoiNc0H51Ae7osQtz7otCxeBugMIxb0NtsozFZWUYFxAA1VkXzr9WkI/Hc7JrP7/S3x+BSiWeysnBUaMRW6ur8b+CfIwPCID3WfmKLBa8W1SIJ8NrVhELUCrRyssLn5aUYKdej41V1ejq4/wzeDTJKaIjNBmLyykpkc6xAgaJMSd+CyQjr3EiO4tKFZ2AXF1ER9EJyIXoTHrRERpkh1cOpvXdgzfuS0B1/y41o4t2tKG6GjkWC8bXM02s0GJBjvnfa1m1CgU+iI1Dha1mI8lHc7IxWKerLSdnmpOfh1uDQxBxxpS3/0ZG4feKctx1IgtTg4OR6hLFpZ3oCE0mybLgJSichN5kRadn/4DVFcb4yK60Kit2BzwMZVWe6Cjkbka/CfSYIjoFubIjK4AvrhGdglzE4YgUXOPrelOeh1Yn4tYN3tBs3is6ikdos+ovqF10A0qOuJzi46VEmzDXXGGBmmd2wm6WFnIMjrhQc3HEhRpBZ6oSHaFJ/vJNx83DDuKTmSmwdGsvOo5bUwQEuGxpAVhc6ugUc/51vsk9qRUyRld+JzoGuSOFCgjn9QnUTP5RgG+I6BTkInSGctERmuVX3VFMvPwIvpnREbaObUXHcUve7Vx3mhjA4lJHpxhe5+Jpnow/CHVZuugY5I7CUgAVl7IlO2ABpgbSGSogwfV2cT/bD/6HcMOYNPxyZ2egXZLoOG5Fk+zai8awuJyhM0dcPM4N5h9FRyB3FclpYmQnkZ1FJyAXoZBt8FE5/8XhDfVZ0AFcPz4Ly2/rCimJG7LagytfmA+wuNTRIdofCtc/UUENNDM+DT5F+0THIHfF61vIXmJ7iU5ALkSncq/tHWQJWBi6FzfckIO107pDinPdzROdgU+nTqIjNAuLyxl8vVRozQv0PcZt+El0BHJnUV1EJyB3kdBPdAJyIX4qb9ERHMIKGW+F78ZNNxVhyy09IUWEi47kchQ6HaeKuRtOF/MME6Ny4J+/RXQMclcqbyCmh+gU5C78IoEgzvOnhtEqvERHcCiTZMUrUTsx5ZYK7JrUC4rQYNGRXIZPly6QmrHp599//43Ro0cjOjoakiThp59+sl+4BmJxOUuvJP4D8AQP+CwVHYHcWVxvXphP9sVRF2ogPzcvLqdVK8x4MW4Hpk034eANvSEF8sTzxfj06N6s76+qqkKXLl3w9ttv2ylR46mEPbKTGtAmVHQEcrDLw4oQmr1adAxyZ4kDRScgdxPfF9j5hegU5AK0klJ0hBZVJhnwdNJ2hNzhi0eO9UbrP/ZDrqgUHcsp+fbs2azvHzVqFEaNGmWnNE3DEZezxAX7IjHEvS5so7qeClgGCbLoGOTOklhcyM444kIN5Oehb+2KFNV4vO123HuXGifG9Ybk4z6rq9mDpNHAp2tX0TGazTP/dl/EgLYcdXFXvQPLEXtymegY5M68dLy+hewvpDWg5cXIdHE62bOXR81RVuDB9tvx8D1a5F7VC5KG03YBwKdrVyi8XH8aIYtLPQa0CRMdgRxkdthfkGSr6BjkzuIvAZSchUsOkDhAdAJyAVqbTXQEp5ChKsW9nXfgPzODUDSyJ6Dy7N/Lvn16i45gFywu9ejXJgRKbujidtpp9WiX87PoGOTuOE2MHKXNcNEJyAX4sbjUcUhdiLu678Ts+8JRNqw7oPSsa4BO0/Vzj+mmLC718PdWc1lkNzQ3Zi0ki0F0DHJ3LC7kKG0vA8CTanRhWqtZdASntMcrH7f13o3/3ReLqoFdAclz/i0pQ0Ph3cU99hZjcTmPS3mdi1uJ8jahW94PomOQu9OGAZHu8eJATkgXzo1N6aL8LCbREZzaZs1J3Np/L965rxUMl3QWHadF+A0ZDMkORa2yshI7d+7Ezp07AQBpaWnYuXMnMjMzm33shmJxOQ8ui+xe5sZtgmSsEB2D3F3KlUAzNvciuqh2l4tOQE5OZzaKjuAS1vhkYPKQA/jwvnYw9+ggOo5D6YYOtctxtm7dim7duqFbt24AgAcffBDdunXD008/bZfjN4QkyzLXha2HxWpD7/+uRHEVz1y4Oj+VBbv8H4SiulB0FHJ3k34E2gwTnYLcWc4u4D1OR6Tz2xvTGTd6lYmO4XLGVrbFDWtsUO4+JDqKXUk+Pmi3cQMUbrK6Gk8NnodKqcDlnSJFxyA7+G/iLpYWcjyfICBpkOgU5O6iugABcaJTkBPTmapFR3BJS3RHcOOVx7D47k6Q27cWHcdutP37uU1pAVhcLmh0arToCNRMGoUNo8q/FR2DPEHyFVwGmVpGstidq8m56fScFt0cXwUcxPXjMvD7HV2A1gmi4zSb31D3mgXA4nIBfZKCEe7nPi3VE81KPABVeZboGOQJ2o8RnYA8RcerRScgJ6YzlouO4BY+Dt6H6687iVXTu0FKiBUdp2mUSuiGDBadwq5YXC5AoZBwReco0TGoiSRJxnWG70XHIE/g5Qe0HiI6BXmK+L5AYLzoFOSkvM0GqBQc/bUHWQIWhO3BjTfmY8MtPSBFu9YlBL49e0IVFCQ6hl2xuFzE6C4sLq7qwfjj0BS710V25KTajQRUHJ2lFiJJQOcJolOQE9OpfEVHcCsWyYbXo3Zh0uQSbL+5F6Rw11h5NmDcONER7I7F5SK6xwchJtBHdAxqgltti0VHIE/RYazoBORpUq8XnYCcGIuLYxglK+bG7sCtU6uxb2IvSEGBoiOdl8LXF/4jR4iOYXcsLhchSRKuTOWoi6uZEn0CuoLtomOQJ9CG8WJpanlh7YCorqJTkJPyU3IE2JEqJROeS9iB22+34siE3pD8/ERHOoffiBFQ+LpfgWVxaQCuLuZ67tUsFR2BPEWXGwGlWnQK8kRdbhCdgJyUVuElOoJHKFHo8VTr7ZhxtwIZ43tD0mpFR6oVcLV7LuLB4tIAnWMD0CHKX3QMaqDR4QUIyflbdAzyFD1uEZ2APFWnawFehE310Ek8mdKS8hVVeCR5Ox642xvZY3pD8vYWmkcdGwvf3r2EZnAUFpcGmtzX9dfy9hRP+P0uOgJ5isRLgRD32aiMXIwuDGgzXHSKZpmz1oheCyvhN6cc4a9UYNzX1ThUaK1zn0qTjHt+0yP2tQr4vFiO9u9UYsEWU4Mf4+u9ZkjPlWPc13U3Zvxitxlxr1cg+KVyPPKnoc7X0kttaPdWJcqNctOfnEA6iW/vRDihKsP9Hbfj8Zn+KBjVE1CLKZABY8dCkiQhj+1o/JvdQGO7xsDfm2e2nF3/oDJEZf8pOgZ5iu5TRCcgT9f7NtEJmmVNhgUzenlh4zQtlt/sC4sNGPF5NapM/xaGB5YZsOyoBZ+P98GBGTo8cIkXZv5uwJKD5oseP6PUhof/NODSeGWd2wurbZj+ix7/u8wbf0zS4pNdZvx6+N/j3fWrHnOHa+Cvcc03fzq+vRPqmKoYM7ruxHP3hqH0sh6AqgXfP0oSAq4e13KP18L4N7uBfLyUuK5nnOgYdBHPha6AJNtExyBP4BMEdOCmkyRY62FASBvRKZps2SQtbunqhY7hSnSJVOLjsd7ILJOxLeffUZcNJ6yY0sULgxNVSAxU4PYeXugSqcDWbOsFjgxYbTJu+lGP5wZr0Cqo7tud4yUyAjQSru+kRq8YJYYkKbG/oOa148s9ZngpJYxv77rTrfxsrjlS5G72eeXj9p678NJ9UagY3A1QOP5tt7ZvX3jFuuiGmQ3A4tIIN1+SADcdeXMLHf2q0Dr7F9ExyFN0uZF7t5B4kgT0cu1RlzOVGWv+G+zz74vtgHglfj5sxslyG2RZxqo0Cw4X2TCyzYXPYj+/xogwrYRp3c+9UL1tsALVZhk7cqwo1svYctKK1AglivUynl5lwNujxF6j0FxaG0/gOZNtXjmY1ncP5t2XAH3/VIc+VvCttzj0+KKxuDRCYqgWl7YNEx2DzuO/kX9DsjZ83jNRs3CaGDmLrhMBL53oFM0myzIe/MOAAfFKdAr/d2rXm6O80SFMidjXK+H1QgUu/6Ia86/wxoD48xeXfzIt+HCHGQtH119AgnwkfDLOB5N/0qP3wkpM7qLGyDYqPPynATN7eyGt1IZu71Wi0/xKfL//4lPSnI2f7cKjUSTGP95ZmDJwP967vw1MvTvZ/fherVtDO2CA3Y/rTHjRRiNNviQBfx8uEB2DzhLrbURq3o+iY5CnaD0MCE8RnYKohrd/zQjgloWikzTLPb8ZsDvPinVT6y4p++YmEzaesOLnG3yQEKjA3xlW3P2bAVF+Cgxvde7bmAqjjEmL9Vg42huhvuc/P3t1ezWuPmM62Op0C/bkW/H2Fd5o82YlvrrGB5E6Cb0/qMLABCXCta5zrldncb2y5UlW+qRj5TDgqj4pmLhWgmrnAbscN3jyZLe9KP80FpdGGpoSjphAH5ws1YuOQmd4KW4jpKwq0THIU1z6oOgERHX1vh3Y8gEA17y2YeZvevx82IK/b9Ei1v/fgqA3y3hypRGLr/fBle1qSkZqhBI7c63433pjvcXlWIkN6aUyRn+lB1DzWn36kg/V8+U4dI8OrYPrlhCjRcbdvxrw+XgfHC22wWIDBiXWHLtdiAKbTlgxOtmFiovZKDoCNcBS3VEsHQVc168jrlljgmLfkSYfSxkUhIBxY+2Yzjm5zr9CJ6FQSFwa2ckEqS3oW/i96BjkKWJ7A4nuPRRPLiisHdB6qOgUjSbLNUsd/3jQgr8m+yLprIvozbaaD8VZJ5GV0r9l5GwpoQrsuUuLnXf++zEmWYUhSUrsvFOLuIBzz0jP/tuIUW1U6B6lhNUGWM44uNkKWF2sD+rMhovfiZzGdwGHcMOYNCy9MxVom9SkYwTecD0UGve/7pLFpQkmXZKAIF/XXW3E3fw3YRsU+iLRMchTDHhAdAKi+g18WHSCRpvxmwGf7zbjy/E+8NNIyK20IbfSBr25pin4ayQMSlDikeVGrE63IK3EhkU7Tfh0txlXp/z7Ojx5sR5PrKh5s+6tktApXFnnI9Bbgp9Xze1eyrrFZV++Fd/ss+D5ITVv+lJCFVBIEj7cbsKvh804WGhDr+i6yyk7O62x+uJ3IqfzadB+XH9NFlbc1hVSYsNXspXUagRPnOjAZM6DU8WaQKtRYfqlrfDKH4dER/F4PkorLivjaAu1kLD2QPIo0SmI6pfQD0gYAGSsE52kwRZsrbkWY/Andd9ofzzWG7d0rVkN7OtrffDESiNu+lGPYr2MhAAFXhyqwZ09/y0umWU2KJqw6aIsy7h9qQGvj9RA61VTaHzUEhaN88aM3wwwWoC3r/BGjL9rnef1M1UDvqJTUFPIEvB+6F58eKOEGXndcemfuZBPZF/we/yvvBKqMM9YPEqSZdnFBkCdQ5XRggEv/YWSal4AJ9LcVntwQ/Yc0THIU4x7F+h6o+gUROd3fA3wKfcX8nTlPgHoHxkgOgbZgZesxH3ZndH7j0zIefnn3kGpRKulv0CT1LQpZq7GtU4hOJHToy4kjlKyYXz1d6JjkKcIiAc6Xyc6BdGFtRoExPcVnYIE0xkqREcgOzFJVrwSsxNTbqnA7pt6QQoJrvP1gKuu9JjSArC4NMuUfom81kWgR+KPwqv0qOgY5Cn6zQSUnF1LLmDgI6ITkGAK2QZfFeeKuZNqhRkvxO/A9NtMOHRDL0gB/oBSidC77hIdrUWxuDSDjqMuQk22LhYdgTxFQBzQgxtOkotoMwyI7SU6BQmmU/mIjkAOUCYZMCtpB+66U0Lxf6bBKzFRdKQWxdOHzTSlXyIWrj2OUl7r0qJuj82Eb+Eu0THIUwx+AlC5/zKT5EYGPw58fo3oFCSQTuWDeq6IaLaqQ1Uo/K0Q+gw9LKUWxM+Mh38P/zr3MWQbkPdtHqoOVQEyoInWIG5GHLxCvOo9ZsnaEpz88OQ5t3d4vwMUXjXn2EvXlyL3+1zIRhlBlwYh8obI2vuZCkxI/186Wj/bGkof11oBrqnKVCYkjb5edIwWx+LSTDqNCrdxhbEWd7fqZ9ERHGrBFhMWbDUhvdQGAOgYrsTTA70wqq0aZquM//xlxG9HLTheYkOARsLwVirMHa5BtN+FB1F/2G/GrFVGHCuxoXVQzco8Z+4c/cVuMx5faUCVSca0bl54ZYR37dfSS20Y8Vk1tt6uhb/GvXfmrSO8Q82u5ESupM1woNUQ4Pgq0UlIEJ3CMSdbbEYbvOO9EXhpILLezjrn68Z8I9JeTEPQwCCEXx0OpY8SxmwjFOoLvz4pfBRoO6dt3dtOlRZLhQUnPz6J2OmxUIepkfF6BrQpWvh19QMAZH+ajYjrIjymtADAhOQJiNZFi47R4jhVzA6m9k9CVID3xe9IdjE+Ih+BuetFx3CoWH8Jc4drsPV2LbbersXQRCXGfq3Hvnwrqs3A9lwrZg3UYPvtWvx4vQ8OF9kw5qsLr9u/IcuC67/X4+ZUNXbdqcXNqWpM+F6PTScsAIDCahum/6LH/y7zxh+TtPhklxm/Hv53JPGuX/WYO1zjWaUFAIY9DSj4q5Jc0MgXgSYsEUzuQadwzLlpv1Q/RFwTgYCe9a9alv99PnSpOkReHwmfBB94hXvBr6sfVP4Xz6MOVNf5OM1UYILSR4mAPgHwbeULbXstDNk1+/aUbiiFpJLOm8cd+ap8cXvq7aJjCMHfaHbg46XE46NSRMfwGI/qfhUdweFGJ6txRVs12oUo0S5EiReHeUPnBWw8YUWAt4TlN2sxoaMayaFKXBKrwlujvLEtx4bMMtt5j/nGJhMua63EE5dqkBJa899hSUq8sckEADheIiNAI+H6Tmr0ilFiSJIS+wtqjvflHjO8lBLGt/ewxSgSL+W+LeS6IjoC3SaJTkGC6KSWn1Qj22RU7K6AJlKD9P+l48DMAzj2/DGUbyu/6PfajDYceugQDj5wEBmvZ0Cfoa/9miZCA5vJVjM9rdICfZoe3nHesFRakL84H1GTohz5tJzOlI5TEOwdfPE7uiEWFzsZ2zUGvRM98y9RSxocXIKIkytEx2hRVpuMr/eaUWUG+sbVPwxeZpQhAQj0Pv9oyIYsK0a0qvtCNrK1CuuzrACAtsEKVJtl7MixolgvY8tJK1IjlCjWy3h6lQFvj/KwUUVJAYz8r+gURM0zdBbg5Sc6BQngJ+AtnqXcApvBhoJfC6DrrEPiw4nw7+6PzLczUXWw6rzfp4nSIHZ6LOLvi0fcnXGQ1BKOv3gcxlwjAECpVSL2tlicWHgCx58/jsB+gfDr7Ifcb3IRPDwY5kIzjj59FEeeOoKyLWUt9XSFiNHFYGqnqaJjCMNrXOzo2TEdMfrtdbDauKenozwTvBxStWf8fPfkWdH3wyoYLIDOC1h8vQ86hJ1bXAwWGY+vMGBiZ/UFp3HlVsqI0NV9IYvQKZBbWfPzDPKR8Mk4H0z+SQ+9WcbkLmqMbKPC1CV6zOzthbRSG8Z8XQ2zFXh2sAbXdnDz0ZcuE4GoVNEpiJpHFw4MuB/4a7boJNTCtCJeKk89pn93f4SODAUA+CT4oPpoNYpXFUOboq3323zb+MK3zb/LN/u29cWxZ46haEURoifVXMfh38O/ziIAlQcqYTxhRPSkaBx+7DDi7oyDKkCFY88fgzZZ26Cpaa7o0V6PwlvlYScSz+Cef6qCdIj2xw294vDFpkzRUdxSV/9KJGa7/zSx05JDFdh5pw6lBhk/7Ddjyk8GrLlFUae8mK0ybvheD5sMzL/y4r/Izq41slz3tqvbq+tcrL863YI9+Va8fYU32rxZia+u8UGkTkLvD6owMEGJcK2bDtp66YBhs0SnILKPvvcA2xYBZedeSE3uSyeguCj9lICyZhWxM2miNag+fOHrMM8kKST4JPnAlGeq9+s2sw05n+Ug9vZYmPJNkK1ybSnSRGpQfawa/t386/1eVzYwdiCGxg8VHUMoN33XIc7DI5IR4OPmZ6IFeTFiNSSb5yw77aWU0CZYgZ7RSswZ7o0uEQrM2/jvL3GzVcaE7/VIK7Vh+c2+F71oPlInIbey7jUw+VU2ROjq/z6jRcbdvxrw3lU+OFpsg8UGDEpUITlUiXYhCmw6YW3+k3RWQ2cBfpEXvx+RK1B7A5c9LzoFtTCdteV/RytUCvgk+cCYY6xzuzHXCHVow98bybIMQ5YBqsD6z68X/FwzFc0n0QeyTQbOeGmTLXU/dxcapQaP935cdAzhWFzsLEjrhQcvayc6httJ9DGgQ+5PomMIJQMwnnodOl1ajhTZsOJmX4T4Xvyfct84JZYfr/tC9udxC/qd57qZ2X8bMaqNCt2jlLDaAMsZUyDNVsDqrjP2YnoCvT1ztRZyY53GA21Hik5BLUhntTjkuFaDFfoMfe3F86ZCE/QZepiKak6shY0KQ/nmchSvLoYxz4iiFUWo2FmB4KH/Xgd84v0TyP0ut/bz/J/yUbGnAqb8mmOd/Ogk9Jl6BA8599phw0kDyjaXIWJ8BICa62MgAcVrilGxswLGHCN8Wrnf5pu3droVcX5xomMIx6liDjDpkgR8tTkTB3MrREdxGy/FboCU1fBhZlf35EoDRrVRIS5AgQpjzcX5q9OtWHaTBhabjGu/02N7jhVLb/SFVUbtSEqwjwQvZc0IyuTFesT4SZgzvGYK2X19vDDw42q8tM6IsSkqLDlowYrjVqy71fecx9+Xb8U3+yzYeUfN0HtKqAIKScKH202I1Ek4WGhDr2g3XC9foQbGvMnlj8k9XfUa8E4fwFQpOgm1AD+L8eJ3agJ9mh7pL6XXfp77VU0BCewfiNjbYuHfwx/RU6JR8GsBcr7IgSZSg/h74qFt9+/1LaYiU515ytZqK7IXZcNSZoHCRwGfBB+0eqIVfFvVfX2SZRnZH2cj8sZIKDQ1v6cVXgrETI9Bzmc5kM0yom6OgjrIvWa+xOhiML3zdNExnIIky7K7njcValtGCa57dz14nX7zhXmZscn3PigMpaKjtJhpS/RYmWZBTmXNEsWpEQo81l+Dy1qrkF5qQ9K8+t94rJrii8GJNecjBi+qQmKgAovG/Xvm6fv9ZvznLyOOl9jQOrhmA8qzlziWZRkDPq7GEwO8cFW7f7+29LAZM34zwGgBXhiqwfTu9e+A7NIGPAgMf0Z0CiLH2fQe8PujolNQC9iQ1Bu3I/fidySn99bQtzA4brDoGE6BxcWBZi/djw/XpYmO4fI+aLsBw7PeEh2D3F1wa+Cu9TXXAxC5K5sN+GgEcGKL6CTkYHtjOuNGL/deGtgTDI0binlD54mO4TQ4H8KBHhmZjKTQ+pf+o4bRKm0YUvyd6BjkCUa/wdJC7k+hAMa8VTMtktya1uQ506vdVbB3MJ7px1kAZ2JxcSBvtRIvX5sKxYUXe6ILeD5xN5RVHOomB+s6CUgaKDoFUcsIbw8MeEB0CnIwPwOvZXJ1z/Z9FsHe3Nz8TCwuDtYrMRhT+yeJjuGSlJINYyq/Fx2D3F1AHDDyBdEpiFrWwEeASG6w6s50hnLREagZxrcdjyHxQ0THcDosLi3gkcuTkRzhJzqGy3ky8TDUZcdFxyB3JimB8QsBnyDRSYhalsoLuPYjQH3uqoLkHrzNeqgUXDzWFcXqYvFYr8dEx3BKLC4tQKNS4vXru8JLyR93Y0w0/Sg6Arm7QY8BCX1FpyASI7QtcPlc0SnIgXQqXmfrahSSAv+99L/w5UmFevGddAvpEO2PB0dwY8qGmhGXDp+ivaJjkDtLGFAzXYbIk/WYAnQYKzoFOYhO5X4bMbq7Wzveim7h3UTHcFosLi3ojoGtMDQlXHQMl3CHYonoCOTOfIKA8e9zo0kiABg9D/CPFZ2CHECn1IiOQI2QEpyCGd1miI7h1Piq3YIkScLrE7oiPpjDfxdyQ1QO/PM2iY5B7mzM20BAjOgURM7BJwi4ZiEg8S2Bu9Ep3HCjYDfl5+WHVwe9CjWXKr8g/pZqYQG+aiyY1B3eav7oz+dBn99ERyB31nMa0P4q0SmInEtCP2DQ46JTkJ3pJF6c7wokSJh76VzE+8eLjuL0+O5ZgI7RAXhxXGfRMZzSZaHFCMv+S3QMcldRXYGR/xWdgsg5DXoUSGGpdyc6SSk6AjXAXV3vwsBY7iXWECwuglzTIxY39WGzPtuswD8gQRYdg9yRLhK48StA7S06CZFzkiTg6neBsBTRSchOdHyb5/SGxA3Bnal3io7hMvg3WqBnRndE17hA0TGcRs+ACsRl/y46BrkjlTdww5eAf7ToJETOTeNX82/FO1B0ErIDnY0nAp1Zon8i/jvgv5AkSXQUl8HiIpCXSoEFk7ojRMuL5wDghfC/INksomOQOxrzFhDbQ3QKItcQ0hq49sOaDVrJpelsNtER6Dy0ai3mDZ0HnZdOdBSXwuIiWFSAD+bf1B1eKs/+o2ir1SM552fRMcgdDXgASJ0gOgWRa2kzHBj+jOgU1Ew6m1V0BKqHBAkv9n8RrQJaiY7icjz73bKT6NMqBK9P6AqFB48Uzo1eB8miFx2D3E3yFcAwvvkiapL+9wGp14tOQc2gs5hFR6B63Nv9XgxLGCY6hkticXESV6ZGYdZVHUTHECJSY0L3/B9ExyB3E94RGL+w5oJjImqaMW8DSYNEp6Am8jMbRUegs0xqPwnTO08XHcNlsbg4kVv7J+GOQZ43bDgnfgskY7noGOROAuKAm74FNJw7TNQsKi/g+s+BCC7h74q0ZoPoCHSGK5KuwKO9HhUdw6WxuDiZxy9PwdXdPGdHbz+VBYOKvhUdg9yJNgyYvAQIiBWdhMg9ePsDN30HBHAJf1ejM3EKtrPoG9UXLwx4gSuINROLi5ORJAkvX5uKS9uGio7SIl5I2AVFdYHoGOQuvAOAmxfXrIpERPbjHwVM/qnmxAC5DD9jlegIBKBjSEe8MeQNqBVq0VFcHouLE1IrFXh3Ug90ivEXHcWh1AoZV1R8LzoGuQu1FrjpeyCSU1qIHCKkNTDpB0Dj3q9N7kRrKBMdweMl+Cdg/vD58FX7io7iFlhcnJRWo8KiW3ujbbj7ztGflXAA6vIM0THIHSg1wA1fAHG9RSchcm9RXYCJ3wLce8Il6AyVoiN4tDCfMLw7/F0EeweLjuI2WFycWKhOg69vvwQpkX6io9idJMmYYORoC9mBQgVc+xHQeojoJESeIaFvzeiml/u9NrkbpWyFj8pHdAyPFOEbgY8v/xixfrze0p5YXJxciE6Dr267BB2j3Wto/v64NHgXHxQdg1ydpATGLQDaXyU6CZHTWrBgAVJTU+Hv7w9/f3/07dsXv//+e/MOmtCX08ZchJ+KU5RaWowuBosuX4QE/wTRUdwOi4sLCNJ64cvplyA1NkB0FLuZip9ERyBXp/QCrlsEpE4QnYTIqcXGxmLu3LnYunUrtm7diqFDh2Ls2LHYt29f8w4c3weY9CPLi5PTccSlRcX7xePjkRxpcRRJlmVZdAhqmHKDGVM+2owdmaWiozTL5OhsPF/8sOgY5MrUvjV7S7ThzsNETREcHIxXXnkF06ZNa/7BTmwDPr8a4IXgTummLoOxu/y46BgeISkgCR+M+ADhvuGio7gtjri4EH9vNT6b1ge9EoNER2mW+zS/iI5ArkwTANz8E0sLURNYrVZ8/fXXqKqqQt++fe1z0NgeNXsneQfa53hkVzouwdsi2gS2wUcjP2JpcTAWFxej06jwydTe6NsqRHSUJrkirBAhOWtExyBXpQ0DbllaM0WFiBpsz5490Ol00Gg0uPPOO7F48WJ06NDBfg8Q3Q2Y+gcQEGe/Y5JdaCWV6Ahur31we3w88mOE+njGHnwisbi4IF8vFRZN7YWxXaNFR2m0JwOWiY5Arso/Frh1GRCVKjoJkctJTk7Gzp07sXHjRtx1112YMmUK9u/fb98HCU8Bpi0HIriXkjPx41s9h+oT2QcfjPwAgRxxbBG8xsXFvbb8MN5ceUR0jAbpG1SGLw33QJKtoqOQqwltV3MRcCDP5hLZw/Dhw9G6dWu899579j+4sQL45mbg+Cr7H5sa7ZVuV+HT0t2iY7ila9peg6cueQpqTsdrMazhLu7By9rhtQld4KV0/j/K50NXsrRQ47UZDkxfwdJCZEeyLMNoNDrm4Bo/4KbvgC4THXN8ahSdbBMdwe1IkPBAjwfwbL9nWVpaGCc+uoHx3WMRE+iDOz7fhtJqs+g49Wqvq0abbF6UT410yQxgxGxAoRSdhMhlPfnkkxg1ahTi4uJQUVGBr7/+GqtXr8ayZQ6cuqtUA1cvAAJigb9fdtzj0EXprCwu9uSj8sGcAXMwLIELxIjg/KfpqUH6tArB4rv7IylUKzpKveZE/Q3J6qCze+R+lF7AmLeBy//L0kLUTHl5ebj55puRnJyMYcOGYdOmTVi2bBkuu+wyxz/40KeAq98HuJeIMDqrRXQEtxHmE4aPR37M0iIQr3FxMyVVJtzx2TZsTi8WHaVWjLcR67xmQjJVio5CrsA3tGaPlgQ7LdVKROLl7Aa+mQSUZohO4nGWt7sUD5r5c2+udkHt8M6wdxCpjRQdxaNxxMXNBGm98MVtfTB9QJLoKLXmxm1iaaGGiegE3L6KpYXI3USlAnesqblmjVqUzszZDs11Vaur8Nmoz1hanABHXNzYiv15ePj7XUKvewlQW7BD9yAU+kJhGchFpF4PXPkaoNGJTkJEjmKzAateBNa+CoBvP1rCnpjOmOhVJjqGS/JR+eCJ3k/g6rZXi45Cp3DExY0N7xCBX++9FN3jA4VlmJOwg6WFLszLD7j6PWD8+ywtRO5OoQCGzQJu+BLQ+ItO4xF0pmrREVxS64DW+PKKL1lanAxHXDyAxWrDK38ewvt/H0dL/mlrFDbsC3kMqoqTLfeg5FqiuwHXfAiEtBadhIhaWkk6sPhOIHOD6CRurdAvAkNCNaJjuJRxbcbhyT5PwoeLSjgdFhcPsupQPh76dheKq0wt8nj/bbUHE7PntMhjkauRgH4zgWFP1yybSkSeyWYD1r0GrJ4L2JxzOX9Xp1f7oHdsmOgYLsFH5YNZl8zC6NajRUeh82Bx8TC5ZQY8+O1OrD9W5NDHkSQZByOfg6bksEMfh1yQLgK4+l2g9VDRSYjIWWTvAH68HSjka4YjdGvVChaZyyJfSPvg9pg7cC5aBbQSHYUugNe4eJjIAG98edslmDO+M/y8Hbf/6CPxR1la6FzJVwJ3/sPSQkR1RXcD7vgb6DVddBK3pFX7io7gtNQKNe7peg++vPJLlhYXwBEXD5ZXbsB/ftqL5fvz7H7sfXEvQ1uw0+7HJRelDQNGvQx0Gi86CRE5uyMrgKUPAGWZopO4jcs79sLJavu/1ru6DiEd8EL/F9A2qK3oKNRAHHHxYBH+3lg4uSfentgNoTovux13WkwWSwv9K/UGYMZmlhYiapi2w4EZG4F+9wIKx80M8CR+Sm/REZyKj8oHD/Z4EF9c8QVLi4vhiAsBAEqrTXh+6X78uL35K4DtSHwHQbn/2CEVubTg1sCVrwKth4hOQkSuKncvsPR+4MQW0Ulc2i1dh2Fb2RHRMZxCv+h+mHXJLMT6xYqOQk3A4kJ1rDlcgFk/7UVmcdPWfR8XkY83yu63byhyLUoNMOB+4NKHABWX4CSiZrLZgG0fAyufAwzcSLEpZnYfidUlB0THECrcJxwP9HwAV7W6SnQUagYWFzqHyWLDpxvS8dZfR1Gmb9zylBtaf4Kok384KBk5vZSrgOHPAaFtRCchIndTmQ/8OQvY/Q0AvnVpjMe7X4FfS/aKjiGEj8oHt3a8FVM6ToEvFylweSwudF6l1Sa8ufIoPt+YAZPVdtH7DwopwaLqmZDki9+X3Exsb2DEbCD+EtFJiMjdZe8Elj8NpK0RncRlvND9SnxTskd0jBalkBQY03oMZnabiXDfcNFxyE5YXOiiMoqqMPf3g/h9b+4F7/dXm+/Q6sTiFkpFTiG4FTDsGaDjONFJiMjTHFkBrHgGyPPMkYTGmNftSnxQ6jnFpU9UHzzc82GkBKeIjkJ2xuJCDbY1vRgv/HoAO7NKz/laqn8llljvgWQ1tXwwanm+IcCgx4CeU8Gd74lIGJsN2PUVsOpFoLz5i8u4qw9SR2FexT7RMRwuKSAJD/V4CIPiBomOQg7C5ZCpwXomBuOnGf2x4Kbu6BDlX+drL0asYWnxBN4BwMBHgHt3An3usHtpmT9/PpKSkuDt7Y0ePXpg7dq1dj0+EbkZhQLodhMwczsw4gVAFyk6kVPys1lFR3CoNoFt8N8B/8XiMYvtXlrmzJmDXr16wc/PD+Hh4Rg3bhwOHTpk18eghuOICzXZygN5eOuvoygtzMUq1UxI5irRkchRdJFA37trRlg0fg55iG+++QY333wz5s+fj/79++O9997DBx98gP379yM+Pt4hj0lEbsZiBHZ8DvwzDyjNEJ3GafyaMgSPG4+JjmF3nUI6YXrqdAyNGwpJkhzyGJdffjluuOEG9OrVCxaLBU899RT27NmD/fv3Q6vVOuQx6fxYXKjZctL2IWrtU8DxVaKjkL0FtwL63wd0udHhSxv36dMH3bt3x4IFC2pva9++PcaNG4c5c+Y49LGJyM3YrMDeH4C1rwEFnr0MMACsad0P99hOiI5hN70ie2F65+noF92vxR+7oKAA4eHhWLNmDQYOHNjij+/puCUtNVtUUkcg6ScgZzew/k1g32LAZhEdi5ojqgvQ/36gw7iaqRgOZjKZsG3bNjz++ON1bh8xYgTWr1/v8McnIjejUAKpE4DO1wGHfgPWvQGc2Cw6lTA6sxFQik7RPBIkXBp7KW7rfBu6hncVlqOsrGYvoeDgYGEZPBmLC9lPVCpwzQc1q0xt/7RmuL4iW3QqaiilF5B8BdDzVqDV4BZ96MLCQlitVkRERNS5PSIiArm5F17NjojovCQJSLmy5iN7B7B5Yc1IjMUgOlmL0pmqAR/RKZom2DsYY1uPxTXtrkGCf4LQLLIs48EHH8SAAQPQqVMnoVk8FYsL2V9gHDD0KWDw48CRP4Fti4AjywHZvS8OdFkhbYDuU4CuEwFtqNAoZ89RlmXZYfOWicjDRHcDxs2vuYh/5xfAtk+AoiOiU7UInanKpYqLBAm9I3vj2nbXYlj8MKidZPXKe+65B7t378a6detER/FYLC7kOAolkDyq5qM8G9j+GbDjM6AsS3QyUnkDHcbWFJbE/qLTIDQ0FEql8pzRlfz8/HNGYYiImsU3GOg3s+YjbS2w/RPg4K+AuVp0MofR6cuBAP+L31EwZxpdOdvMmTPx888/4++//0ZsbKzoOB6LF+dTy7LZgGN/AXu+BQ4tA4xlohN5EAmI6wN0Gl8z99snSHSgOvr06YMePXpg/vz5tbd16NABY8eO5cX5RORYpirg0O8108iOrgDcbHl/q6RE18QY0THq5aPywaUxl2JE4ggMjRvqNKMrp8myjJkzZ2Lx4sVYvXo12rZtKzqSR2NxIXEsJuD4auDAEuDgb4C+WHQi9yMpa0ZU2o8B2o8G/Jx3j4PTyyG/++676Nu3L95//30sXLgQ+/btQ0KCc515IyI3pi8BDvxSU2LS1rrNNOfebZOht+hFxwAAaNVaDIwdiBEJIzAgZgC8Vd6iI53X3XffjS+//BJLlixBcnJy7e0BAQHw8XGh+XdugsWFnIPVAqSvBfYvqRmyr8oXnch1Kb2ApEFAhzFA8pWANkR0ogabP38+Xn75ZeTk5KBTp054/fXXudwkEYlTmV/zmnR0BXB8DWCqEJ2oyYa274oCg7gThH5efhgSNwSXJVyGftH94KX0EpalMc53neXHH3+MW265pWXDEIsLOSGbDTixBUhbA6T9DWRtBqxG0amcW0hbIGkgkHQp0HpozQ73RERkP1YzkLkROLocOLICyN8nOlGjjOnUF2lVJ1vs8VQKFTqFdELvqN7oHdkb3SO6Q61wrmlg5HpYXMj5mQ1A1qaaEpP2N5C9nfvEBMafKiqDgMRLAf8o0YmIiDxL2Ung2EogY0PNHjFFR0UnuqCbugzG7vLjDju+QlIgJTgFfSL7oHdUb3QP7w5fta/DHo88E4sLuR5jJZC5ATixFcjdXbPxZbn77Ah8DrUWiOwERKbWLOeZOAAI4jUfREROpaqopsBknfrI3u5UK5Xd3u0ybCg9ZLfjhXiHICUkBSlBKUgNS0XPyJ7w93L+lcvItbG4kHuoKqopMaeLTO7umrNfsk10ssbRhtUUlKhUILIzENkFCG7VIrvXExGRHVktQP5+oOAgkH+g5r8FB4GSdCGvTQ92H4XlJY2f3iZBQrx/PFKCU5ASnILkoGS0D2mPUB+x+36RZ+I+LuQetCFA6yE1H6eZ9UBJBlCaCZRm1LxYnP7/0syalWNamsobCIirmeoVGF8zchIYDwQm1Hzowlo+ExER2Z9SVXMSKiq17u1mPVB4GMg/CBQfB8pPnvrIrvkwljskjh/OfwIsQBOAaG00onWnPrTRiNJFIUYXg3i/eE75IqfBERfyXIbymhcLfSlgKDv1ccb/60trPjfrAZz1z+TsfzZKL0DjB2h0gJcO0PjX7JPiG/zvf/2iAF0EwJ3giYjofAzlp0rMyZoTbMZywFhx1kd5zbTp2qWaT72unPn6IikAtW/Na5KXFn9HJ+OwWokATQD8vfwRoAlAqHcoonXRLCbkMlhciIiIiIjI6XHiPBEREREROT0WFyIiIiIicnosLkRERERE5PRYXIiIiIiIyOmxuBARERERkdNjcSEiIiIiIqfH4kJERERERE6PxYWIiIiIiJweiwsRERERETk9FhciIiIiInJ6LC5EREREROT0WFyIiIiIiMjpsbgQEREREZHTY3EhIiIiIiKnx+JCREREREROj8WFiIiIiIicHosLERERERE5PRYXIiIiIiJyeiwuRERERETk9FhciIiIiIjI6bG4EBERERGR02NxISIiIiIip8fiQkRERERETo/FhYiIiIiInB6LCxEREREROT0WFyIiIiIicnosLkRERERE5PRYXIiIiIiIyOmxuBARERERkdNjcSEiIiIiIqfH4kJERERERE6PxYWIiIiIiJweiwsRERERETk9FhciIiIiInJ6LC5EREREROT0WFyIiIiIiMjpsbgQEREREZHTY3EhIiIiIiKnx+JCREREREROj8WFiIiIiIicHosLERERERE5PRYXIiIiIiJyeiwuRERERETk9FhciIiIiIjI6f0fK6S7VtFEz68AAAAASUVORK5CYII=","text/plain":["<Figure size 1000x500 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))\n","\n","\n","heart_categorical['sex'] = heart_categorical['sex'].cat.as_ordered()\n","heart_categorical['cp'] = heart_categorical['cp'].cat.as_ordered()\n","\n","sex_counts = heart_categorical['sex'].value_counts()\n","cp_counts = heart_categorical['cp'].value_counts()\n","\n","sex_counts.plot(kind='pie', autopct='%1.1f%%', ax=ax1, title='Sex')\n","cp_counts.plot(kind='pie', autopct='%1.1f%%', ax=ax2, title='Type of Chest Pain')\n","\n","plt.show()"]},{"cell_type":"markdown","id":"6b041f8a","metadata":{"id":"6b041f8a"},"source":["Proportionally, we quickly see there are more males than females, and the most common chest pain type is asymptomatic, followed by non-anginal pain, and that - in fact - only 7.6% of the patients had typical angina.  "]},{"cell_type":"markdown","id":"d759f4c2","metadata":{"id":"d759f4c2"},"source":["# Covariance, Correlation Coefficient and Effect Modification\n","\n","In this section, we'll explore relationships between two or more variables.\n","\n","## Covariance\n","\n","When two measures vary simultaneously, each can be summarized using the Measures of Central Tendency.  However, we may want to know how they vary jointly relative to each other.\n","\n","Let's see and interpret the covariance of age and maximum heart rate achieved ('thalach')."]},{"cell_type":"code","execution_count":null,"id":"49c81874","metadata":{"id":"49c81874","outputId":"1d89f3af-ce38-4af4-9151-dbfaecfe42d8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Covariance between Age and Maximum Heart Rate Achieved: -81.42306515419754\n"]}],"source":["covariance_matrix = np.cov(heart_continuous['age'], heart_continuous['thalach'])\n","covariance = covariance_matrix[0, 1]\n","\n","print(\"Covariance between Age and Maximum Heart Rate Achieved:\", covariance)"]},{"cell_type":"markdown","id":"2f3f313e","metadata":{"id":"2f3f313e"},"source":["The covariance between age and maximum heart rate achieved being large and negative (-81.423) indicates a **strong inverse relationship between these two variables**.\n","\n","In other words, as age increases, the maximum heart rate achieved tends to decrease, and vice versa.\n","\n","The large magnitude of the covariance suggests that this relationship is quite strong."]},{"cell_type":"markdown","id":"250aff22","metadata":{"id":"250aff22"},"source":["## Scatterplots\n","\n","Scatterplots allow us to look at two readings to get a quick overview of their relationship to one another.\n","\n","Here's a quick look at the two variables we just calculated the Covariance for - Age and Maximum Heart Rate Achieved."]},{"cell_type":"code","execution_count":null,"id":"94fd8145","metadata":{"id":"94fd8145","outputId":"c4512873-afde-49ec-aa38-bc14afc7f359"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA1IAAAIhCAYAAABE54vcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydd3gUVRfG3y1pJCEk9N6kSq/SO0ivogLSxIJ0ARGVIqIoIEgTFGkiTZSqgHQEAQFp0qXX0AKBhASyu+f743ybZLMzs5tkN7sJ5/c8+0Bm5s49c3dm9p57z32PjogIgiAIgiAIgiAIgtPoPW2AIAiCIAiCIAhCekMcKUEQBEEQBEEQhGQijpQgCIIgCIIgCEIyEUdKEARBEARBEAQhmYgjJQiCIAiCIAiCkEzEkRIEQRAEQRAEQUgm4kgJgiAIgiAIgiAkE3GkBEEQBEEQBEEQkok4UoIgCIIgCIIgCMlEHCkBAPD333+jffv2KFCgAPz8/JAzZ07UqFEDQ4cOdVude/fuxdixY/Hw4UO7fd9++y0WLlzotrqVqF+/PnQ6XfwnICAA5cuXxzfffAOLxRJ/XM+ePVGoUKEU1eGu63r27Bneffdd5M6dGwaDARUqVHCqXIcOHaDT6dC/f3+X2+TtXL58Of67Hjt2rOIxvXv3jj/GnaTmnvJWevbsiaCgINX9QUFB6NmzZ9oZlITkPouFChWyeT8EBgaiUqVKmDlzJogoRTZovQNdxbp166DT6ZA1a1Y8ffo0ReewPiuTJ092eOzYsWPd/rykluTa2KhRI7z77rvxf+/cudPmXtDpdAgNDUX16tWxaNEiu/KFChXy6L3uLO+//z50Oh1atWqVrHKp+c4XLlwInU6Hy5cvx29bunQpvvnmmxSdz9NYn5W06r8kvQ+tny+//NLmOGs7K33Cw8Pjj4uLi0PRokXTbft7AnGkBPz++++oWbMmHj16hIkTJ2Lz5s2YNm0aatWqhRUrVrit3r179+LTTz/1GkcKAIoUKYJ9+/Zh3759WLFiBfLmzYshQ4Zg5MiRLjm/u65r9uzZ+O677/Dxxx9jz549WLx4scMyd+7cwW+//QYAWLJkCWJjY11uV3ogODgYCxcutHGWASAqKgorV65E5syZ3W7DqFGjsHr1arfXIySQkmexVq1a8e+HxYsXI1OmTBgwYAAmTJiQIhu03oGuYt68eQCAiIgIrFmzxm31WOnTpw/27dvn9nrSirVr1+Kvv/7CqFGj7PZ98cUXNvdDwYIF0bNnT8yYMcMDlqaOuLg4/PTTTwCATZs24caNGx6zJT07Up6gU6dO8feh9dO9e3fFYxcsWGB3bNasWeP3+/j4YPTo0Rg3bhzu37+fVpeQrhFHSsDEiRNRuHBh/PHHH3jttddQr149vPbaa5g8eTKuXr3qafNcBhEhJiZG85iAgAC89NJLeOmll9CmTRusXbsWRYoUwcyZMxEXF5dGliafEydOICAgAP3790eNGjVQtmxZh2V+/PFHxMXFoWXLlnj48CFWrVqVBpZ6H6+++iquXLmCbdu22WxfsWIFzGYz2rRp43YbihYtiooVK7q9HgF48uRJistmyZIl/v3QoUMHbNiwASEhIfjuu+9caKHrCA8Px4YNG9CwYUP4+/vHO1XuJF++fHjppZfcXk9a8cUXX6B9+/bImzev3b5ixYrF3w+tWrXC8uXLUahQISxbtswDlqaOtWvX4u7du2jZsiXMZrPizJqQ9sTExDic8c6ZM2f8fWj95MmTR/HYMmXK2B3r4+Njc8zrr78OnU7nte81b0McKQH3799HtmzZYDQa7fbp9fa3yNKlS1GjRg0EBQUhKCgIFSpUsPmB3rJlC9q2bYt8+fLB398fL7zwAt555x3cu3cv/pixY8di+PDhAIDChQvHTzHv3LkThQoVwsmTJ7Fr16747YnDnh49eoRhw4ahcOHC8PX1Rd68eTF48GBER0fb2GkNWZszZw5KlSoFPz+/ZP84+Pj4oHLlynjy5Anu3r2relxsbCxGjhxpY1O/fv1sRpodXVdKz6vT6fDDDz8gJiYm/rzOjLTPnz8fOXPmxKJFixAQEID58+crHrdnzx7UqFED/v7+yJs3L0aNGoUffvjBLhwDYOejRo0aCAwMRFBQEJo1a4YjR45o2nHs2DHodDrFTt7GjRuh0+mwbt06AMDdu3fx9ttvI3/+/PDz80P27NlRq1YtbN261eH1qlGiRAnUrFnT7vrnz5+PDh06ICQkxK7MihUr0LRpU+TOnRsBAQEoVaoUPvzwQ5t78N69e8ifPz9q1qxp44SfOnUKgYGBeOONN+K3KYX2We/fBQsWoESJEggICECVKlWwf/9+EBEmTZqEwoULIygoCA0bNsT58+dtyquFE9WvXx/169eP/9saprR06VKMGDECuXPnRlBQEFq3bo3bt2/j8ePHePvtt5EtWzZky5YNvXr1QlRUlDNNm2ycfbZnzZqFunXrIkeOHAgMDETZsmUxceJEu8GO+vXro0yZMvjzzz9Rs2ZNZMqUCb17907Rs6hE5syZUbx4cdy+fdtme2rfgVZS8jwlZtGiRTCZTBgyZAg6dOiAbdu24cqVK3bHPXz4EEOHDkWRIkXg5+eHHDlyoEWLFjhz5ozdsVOmTIm/72rUqIH9+/fb7FcL83J0Ld988w10Op3dfQwAI0aMgK+vr037bd26FY0aNULmzJmRKVMm1KpVy24wBOCIiwoVKsDPzw+FCxd2KjzRypEjR3DgwAGbZ1ULvV6PoKAgu45pUpTC2YCEZzHxPQA4f62pYd68efD19cWCBQuQP39+LFiwQLED70x7aoW3aYVSA/zM/v7777hy5YpN+JmV2bNno3z58ggKCkJwcDBKliyJjz76SPPa1NpVyU5rSPL58+fRokULBAUFIX/+/Bg6dKhdaOzNmzfRuXNnBAcHIyQkBK+++qpNmFxiDh06hDZt2iAsLAz+/v6oWLEifv75Z5tjrPfF5s2b0bt3b2TPnh2ZMmVKcUhuSvH19cWrr76K77//PsVhy88VJDz39OnThwDQgAEDaP/+/fTs2TPVY0eNGkUAqEOHDrRy5UravHkzTZkyhUaNGhV/zOzZs2nChAm0bt062rVrFy1atIjKly9PJUqUiD/3tWvXaMCAAQSAVq1aRfv27aN9+/ZRZGQkHT58mIoUKUIVK1aM33748GEiIoqOjqYKFSpQtmzZaMqUKbR161aaNm0ahYSEUMOGDcliscTbAYDy5s1L5cqVo6VLl9L27dvpxIkTqtdWr149evHFF+22V6pUiYxGIz158oSIiHr06EEFCxaM32+xWKhZs2ZkNBpp1KhRtHnzZpo8eTIFBgZSxYoVKTY2lohI87qUcPa8+/btoxYtWlBAQED8ee/cuaN6XiKiv/76iwDQ8OHDiYioW7dupNPp6OLFizbHHTt2jPz9/alcuXK0fPlyWrduHbVo0YIKFSpEAOjSpUvxx37++eek0+mod+/e9Ntvv9GqVauoRo0aFBgYSCdPntS0p2LFilSrVi277Z07d6YcOXJQXFwcERE1a9aMsmfPTt9//z3t3LmT1qxZQ6NHj6bly5drnl+JS5cuEQCaNGkSzZs3j/z9/SkiIoKIiM6cOUMAaPv27dSvXz9K+qr87LPPaOrUqfT777/Tzp07ac6cOVS4cGFq0KCBzXF79uwho9FIQ4YMISK+f0uXLk0lS5akqKio+OOS3lNEfP8WLFiQatasSatWraLVq1dT8eLFKSwsjIYMGUJt27al3377jZYsWUI5c+akcuXK2dz/BQsWpB49ethdd7169ahevXrxf+/YsSO+rp49e9KmTZtozpw5FBQURA0aNKAmTZrQsGHDaPPmzfTVV1+RwWCgAQMGOGzfHj16UGBgIMXFxSl+AgMDbexLzrM9ZMgQmj17Nm3atIm2b99OU6dOpWzZslGvXr3srjUsLIzy589PM2bMoB07dtCuXbuS/Sxa27Nly5Y22+Li4ihXrlxUtmxZm+2pfQcSpe55slK8eHHKnTs3mUwm2rp1KwGgsWPH2hzz6NEjevHFFykwMJDGjRtHf/zxB/366680aNAg2r59OxElPCuFChWil19+mdasWUNr1qyhsmXLUmhoKD18+DD+fGPGjLF7Xpy5lrt375Kvry99/PHHNmVNJhPlyZOHOnToEL9t8eLFpNPpqF27drRq1Spav349tWrVigwGA23dujX+uK1bt5LBYKDatWvTqlWraOXKlVS1alUqUKCAnY1KjBs3jgwGAz1+/Nhmu/WZWbFiRfz9HB4eThMmTCAA9P3339scn/RZXLBggd37M/F5d+zYkexrtVgsqs9a0k9Srl27Rnq9nl555RUiIvrkk08IAO3cudPmOGfb03q/LFiwwK4uADRmzBjVtjh58iTVqlWLcuXKFf9M7Nu3j4iIli1bFt9X2bx5M23dupXmzJlDAwcOtKvHUbuq2dmjRw/y9fWlUqVK0eTJk2nr1q00evRo0ul09Omnn8Yf9+TJEypVqhSFhITQjBkz6I8//qCBAwfGt0Xic27fvp18fX2pTp06tGLFCtq0aRP17NnT7jhrW+TNm5fefvtt2rhxI/3yyy9kMplUrw0AhYaGkr+/P/n6+lKlSpVo/vz5dsdZz50zZ07S6/UUGhpK7du3p3///VfxvCtWrCAAdPz4cc22FYjEkRLo3r17VLt2bQJAAMjHx4dq1qxJEyZMsPkBuXjxIhkMBuratavT57a+3K9cuUIAaO3atfH7Jk2apPhjQkT04osv2nT2rEyYMIH0ej0dPHjQZvsvv/xCAGjDhg3x2wBQSEhIfOfYEVZHyvpjc/PmTfrwww8JQPwPDJF9p3fTpk0EgCZOnGhzPuuLKPGPqtp1KZGc81o7rc7Su3dvAkCnT58mooQfmsQOMRHRK6+8QoGBgXT37t34bWazmUqXLm3z3V29epWMRqNdB/vx48eUK1cu6ty5s6Y906dPJwB09uzZ+G0RERHk5+dHQ4cOjd8WFBREgwcPdvo6tUjsSD1+/JiCgoJo5syZREQ0fPhwKly4MFksFkVHKjHWe3zXrl0EgI4dO2az/6uvviIAtHr1aurRowcFBATY/TipOVK5cuWycbjWrFlDAKhChQo2jsU333xj96OXXEeqdevWNscNHjyYANh1Utq1a0dhYWGq7ZH4mqzvFLVPYvuS82wnxmw2U1xcHP34449kMBhsnvd69eoRANq2bZtdueQ8i0Tcni1atIh/P1y5coXeeust8vHxod9++021XEregal9noiI/vzzTwJAH374YbwdhQsXpoIFC9rcO+PGjSMAtGXLFtVzWZ+VsmXL2nTqDhw4QABo2bJl8duSOlLJuZYOHTpQvnz5yGw2x2/bsGEDAaD169cTETvcYWFhdver2Wym8uXLU7Vq1eK3Va9enfLkyUMxMTHx2x49ekRhYWFOOVLNmzenkiVL2m23PjNJP3q93s4RJEq5I5Wca1WzSemTtF7rPbBp0yYi4t96nU5Hb7zxhs1xzrZnahwpIqKWLVvavQ+JiPr3709ZsmSx2+6I5DpSAOjnn3+2ObZFixZUokSJ+L9nz55t9zwTEb311lt25yxZsiRVrFjRzolt1aoV5c6dO/5+t7ZF9+7dnb62Ll260JIlS+jPP/+kX375hZo3b04A6JNPPrE5buPGjfTxxx/T+vXradeuXTRz5kzKly8fBQYG0tGjR+3O+99//xEAmj17ttO2PK9IaJ+ArFmzYvfu3Th48CC+/PJLtG3bFufOncPIkSNRtmzZ+HCKLVu2wGw2o1+/fprnu3PnDt59913kz58fRqMRPj4+KFiwIADg9OnTqbL1t99+Q5kyZVChQgWYTKb4T7NmzRSn7hs2bIjQ0FCnz3/y5En4+PjAx8cHefLkwddff42uXbti7ty5qmW2b98OAHZhVK+88goCAwNTHILhrvNGRUXh559/Rs2aNVGyZEkAQL169VC0aFE70YVdu3ahYcOGyJYtW/w2vV6Pzp0725zzjz/+gMlkQvfu3W2+F39/f9SrV8/ue0lK165d4efnZxNisWzZMjx9+hS9evWK31atWjUsXLgQ48ePx/79+122bi0oKAivvPIK5s+fD5PJhB9//BG9evVSVaK6ePEiunTpgly5csFgMMDHxwf16tUDYH+PDx8+HC1btsTrr7+ORYsWYcaMGU6tYQOABg0aIDAwMP7vUqVKAQCaN29uY5t1u1LYlrMkVeqynrNly5Z22yMiIpwK7wsICMDBgwcVPwEBATbHJufZPnLkCNq0aYOsWbPGt3/37t1hNptx7tw5m/OGhoaiYcOGyWkKVTZs2BD/fihYsCDmzp2LGTNm2LVRat+BqX2egASRid69ewPgkKqePXvarQfcuHEjihcvjsaNGzs8Z8uWLWEwGOL/LleuHADt+y4519KrVy9cv37dJlR3wYIFyJUrF5o3bw6ABToiIiLQo0cPm/NZLBa8/PLLOHjwIKKjoxEdHY2DBw+iQ4cO8Pf3jz9fcHAwWrdu7fBaAQ7dypEjh+r+r776Kv5+3rJlCz744AN8+eWX8SGbqcXZawWAypUrqz5rST+J188QUXw4X5MmTQBwqGn9+vXx66+/4tGjRwDgkvZMLdWqVcPDhw/x+uuvY+3atTahnq5Ep9PZXVO5cuVs7vMdO3YgODjYbg1tly5dbP4+f/48zpw5g65duwKAzffYokUL3Lp1C2fPnrUp07FjR6dtXbJkCbp06YI6deqgY8eO2LBhA1q1aoUvv/zSZjnCyy+/jPHjx6NVq1aoW7cu+vXrh927d0On02H06NF257Xe954UHUkv2C+KEZ5bqlSpgipVqgBgBZ8RI0Zg6tSpmDhxIiZOnBj/UObLl0/1HBaLBU2bNsXNmzcxatQolC1bFoGBgbBYLHjppZccij044vbt2zh//rxqDHrSF2vu3LmTdf6iRYti+fLl0Ol08Pf3R+HChZEpUybNMvfv34fRaET27Nlttut0OuTKlSvFyjfuOu+KFSsQFRWFzp0726y16ty5MyZMmIAtW7agWbNm8TbkzJnT7hxJt1nXiFStWlWxTqW1dokJCwtDmzZt8OOPP+Kzzz6DwWDAwoULUa1aNbz44os2to8fPx4//PADRo0ahaCgILRv3x4TJ05Erly5nLp+Nd58803Url0bn3/+Oe7evasqVxwVFYU6derA398f48ePR/HixZEpUyZcu3YNHTp0sLvHrR3Y33//Hbly5XJ6vQXA7ZIYX19fze2pUV5MSV1a8uYAf+/Wd4rSvsQ4+2xfvXoVderUQYkSJTBt2jQUKlQI/v7+OHDgAPr162fX/sl9B2hRu3ZtTJ06FWazGf/99x9GjRqF/v3748UXX0Tt2rUBuOYdmNrn6fHjx1i5ciWqVauG7Nmzxz/n7du3x9ixYzFv3rx4x+nu3bsoUKCAU9efWN0LAPz8/ABA85qScy3NmzdH7ty5sWDBAjRt2hQPHjzAunXrMGjQoHgHznq+Tp06qdYZEREBnU4Hi8Wi+F5w9l0RExOj+P6zUqRIEZv7u3Hjxnjw4AG+/vprvPnmm/EDVSnF2Wu1rjtzNu1F4vXQ27dvx6VLl/D+++/HO00A/x7s2LEDy5YtwzvvvIMHDx6kuj1TyxtvvAGTyYS5c+eiY8eOsFgsqFq1KsaPHx/vBLqCTJky2TiLAN/rid+var+NSdvC+h0OGzYMw4YNU6wvtf2WpHTr1g2//fYbDh06FD8AoUShQoVQu3Ztu3WOAOKvP7V9tucBcaQERXx8fDBmzBhMnToVJ06cAID4Dv3169eRP39+xXInTpzAsWPHsHDhQvTo0SN+u9IC4pSQLVs2TWGExDMnAJKd28Lf31+146dG1qxZYTKZcPfuXRunh4gQHh6u2oHw1HmtI9WDBw/G4MGDFfdbHamsWbPaLaQHYLeg1truv/zyS/zIe3Lp1asXVq5ciS1btqBAgQI4ePAgZs+ebVfPN998g2+++QZXr17FunXr8OGHH+LOnTvYtGlTiuq1UqtWLZQoUQLjxo1DkyZNVO/x7du34+bNm9i5c2f8LBQAVQnrW7duoV+/fqhQoQJOnjyJYcOGYfr06amy1Rn8/f0VFynfu3fP7jnxBpx9ttesWYPo6GisWrXK5l47evSoYjlX5jQKCQmJfz9Ur14d1atXR/ny5fHee+/h6NGj0Ov1LnkHpvZ5WrZsGZ48eYIDBw4ozsivXr0aDx48QGhoKLJnz47r168nuw5nSc61GAwGvPHGG5g+fToePnyIpUuX2s1KW883Y8YMVXXAnDlzIi4uzi5HjhU1QQAl2yMiIpw61kq5cuVARDh+/LiqI2XtpCZ9PpN2qJ29VoCjBxo0aOCUjZcuXYoXV7H+HkyZMgVTpkyxO3bevHl45513EBoa6nR7ql2fK+S0e/XqhV69eiE6Ohp//vknxowZg1atWuHcuXOq95ez7Z0csmbNigMHDthtV/ttHDlyJDp06KB4rhIlStj8ndp3Fv1fIMLRgIv1WKXjrPe9N/5WeBviSAm4deuW4giINQTFGgbQtGlTGAwGzJ49GzVq1FA8l/UFYB2ptKIko6k1munn56e4vVWrVvjiiy+QNWtWFC5cWOuy0oxGjRph4sSJ+OmnnzBkyJD47b/++iuio6PRqFGj+G1q15Xa8zrL6dOnsW/fPnTs2FExCe/48eOxdu1a3L9/H1mzZkW9evWwYcMGm863xWLBypUrbco1a9YMRqMRFy5cSFZYQmKaNm2KvHnzYsGCBShQoAD8/f3x+uuvqx5foEAB9O/fH9u2bcNff/2VojqT8sknn+CXX37RDF9Nzj1uNpvjpWQ3btyIJUuWYNiwYahfv77qj6qrKFSoEI4fP26z7dy5czh79qxX/jg6+2wrtT8RaYbfKpGcZ1GNYsWK4YMPPsCnn36KFStWxH/XSe0DkvcOTO3zNG/ePAQHB2PNmjV2naRDhw5h+PDhWLJkCfr374/mzZtj9OjR2L59u8tCIBOT3Gvp1asXJk6ciGXLlmHhwoWoUaOGjUNSq1YtZMmSBadOndJMJO7r64tq1aph1apVmDRpUnxn+vHjx1i/fr1TtpcsWTLZubesDr1WSKDViTl+/LhNJ9qqTmrF2WsFEkL7nMH6m/7gwQOsXr0atWrVwvjx4+2O++GHH7BkyRKcOHECZcqUcbo9c+bMCX9/f7v3z9q1a52yz5lnMzAwEM2bN8ezZ8/Qrl07nDx5UtWRStze1kFCwL69k0ODBg3w888/Y926dTbhfUuXLrU5rkSJEihWrBiOHTuGL774IsX1JYfFixfHKw5rcenSJfz111+KYb0XL14EAJQuXdotNmYkxJES0KxZM+TLlw+tW7dGyZIlYbFYcPToUXz99dcICgrCoEGDAPDL6KOPPsJnn32GmJgYvP766wgJCcGpU6dw7949fPrppyhZsiSKFi2KDz/8EESEsLAwrF+/Hlu2bLGr17pOZNq0aejRowd8fHxQokQJBAcHo2zZsli+fDlWrFiBIkWKwN/fH2XLlsXgwYPx66+/om7duhgyZAjKlSsHi8WCq1evYvPmzRg6dCiqV6+epu3XpEkTNGvWDCNGjMCjR49Qq1YtHD9+HGPGjEHFihVtQrnUriu153UW6+jjBx98gGrVqtntf/z4MbZt24affvoJgwYNwscff4z169ejUaNG+PjjjxEQEIA5c+bEx+VbO2mFChXCuHHj8PHHH+PixYt4+eWXERoaitu3b+PAgQMIDAzEp59+qmmbwWBA9+7dMWXKFGTOnNlOejwyMhINGjRAly5dULJkSQQHB+PgwYPYtGmTjVMybtw4jBs3Dtu2bbOZMXKGbt26oVu3bprH1KxZE6GhoXj33XcxZswY+Pj4YMmSJTh27JjdsWPGjMHu3buxefNm5MqVC0OHDsWuXbvw5ptvomLFim4dDHjjjTfQrVs3vPfee+jYsSOuXLmCiRMn2oWKegvOPttNmjSBr68vXn/9dXzwwQeIjY3F7Nmz8eDBg2TVl5xnUYthw4Zhzpw5+PTTT9G5c2eXvANT8zydOHECBw4cQN++fRUdo1q1auHrr7/GvHnz0L9/fwwePBgrVqxA27Zt8eGHH6JatWqIiYnBrl270KpVK6dnOdRI7rWULFkSNWrUwIQJE3Dt2jV8//33NucLCgrCjBkz0KNHD0RERKBTp07IkSMH7t69i2PHjuHu3bvxM9mfffYZXn75ZTRp0gRDhw6F2WzGV199hcDAQKdmmurXr4/58+fj3LlzKF68uN3+//77Lz4sKjIyElu3bsW8efNQpUoV1KlTR/W8VatWRYkSJTBs2DCYTCaEhoZi9erV2LNnT4qvNTg4ONnRFNZE7AMHDrRJiWAla9asWLJkCebNm4epU6c63Z46nQ7dunXD/PnzUbRoUZQvXx4HDhywczLUKFu2LFatWoXZs2ejcuXK8SHCb731FgICAlCrVi3kzp0b4eHhmDBhAkJCQjQjNHLlyoXGjRtjwoQJCA0NRcGCBbFt27ZU5U7s3r07pk6diu7du+Pzzz9HsWLFsGHDBvzxxx92x3733Xdo3rw5mjVrhp49eyJv3ryIiIjA6dOncfjwYbuBSWeZNGkSTp06hUaNGiFfvny4c+cO5s2bh82bN2Ps2LE2A2aNGzdG3bp1Ua5cOWTOnBn//vsvJk6cCJ1Oh88++8zu3Pv374fBYEDdunVTZNtzhcdkLgSvYcWKFdSlSxcqVqwYBQUFkY+PDxUoUIDeeOMNOnXqlN3xP/74I1WtWpX8/f0pKCiIKlasaKNQc+rUKWrSpAkFBwdTaGgovfLKK3T16lU7tR4iopEjR1KePHlIr9fbqOpcvnyZmjZtSsHBwfHSzFaioqLok08+oRIlSpCvry+FhIRQ2bJlaciQIRQeHh5/HADq16+f0+2gJn+eFCWFtZiYGBoxYgQVLFiQfHx8KHfu3NS3b1968OCBzXFa16WEs+d1RrXv2bNnlCNHDqpQoYLqMSaTifLly2cj57x7926qXr06+fn5Ua5cuWj48OHxSnSJZY+JWFWuQYMGlDlzZvLz86OCBQtSp06dbGR6tTh37ly8slRSFbHY2Fh69913qVy5cpQ5c2YKCAigEiVK0JgxYyg6Ojr+OKtqWFKFpqQkVu3TQkm1b+/evVSjRg3KlCkTZc+enfr06UOHDx+2UWvavHkz6fV6u3v+/v37VKBAAapatSo9ffqUiNRV+5Lev2o2W1WpVq5cGb/NYrHQxIkTqUiRIuTv709VqlSh7du3q6r2JS5LlKAglVRFz9q+iZUclXB0TyaVPydy/tlev349lS9fnvz9/Slv3rw0fPhw2rhxo933rvVMJ/dZVJI/tzJr1iwCQIsWLSIi17wDiVL2PFnVFpWUuKxY1Uj/+ecfIiJ68OABDRo0iAoUKEA+Pj6UI0cOatmyJZ05c4aItJ+VpNekJH+e3Gv5/vvvCQAFBATEy8EnZdeuXdSyZUsKCwsjHx8fyps3L7Vs2dLuPl63bh2VK1eOfH19qUCBAvTll1+q2piUyMhICgoKslNOVVLICwwMpNKlS9OYMWPsbFZS0Dx37hw1bdqUMmfOTNmzZ6cBAwbQ77//rvjucvZak0uFChUoR44c8e8hJV566SXKli1b/DHOtmdkZCT16dOHcubMSYGBgdS6dWu6fPmyU6p9ERER1KlTJ8qSJQvpdLr4cy9atIgaNGhAOXPmJF9fX8qTJw917tzZKYnuW7duUadOnSgsLIxCQkKoW7dudOjQIUXVPqX3ltI1Xr9+nTp27EhBQUEUHBxMHTt2pL179yoqFh47diw+nYePjw/lypWLGjZsSHPmzLFri6TvXDXWrVtHtWvXpuzZs5PRaKTg4GCqU6eOjYqmlcGDB1Pp0qUpODiYjEYj5cmTh7p162ajlJuYOnXq2KlFCsroiCTbliAIyaNp06a4fPmynUKaIAhCRmLAgAHYtm0bTp486dL1doLgrVy4cAHFihXDH3/84VIRj4yKOFKCIGjy/vvvo2LFisifPz8iIiKwZMkSrFq1CvPmzYuXVhYEQciI3L59G8WLF8e8efM01fMEIaNgTUOgFI4s2CNrpARB0MRsNmP06NEIDw+HTqdD6dKlsXjxYodriQRBENI7OXPmxJIlS5K9Bk8Q0iMmkwlFixbFyJEjPW1KukFmpARBEARBEARBEJKJY5F5QRAEQRAEQRAEwQZxpARBEARBEARBEJKJOFKCIAiCIAiCIAjJRMQmAFgsFty8eRPBwcEibyoIgiAIgiAIzzFEhMePHyNPnjzQ69XnncSRAnDz5k3kz5/f02YIgiAIgiAIguAlXLt2Dfny5VPdL44UgODgYADcWJkzZ/awNYIgCIIgCIIgeIpHjx4hf/788T6CGuJIAfHhfJkzZxZHShAEQRAEQRAEh0t+RGxCEARBEARBEAQhmYgjJQiCIAiCIAiCkEzEkRIEQRAEQRAEQUgm4kgJgiAIgiAIgiAkE3GkBEEQBEEQBEEQkok4UoIgCIIgCIIgCMlEHClBEARBEARBEIRkIo6UIAiCIAiCIAhCMhFHShAEQRAEQRAEIZmIIyUIgiAIgiAIgpBMxJESBEEQBEEQBEFIJuJICYIgCIIgCIIgJBNxpLwIIiA8HLh6FTCZPG2NIAiCIAiCIAhqiCPlJSxdCpQrB+TODRQsCOTJA3z6KfD0qactEwRBEARBEAQhKeJIeQGffQZ07QqcOpWw7e5dYNw4oEUL4Nkzz9kmCIIgCIIgCII94kh5mNOngdGj+f8Wi+0+iwXYsQOYNy/t7RIEQRAEQRAEQR1xpDzM998DRqP2MbNmpY0tgiAIgiAIgiA4hzhSHub0aW1hCSLg3Lm0s0cQBEEQBEEQBMeII+VhgoMBvYNvISAgbWwRBEEQBEEQBME5xJHyMJ062a+NSozRCLz2WtrZIwiCIAiCIAiCYzzqSE2YMAFVq1ZFcHAwcuTIgXbt2uHs2bM2xxARxo4dizx58iAgIAD169fHyZMnbY55+vQpBgwYgGzZsiEwMBBt2rTB9evX0/JSUkz79kDJksrrpPR63v7++2lvlyAIgiAIgiAI6njUkdq1axf69euH/fv3Y8uWLTCZTGjatCmio6Pjj5k4cSKmTJmCmTNn4uDBg8iVKxeaNGmCx48fxx8zePBgrF69GsuXL8eePXsQFRWFVq1awWw2e+KykoWvL7B1K1C2LP9tNAI+Pvz/0FBg0yagRAnP2ScIgiAIgiAIgj06IiJPG2Hl7t27yJEjB3bt2oW6deuCiJAnTx4MHjwYI0aMAMCzTzlz5sRXX32Fd955B5GRkciePTsWL16MV199FQBw8+ZN5M+fHxs2bECzZs0c1vvo0SOEhIQgMjISmTNndus1qkEE7NoFbNjAeaOqVuWwPz8/j5gjCIIgCIIgCM8lzvoGDoS305bIyEgAQFhYGADg0qVLCA8PR9OmTeOP8fPzQ7169bB371688847+OeffxAXF2dzTJ48eVCmTBns3btX0ZF6+vQpnj59Gv/3o0eP3HVJTqPTAfXr80cQBEEQBEEQBO/Ga8QmiAjvv/8+ateujTJlygAAwsPDAQA5c+a0OTZnzpzx+8LDw+Hr64vQ0FDVY5IyYcIEhISExH/y58/v6ssRBEEQBEEQBCED4zWOVP/+/XH8+HEsW7bMbp9Op7P5m4jstiVF65iRI0ciMjIy/nPt2rWUGy4IgiAIgiAIwnOHVzhSAwYMwLp167Bjxw7ky5cvfnuuXLkAwG5m6c6dO/GzVLly5cKzZ8/w4MED1WOS4ufnh8yZM9t8BEEQBEEQBEEQnMWjjhQRoX///li1ahW2b9+OwoUL2+wvXLgwcuXKhS1btsRve/bsGXbt2oWaNWsCACpXrgwfHx+bY27duoUTJ07EHyMIgiAIgiAIguBKPCo20a9fPyxduhRr165FcHBw/MxTSEgIAgICoNPpMHjwYHzxxRcoVqwYihUrhi+++AKZMmVCly5d4o998803MXToUGTNmhVhYWEYNmwYypYti8aNG3vy8gRBEARBEARByKB41JGaPXs2AKB+Eqm6BQsWoGfPngCADz74ADExMXjvvffw4MEDVK9eHZs3b0ZwcHD88VOnToXRaETnzp0RExODRo0aYeHChTAYDGl1KYIgCIIgCIIgPEd4VR4pT+ENeaQEQRAEQRAEQfA8zvoGXiE2IQiCIAiCIAiCkJ4QR0oQBEEQBEEQBCGZiCMlCIIgCIIgCIKQTMSREgRBEARBEARBSCYeVe0TBEEQhPTMkyfAsmXA5s2A2Qy89BLQqxeQNaunLRMEQRDcjaj2QVT7BEEQhORz9CjQtClw9y6g1wNEgE4H+PkBP/8MtGrlaQsFQRCElCCqfYIgCILgJh4+BBo3BiIi+G+LhR0piwWIjQU6dABOnfKoiYIgCIKbkdA+QRA8ChGwZw+wdy9gMACNGgEVK3raKkHQZuFCdqKUYjqI+DNtGvDdd2lumiAIgpBGiCMlCILH+O8/Hrk/cYKdKOuIfp06HBqVK5enLRQEZdavV3airJhMwOrV4kgJgiBkZCS0TxAEj3D3LlC3LnDmDP9tNrMTBQD79gENG3KIlCB4IzExjo959sz9dgiCIAieQxwpQRA8wpw5wJ07PHKfFJMJOH0aWL487e0SBGeoWhUwasR0GAxApUppZ48gCIKQ9ogjJQiCR/jxx4QZKCX0emDJkrSzRxCSwzvv8CyqGmYzMHBg2tkjCIIgpD3iSAmC4BEePNDeb7Fw+J8geCOlSwNTp/L/DYaE7fr//6q+8w7Qtm3a2yUIgiCkHeJICekOk4k74UohYUL6oWjRhE6nEkYjULx42tkjCMll0CBOxNuoEd/LOh0rTv70EzB7Nv8tCIIgZFzEkRLSDdeuAe+9B2TODISFAcHBwNtvA5cuedoyISW8+652aJ/JxN+vIHgzTZoAf/zB96vJBBw6BHTtKk6UIAjC84A4UkK64Px5Xrg9d26CWlZsLLBgAVC5siS+TI907QrUq6c8K6XTAa+/ziP9gpAe0Om0Z1gFQRCEjIe89oV0QZ8+yuF8JhPw6BHQs6dHzBJSga8vsHEjMHgwEBSUsD0sDPj0U2DxYhnVFwRBEATBe9ERaaUUfD549OgRQkJCEBkZicyZM3vaHCEJZ88CJUs6Pu7IEaBCBbebI7iB6GieVTQYgDJl2MkSBEEQBEHwBM76BhpZMATBOzhxwvnjxJFKnwQGcl4eQRAEQRCE9IKE9gleT6ZMzh0XEOBeOwRBEARBEATBisxICV5P3bq8hiYqSv0Yf3+gceO0s0kQBO/j+nVg/nzgzBl+Z3TsyKp6IgIhCIIguAP5eRG8nsBAYNgw9f06HTBwIBASknY2CYLgXcyYARQsCIwbB6xYwYqeL78MvPQScO+ep60TBEEQMiLiSAnpglGjOIcUwIlaDQb+F2DFvs8/95hpgiB4mLVreTDFYgHMZv7XqvB5+DDQrh0gskqCIAiCqxHVPohqX3rizBlg0SLgxg0gd26ge3fgxRc9bZUgCJ6kWjXgn3+0Ezzv3QvUqJF2NgmCIAjpF1HtEzIkJUsCEyZ42gpBELyFO3eAgwe1jzEagTVrxJESBEEQXIuE9gmCIAjplthYx8fodM4dJwiCIAjJQRwpQRCeSywW4NgxYM8e4NYtT1sjpJTcuYHQUO1j4uKA8uXTxh5BEATh+UEcKUEQnjuWLAGKFeMEznXqAHnzAm3aABcvetoyIbn4+ADvvssCNErodEDmzMBrr6WtXYIgCELGRxwpQRCeK2bOBLp1s3WaiIANG4Dq1YErVzxnm5AyPv4YqFzZPl+UVeFz6VLnE3sLgiAIgrOIIyUIboYIiIkR+WVv4P59YOhQ5X1mM/DwIfDJJ2lqkuACAgOBHTuAzz4D8uThbQYDy57v2we0bOlR8wRBEIQMijhSguAm7twBhg8HwsJ4NDwoCOjbF7h82dOWPb8sW8brZdQwmTiZ66NHaWeT4BoyZQI++gi4fh2IjgaePgVWrgSqVPG0ZYIgCEJGRRwpQXAD169zqNHUqTzLAQBPngA//ABUqgScPOlR855bLl9OSOSsRlwcEB6eJuYIbkCnY6dKbc2UIAiCILgKcaQEwQ307cudcbPZdrvJxLMdXbtKqJ8nyJpVO2mrFUcqcIIgCIIgCOJICYKLuXYN+P13dpqUMJtZdvvQobS1SwBefVXbkTIYgMaNgezZ084md/Pff8DgwUCZMsCLLwIDBwJnznjaKnUuXgSGDQPKlmV7+/UDTp3ytFWCIAiCYI84UoLgYk6ccG626ehRt5siJKFIEeCttzj8Kyk6HX/GjUt7u9zFzz8DpUsDs2ZxOOmpU8C337KDsmSJp62zZ+1aoGRJ4Jtv+Dk6dQr4/nt2AufP97R1giAIgmCLOFKC4GL8/V17nOBaZs0C+vfntVI6XcJampw5eSaxRg3P2ucqzp3jEFKTyXZ21GzmWbkePbxrrd7ly8Arr7CtiUNiTSYemOjTBzh82GPmCYIgCIId4kgJgoupUQMICdE+xmgEmjVLG3sEW4xGYPp0FgSZMwf48ktg3ToOyWza1HH52Fjgxx95ZuvttzlH0dOn7rc7ucyapb1fp+OcWt7CnDns4KnN5hoM/L0JgiAIgregI5Il748ePUJISAgiIyOROXNmT5sjZADGjwdGjVLep9dzJ3zOnLS1SUg9Bw4ArVoBd+8mqP+ZTECuXMDGjUCFCh41z4bSpYHTp7WPKVoUOH8+bexxRJUqwD//aB+TJw9w40ba2CMIgiA8vzjrG8iMlCC4gY8+At57j/9vNLLzZO14t2sHTJvmMdOEFHLjBgtR3L/PfycOmbt7F2jUCLh3z3P2JcWZITJvGkZLb/YKgiAIgjhSguAG9HoOrfr3X2DQIKBTJ+Ddd4G//wZ++QXw8/O0hUJymTOHc4Epqf6ZzZwvbN68NDdLlYYNtXNmGY1AgwZpZ48jGjTQzv3kjL0WC7BjBzB7NrB4cYLTKwiCIAjuQEL7IKF9giA4pmRJ4OxZ7WMqV/YeWfvTp1ntTk3uXacDjhwBypdPW7vUuHCB21gtbQDAAxHVqinv27sXeOMNlk/X6Xj2yscHGDAA+Oorx4mYBUEQBMGKhPYJgiC4kOho1xyTVpQqBSxYYBtWCiSoFc6d6z1OFMDrtX76iWellOydOVPdiTp6lEMrL1/mv63Dg3FxwNSprNIoCIIgCK5GHClBEAQnqFDBcehZxYppZo5TdO/OTkbv3kDhwkChQrzt8GHgzTc9bZ09r74KHD/OYixFigAFCwLdugEHD3JiXjXGjmWnSWn2jYhzUV244DazBUEQhOcUCe2DhPYJguCYTZuA5s21j/nzT6BOnbSxR2AePQJCQ9VDGAF2gMeMUVfSFARBEITESGifIAiCC2nWDHjnHf6/TpewXf//t+jQoeJEeYKICG0nCuDv6M6dtLFHEARBeH4QR0p4riBy3OnyJlJjr1ZyU3eR3to3Oeh0rAY3bx6LIlgpU4YV4iZN8pxtzzPZs7OohBZmM1CgQNrYIzggo74gBEF4LhFHSnguOHgQ6NwZ8PfntSwvvgh89522QpgnOXIE6NIFCAjgsKSSJVlOPS5OuxwRsHQpL8r38QF8fYEWLYDt291r77//smJaYCDbW6wY58p6+tS99aY1Oh2vNzp5kuXOIyOBY8d4HU/iWSoh7QgMBF5/XVuVT6fj+1PwELduAcOGAWFh/ILIkYOT7XlT4jVBEIQUIGukIGukMjq//sqL2HW6BMfJ2ult3Zr3e5M08vr1QIcO/P+k9jZuDPz2GztISSEC+vZlB1GvTxj4NRh4RH7WrIQkwa5k82ZuR4vF3t66dXltkb+/6+sVBCuXL7P0fGQk3+tJGT8e+PjjNDdLAFjlo2ZNTuqV+MsxGIA8eVi3Pl8+z9knCIKggKyREgTwb3fXrradfICdDiJ2WmbP9px9SXn0iEfXzWZle7dtA775Rrns6tXsRAG20TPWvkv//o7zICWXJ094ps9kUrZ3927O4SMI7qRQIWD/fpZATzwzmCsX8O23PPkheIju3e2dKID/vnUrYeGhIAhCOkQcKSFDs2gRh8NpzbtOm5b2a4nU+Okndk7U7LFYgBkzlPfPmKEtz63XJzhaauf+/XegfXvOL9SkCa/90QrPW7GCZwHUlj1YLDwT5o4QSiLgjz+ATp3Y3kaNOG9STIzr6xK8n2LF+H64eJFnQf/6C7h2jWdpJezSQ/z7L884KU0TAvxi2LgxIQGYIAhCOsOLApoEwfUcPqzdiSLiyJOYGCBTprSzS43Dh9kZ0nI8rl8HHjzg5QaJ+ecf9f4KwPsOHlTe9+wZ8MorwLp1CaGAej2wdSuLKGzfDmTLpmyvj4/22q27d4Hbt4G8edWPSS4mE880/vwzh2WaTGzv9u3AxInAzp1Azpyuq09IPxQqxB/BCzhyxPExRJzsTL40QRDSITIjJWRofH0dj0brdN6zRsrPz7njlNZIKW1LjE6nvlZp9GheewUkOGPWWaZTp1hMQc0OZ2bznL0uZ/n8c2DlSv6/1em02nv+PK+JEwTBwzh6KVlx9QtCEAQhjRBHSsjQtGypPbtjMHAIm7O/9+7GGXtr1waCguz3tWnj2CFs3dp+W3Q0h9+pheeZzRwydeaM/b5WrbTt1euBSpWUZ7NSytOnvE5MzYEzmYBdu3iQW4tDh4ARI1iA45tveBlHRiQujgVVBg4EBgxgB9SR+qMguIRGjRy/lDJlkgRsgiCkWzzqSP35559o3bo18uTJA51OhzVr1tjsj4qKQv/+/ZEvXz4EBASgVKlSmJ1EGeDp06cYMGAAsmXLhsDAQLRp0wbXr19Pw6sQvJk2bYCiRdV/y81m4IMP0tYmLZo1A0qX1rb3ww+V9w0Zwv8qzcAZDBwK2L27/b4jR4CoKG27dDplCfX69YGKFdXttVhcv9D/339ZelwLa5ifEo8fA82bA1WrAlOmAD/8wMl08+ThHFEZiRMn+P7v1AmYM4fXyHXuzFFUjhxNQUg12bMDffokZK1Oik7HHr7SyJAgCEI6wKOOVHR0NMqXL4+ZM2cq7h8yZAg2bdqEn376CadPn8aQIUMwYMAArF27Nv6YwYMHY/Xq1Vi+fDn27NmDqKgotGrVCmatxSLCc4OPD7BlC5A/P/9tFWMwGPgzdy4PmnoLBgMvlC9SJOFv6796PQtKtGypXLZsWeCXX3h2Ta/nPoq1/xIWxuudsmSxL+dsfkyl43Q6FqgoUcLeXp2O11d17Ojc+Z3FGXt1OvXjunThewLg2au4OD722TPu81lDHNM79++zo3vzJv8dF5cwE3X7NtCwIf8rCG7lm28SpsKtIy7Wf7t2BT77zCNmCYIguAKvySOl0+mwevVqtGvXLn5bmTJl8Oqrr2LUqFHx2ypXrowWLVrgs88+Q2RkJLJnz47Fixfj1f8virh58yby58+PDRs2oFmzZk7VLXmkMj7PngGrVgFr1wKxsUC5ctxptjpY3kZcHLBmDX+ePOEEwn36OLce++5d4MsvWXDBaGQRib59OXGpEpGRLBMdG6t93sOHefZJCZMJWL6cZzyiotipGzOGZ0NcTXQ0C0lER2sf99dfnL4mMceOARUqqJfR6zkf0YEDqTbT5URFcYjlw4esUFenjvb6vy+/5NxJag6lXg+MHQsker0Kgnsg4gdy8WIgPJyVZ3r14mnhjMi9ezxaExvLLxy1F6cgCF6L074BeQkAaPXq1Tbb3nnnHapSpQpdv36dLBYLbd++nYKCgmj37t1ERLRt2zYCQBERETblypUrR6NHj1atKzY2liIjI+M/165dIwAUGRnp8usShLTk6lWi+vWtWZz4o9cTde1K9Pixerl33yUyGGzLWT9GI1GNGuplTSaikSOJ/P1ty5UuTXTwoOuvkYhoyBC+LjV7K1Qgsljsy40Zw/uVyiX+3LjhHrtTgsVC9PnnRIGBtjYWLUq0c6d6uYoVHV/niy+m3XUIQobn6VOi994j8vGxfdCqVCE6fdrT1gmCkAwiIyOd8g28Wmxi+vTpKF26NPLlywdfX1+8/PLL+Pbbb1G7dm0AQHh4OHx9fREaGmpTLmfOnAgPD1c974QJExASEhL/ye+t0xKCkAwiIliIYs8e2+0WC88WtWypLo8+aVLC4HDi5Qx6PZA7N7BsmXq9gwbx7EfSGa2zZ4F69Vj1z9V8/nnC+vSk9mbPziGOSrM10dHOhQY6mu1KS8aM4ZmlpDZdusRCKWqzZ47WvQG8XkwQBBdAxKGKc+bYq7kcOQLUqgVcveoZ2wRBcBte70jt378f69atwz///IOvv/4a7733HrZu3apZjoig04h5GTlyJCIjI+M/165dc7XpgpDmfPst55hSUtEzm4E//wQ2bFAuGxTEoYA//MBhj6GhwAsvABMmcDhcwYLK5S5c4HqVAoTNZlbY+/RTx7Y/esRhlxs2OA4xBICAAGDzZk7AW7UqqwIWL87LLf79Vz2kMFcux46UwQDky+fYhrTgzh3+DpSwWPgzcqTy/nLltAXTDAY+JqPx6BHfA5cueU+ibbdx8yZw/DjH8wqe5eBBHsFResGYzXxjTpqU9nYJguBWvNaRiomJwUcffYQpU6agdevWKFeuHPr3749XX30VkydPBgDkypULz549w4MHD2zK3rlzBzk1snH6+fkhc+bMNh9BSO/Mn6/tJBgMwKJF6vuvXGHhiOPHOeHv+fMsvHDypHqZJUvUBbkA7j/8+qv67MijR0DduiyC0a4dz5oFBgJt22rLqgMsqtGzJ7B/P/cjz55lhcCsWdXLOOOkmc2coNkb+Pln7e/UbGZ1wlu37Pf17avdhmYzS79nFG7fBnr35hnJcuVYsKVCBV5nmOHYt4+VRPLmBcqX50WDbdsCp0972rLnl8WLtUcuTCYe+cnw3r0gPF94rSMVFxeHuLg46JP00gwGAyz/71lUrlwZPj4+2GKV4AJw69YtnDhxAjWTrjIXhAyOo0FpszlBwS0pZ84A1aoB69bZdtz/+gto0ICVBJW4fVvbkbLWm2SsAwA7NUWLArt32/YtLBa2o3Rp5xUFneXBgwRlQS3u3XNtvSnl9m3n7L1zx35bw4bAu+/y/xNP0Fv/37s38PLLqbfRG7hzB6henfuyz54lbP/3X6B9e+D77z1nm8vZvp1HHxLH8BLxKEj16qx5L6Q9t287fmFFR/M0vSAIGQaPOlJRUVE4evQojv4/ocmlS5dw9OhRXL16FZkzZ0a9evUwfPhw7Ny5E5cuXcLChQvx448/on379gCAkJAQvPnmmxg6dCi2bduGI0eOoFu3bihbtiwaN27swSsThLQnTx7t/UajeojegAE8a5R0DZXFwtt691ZeX5U3r+O+g4+P8izR4MHaDst//wHTp2ufO7nkzet4QNhgAHLkcG29KSVvXsczczodr2NT2v7tt+xEvPBCwvYiRXj73Lnaqn/piU8/VQ5rtX7XAwbwGsJ0j8XCanfWBzMxZjNLfPbr5xnbnnfy5nU8qpQlC+DnlybmCIKQRqSN9oUyO3bsIAB2nx49ehAR0a1bt6hnz56UJ08e8vf3pxIlStDXX39NlkRyXDExMdS/f38KCwujgIAAatWqFV29ejVZdjirzCEI3sykSepKdtbP5s325S5fdqzuBhD9/rt92atXiXQ69TJGI1H37sr2BgU5rrNAAde2UXi4tmqfwUD0yiuurTM1REQQ+flp29uypePzWCxEt2/z9SupGaZnYmKIAgK07yOdjmj6dE9b6gK2bHHuYT13ztOWPn8cPar9nRgMRMOGedpKQRCcxFnfwGvySHkSySMlZAQeP+bInnPn7Aer9Xpef7Rmjf2g6fbtjpMS6/XAlCms0JeUTz5hFb2kGAxA5szAoUMJCYaTntPR2ycggAfZXck776iHehkMnC/Lm0QYpkwBhg61324w8OD2/v2ct+t55coVx/nVfHw4zNHVM5xpzuzZPOPk6MHZuDHjxG2mJ95+mxV7kn4/BgMr3fzzD69nEwTB63HWN/DaNVKCICSP4GBW5uvY0dZZ8vcHBg5kQSmlyJOQEMfntljUj/vsM+Drr1npLzG1agF79yo7UYBza39cHQXz7Bk7k2ohbUTAypWurTO1vP8+KyonDTesUoWXyTzPThTAzrojiJy7z72ekBDnxAoyxMWmQ2bPBkaP5pexFZ0OaNqUBULEiRKEDIfMSEFmpISMx82bPLNiNAI1amj3qywWFn24fFn9GF9fVoYLC1M/5ulTFqeIigJKlmQ5ci3q1WPHT4vu3bWVBpPLypVA587ax4SGAuHhfM3eRFwcO6aRkbzmqXRpT1vkPTRpAuzYoZ4nDWDhiTJl0s4mtxAZyTMbWvKTefPyNJ0zIxWCe3jyhF+GsbE80uFoylQQBK/DWd9AQ6tTEIT0Sp48jsUnrOj1HJrXtavyfp2OZ0W0nCiAZ48aNnTexhkzWJ5abSjHaOSZLldy8CCHeSXNl5mYBw84b2ZigQZvwMeHnU/BntGj2ZHS6ezvJ72elfvSvRMF8IjIBx8A48apHzN+vDhRniZTJvbuBUHI8EhonyAI6NKFldwCArgz6uPDHVCDgZ2o8eNdX2e5csDq1cqpVwICePYlWzbX1unr61xklLfNRgna1KkDrFqVMPPq45PgS3TuzLLoGYYxY4ARI/gC9Xq+WJ2OY3inTePkaoIgCEKaIKF9kNA+QbASGclJYK9eZcnyzp2dn9lKKSYTMGkS56oyGIBXXmFBCEdKwilhzx7udKuh03FI4unT6uuoTCZgwwYOS9TpOM9Ws2beOwlgNnPb7tzJTmS9ekCLFt5rb2qIjWWH6vRpICgI6NABKFbM01a5ifBwYMUKTiCXPz/w6qssry0IgiCkGmd9A3GkII6UIDwvELFIw+HD6scsXAj06KG8799/gVat2NH08eFtcXEcBvjbb0CJEi43OVWcOcP2Xrhga2/Bgmxvhgh3EwRBEAQXI6p9giAISdDpHCfbVRPWunMHqF8fuHGD/46LS1hrdekS73v40EWGuoCICLbJKiKS2N7r13km7e5dT1knCIIgCOkfcaSEdIXZzIvKlywBtm3jMCtniYzkNTlLlwInT7rPRsF7OXGCw9zU0OvV1/F/9x07SkrKcGYzcPs2sGCBS8x0CfPns6OkZm9EBDB3btrbJXgBZ89ycrKBA/lFKqR/LBaO312yBNi6NXk/joIgpBhxpIR0w5o1QOHCrAzXrRvQuDFQoAAvE9DCZGKhq1y5eM1E164c0lS7NievFZ4fVq3SXhtksXC6l/Bw+33LlvF+NYj4GG9h+XJtey0W77JXSAMePmTd/JIlOdPzjBn8Is2alSUthfTJ+vX849igAf84NmnC6+aWLvW0ZYKQ4RFHSkgXrF3LTtD167bbb90CXnuNO41qvPUWMHmyfeqV/fuBmjWBa9dcb6/gnTx+7JyIxePHzm1LyqNHybfJXThjizfZK7gZiyVBSSUpERGccO6//9LeLiF1/P470Lat/Q9ZeDiPGv70k2fsEoTnBHGkBK/HYgEGD+b/q0mjDBmiHMlw9CiLByiVM5s53G/iRBcZ6gZu3OC+TUyMpy1xLyYTCyJcvqw9i5JaSpRwHPESEMA5TZNSurT2bJbR6F3iDaVLK0vLWzEYvMvexFgsfC9cvJi2EUr37vEsdYZ0MCdP1l4UZzazXKYjYmP5pXT9unO5BAT3QQQMGpTwfyWGDNFOnCcIQqoQR0rwevbt406V1m92eDivnUrKjz9qdyZNJl7X4s7Oe0pYswaoWBHIl48HkbNn599LbxIzcAVxcZwMOG9eVr4rXBgoUgSYNcs9fbTXXuNcmWoYDJyGR+mY995TXm9kxWQC3n031Sa6jL59tZ0Qs9m77AX4O585k++DwoWBokX5GfjiC/c6VPv3czRU9uzsbGfNyoP5VqGODMGcOY6P+fNP9X2PHnFSuRw5+KWUPz9n1P71V5eZKCSTAwd4BErrZXnvHrBlS9rZJAjPGeJICV7PrVspPy483LGTFB0NPHmSfLvcxezZQPv2wPHjCduio9m5qFWLZ9EyAmYz0KkTMGoUK+JZuXIF6N8f6NfP9c5UUBDQu7f6fp2O61aidWt2xJLml7L+/eabQKNGrrHTFTRtCvTqpbxPp2NHoVWrtLVJCyJ2/gYMYHl5K7dvA598wvnFtBzZlLJ1K+cWSzwQYzJxPrUqVbifmiF48MDxMWoN/PgxULcuMH26bYzriRP8EE+b5hobheShtJhTCWd/RAVBSDbiSAleT+7czh2nlDg2d27Ha2ICA7VnKdKS27dZSAuwdwDNZhbb+uqrtLfLHaxYAaxbp+4szZ7NCXRdyePHwLx56vuJuK+ohF7Pyw0mT+ZZEisFC/Ka/e+/V0/i6wl0OuCHH/h6ChZM2J4/P/D118CiRd5l765drIyoBBHP0v7yi2vrNJs5Z5jFYu9DmEw8A2yNnEr3BAY6Pkbthpg8mZ2mpI1kfUm9/35CXgAh7UjNj6MgCC5BHCnB66lRg8N8tDp9uXOzYFFSevTQDgkyGnmGwhkBgrRg0SLtGTSzmSN03DEynxq2bwfKl+d21Om4z9anj73AR2Jmz9Zud6PRuWik5LB8ufZ6M7OZv4PoaOX9BgP3Ga9c4bCvK1d4xqJ/f8f30NOnfD3ly/PMWJ48rCaZVEDFlej1PMNz8WKCzZcv87IJrfVenuC77xyv6fr2W/X91jDdKlWA4GDOBzZoEF+7Gps2ATdvqj9zZjOwYUMG8RESe9NqKI0oEfHD6uilM39+yuzKaJw8yS+/bNn4RqxTh6c33RGrXLUqUKyY9o9j9uyszOhqiDird5MmQObMQFgYKwb+84/r6xIEL8ZLuo+CoI5enxA5ovZ7MW2acsewXDl2lJTKGQxAaCh3Zr2Fs2cdd8gfPHAuSietmD2bQ9qOH0/oKzx5wjM/hQqph02eOaPtNJpMwKlTrrX13DntzjrAzt/Nm9rH6PXcLy1QwDkn/MkT7m+89x7w77/sqN26xQrU5crZhnG6A72ebS1Y0HsGDZJy6pTjNV1nzijvi4tj4bLevYEjR4CoKA4XnTWL23fvXuVy5845bg8i4Px5567Bq/Hzc3yMkihBVJRzmZsllwR73RUr8mjM/fvcdnv3Aq++yjenqxfj6nSOfxy/+Qbw8XFtvUTA8OEc77xjB0/1P3jAYQbVqonsuvBc4aU/qYJgS+vWLIGedFA1Xz5g5UpeP6HG998DH35oP9haqxYLWSQO0/I0wcGOj9HrnYvSSQsePuS1TGrcvg107Ki8z9G16nRAliwptUy9Tmf6Ms58D8lhzBjuTxHZDkybzbyGv3177xM8SWtCQhyHGmbOrLx98uSERMuJ29Fs5hnIdu2AZ8/syzl7P6jVm67IkiVlDezv77icxcLHPc88eMA/RCaT7YiA9QZbuJAdLFfTvDnPDBUqZLs9b16egu/SxfV1rl/P8cGA7UylycTX26OH7UJHQcjAiCMlpBtat+Ywqt27+fdh504OU+rUSbucwcCqX+HhvCbn5595ZHvXLlYF8yY6d9YelTcYgJYtWaLbG/joI8cRK5s3K19Tly6Ow8tee019HxHw99+cJ6xhQ2671au1269TJ+0IJb2ec4vlyqVtV3KIieGwNbV6zWYOP/NGYa2jR3kWrWFDzuO2fLmyQ+IKtL5rgO8VpT6h2czrwNQcIouFJ1RWrbLf16aN4xnKQoU4HDPd07mz9sNqNAKvv6683ZmRG3eN7jx+zNPeLVtyiNqHHwKXLrmnrtSwaBE/7GptrNcDU6e6p+4WLXjadM8efkh37OBY3ldfdU99aiEgVoh4BFMQngdIoMjISAJAkZGRnjZFeM6xWIgaNiQyGKxzFwkfnY7IaCTav9/TViZQrpy9nUqff/6xL3vjBlFoqPK1Go1EBQsSRUUp12s2E731VsKxQMJ5qlQhun9f3eZOnYj0euX21emINm92SdPEc+yY4/YxGok+/dS19aYGi4Vo+HDb9rW2WalSRDdvur7OR4+IChRIqC/xx2AgCgsjunXLvtzVq47b18eHaNAg5XoHD+bvXa3sjz+6/lo9wtOnRCVLqjdwYCDR+fP25aKinHvIu3d3vc3HjhFlz57wcFpt1euJ5s51fX2p4bXXlF8sST9Pn3ra0tQTGOj4Ohs29LSVgpAqnPUNZEZKELwInY5Hzq1rg43GhPD24GDeV7265+xLiqPRfCtKyzPy5OGBU6uglI9PwvlKlOAZR7VB7smTgblz+f/WGSjrjM+RIyztrcaPPyaEGxoMXK9Ox7N8S5bwWiZX4kwbETnflmnBDz8Akybx/63ta53x+e8/Xo/k6rXzwcH8nRcvzn8nvvfz5eN7RWmmMLXtO2kS56HV6WzvB19fHnh/440UXY734esLbNuWML2WuIGzZWMdeKUpemdVSVy9+C46mh/GiAjbmFizmW/Gt9/WznuV1hiNjkMgrTdZeseZa3D1uixB8FJ0RJKa/NGjRwgJCUFkZCQyZ4hgeMFbuH8fWLaMldly5ODwJWeVaI8eZcnnJ0+AMmU4/N5bQvqsjB/PeaC08PPja1DrZ5lMvEb7r7/497lRIw4lU+uTxMVxG967p13vqVNAqVLq+zdsYLGHx4+B0qWBCROcC+mzWDgMb/dutrFBA/6o2Ws2s9CDIwGLQ4eAypUd1+9uiDg58qVL2s7SX39xGKQ76t+2jZUgLRZey9iihXrfjQh48UUO19Wyd8sWbfGyS5d4rfy9exzO9/rrnJjXGXv/+otDWM1m4KWXtO31OFaDN23ih6lqVfaMtTq+ISG8mE+LIUP4gXIVP/zAcbtqGI3c0GvXuq7O1LB4MdC9u/p+gwGoX58d1vTOa69xIma1OGqdju+FwYPT1CxBcCVO+wZpMj/m5Uhon+AOJk8m8vXliBQfH4760OuJPviAQ9MyAnFxfI1aER7vvuvaOg8dchxVotcTTZ2qXP7JE6LXX0+IEvLx4f8HBxOtWKFd9+nTRMWKJYS8WaOkSpdWjoqyMnWquq0GA1Ht2iltDddz8aJzoYijRnna0gQWLtS29cUX3fPMXbtGVKlSQj3We6lAAaLDh11fn0eIiXEutK9XL9fW27Gj41A5X1/X1pkaYmKIcudWjlW2fjZt8rSVruHvv9XjYfV6oixZiCIiPG2lIKQKCe0TBA8ydy4wbBgvzCfigV+LhT8TJwLjxnnaQtdgNPLsgVrYVO3aLEHtSrTEJKzodMpKzgDQqxfPPAA8g2A9LiqKB1q3bVMud+8eUK9eQl6ixOJc587xYLPaoP3AgUDfvvx/a1tZZ+hKlnR9otnUkNr29QTduwMjR/L/k7ZvgQLA77+7PvIsJoZnTq3S9SZTQpvcuMH73JkjLM1wJmmdweD65HbWl6YWJpPrY0xTir8/T3tmzcoPiHWK2mDg/0+dCjRr5lkbXUW1apy0zWCwnXrV6ThGd9Mmzi0iCM8B4kgJgosxmYDRo7WP+eorx5Ey6YXatVnmvFcvzv2YOTOHyi1fzuFvru7AlirlWGnZbOaIpaScPs1OlFL/jIj7AWPHKp9z7lx2ppT6iyYTd57V1I31ek4mu38/d/pr1wZateKwz8OHOXmst1CwoOM+UFwc96W8BZ2OlTmPHOHQ0Lx5OTxx1izgxAnnctEml59/5vViSo6n2cwhozNnur5em0r+/JNDrA4ccJ9DkSmT46SvFovyA5canMlBYHVatLh4keU8N2zg0RJ38uKLrJ43eTLnkypVihfZnT6d8cLcevTgB2DYMKBuXX7wJk/m9vamhbyC4G7SaIbMq5HQPsGV7N7tXCTMsmWetjT90revegSNwcDiZBaLfblx45wT1goPty9bpox2GZ2OqHp19197WvDxx+rtZDAQ5cnDYZ3exJo1HJ6Z1N6WLd0T1vfyy47vpbx5XV8vEfHLI18+28pKliTats099c2apR7KpdOxitvDh66ts0MHxw+qn596+StXiJo0sT0+Uyaijz5y381rMvHDk1TVrnFjosuX3VOnIAhuQUL7BMFDREY6d1xGmZHyBF9+CVSoYBtBA3CUSebMHCqnNFCdmu/m4UPtMkSOj0kvfPIJUKeOffsajSx4snq1d6kMbtnCSXcfP7bf9/vvfC2u5sEDx5FnSvakmp9+YiWMpHGDZ89y6NiOHa6v8513EhL2JZ5itir/rVzJghSuxJmEZU+fKs/E3b4N1KjBiiWJefKEVWX69HGNjUl5+22eGo2Ott2+YwfbEx7unnoFQfAY4kgJgospVsy1x6UnLlzgJLnujqDJnJmjmqZMYblsPz9W3Bs6FPj3X46wUSJnTsedX71eWVmxZEltJTaDQVsl0JMQAbdusXKgo+sHOHRy82YOjXvxRf47WzagXz9eE+RNYX2AtrgbAOzdCxw86No6S5XSdib1ejc840+fAoMGKe8j4i930CDHYX5E7GzcuOH8Gqjlyzl3QOXKfENkycLhXYcPA82bJ/tSHFK8uGNvvUgR5RGTyZP5+pSujYhjcA8fdo2dVo4cAebPV257sxm4c4ftEgQhQ+GU/HnFihWhcxSH/H8Ou/rllAaI/LngaurUAfbtU/4d1+tZXvm//1y/fshTTJwIfP657UxO5cq8fMMd61NSyhdfAB9/rH2MTsepa7Jksd3+668Jg/JqbNwIvPxyqkx0KUTAnDkJSxcAvvfef5+dooxw/4WHA7lzOz6uWTNeA+8q9u1zLP8+bx7Qu7fr6sTatTz15ohjx4By5ZT3LV3KszInTvDfOXMCAwYAw4dzrilv4cwZ7ZEJnY5fPMOG2e8LDdWeHjYaWf1l+vRUmxnP4ME88qCl1hISwlOZTvanBEHwHM76Bk79jLZr1w5t27ZF27Zt0axZM1y4cAF+fn6oX78+6tevD39/f1y4cAHNMooijSCkktmzOZls0hkMg4F/w+fPzxidWAB47z1gxAj7cLh//uFB5StXPGOXEhERjvP7EPHgcVLat+ePUh9IpwO6dfMuUS4ijsh67z3OkWTlyhVWEezd23sEz1LD+fPOHecoj1dyqVGDnVEl9HrOWeXyZL43bzrXCb9xQ3n7p59ytupTpxK23b7N6jitW3uXFGPJkglJ6pJes8HA06JKX0BcnOMYW7NZvY1Sys2bjmf3IiN5VlEQhAyDU125MWPGxH/u3r2LgQMHYt++fZgyZQqmTJmCvXv3YvDgwbh9+7a77RWEdEGZMhzi1q6drcPUsCGwZw/LaGcErlxhp1GNZ8+Ajh3Tzh5H5Mzp2HnQ6TiMLSl6PSu1jR/PyZWt5MnDA+MLF3rXQPPWraw0CNhes/X/ixaxkFl6p0gR545zJrlucpkxg5X5ChRI2BYWxrOev/2mneM2RThzAwPKmaVPnEiQpEwa32nNMr1wYWotdC2ffsoy2y+8kLAtc2ZO/rttm3KGcqORj9HCYHAu+3ZyyJnT8ShNUBDHIQuCkGFwKrQvMSEhITh06BCKJQn+/u+//1ClShVEOrua24uQ0D7BnUREcPhRtmy2HfCMQOfOvM7cEY8fcx/C01y/zqGGauuEDAagZUuOoNLCZOJQOZ2OO/KO+k+eoEMHYP169Ugjg4HDEH/7LW3tcjVPn3J/2tEv2bBhwKRJrq//6FFg2jRuR7OZpe0HD+ZBE5cTG8sOgNrvrE7HMzknT9p79QMH8qiH2g2h1wNly/IFeRtEwOXL/GUXKuQ4/8Hgwezhas0Q/f23axf7HTyofT6jkaeHp01zXZ2CILgNl4b2JSYgIAB79uyx275nzx74O3q5CcJzSFgY51XKaE4UwMsYnOH0affa4Sz58vFSECX0el4i8tlnjs9jNHLYYrFi3ulEATwBobVcw2xmYY70Tni4c7OM7oioWraM1wL+9BPnGHvwgNfJNWrk3H2UbPz91b1Bq+M0ZYry1OjJk9o3hMXCyn/eiE4HFC7MTqIz/Yzhw/nFq/Rw6vU8AuTqvFdVq3JGb6W2Nxh40aXay0cQhHRLsgVsBw8ejL59++Kff/7BSy+9BADYv38/5s+fj9GOspAKgpChyJTJueOUQuU8xYQJnOtzwgRbleKSJTmKSG2NfnrDmXymGWEC3pmZToPB9TOily5xcmWLxXaG0+qrjB7NYhSNGrm2Xrz1FjsDI0YA9+8nbM+Xj7M+q6mdBAdzOS3ZRmcfaG8nb17gr79YVXDfvoTtvr68cHDyZPfE4f74I4+YzZljK99etSrH0ubL5/o6BUHwKMkO7QOAn3/+GdOmTcPp/w8zlypVCoMGDULnzp1dbmBaIKF9gpAyFi0CevbUPiZLFh6p9zaiongd0aNHQIkSHJXjTWucUsvEicDIker9Zr0eGDfOsYphWhMZCSxezEtgLBagVi0WxtByxuvW5X6zlo/wzz9ApUqus/PDD7k/rhY9ZjSyT7N+vcZJLlzghWz//sueXocOrGjijHres2d8A9+9C+TPD9Svr61gs2QJK6KoYTRyHqRZsxzXnZ74918OV/T3Z682LMz9dUZE8A0cGwuUL59xRmcE4TnCWd8gRY5URkMcKUFIOVmzcr9BjcmTOb+TkLbcvw8ULaq+nCY4mBXvvCnkdN8+oEWLBJuJ2Dfw8+Mkyy1aKJfbuhVo2lQ5xM9gAJo04ZA7V1K7NjtvWmgOIkyZwgu39Hr2xgwG/veFF7gTnljBwhU8fcoqOJcv24f4WRv56FGOWRUEQXjOcdsaKQB4+PAhfvjhB3z00UeI+H8P6vDhw7jhajlRQRC8nmPH1BXR3ntPnChPEROjvS7o2TPgyZO0s8cRt2+zfPyjR+wQWZ0ii4UH9tu3B86dUy7buDFHVQUE8Kyij09CLtfGjYEVK1xvrzNr41SPWbuWHwyihCkt67+XL3OCW2cyJycHPz9g+/aE3ExGY4KsYGgoJ9kSJ0oQBCFZJHuN1PHjx9G4cWOEhITg8uXL6NOnD8LCwrB69WpcuXIFP/74ozvsFATBS8mXjxfa//gji4JFR7O4xsSJrh9UT0p0NM9UXLrEETudOrEcucDLNLTSAplM/H199VXa2aTFDz/w96nkPxDx9pkz1XOoduvGqZCWLGFxk6Agvh8qV3aPvc2acSoDNX/HaNTIKzZhgvp6JZOJ8zxt3uz67M758/PIx7Zt7DjFxfH6nU6dnBNxePKEJckPHGCvtX9/9WlCIf1w9iywbh1/v+XKAa1auUG7XxAyJskO7WvcuDEqVaqEiRMnIjg4GMeOHUORIkWwd+9edOnSBZcvX3aTqe5DQvsEIf3x44+cjzMqin/zzWaejejfH/j6a+9V00srypZl5T4tihf3HqG26tW5f65F/vzA1atpY48jbt9mIbmYGOX9Oh1fT5UqSXZERnLMnxZGI/Duu5yoyluYNg14/3175y93buDQIRnBSI9ERbEgx6pV/MLU6diRz5mTJSkbNPC0hYLgMdwW2nfw4EG88847dtvz5s2L8PDw5J5OEAQh2axZw7//UVH8d1wc9+/MZp6x+OADj5rnFSQWDVPDHZLgKcUZW5y5prQiSxYge3b1/ZkysXicHc5ehDdd7C+/cG4mpRm0W7fYa3d1KKLgXog4W7o1aZ7ZnLB27u5dDi89dsxz9glCOiHZjpS/vz8ePXpkt/3s2bPIrvWrIgiC4AKIgI8+UlfYI2Jn6s6dtLXL26hePWGdkBJGI/D/DBZewUsvObbXlflTU8vKldqzY7GxHIpoR9asjmdvzGb3xSQCCZKVmzZxIi5HOFroGBEBfPed9jFEPHP1++8saiE6V55l/34OH1WSnbSOSk2YkPZ2CUI6I9mOVNu2bTFu3DjE/T/4XqfT4erVq/jwww/RsWNHlxsoCIKQmNOn+aPVDzObgdWr084mb6R/f+38qyYTH+Mt9O3r2N4BA9LOHkcsXaqtNm42s4y7HXo9MHCgemGdjhd4deniEjttiItjTfxcuVjKsHlzXuT46qu80FGJ2Fjn4ilnz1bf9/vvnGOgalVef1OxIs9ibd+esusQUs/y5dojFyYT8Ouv3jUzKgheSLIdqcmTJ+Pu3bvIkSMHYmJiUK9ePbzwwgsIDg7G559/7g4bBUEQ4nn40PExBoNzx2VkqlUDrK/kxOvFrP8fM4YlvL2F8uVZERxQtnfECO77ewsREY6j2dSk5zFkCF+MTmc7tWpV0vv5Z9dnECYC3niD1UUSZ6I2m7nDXLs2SyYmRc3BSoo1zjYpa9eyCsj587bbT59mzfpt25w7v+BaHj50PCtoMnmXtKcgeCHJdqQyZ86MPXv24Ndff8WXX36J/v37Y8OGDdi1axcCAwPdYaMgCOmE6GgOr1dLUuoKChd2nDjXZOIcSs87H33E+ZMaNOD+uY8PJ69dvx4YO9a5c5hMHCaZkv5URARw8qTzZYcMAbZsYR/D15ftrVWLZxe//DL59buTEiW0B/T1eo170NeXv4Rvv2WJS4MBCAwEunbl8Ddn1fpiY/nLcWbWYM8e1oFX6jybzcB//ynPKuXK5VymaiWJTrOZFWEA+3otFv4MGOC9YX5Pn3L7etNiQlfhzAsyNBRwJMBFxEnrVEcNBCGDQ8nk0qVLyS3i9URGRhIAioyM9LQpgpAu+fNPoiZNrNl/iMLCiD76iOjRI/fU16oVkcGQUF/ij07H9cfGuqfu54UHD4iGDycKCUlo15Ytifbtc1x26VKi3Lltv5cKFYhOnXK31WnH7t3K91/iz/ffu6nyU6eIXn+dyGjkigICiN59l+j6dfUyPXsmHK/2KVxYuWyNGo4vds8e+3JbtjguBxAdOOCadnEVFy5we/n5sX2+vvz3+fOetsx1XL1KpNerfycGA9GIEerl4+KIpkwhKlAgoUzlykQrV6bdNQiCG3HWN0i2I6XT6ahWrVo0Z84cun//fooN9CbEkRKElPPzz/x7nNSxMRiIypcncsdjde4cUWiofZ16PXf4f/3V9XU+T9y/T1SypPJ3ajQSrV+vXvbrr7X7ZseOpd11uBOLhahiRfVrzZaNKCbGDRUfPEgUGGjvFBmNRDlyEF28qFyuQQPHDo2fn3LZy5eJfHzUyzVpolxuwQLnHClvemBPneKXi1L7ZslCdOKEpy10HV9+mTBKkvRBLVaMKCJCuZzJRNSunX05q2P2+edpex2C4Aac9Q2SHdp36NAh1KhRA+PHj0eePHnQtm1brFy5Ek8z4tS3IAiaPHoE9OzJv6JJw/nMZs5j9MUXrq+3WDHO0dOmje2a/SpVgD/+ADp0cH2dzxOjR3Okl9J3ajZz8lul/EmxscDw4ernNZuBdu1caqrHOHYMOHJEff+9e24QPCHixo+NtVfmMJk4ltIaSpeUTJkcnz8gQHl7wYK8pqlkSdvtRiPQpw+rvynhrJJvjhzOHZcWvPkmv9iU2vfxY6B3b8/Y5Q5GjAAWLbIN8/Pz45f6vn0c2qfETz9xDgqlcE0A+PhjjukVhOeAZDtSlSpVwqRJk3D16lVs3LgROXLkwDvvvIMcOXKgd0Z6wQiC4JAlS7hDrbbEwWwG5sxhsTA19u/npSH58nF/7d13HSeSBfjYDh1YCCxrVnauOnZUSIAqJIsnT4AFC9TXuRHxcohffrHf99lnjgUYLl0CLlxIvZ1JsVjYcWnShNXFixdnQY1bt1xfF8D3tdYaKYMBmDXLxZXu2cMZlNW+HJOJJc2vXLHfp2WsFX9/9X1FirAaSMOGnEQrZ05e1DZqlHqZJk2AsDDtOgsUAGrWdGxbWnDiBDsQau1rNvMIzr//pq1d7qR7d+DcOeDMGR4ZuHMH+OEHfqmqMWOGtmSlwQB8/73rbfUUcXGcAb52bU5AXbYsMGkS8OCBpy3LGJjNLLDToAG3b8mSwPjxvOA6PeCK6a9//vmHKlSoQHq93hWnS3MktE8QUka/ftoRP9aP2tKNKVMSomYSR9AYDETLlqnXGxND1LChbTSJ9f9586pHNwmOOXPG8fdpNCovn0i8Tk7rs3Cha202mYheey0hKilxhFJoKNGRI66tj4jopZccX2dIiIsrnT3buQbetMm+bGpC+ywWXoOl1MCBgcrro6yMG6dd56xZrmkbV7B8uXPtq/Vyeh4ICHDcRvXre9pK1/DkCVG9eso/Nvnzc9irkHKePSNq3dr+3aLXE2XP7tGFtW4L7bNy7do1TJw4ERUqVEDVqlURGBiImYrZBwVByKgEBqrPRiVGKWJo717g/ff5/4mjaEwmHqB64w3g4kXl840aBezcyf9PPANisQC3b/PMlDN2CfY4I75qMil/p84Kt2bJkiyTHDJ9OgvSAbaTCWYzR2m1aqU9K5oSgoMdi9mpRcqlGGcbWCmMzxmD1cL/Fi3iKTjAvoFjYljePLGkemJmzNCuc9Ik7f1pSWra93nC0fXr9a6X7/cUH30E7N7N/0/6Y3PrFvDKK56xK6MwYQLnmQNs3y0WC4cqt23rOMzBwyTbkfr+++9Rr149FC5cGIsWLULnzp1x4cIF7NmzB3379nWHjYIgeCnt22snUdXrORpCKbpn2jTtaCMiZTXm6Gju06m9W00mjlD5+29t2wVlcuVyLgpMafmLs3meGjdOnk1aWCzA1Kna4aU3brDauCtxtA7PYOA8t5pcu8bxh506AT168LoTrQeqcGHnjCte3H5bhw7aowtGo3qn8Ouv1UO5LBYOcVq2zH7f9u2Ow3MuX9aO5X36lLMfd+3K9n35JYefOUN4OCdTe+UVXlu2YoW2VHyDBo6dqUyZOLzxeeaVV7RfEhZLxlioGhXFIYpaPzYHD3LKAiH5xMXxKJha+1rTMmzdmrZ2JZfkTnXly5ePhg0bRkfcESvhISS0TxBShsXCERxaUuRKUUZERDlzOo4OqVLFvtzffzsup9cTTZrk3mvPqPz3n+P21emIRo60L2sVAXP0OXTIdfZeu+ZcKOLgwa6rk4jo8WMOI1W69/V6jn7SVMueNStB7lKnS4hvLV2a6MYN5TLff+9cA2/ebF/2yROiQoWUJdD1eiJ/f+Uwmuhox/UZDEQ9etiXtYYDOvp89JHy9Z49myCvbTCwnXo9xxMvX67RuES0eDEfZ5XytH5RhQuzvLkaY8dq3/ijR2vX+zxw5gzfL0ry6UYjUcGCfN+kd/76y7kfmylTPG1p+uT0aede3qNGecQ8t4X2Xb16FZMmTUKFChVc7tQJgpC+0OmAVas4aSrAg5RGIw9e+/oC8+YBzZqpl3Xm/CkpZ7E4d5xgjzPtpterfzdaa9CTU4ezOFOf1iRPSgkKAnbsYNETgJMHWwfpQ0JY80E15+mGDayuZ7HwqCtRgpHnzgEtWiiP0jrbcErHBQTwDFGRIvYGBwcDv/0GlCqVsjp1upQ/rIDylxgby1OXN27w32ZzQhJfkwno0oWFH5TYs4dFFOLi+HiihLCha9f4vGozU6NGAf378/8NhoSXGgD07csziM87JUpwOFZwMP+duI0KF+b7LCOEP6bm/hUck5r3mReR7G9fp9Nh9+7d6NatG2rUqIEb/3/JLV68GHv27HG5gYIgeDehobxeafdu7n/07MnLHm7eBHr1Ui/XpIlj1bOmTe23lyrl3Hv1hRccH5PWPH3Kandffsnhibdve9oiewoVAvLm1T7GbFaObmrY0HE4e2go8OKLKTbPjty52Wl35jgt7t8H5s7l72b5cmV596QUK8Yiet98w+qRlSsDI0cC168DdetqFPz8c/XOl8nE2urbttnvq1/fsVH+/kC1asr7ChcGTp3iOMe332Yp77lz2Vlp1Ei5TEAAS2FqdRZNJuUbom1bx/YCHNqYlJ9/ZqdHSUGPiO2ZPFn5fBMnarfvpUvq2vR6Pa/rOnIEaNkSqFABaN4cOHyYZRil08w0bMj3zfDhfONXrw7MnMky+VZnPb1ToQKQObP2MRYLh4QKyadoUY4l10Lt3eJNJHeq65dffqGAgADq06cP+fn50YX/T5HPmjWLmjdvnqxz7dq1i1q1akW5c+cmALR69Wq7Y06dOkWtW7emzJkzU1BQEFWvXp2uXLkSvz82Npb69+9PWbNmpUyZMlHr1q3p2rVrybJDQvsEIe05dMg+n2PiCBo/P6KrV+3LHT7sXLTQtGlpf01arFpFFBaWEK1gjeYaMoRV57wJq5qiWqRF6dIc1qlEjRrK0WPW73XsWNfaevu2c/fDsGHK5c1mjhzx8bGNsAsJIfrpJ+2679yxVY+0Ro+9+CJHPykSGelcOMvAgcrlW7ZUj6XV69XLpQZHanbZsytnH3ZWBW/jRvuyr7yiHDqW+OPvb1/ObFZvn8ShiN26qV/vtGkJynTWhzUggGjq1BQ3YYbj5EnO2p047BIgatqUM3pnFEaOVP+hMhpZ0U9IORMnardvuXLqPzZuxm2hfePHj8ecOXMwd+5c+Pj4xG+vWbMmDh8+nKxzRUdHo3z58qpqfxcuXEDt2rVRsmRJ7Ny5E8eOHcOoUaPgnyjXxeDBg7F69WosX74ce/bsQVRUFFq1agWzWh4IQRC8gsqVOfRPr7edmTIYOOro11+B/PntyzmT+9tg4Mggb2HbNlYStKYdMZkSorm++QYYOtSj5tkxaFDCbKL1u7FGb+XOzRMaarOCK1fyrFbiaC+Dgf/t0IFzdboSZ75nnU49kmvcOM5/FRdnG2EXGcn6BGvWKJd79owjxHbt4r+tUXoAp+SpU0dlxtHZ5PVqF7ZoEVCmDP/fOjtibeDGjYGvvnLu/MmhTBnt6eMSJTiRa1JSc60xMY6nN5894y8tMYm/CDWI1Nv3u+/4AbBOSVof1pgYzptlVS98nrl5k2/w//7jv61hlwC/7Jo1c088rScYO5ZVKYGE58z6YnvhBZ6+FlLO+++zRC9g/2OTNy+wdq3Xh/Yle0YqICCALl26REREQUFB8TNSFy5cID+1HBROAIUZqVdffZW6aYwaPXz4kHx8fGh5okWnN27cIL1eT5vUVrgrIDNSguA5Tp0iGjCAqGxZogoViD78UDs1R0QEka+v40FupfX2nqJaNe3Bdb1eXV/AU1gsRFu28MTAiy8S1apFNHMmT6g4IiqK6LvviCpWZK2Axo2Jfv+dJwtczbNnzuUy++IL+7IRETzzqVZGpyMqUUJ5QHTpUseTHoprpM1moly5tAvrdJwzSo2YGKJFizg3VOnSRC1aEK1e7fzUZkwM0e7dRDt2EN275/j4rl3Vpxmtn3377MsdO+b4i9HplBO/1a/vuKzSjBQRz5SojXJbH7jPP7cv9/QpUdas2nWGhfFxzzMffOB41u/XXz1tpeswm4nWreN8R6VL8yzUDz9kDEENb8Bi4R/sjh0TfmxmzSJ69MijZjnrGyTbkSpSpAht2bKFiGwdqUWLFlGpUqVSYOr/DUniSJnNZgoKCqJx48ZR06ZNKXv27FStWjWbY7Zt20YAKCIiwuZc5cqVo9EayjqxsbEUGRkZ/7l27Zo4UoKQjujZU/133GBgYS53dNpTwuXLjvuDej3RN9942lLX8fPPREWL2l5jvXpEx4+7vq4HDxy3L6AsDLdggXNljx2zL9uypXZfHWAnUpHx49U9a52Ok9y6oxNhMnFsZUhIQn0+PkTdu7NXqURcnGNP1Wjk0RAlXnpJ/Vr1eqKXX1YuZ1Xrc/RR4ttvtb8cHx+iW7fsy/3xh3N1KoUiPk/kzq3dPgYDUYcOnrZSEFKF20L73nnnHQwaNAh///03dDodbt68iSVLlmDYsGF47733XDZTdufOHURFReHLL7/Eyy+/jM2bN6N9+/bo0KEDdv0/liI8PBy+vr4IDQ21KZszZ06Eh4ernnvChAkICQmJ/+RXih8SBMFrmTyZF/onXfdtNPLa+J9/9p414RERjo8xGJw7Lj2wYAHQuTNw4YLt9j17gBo1tFMGpYSHDx0fYzRyYt6kREQ4d58ofTd373KvMbnlAADDhgH16tmr3RmNfDMsW5agiOYqiDhe89NPOW7RSlwcsGQJh2o9fmxf7skTx9mMidQvtndv9RA9i4X3K+FsbK7Sud96C2jTRrl9dTpg/nzlRe7OPoQZ5WFNKdYYZTXMZuDevbSxRRA8TLK7Gh988AHatWuHBg0aICoqCnXr1kWfPn3wzjvvoL9VMtQFWP7/cmzbti2GDBmCChUq4MMPP0SrVq0wx0GMMhFBpxFTOXLkSERGRsZ/rl275jK7BeF55tkz7rA66mCmlqxZgf37WanY2h8KCOA+2ZEjLDLmLeTP77izHheXMYSuoqOBgQOV95nN3DcePty1debMyUJ1Wlgsyu1buLDjZTgAr/lKijN+TkiIyg4/P2DjRmDKFFs58g4d+Ma2rslwJfv2AYsXKz+cZjOrrX33nf2+oCAgSxbH51dqYIuFF6Cp/R7r9bxITckmR2peADudSg+X0cjymLNmASVLJhzbogXLi3brpnw+ZxMeu/NhJWKHNiWLPM1mdpLdvT7JughSDaORR7oE4TkgRWO2n3/+Oe7du4cDBw5g//79uHv3Lj777DOXGpYtWzYYjUaULl3aZnupUqVw9epVAECuXLnw7NkzPEgyOnLnzh3kzJlT9dx+fn7InDmzzUcQhJSzfz8rHQcEcOcxTx7uP0VHu6/OkBBeB3zrFjtw0dHcD/Q22fNs2YB27RLWKSsRGKisAJ3eWL0aiIpS3282A3/8wWvVXUVAAPeLtdrXYFDuO7dsyd+PWp/QYGBlYyVHyhk0BxT8/IDBg4Hz59mTfvoUWLGCVVjcwbx52oIRFouykIJez1LpWg1ssSjnOti1iyXM1RrCYuEpyiNH7PeNGqVenxUtWWSjkfM+nTrF7RsXxwvXrUnvlKhWjfMrqI186PUsrFG9umPbkovJBEyfzi+wzJn5xm7QgBOSOeLmTR7BCAlhpzc4mGflLl50vZ0A8O672vtNJuDNN91TtyB4GSkOfsmUKROqVKmCatWqISgoyJU2AQB8fX1RtWpVnD171mb7uXPnUPD/GRArV64MHx8fbNmyJX7/rVu3cOLECdSsWdPlNgmCYM+aNUDt2pyf0Tq6Hx7OTk6DBu51pqz4+Hi3sM9XX3HfKGlf1GrzrFnsTKV3rlzR7m8D3Kd2dRDA8OHas37duwPZs9tv9/VNmIRJev8YDNyXnTZN+ZxaDqMVpUg5RawhZ+7k0iXHMxXXrytv/+AD9ibVHLHRo5Vnc65ccc62y5ftt3XqxHl81PDzAxYudO78zravTsc3hMFgfyPr9bzt++9d/12ZTCzrOXgwf09Wdu/mHFazZqmXvXQJqFQJmD074WUbG8ttU7my62NpAXasK1dWf9j79AFeesn19QqCF+KUI9WhQwc8+n+AeYcOHTQ/ySEqKgpHjx7F0aNHAQCXLl3C0aNH42echg8fjhUrVmDu3Lk4f/48Zs6cifXr18evxQoJCcGbb76JoUOHYtu2bThy5Ai6deuGsmXLonHjxsmyRRCE5PP4MY/0KykOWyycw/KLLzxjmzfxwgvA339znyhxH6xkSZ7F6dHDc7a5krAwx8rTAM8CuZIFC7TrXbFC3aHv0IGj7MqVs93esCFHw5Utq1wuIMCxXY5CDtOUHDkce7lhYcrbs2YF9u4FXnvN1pnKl49nscaMUS7n7Bet5OUCwD//8HRuUi+5eHHOhJwnj3PnTw516vBMWlJH4KWXOPO4ZqblFLJwIbBuXYJcgxXrTT1woK2DlZh33uFs0kmdZJOJX9BWaWlXEhAAbN/OdSe+ybNl44zW333n3SNbguBCNOb5EwgJCYlfcxSiGvSdfA4dOoQGiTJCv//++wCAHj16YOHChWjfvj3mzJmDCRMmYODAgShRogR+/fVX1K5dO77M1KlTYTQa0blzZ8TExKBRo0ZYuHAhDI5+MARBSDXLlvFadLXIHbOZB0rHjuVZo+eZYsU4/9KtWzwAnyULO1IZqb/hzM+DTqfeXweAc+eAmTM5BNBiYU2GAQOA8uWVj4+L47681lqnqChO96IWbdSsGdC0KafFuXcPKFCAfQQtfH219wPOOVtpRrdu7FGqYTAAPXuq78+Rg9dYffMNN1RAAOeX0vqtbdKEb3QtRZB8+QC1CBK9nqcEX3iBBTHi4lixZMQI4P+RKW6hYkW+WWJjgRs3OJ/Nm2/yzI87mDGDHwy1F6lOB8ydaz8qdfEikCgixw6zGTh6FDh0yPULR4ODeaZswgReX2c08mjE8/6iF54/0kZE0LuRPFKCkDL69XMuh8/16562VEgLPv/csSQ4QHTggHL5lStZSTtxyiKjkc85Z45ymStXHNfn40M0cKBrr7VaNcf1Bge7ts5UYTJxfhalvAEGA1H27EQ3b7q+3kmTtBvphx/Uy+7YQRQQYGuz9eYYO9b1thKxnn7lygny7In/rVSJ97sarSRz1k/Llvbl1q51Tq59/nzX2ywIGRy3yZ8LgiBYCQhwTqHPq0KcBLfh7AyM0nEXLwJduvAgeuIoJZOJ77G+fYGDB1NWJ5HrZ4ecUe3LlMm1daYKgwHYsIFD5ayy4Nbp0LJlWZ8+d27X17tnj/q0q07H+5V48IDVC58+tY3btN4cY8cCv/3mUlMB8I32/+UG8dOc1n+PHeNwNlfj56e9X69XvplS88AJguASku1I3b59G2+88Qby5MkDo9EIg8Fg8xEEb8Rs5rCqt97iCJdJkzgPjJA62rbVXr+u13MkTtasaWeTN3P5Mi8n6doV6NcP+PNP5xxRIl620a8flx071vl1/GlJmzaOr6dgQSCJGCuAhPA8tfIGA4uaJSV7dqBqVW2xCZOJ/QdX0r69YwVor1NizJyZZcHPn+eY2+nTWXLz8GFed+SIO3eAiRP5JfrWW+zIaC1Ou3w5Ye2PEkTATz8pv4wXLeKFbWoxmwYD8PXXjm1ODjdvchI6tWsym7n9btxwbb0dOjhWVGzb1n67VdrdESJFLgjuI7lTXS+//DKVLl2avv32W1q9ejWtWbPG5pMekdC+jM21a0SlSydEhRgMHEnh40O0ZImnrUvfWCzq0ULWz2+/edpK78Aa9mYw8McaodSwIdGjR+rlIiOJ6te3vX+t9/CECWlnv7N07qx9P8ydq1yuUiXHEUp58iiXXbdOu9xLL/G96koiI4myZtUOJzx71rV1epTFi/mi9HrbG7hMGaIbN9TLOBN6tm6dfdl27RzHiRqNrv1iV650zt6ff3ZdnURER44kxLAqXWOhQkSxsfblfvvNOXsXLXKtvYLwHOCsb+CU2ERi9uzZg927d6OCliypIHgJZjMvJD93jv9OPHtisfDAav78LNQkJB+djuXPW7YEDhzgQVXrr7dez2uoW7b0tJWe58cfgY8/5v8nHezetYvvw7Vrlct26cIqyID97N/IkbxWXy2/qCeYP5/FwjZuTLgfAH7exo5VF3xIjeiGo1kwZ2b9BA127WINeaWGPHOGX7JHj6rr+6cET6iweEr5pUIF4NdfgddfB2Ji+OWp0/EDX7AgsHmzcvhfRlKqEYR0SrIdqfz584PkV0lIJ2zYwPkY1dDrOcePOFIpJ1s2jg7avp37Ao8fc+hWr15Arlyets7zEAHjxqmLcpnNHP10+jTnAk3MyZOcn0sNnY4TH3ft6j19qsBAtvnvv1nVMSICKFKE7wetxLYNG3JfXC2qymgEGjVS3jd+PD/LalFgf//NH63UNpGR/D3cv89915YttZX5Fi/ma1ODiCPnZs5UPwYWCz84J09yw7Vs6Z51Sqnlyy+5gZW+HJOJcxX98QfQooXtvlq1tNXoAP5ilb6YevV4lEYNg4Ff3K688WvU4PNqhSvq9eoqg6mhTRvO4zVuHPDXX3zzvfEGjzyohf1Vq8YqeXFx6ufV6TjRnyAI7iG5U11//PEHNW3alC5dupTCyTLvQ0L7Mi5vvmmrAKb00euJnj3ztKVCRuXMGceRNwYD0Zdf2pf94gvtMDnr59y5tL8uV3PhAkeOqUVz6XREBw/al7tzx3H7GI1EI0Yo12uxcNilv3/C+wDgsL2VK9XtbdTIceRZjhwaF7x3L4dsJa7UYCB6+22ip0+T1XZuJTbWuRC7t95SLt+unfpNbDAQ9eypXO7BA6KgIO161693/fW+/rq2vZ07u75OIqKTJ4lefDHhftDp+PPKK9qxv717a9vburV77BWEDI5LVftCQ0MRFhaGsLAwvPbaa9i5cyeKFi2K4ODg+O3WjyB4EzExjsN6LBbtAT1BSA0xMY6P0es5ZY1SWS0RBStKZdMbRYrwDJbBYBshZjTyoPqcOcqpcJxpX51O/bgvvuCwS2sbWme17t8HOndWF4aLjnb8bnn6VGXHv//y9Nr/k8/HV2o2Az/8oJ3PKa159sy52Em1m3DePM43BSTMHllv6mrVlBVEABbGeOEF9Tr9/e2zKLuC2bM5jxSQYKf13woVONmsq7l2jWeNzpzhv62qK0TAqlWsXqg23TptGlC9urK9pUtztmpBENyGU6F933zzjZvNEAT3UK4cJ+LUomBBUYcV3EfRotzn03J24uKU+4Tlyjl28gMCgMKFU2ejt9CxI4fiTpvGDozFAjRuDAwerN5nzp3bcc5XtfaNjOSwQC1GjOCIu6QRZJUqcZ5TNdVKg0E9iTDGj2cHRalzbLGwRzlyJMuSe5qgIF5Ieu2a+jFE6l9QWBiwbx9f04IFrHhXoADQpw97qmrxk9u3J8iQK2EyAVOmcIJgVxISwpLsy5dzwtnr1zkhb79+wGuvOZfLwWQCjh/nh75kSe0M1AAwdSrw6JFySKHZzGvUtm7lrNFJCQoCduwAVq5kJ/zKFSBPHo6l7dJFftwEwd2k0QyZVyOhfRmX27e1E8bq9URff+1pK4WMzjvvqEff6PVEOXMqh5c+fcp5UtXydRoMRO+9l/bX4y6ePSMaNYooJCThGjNlIho8mOjJE/VyH36o3r46HSfGjYqyL7dokXOiZ//+a1/2+HHH5RRDA588cRyvaTTyRXkLX32lHd7n60t0965r6+zZ03FcdpYsrq3Tyl9/EdWoYVvXSy8R7dmjXc5iIZo+nSh37oRyPj5E3btrt09YmPZ1Ggx8DkEQ0gy3JeTdsGED/vjjD7vtmzdvxsaNG13g2gmC68iRg1XEdDp7QSm9HmjQAOjf3zO2Cc8PX3zBqVyS3oNGIw/Ir1jBa8aTknhf0vXmBgNQooTjGZX0gsXComXjx/NMkZUnTzj66+WXeRJHiY8/5hmipGGQRiO309KlrOWQlHv37L8TJe7ds99WtixrMAC257DOXPXowTNsdqjNPDhTqafo1k07aWyLFqw640ru3dNOUgfwjeIo7DC5/PknUL8+q5Mk5sAB3r5zp3rZkSOBgQOBW7cStsXFAUuWsEDFgwfK5bSmUwG+XyTxoSB4Jcl2pD788EOYFX4ELBYLPvzwQ5cYJQiupFs3jnxo2jShk1OwIKv1bdigrcwlJA+LRb2z642klb1hYaxs+OGHCcmJfXyAV17h/lm9euplGzTgYzp1SnC2smXjPtvevUBoqPvtTwv++INVH5X6xRYL92+XLFEua41u+vTTBKVIvZ6F0PbuBVq1Ui5XsKBzPk2BAsrbR4xgYbnEonMlSwLff58wgGNHaKjjcCsiNs4RRLwQy9XORFK++Ub7QVm7Frh40bV1FiyonaQW4BA2V6r2EQHvvMM3RdKwS4uFP++8o9zeZ8/yj4oSZjO3z5QpyvuDgx3bFhTk+BhBENKe5E51+fv7Kyr2Xbp0iTJlypTc03kFEtr3/PD0KYf4uDo55/PO4cMsZmUNoyxQgGjiRO1wLE9y7BhRly4J9ubNywp50dHur9tiIXr8mCguLvll4+K4bEa8f9u3147k0uuJqld3fB5r+zqjxBkbSxQaqh61ZjAQ1a3rnP2xscm4f957Tzu8T6cjunpVvfylS0R9+3LcI8AXMWKE68PriIjMZg6hcxR69sknrq338GHtOvV6onHjXFvn/v3OxXru3Wtf9oMPHIciZsum/PA6al+A1fsEQUgz3BbaFxISgosKI0/nz59HoFLshCB4Eb6+HOLjLTl3MgIbNrD41qpVCcIIV6/y7Evjxs6pqqUl27YBVasCP/+cYO+NG8Ann3DkTlSUe+vX6Xhw2dFguxJGI5fNiPfv+fPakVwWi3OTHtb2VQqVTIqfH/DttwnlEmMw8PvCWS0DPz8gUybnjsUnn3DcsdpNMGYMCzwocfIkxzHOnctxjwCHjE2eDFSuDNy86aQRThIV5Tj0DAAuXXJtvRUrAu++q7zPYOBY2YEDXVuns9egdNylS+rKelbu3VOWckwcy6qGu19MgiCkiGQ7Um3atMHgwYNx4cKF+G3nz5/H0KFD0aZNG5caJwiCdxMdzetazGb7TrDFwuFs1nUk3kBsLAuFmUzK9h4+zAluhbQnWzbHUu/WsEhX8tprHJ5XvLjt9po1OS+qVQnbpeTOzWtwWrWyvejcudmzGz1auRwRP3CPHtnfwGYzjwi4etFnpkyOvVKdzj1fzqxZwIQJtqp3RiO3wV9/scKeK3E2hYvStYaFOb6B/f2V15plyaJdTq93/Ro0QRBcQrIdqUmTJiEwMBAlS5ZE4cKFUbhwYZQqVQpZs2bFpEmT3GGjIAjJ4PhxHsgtU4bllz/+WFu5ODUsX859OrUlGhYL94UcrRlPK1atAiIi1AeOzWZOE6Oa/we8FGLQIBYbKFcOGDqUZ1McQQRs3Ai0aweUKgXUqsVt8/ix47KPHwMzZ3LnvlQpoH17YNMm9y+NSUveeEN7QF+vZwEHd9C4MX+PlSrxRFC9esD77zuXpujKFV6vVr48P3N9+3KaKIfkzw+sXs0P544d7FhdvconUJtyPHCAT662sMts5vVKN244YYCTGI3Aq69qT6GaTCy17Wr0ep7avnWLF7vt2MH/X7zYPY5b/fqOHZasWXnhYlK6dNF+0RmNQNeuyt9t9+7a7WuxuKd9AZ4l+/JLnqYvXZpt/Osv99QlCBmRlMQNWiwW+uOPP2jixIk0Y8YM2rVrV4riD70FWSMlZBRmzeLlFYlD9Q0GIn9/os2bXV9f//7a8vLWz/Xrrq87JQwf7py9//2nXH7xYm7PpO3r40P0yy/q9ZpMRK+9lnC8dRmMTkdUqBDRlSvqZS9fJipYMOH4xOfo0oXPnRF48oSoZEnlZSZGI69ju3fP9fWGh3O91u8kcfu2bMnrKtXYtImfrcTLnYxGPs/s2a63lebMcW4Nz6ZNrq335Ent9T916mSchXvffafdtnPmKJezWIiaNFFe+2YwEAUFEZ07p1z28mVeJ6VWtlYtXqvmao4cYen1xPkVrN/ziBEZ5zsVhBTgrG+QIkcqKWazmdatW0dt27Z1xenSHHGkhIzAnj3a67IDAohu3XJtnUOHOueYuKMDnBI++cTxenCA6No1+7L//quez8nqvJ4/r1zvF1+oCxoYjUQVKyr3WSwWovLl1W3W6Yi+/NKlTeRRwsOJ6tdPuGet7V2xItGFC+6ps2FD9fbV61lDQImbN/mZUvtedTqifftcbOyCBc45Ujt3urbe5cvV69LpONlZTIxr6/Qk06cnCHlYnZtMmYi++Ua73OPHrLpjHfWwli1cmOjAAe2yx44RFS+eUKf15m/ThujhQ9ddm5XYWE5gpyV6snSp6+sVhHSCs76BjogopbNZ//33H+bPn49FixbhwYMHaNasGdasWeOiubK049GjRwgJCUFkZCQyZ87saXMEIUW88gqv9VCLLtHrWR76k09cV+euXRwNo4Zez+vfDxxwXZ2p4cABoHp19f16PUe3HD9uH4Hz7rvAvHnq7WswcMjf11/bbo+LA/LmdZwGZs8eDvdLzO7dQN262uVy5ACuX3dOXCG9cPw4i4JYLEDt2ixm4g6BjRMnOERTi6AgIDzcPg/VuHH8PKmFIxqNQIcOnAfMZdy6xSGBWprtWbLwcf7+rqu3ShXgyBHt2MtFizhELaMQFcWhlzdvssx6u3bOyZQDrIry+++8KLNiRaBhQ8frpwB2X3btAg4e5LVUzZuzqIYzXLjA38GNG0DOnJz3o3Rp9eOXLOFj1NDrOV718GHn6k8uly+zvVevAtmzc0iho4cRSGij1at5kW7p0nzfyRoywcU47Rsk10N78uQJLVy4kOrUqUM+Pj6k1+tp2rRp9Pjx4xT6fJ5HZqSEjIAzCrr16rm2TouFqGpV7UHNNWtcW2dqsFhYzlprVmr5cuWyhQs7bt8XX7Qv9++/jssZDESffWZf9tNPnZtBO3nSte30vDB9uvqMUuLPnj32ZevUcVwuLMwNRr/5pvrUKED0+eeure/JE8cXajQS9ezp2noF57BYODTA+iIxGhNeGr17q+dZePNN514uru7bWSxEH32UMGOX2N4uXbRjae/dI6pZM+GeMxr5WfD1JVq0yLV2Cs89Lpc/P3DgAN5++23kypULM2fORMeOHXHt2jXo9Xo0btwYQZIsThA8ijNzyymff1ZGpwPWr08YSDQaeSDTYOB/p00D2rZ1bZ2pQadjwQmrEltie3U6zqf56qvKZR0pGwPK7etMm+t0KS+bnOMEW4icm+lK6Xfjlu9l5syEDMNGI1+AVaigb18WZ3Alzl6o3ISe4auvEqbBrfKp1mnzBQvU7weLxTkVIFd/r9OnA198wedNau+yZcCQIep2tG3LoixAQjlrVvWePYGtW11rqyA4gdOZTGrWrIkBAwbgwIEDKFGihDttEgQhBdStyzmd1KJ+DAbtMLyUkjMncOgQq8j9+itHxJQqBfTpo54KJyknT7Iol9XGIkVcb6eVrFlZln3LFmDlSlYdLFECePNNoFAh9XINGgA//aTe9zAagUaN7LcXLw6EhnKqHzVMJqBOHfvttWs77utkzeo4+ue//4A//+S+SJ06fL0Ct4UjBzkggCOcklKvHrBvn/rzZjTyMS7H359jeHfuZLW1u3eBAgU495SzWu3XrnGn02RitbYKFdSPzZSJR0pOntSWu1S6gZ9XwsOBP/5g+c/y5d0Xmxobq51fgogd748/5pdQYpyReg8KchzOePQohyJaX4AFCqgfGxcHfP65tr3ff8/3co4ctvv27NFWE9Tr+dyNG2vbKwiuxtkpriZNmlBwcDB16dKFNm7cSJb/r4w2Go10Mp3HlUhon5AR2LZNez24r6+yiIInuXaNww2T2tquHVFEhKets+Wff7TDwPR6otOnlcuOHq0tNlG6tLrYRMmS6qGTOh3R2LHqNt+5Q/Tyy/blmjRhYQeBqEYNbbGJgQOVy129ykIrWveEqzUf4pkxgyhzZtvKcuQgWrlSu9zjxywfmdToatXUlVKIiBYu1L7xw8KIoqNde43pkZgYoj597B/YcuU4xtfVbNniODQPUI5X7tPHubJqIiIXLxK99JL9C+nVV4kePVIuo6WIlPizYIF92SFDnAtFlH6c4CJcHtq3efNmnDx5EiVKlEDfvn2RO3duDBo0CACgc8dIiyAIyaJhw4TBycQpSYxGnun5+WcgXz7P2KbEw4c8iJ10kJGIwwWbNOGIDW+hUiXOlZo4kgpICA9csAAoWVK57McfAy1a8P8NhoTtej2vs16zRnnAWqfjtEBJk9Vaz9GqFfDRR8p1xsTwLNqWLfb7duzg2ZKoKNXLfW5YsYKfC50u4TuwtnWdOuoD/vnzcx41g8H+fgCAiRPdNCM1cyYwYABPpSbmzh3ONr1unXI5i4VvmJUr7cO1Dh/m6c/bt5XLdu8OvPce/z/xxRoMPGO1fj3/+zxDxImC58+3n6Y8eZJvpsuXXVvnkycpPy4uzvZlpIZSUr07d1gZ59Ah2+1EwC+/AC1bKk/VpsbeJ0+cm9WLiXGuDkFwFSn11DZv3kyvvfYa+fv7U7FixWjkyJH0zz//pPR0HkVmpISMxF9/8aBz/vwskNC/P9GZM562yp6vvtJeMw8QLVvmvvotFqKzZ4kOHUqePPs///C6+gIFOL/TW2+xcrEjTCYeGK5fnyhPHhammDDBubrv3mUJ9dKluWyDBkQrVmjnkJo7V7ttdTrOO+aNPHvGbXrkCGsduJuHD4mmTGGp+Tx5iGrX5rXrz545Lnv6NFG/fvys5c9P9PrrRHv3usnQ6Gii4GDtL7VYMeXpzd9/174hDAaijz9Wr9tiIdq4kahVK07qVawY0ciRPDUn8Jeu1b5GI9F777m2zv/+c26G5++/7ctOmuT4BZw7t/K9NGqUtsIQQLR+vX25q1eds3fjRvuyM2Y4VoYJC1MX1xCEZJJmeaQiIiJo+vTpVKFCBdLr9ak9nUcQR0oQ0p5SpbR/E/V6DktzB7/+yk5J4j7Oa695X+hjaqhVS7ufpNOx4qI3YTKx6Fy2bAl2hoQQffghp7157lm50rmO6KFD9mVff91x5zdPnrS/pozCe+85Dj3LlMn1SW4bNlT/Xg0GorJlleu8c0c7CaBeTzR+vHKd+fI5dspffdW+3OHDzt2/SqGIDx5oJ25zNBAgCMnE5aF9aoSGhmLAgAE4cuQIDh48mNrTCYLwnHDnjvZ+i4XT4biaH34AOnYETp9O2GYycURKtWqchiUjcPu2tpACEa+J9xaIOILsk0+Ae/cStkdGcphcq1YcjfRcc/euc+FNSg9XeLh2/ikAuH8/ZXYJ/N04Ui558kQ5VC41fP89C0kYk2iHGY0cbrl4sfI9kz07MHcu70sa4qfXc8K9999XrjPxA6qE2az88naUTE/ruCxZOH4asLfXYGDBFFcrVgqCE6TakUpMpUqVXHk6QRAyMAUKaPcJDQZtFb2U8PAhLy8BuOOeGJOJf7+dTVhsHQr1VgoW1F4Code7vn1Tw5YtwNKlym1qsbDI3PLlaW+XV5E/v3M3nZJcZsGC9p3tpOTNmzK7BG5zR0l3Q0M50a4rKVqU17i9+SZLTAKcnbtrV+Cff5QlJ6306MEPXuLFfDlyAGPHckZs6/mS4mixrdGo/HJxVsZV7bhXX2W1ysaNE348smXjRag7d7LKoCCkMS51pARBEJzlrbe095vNjo9JLsuWaQ8Im0zcmdcSYVi7lvsdPj6Ary+LYmzc6Fo7XcFbb2lPQFgsrm/f1DB3rnY/X68H5sxJO3u8kmbNuOOohl7Pqihlytjv691bW0tfrwfefjv1Nj6v9Oyp3b4GA7evO8S5MmXiGSarVHlwMP/tjGPRqBE7TVFRPNN06xYwapS6EwXwdWg5jSYT329JKVUKqFJFvaxOx/d38+bq565bl3NtPH7MI1/h4cCnn4oTJXgMcaQEQfAIPXty2hulWRO9nlXutH5PU8KFC44H5Z89Uw8p/OQToF07Vhq05pLcsYNtnTDBtbamlo4dOSeXUp/FYGDRLbXkw57g7FntfqjFApw/n3b2eCU+PgnSkUk75NbM0tOnK5etXZtzRqkRHAy8+67rbH3eKFs2QdkwKUYjkCcPMHSo6+u9eZOdkwkTEkI6IyKAqVM53O3CBefOExjISekczaoBfJ+UKKH88tbpWD2ybl3lstOnJ2RsT1qOCJgxg0eonLE3WzbnlAcFwY2IIyUIgkcICAC2bwfeeIP7h4m3DxoErFrl3G96cggNdbyMAQBCQuy37dqVkEsy8UyP9f8ffWSvBuxJjEbg99+5z+Pvn7Ddz4+jgP74w7n+SlqRNavjwfosWdLEFO/mlVf44Sha1HZ7hQrs1deqpVzu8mXtGzQykkf6hZQzYwa/JBLfqDody4Hv38+zRK7m3Xc5wXLS6Wezmde8devm+jqDg4Hdu3kkJvHIVFAQMGIEZy5Xe5hr1OD7NGkS6CJF+L5+7TXX2ysIbkRHlPwof5PJhJ07d+LChQvo0qULgoODcfPmTWTOnBlB6XB69dGjRwgJCUFkZCQyZ87saXME4bnj3j0O5zcYWPDBXY/hf/8BxYur7zcYON3Ljh32+155hfM9qc2aGI3cZ7Guh04KEYfxf/89cOYMOw6vv84fd6fgefgQOHiQbahalR1Kb2PuXO3IMr2eI3i01rDt3cvhfydO8D3UuTM76taIJ3dw6xZ/pxs3ct+1du2EAXu3QsQPzZ07vOBQKZwvMSNHApMmqcd7GgzASy8Be/a43tbniYgI4LvvOEY4NpZFG4YNs3ccXMHVq7wWyVE37uhR7bVSqeH2bT6/0cj3T2Cg82VPnOBryJEDqFzZPWGPgpBCnPUNku1IXblyBS+//DKuXr2Kp0+f4ty5cyhSpAgGDx6M2NhYzEmHQeziSAnC80OPHjxgmnRmSqfjzvr27cpRKQUK8MCvFiVL2qoBWrFYeBZo4ULub5hMXJfFAhQrxo7b877O/8kT7utdvmzvrBoMHMXz77/Kg/pELDD2zTcJ7Wvtk+XNy+37wguut3n7dqB1a+4vW+8no5H//8MPQK9erq8zxTRuzGthtPD3l4SmqeHIEW7nhw9tbwiTiUPvXK0qt3490KaN4+Pmz/eym1EQvB9nfYNkB84MGjQIVapUwYMHDxCQaDFi+/btsc3RS1oQBMHDzJ3L66CtjpM1rDBLFo4sUQvtd2awVO2Yr78GFi3i/1udBGs/69IloEMH71YATAsyZeIZu8qV+W+DISFqqEQJ4M8/1SOj5s1jJwpIaF+rqmJ4OEdWORPSmRxu37Z3oqz1Wx1nr8oI4ufn+Cb2pljP9MaTJywGEhlpf0MAPCP422+urdPZ78vVSoGCIMTjYNm1PXv27MFff/0F3yQPcMGCBXEjoyRgEQQhw+Lry87UqFHA6tXAo0fcUW/bVru/ERbGUShaKIXMmUzsSKk5SiYTcOAA8PffHBnzPJM3Ly8lOXCAZ3ssFl7yU7euug9AxHmmrGvVk2IyAefO8ZowV4qX/PCDvROVGIOBnbslS1xXZ6po3VpbXtJodDy78fgxS18eP86LGdu25S/IW0OyoqNZM//oUX64W7fWvpkSc/kyT13fugXkzs1xu1r5Alas0M6TZDBwaGWrVsm8CA1q1eJQuuho9WOMRp4lyygQ8cty9Wq+7tKlWepdaWGrIKQFyc30GxoaSidPniQioqCgILpw4QIREe3evZty5MiR3NN5Bc5mLxYE4fklXz7rHIf6p2RJ+3InTjguZzAQjR+f9teUEbhxw3H7+vgQvf++a+utW9dxvVmzurbOVPHoEVH27HyzJTVUp+Pt//yjXn7NGqLAQD7Wx4fIaOSyNWoQ3b2bdtfhLBs2EAUHJ9wAVnurVCG6dUu9nNlMNGxYQpv4+PC/Oh3fRGazcrmuXZXbNuknLs611/nRR2ybUl16PdFbb7m2Pk/y4AFRgwZ8bUYjfzc6HVFAANGyZZ62TshgOOsbJDu0r0mTJvjGGkMBQKfTISoqCmPGjEGLFi1c5+EJgiB4Ec6o7CoNdGvlckpczpnjBHucDdlzdfs6cz6v+k6Dgzmrcdas/LdVElOv51mLZcs4B5US+/eznv6TJ9xFj4tLCFk7cID1/10dO5kajhzh2TVrQrjE9h49yiF4al/OhAnA5Ml8nWYzlzWb+e8pUxKkO5NiPcYRro7h/fRTnpEBEmJhrf+2aKEuh5/eIALat+cYX4C/z7g43h4TA3TpwrHBgpDGJNuRmjp1Knbt2oXSpUsjNjYWXbp0QaFChXDjxg189dVX7rBREATB49Stq52DymgEGjSw3168uOOoE5NJXbVa0CZPHsdCHXFxrKbnSurU0XaujUY+RhMiliRfuBBYuZLX17iTcuWAixc5LrFDBw7NGzeOY1ZfeUW9nDVJmpITYDbzYrDt27Xrjovj0MIFC4DNm7WThqWWiRP5X7VYz+PHgQ0b7Pc9eQI46sdMnKgcSlezpraTpNdz4rzEuR5cgdEI/Pgjh7v16cOhgz16cL6Gdetscx+kZ/btY0dJzQHW64Hx49PUJEEAUih/HhMTg+XLl+Off/6BxWJBpUqV0LVrVxvxifSEqPYJguCIgwdZml0NnY5V5V580X7fRx9x/0xp0N5g4JRAZ85471ITb2fyZOCDD5T7sQYDqytfueLaPuzly6y4qOUPbN0KNGqksvPYMe7wHjuWsM3fn5OojR/vOHN0WvHsGa+H0ppxMhq5Ez97tvL+H3/kZLT37iVsy52bZ0s6dXKtvUS8HiouTv0Yg4HXPC1caLv9t994HZUj1q2zPy4yEsiXj50xtbZavNg9eZ2eB4YO5fvFkQMeGem+/BnCc4XbVPv+/PNP+Pj4oFevXpg5cya+/fZb9OnTBz4+PvjTOuUqCIKQwahaNUEZLnEf12hkB+i775SdKAAYMwZo2JD/nzjJsMHAIhZr1ogTlRoGD+aoH8B2lshg4Byh69a5fiKgUCHuFydWFwQS/v/ZZxpO1LlzPF114oTt9thYnvHo18+1xqaGp08dh+0RJYTRJeXHH9lhTOxEASzi0Lkziwa4EmvIlxZmMwtnJEVLtCExStcaEsLX4utre0NYb8i+fRNC8ITkEx3t3EvyyRP32yIIiUi2I9WgQQNERETYbY+MjEQDpbgWQRAEL+XqVeDkSVbuc4ZBgzjpa8eOPMuRMyfw2mu8TOStt9TL+flxJNGiRRzdExICFCzIyoH//guUKuWa63leMRqBn39mgbZatXgZUMGCwPDh7KtUqeKeel97DTh8GOjenSdYsmfnpTk7dmgnDsZnn/G6DqUwJSLO8Hv2rGMDwsP5Au/fT/E1OCQoCMiVS/sYIlZPS0pcHM8kaDF0qHOO2vnzwKlTjvNc+fg4l4E5Sxb7bVrZuhOjlm25cWN+oPv25TbLkoXjfdevB2bNcs4RiI3l6zx/3rvWnXmaUqUcz0ZlycIJ5wQhDUm2I0VE0Cm8DO7fv4/A5GS0FgRB8BAbNnC+ooIFgTJluAPcqxcPkjuiRg3usN++zf3YxYud66iHh/PSkGPHOPrkyhVg0ybudwmpx2AAXn2Vl4bcu8ehdxMmcLSVOylXjvNY3bwJ3LkD/PorUL++RoHYWJbK1uoUGgwsva3G/v08xZk7N1C2LHv17dtzfKir0emA997TdgL0euWEr1u32s9EJYaIE6nt36++f/58jqEsVoynfHPmBIYNU58BI+JwREcozVo9fOi4nKPj9u1jrf3wcD5u3z5gyxblGbDEREez558zJ1+n9Zp/+EGSzAHAG29o580yGIB33/WekFjhucHpO65Dhw4AWKWvZ8+e8EuUcMVsNuP48eOoWbOm6y0UBEFwIdZIo8Qhds+ecb912zaeXXI0AJ9crl7l9VX379v2nw8cAJo25dkUVy8VEbyUyEjHoWc6HXfEldixg1XnEs9WWCw867F9O0+ZqsWYppS33mKvVG02qE0b5Yfm9m3nzq92raNH2wsIPH7MMba7d7P4QNK12SYThyM6QimML7X2fvEF8PHHtk5ndDTPRu3aBezZwzN8SYmJ4TjQQ4dsZykvXeK2v3RJXS3weSEsjGdqe/bkl3fidjIYeEZ05EiPmSc8vzg9IxUSEoKQkBAQEYKDg+P/DgkJQa5cufD222/jJ60RNEEQBA/z6BEPWgL2UTMmE88qjBrl+npHjLB3ohLb0KeP44glIYOQJYt25meAZyCUpAgtFu5Ims32YYFmM3fa3bG+atYs7VmeNWuAa9fstzuSU7SiNG14+rS6CpvZzE7HrFn2+3x8lDNjJ8ZoVLbNWXuVjrt4MSGeM+kMktnMU8+JUsfYMGcOq9kk/U6t5/niC45Bft7p3p1n+xJLnIaEcHjonj0iMiF4huQmqBo7dixFRUWlKLmVtyIJeQUh/REdTfT110TFihH5+hLlyEE0dCjRtWvqZebMUc9daf34+xO58hV3/35CLlCtz08/qZ/j0iWigQOJsmXjay1Vimj6dKKYGNfZ6S3s3EnUpg3nfg0IIGrcmOi339xb540bRB98QJQzJ7dv0aJEEycSPX7spgp799a+KXQ6ov8nu7dhyxbHNxJA9N9/6nXv30/UuTNRUBDf7HXqEK1cSWSxKB9vNnNmYUcZpT/91L5sXBxR7tza11m8uHLdQ4Y4fnCKFFG2efhwx8lxDx9WvtZChdRfEjodUYECykl5P/rIcZ25cyvb+8IL2i8mo5Fo8GDlss8rERH8sn/2zPkya9dyQl9/f37BtGtHtHu3+2wU0jXO+gbJdqQyIuJICUL6IjKSqFIl7nsk7n8YDEShoUT//qtcbuhQIh8fx/3Qc+dcZ+vhw47r8/EhGjNGufyhQ0SZM9v20azXXbMmO5QZhRkzEvqNib9TgGjUKPfUefo0O6hJ+8B6PVG5ckQPHrih0suXicLC1DveQ4cql/v2W8cjAQDRxo3K5X/8kcsrNXC/fsoOTWSk4/oMBqJu3ZTrnD1bu+yyZcrlWrVyXK9Op2zz7dtEefMqO2I6HVHPnsp1EhGtX2//Ykn80K1dq1zulVf4pnFkc2ysfVlnyrVooW6z4JgRI2zvd+uLRqcj+u47T1sneCHO+gbJFpsAgF9++QWdO3fGSy+9hEqVKtl8BEEQ3M2HH7Jog/UX0YrZzOF7nTrZbrcSEuKcEJYrI0QcJeMF2G6lOs1mzpsaHW0b9WO97v37WVo9I3DiBDBwIP8/cQik9bo/+8xxztfkQsQK3A8e2EdVWSwcTeVIdC5FFCzIIgRJswSHhHAY16RJyuVCQpRv7KQo3UxXr7IgBJFyA8+aBaxaZV8uIMDxAn6dTv2hWb7cdkFiYvR63q9ESIh2xmMACAxUFsHIkYPbt3Fj2/2BgZzU7Ycf1M/ZqhWvNytSxHZ74cLA2rW8HkzNXrXrtOLrq6zDr7RuKjEGg3MvEkGZTZsSEi0nftBNJn4e+vbllASCkAKS7UhNnz4dvXr1Qo4cOXDkyBFUq1YNWbNmxcWLF9G8eXN32CgIghDPo0fAggXqCe7NZlaO3rXLft8rr6iXA7gfVLs2C2e5isKFgfLltftYRCypnpSNG7n/q2azxcL5qzLC+qpvv9XuNxuNwIwZrq1z715euqJ1L/30E6CQ8SP1FC/OYglnz/Iao82bWcRg5Eh1hbwWLThprxZ58wLVq9tv//577XIGAyc8TYqPDysCajlTJhNLJibl9Gl+ENVGLywWTvKltL6qc2fth9VoBLp0Ud+fPz8/QBcucPtu2sRiEuPHO3bQWrYE/vuPnbFVq/jf8+e1k/V27qytxGg08jFKL4IuXbTb12xWbl9Pc/EiLwCtW5fFMiZPdq8Uf0qZPl37O9fr1ZNJC4IjkjvVVaJECVq6dCkREQUFBdGF/8dxjxo1ivr165eCyTPPI6F9gpB+2LfPuUijiROVy7/2mnIkjTVyZ9s219tsjRZSi07q00e53OjRzq2vUgtlTE+UL+/4OnPlcm2dU6c6F1X155+urTdVfPCBtrFz5yqXa9TI8YUGBCiXPXKEF48pNZbBQFS/vnKI3aJFjusEiNassS9rMvFiQLUyPj5EZ8+muBldjtnMsbZK4Zp6Pa/LUXtQz53jNTtKZY1GoooVeb2ZN7FgAV9XYpv1eqKQEH5JexOhoY7vwerVPW2l4GW4LbTv6tWr8TLnAQEBePz/3AhvvPEGli1b5kofTxAEwQ6lyJikEKkft2AB8Prr/H+DIeG4oCCWIW/Y0DV2JqZVK5Zdz5SJJxt8fLhunQ7o3VtZfAzg45yJ5HKmTbwdR4rggON8nMklXbbvhQvaOZ3On1fe7ufnOCGs2qxIhQqcfM2qhme9gQHW71+7Vvnczjac0nExMdpy5HFxwI0bzp0/LdDrgd9+S3iBJH65ZM3KM2JlyiiXLVaMc27lyMF/+/gkfBc1a7JSnTflR/r7b35xWSy2s4YWC8vTv/yym6ZxU4gz96FWjipB0CDZT2auXLlw//59FCxYEAULFsT+/ftRvnx5XLp0CeTML5IgCEIqKFeOE+jevat+jMXCqXaU8PfncK0xYzhq59EjoEQJXleVKZN7bAaAbt2Adu3YWbtwgVWwO3Xi0D81Xn7ZsRx7gQLcD0vvKKX1SYpWpFdKaNrUsSMVGgpUrOjaelPM5ct802oZPXMmy3AnXXfTvDmHuqlhNHJImxqNGnF+gNWrOR4yIIDXC5Utq16mdGn1fYlRcjCWLuXFa2oYDMDXXwMNGjhXR1oQGsohmkePslMVG8tOaNu2jjvzL73Ecby//cbS7r6+HMrpTLbvtGbqVG5/pZENi4VfqosWAUOGqJ/j7l1gyRK+5uzZeXSrUCH32GsdyVIbidHrte99QdAiuVNdb775Jo0dO5aIiGbPnk0BAQHUuHFjypIlC/Xu3TtF02eeRkL7BCF9MXGidlhf8+aettB11KmjHd43a5anLXQNAQGOo2/0etfX27atunieTkc0frzr6/xfe/cdHkW5/QH8O7ubhBAgkAiE0EEuSkdAlI5S5FJFKVIVCwjSEURFAQWUq6jARUXpouBV4Id0FAERlWboTQg9IQIhDUiyu/P747hJNtkKsy35fp5nnyS7s5mzyWQzZ97znveuffmla6VytupTk5IclzjpdKr6xx/axrt8uWvx2upv37278+cFBdlv206eU7So899N+/b2n//++/KmptPJ71Cvlz+2YcOkpFNrhw5ld+izddwXK6aqf/+t/X4poLmaG7g9IjV//nyY/5k4OmTIEERERGDXrl3o3LkzhlhWuiQi8qCxY2VU5/PP5UK60SgXSE0m4KGH5EJnfvHdd0DbtsChQ3Lh1GzOfs1jxkjDKbp7S5bIhf/du7OPIcvP97nnpPeD33B1SM7WlfdChaSznr1RnqAgKUHT0r3E64taT3KNK61P7f3+vvxSGlTY+l7z5klZwMyZ9xZfbrVrAytXAr17W5cjKgpQtKiM1N53n7b7pAJDUVXt6vEuX76Msq6uDO5HkpOTER4ejqSkJBTjytiUD8TGAnv3yolhy5b593/E3r3yf/mvv+Q19ukjFRr+NJ1AC5mZ0txsxQo5D/7Xv4AXX/SjkjMN1KkjFWOOlC0LXLqk/b5NJjmXWr4cSEiQztfPPy/N75xNK/KqgwelVMwRg0FK8EqWtL7/m28cd7kzGIAhQ5y3Rjx2TLL60FApq3P0P/PkSeCBBxx/P51Oyrtynzv07y81uI6ULCm/MEdOnpRSu5AQidfVNuImE/DLL0BcHFCmDNC8ufNuf75kNgO7dsm8sdKlpZOep94I27SRjpP2EmW9Xq5AvPOO9f0mk3RTjIuz/72Dg+XxiAjNws1y5Yr8w/j1V/nZtG0LPPus1FkT5eJybqDF8FdcXJz6yiuvqIUKFdLi23kdS/sov4iLU9WOHa0rGIKCVPWll1T11i1fR0dk3w8/OK8Wmj3b11H6gUcftV/rqderav/+tp/35JPOWxRGRtrf78mTqtqsmfX2oaGq+vrrjsuxHn/c/n51OlV96inbz3OltC8kxP5+z5xR1ZYtrbcvVEhVx41T1YwM+89TVVVdtUpVy5e3fm65cqr63XeOn+cr69apaqVK1vGWKWN/seN7tXat49+LwaCq58/nfZ4rLVcB6fZI5GOad+27efMm+vbti5IlSyI6OhqzZ8+G2WzGW2+9hSpVquD333/HwoUL3cr2du7cic6dOyM6OhqKomDNmjV2tx08eDAURcHHH39sdX96ejqGDx+O++67D2FhYejSpQsueeKSJZGfu3lT1kDavFn+G1lkZspFuG7dXKvIIPKFTp2Avn3tP96mDTB8uPfi8Vtffy0jDjnXI1IUudWoAXzyie3nJSU5fwOw1/HjwgXpHvfbb9b3374NzJgBDB5s/3s++aTjdaSefNL2Y66U9mVkWL/ZWcTFAU2byshDTnfuSIOK556z/z1Xr5ZF3XKfR1y6JN1hvv/eeVzetGGDrG91/rz1/XFx0sDBE3XOnToBr74qn+ccpTMY5LhculS64OT2T5dnhxTFte2I/ITLidTrr7+OnTt3YuDAgYiIiMDo0aPRqVMn7Nq1Cxs3bsTevXvxjKWnsIvS0tJQt25dzJ071+F2a9aswR9//IHo6Og8j40aNQqrV6/GihUrsGvXLqSmpqJTp04wad3eicjPffaZlPTZa6S0ZYskWVTwmM1ynnXunPud727dktJJZxVUWvjqK+CLL6T6x1JSV6qUTJnYutXz+w8IlSpJqdrbbwMVK0p3vurVgVmzJNGxtCjP7cEHHZd6KYr99o/vvSeJmK2DR1WBBQuAI0fyPpaZCUyd6vj1vP227WSoenXn8Vatarv28oMPpCucrTdDVZXkYt++vI+ZTMDIkdnb2TJqlPbtI++WqjqPd/Ro15JSdyiK/FGuXy/dHIsVk1K8Z56Remt754KutBdVVfndEwUKV4e4KlSooG7dulVVVVU9c+aMqiiKOnLkyHsaNssJgLp69eo891+6dEktW7aseuTIEbVixYrqRx99lPXYzZs31aCgIHXFihVZ912+fFnV6XTqpk2bXN43S/soP6ha1XG1hF6vqj16+DpK8iaTSVU//ti6SqlsWVX94APnzbGuXlXVwYOlGsry3GbNVPXHH70TO2ns4EHHbxCKoqqffpr3eUaj85aKBoOUzOW2aZNrpVy2FnA9edJ5vLNm5X2e2ayqxYs7j3fYsLzP3b7dtXi3bXP/5+8Jf/zhWrzr1/s60mxt2thvk6nTqWrFivLGReRjmpf2XblyBTX+WROiSpUqKFSoEF544QXPZHf/MJvN6N+/P1599VXUrFkzz+P79+9HZmYm2rVrl3VfdHQ0atWqhd27d9v9vunp6UhOTra6EQU6R2tXAnIRlVWvBYeqSsOEUaOAixez7798Wapy+ve3fxE7IUEaLXz5pVRDWezeLfOzv/3Wo6GTJ9SpA7z+unyeexRHp5NGDIMG5X1eWpqU8DmiqrYbCDhqKpDTlSt57/vXv7KbFdiKt0kT2y0rjUapc3bEZLq3eF3dztMCLV5AVh8vVizvaKNeL/ctWWJdtkrk51w+Ws1mM4JyLCin1+sRFhbmkaAs3n//fRgMBowYMcLm4/Hx8QgODkaJXKUMpUuXRnx8vN3vO2PGDISHh2fdypcvr2ncRL5QurTjx/V6oFw578RCvrdlC7B4se3HVFWauK1bZ/vxN96Q5Ct3BZNlqsvzz7u2gC75mXffBRYtkiTFIjJSFvDdsEE6puUWFiYd+hxRFOlsl5ut+2yxUbYPQOL66ispS7QoUQJ47TWp9SxUKO9zDAbnXdj0+nuL19XtPC3Q4gXk2Nu7F+jZMzuZUhRZQf3XX6XNLFEAcTmRUlUVzz77LLp3747u3bvjzp07GDJkSNbXlptW9u/fj08++QSLFy+G4mb/WVVVHT5n4sSJSEpKyrpdzHm5lihAvfCC4wt5JpPjOdbkmu3bpXFHiRIyLaBHj7xz2v3Bp586nmKi18s2uaWmyrmrvWkgqirbrFypTZzkRYoi7Z6PH5cGEmfOyGjFlCnSHtwWvV6e4+hgMhqBgQPz3v/44zLJzVE8VavK8Kc9ffvK/KuLFyXe+Hhg2jT7yZ2iyMiaozdDo1FeU27NmllP0LOlXDlpLe4PGjUC7r/fcbwlS8owsj2nTgHDhsmVuGLF5GewYoVnOxNVrSrz1G7ckP1fuybzrRo29Nw+iTzE5URq4MCBKFWqVNYoTr9+/RAdHW01shPu6voMLvjll1+QkJCAChUqwGAwwGAw4Pz58xg7diwqVaoEAIiKikJGRgYScy0wmJCQgNIOLs+HhISgWLFiVjeiQDdkCFC5su3zHZ1O/pe2b+/9uPKTmTOlAmr9eqkeSkwE1qyRc4///tfX0Vk7dszxeqUmE3D0aN77L1+2LuezJShIluehAKUokjBUqSK/TGdee03WX7K1jpKiyBBlrVp5HzMYgFyddq2eB8jjzi6WKookMFWq2B41y23sWMfbtWxp+6Rdr8/uemgvpo8/9p/1pBTFebwffWT/d/zTT0DdusD8+VLPm5IC/P67NIvo18/zTTWKFpUGFJ5YM4rIW7wyY8sFyNVs4tq1a+rhw4etbtHR0eqECRPUEydOqKqa3Wxi5cqVWc+7cuUKm01QgcV1pDzH2RIoiiLz+f1FgwbO56DXqpX3eZcvO3+eXq+q77zj/ddEPnTqlKo2bWp9IISGqurEic47l6xYoarR0dbPrVhR1iPyhCVLHB/AoaGqev26/ecXhHWkUlJUtWhR+2t8KYqqzpvnvddA5GdczQ08tOy1a1JTU/HXX39lfR0bG4uYmBhERESgQoUKiIyMtNo+KCgIUVFRqP5Pa8zw8HA8//zzGDt2LCIjIxEREYFx48ahdu3aaNOmjVdfC5E/iIqSeS+xsVKGbjBIFcp99/k6ssA3Z478PO2N8uj1wLx50obeH/TuDfz5p/0KHZ0O6NMn7/3R0VIxtH+//eeaTLKkDhUg1aoBu3bJUOfhw1Ja17q1jCo406uXHDA7d0ppXtmyMozrqaYCH30k39veAXznjkwgHDPG9uNPPgl07Qr88os0wihTBmje3H9GonLr2BHo0EFqjC9dkjK9li0dx7t8udTo2us4A8jo25AhzkcMiQownyZS+/btQ+vWrbO+HvPPm9rAgQOx2N4s6Vw++ugjGAwG9OzZE7dv38bjjz+OxYsXQ++vb3hEXlC5stxIO7t2OS6VMxrlPNFfPP+8rD369995K3T0eqmmefFF28+dMkXOzWzR6eSc+IEHtI2XAkSNGnJzl14viZenmUyyzpYjiiIlbI7odIHV+ECnk2TPVX/8Ib8Te29qqirzl1JSZO4UEdnk00SqVatWUB1dDcnl3Llzee4rVKgQ5syZgzlz5mgYGRGRNUdz7S1cmW7iLSVKSGOMTp1kQV2DQc4fMzOBChVk5NLeSGWHDtKFePBguXhvMMjFfZMJ6N7dfjdAIp9TFLk5OrcwmznK4urFZl6UJnLIp4kUEdG9SE6W7nGxsTLC0rOnJAme0LGjlO7Zm3+t19sfxfGV6tWlKcTmzZJUqaqUenbo4Pz8qH9/qW765hu5MF2smHQovJvBCCKv0enkzeD6dcfbadgcK4+DB4G1a4H0dGnm0LWra00yvKldO1kozh6dTropeniZG6JAp6juDAkB2LlzJ5o0aQJDrsuzRqMRu3fvRgt/aQvqhuTkZISHhyMpKYkd/IgCxJdfAiNGZI+YmEySKAwZAsye7doIkjtOnpTGZJb95KQo0j365EnPJXJE5AKzWf74nZ3a9OypfQ//xESZD7Z1q1yp0OlkCLhUKVnF2p9KBTMzpXX65cv2rw6tXi1rPRAVQK7mBm7P9GzdujVu3LiR5/6kpCSr+U5ERJ7y/fcyv+f2bTlfysyU8ydVlWYPY8dqv8/q1YH//U/K93KO5uh0si7omjVMooh8zvJG4Iiz0r+73W+nTsC2bfK1ySRvTICsk/TEE7bXG/CVoCAZqi5VKrscEsi+AjVtGpMoIhe4nUipdha7vX79OsI4BExEHqaqwBtv2J/ioKqyplN8vPb77tZN1gR9/XWZ192iBTB5MnD2LNfoIvILBgNQu7bjOVCKIq0ptfTTT8Du3bZHd8xmaerw/vva7vNePfCA1O3Omwe0aQM0bSoTIw8dkjc5InLK5dK+7t27AwD+7//+D0888QRCcqyCbjKZcOjQIVSvXh2bNm3yTKQexNI+osBx9KjttT9zUhQ5NxgyxDsxEZEfWbhQ2lbaoigyX+nSJW3XhXjxRenC4qi1Z3CwDKN7qu07EWnG1dzA5VkE4f9MzFRVFUWLFkVoaGjWY8HBwXjkkUfwor1eukREGklKcr6NXu/adkSUDz37rJTYLV9uvZ6UpWztm2+0X1wvKcn+ulUWGRlS7pfjQjQRBTaXE6lFixZBVVWoqoo5c+agqCuL8BERaaxKFedTHIxGmUdNRH4kLU0aMkREAIULe24/Oh2wdKm0p5wzR9aVCgmR7nljxgD16mm/z2rVnLdUL1OGSRRRPuPW+LKqqvj6668R74nJB0RELoiKAjp3tt++W1GAyEjZhoj8wPHjQO/eQPHiQPny8nHAAJlc6Ck6HdC3ryy8e+eOjBgtXeqZJAqQUkJHI1I6HTB0qGf2TUQ+41YipdPpUK1aNVx3tj4DEZEHzZol52K5W5zrdJJILVzof8u2EBVIBw5IY4fvvsueP5SZKeV1DRtKkpUfVKkCvPOOfJ57ZEqvlwYYo0Z5PSwi8iy3ZzzOnDkTr776Ko4cOeKJeIiInKpaFdi7F3jqKetkqkkTaZ7VpYvvYiOif6gqMHCgjAjl7mZnNMqK2oMH+yY2T3jjDRn1qlYt+76wMGDYMGDnTqBIEd/FRkQe4faCvCVKlMCtW7dgNBoRHBxs1XQCgM01pvwdu/YRBa7ERFlTMiICiI72dTREXnDzJjB/vnSJS0iQBcxeekmSllz/k31qzx6gcWPn2504IQu15ReqCsTGSgJZqZJr88FUFdiyReZ07dkjQ+pduwLDh0ubciLyKs279ll8/PHH9xIXEZGmSpSQG1GBcOmSLGJ24UL2nJwbN2T+zZdfypDsP112fc7Vsr1jx/JXIqUoUurnKlUFxo8HPvhAygAto3fz5wNffAGsWiWL/RKR33E7kRo4cKAn4iAiIiJn+vaVZCpnYwNLYUlMDDB6tEwS9AdhYa5tV9BL3taskSQKsC6BNBolKevRQxLnkiV9Eh4R2ed2aV9Ot2/fRmZmptV9gVgax9I+onuTmQn83//JhdPUVODBB2V9yvzYgvzGDamo+uUXOcdp3VoakPnLIAAFkNhYGXU4elSSjieflFtQkO3tjxyRpgWOBAUBcXHSutLXkpOB0qWlxM2e4sWB+Hj7bcFv3wZWrgTWr5d1mBo0AF54wX/reO/ckcYaa9cC6elA3bryZli+vP3ntGgB7N6ddx6ZhU4HTJ8OTJigfbwZGcD338sb+K1bQJ06Em/FitrviyiAuJwbqG5KTU1Vhw0bppYsWVLV6XR5boEoKSlJBaAmJSX5OhSigHP5sqrWqKGqgKrq9dkfFUVV33/f19Fpa+tWVQ0Lk9eW81asmKr+8ouvo6OA8tFHqqrTWf/RAKr6r3+p6oULtp/z+eeyjbPbli1efSkOvfGG/JHYi3XmTPvPPXZMVaOjZTudLvujwaCqy5Z57zW46vRpVa1QwTpevV5uX35p/3kGg/PfaZcu2scbG6uqVavmffPW6VR17lzt90cUQFzNDdzu2jd+/Hhs27YN8+bNQ0hICL788ktMmTIF0dHRWLp06d2nfkQUcFRVSvdPnZKvLRdUTSZ5bMIEudiZH8TGytpUt27lPctJTQWeeAK4csXXUVJAWLtWSvDMZus/GkDWVurQwfaaRPYWT8tN5/a/ds+ZMkXmbymKxB8UJB8VBXjtNWDcONvPu30baNMGuHpVvrb8PMxmKXkbMAD47TfvvAZXZGQAbdtK5xsgO16TSW4vvghs3277uc4W8s35/bRiMgHt2wPnz2d/bfloNgOvvAJs2qTtPonyIbffbX/44QfMmzcPTz/9NAwGA5o3b44333wT06dPx/Llyz0RIxH5qZ9/Bv78M3t5mNwsFSn5wbx5UsJoqxjabJbzvi++8H5cFIBmzLCf7BiNUuq3dWvex1q3dn7SXbgw8PDD9x6jLXfuAK+/Djz+uFxB2bzZ+XP0emDuXOD0aeCtt6Qsb8oU4Nw5+TnYez0rV8qVCXvlbno98OGHd/1SNLd6tbwmR+V5M2fafsyVMkyta4fXr5crYPbevPV64P33td2nVs6elcUE33lHrtTlmmJC5E1uN5u4ceMGKleuDEDmQ1nanTdr1gwvv/yyttERkV9bv17WcbL3v9hslvU4r10D7rvPu7Fpbc0a++dIgLzWNWuAt9/2VkQUkJKSgN9/d7yNwSB/XO3bW99fpYoskrZune2DUacDhgwBihbVLl6LOXNkQdmcIyPr18tcpf37gagox8+vWlUSKVetXy+vx95IjNEoPwd/sX69dce93EwmSTxNprwji9evO//+KSn3HmNOzt68TSYZQbt9239a6t++LYn4N99IAq7TSfwlSwLLl8uIIJGXuT0iVaVKFZw7dw4AUKNGDXz77bcAZKSqePHiWsZGRH4uI8O1qpT0dM/H4mmuvAZHc+qJAMgfjSvsHXCLFgH16snnllEty4n5E094Zgj4+++BESNsJzVXrgC1amlfepaR4fx72hsi9oWMDOex5CzlzH2/M/YSnrvl6nHo6nbe0LcvsGKF/JwtJZ6AJKIdOwL79vk2PiqQ3E6knnvuORw8eBAAMHHixKy5UqNHj8arr76qeYBEWklPB3bskLLvS5d8HU3+0KCB86qKUqWcX6wOBI0bywVcewwG4JFHvBcPBajISOcd50wm+eOypUQJ6fD2zTdAu3bSFa5zZxlh+OEH+93v7sWYMY4fv35d+7rWBg0cz/XS6eS1u3Ilxxseesjx44oiC+sGB+d9LCLC+ffXurTvoYccD7EDspiwv3QyjomR8klbSafZLLd33vFsDMePAxs3yoLJWl84oMB1r10tzp8/r37//fdqTEzMvX4rn2HXvvzNZFLVGTNUtXjx7PYAiqKqnTur6sWLvo4usKWlyc/V0qAq902nU9UpU3wdpTZ+/tl5Y609e3wdJQWE995z3MkuLExVU1J8HaW4fdu1ToG1a2u738uXnXezW7RI233ei4QEVQ0Otv97VRRV/fRT288NDXX+833ySW3jTUxU1cKFHcc7a5a2+7wXEyY4Px50OlVNTdV+33v2qOrDD1vvq1IlVV25Uvt9kd/wWNe+nO7cuYMKFSqge/fuqFu3rjaZHZHGRo8GJk4Ebt7Mvk9V5cLSo49mN4Ui9xUuDHz7rTTiyjlaoyhya9HCM0uf+EKrVjLPHrCe4mD5fPp0oFEjr4dFgahHD/trRQHSyMFfFqm9ds217VJTtd1vdLSUMSqK9ZuLZZSqTx/p3OcvSpYEvvpK4rMVb/fu0rnPFldqgrWujy5eXEY19Xrbb97//rd07vMXiYnORx/NZu3nku3bJ//IcpcNnjsH9OoliwpSgeZ2ImUymfDOO++gbNmyKFKkCM6ePQsAmDRpEhYsWKB5gET34vhxYPZs248ZjbJu5X/+492Y8pu2beV/TL9+klgpClCtGvDxxzK32hOVRr4ybZo0lGjaNPv8o3VrYMMGSdaJXDJzpuPSoG+/zW5L7Wuu1uV6YgHgfv2AX38FunaVNxJLOd+iRcCyZf7V5h2QBPm33yRpKlRI4qtVS8oeV660376+UiXH39dgkGYdWuvSRcrUevaUhhI6HVCjhrQoXbPGcbJvoaqS5GidwORWtarzUsSwMNfKJN0xdqzUr9v7ex01SppgUMHl7lDXlClT1CpVqqhfffWVGhoaqp45c0ZVVVVduXKl+sgjj9zd+JmPsbQv/xo/3nk1QLFiUv5H2jCbfR2Bd5jNBee1kobS052Xcun1/lMTazLZr93Neevb1/OxBNofnKvxzpzp/Gd88KBnY1VV936+mZmq+vHHUuJmibFRI1X9/nvPxBYf7/ifuV6vqsOHa7vPs2ddK2tdsULb/ZJf8Fhp39KlSzF//nz07dsX+hxXV+rUqYMTJ05omOIR3buLF503UkpOlkVWSRv+Mvfb0ywVMERuSUx0fgVbUeTNyx+kpvrPxPpA+4NzNd6hQ6UTo70RqzFjgDp1NAvLLlfjNZlkFGv0aClxs9i/H3jqKeC997SPrXTp7PKR3HEaDED58sCkSdru05W/Qb3ef/5WySfcTqQuX76M+++/P8/9ZrMZmVwUjfxMyZLO/zeEhkpJGhGRxxUr5rj9o0XJkp6PxRWFCzuvzzUY/CfeQBQWJqubDxlivWZTuXKyftcHH/guNluWL5cOermvUloS7okTgWPHtN/vqFGy72rVsu8LCgKeeUZKKrU+Bl35fiYTj/0Czu1EqmbNmvjll1/y3P+///0P9evX1yQoIq306+d4+Q2DAejf3/9K7YkCWWKiXDyuUwcoW1YadXzzjfZL4QSk0FCZS+MomTIa5c3LHxgM0tjBWbz9+3svpvyoWDFg7lwgIUFGdg4fltGeV17xv5G4OXMc/9M0GIDPP/fMvvv0AU6ckAnQ+/ZJt6ilSz2zxsYDD8icPEevtVAhoFs37fdNAcOFy2LW3n77bfTv3x+XL1+G2WzGqlWrcPLkSSxduhTr/GmVcSJIF7WnnwZWrcpbnaLXy4XA/NJVjsgfnD0rTa7i4rL/5q5elTXcFi3y3FJHAWXSJGDtWrmin3sCvaIAAwfKpH9/8frrsihvWlreeHU6SQydraNErilSxP9/lkePOi73NBqBQ4c8t3/LmlyepihyReiJJ+RzW/ME3n5b+zW+KKC4fR2+c+fOWLlyJTZs2ABFUfDWW2/h+PHj+OGHH9C2bVtPxEh0T776SrrO5r6g+uCDwM6dQJUqvomLKL9RVZkicfWq9XmW5dz7p5/kvKPAe/BBYPt2IHeZfFAQMHw4MH++T8Ky6/775c0y98mrwQAMHiwjAlRw5Cw/tEWn85/2/feqbVvpYJh7xCssTLpv8kpsgaeoqrOp+PlfcnIywsPDkZSUhGL+soo3ae7qVWDTJlmyo25doHFj/6uYIApku3dLa3hHihUD4uOdn4sVCKoq7b2PHpW5SB06APfd5+uo7FNVmYty+LD8Ap94AihVytdREQAcPChDvpcvS2OG/v2Bhx/2zD+5IUOABQsc1+ouXAg895z2+/YVoxH48UcgNlZa/XfsKMkU5Vuu5gZMpMBEiohIC++/D7zxhvPlXvbtAxo08E5MRPmayQS8/LKsVWUwyNd6vZz4P/20NGcIDtZ2nydOAPXrAxkZtmvmy5aVOUzs4kQBzNXcwOU5UiVKlIDiwpWNGzduuPotiYgoH3H14jdHgok08s47wJdfyueWESLLx1WrpEX5f/+r7T4feABYt04WHk5JyW7bbjTK4sKbNzOJogLD5RGpJUuWZH2uqipefvllTJ06FaVyDesPHDhQ2wi9gCNSRER5ZWYC69cDp07JfOquXR03x9q3Txq8OFKihDSiyE8NJ44eBbZulcGARx4BmjTx82TxxAk52TUa5RfWvLmfB0w2paXJH2Rqqv1tDAb5g/NEyWhamrTj/OMP2U+HDsC//+1ae38iP+fx0r6iRYvi4MGDqJIPZuozkSIisrZxI/Dss9KNWa+XCh6dTtYOnTXL/rlS48bA3r32F8J++21g8mRPRe1d167JEjY//pjdIdlsBmrXBr77DvjXv3wbXx6JidJWfcMGCVhRJPt78EHgf/8Datb0dYTkji1bgPbtnW/39ddyoBKRy1zNDbh6DhERWdm1C+jSBfj7b/naZMru1D13rjSWs6dhQ/tJFCDz3/ODjAygTRtZRxWQBMoyXeTYMRnkSUjwXXx5GI3SHGLzZvnabM6ezHbqlPSsv3zZd/GR+9LTXdvuzh3PxkFUgDGRIiKfS0qS7sq//grcuuXraGjSJEmGbCVEqiprbZ47l/exhATHnbsVBXjzTef7v3VLjoVffpFjwx+tWiWN0mw11jCZgOvXtZ+ack/Wrwf27LEfcFISMHu25/avqpJh/vyzJG7e8vffsojZ779LrWp+Uru2ayWZ9ep5LoYbN+TNe/fuwEjYbt7M/mdz+7Z7zz17Vo7fQ4ccXy2iAoWJFBH5TGqqNJyKigJatgSaNZPPX389/53zBIqrV2WJI0ed93Q6YOXKvPevWuX4eaoK/Pkn8Ndfth/PyAAmTpTuzc2aySBJVBQwbJhMx/AnX32VXc5ni8kE5Jha7Htff53dFMAWk8lz60H99JN0eatZE3jsMaB6dRma3L3bM/sDJKvv0weIjgZatQIefVQ+/+CD/HMSXKmSjDLa+73q9TIHrn597fd98ybw/PNAmTLy5t20qfyxTpnivG2nL6SkyJpnuf/ZvPmm8382MTHynKpV5fitW1fKYf/v/7wSOvk3l2cEjhkzxurrjIwMTJs2DeG5VnSeNWuWNpERUb6Wni5rHe7da/1/NyUFeO896Z77/feOT1ZJe4mJzrfR6WTEJbfr17M7Lzty/XretWjNZqBHD2kGlrOj8p07MgIWEwNs2+Y/TSquXcvb+Tk3v2pi+/ffzk9wXfnlu2vjRqBTp7z3798vCc5PP0kdpJYSE6Xjx/nz1gfjtWvAq68CFy8Cn3yi7T595fPPJUmMj7f+/RoMsmibJ5LjtDRJLI4etd5nUpIkUmfOyFUEf2lgcvs28PjjwIED1vEmJwPTpwMnTwLffms73oMHJUnMXUZ56hTQrZu0l+/Tx6Phk39z+RTlzz//tLo1adIEZ8+etbovJibGg6ESUX6ydKlU29g6t1NVWUx+yxavh1XglSnjvOmW0QhUrpz3/sqVnSdRigJUqJD3/o0bgbVrbScnJpMMXnz1lePv7U333+/456Qotn9GPlO1qvOAK1bUdp9msyzeqqp5f7GWOVpDh2o/QjRrltSe2jsYZ8+WJCA/KF9eEoQxY4DixeW+IkVkqD8mRlqVa+3zz2VRZntv3suWSemcv1i8WFqK2ov3u+8kobdl9GhJonI/13LMDhvm+lw1ype4IC/YtY/IFxo2lP//9t6BDAZpt/3dd96Ni4C+feUCrb3z0EKFpKOy5bzN4vZtqZZJTrb9PL1emoytX5/3sW7dZDTK3qCJTifHzB9/uPoqPGv7dqB1a/uPK4rMkXr5Za+F5Ngff0hvdnsURcreclWf3JOff5ZSKGf27wceeki7/ZYqld0pxRaDARgxAvjwQ+326Q9UVepjg4M9OxpUvTpw+rTjN+/+/YGFCz0Xgzvq1XM8r8lgAJ56Clixwvr+c+dcuxry7bcynE75Crv2EZFfO3fO8YVoo1EqRMj7pk2TJCn31AvLudmsWXmTKAAIDQXmzbPe1kKvB8LC7J+7nj3ruPLMbLbd4MJXWraUTuK2zlf1emkD/9xz3o/LrocfdpxIhYcDL76o7T7Pn9d2O1dkZjpOogA50LTcp79QFKl99XRJ3YULzt+8z571bAzuOH/+7uK9cMH599br8+exRC5jIkVEPuFsfUidTi4sk/dVqiQDGB07Wp+TVa0qF20djbL07StzsB98MPs+RZH5cL//br/SqFQp5/PhIiNdfgkepyhSMTRliiwybBEaKj+frVtl5M5v/PWX/ALsuXlT6mm15OoisFr+Yg0Gydgd0ev962AKNM5+dno9ULKkd2JxhbN4dTrb8bpy/JrNnlnsmAIGEyki8omBAx2fOJvNwIAB3ouHrFWpIgnR5cuyrtShQzK/ulcv58/t0gU4ckSmofzyi1zY3bjROrnKrX9/x80bdDpZINif6PXSKj4uTjqL794tXQ/nzJFpKn5l/nzHXft0OglcS23a2B66zKlsWZnMrxVFkYPJ0Xwwo1GGE+nuDBzovAOkP/187/afzYMPArVqOR7hCw6WumQqsDhHCpwjReQLN25IF9n4+LxzcfR66ZS8Z4//dGkjz7pzR6Ze2KumKVFCSj1zjv6QGx5/XNoeOlKokPtr6zjz6afSUMKer76SYUwtxcZKy+/U1Lz1onq9JHgbN/pPV7lAEx8vb97Xr9v++TZqJFdQnHWt8ZZr1yTehATb/2zq1JHR2uDgvM/dsEG6Tto7VZ4yBXjrLe1jJp/jHCki8msREfK/1jLHXFGyz2vatJEmSkyiCo64OODKFfuPJyZyztw9cTSC4M427nr5ZWk1bhmis4wMhIcDCxZon0QB0iBg+/bsHvs6XfYbTK9esq4Ck6i7FxUlb961asnXlp8vIPXAGzf6TxIFSOndrl3ZCxPnjLddO6nDtZVEAcC//y3NJCzlgZbjt1AhYOpUGZKmAu2uRqTu3LmDQ4cOISEhAeZctRhdunTRLDhv4YgUke+kpgIzZgA//ij/o7p3l4ZaTKIKlvHjpYmFvYYTBgPwzDOeWzM233vqKVkx2ZFy5WSNJXuuXZOJYYcOyWSwrl2lDaMrCVhamvS3j4+Xcr7OneV7eJKqygn/n3/KiW+HDrZ77/uLmzflAN+/X07s//1v+Tn5U1KSk6rKZMo9eyTGdu3yLhDnb/buBX77TeJ9/HEZBndFRoaMTsXGSlLVtatcDKB8y9XcwO1EatOmTRgwYACuXbuW95spCkz+uKK1E0ykiHxjxw75f5ScnH0uZjTKWkabNwO1a/s2PvKehg3l/NGR6GiZs0V3oW1buVrhSGgocOuW7ce+/lraEBqN2aM7RqP8kW7aJL8cuns//AD07i2llZYRE6NROrxs2SKTFonIazxW2vfKK6+gR48eiIuLg9lstroFYhJFRL4RGysXiFNS5MKm0Zhdvp6QIMvP3Lzp0xDJizIznW/jbLFfcsCVUSN7E/J37pTmARkZ2QvpWn4Zx48DTzzhuHc9ORYTI0Pxt2/Lm2HOn+/58zJywkVfifyS24lUQkICxowZg9KlS3siHiIqIObMyT4vy81kknnMixZ5Py7yDVeaSLBj9T1o2dJx5zK9XraxZcYM+881GoHDh2UIme7OBx/IR1sFQkajLKDGlcnJHUajrHw+ezawbBmvSnqQ24nU008/je3bt3sgFCIqSL77zvFFbFV1PqWD8o/kZOfb8FzgHjz/vOPFWk0mYNSovPenp0uS5OiP1WCQXvl0d1atcjzcqtPx50uuW78eKF9eug2OHi2t3aOigLffdrzGBN0Vt2cwzp07Fz169MAvv/yC2rVrIygoyOrxESNGaBYcEeVfrnRZTkvzfBzkH1ypDOM5wD0oVQpYvVrWvMnMzP6BGwxyEv/eezKPKreMDPutny1UVfu26QWJs7I9s9n+3DWinH7+WRbys/zNWt4009Oly6DRCEyb5rv48iG3E6mvv/4amzdvRmhoKLZv3w4lx9UtRVGYSBGRS+rVk/d8R13a6td3/n0uX5ZpBBER0oDJ37sax8XJ/LASJYAHHvD/eL3loYeAY8fsX5jX62UpGLoH7dvLD/nTT6W5QUYG0KQJ8MorQOPGtp9TpIh087t0yf73VVV2hrkXtWpJeaS9hNWy1pGnmM2y/1u35E00IsJz+yLPeu01+WjvWJo5U0aeS5b0Wkj5ndulfW+++SamTp2KpKQknDt3DrGxsVm3s2fPeiJGIsqHXnnF8SiE0eh4Hc/jx6U7cPnyQNOmsgh93bpS1eCPTp+WTsZly0q8NWrIosNr1vg6Mv/w8suOq5tMJjlm6B5VriwnU8ePy8Jcy5bZT6IAyfSHD3c+v+rZZzUPtcAYPtzxqJ+qAi++qP1+VRX44gs5JurVk6S6dGmgf3/g6lXt90eeFRsrregdDd2bTJxvpzG3E6mMjAz06tULOkdvqi7auXMnOnfujOjoaCiKgjU5zigyMzMxYcIE1K5dG2FhYYiOjsaAAQNwJdeKjenp6Rg+fDjuu+8+hIWFoUuXLrjk6MoZEfmFLl2AQYPk85yjMpa3lsmTgQYNbD/3+HHgkUekK3DO848jRyRZWbnSIyHftb/+knPVjRut4z1xAnjySWDJEt/F5i8efjh7bcuc/14sx8aLL0rJP/nAyJFAixZ5kym9Xn5BCxfyCve9ePZZeUPMuSo5kN1pcfZsSXa09s47wEsvARcuZN9nNAIrVgCPPirrhlHgcOX3pdfz96oxt7OhgQMHYqVGZylpaWmoW7cu5s6dm+exW7du4cCBA5g0aRIOHDiAVatW4dSpU3kW/B01ahRWr16NFStWYNeuXUhNTUWnTp3Yip3IzymKXAz94gsZTbJo2BD43/9kXqw9Y8bI/Kncf+aWJGXIEODOHe1jvlsTJkgzBXvxDhvG+WCAlPB/+611Al2jBrBgAfD55yyD9JmQEFkravp0KfMD5JfRtq3U5/br59v4Ap3BAHz/PfDRR9YJU/PmcvVl2DDt93nunFytssVolOTq/fe13y95Trlyzt8kjUagYkXvxFNAuL0g74gRI7B06VLUrVsXderUydNsYtasWXcXiKJg9erV6Natm91t9u7di4cffhjnz59HhQoVkJSUhJIlS2LZsmXo1asXAODKlSsoX748NmzYgPbt27u0by7IS+R7qalysSw01PF2ly9LOZ+zd66VK4GePbWL725duybVMs4aJSxeDAwc6JWQAsKtW/I7DgvzdSRkRVXljzU4WBIs0pbl5xsUBBQq5Ln9TJ4MvPuu4/rq8HBZh8KVNcjIP/z731KqYe/3GhYmZZt8Y3XK1dzA7WYThw8fRv1/ZoAfOXLE6jHFw5cLk5KSoCgKihcvDgDYv38/MjMz0a5du6xtoqOjUatWLezevdtuIpWeno70HF1ykl3pu0tEHlWkiGvbnT/vPIkyGAB/mbJ58aLzJCooyH/i9ReFC/s6ArJJUYCiRX0dRf7lrZ9vbKzzbZKSZCjdlUXeyD/85z/AL79IF01bydQnnzCJ0pjbidTPP//siTicunPnDl577TX06dMnKzOMj49HcHAwSuT6Iy9dujTi4+Ptfq8ZM2ZgypQpHo2XiDzDlYZSJpP/NJ4KtHiJqACIiHBeBhYUxJPuQFOzJrB7NzBiBJBzzdcqVaQ095/qLdLOvXeM8ILMzEz07t0bZrMZ8+bNc7q9qqoOR8cmTpyIpKSkrNvFixe1DJeIPKh6dekW7OgcwGAAunf3XkyOVKwojRSc9efp0cP+YykpwLx5MmWiZk3gqafyNtogInLZM884bpNpMMibUnCw92IibdSuLXMXz5wBfvwR2LdP2sYyifIIt0ekWrdu7TBJ2bZt2z0FlFtmZiZ69uyJ2NhYbNu2zapOMSoqChkZGUhMTLQalUpISECTJk3sfs+QkBCEsLabKCApiqwd2rmzfG4rmRg7FrjvPu/HZs+0aUC7drbjVRRp6x0dbfu5588DrVrJR0Cef/IksGqVzPFfvJhTGIjITY0aSRvMDRvy1h7rdJJITZzom9hIG1WqyI08yu0RqXr16qFu3bpZtxo1aiAjIwMHDhxAbY0X5LMkUadPn8aPP/6IyMhIq8cbNGiAoKAgbN26Neu+uLg4HDlyxGEiRUSBrWNH4OuvZS40IP/zFUU+Tpjgfwu3t2kjTbks13tyxjtqFGCvR4+qAt26yVqoqpqdhFlK35cvBz780NPRE1G+oyjSkadXL/lcp8u+IhMdDWzdKkP/ROSQ21377Jk8eTJSU1PxwQcfuPyc1NRU/PXXXwCA+vXrY9asWWjdujUiIiIQHR2Np556CgcOHMC6detQunTprOdFREQg+J/h5pdffhnr1q3D4sWLERERgXHjxuH69evYv38/9C5epmXXPqLAdOcOsHatNGqIiJA1mfx5OZv0dOCHH2RdqeLFJd4cb2157Nol5XyOlC4tiZbB7foC8rkbN2RB3KNHZS5Kt26yXhP7vJM3xcYC69ZJq8w6dWT4nMPcVMC5mhtolkj99ddfePjhh3Hjxg2Xn7N9+3a0bt06z/0DBw7E5MmTUdnOAnQ///wzWrVqBUCaULz66qv4+uuvcfv2bTz++OOYN28eypcv73IcTKSIyB9NnSprZjqaygDIeXiNGt6JiTSyfDnw/PNARkb2SavRCDRpIlcHclVgEBGR93is/bk9v/32Gwq5ueZBq1at4CiPcyXHK1SoEObMmYM5c+a4tW8iIn/n6mUuNp0IMD//DPTvn/2Ly5kp//GHTAD89VeOTBER+Tm3E6nuuVphqaqKuLg47Nu3D5MmTdIsMCKigq5pU+ejURERQLVq3omHbLtwAdi2TX5Xjz4qnRUdevddmZNia50Xkwn47TdZC6ZFC4/ES0TkEadPy3sXIO9f99/v23i8wO1EKtwyu/sfOp0O1atXx9SpU60WxiUionvz2GPS7v3MGdsJlaIAw4ezQ7GvJCcDL7wAfPed9ahgixYy9alCBRtPSkmRrMsRgwFYvZqJFBEFhr//BgYMADZtsr6/QwdgyRL/nrx8jzSbIxXIOEeKiPzVyZNyPn3tWnaXYp1OPu/USdqgBwX5NsaCyGgEWraUSrzcA0sGA1CmDBATY2Oh5YQExx1GLN9g0CDg88+1DJmISHu3bgENG8poVO4rfgYD8K9/AXv3AoUL+ya+u+RqbnBPC/KmpqYiOTnZ6kZERNqpXh04ckQaTzzwgJygN28OfPMNsGYNkygtnT8PHDgguY4za9cCu3fbrs4zGoHLl4HPPrPxxMhI540kTCbPdg8xmaRDycGDchJE5AtXr8of3IULvo6E7sWyZcCJE7bLJoxG4Phxaa6TT7mdSMXGxqJjx44ICwtDeHg4SpQogRIlSqB48eJWi+ISEZE2SpYE3nhD/h9duQJs3w707s0OxVr5+WeZ21SpEtCgARAVBXTtKqOB9ixb5vjnbzYDCxfaeECvB4YOdfzk4GApk9GaqgKzZwMVK8oaQfXqyejYmDFAWpr2+yOy5dgxGU4vU0b+4CpWBJo1y55bQ4Fl0SLHjyuK820CmNulfZaFbkeOHInSpUtDydVVqGXLltpF5yUs7SMiKpjWrZOkCcgunQQkzylSRPo+PPhg3uc98oiU9TlStKjMo8ojLQ1o1Qr480/rIS29XoJYtgzo29fdl+LciBGArQ63ej3QqJFklG523yVyy+HD0uL/9m3rY1+nk9u6dUD79r6Lj9xXuTJw7pzzbc6e9Uo4WvFY+/NDhw5h//79qF69+j0FSERE5EuZmbKUk6rmbSFvMgGpqcDIkcCWLXmfW7EisG+f7dI+QC7C2l3OMCxMkpaZM4F584Dr1+X+1q2B11+Xj1rbv992EgXIi/jjD2D+fEm2iDzllVfyJlGAXEBQVfmDPH+ew+2BpEIFKc/MeSUqJ51OhvvzKbdL+xo1aoSLFy96IhYi8qGkJGDaNHlP1OtlGseYMcClS76OjMgzNm6U+VD26jJMJmDrVttTOAYNsp9EWbz0koMHixSRiW9Xr0oQKSmyM08kUQDwxRcy8dsRm5O6yO/duAG8/TYQHS1v3qVKARMmAPHxzp8bEwP06yfJvV4P1K4tCbWzdRfuxpkzwM6d9v9wVFUmF27dqv2+yXNefNF+EgXIYy+84L14vMzt0r4zZ85gyJAh6NevH2rVqoWgXDOd69Spo2mA3sDSPirorl+XEvVTp/KWN4WHS+m6J+e+E/nCrFnAq686PgcAZE5a7qp1sxl48kmpRMr9fL1eygF//13OT/3CY4/JKJgjhQrJaAEFjrg4WXDuwoW8ZaL33ScdUapUsf3cdevkIAayEyfLdA1LS1Bnybc7tmxxXranKDJyOmyYdvslz8rIkPeX33/PmyTrdHJ8/vRTwHVG8ljXvr///htnzpzBc889h0aNGqFevXqoX79+1kciCjyjRknn0twnhCaTjFT16mX/qj1RoCpe3LXjunjxvPfpdMD//id/O6Gh2fcbDNIIZOdOP0qiABlidlYuxQuJgWfIEODixbwnsCaTXCHr18/28yxv7CaT9eiTpc513Tpg7lxtY7X1h5Sbqrq2HfmP4GBg82Ypy8y5qGFwsIxEbdwYcEmUO9wekapRowYefPBBjB8/3maziYoVK2oaoDdwRIoKsmvXpHmSs0qOX3+VOcJE+cX163LsZ2baflxRgKpVZaQ217+6LPHxcr65erX8DT32mJTEVqvmubjvynffAT162H9cr5f5UbNmeS8mujcXL8pkPWencQcPArmrhebOld+3vecqisxrOXPG/sHvLrNZmg44andeqJD8UYWHa7NP8q4bN2TNKAB4+GEggLt5e6zZxPnz57F27Vrcf//99xQgEfmHI0ecJ1E6nUysZyJF+UlkJDB2LPD++7bPJ1VV5g3aO4/cvh3o2BG4cyd7NPfMGZlismAB8Oyznor8LnTtCtStK3/wuUcvLC0KR470TWx0d2JiXBtS3b8/byK1f7/83u29+asqEBsrbSe1Smp0OvmD6t/f/jbjxzOJCmQREQWu66LbpX2PPfYYDh486IlYiMgHco7E26OqQEiI52Mh8rZp04Bx4+ScUqeTChRFAQoXBr78EujZ0/bzrl7Nm0QBkqOYzdKMYt8+77wGlwQFyST+5s3la70+e/5L+fIyfyoAK0oKNFfevO1t5+pztS7J6tcP+PRTqYdVFPn+Op0ci6+/Lk0ziAKI26V98+fPx7vvvotBgwahdu3aeZpNdOnSRdMAvYGlfVSQpadLeVNiov1tFEUuTvI8i/Kr+Hipfrt2TSqann5aBmnsmT4dmDTJfqMKg0GSsOXLPRLu3TObZchs+XIZjWjeHJg8WTJHCiypqbKg8q1b9rcxGKT1aunS1vf/3/8B3brZf55eLx2Itm/XItK8UlJkkuGFC7LieI8e0m3QkzIypAb3t9/k9bVpI6MnOrfHFKgAcDU3cDuR0jk44BRFgclZP1g/xESKCrrp04E33rD9mF4PPPUUsHKld2Mi8mctW0pDCUciIyUx8xsXL8ow2uHDcoKtKDJBLDJSOrS1aOHrCMldEyfar03V6WRo9Isv8j5mNEor1thY++V9GzYAHTpoG6+v7NkDdOkiQ8mWAYDMTKB6dXmd9jobUoHlsa59ZrPZ7i0QkygiAl57TZaCALKrfSwfmzeX+R5ElM2Vf3d+9S8xPV06YRw/Ll8bjdldNhITgSeekK4aFFjeeQfo00c+z/3m/cQTwOzZtp9nMEinNcuq0ZaL5JYa1zlz8k8Sdf68jD79/bd8nZmZfeyfOQO0agWkpfksPApsGi4QQESBSqeTap+XXgIWLpT/O/fdJ+Xsjz/OygfK/9LTZQrR9etS2te8uePjvlkz28umWBgMso1TBw5IA4iwMDnZ89RE++++A/76y/ZjZrOcWH7yCfDf/zr+PjExwKFDMselTZuA7sqVLxgMwLJlwCuvAIsWyahjVBQwYIAMmzrquFe5siTW338PrFkjJYJ16shVtcqVvfYSPG7uXHlttupwjUYpfVy+3MkK2kS2uVTaN3v2bLz00ksoVKgQZtu7uvGPESNGaBact7C0j4io4Pr0UyltzTlPsFIl4PPPgXbtbD8nNhb4178cd7zculVyDZsOHZK2fn/+mX1foULA6NEyyuBszSd3desG/PCD49WHIyIkk7Tl+HFg4MDs1saAdKB55RXgvfe0XbiVSEvly0uyZI+iyGjtjz96Lybye5rOkapcuTL27duHyMhIVHZwlUJRFJw9e/buIvYhJlJERAXTnDmynE5uiiIjUlu3Aq1b237uN9/IqK1Ol51QGQzy+ZQpwFtv2dnp6dNAw4ZSTpR7SEtRgMGDJbvT0uOPA9u2Od6mUCHg9u289587Bzz0kLTCthXvgAHA4sVaRUqkrYgIx92UAKBRI5lHRfQPjzWbyI+YSBERFTypqVIFZW96hE4H1KsnS+7Yc/Ag8PHHwMaNkkA1ayaJ2WOPOdjxgAGShTkazjp5Uoa8tDJihCRn9vapKFLWFROT97EhQ2SipKN4bS36SuQPmjWTTn2OWmz27y917UT/8Fizidu2rlb9Iy4uzt1vR0RE5BNr1zqeY242yxSmEyfsb1O3rkxNiY+XDn1r1jhJou7cAVascJyUWOa9aOmll5yvvD1sWN77TCZg6VLn8S5dem/xEXnKyy87Lmk1GmUborvgdiJVv359HDhwIM/93333HerwahQREQWI+HjXpiLFx2u406Sk7I5hXtspgFq1shc7zd1FQ6cD2raVOVu5paXZLvfLSVW1j5dIK717yxzB3I03LF+PGyelfUR3we1Eqm3btmjSpAnee+89qKqK1NRUPPvssxg4cCDeslsQTkRE5F+io11rUV62rIY7LV5cmjQ4oqoa7/QfkydLd7IaNbLvi4oCpk6VRhSW9XVyCgtzvlivongmXiIt6PWy+O/771sfp9Wry3DyzJm+i40CntuJ1Jw5c7BmzRp88sknaNGiBerWrYuDBw9i7969GD58uCdiJCIi0lyXLoCjabE6HdC4MVCtmv1tfvsN6NFDvk9YmPR0+OEHBzsNCQH69nXc5c5sljkbntCnj3QMjI+XVtmXLknLwuBg29vr9cBzzzmO12iUjn5E/spgAB58EHjgAWmqEhYmFxT+9S/HLeKJnLir1WHatWuH7t2749dff8XFixfx3nvvoUbOK1xERER+rnBh4IMPbD+m00kO8eGH9p+/cCHQtKnMi0pJkaVqduyQBG3CBAc7njRJMi97dYWjRgFVq7r4Ku6CogClSwPlyrlW2/jaa9L5zNa2li6DPAcgf/b660DnzsD27TJPMS1NJkk2awZ88YWvo6MA5nYidebMGTz66KNYt24dNm/ejPHjx6Nr164YP348Ml2p+yYiIvITL74ofRLKlLG+/8EHgZ9+kkTJlr/+kueqqnUfBkup4MyZ0snPpkqVZCirSRPr+4sVA6ZNs5/d+Uq5chJvixbW9xcpIj3e583zTVxErti8GZgxQz7PWctrNMof8JAhwKlTvomNAp7b7c+LFi2Kjh074rPPPkPx4sUBALt378aAAQNQtGhR/JlzccEAwfbnREQFm9EI/PqrdN6rVEmWTXJU8TNunLQ9tzfHSq+X/g12kymLkyeBY8dkeKxFCyA09C5fgZecPg0cOSJxNm8uJVJUMMXGAvPnS5IdFAR06CANSyIifB2ZtY4dJZly9Mc6YgQwa5Z34yK/5rF1pJYtW4b+Nmq3U1JSMGrUKCxYsMD9aH2MiRQREbmjaVNg927H2xQv7nwdUKKAtHQpMGiQfG5JUBQFCA8HNm2SyYX+IjISuHHD8TaNGwO//+6deCggeGwdKVtJFCAjVYGYRBEREbmLS9lTgbVnj4w8mUzWozyqCiQnA0884V9XEBw1SrGw12yFyAkXji7bjh07hgsXLiAjIyPrPkVR0LlzZ00CIyIi8lf/VLY7FBnp8TCIvG/WLCmHs7VIs9ksa6UtWSJNU/xBp06OF5XW6aT8j+guuJ1InT17Fk8++SQOHz4MRVFgqQxU/ikmN7myKAcREVEAS0pyvs31656Pg8jrNm60n5QAMjK1aZP/JFIjR0oipSh5h5J1OpnnZylTJHKT26V9I0eOROXKlXH16lUULlwYR48exc6dO9GwYUNs377dAyESERH5Fy49QwWWKxfM/amLc506wDffSIlfzhb+iiKdJzdtAkqW9F18FNDcHpH67bffsG3bNpQsWRI6nQ46nQ7NmjXDjBkzMGLEiIDs2kdEROSOJk1kbrqjRmCPPurdmEhDZjOwaxcQFye98Zs1k9ELksYMO3YE1sH/9NMS0xdfyO81KEjaaj73HFCihK+jowDmdiJlMplQpEgRAMB9992HK1euoHr16qhYsSJOnjypeYBERET+ZvBgx4v1mkxSUUQB6P/+T9phX7iQfV+FCtLv/sknfRaW3xg5Eti2zf7jigK89JL34nFV2bLA5Mm+joLyGbcvr9SqVQuHDh0CADRu3BgzZ87Er7/+iqlTp6JKlSqaB0hERORvqlYFFiyQc8acTcEslUMTJwLt2/smNroHa9ZIsnTxovX9Fy8CTz0FrF7tk7D8SufOwJgx8nnOUjmDQUbtliyRxJOoAHB7HanNmzcjLS0N3bt3x9mzZ9GpUyecOHECkZGRWLlyJR577DFPxeoxXEeKiIjuxp49wEcfyTQLo1HWlxo5UtYm9bibN2V9nJIlgaJFvbBDABkZwOXLsiBvVJR39uktZjNQubIkTbZOjRQFKFdOFqLNmUBoKTNTfr7BwVJS6K+T8VQVWLcOmD1balwNBul8N3o00KCBr6MjumceW5DXlhs3bqBEiRJZnfsCDRMpIiIKGH/+Cbz1FrBhg5z8GwxAr17A1KmApypDUlKAd94B5s/Pbln40EPApElAt26e2ae37dgBtGrlfLuff3ZtO3fcuQNMnw7897/Zi8fWrAm8/jrQp4+2+yIipzy2IK8tERERAZtEERERBYxff5VOFxs3ShIFyFDYypVAo0bAqVPa7zM1VRKHWbOs+77HxEgZ3H//q/0+fSEuzrXtrlzRdr/p6bKI7bRp2UkUABw7BvTtC7z7rrb7IyLNuNxsYpCLPfYXLlx418EQERGRHWYzMGCAlNdZkigLo1GSnGHDgK1btd3vhx9K0pR7n5avR46UhCo6Wtv9epurpYplymi73/nzgZ0785YTWr6eNAno0QOoXl3b/RLRPXN5RGrx4sX4+eefcfPmTSQmJtq9ERERkQfs2AGcPZs3obEwmYAff5RttKKqwLx59vdp2WbRIu326SvNm8scKEfKlgVatNB2v85G9AwGadtNRH7H5RGpIUOGYMWKFTh79iwGDRqEfv36ISIiwpOxERERkcXJk9J8wNnU5lOntJsrlZoKJCQ43kZRgBMntNmfL+n1wCefyJpD9n7GH3+sfaOJ06cd/06NRuD4cW33SUSacHlEat68eYiLi8OECRPwww8/oHz58ujZsyc2b94MDfpVEBERkSNFizpPoizbaaVQIecL0SqK97oGelr37sD//icjTzlFRwPffitJltbCwhw/rtcDbIRF5JfcajYREhKCZ555Blu3bsWxY8dQs2ZNDB06FBUrVkRqaqqnYiQiIqIOHaQttiNRUUDjxtrtMygI6NLF8SiM0eiZBMNXuneXtZDat5fOee3aydeeeo09e1ovRpabyZS/fr5E+chdd+1TFAWKokBVVZgd1U4TERHRvYuIAIYOdbzNm286Pim/G6+/LqNOtrrz6vXSRbB1a2336Svp6dI4o00b4KefgKNHgW3bgLZtga5d5XGtjRuXvZhtbgaDJHNdumi/XyK6Z24lUunp6fjmm2/Qtm1bVK9eHYcPH8bcuXNx4cIFFClSxFMxEhEREQBcvep4kdb4eO332agRsGZNdvleUFB2sta8OfDDD/67cKy7xo2T1wPISFvOj+vXA2PGaL/PBx6QFZ1LlJCvc/5869WTLoxBQdrvl4jumcsL8g4dOhQrVqxAhQoV8Nxzz6Ffv36IjIz0dHxewQV5iYjI7128CFSs6HieVLFish5S4cLa7//WLVmv6vBhIDRURmgeflj7/fhKYqKURmZk2N8mOFh+vp5otpWeDnz/PbBvn+ynY0egWbP8k6QSBRBXcwOXEymdTocKFSqgfv36DhffXbVqlfvR+hgTKSIi8nuLFgGurOm4bVv+KbXzpjVrpKzPme+/l3lURJRvuZobuFxIPWDAAIcJFBEREXmQo5GSu9mOrPHnS0RucjmRWrx4sQfDICIiIocaNXK+jV4P1K3r+VjyowYNXNuuYUPPxkFEAeOuu/YRERGRFz30kJzE22tFrtcDPXrIPB9yX9Wq0urcXtdDg0G6+d1/v3fjIiK/xUSKiIgoUHzzDRAZmTeZ0ukkEZgzxzdx5RcLF8riu7l/vno9UKaMzFMjIvoHEykiIqJAcf/9QEwM8OqrQOnS0ha7QgXg3XeBPXuA++7zdYSBrWxZ4MABWY+rXDn5+ZYtC7zxhtxfrpyvIyQiP+Jy1778jF37iIiIiIgIcD038OmI1M6dO9G5c2dER0dDURSsWbPG6nFVVTF58mRER0cjNDQUrVq1wtGjR622SU9Px/Dhw3HfffchLCwMXbp0waVLl7z4KoiIiIiIqKDxaSKVlpaGunXrYu7cuTYfnzlzJmbNmoW5c+di7969iIqKQtu2bZGSkpK1zahRo7B69WqsWLECu3btQmpqKjp16gSTyeStl0FERERERAWM35T2KYqC1atXo1u3bgBkNCo6OhqjRo3ChAkTAMjoU+nSpfH+++9j8ODBSEpKQsmSJbFs2TL06tULAHDlyhWUL18eGzZsQPv27V3aN0v7iIiICACQliZNJxYsAC5fluYTzz8viyEXKeLr6IjICwKitM+R2NhYxMfHo127dln3hYSEoGXLlti9ezcAYP/+/cjMzLTaJjo6GrVq1craxpb09HQkJydb3YiIiKiAu3YNePhhYORI4NAh+frwYWDUKLn/2jVfR0hEfsRvE6n4+HgAQOnSpa3uL126dNZj8fHxCA4ORokSJexuY8uMGTMQHh6edStfvrzG0RMREVHAeekl4ORJQFXlBmR/fuoU8MILvo2PiPyK3yZSFoqiWH2tqmqe+3Jzts3EiRORlJSUdbt48aImsRIREVGAungRWLMGsDfH2mQC1q4Fzp/3alhE5L/8NpGK+mdl9twjSwkJCVmjVFFRUcjIyEBiYqLdbWwJCQlBsWLFrG5ERERUgP3xR/YolD2qKtsREcGPE6nKlSsjKioKW7duzbovIyMDO3bsQJMmTQAADRo0QFBQkNU2cXFxOHLkSNY2RERERE7pXDwlcnU7Isr3DL7ceWpqKv7666+sr2NjYxETE4OIiAhUqFABo0aNwvTp01GtWjVUq1YN06dPR+HChdGnTx8AQHh4OJ5//nmMHTsWkZGRiIiIwLhx41C7dm20adPGVy+LiIiIAk2zZoDBABiN9rcxGGQ7IiL4OJHat28fWrdunfX1mDFjAAADBw7E4sWLMX78eNy+fRtDhw5FYmIiGjdujC1btqBo0aJZz/noo49gMBjQs2dP3L59G48//jgWL14MvV7v9ddDREREAapUKaBfP2DJEtslfooCPPMM8M/UAyIiv1lHype4jhQRERHh44+B0aPtP/7hh8A/F32JKP9yNTdgIgUmUkRERH7rzh1p8JCRAdSu7bkRoYwMoEwZ4MYN+9uUKAHExQEhIZ6J4epVWb8qOFjWrQoN9cx+iMihgF+Ql4iIiAowkwmYOlUSp1atgHbtgHLlgF69gIQE7ff388+OkygASEwEtm3Tft9//w307g2ULSuvs1UrSeqmTLHfjp2IfM6nc6SIiIiIbBo8GFi40Hq+kskEfP89cOAAsGePjBBpJddSKnbdvKndPi3fr2lT4OxZ66QpKUkSqXPngEWLtN0nEWmCI1JERESQyq4bNxw3bSMv2bcPWLDAdtMHk0mSjtmztd1nlSrabueqOXOAM2dsjzypKrB4MbB3r7b7JCJNMJEiIqIC7dgxoG9foEgRIDJSBjlGjZLpKuQjixZJq3F7zGZg/nxt99moEVCjhv11onQ64IEHZO6SlubPl9djj8EgI3NE5HeYSBERUYH1++9Aw4bAt98CmZlyX2oqMHeunFdfvuzb+AqsCxecDw3GxdkesbpbiiJJjcEA5F5CRa+X+7/4QrbT0pUrjh83GoHz57XdJxFpgokUEREVSGYz0KcPkJ6e95zdZJLz21GjfBIalSzpeEQKAIoX1z6padoU+OWXvIvuNm0K7NzpmcV4nc3zMhhkjSsi8jtMpIiIqEDatg2IjbVfVWUyAatXs8TPJ/r1czwipdcDzz3nmX0//DCwfbuMiv3xh4wG7dgBNG7smf0991zeEbCcjEagf3/P7JuI7gkTKSIiKpAOH7Y/HcbCZAJOnvROPJRD69ZA27a2Ewy9XkajHC2cq4Xy5SWpqlDBs/sZNUpGpey91jZtgMce82wMRHRXmEgREVGBVLiwa1NsChf2fCyUi6IAa9YAzzyTne1ayvhq1wZ27ZI1pfKDsmXl9dSpI19bXqeiyJpZa9ZoX8JIRJpQVFXLmZqBydXVi4mIKP+4dAmoWNFxw7ToaKnwclR5RR526RKwZYtMZmvQQLqA5NfEYu9eaf0eHAy0b59/kkWiAONqbsAFeYmIqEAqVw549llZpsdeMvXGG0yifK5cOWDQIF9H4R2NGsmNiAICS/uIiKjA+u9/gSeflM8NhuzO14oCTJoEvPyyb+MjIiL/xREpIiIqsAoVAr77DvjzT2D5cuDaNaBSJRmpqlTJx8EREZFfYyJFREQFXv36ciMiInIVS/uIiIiIiIjcxESKiIiIiIjITUykiIiIiIiI3MREioiIiIiIyE1MpIiIiMjzzGYgLc3xCshERAGEiRQRERF5zvnzwLBhQNGiQJEiQPHiwOjRQFycryMjIronTKSIiIjIM06cAB56CJg/H7h1S+5LSQHmzpX7z53zaXhERPeCiRQRERF5xoABQFISYDRa3280yurHL73km7iIiDTARIqIiIi0FxMD7N0LmEy2Hzcaga1bgbNnvRoWEZFWmEgRERGR9g4edG27Q4c8GwcRkYcwkSIiIiLthYa6tl2hQp6Ng4jIQwy+DoCIiIi8xGgE1q4FNm2Szxs1Avr2BYoV035fbdoAwcFARob9bYoWBVq00H7fRERewBEpIiKiguCvv4Dq1YGnngIWLQKWLZO25NHRwObN2u8vIgIYMgRQFNuPK4q0QS9cWPt9ExF5ARMpIiKi/O72beCxx2RNJ0BGo4xGQFXlsS5dgKNHtd/vBx8AvXvL5wYDoNPJRwB48UXgrbe03ycRkZewtI+IiCi/W7ECuHjR9mNms9w++gj48ktt9xsUBHz9NTBunIyAxccDZcsCzz4L1Kql7b6IiLxMUVVV9XUQvpacnIzw8HAkJSWhmCfqxImIiHypSxdg/XpJmOwpXhxITPRaSERE/srV3IClfURERPldWprjJAoA7tzxTixERPkEEykiIqL8rl697LlJtuh0QM2aXguHiCg/YCJFRESU3730EmAy2X/cbAaGD/dePERE+QATKSIiovyuenXgP/+Rz/X67PsVRW7duwP9+vkmNiKiAMVEioiIqCAYO1YW433kkez7KlcGPvkE+PZb6wSLiIicYvtzIiKigqJzZ7nduiXrSBUtan/BXCIicoiJFBERUUFTuLCvIyAiCngs7SMiIiIiInITEykiIiIiIiI3MZEiIiIiIiJyExMpIiIiIiIiNzGRIiIiIiIichMTKSIiIiIiIjcxkSIiIiIiInITEykiIiIiIiI3MZEiIiIiIiJyExMpIiIiIiIiNzGRIiIiIiIichMTKSIiIiIiIjcxkSIiIiIiInITEykiIiIiIiI3MZEiIiIiIiJyk18nUkajEW+++SYqV66M0NBQVKlSBVOnToXZbM7aRlVVTJ48GdHR0QgNDUWrVq1w9OhRH0ZNRERERET5nV8nUu+//z4+++wzzJ07F8ePH8fMmTPxn//8B3PmzMnaZubMmZg1axbmzp2LvXv3IioqCm3btkVKSooPIyciIiIiovzMrxOp3377DV27dkXHjh1RqVIlPP3002jXrh327dsHQEajPv74Y7zxxhvo3r07atWqhSVLluDWrVv4+uuvfRw9ERERERHlV36dSDVr1gw//fQTTp06BQA4ePAgdu3ahX//+98AgNjYWMTHx6Ndu3ZZzwkJCUHLli2xe/duu983PT0dycnJVjciIiIiIiJXGXwdgCMTJkxAUlISHnjgAej1ephMJkybNg3PPPMMACA+Ph4AULp0aavnlS5dGufPn7f7fWfMmIEpU6Z4LnAiIiIiIsrX/HpEauXKlfjqq6/w9ddf48CBA1iyZAk++OADLFmyxGo7RVGsvlZVNc99OU2cOBFJSUlZt4sXL3okfiIiIiIiyp/8ekTq1VdfxWuvvYbevXsDAGrXro3z589jxowZGDhwIKKiogDIyFSZMmWynpeQkJBnlCqnkJAQhISEeDZ4IiIiIiLKt/x6ROrWrVvQ6axD1Ov1We3PK1eujKioKGzdujXr8YyMDOzYsQNNmjTxaqxERERERFRw+PWIVOfOnTFt2jRUqFABNWvWxJ9//olZs2Zh0KBBAKSkb9SoUZg+fTqqVauGatWqYfr06ShcuDD69Onj4+iJiIiIiCi/8utEas6cOZg0aRKGDh2KhIQEREdHY/DgwXjrrbeythk/fjxu376NoUOHIjExEY0bN8aWLVtQtGhRH0ZORERERET5maKqqurrIHwtOTkZ4eHhSEpKQrFixXwdDhERERER+YiruYFfz5EiIiIiIiLyR0ykiIiIiIiI3MREioiIiIiIyE1MpIiIiIiIiNzERIqIiIiIiMhNTKSIiIiIiIjcxESKiIiIiIjITUykiIiIiIiI3MREioiIiIiIyE1MpIiIiIiIiNzERIqIiIiIiMhNTKSIiIiIiIjcxESKiIiIiIjITUykiIiIiIiI3MREioiIiIiIyE1MpIiIiIiIiNxk8HUARERERBSAjEYgLg4wGICoKEBRfB0RkVdxRIqIiIiIXJeRAUybBpQtC1SoAERHAzVrAkuXAqrq6+iIvIYjUkRERETkmsxMoGtXYMsWwGzOvv/ECWDgQOD0aeCdd3wXH5EXcUSKiIiIiFyzdCmwaZN1EgVkj0S9+y5w6JD34yLyASZSREREROSauXMBnYPTR4MBmD/fe/EQ+RATKSIiIiJyzcmTeUejcjIagSNHvBcPkQ8xkSIiIiIi14SFOX5cpwOKFfNOLEQ+xkSKiIiIiFzTu7eU79ljNgM9engvHiIfYiJFRERERK4ZNQoIDrY9T8pgAO6/n4kUFRhMpIiIiIjINVWrSuvzyEj5Oigoe4TqwQeBbduAQoV8Fx+RF3EdKSIiIiJyXdOmwMWLwKpVwJ49kkg98QTw2GOAovg6OiKvUVSVS1AnJycjPDwcSUlJKMYJkkREREREBZaruQFL+4iIiIiIiNzERIqIiIiIiMhNTKSIiIiIiIjcxESKiIiIiIjITUykiIiIiIiI3MREioiIiIiIyE1MpIiIiIiIiNzERIqIiIiIiMhNTKSIiIiIiIjcxESKiIiIiIjITUykiIiIiIiI3MREioiIiIiIyE1MpIiIiIiIiNzERIqIiIiIiMhNBl8H4A9UVQUAJCcn+zgSIiIiIiLyJUtOYMkR7GEiBSAlJQUAUL58eR9HQkRERERE/iAlJQXh4eF2H1dUZ6lWAWA2m3HlyhUULVoUiqL4Opy7lpycjPLly+PixYsoVqyYr8OhAMZjibTA44i0wmOJtMJjiVyhqipSUlIQHR0Nnc7+TCiOSAHQ6XQoV66cr8PQTLFixfjmQJrgsURa4HFEWuGxRFrhsUTOOBqJsmCzCSIiIiIiIjcxkSIiIiIiInITE6l8JCQkBG+//TZCQkJ8HQoFOB5LpAUeR6QVHkukFR5LpCU2myAiIiIiInITR6SIiIiIiIjcxESKiIiIiIjITUykiIiIiIiI3MREioiIiIiIyE1MpALMp59+ijp16mQtJPfoo49i48aNWY+rqorJkycjOjoaoaGhaNWqFY4ePerDiCkQzJgxA4qiYNSoUVn38VgiV02ePBmKoljdoqKish7nsUSuunz5Mvr164fIyEgULlwY9erVw/79+7Me57FErqhUqVKe9yRFUTBs2DAAPI5IO0ykAky5cuXw3nvvYd++fdi3bx8ee+wxdO3aNesNYObMmZg1axbmzp2LvXv3IioqCm3btkVKSoqPIyd/tXfvXsyfPx916tSxup/HErmjZs2aiIuLy7odPnw46zEeS+SKxMRENG3aFEFBQdi4cSOOHTuGDz/8EMWLF8/ahscSuWLv3r1W70dbt24FAPTo0QMAjyPSkEoBr0SJEuqXX36pms1mNSoqSn3vvfeyHrtz544aHh6ufvbZZz6MkPxVSkqKWq1aNXXr1q1qy5Yt1ZEjR6qqqvJYIre8/fbbat26dW0+xmOJXDVhwgS1WbNmdh/nsUR3a+TIkWrVqlVVs9nM44g0xRGpAGYymbBixQqkpaXh0UcfRWxsLOLj49GuXbusbUJCQtCyZUvs3r3bh5GSvxo2bBg6duyINm3aWN3PY4ncdfr0aURHR6Ny5cro3bs3zp49C4DHErlu7dq1aNiwIXr06IFSpUqhfv36+OKLL7Ie57FEdyMjIwNfffUVBg0aBEVReByRpphIBaDDhw+jSJEiCAkJwZAhQ7B69WrUqFED8fHxAIDSpUtbbV+6dOmsx4gsVqxYgQMHDmDGjBl5HuOxRO5o3Lgxli5dis2bN+OLL75AfHw8mjRpguvXr/NYIpedPXsWn376KapVq4bNmzdjyJAhGDFiBJYuXQqA70t0d9asWYObN2/i2WefBcDjiLRlNCI0oQAABppJREFU8HUA5L7q1asjJiYGN2/exPfff4+BAwdix44dWY8rimK1vaqqee6jgu3ixYsYOXIktmzZgkKFCtndjscSuaJDhw5Zn9euXRuPPvooqlatiiVLluCRRx4BwGOJnDObzWjYsCGmT58OAKhfvz6OHj2KTz/9FAMGDMjajscSuWPBggXo0KEDoqOjre7ncURa4IhUAAoODsb999+Phg0bYsaMGahbty4++eSTrC5Zua+oJCQk5LnyQgXb/v37kZCQgAYNGsBgMMBgMGDHjh2YPXs2DAZD1vHCY4nuRlhYGGrXro3Tp0/zfYlcVqZMGdSoUcPqvgcffBAXLlwAAB5L5Lbz58/jxx9/xAsvvJB1H48j0hITqXxAVVWkp6ejcuXKiIqKyupOA0ht8I4dO9CkSRMfRkj+5vHHH8fhw4cRExOTdWvYsCH69u2LmJgYVKlShccS3bX09HQcP34cZcqU4fsSuaxp06Y4efKk1X2nTp1CxYoVAYDHErlt0aJFKFWqFDp27Jh1H48j0hJL+wLM66+/jg4dOqB8+fJISUnBihUrsH37dmzatClrHaDp06ejWrVqqFatGqZPn47ChQujT58+vg6d/EjRokVRq1Ytq/vCwsIQGRmZdT+PJXLVuHHj0LlzZ1SoUAEJCQl49913kZycjIEDB/J9iVw2evRoNGnSBNOnT0fPnj2xZ88ezJ8/H/PnzwcAHkvkFrPZjEWLFmHgwIEwGLJPd3kckZaYSAWYq1evon///oiLi0N4eDjq1KmDTZs2oW3btgCA8ePH4/bt2xg6dCgSExPRuHFjbNmyBUWLFvVx5BRoeCyRqy5duoRnnnkG165dQ8mSJfHII4/g999/zxpJ4LFErmjUqBFWr16NiRMnYurUqahcuTI+/vhj9O3bN2sbHkvkqh9//BEXLlzAoEGD8jzG44i0oqiqqvo6CCIiIiIiokDCOVJERERERERuYiJFRERERETkJiZSREREREREbmIiRURERERE5CYmUkRERERERG5iIkVEREREROQmJlJERERERERuYiJFRERERETkJiZSREREREREbmIiRURE+d7u3buh1+vxxBNP+DoUIiLKJxRVVVVfB0FERORJL7zwAooUKYIvv/wSx44dQ4UKFXwdEhERBTiOSBERUb6WlpaGb7/9Fi+//DI6deqExYsXWz2+du1aVKtWDaGhoWjdujWWLFkCRVFw8+bNrG12796NFi1aIDQ0FOXLl8eIESOQlpbm3RdCRER+hYkUERHlaytXrkT16tVRvXp19OvXD4sWLYKlGOPcuXN4+umn0a1bN8TExGDw4MF44403rJ5/+PBhtG/fHt27d8ehQ4ewcuVK7Nq1C6+88oovXg4REfkJlvYREVG+1rRpU/Ts2RMjR46E0WhEmTJl8M0336BNmzZ47bXXsH79ehw+fDhr+zfffBPTpk1DYmIiihcvjgEDBiA0NBSff/551ja7du1Cy5YtkZaWhkKFCvniZRERkY9xRIqIiPKtkydPYs+ePejduzcAwGAwoFevXli4cGHW440aNbJ6zsMPP2z19f79+7F48WIUKVIk69a+fXuYzWbExsZ654UQEZHfMfg6ACIiIk9ZsGABjEYjypYtm3WfqqoICgpCYmIiVFWFoihWz8ldqGE2mzF48GCMGDEiz/dn0woiooKLiRQREeVLRqMRS5cuxYcffoh27dpZPfbUU09h+fLleOCBB7Bhwwarx/bt22f19UMPPYSjR4/i/vvv93jMREQUODhHioiI8qU1a9agV69eSEhIQHh4uNVjb7zxBjZs2IBVq1ahevXqGD16NJ5//nnExMRg7NixuHTpEm7evInw8HAcOnQIjzzyCJ577jm8+OKLCAsLw/Hjx7F161bMmTPHR6+OiIh8jXOkiIgoX1qwYAHatGmTJ4kCZEQqJiYGiYmJ+O6777Bq1SrUqVMHn376aVbXvpCQEABAnTp1sGPHDpw+fRrNmzdH/fr1MWnSJJQpU8arr4eIiPwLR6SIiIhymDZtGj777DNcvHjR16EQEZEf4xwpIiIq0ObNm4dGjRohMjISv/76K/7zn/9wjSgiInKKiRQRERVop0+fxrvvvosbN26gQoUKGDt2LCZOnOjrsIiIyM+xtI+IiIiIiMhNbDZBRERERETkJiZSREREREREbmIiRURERERE5CYmUkRERERERG5iIkVEREREROQmJlJERERERERuYiJFRERERETkJiZSREREREREbvp/+lED2TyIO2wAAAAASUVORK5CYII=","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["colors = ['blue' if value < 55 else 'red' for value in heart_continuous['age']]\n","\n","plt.figure(figsize=(10, 6))\n","plt.scatter(heart_continuous['age'], heart_continuous['thalach'], c=colors)\n","plt.xlabel('Age')\n","plt.ylabel('Maximum Heart Rate Achieved')\n","plt.title('Scatter Plot of Age vs. Maximum Heart Rate Achieved (Blue=Adults under 55)')\n","plt.show()"]},{"cell_type":"markdown","id":"1e1f1ebb","metadata":{"id":"1e1f1ebb"},"source":["The scatterplot above shows people 55 and under in blue, and those over 55 in red.\n","\n","We can see the negative relationship between the two variables, as illustrated by the covariance metric we calculated.\n","\n","Later, when we learn regression, we'll be able to confirm this relationship by fitting a line to the data."]},{"cell_type":"markdown","id":"7aa2d4f8","metadata":{"id":"7aa2d4f8"},"source":["### The Correlation Coefficient for Continous Variables\n","\n","The Pearson correlation coefficient, is a standardized covariance indicator that measures the relationship between two variables on a scale between -1 (when one variable increases the other always decreases) and 1 (when one variable increases the other always increases).  The closer to zero the correlation coefficient value, the more likely it is that there is **no relationship at all** between the two variables.\n","\n"]},{"cell_type":"code","execution_count":null,"id":"7e1f2560","metadata":{"id":"7e1f2560","outputId":"3876665f-6718-41fc-b9ec-d93fc555dd51"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import seaborn as sns\n","correlation_matrix = heart_continuous.corr()\n","plt.figure(figsize=(10, 8))\n","mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n","sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5, fmt='.2f', mask=mask)\n","plt.title('Pearson Correlation Matrix')\n","plt.show()"]},{"cell_type":"markdown","id":"49e315b7","metadata":{"id":"49e315b7"},"source":["The Pearson correlation coefficients between all the continuous variables in the heart disease data set are shown in the above heatmap.  Quickly, our eyes are drawn to the darker blue (stronger negative correlation) and darker red (stronger positive correlation) areas.\n","\n","In this dataset, 'Num' (An indicator of the diagnosis of heart disease based on angiographic disease status, where 0 indicates < 50% narrowing in any major vessel and 1 indicates > 50% narrowing in any major vessel, is considered the outcome variable.\n","\n","To brush up on the major vessels, click here:\n","https://watchlearnlive.heart.org/CVML_Mobile.php?moduleSelect=corart\n","\n","What more-strongly correlated continuous variables in the dataset do you think might help to predict the value of 'Num'?\n","\n","**Note that correlation methods only measure relationships along a straight line.**  Some variables may be related in a nonlinear way, but are still very much related.  For example, weight is a power of height, and growth is a log(time).  "]},{"cell_type":"markdown","id":"23d4e572","metadata":{"id":"23d4e572"},"source":["### The Correlation Coefficient for Rank-Order Variables\n","\n","While Pearson measures the correlation between continuous variables, rank-ordered variables can also have correlation, and this correlation can be computed using the Spearman Rank correlation coefficient.  \n","\n","We can use this to look at the relationship between 'exang', exercise-induced angina where 0 is 'no' and 1 is 'yes', and 'restecg', where 0 is normal, 1 is ST-T wave abnormality (T inversion and/or ST elevation or depression of > 0.05 mV (clinically significant)), and 2 is probable or definite LVH by Estes' criteria: https://www.merckmanuals.com/medical-calculators/RomhiltEstes.htm"]},{"cell_type":"code","execution_count":null,"id":"51f91e42","metadata":{"id":"51f91e42","outputId":"4254f6be-c1e4-4aed-da9a-27a880aab9bd"},"outputs":[{"name":"stdout","output_type":"stream","text":["Spearman Rank Correlation Coefficient: 0.085\n","P-value: 0.140\n"]}],"source":["from scipy.stats import spearmanr\n","\n","# Calculate Spearman rank correlation coefficient\n","corr, p_value = spearmanr(heart_categorical['exang'], heart_categorical['restecg'])\n","\n","# Print the results\n","print(f\"Spearman Rank Correlation Coefficient: {corr:.3f}\")\n","print(f\"P-value: {p_value:.3f}\")"]},{"cell_type":"markdown","id":"52709841","metadata":{"id":"52709841"},"source":["This result indicates there's no significant relationship between the two variables.\n","\n","Can you explain why?"]},{"cell_type":"markdown","id":"38ccfa3e","metadata":{"id":"38ccfa3e"},"source":["### Tetrachoric and Polychoric Correlation\n","\n","An estimate of association using joint frequencies of categorical variables.\n","\n","First, let's look at joint frequency / contingency table of Sex and Num (Heart Disease, 0=No, 1+=Yes):"]},{"cell_type":"code","execution_count":null,"id":"50e04729","metadata":{"id":"50e04729","outputId":"6e05f24c-7db4-455d-a22d-fc3e138ab7df"},"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>num</th>\n","      <th>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>All</th>\n","    </tr>\n","    <tr>\n","      <th>sex</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>72</td>\n","      <td>9</td>\n","      <td>7</td>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>97</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>92</td>\n","      <td>46</td>\n","      <td>29</td>\n","      <td>28</td>\n","      <td>11</td>\n","      <td>206</td>\n","    </tr>\n","    <tr>\n","      <th>All</th>\n","      <td>164</td>\n","      <td>55</td>\n","      <td>36</td>\n","      <td>35</td>\n","      <td>13</td>\n","      <td>303</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["num    0   1   2   3   4  All\n","sex                          \n","0     72   9   7   7   2   97\n","1     92  46  29  28  11  206\n","All  164  55  36  35  13  303"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["freq_table = pd.crosstab(heart['sex'], heart['num'], margins=True)\n","\n","freq_table"]},{"cell_type":"markdown","id":"6877a300","metadata":{"id":"6877a300"},"source":["By the looks of the joint frequencies, a far larger % of males in our sample dataset appear to have heart disease."]},{"cell_type":"code","execution_count":null,"id":"84f4c8dc","metadata":{"id":"84f4c8dc","outputId":"7bd35e8c-7f1a-4b0b-ea83-5f2af7eb850e"},"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>lval</th>\n","      <th>op</th>\n","      <th>rval</th>\n","      <th>Estimate</th>\n","      <th>Std. Err</th>\n","      <th>z-value</th>\n","      <th>p-value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>num</td>\n","      <td>~</td>\n","      <td>sex</td>\n","      <td>0.590144</td>\n","      <td>0.147174</td>\n","      <td>4.009828</td>\n","      <td>0.000061</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>num</td>\n","      <td>~~</td>\n","      <td>num</td>\n","      <td>1.428438</td>\n","      <td>0.116053</td>\n","      <td>12.308534</td>\n","      <td>0.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  lval  op rval  Estimate  Std. Err    z-value   p-value\n","0  num   ~  sex  0.590144  0.147174   4.009828  0.000061\n","1  num  ~~  num  1.428438  0.116053  12.308534  0.000000"]},"execution_count":30,"metadata":{},"output_type":"execute_result"}],"source":["from semopy import Model\n","\n","model_desc = \"num ~ sex\"\n","model = Model(model_desc)\n","model.fit(heart)\n","model.inspect()\n"]},{"cell_type":"markdown","id":"334de7eb","metadata":{"id":"334de7eb"},"source":["The polychoric correlation coefficient estimate, 0.59, along with the p-value <0.0001, confirms that there is a positive correlation between sex=1 (Male) and an increase in Num, the Heart Disease marker."]},{"cell_type":"code","execution_count":null,"id":"0898cb6d","metadata":{"id":"0898cb6d"},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.3"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}