EDACheckpoint_Group035_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**If you lost points on the last checkpoint you can get them back by responding to TA/IA feedback**  \n",
    "\n",
    "Update/change the relevant sections where you lost those points, make sure you respond on GitHub Issues to your TA/IA to call their attention to the changes you made here.\n",
    "\n",
    "Please update your Timeline... no battle plan survives contact with the enemy, so make sure we understand how your plans have changed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - EDA Checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Erick Amaro Hernandez\n",
    "- Sanjith Devineni\n",
    "- Alex Macias\n",
    "- Lawrence Ong\n",
    "- Mingyang Yao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our question is: Does weight class, event (type of lift: squat, bench press, or deadlift), and gender affect the percentage(%) increase in attempt weight selection(amount of weight the competitor chooses for their next attempt)? For example, given that an athlete has lifted 100kg in an event defined previously, would something like gender or weight class influence whether they pick 105 kgs, 110 kgs, or more for their next attempt? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sport of powerlifting consists of 3 main **events**. The squat, the bench press, and the deadlift. A powerlifting competition gives you 3 attempts at the squat, 3 for the bench press, and 3 for the deadlift, and per event, each attempt must be heavier than the last (within the event), with the max weight lifted in each event summing up to the competitor's \"**total**\" for a meet. \n",
    "\n",
    "When competing in a powerlifting competition, choosing how much weight you'll be lifting has a lot of factors incorporated into it such as your own ability to judge the difficulty of your previous attempts and how you performed on those attempts. As such, attempt selection is an incredibly important skill a powerlifter must attain if they want to be successful in the sport. Seasoned coaches in the sport suggest that attempts start at around 90% of an athlete's max in the event. With 5% jumps between attempts being suggested from attempt 1 to 2, and <3% between attempt 2 and 3. It is also noted that heavier jumps may occur in the last event, the deadlift, due to it being the most exciting event of a powerlifting meet and/or the unique rules that come from it being the final event of a meet. <a name=\"cite_note-1\"></a> [<sup>1</sup>](#cite_ref-1) \n",
    "\n",
    "Most of the hype around the deadlift event comes from it contributing on average >40% of an athelete's total regardless of gender according to a previous analysis of the dataset by Kaggle user Steven Wilson <a name=\"cite_ref-2\"></a>[<sup>2</sup>](#cite_note-2). The importance of a lifter's deadlift fuels our hypothesis that maybe this lift comes with even higher/riskier jumps in weight per attempt on average. Another thing we can learn from Wilson's analysis and build on is that a larger portion of Men's totals is made up by their bench press, with it making up around 25% of their total in comparison to female lifters where it only makes up 20%. The study also shows the almost trivial trend of a higher athlete body weight being associated with higher totals.\n",
    "\n",
    "The prior work mostly looks at statistics of powerlifting like athlete totals rather than seeing some psychology behind the sport. We set out to uncover unconscious biases in weight selection for each attempt. If we discover that these biases exist and are significant, then this would help athletes plan out their attempts more efficiently. Athletes could then choose to be more conservative if planning a risky jump (being concious that they may be susceptible to biases found in this project), potentially adding more pounds or kilograms to their meet total rather than failing their next attempt or vice versa if we find out that their category tend to play more conservative. \n",
    "\n",
    "[^](#cite_ref-1) Krawczyk, Bryce (6 August, 2023) So You Wanna Be a Powerlifter? Attempt Selection During a Meet. *BarBend.com* https://www.barbend.com/powerlifting-meet-attempt-selection/\n",
    "\n",
    "[^](#cite-ref2) Steven Wilson (23 Sept. 2020) Strength Differences in Powerlifting by Gender https://www.kaggle.com/code/stevenwilson8/strength-differences-in-powerlifting-by-gender\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. We believe there will be no difference between genders when it comes to attempt selection in general. We think this because the different distributions of weight selection a per-event basis will balance eachother out to be equal when comparing just by gender. For example, although women may choose higher squat attempts, their bench attempts will might be lower and vice versa for men.\n",
    "2. The deadlift will have the higher jumps due to the hype around the amount of weight lifted and it being the main contributor to an athlete's total.\n",
    "3. Following from the last prediction, and from Wilson's past research on contribution of an event to an athlete's total, we predict that on a per-event basis there will be a difference between men and women's jumps, e.g. since more of men's totals is made up of the bench press, they might have higher jumps than women in the bench press.\n",
    "4. Based off our previous hypothesis and because heavier weight classes lift more weight, we think weight class will also effect % of weight increase.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data overview\n",
    "\n",
    "For each dataset include the following information\n",
    "- Dataset #1\n",
    "  - Dataset Name: Open Powerlifting Database\n",
    "  - Link to the dataset: https://www.kaggle.com/datasets/open-powerlifting/powerlifting-database\n",
    "  - Number of observations: 3,043,013\n",
    "  - Number of variables: 38\n",
    "\n",
    "The Openpowerlifting dataset is a collection of archived powerlifting competition results from 1946 - 1/1/2023.\n",
    "\n",
    "Every row of the OpenPowerlifting dataset consists of a record of how a lifter did during a competition. A row contains their age/weight data, what events they decided to compete in, whether they competed Equipped (certain competitions dont allow certain equipment to be used during a lift), geographic data like country,state,town, as well as the name of the Competition. It also includes rows of different scoring systems that take weight, gender, and total lifted in order to compare one's performance to another across varying weight/sex classes. All weight data is stored in Kilograms.\n",
    "\n",
    "Powerlifting is a sport where the philosophy behind the sport varies drastically. As such, there are results from 300+ federations in this dataset with their own rules behind what counts as a \"valid lift\". This also includes each federation having their own weight classes. Thankfully, the raw bodyweight a lifter weighed in at on the day of the competition is included as one of the columns, and 5 different scores are precomputed as columns if a lifter's performance was able to be scored.\n",
    "\n",
    "In order to compare lifters in this dataset, we will standardize to one weight class category, and one scoring system. Weight classes will be standardized to those that the International Powerlifting Federation uses (with some caveats, since the cutoffs are different for Female and Male lifters). DOTS will be our selected scoring system because it does not require that an athlete complete all 3 lifts to be calculated and is actively used today.\n",
    "\n",
    "We also need to calculate the jumps between a lifter's Squat 1 and Squat 2 attempt, which will be calculated in the following section. In this dataset, a failed attempt is set to the negative of the attempt weight. This means if there is a -100 in the Squat1Kg column, the lifter attempted 100kg and failed the attempt. Even if an attempt is failed, they are not allowed to lower the weight for the next attempt, so these 0% jumps should be ignored as to not skew any averages we compute."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Openpowerlifting Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3043013, 41)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "df = pd.read_csv(\"./dataset/pl_data.csv\")\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NaN values\n",
    "\n",
    "Differences between how federations categorize their lifters are shown here, some recorded an age (Age), or just the range in which they fell into (AgeClass), some decide to combine the two in a \"Division\" column. There is also the \"Tested\" column which indicates whether or not athletes were drug tested, which is True if they were, NaN if they werent. Some Federations do not test for PEDs.\n",
    "\n",
    "Attempt values like Squat\\[1-3\\]kg are NaN if an athlete did not show up, or if an athlete decided to not do 3 attempts. There is also a 4th attempt column in the dataset, which is when there is an error in competition organization and the lifter is granted an extra attempt. \n",
    "\n",
    "BodyweightKg is also null if someone is No-Show to a competition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                      0\n",
       "Sex                       0\n",
       "Event                     0\n",
       "Equipment                 0\n",
       "Age                 1070337\n",
       "AgeClass             814256\n",
       "BirthYearClass      1000666\n",
       "Division               1435\n",
       "BodyweightKg          38368\n",
       "WeightClassKg         40757\n",
       "Squat1Kg            2137906\n",
       "Squat2Kg            2147142\n",
       "Squat3Kg            2171053\n",
       "Squat4Kg            3034560\n",
       "Best3SquatKg        1009169\n",
       "Bench1Kg            1652910\n",
       "Bench2Kg            1668528\n",
       "Bench3Kg            1710945\n",
       "Bench4Kg            3022026\n",
       "Best3BenchKg         358566\n",
       "Deadlift1Kg         2021904\n",
       "Deadlift2Kg         2039602\n",
       "Deadlift3Kg         2080157\n",
       "Deadlift4Kg         3020077\n",
       "Best3DeadliftKg      836823\n",
       "TotalKg              202421\n",
       "Place                     0\n",
       "Dots                 227397\n",
       "Wilks                227397\n",
       "Glossbrenner         227397\n",
       "Goodlift             476889\n",
       "Tested               804298\n",
       "Country             1284501\n",
       "State               2408023\n",
       "Federation                0\n",
       "ParentFederation    1068468\n",
       "Date                      0\n",
       "MeetCountry               0\n",
       "MeetState            859344\n",
       "MeetTown             411193\n",
       "MeetName                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anomalies \n",
    "There are also some rows that are anomalies. The following competitor competed in a Bench Press/Deadlift-only competition, yet is missing bodyweight and has a best bench despite not having any Bench\\[1,2,3\\]Kg values filled in. It is important to note there is no official way to keep track of powerlifting events, so each is recorded differently through either a Excel- and we're subject to errors in data entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                              Fabricio Ubirajara de Assis\n",
       "Sex                                                         M\n",
       "Event                                                       B\n",
       "Equipment                                                 Raw\n",
       "Age                                                       NaN\n",
       "AgeClass                                                  NaN\n",
       "BirthYearClass                                          40-49\n",
       "Division                                                   MO\n",
       "BodyweightKg                                              NaN\n",
       "WeightClassKg                                            140+\n",
       "Squat1Kg                                                  NaN\n",
       "Squat2Kg                                                  NaN\n",
       "Squat3Kg                                                  NaN\n",
       "Squat4Kg                                                  NaN\n",
       "Best3SquatKg                                              NaN\n",
       "Bench1Kg                                                  NaN\n",
       "Bench2Kg                                                  NaN\n",
       "Bench3Kg                                                  NaN\n",
       "Bench4Kg                                                  NaN\n",
       "Best3BenchKg                                            200.0\n",
       "Deadlift1Kg                                               NaN\n",
       "Deadlift2Kg                                               NaN\n",
       "Deadlift3Kg                                               NaN\n",
       "Deadlift4Kg                                               NaN\n",
       "Best3DeadliftKg                                           NaN\n",
       "TotalKg                                                 200.0\n",
       "Place                                                       1\n",
       "Dots                                                      NaN\n",
       "Wilks                                                     NaN\n",
       "Glossbrenner                                              NaN\n",
       "Goodlift                                                  NaN\n",
       "Tested                                                    NaN\n",
       "Country                                                   NaN\n",
       "State                                                     NaN\n",
       "Federation                                            CONBRAP\n",
       "ParentFederation                                          GPC\n",
       "Date                                               2018-04-14\n",
       "MeetCountry                                            Brazil\n",
       "MeetState                                                 NaN\n",
       "MeetTown                                                  Jaú\n",
       "MeetName            Campeonato Paulista de Supino e Lev.Terra\n",
       "Name: 579, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Name'] == \"Fabricio Ubirajara de Assis\"].iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping Data\n",
    "\n",
    "Since we are looking at attempt selection, we will be dropping any rows where the lifter signed up, but didn't compete. These can be found by those rows with NaN in their 1st attempt of every event, as well as those whose placement in a competition was \"NS\", for \"no-show\". \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter out any rows where there is no lift data, or lifter was no-show to the competition\n",
    "df = df.drop(['Goodlift','Wilks','Glossbrenner'], axis=1)\n",
    "df = df[~((df['Squat1Kg'].isna()) & (df['Bench1Kg'].isna()) & (df['Deadlift1Kg'].isna()))]\n",
    "df = df[df['Place'] != 'NS']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardizing Weight classes\n",
    "\n",
    "Earlier we discussed how there are 300+ federations, each with possibly their own defined weight classes. In this study, we will standardize the weight classes used by the International Powerlifting Federation, which hosts world championships, and can be seen as a \"Parent Federation\" in the dataset for federations worldwide, this means they all follow and conform to IPF guidelines to some degree. It's popularity worldwide made us choose it for this standardization. \n",
    "\n",
    "The weight classes are as follows, defined on [page 4 of the IPF rulebook](https://www.powerlifting.sport/fileadmin/ipf/data/rules/technical-rules/english/IPF_Technical_Rules_Book_2023__1_.pdf). A weight class like 80kg means you weigh under 80kg, but over the next smallest weight class. \n",
    "\n",
    "- Men\n",
    "    - \\[59kg, 66kg, 74kg, 83kg, 93kg, 105kg, 120kg, 120+kg\\]\n",
    "- Women\n",
    "    - \\[47kg, 52kg, 57kg, 63kg, 69kg, 76kg, 84kg, 84+kg\\]\n",
    " \n",
    "This however excludes people who do not identify as either (labeled as Mx in federations who support this), so we will merge the two categorizations to account for more bodyweights and identities. The following is the resulting weight classes, we use men's weight classes and append any lower weight classes that the women's classes support.\n",
    "\n",
    "\\[43kg, 47kg, 53kg, 59kg, 66kg, 74kg, 83kg, 93kg, 105kg, 120kg, 120+kg\\]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Standardizing weight classes\n",
    "def standardize_weight_class(weight):\n",
    "    if weight == np.nan:\n",
    "        return np.nan\n",
    "    \n",
    "    combined_ipf_classes = [43, 47, 53, 59, 66, 74, 83, 93, 105, 120]\n",
    "\n",
    "    unlimited_class = str(combined_ipf_classes[-1]) + \"+\"\n",
    "    for weight_class in combined_ipf_classes:\n",
    "        if weight < weight_class:\n",
    "            return str(weight_class)\n",
    "\n",
    "    return unlimited_class\n",
    "\n",
    "df['WeightClassKg'] = df['BodyweightKg'].apply(standardize_weight_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WeightClassKg\n",
       "83      278594\n",
       "93      243159\n",
       "105     218003\n",
       "74      215414\n",
       "120     152866\n",
       "66      143427\n",
       "120+    105640\n",
       "59       99374\n",
       "53       55073\n",
       "47       14903\n",
       "43        6980\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['WeightClassKg'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_absolute_value = df[['Squat1Kg','Squat2Kg','Squat3Kg','Bench1Kg','Bench2Kg',\n",
    "                        'Bench3Kg','Deadlift1Kg','Deadlift2Kg','Deadlift3Kg']].abs()\n",
    "\n",
    "df['squat12_percent_increase'] = ((df_absolute_value['Squat2Kg'] - df_absolute_value['Squat1Kg']) / df_absolute_value['Squat1Kg']) * 100\n",
    "df['squat23_percent_increase'] = ((df_absolute_value['Squat3Kg'] - df_absolute_value['Squat2Kg']) / df_absolute_value['Squat2Kg']) * 100\n",
    "\n",
    "df['bench12_percent_increase'] = ((df_absolute_value['Bench2Kg'] - df_absolute_value['Bench1Kg']) / df_absolute_value['Bench1Kg']) * 100\n",
    "df['bench23_percent_increase'] = ((df_absolute_value['Bench3Kg'] - df_absolute_value['Bench2Kg']) / df_absolute_value['Bench2Kg']) * 100\n",
    "\n",
    "df['Deadlift12_percent_increase'] = ((df_absolute_value['Deadlift2Kg'] - df_absolute_value['Deadlift1Kg']) / df_absolute_value['Deadlift1Kg']) * 100\n",
    "df['Deadlift23_percent_increase'] = ((df_absolute_value['Deadlift3Kg'] - df_absolute_value['Deadlift2Kg']) / df_absolute_value['Deadlift2Kg']) * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isNoIncrease(weightPercent):\n",
    "    if weightPercent == 0:\n",
    "        return np.nan\n",
    "    else:\n",
    "        return weightPercent\n",
    "\n",
    "df['squat12_percent_increase'] = df['squat12_percent_increase'].apply(isNoIncrease)\n",
    "df['squat23_percent_increase'] = df['squat23_percent_increase'].apply(isNoIncrease)\n",
    "df['bench12_percent_increase'] = df['bench12_percent_increase'].apply(isNoIncrease)\n",
    "df['bench23_percent_increase'] = df['bench23_percent_increase'].apply(isNoIncrease)\n",
    "df['Deadlift12_percent_increase'] = df['Deadlift12_percent_increase'].apply(isNoIncrease)\n",
    "df['Deadlift23_percent_increase'] = df['Deadlift23_percent_increase'].apply(isNoIncrease)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                               Alona Vladi\n",
       "Sex                                          F\n",
       "Event                                      SBD\n",
       "Equipment                                  Raw\n",
       "Age                                       33.0\n",
       "AgeClass                                 24-34\n",
       "BirthYearClass                           24-39\n",
       "Division                                     O\n",
       "BodyweightKg                              58.3\n",
       "WeightClassKg                               59\n",
       "Squat1Kg                                  75.0\n",
       "Squat2Kg                                  80.0\n",
       "Squat3Kg                                 -90.0\n",
       "Squat4Kg                                   NaN\n",
       "Best3SquatKg                              80.0\n",
       "Bench1Kg                                  50.0\n",
       "Bench2Kg                                  55.0\n",
       "Bench3Kg                                  60.0\n",
       "Bench4Kg                                   NaN\n",
       "Best3BenchKg                              60.0\n",
       "Deadlift1Kg                               95.0\n",
       "Deadlift2Kg                              105.0\n",
       "Deadlift3Kg                              107.5\n",
       "Deadlift4Kg                                NaN\n",
       "Best3DeadliftKg                          107.5\n",
       "TotalKg                                  247.5\n",
       "Place                                        1\n",
       "Dots                                    279.44\n",
       "Tested                                     Yes\n",
       "Country                                 Russia\n",
       "State                                      NaN\n",
       "Federation                                 GFP\n",
       "ParentFederation                           NaN\n",
       "Date                                2019-05-11\n",
       "MeetCountry                             Russia\n",
       "MeetState                                  NaN\n",
       "MeetTown                               Bryansk\n",
       "MeetName                       Open Tournament\n",
       "squat12_percent_increase              6.666667\n",
       "squat23_percent_increase                  12.5\n",
       "bench12_percent_increase                  10.0\n",
       "bench23_percent_increase              9.090909\n",
       "Deadlift12_percent_increase          10.526316\n",
       "Deadlift23_percent_increase           2.380952\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning up extreme/not so extreme outliers\n",
    "\n",
    "We have a ton of anomalies when it comes to outliers as well, we believe this to be a result of the inconsistent data entry methods that happen across powerlifting competitions. As such, there are lifters with 100+% increases which is just not a good idea at all in a sport like powerlifting, and there are lifters with negative attempt % increases which shouldn't be allowed and could be a result of earlier days of powerlifting when the \"no going down in weight\" rule wasn't in place.\n",
    "\n",
    "We will use the common filter of 1.5 * IQR to get rid of these rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_outliers(df_toclean, col):\n",
    "\n",
    "    #under no circumstances would anyone need a 100% increase\n",
    "    df_toclean = df_toclean[df_toclean[col] < 100]\n",
    "\n",
    "    #not possible to decrease weight, data entry error.\n",
    "    df_toclean = df_toclean[df_toclean[col] > 0]\n",
    "\n",
    "    Q1 = df_toclean[col].quantile(0.25)\n",
    "    Q3 = df_toclean[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    \n",
    "    upper_whisker = Q3 + (1.5 * IQR)\n",
    "    # Filter out the data points that are greater than the upper whisker value\n",
    "    df_toclean = df_toclean[df_toclean[col] <= upper_whisker]\n",
    "\n",
    "    return df_toclean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>squat12_percent_increase</th>\n",
       "      <th>squat23_percent_increase</th>\n",
       "      <th>bench12_percent_increase</th>\n",
       "      <th>bench23_percent_increase</th>\n",
       "      <th>Deadlift12_percent_increase</th>\n",
       "      <th>Deadlift23_percent_increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.266517</td>\n",
       "      <td>4.500666</td>\n",
       "      <td>6.503309</td>\n",
       "      <td>4.495437</td>\n",
       "      <td>6.490803</td>\n",
       "      <td>4.536791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.117106</td>\n",
       "      <td>1.859871</td>\n",
       "      <td>2.385232</td>\n",
       "      <td>1.975026</td>\n",
       "      <td>2.127265</td>\n",
       "      <td>1.878748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010754</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.302731</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.819277</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.174603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.024096</td>\n",
       "      <td>4.347826</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>5.617978</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>7.843137</td>\n",
       "      <td>5.747126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.947658</td>\n",
       "      <td>10.013333</td>\n",
       "      <td>13.677812</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>12.608696</td>\n",
       "      <td>9.865471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       squat12_percent_increase  squat23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.266517                  4.500666   \n",
       "std                    2.117106                  1.859871   \n",
       "min                    0.010754                  0.156250   \n",
       "25%                    4.819277                  3.125000   \n",
       "50%                    6.024096                  4.347826   \n",
       "75%                    7.500000                  5.617978   \n",
       "max                   12.947658                 10.013333   \n",
       "\n",
       "       bench12_percent_increase  bench23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.503309                  4.495437   \n",
       "std                    2.385232                  1.975026   \n",
       "min                    0.302731                  0.095238   \n",
       "25%                    4.761905                  3.125000   \n",
       "50%                    6.250000                  4.166667   \n",
       "75%                    8.000000                  5.555556   \n",
       "max                   13.677812                 10.937500   \n",
       "\n",
       "       Deadlift12_percent_increase  Deadlift23_percent_increase  \n",
       "count                365540.000000                365540.000000  \n",
       "mean                      6.490803                     4.536791  \n",
       "std                       2.127265                     1.878748  \n",
       "min                       0.212766                     0.144928  \n",
       "25%                       5.000000                     3.174603  \n",
       "50%                       6.250000                     4.347826  \n",
       "75%                       7.843137                     5.747126  \n",
       "max                      12.608696                     9.865471  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LIFT_INCREASE_COLUMNS = ['squat12_percent_increase','squat23_percent_increase', 'bench12_percent_increase', 'bench23_percent_increase', 'Deadlift12_percent_increase', 'Deadlift23_percent_increase']\n",
    "\n",
    "df = clean_outliers(df, 'bench23_percent_increase')\n",
    "df = clean_outliers(df, 'squat12_percent_increase')\n",
    "df = clean_outliers(df, 'squat23_percent_increase')\n",
    "df = clean_outliers(df, 'bench12_percent_increase')\n",
    "df = clean_outliers(df, 'Deadlift12_percent_increase')\n",
    "df = clean_outliers(df, 'Deadlift23_percent_increase')\n",
    "\n",
    "df[LIFT_INCREASE_COLUMNS].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "### Basic Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "LIFT_INCREASE_COLUMNS = ['squat12_percent_increase','squat23_percent_increase', 'bench12_percent_increase', \n",
    "                        'bench23_percent_increase', 'Deadlift12_percent_increase', 'Deadlift23_percent_increase']\n",
    "palette = sns.color_palette(\"pastel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Event</th>\n",
       "      <th>Equipment</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeClass</th>\n",
       "      <th>BirthYearClass</th>\n",
       "      <th>Division</th>\n",
       "      <th>BodyweightKg</th>\n",
       "      <th>WeightClassKg</th>\n",
       "      <th>...</th>\n",
       "      <th>MeetCountry</th>\n",
       "      <th>MeetState</th>\n",
       "      <th>MeetTown</th>\n",
       "      <th>MeetName</th>\n",
       "      <th>squat12_percent_increase</th>\n",
       "      <th>squat23_percent_increase</th>\n",
       "      <th>bench12_percent_increase</th>\n",
       "      <th>bench23_percent_increase</th>\n",
       "      <th>Deadlift12_percent_increase</th>\n",
       "      <th>Deadlift23_percent_increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Galina Solovyanova</td>\n",
       "      <td>F</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>43.0</td>\n",
       "      <td>40-44</td>\n",
       "      <td>40-49</td>\n",
       "      <td>M1</td>\n",
       "      <td>73.10</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.407407</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Aleksey Krasov</td>\n",
       "      <td>M</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35-39</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>66.65</td>\n",
       "      <td>74</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>6.521739</td>\n",
       "      <td>4.081633</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.030303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Ruslan Gasanov</td>\n",
       "      <td>M</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>31.5</td>\n",
       "      <td>24-34</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>102.55</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>3.846154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Yuriy Solovyev</td>\n",
       "      <td>M</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Wraps</td>\n",
       "      <td>26.5</td>\n",
       "      <td>24-34</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>98.90</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bryansk</td>\n",
       "      <td>Open Tournament</td>\n",
       "      <td>11.111111</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>8.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Irina Syanova</td>\n",
       "      <td>F</td>\n",
       "      <td>SBD</td>\n",
       "      <td>Raw</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35-39</td>\n",
       "      <td>24-39</td>\n",
       "      <td>O</td>\n",
       "      <td>53.60</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>Russia</td>\n",
       "      <td>ORL</td>\n",
       "      <td>Oryol</td>\n",
       "      <td>Fortior est ferrum Tournament</td>\n",
       "      <td>7.142857</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>5.263158</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>9.375000</td>\n",
       "      <td>5.714286</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 44 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name Sex Event Equipment   Age AgeClass BirthYearClass  \\\n",
       "1   Galina Solovyanova   F   SBD       Raw  43.0    40-44          40-49   \n",
       "3       Aleksey Krasov   M   SBD       Raw  35.0    35-39          24-39   \n",
       "8       Ruslan Gasanov   M   SBD       Raw  31.5    24-34          24-39   \n",
       "12      Yuriy Solovyev   M   SBD     Wraps  26.5    24-34          24-39   \n",
       "70       Irina Syanova   F   SBD       Raw  35.0    35-39          24-39   \n",
       "\n",
       "   Division  BodyweightKg WeightClassKg  ...  MeetCountry  MeetState  \\\n",
       "1        M1         73.10            74  ...       Russia        NaN   \n",
       "3         O         66.65            74  ...       Russia        NaN   \n",
       "8         O        102.55           105  ...       Russia        NaN   \n",
       "12        O         98.90           105  ...       Russia        NaN   \n",
       "70        O         53.60            59  ...       Russia        ORL   \n",
       "\n",
       "    MeetTown                       MeetName  squat12_percent_increase  \\\n",
       "1    Bryansk                Open Tournament                  5.263158   \n",
       "3    Bryansk                Open Tournament                  5.600000   \n",
       "8    Bryansk                Open Tournament                  7.142857   \n",
       "12   Bryansk                Open Tournament                 11.111111   \n",
       "70     Oryol  Fortior est ferrum Tournament                  7.142857   \n",
       "\n",
       "    squat23_percent_increase  bench12_percent_increase  \\\n",
       "1                   5.000000                  8.000000   \n",
       "3                   4.166667                  6.521739   \n",
       "8                   3.333333                  6.666667   \n",
       "12                  5.000000                  7.142857   \n",
       "70                  6.666667                  5.263158   \n",
       "\n",
       "    bench23_percent_increase  Deadlift12_percent_increase  \\\n",
       "1                   7.407407                    10.000000   \n",
       "3                   4.081633                    10.000000   \n",
       "8                   3.125000                     8.333333   \n",
       "12                  5.000000                     5.263158   \n",
       "70                  5.000000                     9.375000   \n",
       "\n",
       "    Deadlift23_percent_increase  \n",
       "1                      9.090909  \n",
       "3                      3.030303  \n",
       "8                      3.846154  \n",
       "12                     8.750000  \n",
       "70                     5.714286  \n",
       "\n",
       "[5 rows x 44 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>squat12_percent_increase</th>\n",
       "      <th>squat23_percent_increase</th>\n",
       "      <th>bench12_percent_increase</th>\n",
       "      <th>bench23_percent_increase</th>\n",
       "      <th>Deadlift12_percent_increase</th>\n",
       "      <th>Deadlift23_percent_increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "      <td>365540.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.266517</td>\n",
       "      <td>4.500666</td>\n",
       "      <td>6.503309</td>\n",
       "      <td>4.495437</td>\n",
       "      <td>6.490803</td>\n",
       "      <td>4.536791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.117106</td>\n",
       "      <td>1.859871</td>\n",
       "      <td>2.385232</td>\n",
       "      <td>1.975026</td>\n",
       "      <td>2.127265</td>\n",
       "      <td>1.878748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.010754</td>\n",
       "      <td>0.156250</td>\n",
       "      <td>0.302731</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.212766</td>\n",
       "      <td>0.144928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.819277</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>4.761905</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.174603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.024096</td>\n",
       "      <td>4.347826</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.347826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>5.617978</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>7.843137</td>\n",
       "      <td>5.747126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.947658</td>\n",
       "      <td>10.013333</td>\n",
       "      <td>13.677812</td>\n",
       "      <td>10.937500</td>\n",
       "      <td>12.608696</td>\n",
       "      <td>9.865471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       squat12_percent_increase  squat23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.266517                  4.500666   \n",
       "std                    2.117106                  1.859871   \n",
       "min                    0.010754                  0.156250   \n",
       "25%                    4.819277                  3.125000   \n",
       "50%                    6.024096                  4.347826   \n",
       "75%                    7.500000                  5.617978   \n",
       "max                   12.947658                 10.013333   \n",
       "\n",
       "       bench12_percent_increase  bench23_percent_increase  \\\n",
       "count             365540.000000             365540.000000   \n",
       "mean                   6.503309                  4.495437   \n",
       "std                    2.385232                  1.975026   \n",
       "min                    0.302731                  0.095238   \n",
       "25%                    4.761905                  3.125000   \n",
       "50%                    6.250000                  4.166667   \n",
       "75%                    8.000000                  5.555556   \n",
       "max                   13.677812                 10.937500   \n",
       "\n",
       "       Deadlift12_percent_increase  Deadlift23_percent_increase  \n",
       "count                365540.000000                365540.000000  \n",
       "mean                      6.490803                     4.536791  \n",
       "std                       2.127265                     1.878748  \n",
       "min                       0.212766                     0.144928  \n",
       "25%                       5.000000                     3.174603  \n",
       "50%                       6.250000                     4.347826  \n",
       "75%                       7.843137                     5.747126  \n",
       "max                      12.608696                     9.865471  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[LIFT_INCREASE_COLUMNS].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "\n",
    "Looks like we lost a lot of data when taking outliers out (from 3043013 to 365540), this was unfortunately expected when working with a sport where methods of tracking scores are prone to errors in human input, and where data input isn't strongly supervised. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gender/Weight Class Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove duplicate names/sex, get the number of times they've competed, and then count the sexes\n",
    "df_times_competed = df.groupby(['Name','Sex']).size().reset_index(name='times_competed')\n",
    "df_times_competed['Sex'].value_counts()\n",
    "\n",
    "sns.countplot(data=df, x='Sex', color='#258fdf')\n",
    "plt.title('Gender Distribution', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Powerlifting has a majority Male audience and it shows here, with little to no Mx lifters as only rarely do they get their own category in federations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "WEIGHT_CLASS_ORDER = ['43', '47', '53', '59', '66', '74', '83', '93', '105', '120', '120+']\n",
    "\n",
    "weight_class_chart_font_size = 16\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.title(\"Distribution of Weight Classes, seperated by Sex\", fontsize = weight_class_chart_font_size)\n",
    "plt.xticks(fontsize = weight_class_chart_font_size)\n",
    "plt.yticks(fontsize = weight_class_chart_font_size)\n",
    "plt.xlabel(xlabel=\"Weight Class (kg)\", fontsize=weight_class_chart_font_size)\n",
    "plt.ylabel(ylabel=\"Count\", fontsize=weight_class_chart_font_size)\n",
    "\n",
    "sns.countplot(data=df, x='WeightClassKg', hue='Sex', order=WEIGHT_CLASS_ORDER, linewidth=10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that women are more common in the lighter weight classes, and are more evenly distributed across the weight classes. Meanwhile, men tend to hover around mainly the 74-105kg weight classes. There are very few competitors in the lighter weight classes (43-47kg), so we mayu not have enough data to come to any conclusions in regards to those weight classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effects of Sex on % attempt increases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jumps between Attempts 1 and 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BINS_INCREASE_RANGE = [n * 1 for n in range(30)]\n",
    "HIST_TICKS = [n * 5 for n in range(7)]\n",
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "xp = 'squat12_percent_increase'\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Squat 1 and Squat 2', fontsize=14)       # Set title with font size\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "xp = 'bench12_percent_increase'\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.title('Percent increase between Bench 1 and Bench 2', fontsize=14)       # Set title with font size\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "xp = 'Deadlift12_percent_increase'\n",
    "plt.subplot(1,3,3)\n",
    "sns.histplot(data=df,x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Deadlift 1 and Deadlift 2', fontsize=14)       # Set title with font size\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumps in Squat 1, 2\n",
    "From the graph, there seems to be are a large number of men who jump between 5 percent - 7 percent between squats 1 and 2, and for women, the most common percent increase in weights seems to be around the same percentage, however, there seems to much less disparity for women as compared to men. \n",
    "\n",
    "### Jumps in Bench 1, 2\n",
    "The graph shows that the most common percent increase between bench 1 and 2 lie between 5 - 7 percent but the jumps are slightly more spread out as compared to the percent increases in weight for the squats. For women, the most common percent increase in weights seems to be more spread out, and lie between 5 and 9 percent, and there seems to be no clear peak as compared to men. \n",
    "\n",
    "### Jumps in Deadlift 1, 2\n",
    "For the deadlifts 1 and 2 percent increases, men lie between 5 and 7 percent once again, with women having a more clear peak for deadlifts, with their most common percent increases lying in between 6 and 8 percent. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jumps between Attempt 2 and 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "\n",
    "xp = 'squat23_percent_increase'\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Squat 2 and Squat 3', fontsize=14)       # Set title with font size\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "xp = 'bench23_percent_increase'\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.histplot(data=df, x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Bench 2 and Bench 3', fontsize=14)       # Set title with font size\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "xp = 'Deadlift23_percent_increase'\n",
    "plt.subplot(1,3,3)\n",
    "sns.histplot(data=df,x=xp, bins=BINS_INCREASE_RANGE, hue='Sex')\n",
    "plt.xticks(ticks=HIST_TICKS)\n",
    "plt.title('Percent increase between Deadlift 2 and Deadlift 3', fontsize=14)       # Set title with font size\n",
    "plt.xlabel(\"% increase in weight between attempts\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jumps in Squat 2, 3\n",
    "The percent increase between squats 2 and 3 are expected to be slightly lesser than the increases between 1 and 2, and the graph shows this. The most common percent increases in weight in this case are between 3 and 6 percent, with most being near 3-5 percent increases. Women have also slightly reduced their percent increases as they lie primarily between 4 and 6 percent.\n",
    "\n",
    "### Jumps in Bench 2, 3\n",
    "The difference in percent increases are also slightly lesser for the bench 2 and 3 attempts. The most common percent increases are between 3-5 percent increases, while women are majorly between 4-6 percent with most being in between 4-5 percent, which is a big change from 5-9 percent between attempts 1 and 2. \n",
    "\n",
    "### Jumps in Deadlift 2, 3\n",
    "The men had a common percent increase of 3-6 percent, with most being from 3-5 percent and this is again a smaller percent increase as compared to attmpts 1 and 2. For the women, the most common percent increase is between 4-6 percent and this is also a smaller jump compared to 6-8 percent between attempts 1 and 2. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Closer Look at Effect of Sex on % Increase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define your figure size\n",
    "plt.figure(figsize=(12, 6))\n",
    "min_value = 0\n",
    "max_value = 15\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, y='squat12_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Squat 1 and Squat 2\")\n",
    "plt.title('Percent increase between Squat 1 and Squat 2 By Sex', fontsize=12)       # Set title with font size\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data =df, y='squat23_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Squat 2 and Squat 3\")\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.title('Percent increase between Squat 2 and Squat 3 by Sex', fontsize=12)       # Set title with font size\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "There seems to be very small differences between men and women when it comes to the Squat, but the median is overall higher for Women in the Squat. There also seems to be a lot more outliers when it comes to men, which might suggest they more often take extreme jumps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "min_value = 0\n",
    "max_value = 15\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, y='bench12_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Bench 1 and Bench 2\")\n",
    "plt.title('Percent increase between Bench 1 and Bench 2 by Sex', fontsize=12)       # Set title with font size\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data =df, y='bench23_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Bench 2 and Bench 3\")\n",
    "plt.title('Percent increase between Bench 2 and Bench 3 by Sex', fontsize=12)       # Set title with font size\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "From these two graphs, our hypothesis that men might take higher jumps in the Bench Press may seem flawed. It looks like the median for women is almost a percent higher than it is for men especially in the 3rd quartile, so on the extreme end of jumps, women seem to take bigger jumps in the bench press."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "min_value = 0\n",
    "max_value = 15\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, y='Deadlift12_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Deadlift 1 and Deadlift 2\")\n",
    "plt.title('Percent increase between Deadlift 1 and Deadlift 2 By Sex', fontsize=12)       # Set title with font size\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data =df, y='Deadlift23_percent_increase', hue=\"Sex\", dodge=True, gap=0.1)\n",
    "plt.ylim(min_value, max_value)\n",
    "plt.ylabel(ylabel=\"Percent Increase between Deadlift 2 and Deadlift 3\")\n",
    "plt.title('Percent increase between Deadlift 2 and Deadlift 3 By Sex', fontsize=12)       # Set title with font size\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion\n",
    "The same trend is seen here like in the last visualization for Bench press, where women are making slightly higher jumps, but it is not very significant, probably a 0.5% difference. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A look at effect of Weight Class on % Increase\n",
    "\n",
    "Now lets see if we can see any relationship between weight class and attempt selection, we will hold off on any discussion until after the three visualizations as they all follow the same trend."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NFVdcYYw58EblcN8WNW3a9LDPc0NH1pP1ZH14Zr0xxuzbt890797dDBs2rE6PI+uP7E9/+pMvs3v06OH74KgmZH10Z32dr0l+77331KRJEyUkJKhnz57q06ePnn32Wa1Zs0YlJSU677zz1KRJE99///73v7Vhwwa/dZx++um+f69du1alpaU699xzq93e8uXLtX79ejVt2tS3zpYtW6qkpMRvvZ06dVJsbKzv55SUFG3evLmuu1etU045xW+9knzrXrlyZY21Vzp4f6UD+/Tpp5/6PU8nnXSSJPn2acOGDRo6dKiOPfZYNWvWTOnp6ZKk/Px8SQdurLJy5UqdeOKJuuuuu/wu2l++fLl2796tpKQkv23k5eVV+V0cqmfPnr5/x8XF6fTTT9fatWtrXXd1+vTpo127dunbb7/VokWL1LdvX/Xv31+LFi2SdOCOe3379pUkrVixQsYYZWRk+G1n0aJFvm0sX75cM2fO9Jt//vnny+v1Ki8vz7fdg39vNptNycnJh31NXHHFFdq0aZPmzp2r888/X5999plOO+00zZw5U9KB12paWpratm1b7fNVW+Xl5Zo4caJOOeUU3+9o3rx5vt9tsG3evFmZmZnKyMiQ0+mU0+nU7t27q2z/4OcvMTFRTZs29T1/a9euVY8ePfyubanNczFx4kQVFhYqOztbHTt2VHZ2tk466SStWrWqXus92n2si02bNumCCy7QVVddpVtuueWIyw8bNkwrV67Ud999p88//1zHH3+8Bg0apF27dkk6cCy5+OKL9a9//UvSgWNrSUmJrrrqqhrX2bt3b9ntdn322Wdas2aN9u3bp9NOO03du3fXzp079dNPP+nTTz+Vw+FQr1696nRcrrRu3TqdeeaZftMO/Vk6cIOkpk2b+n4+mmOuMUaSjuoaqezsbJ1++ulq3bq1mjRpohdeeMH3+23ZsqVGjBih888/X5deeqn+/ve/q6CgwPfYe+65R7fccosGDhyoxx577IjHxYaIrCfryfrwy/qysjJdc8018nq9mjp16mGXJevr7v7779e3336refPmKTY2VjfccIMvp2pC1h9ZpGZ9XF2L7d+/v6ZNm6b4+HilpqYqPj5eknwHrPfff1/HHHOM32MOvTlGYmKi79+NGjU67Pa8Xq+6d++ul19+ucq81q1b+/5dWUclm80mr9dbiz06soPXXfkLrlz3keqX/Pe38rGXXnqpHn/88SrLVgbzpZdeqnbt2umFF15QamqqvF6vOnfu7LsZyGmnnaa8vDx9+OGHWrBggYYMGaKBAwfq9ddfl9frVUpKit9F/5WaN29eq30+2MH7fKS6q+N0OtWtWzd99tlnWrJkiQYMGKBzzjlHK1eu1E8//aTc3Fz169fPt43Y2FgtX77c742QJDVp0sS3zG233aa77rqryrbS0tJ8/z6a10RCQoLOO+88nXfeefrLX/6iW265RRMmTNCIESOqPVAe+gcfE3Pgc6eDly0rK/Nb5m9/+5smT56sZ555xne3ylGjRoXsRi8jRozQli1b9Mwzz6h9+/ZyOBzq2bNnle0f7vk7UmgcTlJSkq666ipdddVVevTRR3Xqqafqqaee0qxZs2q13piYmCrLHfoc13Yfa2vTpk3q37+/evbsqenTp9fqMU6nU8cff7wk6fjjj9eMGTOUkpKiV155xddk33LLLbr++us1efJkvfjii7r66qvVuHHjGtfZuHFjnXnmmfr000+1bds2nX322b6/k169eunTTz/Vl19+qZ49eyohIcH3+6rNcbmSMabK67q630sgjrkZGRmSDrxhqsswE6+++qpGjx6tv/3tb+rZs6eaNm2qJ598Ul999ZVvmRdffFF33XWXPvroI73yyisaN26c5s+frx49eigrK0tDhw7V+++/rw8//FATJkzQnDlz9Ic//KFO9Uczsp6sJ+v913kwK7K+rKxMQ4YMUV5enhYuXKhmzZoddnmyvu5atWqlVq1aKSMjQyeffLLatWunpUuXHraBJ+uPLFKzvs5NcmJiou/FcLCOHTvK4XAoPz/f90lhbZxwwglq1KiRPvnkk2q/nTnttNP0yiuvqE2bNkc8IByO3W5XeXn5UT++Jqeccoo++eQTPfTQQ7V+zGmnnaY33nhDHTp0UFxc1V9BcXGx1q5dq+eff17nnHOOJOnzzz+vslyzZs109dVX6+qrr9aVV16pCy64QNu2bdNpp52mwsJCxcXFqUOHDnXan6VLl6pPnz6SpP3792v58uW68847a1W3VPPz3K9fP3366af66quv9Ne//lXNmzdXx44d9cgjj6hNmzY6+eSTJUmnnnqqysvLtXnzZt++H+q0007T6tWrq30dBlrHjh19Qwd07NhR+fn52rRpk1JTUyWpypARlW/mCgoK1KJFC0mqMizT4sWLdfnll/vufuj1evXTTz/5ngMpeK/Xyu1PnTpVF110kSTp119/rfOwDAc/L5WWLl1a51rsdruOO+4439AytVlv69atVVhY6HeAr+45PtI+xsfH1+o5/v3339W/f391795dL774ou/NUV1VBty+fft80y666CIlJiZq2rRp+vDDD5WTk3PE9fTv319z5szR9u3bfW84Jalv37767LPP9OWXX+rGG2+UdHTH5ZNOOknLli3zm3Y0wzPU5jXcrVs3dezYUX/729909dVXV3lud+zYUe2b/cWLF6tXr166/fbbfdOq+4T41FNP1amnnqoxY8aoZ8+emj17tnr06CHpQGhnZGRo9OjRuvbaa/Xiiy/SJB+ErPdH1vsj60Ob9ZUNcuU3iElJSUd8DFl/QG2z/lCVDWNpaWmdHkfWVxWpWX907/aq0bRpU913330aPXq0Zs2apQ0bNujbb7/Vc889p1mzZtX4uISEBD3wwAP605/+5DstYOnSpZoxY4akA6cxtGrVSpdffrkWL16svLw8LVq0SHfffbd+++23WtfXoUMH5eTk6Pfffw/oOG1jxozR119/rdtvv13ff/+9fvzxR02bNu2w27jjjju0bds2XXvttVq2bJl+/vlnzZs3TzfddJPKy8vVokULJSUlafr06Vq/fr0WLlyoe+65x28dkydP1pw5c/Tjjz8qNzdXr732mpKTk9W8eXMNHDhQPXv21ODBg/Xxxx/rl19+0ZIlSzRu3Lgj/gE899xzeuutt/Tjjz/qjjvu0Pbt23XTTTfVqm7pwPP81Vdf6ZdfftHWrVt9nzb169fPN/Zbx44dfdNefvllvz/ojIwMDRs2TDfccIPefPNN5eXl6euvv9bjjz+uDz74QJL0wAMP6Msvv9Qdd9zh+4R67ty5GjlyZB1/e/9TXFysAQMG6KWXXtL333+vvLw8vfbaa3riiSd0+eWXS5IGDhyoE088UTfccIO+++47LV68WH/+85/91nP88cerXbt2ysrKUm5urt5//3397W9/q7LM/PnztWTJEq1du1a33XZblfHwanoeD7Vt2zatXLlSa9askXTg9JmVK1dWWd+h2//Pf/6jtWvX6quvvtKwYcNq9S3JwTIzM7Vhwwbdc889WrdunWbPnu07Va0m7733nq677jq99957ys3N1bp16/TUU0/pgw8+8D3HtVlvv379tGXLFj3xxBPasGGDnnvuOX344Yd13scOHTrok08+UWFhobZv315tzZs2bVK/fv3Url07PfXUU9qyZYsKCwsP+/xW2rt3r2/Z7777TrfffrsSEhI0aNAg3zKxsbEaMWKExowZo+OPP75Wp5v1799fP/30kz766CO/v52+ffvqvffe0y+//KL+/ftLOrrj8siRI/XBBx/o6aef1k8//aTnn39eH374YZ1Pk6rNa9hms+nFF19Ubm6u+vTpow8++EA///yzvv/+e02cONH3ujjU8ccfr2+++UYff/yxcnNzNX78eH399de++Xl5eRozZoy+/PJLbdy4UfPmzVNubq5OPvlk7du3T3feeac+++wzbdy4UV988YW+/vprvzeuqBlZT9ZLZH0os37//v268sor9c033+jll19WeXm5L1sO940pWX9AbbJ+2bJlmjJlilauXKmNGzfq008/1dChQ3XccccdMZfJ+ijO+rpcwFzdHS8P5vV6zd///ndz4oknmvj4eNO6dWtz/vnnm0WLFhljqr/RgTEH7iT4yCOPmPbt25v4+HiTlpbmN8RBQUGBueGGG0yrVq2Mw+Ewxx57rLn11lt9d/Wrrq67777b9O3b1/fzl19+aU455RTjcDjM4XZb1dzM4+ALyCuHDzh4mILPPvvM9OrVyzgcDtO8eXNz/vnn+/axpgvac3NzzR/+8AfTvHlz06hRI3PSSSeZUaNG+W5iMH/+fHPyyScbh8NhTjnlFPPZZ5/51TZ9+nTTrVs3k5iYaJo1a2bOPfdcvwvid+7caUaOHGlSU1NNfHy8adeunRk2bJjJz8+vdr8r93X27NnmrLPOMna73Zx88snmk08+qVPd69atMz169DCNGjXyu4HFjh07TGxsrLnyyit963rrrbeMJDNlyhS/bVTeza5Dhw4mPj7eJCcnmz/84Q/m+++/9y2zbNkyc95555kmTZqYxMREc8opp5iJEyf65ld385auXbuaCRMmVLv/JSUl5sEHHzSnnXaacTqdpnHjxubEE08048aNM3v37vUtt27dOnP22Wcbu91uMjIyzEcffVTlphCff/656dKli0lISDDnnHOOee211/yei+LiYnP55ZebJk2amDZt2phx48aZG264we81XNPzeKjKIRMO/a+m/TTmwF0GTz/9dONwOMwJJ5xgXnvttSrP16H7ZIwxTqfTvPjii76f3333XXP88ccbh8NhzjnnHPOvf/3rsDfz2LBhg7n11ltNRkaGbwiVM844w2+dtV3vtGnTTLt27UxiYqK54YYbzMSJE/1u5lGbfZw7d645/vjjTVxcXI03Aqnp+T3SofPQoWhatGhh+vbtaxYuXFjt8yLJPPHEE4ddZ6V9+/YZh8NhmjRp4jfERmlpqWncuLFp1KiR727AxhzdcXn69Om+YUsGDx5sHnnkEb+70tbmhiq1fQ1XLnvDDTeY1NRUY7fbTfv27c21117rO6YdeiwuKSkxI0aMME6n0zRv3tz83//9n3nwwQd9NRUWFprBgweblJQU3/r+8pe/mPLyclNaWmquueYa065dO2O3201qaqq58847fTcCAllvDFlP1odP1lf+zqr779Ahsw5G1h9Qm6z//vvvTf/+/U3Lli2Nw+EwHTp0MJmZmea3336r8fk1hqw3Jrqz3mZMPS44ACCbzaa33npLgwcPtrqUqPTZZ5+pf//+2r59+1FdZxfuvvjiC/Xr10+//fabXC6X1eVU69Zbb9WPP/6oxYsXW10KAFiCrA8ust56ZL2/Ol+TDACov9LSUv36668aP368hgwZElah+dRTT+m8885TYmKiPvzwQ82aNeuId1IFAAD+yPrIFbBrkgEAtfff//5XJ554otxut5544gmry/GzbNkynXfeeerSpYuys7P1j3/8o1bDXgEAgP8h6yMXp1sDAAAAAFCBb5IBAAAAAKhAkwwAAAAAQAWaZAAAAAAAKgT07tZer1ebNm1S06ZN6zwYNQAAwWCM0a5du5SamqqYGD4bri+yHgAQbgKd9QFtkjdt2qR27doFcpUAAATEr7/+qrZt21pdRsQj6wEA4SpQWR/QJrlp06aSDhTXrFmzQK4aAICjsnPnTrVr186XUagfsh4AEG4CnfUBbZIrT7tq1qwZwQkACCucGhwYZD0AIFwFKuu5OAsAAAAAgAo0yQAAAAAAVKBJBgAAAACgQkCvSa4NY4z279+v8vLyUG86asXGxiouLo7r7QAAYYGsD474+HjFxsZaXQYARL2QNskej0cFBQXau3dvKDfbIDRu3FgpKSmy2+1WlwIAaMDI+uCx2Wxq27atmjRpYnUpABDVQtYke71e5eXlKTY2VqmpqbLb7XzzGQDGGHk8Hm3ZskV5eXk64YQTAjKANgAAdUXWB48xRlu2bNFvv/2mE044gW+UASCIQtYkezweeb1etWvXTo0bNw7VZhuERo0aKT4+Xhs3bpTH41FCQoLVJQEAGiCyPrhat26tX375RWVlZTTJABBEIf/KkW85g4PnFQAQLsik4OBbeQAIDVIMAAAAAIAKIb+79aGKiorkdrtDtj2n0ymXyxWy7QEAgNDmPVkPAKgPS5vkoqIijRg+XKUeT8i26bDbNXPWLMITAIAQCXXek/UAgPqwtEl2u90q9Xh0+8mnK7Vx06Bvb9PeXZq69hu53e6jDs5HH31UY8eO1d13361nnnlGkpSVlaU5c+bo119/ld1uV/fu3TVx4kSdddZZAaweAIDIFMq8J+sBAPVl+enWkpTauKnSm7awuowj+vrrrzV9+nSdcsopftMzMjI0ZcoUHXvssdq3b58mT56sQYMGaf369WrdurVF1QIAEF4iIe/JegAAN+6qpd27d2vYsGF64YUX1KKFf8APHTpUAwcO1LHHHqtOnTrp6aef1s6dO/X9999bVC0AAKgrsh4AINEk19odd9yhiy++WAMHDjzsch6PR9OnT5fT6VTXrl1DVB0AAKgvsh4AIIXJ6dbhbs6cOVqxYoW+/vrrGpd57733dM0112jv3r1KSUnR/Pnz1apVqxBWCQAAjhZZDwCoxDfJR/Drr7/q7rvv1ksvvaSEhIQal+vfv79WrlypJUuW6IILLtCQIUO0efPmEFYKAACOBlkPADgYTfIRLF++XJs3b1b37t0VFxenuLg4LVq0SP/4xz8UFxen8vJySVJiYqKOP/549ejRQzNmzFBcXJxmzJhhcfUAAOBIyHoAwME43foIzj33XK1atcpv2o033qiTTjpJDzzwgGJjY6t9nDFGpaWloSgRAADUA1kPADhYWDTJm/buCtvtNG3aVJ07d/ablpiYqKSkJHXu3Fl79uzRxIkTddlllyklJUXFxcWaOnWqfvvtN1111VWBKh0AgIgXirwn6wEA9WVpk+x0OuWw2zV17Tch26bDbpfT6QzY+mJjY/Xjjz9q1qxZ2rp1q5KSknTGGWdo8eLF6tSpU8C2AwBApAp13pP1AID6sLRJdrlcmjlrltxud8i26XQ65XK56rWOzz77zPfvhIQEvfnmm/WsCgCA6BXqvCfrAQD1Yfnp1i6Xq95BBgAAwht5DwCIFNzdGgAAAACACjTJAAAAAABUoEkGAAAAAKACTTIAAAAAABVokgEAAAAAqECTDAAAAABABZpkAAAAAAAqWD5OclFRkdxud8i253Q6GacRAIAQC2Xek/UAgPqwtEkuKirSiOHDVerxhGybDrtdM2fNIjwBAAiRUOc9WQ8AqA9Lm2S3261Sj0fXdIlTm0Rb0Le3eY/RnFUeud3uow7ORx99VGPHjtXdd9+tZ555RpJks1Vf+xNPPKH777//aMsFACAqhDLvyXoAQH1Zfrq1JLVJtKlts1BcHu2t16O//vprTZ8+Xaeccorf9IKCAr+fP/zwQ91888264oor6rU9AACiSWjynqwHANQPN+6qpd27d2vYsGF64YUX1KJFC795ycnJfv+988476t+/v4499liLqgUAAHVF1gMAJJrkWrvjjjt08cUXa+DAgYddrqioSO+//75uvvnmEFUGAAACgawHAEhhcrp1uJszZ45WrFihr7/++ojLzpo1S02bNtUf//jHEFQGAAACgawHAFSiST6CX3/9VXfffbfmzZunhISEIy7/r3/9S8OGDavVsgAAwHpkPQDgYDTJR7B8+XJt3rxZ3bt3900rLy9XTk6OpkyZotLSUsXGxkqSFi9erHXr1umVV16xqlwAAFBHZD0A4GA0yUdw7rnnatWqVX7TbrzxRp100kl64IEHfKEpSTNmzFD37t3VtWvXUJcJAACOElkPADhYWDTJm/cY1XfIhtpvp26aNm2qzp07+01LTExUUlKS3/SdO3fqtdde09/+9rd61wkAQDQKRd6T9QCA+rK0SXY6nXLY7ZqzyhOybTrsdjmdzoCvd86cOTLG6Nprrw34ugEAiGShznuyHgBQHzZjTN0/cq3Bzp075XQ65Xa71axZM795JSUlysvLU3p6ut+NLoqKiuR2uwNVwhE5nU65XK6QbS9Uanp+AaChO1w2oe6OJuul0OY9WQ8ADUugs97y061dLldUBhkAAPgf8h4AECksb5KBUAj1GQuonWj9tgcArETmBRZZBTQ8NMmIekVFRRoxfLhKPaG79h2147DbNXPWLN58AECAkHmBR1YBDQ9NMqKe2+1Wqcej208+XamNm1pdzmFt2rtLU9d+ExG11lflvrrdbt54AECAhGPmRXK2kVVAw0STjAYjtXFTpTdtYXUZtRJJtQIAwk845kg41gQA1YmxugAAAAAAAMIFTTIAAAAAABVokgEAAAAAqGD5NcmhHqagrrfx93q98ng8stvtionhMwUA4amkpET5+flKS0tTQkKC1eUAVYQy7w/NerIciCxkGqxmaZNsxTAFdb2Nv8fj0caNG9W+fXv+SAGErfz8fGVmZio7O1sZGRlWlwP4CXXeH5r1ZDkQWcg0WM3SJrlymIJ+p0nNQzAiwI5d0mcrPHW6jf8jjzyiiRMn+k1zuVwqLCyUJGVlZWnOnDn69ddfZbfb1b17d02cOFFnnXVWwOsHACAShTLvjybrpQN5/tBDD/lNI+8BoGGy/HRr6UBgtmpuC8GWzFE96oQTTtC8efN8nz7Hxsb65mVkZGjKlCk69thjtW/fPk2ePFmDBg3S+vXr1bp164BUDQBANAhN3h9d1ktSp06dtGDBAt/P5D0ANExh0SSHu9jYWCUnJ1d7itbQoUP9fn766ac1Y8YMff/99zr33HNDVSIAAKinuLg4JScnVzuPvAeAhoO7V9TCxo0blZ6ervT0dF1zzTX6+eefq13O4/Fo+vTpcjqd6tq1a4irBAAA9fHTTz8pNTWVvAeABo4m+QjOOOMMPfbYY3r33Xf1wgsvqLCwUL169VJxcbFvmffee09NmjRRQkKCJk+erPnz56tVq1YWVg0AAOrirLPO0r///W99/PHH5D0ANHA0yUdw/vnn6/zzz1fnzp01cOBAvf/++5KkWbNm+Zbp37+/Vq5cqSVLluiCCy7QkCFDtHnzZqtKBgAAdXThhRfqiiuuUJcuXch7AGjguCa5ljwVw1bExsaqU6dO+vHHH1VSUuKb1rZtW7Vt21bPPfecOnfurOeff173339/yOorLS1VWVmZ8vLy/G40ggPDCCB88fsJDJ5H4Mg8dRiCKhzzvjZZz7EgOHheQ4vnG1ajSa6lgoICSQcCdvXq1erUqZM2btxY7bJlZWUqKiqqcX4weL1ebdu2TdOmTdOWLVtCtl2gviZNmmR1CQAaiMosr41wzHuy3jpkFdCwhEWTvGOXVJ8hG+q2nbp58MEH1b17d3Xt2lU7duzQ448/rr179+qOO+5Qq1at9Pjjj+viiy9WcnKytm3bpunTp6uoqEg33XST2rdvH/idqEHlp8tZWVl8k3yI/Px8wi2MjR07VmlpaVaXEfF4nSMShCLvD5f1KSkpstvt1c578MEHdfHFF6tdu3bavHlzWOZ9bbKeY0FwkFWhxesYVrO0SXY6nXLY7fpsRe1Pf6ovh90up9NZ6+V///13zZ49Wzt27FDr1q3Vo0cPLV26VCeeeKJKSkq0fv16DR06VFu3blVSUpLOOOMMLV68WKeddloQ96J68fHxSk9Pr3aoKiBcpaWlKSMjw+oyAARRqPO+pqy32+01ZmRhYaGGDx+urVu3hnXek/XWIKuAhsXSJtnlcmnmrFlyu90h26bT6ZTL5ar18v/5z3+0ceNGtW/fvkogJSQk6M033wx0iQAARJVQ531ds16S5syZU+M88h4AGhbLT7d2uVx1DjIAABBZyHsAQKRgCCgAAAAAACrQJAMAAAAAUIEmGQAAAACACjTJR2C329W+ffsah4wAgHCQlpam7OxshigBqkGWA5GFTIPVLL9xV7iLiYlhmAUAYS8hIYHhSYAakOVAZCHTYDW+SQYAAAAAoAJNMgAAAAAAFSw/3bqoqEhutztk23M6nYzTCABAiIUy78l6AEB9WNokFxUVafiI4fKUekK2TbvDrlkzZxGeAACESKjznqwHANSHpU2y2+2Wp9SjjmcZJTYL/vb27JTWfOWR2+2udXBmZWXpoYce8pvmcrlUWFgo6UDwP/DAA5o3b5527NihPn366Nlnn9UJJ5wQ8PpRP5v27rK6hCOqrDESaq2vhrCPAA4IZd4fTdZL0Zf34XSMjeRsi8SaAdSf5adbS1JiM6lpS6urqFmnTp20YMEC38+xsbGSJGOMBg8erPj4eL3zzjtq1qyZnn76aQ0cOFBr1qxRYmKiVSXjIE6nUw67XVPXfmN1KbUWSbXWh8Nul9PptLoMACFC3gdfOGdeONZUG2QV0PCERZMc7uLi4pScnFxl+k8//aSlS5fqhx9+UKdOnSRJU6dOVZs2bfTf//5Xt9xyS6hLRTVcLpdmzpoV0mvfUTtcNwggnERD3pN5gUdWAQ0PTXIt/PTTT0pNTZXD4dBZZ52lSZMm6dhjj1Vpaakk+Y29GBsbK7vdrs8//zysQjNShPpGbg0ZoQ8A/sIl78vKylReXl5lemlpqcrKypSXl+f7lhv/Q64BCBSa5CM466yz9O9//1sZGRkqKirSI488ol69emn16tU66aST1L59e40ZM0bPP/+8EhMT9fTTT6uwsFAFBQVWlx5xioqKNGL4cJV6Qncjt4bMYbdr5ixubAMAUvjkfWUTbIypMs/r9Wrbtm2aNm2atmzZEtDtRgNyDUCg0CQfwYUXXuj7d5cuXdSzZ08dd9xxmjVrlu655x698cYbuvnmm9WyZUvFxsZq4MCBfo9B7bndbpV6PLqmS5zaJNqsLqeKzXuM5qzaH7b11cWBfan7jW0AIFqFS96Xl5fLGKOWjWyKi/Gf59kv7bVL13eL1/5d8QHfdm2EaxaSawACiSa5jhITE9WlSxf99NNPkqTu3btr5cqVB+7c6fGodevWOuuss3T66adbXGnkapNoU9tmMUdeMOS8ksK5vrrwWl0AAIQ1q/M+LkayH3JGtfFKsTE2JSfaJJtVORSuWUiuAQiccDq6RYTS0lKtXbtWKSkpftOdTqdat26tn376Sd98840uv/xyiyoEAAD1Rd4DQMMVFt8k79kZvtu57777dOmllyotLU2bN2/WI488op07d2r48OGSpNdee02tW7dWWlqaVq1apbvvvluDBw/WoEGDAlw9AACRLRR5f7TbIO8BAJUsbZKdTqfsDrvWfBW6GzXZHXUb6+63337Ttddeq61bt6p169bq0aOHli5dqvbt20uSCgoKdM8996ioqEgpKSm64YYbNH78+GCVDwBAxAl13tc16yXyHgDwP5Y2yS6XS7NmhnYsv7oODzBnzpzDzr/rrrt011131bcsAACiVqjz/miGAiLvAQCVLD/d2uVyhd1dCL1erzwej+x2u2JiuGwbQHQpKSlRfn6+0tLS/MZ9BQLF6/WqpKTEL0fDMe8BhCdyClajA6yGx+PRxo0b5WG8XgBRKD8/X5mZmcrPz7e6FESpsrIychTAUSOnYDWaZAAAAAAAKtAkAwAAAABQgSYZAAAAAIAKNMkAAAAAAFSgSQYAAAAAoILlQ0CFs0i6K2dpaanKysqUl5en2NhYq8s5KtzBMPR4zhsmfu8IlUjK0XDBc1Y/HN+iA79HWM3yJrmoqEhutztk23M6nbUep7GgoCDI1QSO1+vVtm3bNG3aNG3ZssXqchAhJk2aZHUJAKLYwTm6bds27d69OyTbbdKkiVq2bBmSbSG8kGsAAsHSJrmoqEjDRwyXpzR0n5raHXbNmjmrVo1ySkqK7HZ7CKqqv8pvkrOysiL6m2TCLbTGjh2rtLQ0q8tAiPG3hlCpzNHNmzcr66GHVBaib0nj7Xa9MH262rRpE5LtBZLH44moD+nDDbkWHcgpWM3SJtntdh9okPskSc74EGywTJ6cYrnd7lo1yXa7XQkJCfr999/1wAMP6MMPP9S+ffuUkZGhGTNmqHv37r5l165dqwceeECLFi2S1+tVp06d9Oqrr4b0QB0fH6/09HQlJCSEbJuIbGlpacrIyLC6DABRqjJHS0pKDjTIoch7d5nKcopVUlJSpzyMlKzH4ZFrAALB8tOtJUnOeNlaBf8bW3MUj9m+fbt69+6t/v3768MPP1SbNm20YcMGNW/e3LfMhg0bdPbZZ+vmm2/WQw89JKfTqbVr19KsAgBwsBDkPVkPAKiv8GiSw9jjjz+udu3a6cUXX/RN69Chg98yf/7zn3XRRRfpiSee8E079thjQ1UiAACoB7IeAHAwhoA6grlz5+r000/XVVddpTZt2ujUU0/VCy+84Jvv9Xr1/vvvKyMjQ+eff77atGmjs846S2+//bZ1RQMAgFoj6wEAB6NJPoKff/5Z06ZN0wknnKCPP/5YmZmZuuuuu/Tvf/9bkrR582bt3r1bjz32mC644ALNmzdPf/jDH/THP/5RixYtsrh6AABwJGQ9AOBgnG59BF6vV6effrrvDnunnnqqVq9erWnTpumGG26Q1+uVJF1++eUaPXq0JKlbt25asmSJsrOz1bdvX8tqBwAAR0bWAwAOxjfJR5CSkqKOHTv6TTv55JN9g5y3atVKcXFxh10GAACEL7IeAHAwmuRq2O12tW/fXna7Xb1799a6dev85ufm5qp9+/a+Zc8444zDLgMA4SQtLU3Z2dkMW4OgiY+P9+VoJCDrgfBCTsFq4XG6tbvsqIZsOJrt1EZMTIxvSIfRo0erV69emjRpkoYMGaJly5Zp+vTpmj59um/5+++/X1dffbX69Omj/v3766OPPtK7776rzz77LBh7AQD1kpCQwDiiCKqDc9RPKPK+lll/MLIeCC/kFKxmaZPsdDpld9jlySkO2TbtDrucTmetlz/jjDP01ltvacyYMfrrX/+q9PR0PfPMMxo2bJhvmT/84Q/Kzs7Wo48+qrvuuksnnnii3njjDZ199tnB2AUAACJKqPOerAcA1IelTbLL5dKsmbPkdrtDtk2n0ymXy1Wnx1xyySW65JJLDrvMTTfdpJtuuqk+pQEAEJVCnfdkPQCgPiw/3drlctU5yAAAQGQh7wEAkcLyJhk41OY9RpLX6jKqOFBX+NZXF5X7AgAIT/uriZkyr1TuNSrcY7R/lzU5FK5ZSK4BCCSaZIQNp9Mph92uOas8VpdyWHNW7be6hIBw2Ot2zR4AIPhiY2Nls9m0bV/Vps/rlXZ5pP+sLNOWLXW/QVkghWMWkmsAAoUmGWHD5XJp5qzQXqPekB3NNXsAgOCKj49Xenq6ysvLq8wrLS1VWVmZsrKyFBsba0F14Y1cAxAoNMkIK1yzBgBo6OLj4xUfH1/jvPT09OqH2AIABARNMsJKUVER3ySHCJ+4A0B4KisrO+w3yXl5eXyTXA1yDUCg0CQjbBQVFWnE8OEq9YT3NcnRwmG3a+asWbyhAIAwUtkEG1PdNclebdu2TdOmTdOWLVssqC68kWsAAoUmGWHD7Xar1ONRv9Ok5k2trqaqHbukz1YobOuriwP74pHb7ebNBACEkfLychlj1LSxdOiXxWX7pZ0OaeAZ0r491tQXrllIrgEIJMub5FCfXsupOOGveVOpVXOb1WVU48Cn+uFbX10wVAaA0Apl3kdD1sfGSnGHNMlerxQbI7VoZlNivFU5FK5ZSK4BCBxLm+SioiINHzFcntLQnV5rd9g1ayan4gAAECqhznuyHgBQH5Y2yW63W55Sj2xnd5PN2STo2zPu3fJ8vrJOp+J06NBBGzdurDL99ttv13PPPec37bbbbtP06dM1efJkjRo1KhAlAwAQ8UKZ90eT9RJ5DwD4H8tPt5Ykm7OJbEmhGfy9rifjfP311353mPzhhx903nnn6aqrrvJb7u2339ZXX32l1NTUAFQJAED0CVXeH82Jt+Q9AKBSjNUFWMXr9aqkpERer/ewy7Vu3VrJycm+/9577z0dd9xx6tu3r2+Z33//XXfeeadefvnlGsc1BIBwUVJSotzcXJWUlFhdCqJYbXM2XJD3QPggp2C1Btskezwebdy4UZ46DDfk8Xj00ksv6aabbpLNduBmFV6vV9dff73uv/9+derUKVjlAkDA5OfnKzMzU/n5+VaXgih2NDkbLsh7wFrkFKzWYJvko/H2229rx44dGjFihG/a448/rri4ON11113WFQYAAAKGvAeAhi0srkmOFDNmzNCFF17ouw5p+fLl+vvf/64VK1b4PmkGAACRjbwHgIYtKN8kv/3225o/f75WrlzpdxOMSLZx40YtWLBAt9xyi2/a4sWLtXnzZqWlpSkuLk5xcXHauHGj7r33XnXo0MG6YgEAwFEh7wEAQfkm+Z///Kfi4g6sOjk5WZmZmTrzzDODsamQefHFF9WmTRtdfPHFvmnXX3+9Bg4c6Lfc+eefr+uvv1433nhjqEsEAAD1RN4DAILSJI8cOVLLly/XsmXL1KxZMz300EPKyspSq1atql3euHcHo4yAbcfr9erFF1/U8OHDfc2/JCUlJSkpKclv2fj4eCUnJ+vEE0+sV60AAESbUOR9fbZB3gMApCA1yeeff76uuOIKjR8/Xnl5eTrrrLP0yiuv6Pbbb/dbzul0yu6wy/P5yqMa0/Bo2B12OZ11G6NxwYIFys/P10033RSkqgAAiF6hzvujyXqJvAcAHBC0G3fFxMRo6NChGjlypIYMGaL//ve/Kisr81vG5XJp1sxZcrvdwSqjCqfTKZfL5Rt3rTZDU/Tp00f79u2TpCOO1/bjjz/WarlAKy0tVVlZmfLy8hQbGxvSbQcKt/kPPZ7zhonfO0LJ4/HI6XRq+vPTtXPnzpBss1mzZnI6nXXO4nDI+0gcMiuccHyLDvweYbWg3t06PT1dkmS32yUdOI3pUC6XSy6XK5hlHFZBQYFl2w4kr9erbdu2adq0adqyZYvV5SBCTJo0yeoSAES5g3M2Pj4+JNvct2+fNm7cGJJtIbyQawACIahNcl5enqT/fSoaExN+wzKnpKT4mvhIVvlNclZWVkR/k0y4hdbYsWOVlpZmdRkIMf7WEErRkrOh4vF4ouYDfCuQa9GBnILVgtYke71ezZ49W8nJyVq2bJlat24dsk+Q68JutyshIcHqMgIiPj5e6enpUbM/CL60tDRlZGRYXQaAKBZNOYvwR64BCISgNMkfffSRVqxYoWXLlikjI0NfffWVsrKyZLPZgrE5AAAAAAACIihN8pQpU3xDJ+zatUsTJkzQGWec4Tv9GgAAAACAcBSUJvmWW25RSkqKWrdurS5duig2Njbkd3sGAAAAAKCugtIkDx48WM2aNQvGqgEAAAAACJrwu900AAAAAAAWCeoQULVRVFQkt9sdsu05nU65XC7Z7Xa1b9+eYSkANDhpaWnKzs5mmBQE1aE5G8q8r8x6AJGJnILVLG2Si4qKNHzECHlKS0O2TbvDoVkzZ8rlctVqSIr9+/crKytLL7/8sgoLC5WSkqIRI0Zo3LhxvnGfs7KyNGfOHP3666+y2+3q3r27Jk6cqLPOOivYuwMAdZaQkMAQKQi6mJgYX86GOu8PzvraIOuB8EJOwWqWNslut1ue0lLF9u4rm9MZ9O0Zt1ueLxbJ7XbXOjgff/xxZWdna9asWerUqZO++eYb3XjjjXI6nbr77rslSRkZGZoyZYqOPfZY7du3T5MnT9agQYO0fv16tW7dOpi7BABA2Atl3pP1AID6svx0a0myOZ2yJbWyuoxqffnll7r88st18cUXS5I6dOig//73v/rmm298ywwdOtTvMU8//bRmzJih77//Xueee25I6wUAIFyFa96T9QCAg3HjriM4++yz9cknnyg3N1eS9N133+nzzz/XRRddVO3yHo9H06dPl9PpVNeuXUNZKgAAOApkPQDgYGHxTXI4e+CBB+R2u3XSSScpNjZW5eXlmjhxoq699lq/5d577z1dc8012rt3r1JSUjR//ny1ahV+n5YDAAB/ZD0A4GA0yUfwyiuv6KWXXtLs2bPVqVMnrVy5UqNGjVJqaqqGDx/uW65///5auXKltm7dqhdeeEFDhgzRV199pTZt2lhYfWTasUuSjNVlVHGgrvCtry4q9wUAEJ5ZX15ezTTvgf+27zTat8eaHArXLCTXAAQSTfIR3H///XrwwQd1zTXXSJK6dOmijRs36tFHH/ULzsTERB1//PE6/vjj1aNHD51wwgmaMWOGxowZY1XpEcfpdMpht+uzFR6rSzmsz1ZYXUFgOOx2OUNwwzwACHfhlPWxsbGy2WzatbdqA+r1SvtKpQVfS1u2BGyTRyUcs5BcAxAoNMlHsHfvXt/wD5ViY2Pl9XoP+zhjjEpDOLRVNHC5XJo5a1ZIx81uyBhHFAAOCKesj4+PV3p6usqr+Sq5tLRUZWVlysrKUmxsbEC3Gw3INQCBQpN8BJdeeqkmTpyotLQ0derUSd9++62efvpp3XTTTZKkPXv2aOLEibrsssuUkpKi4uJiTZ06Vb/99puuuuoqi6uPDEVFRTTGQcIbBgA4snDL+vj4eMXHx0uSysrKqm2YAQDBExZNsglRg3Q023n22Wc1fvx43X777dq8ebNSU1N122236S9/+YukA580//jjj5o1a5a2bt2qpKQknXHGGVq8eLE6deoU6F2IOkVFRRo+Yrg8peF9inWksjvsmjVzFo0ygLAQiryPpqwvKytTXl6ejDlw6rXX69W2bds0bdo0bbH6fOswROYBCBRLm2Sn0ym7wyHPF4tCtk27w1Gn61WaNm2qZ555Rs8880y18xMSEvTmm28GqLqGx+12y1PqUcezjBKbWV2Nvz07pTVf2cKytto4UL9HbrebNwwALBXqvI+WrC8vL5cxRo0SpZhYaX+ZZE+QuvQy1V6zHArhmo1kHoBAsrRJdrlcmjVzZkhPteX00/CU2Exq2tLqKqoXzrUBQCQIdd5HW9bHxEoxcVKMkWJipMTmkhKsrYlsBBDNLD/d2uVyRVWQAQCAqsh7AECkiDnyIgAAAAAANAw0yQAAAAAAVKBJBgAAAACgQtg2yV6vVyUlJfJ6vVaXAgARr6SkRLm5uSopKbG6FDQAZDiA2iKfEI7Ctkn2eDzauHGjPB7GzwWA+srPz1dmZqby8/OtLgUNQFlZGRkOoFbIJ4SjsG2SAQAAAAAINZpkAAAAAAAqWD5OclFRkdxud5XpHo9HBQUFKisrk91uD9j2nE4n4zQCaFDKy8u1fPlySVJmZqbfvOeee04nn3yyFWWhgakp74OBrAciz/79+/X6669r6tSpVeYtXLjQgorQkFnaJBcVFWn4iBHylJaGbJt2h0OzZs6sU3ju2rVL48eP11tvvaXNmzfr1FNP1d///nedccYZkqSsrCzNmTNHv/76q+x2u7p3766JEyfqrLPOCtZuAECt5OTkKCsrq8b5d9xxhyTegCC4Qp33ZD0Qee66664ab/Y3YMAAcgohZWmT7Ha75SktlePsKxTjbB307XndW1T6+Rtyu911Cs5bbrlFP/zwg/7zn/8oNTVVL730kgYOHKg1a9bomGOOUUZGhqZMmaJjjz1W+/bt0+TJkzVo0CCtX79erVsHf78AoDpHapAPxhsQBFMo856sByLLG2+8IUlHvBs+OYVQsvx0a0mKcbZWbFKq1WVUa9++fXrjjTf0zjvvqE+fPpIOfJr89ttva9q0aXrkkUc0dOhQv8c8/fTTmjFjhr7//nude+65VpQNoIErLy/XlClTqkwfO3asBgwYoAcffFDffPON37y1a9dy6jWCKlzznqwHrOHxePTJJ59Umb5w4ULt379fV199tbZv3+6bTqOMUAmLJjnUPB5Prcdi2717t8rLyxUTE+P3GIfDoZycnCrr8Xg8eu655+R0OnXiiSeGbMy30tJSlZWVKS8vT7GxsSHZZiBwu//g4zlumNatW6etW7dWmZ6WlqaYmBgNHz68SpN8xx138OYDAVU5BJQVQ0FFQ9YzhFbdkXmRZcGCBVW+Qa7Mobi4ON144416+umnrSgNDVyDbJILCgoUHx9f6+W7deumCRMmKDExUa1atdL777+vr7/+Wu3bt9fGjRslSZ9++qnuvfde7du3T61bt9Y///lP7dmzR3v27AnWbvjxer3atm2bpk2bpi1btoRkm4gMkyZNsroEhKH09HSrS0ADUFBQ4Pf/od52tGU9jozMiy49evSwugQ0UA2ySU5JSVH79u1rvfzLL7+s2267TX379lVsbKy6deumq6++WitXrvSt56qrrtI555yjrVu36sUXX9R9992nnJwctWnTJli74afym+SsrKyI+yaZQAuusWPHKi0tzeoyEGLr1q3T5MmTa5yfl5cXwmrQUKWkpMhut6usrMySbUd61leO9IHaI/Miy4IFC/T666/XOH/p0qUhrAb4nwbZJNvtdiUkJNR6+Y4dO2rx4sXas2ePdu7cqZSUFF199dU69thjfetJSEhQUlKSJKlv37464YQT9PLLL2vMmDFB2YfqxMfHKz09vU77huiXlpamjIwMq8tAiB133HH6z3/+U+WU6/z8fB1//PGaNWtWlcc899xzoSoPDURl3gZyKMe6bru2IiXrcXhkXmTp0KGD3nzzTb9TriuvO96/f79efPFFC6tDQxZjdQGRJDExUSkpKdq+fbs+/vhjXX755TUua4xRaQiHtgKAg8XGxurOO++sMn3SpEkaOHBgleuRJXHTLkBkPRBKdru92hvfDRgwQIMGDfK7aZfEcIUIHZrkWvj444/10UcfKS8vT/Pnz1f//v114okn6sYbb9SePXs0duxYLV26VBs3btSKFSt0yy236LffftNVV11ldekAGrA+ffrUeggo3nigoSPrAWtcccUVkqSYmMO3JeQUQiksTrf2ukNzo6mj3Y7b7daYMWP022+/qWXLlrriiis0ceJExcfHq7y8XD/++KNmzZqlrVu3KikpSWeccYYWL16sTp06BXgPAKBu+vTpo/nz5+vVV1/VCy+8UGX+c889xzfICJlQ5D1ZD0Smf/zjH1qzZo2mTp1aZR4NMkLN0ibZ6XTK7nCo9PM3QrZNu8Mhp9NZp8cMGTJEQ4YMqXZeQkKC3nzzzUCUBgBBERsbq+7du+uFF15QdnY21+sh5EKd92Q9EHni4uJ05ZVX6sorr7S6FMDaJtnlcmnWzJlyu91V5lXe0bHyzpiB4nQ65XK5ArY+AABweIfL+2Ag6wEA9WH56dYul6vaIPN6vTr++ONlt9uPeI0CAODw0tLSlJ2dzdAoCIn4+Hi1b9/e70PumvIeQMNGPiEcWd4k1yQmJoahjAAgQBISEjjNGiFDhgOoLfIJ4YivaAEAAAAAqECTDAAAAABABZpkAAAAAAAq0CQDAAAAAFAhbG/chYZlz06rK6iqsqZwrK02IrVuAIA/b3nF/++XvF5pzw5p115ragnXbAy3egBENppkWMrpdMrusGvNVx6rS6nRmq9sVpdw1OwOu5xOp9VlAACOQmxsrGw2m/btMZIONMieEmnVEpu2bLE2m8IxG8k8AIFieZNcVFQkt9sdsu05nU7GaQwjLpdLs2bOCulroCHh9Q4gXIQy76Pl2BcfH6/09HSVlx/4Krm0tFRlZWXKyspSbGysxdWFn2j5vQOwnqVNclFRkUaMGKHS0tKQbdPhcGjmzJl1Oojm5OToySef1PLly1VQUKC33npLgwcP9s03xuihhx7S9OnTtX37dp111ll67rnn1KlTJ98y/fr106JFi/zWe/XVV2vOnDn13ieANwYAwlmo8z6asj4+Pl7x8fGSpLKysqNeDwCg9ixtkt1ut0pLS9Xz7DvkdB4Tgu39ri8/f05ut7tOwblnzx517dpVN954o6644ooq85944gk9/fTTmjlzpjIyMvTII4/ovPPO07p169S0aVPfcrfeeqv++te/+n5u1KhR/XYoChQVFWn4iOHylIbv6daRwO6wa9bMWTTKAMJSKPM+WrO+rKxMv/32m7Zt26Zp06Zpy5YtAVlvNCELAQSK5adbS5LTeYxaJqVbXUaNLrzwQl144YXVzjPG6JlnntGf//xn/fGPf5QkzZp14AA9e/Zs3Xbbbb5lGzdurOTk5JDUHCncbveBBrlPkuSMt7aYHWXS4mLpnCSpucW11IW7TJ6c4jq/IQSAUAvnvA/3rK885VqOGKl/a2mfRadbh2tWkoUAAigsmuRIlpeXp8LCQg0aNMg3zeFwqG/fvlqyZIlfcL788st66aWX5HK5dOGFF2rChAl+nz43aM542VrZLS3BVP6jufW11IU58iIAgHoIq6yPscnWIl62RGtyKlyzkiwEEEg0yfVUWFgoSVU+tXS5XNq4caPv52HDhik9PV3Jycn64YcfNGbMGH333XeaP39+SOsFAAB1Q9YDQMNCkxwgNpv/UAjGGL9pt956q+/fnTt31gknnKDTTz9dK1as0GmnnRayOgEAwNEh6wGgYYixuoBIV3ndUeWnzJU2b9582GtiTjvtNMXHx+unn34Kan0AAKB+yHoAaFhokuup8rSqg0+l8ng8WrRokXr16lXj41avXq2ysjKlpKSEokwAAHCUyHoAaFg43boWdu/erfXr1/t+zsvL08qVK9WyZUulpaVp1KhRmjRpkk444QSdcMIJmjRpkho3bqyhQ4dKkjZs2KCXX35ZF110kVq1aqU1a9bo3nvv1amnnqrevXtbtVsAIkhJSYny8/OVlpamhIQEq8sBamSMUWlpqex2u2JiIuezeLIeCA3yDJEgLJpkt/v3sN7ON998o/79+/t+vueeeyRJw4cP18yZM/WnP/1J+/bt0+23367t27frrLPO0rx583x3s7Tb7frkk0/097//Xbt371a7du108cUXa8KECYqNtWgIBwARJT8/X5mZmcrOzlZGRobV5QA12r9/vzZt2qT09PQqb4BDkfdkPRDeyDNEAkubZKfTKYfDoS8/fy5k23Q4HHI6nXV6TL9+/WRMzYML2Gw2ZWVlKSsrq9r57dq106JFi+q0TQAAokWo856sBwDUR1Ca5O+//149e/Y84ienLpdLM2fOlNvtPqrteDweFRQUKCUlRXZ77cbqczqdDDIPIOJs3bpVkpSZmSlJSkxMVOfOnXXqqacqOzu7yvILFy6Ux+PRnDlz9Nprr2nPnj3Vrnfy5Mnq2rVr8AoHVP+8ryuyHghfHo9H0v/yrFLTpk11/vnn6/XXX6/ymIULF4akNqBSUJrksWPHqm3btsrMzFSfPn0Ou6zL5TrqICspKVF8fLzat2/PNQ0AotbAgQPl9Xr9pu3Zs0dfffWVvvrqq2ofM2DAgFqte/To0ZJ4A4Lgq0/eA4gO48aN05IlS6qdt2vXrmobZOlAppFTCKWg3FHjqaeeUnp6uh566CHl5OQEYxMA0CBU1yAHQ22bagAAjsbhGuTaIKcQSkFpkk866SQ9/PDD6tGjh55//nmVl5cHYzMAENV+//33Kg1ykyZNNG/evCrL2mw2xcTEaO7cuYddZ+PGjWuc99133x1doQAAHMa+ffuqbZDtdrvOPPPMah9jt9t9Y5RXolFGqATtxl0xMTEaOnSoRo4cqVWrVumkk04K1qZ81zY0ZKWlpSorK1NeXl5E3UUzPz/f6hKiBs9l9LnrrruqTBs8eLDi4qoeuo0xMsbo8ccfP+w6MzMz9fTTT1c7b/To0ZzOhoAhmwOL57N2yMLwNHv27Gqnezwe3XDDDVq2bFm18woLC/X000/77jYPhEpQ726dnp4uSSouLg7mZlRQUBDU9UcCr9erbdu2adq0adqyZYvV5cACkyZNsroEhECXLl0OO3/Tpk01zhswYIB69OgR6JKAapHNsAJZGHkq+4WaBLuPAKoT1CY5Ly9PkpSUlBTMzdTp7tbRqvKb5KysrIj7JplAC4yxY8cqLS3N6jIQQHfddVeVb49WrVqlM844o8bHpKam+o69h1q4cKG6desWyBKBGpHNgeXxePT778EfZzrSkYXhafbs2TXep6imzKoU7D4CqE7QmmSv16vZs2crJSVFXbp0UVlZWbA2Jbvdzt2tJcXHxys9PZ3nooFKS0tTRkaG1WUggGbMmKHrr7/eb9rbb7+tG264ocqyNptNNptNDzzwgC677LIa11ndcFGVJk+efPTFAocgm2EFsjA8PfDAA9U2yXa7Xf/+97+rfYzdblfLli051RqWCMqNu9auXavx48dr6dKluu222yLqm00ACBfHHHOMYmL8D9O7d+/WoEGDqixrjJHX6z1sgyxJe/furXEe4yUDAIKhUaNG6tWrV5XpHo+n2uuRK+cVFhb6TeO+GQiVoHyTfP/996tdu3aaMGHCEcdJLioqktvtPqrteDweFRQUqKysrNandDmdTsZpBBAxFixYEJJhoHjjgWCrT97XFVkPhJ9HHnmkXsNAkVMIpaA0yZMmTVLPnj2P+A1yUVGRRgwfoVJPaTDKqJbD7tDMWTPrFJ45OTl68skntXz5chUUFOitt97S4MGDJUllZWUaN26cPvjgA/38889yOp0aOHCgHnvsMaWmpvrWUVpaqvvuu0///e9/tW/fPp177rmaOnWq2rZtG+hdBBBlFixYoCVLlmjcuHG+aYmJiercubNOPfXUak+hXrhwoTwej+bMmaPXXntNe/bsqXbdkydP5htkBF2o856sB8LTI488oh9++KHa0RuaNm2q888/X6+//nqVeTTICLWgNMmnnHJKrU6xdrvdKvWU6rqTM+VqfEwwSvFTtPd3vbQ2W263u07BuWfPHnXt2lU33nijrrjiCr95e/fu1YoVKzR+/Hh17dpV27dv16hRo3TZZZfpm2++8S03atQovfvuu5ozZ46SkpJ077336pJLLtHy5cs5HR3AEbVq1UrSgWuKD73ebsiQIdU+xm6364Ybbqj2GmYglEKZ92Q9EN4qz/6sLs8k6fbbbw91SUAVQb27dW25Gh+jdk07WF1GjS688EJdeOGF1c5zOp2aP3++37Rnn31WZ555pvLz85WWlia3260ZM2boP//5jwYOHChJeumll9SuXTstWLBA559/ftD3AQAAq4Vz3pP1AIBKQblxV0Pndrtls9nUvHlzSdLy5ctVVlbmd7Od1NRUde7c+aivywAAANYh6wEgetEkB1hJSYkefPBBDR06VM2aNZMkFRYWym63q0WLFn7LulyuKnftA4DqpKWlKTs7m/E/Efbi4uKUmpoa1WMkk/XA0SPPEAnC4nTraFFWVqZrrrlGXq9XU6dOPeLyxhjZbLYQVAYg0iUkJDD2JyKCzWaTw+GoMnxZtCDrgfohzxAJojPBLFBWVqYhQ4YoLy9P8+fP932yLEnJycnyeDzavn2732M2b97MEBUAAEQIsh4AGgaa5ACoDM2ffvpJCxYsUFJSkt/87t27Kz4+3u+mHwUFBfrhhx+qHVgdAACEF7IeABoOTreuhd27d2v9+vW+n/Py8rRy5Uq1bNlSqampuvLKK7VixQq99957Ki8v91171LJlS9ntdjmdTt1888269957lZSUpJYtW+q+++5Tly5dfHfABAAA1iHrAQCVwqJJLtr7e1hv55tvvlH//v19P99zzz2SpOHDhysrK0tz586VJHXr1s3vcZ9++qn69esnSZo8ebLi4uI0ZMgQ7du3T+eee65mzpzJuImV3GUyVtewo8z3/5bXUhfuMqsrAIBaCUXeR33We43M9jKZfZ7ArbMuwjUryUIAAWRpk+x0OuWwO/TS2uyQbdNhd8jpdNbpMf369ZMxNUfB4eZVSkhI0LPPPqtnn322TtuOdk6nU3aHXZ6cYqtL+Z/FYVRLLdkd9jq/rgEgVEKd99GY9b5Gu9QrfbpF2rIl4NuokzDMSrIQQKBY2iS7XC7NnDVTbrc7ZNt0Op3cQCOMuFwuzZo5K6SvgWjE6xpAOAt13kfjMTE+Pl5t27ZVWVmZsrKyOBOtGtH4ewdgDctPt3a5XBzQGjheAwAQ/TjW1198fLzi4+OVnp6uhIQEq8sBgKhleZOMhqmoqIhvj48Sn5QDQMNQVlam8vJy38+lpaUqKytTXl4e3yRXIBMBBANNMkKuqKhIw0cMl6fUopuORDi7w65ZM2fxpgAAolhlM3zwtdBer1fbtm3TtGnTtMXqa5LDBJkIIBhokhFybrdbnlKPbGd3k83ZxNJajHu3zOcrw6KW2jDu3fJ8vlJut5s3BAAQxcrLyw80yE0aS7ExByaW7Zd2OhTTr7ti9u4Oeg3hnpFkIoBgoUmGZWzOJrIlWX8XShNGtdRGWA25AQAIrtgY2eIqTq02XikmRrbmTWVrFJrTrcM9I8lEAMEQY3UBAAAAAACEC5pkAAAAAAAq0CQDAAAAAFDB8muSjzQUkDFGZWVlio+Pl81mq/f2GCoAQDgpKSlRfn6+0tLSGPcUEa9yiCKv11tlXiiH/iPrgfBE5iFSWNokFxUVacTwESr1lIZsmw67QzNnzaxTeObk5OjJJ5/U8uXLVVBQoLfeekuDBw+WdGCIhnHjxumDDz7Qzz//LKfTqYEDB+qxxx5Tamqqbx2lpaW677779N///lf79u3Tueeeq6lTp6pt27aB3kUAESQ/P1+ZmZnKzs5WRkaG1eUA9bJp0yZt27ZNZWVlftNDnfdkPRCeyDxECkubZLfbrVJPqTKP/4OOadQ66Nv7fd8WZa9/q85DBezZs0ddu3bVjTfeqCuuuMJv3t69e7VixQqNHz9eXbt21fbt2zVq1Chddtll+uabb3zLjRo1Su+++67mzJmjpKQk3Xvvvbrkkku0fPlyxcZWvUPlL7/8ovT0dL/xEQEAiEShzHuyHgBQX5afbi1JxzRqrQ6JKVaXUaMLL7xQF154YbXznE6n5s+f7zft2Wef1Zlnnuk7ncTtdmvGjBn6z3/+o4EDB0qSXnrpJbVr104LFizQ+eefH/R9AADAauGc92Q9AKBSSJtkY4z27dsnj8ejuLi4qP3k1O12y2azqXnz5pKk5cuXq6ysTIMGDfItk5qaqs6dO2vJkiUEJ9BArV27VnfccYckKTMz86jW4XK5VFRUVGX6U089peLiYq1fv16vvfZajY9v27at7rzzTnXv3r3ab7oAVI+sB+ruoYceklRz5iUmJmrPnj1Vpi9cuDCodQGHClmT/PXXX0uSbDabYmIO3FR706ZNodp8yJSUlOjBBx/U0KFD1axZM0lSYWGh7Ha7WrRo4besy+VSYWGhFWUCsNiAAQMCsp7qGmRJuu+++2r1+N9++00PPvig7Ha7xo4dqz59+gSkLiCakfVA3dUm96prkCsfS6OMUArJEFA5OTl67rnnFBcXp5SUFJ1wwglKS0vzNcvRoqysTNdcc428Xq+mTp16xOWNMX537O7UqZOaNGmiJk2aqFOnTpLk+/ngaQAiW6Aa5EDyeDzKyspSTk6O1aUAYY2sB+ouELkXjtmJ6BX0b5LLy8uVnZ2trl27yul0KiEhQTExMUpISKh2iIhIVVZWpiFDhigvL08LFy70fbIsScnJyfJ4PNq+fbvfJ8ybN29Wr169fD9/8MEHvjuC/v777+rXr59Wrlzpmx8fHx/8HQEQVGvXrvX7OTk5Oay+ZZo6dap69+7NqddANch6oO4qLysKBL5RRqjUq0kuLS1Vaen/hnPYuXNnlWVWrVqlwsJCjR071u+T1H379mn//v312fxR83g8KikpCdjjy8rKNGzYMG3YsEEfffSREhMT/eZ36tRJ8fHxev/993XllVdKkgoKCvTDDz/okUce8S178F04y8vLJanKsBE11V05NmVeXl7Yv7nNz8+3uoSIx3MYuQ59sxBODbJ04A39qlWr1K1bN6tLQZioTdYfqqyszC+vPB5PUGo7nEjPeiues0hFJoa3Qz8crqvLL79c77zzToCqAWqnXk3yo48+6rsAvybFxcWSDgRAQUGBb7pVDbJ0ILTq8kntnj17/A7A3377rex2u5xOp9q0aaO77rpLa9asUXZ2tjZu3KiNGzdKOnA3TLvdLkm64oordP/992v//v1yOp164oknlJGRoeOOO863/MF+++03Sap2XnW8Xq+2bdumadOmacuWLbXeN0SmSZMmWV0ColjlcRuQapf1h9qyZYvf6+jg/A+VaMx6VI9MjG6///671SWgAapXkzxmzBjdc889vp937typdu3a+S2TlJQk6UAQHPwNZ1zc/zb9+77QNHWV20lJSVH79u1r/bicnBz94Q9/8P382GOPSZKuu+46jRs3znfax+DBg/0e9/HHH/tugjNt2jSNGTNG9957r/bt26f+/fvr3//+d5Xn61C1rbPym+SsrKyI+CaZQKufsWPHKi0tzeoycBSO9i7WoVR53Aak2mX9oVq3bq0mTZr4fvadXhyCvI+WrPd4PJZ8uBCJyMTwVt/cO+aYY/zGIwdCoV5NssPhkMPhOOwyXbp0UXJyst59911dfvnlvumNGjVS8+bNZY+3K3v9W/Upo04cdodat26thISEWj9m0KBBhx2uqjZDWSUkJGjatGmaNm1arbZ54okn1nmIrPj4eKWnp9dp3xCZ0tLSlJGRYXUZOArPPfec3ynX4XZNcps2bdSlSxery0AYqU3WHyo+Pt4vi1q3bi2H3RGyvI/mrEdVZGJ4O/nkk+t1yjWnWsMKQb9xV2xsrDIzMzV16lT169dPJSUlio+Pl8fjUZs2bTQha4I8Ho9atmyp+Ph4lZWVadu2bfJ4PLLb7WratKmKi4uVlJSkffv2ae/evWrdurUaN258VPU4nU6/64EAIJROPvlkv5/DqUGWpNtvvz3sz0ZB5HG5XJo5a6bcbndItkfWA+HjueeeC9idqblpF0IlJOMk9+nTRzabTfv371dBQYFvXM/4+Hgdd9xx2r17t+9ULLvdrnbt2qlJkya+6YmJiZKk5s2bq3Xr1mratGkoygaAoFi4cGHYDWXBOMkINpfLReMKNFCByD0aZIRSSJpkSTrjjDP0888/Kzk5WbGxsYqLi1OjRo1ks9nUunVr392uD53udrtVVFQkl8slp9Ppd4dsAIhUCxcu1Nq1a+s9NIbL5fJ98Hiwp556SsXFxVq/fr1ee+21Gh/ftm1b3XnnnerevTvfIAMAgmbhwoUaNmzYYa+1T0xM1J49e6p9LBBKIWuSJclms6lRo0ZVrhGy2WzVnj5ts9l8yyYkJNAgA4gqJ598srKzs5WZmans7OygXFN33nnn6f/+7/8Cvl4AAOpqwoQJQc08IFBiQr1BblARHDyvAACr2Ww2GWPIpGAxB/6H5xcAgitk3yRXjlW4d+9eNWrUqNaPs9vtat++vW8MQlRv7969klSnMSEBWC8tLU3Z2dkMX4Ko0K5dO9+QhAg8U16ucq/RvvL9VpcCHBUyD5EiZE1ybGysmjdvrs2bN0uSGjduXKfTpz0eT7BKi2jGGO3du1ebN29W8+bNuaYQiDAJCQmccoaokZiYqDZt2mjr1q2KiYmpc9bjf0pLS+X1eqWy/ZLxSsZozw63ftpVrL3lfAiByETmIVKE9Jrk5ORkSfI1ygic5s2b+55fAACsQtYHRuWQmNrpkGJiZGS001Oq+YV54mRrAAiukN+4KyUlRW3atOFUrACKj4/nG2QAQFgg6wMjLy9P06ZNU0y/7rI1byqvMdpRVqJyrkcGgKALaZNcKTY2lqYOAIAoRtbXT2xsrLZs2aKYvbtla8TzCAChFPK7WwMAAAAAEK4s+SYZkCTj3m11Cb4awqGW2oiUOgEAgWHlcT/cMzJc6wIQ+WiSEXJOp1N2h12ez1eGzc1HTBjVciR2h11Op9PqMgAAQRROWRnOGUkmAggGmmSEnMvl0qyZs+R2u60uJSI5nU65XC6rywAABBFZWTtkIoBgoElGyBQVFRH2h0HQA0DDRk7WjIwEEEo0yQiJoqIiDR8xQp7SUqtLCVt2h0OzZs7kTQAANEDk5OGRkQBCiSYZIeF2u+UpLVVs776yWXTtkHHvUPkXOYrt3Uc2Z3NLaqiJcbvl+WKR3G43bwAAoAEKh5ysFG55SUYCCDWaZISUzemULamVxTU0t7wGAACqEw45WYm8BNBQMU4yAAAAAAAVaJIBAAAAAKhAkwwAAAAAQAWaZAAAAAAAKkR0k1xSUqLc3FyVlJRYXQqABoBjDlA9/jaAho1jAKJNRDfJ+fn5yszMVH5+vtWlAGgAOOYA1eNvA2jYOAYg2kR0kwwAAAAAQCCFbJzk8vJyrVq1SsXFxWrRooUkafv27UpKSlKXLl0UGxvrW9bj8Wju3LnatGmTUlNTdcEFF+iDDz7Qd999py1btqikpERFRUW+5Xfv3h2q3QAA/fjjj3r//ff17rvvWl2KEhISqj29LS0tTQkJCSooKNCuXbtqfPxxxx2n8847T4MHD5bdbg9mqQCAKPTWW2/p2WeflSRlZmYGZRsLFy4MynqBmoSkSc7JyVF2drYKCwurnZ+cnKzMzEz16dNH2dnZeuONN1ReXu6bP3Xq1MOu/7777lOLFi30xhtvBLRuADjYt99+K0l65plnrC3kIDVd/1XbU942bNigDRs2aPr06bryyiuD9gYHABB9BgwYELLt0CgjlIJ+unVOTo4eeughpaen6+abb5Ykde7cWZ07d5bNZtMtt9yi9PR0PfTQQxo3bpxeffVVNWvWTPfee68uu+yyWm9n+/btuuKKK4K1GwAauJycHD3//PNWlxE0Xq9Xr776qrKzs60uBQAQAULVIFu1PTRsQW2Sy8vLlZ2drR49eigrK0vvv/++evbsqWeeeUbPPPOMevTooffff19ZWVk688wztWTJErVo0UKvvPKKzjvvPL333nuKiala4ptvvuk7Zftg27dv17Zt24K5SwAaoMpjWUPw+uuvy+PxWF0GACCMvfXWW5Zsl0YZoRLU061XrVqlwsJCjRs3TqtXr/b9u7LxHTp0qEaOHKnVq1frmGOOkSQNHDhQcXFxev311+X1equs0+VyqXnz5rrxxhv19NNPV5k/cuRITZgwIZi7haPA3Q5rh+cpPK1bt67Gy0Wijdfr1dy5c3XllVdaXQoiEMewo8dzd2Q8R+Gj8hrkULj99tuPeOklEGhBbZKLi4slSenp6VqyZInv35Uq/11cXOz75iI1NVWStGnTpmrXefzxx0uSevToUe38goICrqlDxJo0aZLVJQA1Hn+BI+EYhmDi9dUwkUmwQlCb5KSkJElSXl6e3787duzo+3flcpV3Va38Q6hslg+1fv16SdLSpUurnZ+SksI3yWEoPz+fcKuFsWPHKi0tzeoycIh169Zp8uTJVpcRMjUdf4Ej4Rh29MjJI+P1FT5C+YUUmQQrBLVJ7tKli5KTk/Xyyy8rKyvL9++HH35YkjR79mylpKSoU6dOeuWVVyRJCxYs0K233qrLLrvMdw3gwaddFxUVaceOHXrxxRer3eazzz6rli1bBnO3gKBJS0tTRkaG1WXgEMcdd5z++9//NohTrmNiYup000TgYBzDEEy8vsLHyJEjQ3bKNadawwpBvXFXbGysMjMztXTpUmVlZeniiy/Wl19+qVGjRmnUqFFaunSpLrroImVlZWnZsmXq1auXtm/frquvvlrz5s3TJZdcUu11yX/84x+1ffv2KtNbtGhBgwwg4CqPZQ3BlVdeyXjJAIDD+sMf/mDJdhkGCqES9CGg+vTpowkTJigvL08zZsyQJP3www/64YcfZIzRjBkz9Msvv2jChAl65JFHNGTIEO3cuVNPP/205s6dW+vtME4ygGDq06ePbrvtNqvLCJqYmBgNGTKkwXwYAACon1A3rDTICKWgnm5dqU+fPurdu7dWrVql4uJi3/BN27dvV1JSkrp06aLY2FhJB65xuOmmmzR37lxt2rRJqampuuCCC/TBBx/ou+++05YtW1RSUqKioiJJUllZmZ566imddtppodgVAA3YqaeeKkkaNWqUNmzYoHfffdfiiqSEhASVlJRUmZ6WlqaEhAQVFBRo165dNT7+uOOO03nnnafBgwfzDTIAoE4WLlyot956K+inXtMgI9RC0iRLB05X7NatW62WtdvtVYYfGTJkiIYMGeI3LTc3V5mZmWrSpEmgygSAIzrppJN02WWXafTo0VaXAgCApf7whz+oU6dOyszMVHZ2NteNIyoE/XRrAAAAAAAiBU0yAAAAAAAVIrpJTktLU3Z2NmPmAQgJjjlA9fjbABo2jgGINiG7JjkYEhISuO4BQMhwzAGqx98G0LBxDEC0iehvkgEAAAAACCSaZAAAAAAAKtAkAwAAAABQIaKvSUbkMW63hdve4ff/4cTK5wUAED7CIQ/CLS/D4TkB0LDQJCMknE6n7A6HPF8ssroUlX+RY3UJ1bI7HHI6nVaXAQCwQDjlZKVwyksyEkAo0SQjJFwul2bNnCk3nwbXyOl0yuVyWV0GAMAC5OThkZEAQokmGSHjcrkIOAAAakBOAkB4oElGSBQVFfHpeDX4ZBwAIJGTNSEnAViBJhlBV1RUpOEjRshTWmp1KWHH7nBo1syZvAEAgAaMnKwZOQnACjTJCDq32y1PaakcZ1+hGGfrkG/f696i0s/fsGz7Namsy+12E/4A0IBZnZOVwi0vyUkAVqFJRsjEOFsrNim1wW4fAIDDCZecCpc6AMAqMVYXAAAAAABAuKBJBgAAAACgAk0yAAAAAAAVIr5JLikpUW5urkpKSqwuBUCU4fgC1A5/K0DDwd87GoKIb5Lz8/OVmZmp/Px8q0sBEGU4vgC1w98K0HDw946GIOKbZAAAAAAAAoUmGQAAAACACiEbJ7m8vFyrVq1ScXGxkpKS1KVLF8XGxvrN27Jli7Zv365du3bJZrOpc+fOysvL06pVq7Rv3z653W5t2bJFpaWl8ng8futftmyZjjvuON86AaA+vvjiC40fP16SlJmZaXE1NUtLS6v2lLczzzxTa9as0e7duw/7+DPPPFNDhgxR165dOX4CAGrt5Zdf1uLFi0OyrYULF4ZkO0ClkDTJOTk5ys7OVmFhoW9acnKy743nofOOxr/+9S/961//UlZWlvr06VOvdQFo2AYMGGB1CbVW0zVhy5Ytq9Xjly1bpmXLlql58+YaNWoUx08AwGG98cYbkhSyBlk6kMs0ygiloJ9unZOTo4ceekjp6emaMmWK3n//fU2ZMkXp6enKyspSVlaWmjVr5lv+2GOPVcuWLY96e1lZWcrJyQlE6QAaoEhqkANpx44dHD8BAIeVnZ2t+fPnW7LthprPsEZQm+Ty8nJlZ2erR48eevjhh9WxY0c1atRIHTt2VFZWlhwOh+x2u9xutxwOh3r06KEpU6Zox44d9druP/7xD5WXlwdmJwA0GF988YXVJVguOzub4ycAoAqPx6NXX33V0hpolBEqQT3detWqVSosLNS4ceMUE+Pfj69evVqlpaWSpKKiIknSddddp/fee09er7de2922bZs++OADnXjiifVaDwKDIQIOj+cnfFRegyxJLVu21LZt23w/d+3aVd99950VZYVUYWGhVq1apW7dulldCiIQx7Ojw/N2eDw/4WHBggWWbNflcvl6BSBUgtokFxcXS5LS09NrnHew9PT0gP0BTp48OSDrAYJt0qRJVpeAahzcIEuSMcaiSkKvuuMzUBsczxAMvK4aNrfbbXUJaICC2iQnJSVJkvLy8tSxY8dq5x0sLy9PqampAdn26NGj+SY5TOTn5xNwhzF27FilpaVZXQbkfxfrQ79JttlsVpRkieqOz0BtcDw7OuTk4fG6Cg8LFizQ66+/HvLtOp1OlZSUhHy7aNiC2iR36dJFycnJevnll/Xwww/7nXLdqVMnORwOGWPUokUL7dixQy+99JL+8pe/KDs7u16nXLds2VIXXXQRw5kgIqSlpSkjI8PqMiDp4Ycf9p1yfeg3yQ3hVGvpwMgDXbp0sboMRCiOZwgGXlfhoUOHDpY0yZxqDSsE9cZdsbGxyszM1NKlSzV+/HitXr1ae/fu1erVq5WVleUb79jpdKq0tFRLly7VnXfeqebNm9dru3fddRcNMoA66927t9UlWC4zM5PjJwCgCrvdriFDhlhaA8NAIVSCPk5ynz59NGHCBGVnZ2vkyJG+6SkpKcrKypJ04G6qlX7++ed6bY9xkgHUx8KFCxvk3TMZJxkAcCSZmZnavn27JcNA0SAjlILeJEsHGuXevXtr1apVKi4uVlJSkrp06eL7tqJy3pYtW7R9+3bt2rVLNptNnTt3Vl5enlatWqV9+/bJ7XZry5Ytvm+gD3bTTTfp2muv5RsQAPW2cOFCffHFF353uw5XaWlp1d759cwzz9SaNWu0e/fuwz7+zDPP1JAhQ9S1a1eOnwCAI7riiis0f/58nXPOOVq8eHFItkmDjFALSZMsHTj1uqYhRQ4378wzz9TVV19d43pzc3OVmZmpM888kzd4AAKmd+/eys7OVmZmprKzs7keDgCAgwwbNkwPPfSQ1WUAQRHUa5IBAAAAAIgkEd8kp6WlKTs7m6EBAAQcxxegdvhbARoO/t7REITsdOtgSUhI4DRIAEHB8QWoHf5WgIaDv3c0BBH/TTIAAAAAAIFCkwwAAAAAQAWaZAAAAAAAKtAkAwAAAABQIeJv3IXI4XVvsXS7Vm2/JuFWDwDAWlbnQrjlZbjUAaDhoUlG0DmdTtkdDpV+/oaldVi9/erYHQ45nU6rywAAWChccrJSuNQhkZMArEGTjKBzuVyaNXOm3G631aWEHafTKZfLZXUZAAALkZM1IycBWIEmGbCQ2+2W2+3mTQAANHCVGUCj/D9kIwCr0CQj6IqKijRixAiVlpZaXUrYcjgcmjlzJm8GAKCBIiurIhsBWIUmGUHndrtVWlqqnmffIafzGAu2/7u+/Pw5y7Z/JJX1ud1u3ggAQANldVYeqCF88pJsBGAlmmSEjNN5jFompTfY7QMAcCThkFXhUAMAWIlxkgEAAAAAqECTDAAAAABABZpkAAAAAAAq0CQDAAAAAFAhIpvkkpIS5ebmqqSkxOpSADQgHHuA6vG3AUQ3/sbR0ERkk5yfn6/MzEzl5+dbXQqABoRjD1A9/jaA6MbfOBqaiGySAQAAAAAIBppkAAAAAAAqxIViI+Xl5Vq1apWKi4uVlJSkLl26KDY2VpLk8Xg0d+5cbdq0Sampqbr44ou1bt06FRcXq2nTplq0aJGWLFmiPXv2yG63q0WLFvJ4PJKk2bNn64EHHlCjRo1CsRsAIEnyer2aO3eunnnmGatLkd1u9x0TD/boo49qzpw5+vHHH1VaWlrj4y+//HL17dvX77gMAEB1MjMzLdnuwoULLdkuGq6gN8k5OTnKzs5WYWGhb1pycrIyMzO1Zs0avfHGGyovL/fNmzp1ao3r2r9/v/bu3eu37pycHPXq1UuPPPJIcHYAAA5x++23W12CT3UNsiSNGTOmVo9/55139M477/iOy3369AlkeQCAKGBVc1xpwIABNMoIqaCebp2Tk6OHHnpI6enpmjJlit5//31NmTJF6enpysrK0quvvqpmzZrp3nvv1b333itJio+Pr/N2lixZonHjxgW6fADw8+2331pdQtAUFhbqoYceUk5OjtWlAADCyIABA6wuQVL41IGGIWhNcnl5ubKzs9WjRw89/PDD6tixoxo1aqSOHTtq/PjxBzYeE6PZs2frggsu0Msvv6yePXvq1VdfrXGdcXFxiompvuQlS5Zo3759QdkXACgvL9e//vUvq8sIqg4dOuj555/3O7sHANBwhVtjGm71IHoF7XTrVatWqbCwUOPGjavS2L733nuSDlzXt3btWknyLTtz5swa19mnTx+1adNGc+bMqXb+448/rqFDhwZmBxAwDBdQOzxP4W3dunUqKyuzuoygysvLk3Tg+N2tWzdri0FE4jh29HjuqsfzgoSEBMZnRsgFrUkuLi6WJKWnp1eZt2nTpirLVS77+++/17jOE088UT169KixSa68RhmIRJMmTbK6BECS/3EZqAuOYwg0XlOI9g+oEZ6C1iQnJSVJOvDNRMeOHf3mpaamVlmuctljjjlG33zzTbXrXLdunbZv317jNvv06cM3yWEoPz+fkKuFsWPHKi0tzeoyUIN169Zp8uTJVpcREgcfl4G64Dh29MjK6vGaspbVN+ySDtyviMuAEGpBa5K7dOmi5ORkvfzyy3r44Yf9Trm+5JJLNHXqVMXExOjkk09WXFycb9k//elPeuedd6pdZ05Ojrxeb43bZDgoRLK0tDRlZGRYXQZqcNxxx2nKlClR/Yl2enq6SkpK1KVLF6tLQYTiOIZA4zUFTrWGFYJ2467Y2FhlZmZq6dKlGj9+vFavXq29e/dq9erVevjhhyUduCZ56NCh+vDDDzV06FB9+eWXGjJkSI3r3L9/f41Ncq9evWiQAQRNbGysbrrpJqvLCKpffvlFt912G+MlAwAkhd/4xOFWD6JXUMdJ7tOnjyZMmKDs7GyNHDnSNz0lJUVZWVm+cZKffvpp37yj+ZaGcZIBhMKpp55qdQlBk5KSottuu41xkgEAfhYuXBgWd5WmQUYoBbVJlg40yr1799aqVatUXFyspKQkdenSRbGxserTp49uuukmzZ07V5s2bVJqaqouvvhirVu3TsXFxWratKkWLVqkJUuWaM+ePbLb7WrRooU8Ho+2bNmiPn36cIo1gJCbOnWqcnNz9cwzz1hdiux2uzweT5Xpjz76qObMmaMff/xRpaWlNT7+8ssvV9++fX3HZQAADpWdnW3p9ck0yAi1oDfJ0oHTFGsaTsRut+vKK6/0m3bwsmeeeWaVx+Tm5iozM1NDhw6lQQYQcjExMbrssst02WWXWV3KYZ111llWlwAAiCLZ2dlcI44GIWjXJAMAAAAAEGkisklOS0tTdnY2QwIACCmOPUD1+NsAoht/42hoQnK6daAlJCRwqgeAkOPYA1SPvw0guvE3joYmIr9JBgAAAAAgGGiSAQAAAACoQJMMAAAAAEAFmmQAAAAAACpE5I27EJnc7t8t3a5V2z+ScK0LABB6VmZCOOVlONQAoOGiSUbQOZ1OORwOffn5c5bWYfX2D8fhcMjpdFpdBgDAIuGSlVL45CXZCMAqNMkIOpfLpZkzZ8rtdltdSthyOp1yuVxWlwEAsAhZWRXZCMAqNMkIqqKiIgK/Ftxut+954k0BAKChIwsBWIkmGUFTVFSkEcNHqNRTanUpEcVhd2jmrJm8OQCABoTM9EcWArASTTKCxu12q9RTqutOzpSr8TGW1VG0d5NeWjtN1538f3I1TrWsjtoo2vu7XlqbLbfbzRsDAGhArM7McMpKshCA1WiSEXSuxseoXdMOVpchV+PUsKgDAICaWJ2ZZCUAME4yAAAAAAA+NMkAAAAAAFSgSQYAAAAAoAJNMgAAAAAAFcK2SS4pKVFubq5KSkqsLgUAao1jFxoaXvNAdONvHA1R2DbJ+fn5yszMVH5+vtWlAECtcexCQ8NrHohu/I2jIQrbJhkAAAAAgFCzZJzk8vJyrVq1SsXFxUpKSlKXLl0UGxtrRSkAEFDbtm2TJGVmZgZ9WzabTUOGDFFxcbEWLFjgm56enq5GjRpp27ZtatSokfLy8mpcx7nnnqsLL7xQXbt25TgMAKhixYoVkqrPtbi4OO3fvz/oNSxcuDDo2wAOFvImOScnR9nZ2SosLPRNS05OVmZmpvr06RPqcgAgYAYNGhSSNwuVjDF65ZVXqkw/XFN8qE8++USffPKJmjdvrlGjRnEcBgD4DBgw4LDzQ5V5AwYMoFFGSIX0dOucnBw99NBDSk9P15QpU/T+++9rypQpSk9P10MPPaScnJxQlgMAARPqBjnQduzYoaysLI7DAABJR26QQy3c6kF0C1mTXF5eruzsbPXo0UMPP/ywOnbsqEaNGqljx456+OGH1aNHDz3//PMqLy8PVUkAEBAFBQUR3SAfLDs7m+MwADRwX375pdUlVItGGaESstOtV61apcLCQo0bN04xMf69eUxMjIYOHaqRI0dq1apV6tatm28ed9KLXPzujh7PXWQZPXq01SX4sdlsMsYc1fzCwsIqx2GgNjhu1Q/PX1U8J9b585//XGXascceq59//tmCaoDQC1mTXFxcLOnADWWqUzm9crlKkyZNCm5hQBjidY/6OFyDLB14o7Nhw4Ya5x96HAZqg+MWAo3XVHipvDEl0BCErElOSkqSdOCGMh07dqwyv/JGM5XLVRo7dqzS0tKCXyACLj8/n4A7SrzuI8vo0aO1b98+q8vwOdI3yUf6JuDQ4zBQGxy36ofMrIrXlHWqu5N1y5YttWPHjtAXA1ggZE1yly5dlJycrJdfflkPP/yw3ynXXq9Xs2fPVkpKirp06eL3uLS0NGVkZISqTCAs8LqPLP/85z81bNgwq8vwOdI3yYebn5ycXOU4DNQGxy0EGq8p60ycOLHKKdecao2GJGQ37oqNjVVmZqaWLl2q8ePHa/Xq1dq7d69Wr16t8ePHa+nSpbrtttsYpxNAxElJSVFcnCXDzgdcZmYmx2EAaOB69uxpdQnVYhgohEpI39X16dNHEyZMUHZ2tkaOHOmbnpKSogkTJjA+J4CINW/evIgeBopxkgEAB1u4cGFY3U2aBhmhFPKvPvr06aPevXtr1apVKi4uVlJSkrp06cI3FwAi3rx587R06VKNHTs2JNuz2WwaMmSIiouLtWDBAt/09PR0NWrUSNu2bVOjRo1893yozrnnnqsLL7xQXbt25TgMAPCzcOFCzZkzR9OnT692flxcXEg+HKZBRqhZcn5gbGwsw4sAiEotW7aUdGC84VBeSxeqxhwA0LCcdtppkkKfa4CVQnZNMgAAAAAA4Y4mGQAAAACACmHbJKelpSk7O5vx8QBEFI5daGh4zQPRjb9xNERhO2ZJQkIC1z0AiDgcu9DQ8JoHoht/42iIwvabZAAAAAAAQo0mGQAAAACACjTJAAAAAABUCNtrkhE9ivb+bvH2N/n9fziz+rkCAFjLqhwIp6wkCwFYjSYZQeN0OuWwO/TS2myrS5EkvbR2mtUl1IrD7pDT6bS6DABACIVLZoZLVpKFAKxEk4ygcblcmjlrptxut9WlRBSn0ymXy2V1GQCAECIz/ZGFAKxEk4ygcrlchBwAALVAZgJAeKBJRlAVFRXxqfhR4lN0AGh4yM3qkYkAQokmGUFTVFSkEcNHqNRTanUpEclhd2jmrJm8KQCABoLcrBmZCCCUaJIRNG63W6WeUmUe/wcd06i11eVIkjbt26pp69/U/x3/R6U2amV1OTX6fd8WZa9/S263mzcEANBAWJ2b4ZqRZCKAUKNJRtAd06i1OiSmWF2Gn9RGrcKuJgAAJOtzk4wE0NDFWF0AAAAAAADhgiYZAAAAAIAKNMkAAAAAAFQIuya5pKREubm5KikpsboUAAg4jnGIdrzGgYaFv3lEo7BrkvPz85WZman8/HyrSwGAgOMYh2jHaxxoWPibRzQKuyYZAAAAAACr0CQDAAAAAFCBcZIBIIQWLlwoScrMzAzI+mw2m4wxftMuueQSbd26VatXr1Z5ebnKy8tVWlrqWz4+Pl4tWrRQs2bN1L17d51++unq2rWrYmNjA1ITAKBhKCgo0MiRIyXVP9eaNGmi2NhYud1uv+kZGRnKzs6u17qBuqJJBoAQGTBgQMDXeWiDLEnvvffeYZf3eDwqKipSUVGRfvrpJ82ZM0fNmzfXqFGj1KdPn4DXCACIPoMGDdL+/fsDtr7du3dXOz03N1cDBgzwfcgMhAKnWwNACASjQQ6kHTt2KCsrSzk5OVaXAgAIc4FukGsj3HMU0YUmGQCC7MMPP7S6hFqx2+3Kzs5WeXm51aUAAMJUQUFBSBvks846y/fvQF2qBBxJ2J5uzW3kIx+/w/rjOYwOTz75pNUl+ElISKh2PEuPx6PCwkKtWrVK3bp1C31hiCocv+qO5+zweH7Cw+jRo0O6vT179vj+nZubG9Jto+EK2yZ50qRJVpcAWI6/AwSDy+XSxo0ba5xfXFwcwmoQrTh+IdB4TTVMRUVFVpeABihsm+SxY8cqLS3N6jJQD/n5+QRaPfF3EB3C7fSwI73hSEpKClEliGYcv+qO3Dw8XlPhYfTo0dq3b1/ItudyubRly5aQbQ+QwrhJTktLU0ZGhtVlAJbi7yA63H///WF1ynV1p1pLB65Jbtmypbp06RLiihCNOH4h0HhNhYd//vOfGjZsWMi2l5iY6Ps3v3+ECjfuAoAgu/DCC60uoVY8Ho8yMzMZLxkAUKOUlBTFxYXue7avvvrK92/GS0ao0CQDQAiE+/iOzZs3V1ZWFuMkAwCOaN68eSFtlKXwz1FEl7A93RoAos3ChQuVnZ2tV199NWDrtNlsMsb4Tbvkkku0detWrV69WuXl5SovL1dpaalv+fj4eLVo0ULNmjVT9+7ddfrpp6tr1658gwwAqLV58+apoKBAI0aMUFlZWb3X16RJE8XGxsrtdvtNz8jI4BtkhBxNMgCE0IABA/Tqq68qOzuba6sAABEtJSVFzz77rDIzM8k1RBVOtwYAAAAAoELYNclpaWnKzs7mFv8AohLHOEQ7XuNAw8LfPKJR2J1unZCQwKkaAKIWxzhEO17jQMPC3zyiUdh9kwwAAAAAgFVokgEAAAAAqECTDAAAAABABZpkAAAAAAAqhN2NuxB9ft+3xeoSfDbt2+r3/+EqnJ4zAEBoWZUB4ZqRZCKAUKNJRtA4nU457A5lr3/L6lKqmLb+TatLOCKH3SGn02l1GQCAEAmX3AzHjCQTAYQSTTKCxuVyaeasmXK73VaXEpGcTqdcLpfVZQAAQoTcrBmZCCCUaJIRVC6Xi1ADAKCWyE0AsB437gIAAAAAoAJNMgAAAAAAFWiSAQAAAACoQJMMAAAAAEAFmmQAAAAAACrQJAMAAAAAUIEmGQAAAACACjTJAAAAAABUoEkGAAAAAKACTTIAAAAAABVokgEAAAAAqECTDAAAAABABZpkAAAAAAAq0CQDAAAAAFCBJhkAAAAAgAo0yQAAAAAAVKBJBgAAAACgAk0yAAAAAAAVaJIBAAAAAKhAkwwAAAAAQAWaZAAAAAAAKtAkAwAAAABQgSYZAAAAAIAKNMkAAAAAAFSgSQYAAAAAoAJNMgAAAAAAFWiSAQAAAACoQJMMAAAAAEAFmmQAAAAAACrQJAMAAAAAUIEmGQAAAACACjTJAAAAAABUoEkGAAAAAKACTTIAAAAAABVokgEAAAAAqECTDAAAAABABZpkAAAAAAAq0CQDAAAAAFCBJhkAAAAAgAo0yQAAAAAAVKBJBgAAAACgAk0yAAAAAAAVaJIBAAAAAKhAkwwAAAAAQIW4QK7MGCNJ2rlzZyBXCwDAUavMpMqMQv2Q9QCAcBPorA9ok1xcXCxJateuXSBXCwBAve3atUtOp9PqMiIeWQ8ACFeByvqANsktW7aUJOXn50flG5GdO3eqXbt2+vXXX9WsWTOrywmKaN9H9i/yRfs+sn+BZ4zRrl27lJqaGpLtRTuyPvJF+z5G+/5J0b+P7F/kC/U+BjrrA9okx8QcuMTZ6XRG7S9ckpo1axbV+ydF/z6yf5Ev2veR/QusaGzmrELWR49o38do3z8p+veR/Yt8odzHQGY9N+4CAAAAAKACTTIAAAAAABUC2iQ7HA5NmDBBDocjkKsNG9G+f1L07yP7F/mifR/ZP4S7aP8dRvv+SdG/j9G+f1L07yP7F/kifR9thjExAAAAAACQxOnWAAAAAAD40CQDAAAAAFCBJhkAAAAAgAo0yQAAAAAAVAhYkzx16lSlp6crISFB3bt31+LFiwO1ass9+uijOuOMM9S0aVO1adNGgwcP1rp166wuK2geffRR2Ww2jRo1yupSAub333/Xddddp6SkJDVu3FjdunXT8uXLrS4rYPbv369x48YpPT1djRo10rHHHqu//vWv8nq9Vpd2VHJycnTppZcqNTVVNptNb7/9tt98Y4yysrKUmpqqRo0aqV+/flq9erU1xR6lw+1jWVmZHnjgAXXp0kWJiYlKTU3VDTfcoE2bNllXcB0d6Xd4sNtuu002m03PPPNMyOrD0YvWvCfro0M05320Zb0U/XlP1v9PpGV9QJrkV155RaNGjdKf//xnffvttzrnnHN04YUXKj8/PxCrt9yiRYt0xx13aOnSpZo/f77279+vQYMGac+ePVaXFnBff/21pk+frlNOOcXqUgJm+/bt6t27t+Lj4/Xhhx9qzZo1+tvf/qbmzZtbXVrAPP7448rOztaUKVO0du1aPfHEE3ryySf17LPPWl3aUdmzZ4+6du2qKVOmVDv/iSee0NNPP60pU6bo66+/VnJyss477zzt2rUrxJUevcPt4969e7VixQqNHz9eK1as0Jtvvqnc3FxddtllFlR6dI70O6z09ttv66uvvlJqamqIKkN9RHPek/WRL9rzPtqyXor+vCfrD4jIrDcBcOaZZ5rMzEy/aSeddJJ58MEHA7H6sLN582YjySxatMjqUgJq165d5oQTTjDz5883ffv2NXfffbfVJQXEAw88YM4++2yrywiqiy++2Nx0001+0/74xz+a6667zqKKAkeSeeutt3w/e71ek5ycbB577DHftJKSEuN0Ok12drYFFdbfoftYnWXLlhlJZuPGjaEpKoBq2r/ffvvNHHPMMeaHH34w7du3N5MnTw55baibhpT3ZH3kifa8j+asNyb6856sj6ysr/c3yR6PR8uXL9egQYP8pg8aNEhLliyp7+rDktvtliS1bNnS4koC64477tDFF1+sgQMHWl1KQM2dO1enn366rrrqKrVp00annnqqXnjhBavLCqizzz5bn3zyiXJzcyVJ3333nT7//HNddNFFFlcWeHl5eSosLPQ75jgcDvXt2zdqjznSgeOOzWaLmm9EvF6vrr/+et1///3q1KmT1eWgFhpa3pP1kSfa874hZb3UMPOerA8fcfVdwdatW1VeXi6Xy+U33eVyqbCwsL6rDzvGGN1zzz06++yz1blzZ6vLCZg5c+ZoxYoV+vrrr60uJeB+/vlnTZs2Tffcc4/Gjh2rZcuW6a677pLD4dANN9xgdXkB8cADD8jtduukk05SbGysysvLNXHiRF177bVWlxZwlceV6o45GzdutKKkoCspKdGDDz6ooUOHqlmzZlaXExCPP/644uLidNddd1ldCmqpIeU9WR+Zoj3vG1LWSw0v78n68FLvJrmSzWbz+9kYU2VaNLjzzjv1/fff6/PPP7e6lID59ddfdffdd2vevHlKSEiwupyA83q9Ov300zVp0iRJ0qmnnqrVq1dr2rRpURGa0oHrBF966SXNnj1bnTp10sqVKzVq1CilpqZq+PDhVpcXFA3lmFNWVqZrrrlGXq9XU6dOtbqcgFi+fLn+/ve/a8WKFVH5O4t2DeFvj6yPTNGe9w0x66WGccwh68NPvU+3btWqlWJjY6t8irx58+Yqn/xEupEjR2ru3Ln69NNP1bZtW6vLCZjly5dr8+bN6t69u+Li4hQXF6dFixbpH//4h+Li4lReXm51ifWSkpKijh07+k07+eSTo+JGM5Xuv/9+Pfjgg7rmmmvUpUsXXX/99Ro9erQeffRRq0sLuOTkZElqEMecsrIyDRkyRHl5eZo/f37UfLK8ePFibd68WWlpab5jzsaNG3XvvfeqQ4cOVpeHGjSUvCfrI1e0531Dynqp4eQ9WR+e6t0k2+12de/eXfPnz/ebPn/+fPXq1au+qw8LxhjdeeedevPNN7Vw4UKlp6dbXVJAnXvuuVq1apVWrlzp++/000/XsGHDtHLlSsXGxlpdYr307t27yjAeubm5at++vUUVBd7evXsVE+P/5xwbGxvRw0LUJD09XcnJyX7HHI/Ho0WLFkXNMUf6X2j+9NNPWrBggZKSkqwuKWCuv/56ff/9937HnNTUVN1///36+OOPrS4PNYj2vCfrIzvrpejP+4aU9VLDyHuyPnwF5HTre+65R9dff71OP/109ezZU9OnT1d+fr4yMzMDsXrL3XHHHfr/7d17UFNnGgbwJ2pIAgkoCKKLBCgIi1SsYhW3CE4XxbaK1kpX7Kr1MmtVFGe81EuXjkuxjnTX3UqtON6qtVNtpa2u66UuIjsoKIKgIIig1tt66xZhFASe/UM8NUAgeClC399MZjjJd855vy8nefKF5GTr1q349ttvYTAYlHe07OzsoNPpWri6x2cwGOp958rGxgYODg5t4rtYc+bMwcCBAxEXF4eIiAhkZGQgMTERiYmJLV3aEzN8+HB88MEHcHV1Rc+ePZGVlYW//vWvmDRpUkuX9kjKyspQVFSkLJeUlCA7Oxv29vZwdXVFdHQ04uLi4OXlBS8vL8TFxcHa2hqRkZEtWHXzNNbHbt264Y033sDx48exa9cuVFdXK8879vb2sLKyaqmyLdbUfVj3hYBarYazszO8vb1/6VJFM7TlvJesb/3aet63tawH2n7eS9a34qx/UqfJTkhIoNFopJWVFfv06dOmfjIBQIOXDRs2tHRpT01b+1mInTt30s/PjxqNhj4+PkxMTGzpkp6o0tJSzp49m66urtRqtfTw8ODixYtZUVHR0qU9kuTk5AYfcxMmTCB5/2chYmJi6OzsTI1Gw0GDBjE3N7dli26mxvpYUlJi9nknOTm5pUu3SFP3YV2t6Wchfu3aat5L1rcNbTnv21rWk20/7yXrTbWmrFeR5JOcdAshhBBCCCGEEK3VY38nWQghhBBCCCGEaCtkkiyEEEIIIYQQQtSSSbIQQgghhBBCCFFLJslCCCGEEEIIIUQtmSQLIYQQQgghhBC1ZJIshBBCCCGEEELUkkmyEEIIIYQQQghRSybJQgghhBBCCCFELZkki18dlUqFb775pqXLEK3UuXPnoFKpkJ2d3dKlCCGEMEOyXjwOyXohk+QGTJw4ESqVCiqVCmq1Gh4eHpg7dy7Ky8tburQmubm5YeXKlU+snWjYxIkTMXLkyJYu45GUl5djwYIF8PDwgFarhaOjI0JCQrBr165fvBZLx/HQoUMYPnw4unXr1ipe+BQXF2Ps2LHo1q0btFotXFxcEB4ejsLCwl+8Fkse67du3UJUVBS8vb1hbW0NV1dXzJo1Cz/99NMvU6QQLUCyXjRFsv7JsHQcly1bhn79+sFgMMDJyQkjR45EQUHB0y/wEbW2rAeAP/3pT3juueeg0+ng6OiI8PBwnD59+ukX2Ap1aOkCnlVhYWHYsGED7t27h9TUVEyZMgXl5eVYvXp1s7dFEtXV1ejQ4dcz3JWVlbCysmrpMkQDpk2bhoyMDKxatQq+vr64efMm0tLScPPmzZYuzazy8nL4+/vj7bffxujRo1u6nEZVVlYiNDQUPj4+2LFjB7p27YqLFy9i9+7dz+yk8/Lly7h8+TLi4+Ph6+uL8+fPY9q0abh8+TK++uqrli5PiKdGsv7xSNY/u1pj1qekpGDGjBno168fqqqqsHjxYgwZMgR5eXmwsbFp6fJMtMasB4C+ffti3LhxcHV1xa1bt/D+++9jyJAhKCkpQfv27Vu6vGcLRT0TJkxgeHi4yXVTpkyhs7MzSbKmpobLly+nu7s7tVote/Xqxe3btyttk5OTCYB79uxh3759qVar+e9//5vV1dX88MMP+dxzz9HKyordu3dnbGysst7FixcZERHBjh070t7eniNGjGBJSUm9ulasWEFnZ2fa29tz+vTprKysJEkGBwcTgMnFHKPRyL/97W/KMgCuXbuWI0eOpE6no6enJ7/99luTdU6ePMlXXnmFBoOBer2eL730EouKikxqi4uLY9euXWk0Gi3qU0ZGBn//+9/TwcGBtra2HDRoEDMzM032GxMTw+7du9PKyopdu3ZlVFSUcltFRQXnzZvHbt260drami+++CKTk5PN9vtBXz/55BOGhYVRq9XSzc2N27ZtM2nTWN0xMTH1xjk5OZmvv/46Z86cqWxj9uzZBMCTJ0+SJO/du0e9Xs89e/aQbPo4IslTp05x2LBhtLGxoZOTE9966y1ev35duT04OJhRUVGcN28eO3XqxC5dujAmJqbR/tvZ2XHjxo2Ntvnvf//L1157TRmfLVu2mBwzJSUlBMCsrCxlnR9//FEZC5KsqqripEmT6ObmRq1Wyx49enDlypVKe3Pj2BQATEpKarJdUVERR4wYQScnJ9rY2DAgIID79+83aWM0GvnBBx/w7bffpl6vZ/fu3blmzRqTNunp6ezduzc1Gg379u3LHTt21Ov7w7KysgiA586da7S+pra7YcMG2tnZmayTlJRk8rhuqo/NeU6oa9u2bbSysuK9e/csXkeI1kSyXrJesv7ZzXqSvHbtGgEwJSXFbBvJ+sfL+hMnThCA8hgXP5NJcgMaCs6oqCg6ODiQJBctWkQfHx/u2bOHZ8+e5YYNG6jRaHjw4EGSPwdnr169uG/fPhYVFfHGjRucP38+O3XqxI0bN7KoqIipqalcu3YtSbK8vJxeXl6cNGkSc3JymJeXx8jISHp7e7OiokKpy9bWltOmTWN+fj537txJa2trJiYmkiRv3rxJFxcXLl26lFeuXOGVK1fM9rGh4HRxceHWrVt55swZzpo1i3q9njdv3iR5P0js7e35+uuv8+jRoywoKOD69et5+vRppTa9Xs8//vGPPHnyJHNzcy3q04EDB7h582bm5eUxLy+PkydPZpcuXVhaWkqS3L59O21tbbl7926eP3+e6enpSn9JMjIykgMHDuShQ4dYVFTEFStWUKPRsLCw0GzfAdDBwYFr165lQUEBlyxZwvbt2zMvL8+i++L27duMiIhgWFiYMs4VFRX8xz/+QT8/P2U/vXv3ZufOnZmQkECSTEtLY4cOHXj79m2LjqPLly+zc+fOXLhwIfPz83n8+HGGhoZy8ODByj6Cg4Npa2vL999/n4WFhdy0aRNVKhX37dtntv/e3t6MiIhQxrghw4YNo5+fH9PS0njs2DEOHDiQOp2uWcFZWVnJP//5z8zIyGBxcTG3bNlCa2trfvnllyRpdhybYukkOTs7m59++ilzcnJYWFjIxYsXU6vV8vz580obo9FIe3t7JiQk8MyZM1y2bBnbtWvH/Px8kmRZWRkdHR355ptv8uTJk9y5cyc9PDwaDc6LFy+yXbt2jI+PZ1VVVYNtLNmuJcHZVB+b85xQ19q1a9m5c2eL2wvR2kjWS9ZL1j+7WU+SZ86cIQDm5uaabSNZ/+hZX1ZWxujoaLq7u1t8n/yayCS5AXWDMz09nQ4ODoyIiGBZWRm1Wi3T0tJM1pk8eTLHjh1L8ufg/Oabb5TbS0tLqdFolKCsa926dfT29mZNTY1yXUVFBXU6Hffu3avUZTQaTR6MY8aM4Ztvvqks1w1EcxoKziVLlijLZWVlVKlU/Ne//kWSXLhwId3d3ZV3suuaMGECu3TpYvIgs6RPdVVVVdFgMHDnzp0kyY8++og9evRocL9FRUVUqVS8dOmSyfUvv/wyFy5caLbvADht2jST6/r378933nnH4robenGVk5NDlUrF69ev89atW1Sr1YyNjeWYMWNIknFxcezfvz9JWnQcvffeexwyZIjJ7T/88AMBsKCggOT94HzppZdM2vTr148LFiww2/+UlBS6uLhQrVYzICCA0dHR/M9//qPcXlBQQAA8cuSIcl1+fj4BNCs4GzJ9+nSOHj1aWW5oHJti6SS5Ib6+vvz444+VZaPRyLfeektZrqmpoZOTE1evXk2SXLNmDe3t7VleXq60Wb16daPBSZKrVq2itbU1DQYDBw8ezKVLl/Ls2bPK7ZZs15LgtLSPljwnPOzGjRt0dXXl4sWLm7WeEK2JZL1kvWT9s5v1NTU1HD58eL1+W0KyvnEJCQm0sbEhAPr4+Mh/kc2QE3eZsWvXLuj1emi1WgQGBmLQoEH4+OOPkZeXh7t37yI0NBR6vV65fPbZZzh79qzJNgICApS/8/PzUVFRgZdffrnB/WVmZqKoqAgGg0HZpr29Pe7evWuy3Z49e5p8Z6Br1664du3aE+lzr169lL9tbGxgMBiUbWdnZyMoKAhqtdrs+s8//7zJd5Ms6dO1a9cwbdo09OjRA3Z2drCzs0NZWRkuXLgAABgzZgzu3LkDDw8PTJ06FUlJSaiqqgIAHD9+HCTRo0cPk/siJSWl3n1RV2BgYL3l/Px8i+tuiJ+fHxwcHJCSkoLU1FT4+/tjxIgRSElJAQAcPHgQwcHBAGDRcZSZmYnk5GST2318fADApI6H7zeg6WNi0KBBKC4uxoEDBzB69GicOnUKQUFB+Mtf/gLg/rHaoUMHk+PXx8cHHTt2bHRMG/Lpp58iICAAjo6O0Ov1WLt2rXLfPm3l5eWYP38+fH190bFjR+j1epw+fbre/h8eP5VKBWdnZ2X88vPz4e/vD2tra6VN3WOnITNmzMDVq1exZcsWBAYGYvv27ejZsyf279//WNt91D42R2lpKV599VX4+voiJibmkbcjRGsgWS9ZL1n/bGb9zJkzkZOTgy+++KLRdpL1zTdu3DhkZWUhJSUFXl5eiIiIwN27dx9pW23Zr+fsEs00ePBgrF69Gmq1Gt26dVMCo6SkBADwz3/+E7/5zW9M1tFoNCbLD59kQKfTNbq/mpoa9O3bF59//nm92xwdHZW/6waXSqVCTU2NBT1qWmPbbqp+APVOqmBJnyZOnIjr169j5cqVMBqN0Gg0CAwMRGVlJQCge/fuKCgowP79+/H9999j+vTpWLFiBVJSUlBTU4P27dsjMzOz3skG9Hq95R1/qL+W1m1u/UGDBuHgwYOwsrJCSEgI/Pz8UF1djdzcXKSlpSE6OlrZB9D4cVRTU4Phw4dj+fLl9fbVtWtX5e9HOSbUajWCgoIQFBSEd999F7GxsVi6dCkWLFgAkibj0ZB27e6/v/agLQDcu3fPpM22bdswZ84cfPTRRwgMDITBYMCKFSuQnp7eaG1Pyrx587B3717Ex8fD09MTOp0Ob7zxhnJsPdDY+D3cv+YyGAwYMWIERowYgdjYWAwdOhSxsbEIDQ21aLvt2rWr167uGFvaR0vdvn0bYWFh0Ov1SEpKavSFshBtgWS9ZL1k/bOX9VFRUfjuu+9w6NAhuLi4NNpWsr75HrxR5eXlhQEDBqBTp05ISkrC2LFjH2l7bZVMks2wsbGBp6dnvet9fX2h0Whw4cIF5Z1CS3h5eUGn0+HAgQOYMmVKvdv79OmDL7/8Ek5OTrC1tX3kuq2srFBdXf3I65vTq1cvbNq0Cffu3bP4hbMlfUpNTcUnn3yCV155BQDwww8/4MaNGyZtdDqd8gQ0Y8YM+Pj4IDc3Fy+88AKqq6tx7do1BAUFNas/R44cwfjx402WX3jhBYvrNjfOISEhSExMhJWVFZYuXQqVSoWgoCDEx8fjzp07+N3vfgfAsuOoT58++Prrr+Hm5vbUz5bq6+uLqqoq3L17F7/97W9RVVWFY8eO4cUXXwQAFBQU4H//+5/S/sELiCtXrijjVve3BFNTUzFw4EBMnz5dua7uu/NP63h9sP+JEydi1KhRAICysjKcO3euWdvw9fXF5s2bcefOHeXF45EjR5pdi0qlgo+PD9LS0izerqOjI27fvo3y8nLlRWlDY9xUHy0d49LSUgwdOhQajQbfffcdtFpts/spRGsjWW9Kst6UZP0vm/UkERUVhaSkJBw8eBDu7u5NriNZf9/jPCeQREVFxSOt25bJx62byWAwYO7cuZgzZw42bdqEs2fPIisrCwkJCdi0aZPZ9bRaLRYsWID58+crH7M5cuQI1q1bB+D+Rx86d+6M8PBwpKamoqSkBCkpKZg9ezYuXrxocX1ubm44dOgQLl26VC+AHsfMmTNRWlqKP/zhDzh27BjOnDmDzZs3N/r7dZb0ydPTE5s3b0Z+fj7S09Mxbtw4k3eyN27ciHXr1uHkyZMoLi7G5s2bodPpYDQa0aNHD4wbNw7jx4/Hjh07UFJSgqNHj2L58uXYvXt3o/3Zvn071q9fj8LCQsTExCAjIwMzZ860uG43Nzfk5OSgoKAAN27cUN71CwkJwalTp5Cbm6uEeUhICD7//HP06dNHCWJLjqMZM2bg1q1bGDt2LDIyMlBcXIx9+/Zh0qRJj/XiKCQkBGvWrEFmZibOnTuH3bt3Y9GiRRg8eDBsbW3h7e2NsLAwTJ06Fenp6cjMzMSUKVNM7hedTocBAwbgww8/RF5eHg4dOoQlS5aY7MfT0xPHjh3D3r17UVhYiPfeew9Hjx41aWNuHOsqKytDdna2EhwlJSXIzs5u9KNGnp6e2LFjB7Kzs3HixAlERkY2+z8xkZGRaNeuHSZPnoy8vDzs3r0b8fHxja6TnZ2N8PBwfPXVV8jLy0NRURHWrVuH9evXIzw83OLt9u/fH9bW1li0aBGKioqwdetWbNy4sdl9tOQ54fbt2xgyZAjKy8uxbt06lJaW4urVq7h69epTexNDiGeZZL1kPSBZ/0tn/YwZM7BlyxZs3boVBoNByaE7d+6Y7adk/X2WPCcUFxdj2bJlyMzMxIULF3D48GFERERAp9Mpb2CJh7TEF6GfdU2dYKCmpoZ///vf6e3tTbVaTUdHRw4dOlQ5Rf2Dk3n8+OOPJutVV1czNjaWRqORarWarq6ujIuLU26/cuUKx48fz86dO1Oj0dDDw4NTp07lTz/9ZLau2bNnMzg4WFk+fPgwe/XqRY1G0+yfhah7MiQ7Oztu2LBBWT5x4gSHDBminKQgKChIOUGBuTFrqk/Hjx9nQEAANRoNvby8uH37dpPakpKS2L9/f9ra2tLGxoYDBgzg999/r2z/wVkV3dzcqFar6ezszFGjRjEnJ8ds3wEwISGBoaGh1Gg0NBqN/OKLL5pV97Vr1xgaGkq9Xm9yAouamho6OjoyICBA2daDnwmYO3euyT6aOo5IsrCwkKNGjWLHjh2p0+no4+PD6Oho5UQjwcHBnD17tsl2w8PDOWHCBLP9j4uLY2BgIO3t7anVaunh4cFZs2bxxo0bJv1/9dVXqdFo6Orqys8++6zeMZOXl8cBAwZQp9Oxd+/e3Ldvn8lY3L17lxMnTqSdnR07duzId955h++++y79/f2VbZgbx7oePKbqXhrrZ0lJCQcPHkydTsfu3btz1apV9caroRNd+Pv7m/y0xuHDh+nv708rKyv27t2bX3/9daMn87h+/TpnzZpFPz8/6vV6GgwGPv/884yPj2d1dXWztpuUlERPT09qtVq+9tprTExMNHlcW9JHS54TzI0vAJOfcRGiLZGsv0+yXrL+Wcl6czn08PFZl2T9z/tp6jnh0qVLHDZsGJ2cnKhWq+ni4sLIyEjl7PXClIp8jA/iCyF+Ndzc3BAdHa1810o8WefOnYO7uzuysrLQu3fvli5HCCHEr5Bk/dMlWd96yMethRBCCCGEEEKIWjJJFkIIIYQQQgghasnHrYUQQgghhBBCiFryn2QhhBBCCCGEEKKWTJKFEEIIIYQQQohaMkkWQgghhBBCCCFqySRZCCGEEEIIIYSoJZNkIYQQQgghhBCilkyShRBCCCGEEEKIWjJJFkIIIYQQQgghaskkWQghhBBCCCGEqPV/iU/08DMELowAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, x='squat12_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('Percent increase between Squat 1 and Squat 2 By Weight Class', fontsize=10)       # Set title with font size\n",
    "plt.xlabel(xlabel=\"Percent Increase between Squat 1 and Squat 2\")\n",
    "plt.xlim(0,15)\n",
    "\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=df, x='squat23_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.legend(loc='upper left')\n",
    "plt.title('Percent increase between Squat 2 and Squat 3 By Weight Class', fontsize=10)       # Set title with font size\n",
    "plt.xlabel(xlabel=\"Percent Increase between Squat 2 and Squat 3\")\n",
    "plt.xlim(0,15)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, x='bench12_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.title('Percent increase between Bench 1 and Bench 2 By Weight Class', fontsize=10)       # Set title with font size\n",
    "plt.xlabel(xlabel=\"Percent Increase between Bench 1 and Bench 2\")\n",
    "plt.xlim(0,15)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=df, x='bench23_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.title('Percent increase between Bench 2 and Bench 3 By Weight Class', fontsize=10)       # Set title with font size\n",
    "plt.xlabel(xlabel=\"Percent Increase between Bench 1 and Bench 2\")\n",
    "plt.xlim(0,15)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(data=df, x='Deadlift12_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.ylabel(\"Weight Class\")\n",
    "plt.title('Percent increase between Deadlift 1 and Deadlift 2 By Weight Class', fontsize=10)       # Set title with font size\n",
    "plt.xlabel(xlabel=\"Percent Increase between Deadlift 1 and Deadlift 2\")\n",
    "plt.xlim(0,15)\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(data=df, x='Deadlift23_percent_increase', hue=\"WeightClassKg\", dodge=True, gap=0.1, hue_order = WEIGHT_CLASS_ORDER)\n",
    "plt.ylabel(\"Weight Class\")\n",
    "plt.xlabel(xlabel=\"Percent Increase between Deadlift 2 and Deadlift 3\")\n",
    "plt.title('Percent increase between Deadlift 2 and Deadlift 3 By Weight Class', fontsize=10)       # Set title with font size\n",
    "plt.xlim(0,15)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discussion on Weight Class\n",
    "\n",
    "Between attempt 1 and attempt 2, weight classes usually hovered around 6% jumps, and between the second and third attempts around 4%. There was a downward trend in the median when looking across weight classes, but only around a 0.5% decrease when going from the 43 kg weight class to the 120+ weight class, so per kilo there doesn't appear to be a huge effect on the attempt selection across all the lifts.\n",
    "\n",
    "There seems to be a sharp decline from the 43 kg weight class to the 47 kg weight class, but we believe there is just not enough data (< 500 datapoints) from the 43 kg weight class to accurately say anything from this visual inference. The bias could cause this difference. Around the middle weight classes (74-93kg) which we have the most data from according to the weight class distribution, there seems to be only about a 0.1% difference in attempt selection between weight classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "M     56173\n",
       "F      7499\n",
       "Mx        5\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['WeightClassKg'] == '93']['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regressions\n",
    "\n",
    "Ok, the data looks sufficient to answer our question, and we want to answer our hypothesis on the effect Sex and Event has on percentage increase in events.\n",
    "\n",
    "For this we'll run multiple regressions.\n",
    "\n",
    "- The first regression, we'll filter to only men, and see what each event's coefficient is when it comes to weight increases.\n",
    "- The second regression, we'll filter to only women, and see what each event's coefficient is when it comes to weight increases.\n",
    "- The third regression, we'll mesure the effect of Sex on weight increases.\n",
    "\n",
    "We just need a bit of extra data wrangling to achieve this:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conducting our research on attempt selection in powerlifting, it's paramount to address ethical and privacy concerns throughout the data science process. Our dataset, sourced from Openpowerlifting.com, provides valuable information on powerlifting competitions, but we recognize the importance of safeguarding the privacy of individuals involved. We will ensure that the data used is anonymized and that participants' consent to data usage is respected, given that the information is publicly available. Additionally, we acknowledge potential biases within the dataset, including underrepresentation of certain demographic groups in powerlifting competitions. To mitigate these biases, we will conduct thorough exploratory data analysis to identify any imbalances and employ strategies such as stratification or oversampling to ensure equitable analysis.\n",
    "\n",
    "Throughout the analysis, we will maintain transparency regarding the limitations and potential biases of the data. We will present findings in a fair and balanced manner, highlighting uncertainties and caveats associated with the analysis. Post-analysis, we will critically review our findings to assess their implications on different demographic groups and engage in ongoing dialogue with stakeholders to address concerns related to equity and fairness. Our research will adhere to ethical guidelines and best practices in data science, prioritizing transparency, accountability, and respect for the rights and dignity of research participants. By upholding these principles, we aim to contribute responsibly to the understanding of attempt selection in powerlifting while promoting inclusivity and ethical data use."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Expectations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Communication\n",
    "* We understand that we are all students with different coursework and load and may not be available for days to respond with input to someone's ideas or questions. \n",
    "\n",
    "* To give some semblance of \"I have seen your message\", since Discord doesn't provide read receipts, we hope to adopt a practice of reacting with an emote to messages to acknowledge them if we don't have time to craft a response at the time of reading it. [Read about reacting to messages on Discord](https://support.discord.com/hc/en-us/articles/12102061808663-Reactions-and-Super-Reactions-FAQ). The tutorial shows how to do it on PC, and can be done on mobile by holding down on a message.\n",
    "\n",
    "## Collaboration\n",
    "* Since this project is a group project, and every one should contribute equally or close to equally. Everyone should take the responsibilities of a suitable amount of the work in data wrangling, analysis, report writing, revision, etc. When the work is distributed in the meeting/discord, every team member should keep track of updates on the task, report difficulties encountered (if any) so that we can adjust the work distribution, and help each other out when in need.\n",
    "* The deadline set by the group need to be maintained and every member, though has difference schedules, should complete his or her part of the work before the deadline, too.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Timeline Proposal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our main form of communication is through Discord, to account for changing schedules like midterms/other class deadlines, meeting times should be discussed every week to be as early as all our schedules allow so we can all be on the same page and have time to work on how we decide the split the tasks each week. We believe the table below, which was provided in the template of this proposal, is good for the quarter. \n",
    "\n",
    "\n",
    "| Meeting Date  | Meeting Time| Completed Before Meeting  | Discuss at Meeting |\n",
    "|---|---|---|---|\n",
    "| 1/20  |  Discuss on Discord | Read & Think about COGS 108 expectations; brainstorm topics/questions  | Determine best form of communication; Discuss and decide on final project topic; discuss hypothesis; begin background research | \n",
    "| 1/26  |  Discuss on Discord |  Do background research on topic | Discuss ideal dataset(s) and ethics; draft project proposal | \n",
    "| 2/11  | Discuss on Discord  | Edit, finalize, and submit proposal; Search for datasets  | Discuss Wrangling and possible analytical approaches|\n",
    "| 2/18  | Discuss on Discord  | Assign group members to lead each specific part; Import & Wrangle Data (Ant Man); EDA (Hulk) | Review/Edit wrangling/EDA; Discuss Analysis Plan|\n",
    "| 2/25  | Discuss on Discord  | Finalize wrangling/EDA; Begin Analysis | Discuss/edit Analysis; Complete project check-in |\n",
    "| 3/13  | Discuss on Discord  | Complete analysis; Draft results/conclusion/discussion| Review project report; Edit full project |\n",
    "| 3/20  | Before 11:59 PM  | NA | Turn in Final Project & Group Project Surveys |"
   ]
  }
 ],
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