{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SolasAI Disparity Introduction"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Start Summary\n",
    "\n",
    "This example serves as an introductory text aiming to teach a user how to measure disparities with the SolasAI library. While the example in this notebook is based on the calculation of a single metric, the Adverse Impact Ratio (AIR), there are numerous other metrics available in SolasAI, as well as a generic metric interface for developing custom disparity metrics. Usage of the other metrics is discussed in other examples.\n",
    "\n",
    "This notebook provides:\n",
    "\n",
    "1. A short background on terms used throughout the SolasAI library.\n",
    "\n",
    "2. An explanation of how to import and call functionality in the SolasAI disparity testing library.\n",
    "\n",
    "3. An example of how to calculate an Adverse Impact Ratio using U.S. Home Mortgage Disclosure Act (HMDA) data.\n",
    "\n",
    "4. An overview of how to make customized or formatted tables and charts using the SolasAI disparity testing library."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "Before jumping into the code, we provide a short explanation of several terms used in the code."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### \"Protected\" and \"Reference\" Groups\n",
    "\n",
    "In SolasAI's current list of curated metrics, there is an assumption that one will test whether some group(s) achieve outcomes that are _at least as good_ as another group's outcomes. The first group(s), identified in SolasAI as `protected_groups`, typically consists of people who have been or are continuing to experience some form of discrimination or disadvantage.  The outcomes of these groups are then compared to those of the `reference_groups`, which typically have not been subject to the same type of discrimination or disadvantages.\n",
    "\n",
    "Outside SolasAI, there are numerous ways that these classifications are made and described. For example, often the word \"protected\" may be replaced with \"minority\", \"disadvantaged\", \"test\", or some other description, while \"reference\" may be replaced with \"majority\", \"advantaged\", \"control\", or some other description. Our use of the \"protected\" and \"reference\" categorization comes from our experience working in litigation and regulatory compliance in the United States. It is not meant to imply any particular value judgement regarding the classifications used.\n",
    "\n",
    "A third attribute, `group_categories` is also used throughout the SolasAI library to delineate between different protected-reference group combinations. For example, `group_categories` might be race, sex, age, medical diagnoses, etc. By way of example, classifications most commonly used by SolasAI's U.S. customers are:\n",
    "\n",
    "<center/>\n",
    "\n",
    "| `group_categories` | `protected_group` | `reference_group` |\n",
    "|--------------------|-------------------|-------------------|\n",
    "| Race               | Black             | White             |\n",
    "| Race               | Hispanic          | White             |\n",
    "| Race               | Asian             | White             |\n",
    "| Sex / Gender       | Female            | Male              |\n",
    "| Age                | Age >= X          | Age < X           |\n",
    "</center>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Outcomes, Labels, and Segments\n",
    "\n",
    "SolasAI can be used to measure disparities created as the result of automated systems, semi-automated systems, or entirely subjective processes.  Regardless of the use case, when the user calculates disparity, they will need to specify the `outcome` attribute.  In some cases, this will be a binary (i.e., \"Yes\" or \"No\") outcome, such as whether a person was offered a job, loan, or sent a marketing offer.  In other cases, it may be a continuous value, such as a model's probability of loan default, the amount of time it takes for a person to be promoted, or an employee's pay rate.  Of the metrics SolasAI provides, some are appropriate for the binary case (e.g., `adverse_impact_ratio`), while others are appropriate for analyses of continuous values (e.g., `standardized_mean_difference`).\n",
    "\n",
    "When measuring disparities that arise from the use of a model, and when the true outcome is known, a user can specify the `label` attribute.  Certain metrics, such as the `residual_standardized_mean_difference`, require the label to be present because the disparity measurement incorporates the label.\n",
    "\n",
    "Some metrics, including the `segmented_adverse_impact_ratio` perform analyses on subsets of the data and then aggregate the results.  SolasAI refers to these subsets using the `segment` attribute.  Examples of segments might be different store locations, job openings, or job types.  Care should be used when deciding whether to incorporate segmentation into an analysis."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical Significance and Practical Significance\n",
    "\n",
    "SolasAI uses thresholds of statistical significance and \"practical\" significance to determine whether any potential disparities found are sufficiently large to warrant further review.  The SolasAI does not provide guidance as to which standards are appropriate.  What constitute appropriate and reasonable standards may be driven by regulatory and legal requirements, business decisions, or other factors.  We suggest consulting with one's compliance department, legal advisors, or consultants such as [BLDS, LLC](https://www.bldsllc.com/) (the consultancy from which SolasAI was founded) for advice.\n",
    "\n",
    "That said, after one decides what thresholds to use, SolasAI allows users to test whether the disparity metrics exceed those thresholds.  In the case of statistical significance, each metric uses a measurement of statistical significance that is appropriate or commonly used for that metric.  As an example, in the case of the AIR, statistical significance is calculated using either a Fisher's Exact test or a Chi-Squared test (depending on the size of the data).  For the SMD, a t-test is used.  In certain cases, the user can specify either the test itself or certain attributes of the test.  In United States legal and regulatory standards, a two-sided p-value less than 5% (or, equivilently, a one-sided p-value less than 2.5%) is generally considered statistically significant.  SolasAI does not provide guidance on whether this standard is reasonable or appropriate for any particular use case.\n",
    "\n",
    "Importantly, for SolasAI to identify that a result is \"practically significant\", it must _both_ be found to be statistically significant and exceed the practical significance thresholds set.  Most metrics in SolasAI provide the option to specify two thresholds.  The first is a `percent_difference_threshold`.  If a user sets a value greater than zero, then the raw difference in outcomes between the protected and reference groups must exceed a particular value before a result is considered practically significant.  For example, if `percent_difference_threshold = 0.02` for the AIR, and we find that Black applicants receive offers 1% of the time, but White applicants receive them 2.5% of the time, then this disparity will not be significant because the difference in outcomes is only 1.5%, which is less thna the 2% threshold.\n",
    "\n",
    "The second practical significance metric relates to the value of the metric itself.  For example, when calling the `adverse_impact_ratio`, one sets the `air_threshold` to a particular value.  In the example below, we use `air_threshold=0.80`.  This means that only results that are statistically significantly different from parity _and_ that have AIR values less than 0.80 will be considered practically significant."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing the Library and Data for Use in the Analysis\n",
    "\n",
    "Below, we import the `pandas` and `plotly` libraries used to prepare and graph the data.  SolasAI relies on the plotly library for graphing.  The final line of code, `pio.renderers.default = 'svg'` is only necessary because this workbook is hosted in GitHub, which cannot render plotly graphics in their native format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pathlib import Path"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Certain notebook environments have limited rendering functionality.\n",
    "Uncomment this cell as a potential workaround if plots are not\n",
    "displaying."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import plotly.io as pio\n",
    "# pio.renderers.default = \"png\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's preferable to explicitly and specifically handle warnings. For the\n",
    "purposes of this notebook, we will filter out all warnings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from warnings import simplefilter\n",
    "simplefilter(\"ignore\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SolasAI's disparity library is imported just like any standard Python package.  Here, we import the library itself, which will be accessed as `sd`.  Within sd, we will also access several other types of functions, including:\n",
    "\n",
    "1. The interface functionality, `sd.ui`, which allows users to do things like create nicely formatted tables.\n",
    "2. The SolasAI constants file, `sd.const`, which allows users to customize numerous settings including column names and plot headings.\n",
    "3. Access to a set of utility functions, `sd.util`, which provide additional useful functionality.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import solas_disparity as sd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation\n",
    "\n",
    "The following code imports a sample of the 2018 Home Mortgage Disclosure Act (HMDA) data.  The HMDA dataset includes information about nearly every home mortgage application in the United States.  This dataset includes information about the mortgage itself, such as the loan term and APR; information about credit characteristics of the borrowers themselves, including the borrowers income and debt-to-income (DTI) ratio; and information about the home being purchased, such as its location and the value of the property.  Importantly, it also includes information about each borrower's race, gender, and ethnicity.  The data we are using is based only for applications where the borrower was approved for the loan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "       Low-Priced  Interest Rate  Rate Spread  Loan Amount  \\\n",
       "id                                                           \n",
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       "19675        775000.0  209000.0                  0.25  ...   \n",
       "\n",
       "       Hawaiian Or Pacific Islander  Hispanic Non-Hispanic Male  Female  \\\n",
       "id                                                                        \n",
       "13451                           0.0       1.0          0.0  1.0     0.0   \n",
       "18248                           NaN       NaN          NaN  NaN     NaN   \n",
       "19610                           0.0       0.0          1.0  NaN     NaN   \n",
       "3339                            0.0       0.0          1.0  NaN     NaN   \n",
       "19675                           0.0       0.0          1.0  NaN     NaN   \n",
       "\n",
       "       Age >= 62  Age < 62     Race     Ethnicity      Sex  \n",
       "id                                                          \n",
       "13451        NaN       NaN    White      Hispanic     Male  \n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"hmda.csv.gz\", index_col=\"id\")\n",
    "df.sample(random_state=161803, n=5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We next specify the groups that we will use to test disparities.  In SolasAI, each protected and reference group must be its own variable in the input data.  These variable names are then included in the `protected_group`, `reference_group`, and `group_categories` lists that are used throughout the SolasAI library.\n",
    "\n",
    "For example, if we are going to test for evidence of disparities between Black and White applicants, we must have one variable that identifies whether the person represented by the observation is Black and one variable that identifies whether the person is White.  Importantly, missing values for these characteristics are generally allowed in SolasAI.\n",
    "\n",
    "While more groups are available for analysis in the HMDA data, we limit the analysis in order to make the output more tractable.  The categorization used in this example is as follows:\n",
    "\n",
    "<center/>\n",
    "\n",
    "| `group_categories` | `protected_group` | `reference_group` |\n",
    "|--------------------|-------------------|-------------------|\n",
    "| Race               | Black             | White             |\n",
    "| Race               | Native American   | White             |\n",
    "| Race               | Asian             | White             |\n",
    "| Sex                | Female            | Male              |\n",
    "| Ethnicity          | Hispanic          | Non-Hispanic      |\n",
    "\n",
    "</center>\n",
    "\n",
    "The three lists must all be the same length, with each element of the list corresponding to the same comparison (e.g., the first element of the lists below have `Black`, `White` and `Race`, meaning that Black applicants are being compared to White applicants, which is a comparison by race. The fifth elements of each list are `Female`, `Male`, and `Sex`, which means that women are being compared to men, and the type of comparison is by Sex).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "protected_groups = [\"Black\", \"Asian\", \"Native American\", \"Hispanic\", \"Female\"]\n",
    "reference_groups = [\"White\", \"White\", \"White\", \"Non-Hispanic\", \"Male\"]\n",
    "group_categories = [\"Race\", \"Race\", \"Race\", \"Ethnicity\", \"Sex\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While not demonstrated in this notebook, a key benefit to the SolasAI library is its ability to calculate disparities on groups where the characteristics are estimated, rather than known.\n",
    "\n",
    "In this case, each person's probability estimates are put into the fields identifying group membership.  For example, if the estimation procedure finds that a person has a 75% chance of being Black and a 25% chance of being White, then that person's Black and White variables would have values of 0.75 and 0.25, respectively.\n",
    "\n",
    "A common example of this occurs in race and ethnicity estimation, where a person's home address and last name are used to calculate the probability that the person is Black or White (This is known as the Bayesian Improved Surname Geocoding (\"BISG\") method.  See [here](https://github.com/cfpb/proxy-methodology) for more detail)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the Adverse Impact Ratio on Prior Lending Decisions\n",
    "\n",
    "Determining whether there is evidence of discrimination requires, to the extent possible, testing a model or process before it is put into use as well as testing it when it is being used in production (i.e., having an effective monitoring process).  In this example, we focus on how an organization would monitor a process that is already in production.  Here, we use the HMDA data to test whether there is evidence that members of the protected groups were less likely to receive low-priced loans than members of the reference groups.\n",
    "\n",
    "This type of analysis can be performed on subjective decisions, such as loan officer decisions to underwrite a loan, or a manager deciding whom to promote.  It can also be performed on the outcomes of automated decisioning processes, such as the use of a model to screen applicants, give job offers, or some other similar process.  It can also be performed on a model's training or validation datasets prior to the model being used in production.\n",
    "\n",
    "Below, we use the `sd.adverse_impact_ratio` function to calculate the AIR.  More detail about the AIR can be found in the API documentation for <a href=\"../_api/solas_disparity.adverse_impact_ratio.html\">solas_disparity.adverse_impact_ratio</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "air = sd.adverse_impact_ratio(\n",
    "    group_data=df,  # dataset containing the protected and reference group information\n",
    "    protected_groups=protected_groups,\n",
    "    reference_groups=reference_groups,\n",
    "    group_categories=group_categories,\n",
    "    outcome=df[\"Low-Priced\"],\n",
    "    sample_weight=None,\n",
    "    air_threshold=0.80,\n",
    "    percent_difference_threshold=0,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview of SolasAI Disparity Objects with the AIR as an Example\n",
    "\n",
    "Before jumping into the results of the analysis, below we discuss common elements of the SolasAI disparity objects."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disparity Object Output\n",
    "\n",
    "In a notebook, a user can display a formatted summary of results by displaying the results object using standard IPython notebook methods such as referencing the object in the last line of a cell or by calling the `display` function. This summary includes three elements:\n",
    "1. The disparity card, which summarizes information about the inputs and results of the test run.\n",
    "2. A summary table, which prints more detailed results (and is discussed below).\n",
    "3. A Plotly graph of the AIR metric.\n",
    "\n",
    "Two examples are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Disparity Calculation: Adverse Impact Ratio"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Adverse Impact Ratio Summary Table"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\\* Percent Missing: Ethnicity: 13.97%, Race: 13.88%, Sex: 46.40%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Disparity Calculation: Adverse Impact Ratio"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┌───────────────────────────────────────────┬─────────────────────────────────────────────────────────────────────┐\n",
       "│ Protected Groups                          │ Black, Asian, Native American, Hispanic, Female                     │\n",
       "│ Reference Groups                          │ White, White, White, Non-Hispanic, Male                             │\n",
       "│ Group Categories                          │ Race, Race, Race, Ethnicity, Sex                                    │\n",
       "│ AIR Threshold                             │ 0.8                                                                 │\n",
       "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
       "│ Shortfall Method                          │ to_reference_mean                                                   │\n",
       "│ Affected Groups                           │                                                                     │\n",
       "│ Affected Reference                        │                                                                     │\n",
       "│ Affected Categories                       │                                                                     │\n",
       "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Adverse Impact Ratio Summary Table"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "\\* Percent Missing: Ethnicity: 13.97%, Race: 13.88%, Sex: 46.40%"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "display(air)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Percent Missing</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from solas_disparity import ui\n",
    "ui.show(air.summary_table)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disparity Summary Table Output\n",
    "\n",
    "Nearly all the important information about the results of the analysis is contained in the `summary_table`.  The discussion below describes how to access, format, and graph the information in the `summary_table`."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table can be accessed as a pandas DataFrame by referencing the `summary_table` attribute of the results object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Percent Missing</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Black</th>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>1337.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.796559</td>\n",
       "      <td>0.112230</td>\n",
       "      <td>0.876506</td>\n",
       "      <td>1.242621e-38</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asian</th>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>1286.0</td>\n",
       "      <td>1224.0</td>\n",
       "      <td>0.951788</td>\n",
       "      <td>-0.042999</td>\n",
       "      <td>1.047315</td>\n",
       "      <td>2.389532e-08</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Native American</th>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.861702</td>\n",
       "      <td>0.047087</td>\n",
       "      <td>0.948187</td>\n",
       "      <td>1.467790e-01</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White</th>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17224</td>\n",
       "      <td>0.13880</td>\n",
       "      <td>14461.0</td>\n",
       "      <td>13142.0</td>\n",
       "      <td>0.908789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hispanic</th>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17207</td>\n",
       "      <td>0.13965</td>\n",
       "      <td>2032.0</td>\n",
       "      <td>1593.0</td>\n",
       "      <td>0.783957</td>\n",
       "      <td>0.134857</td>\n",
       "      <td>0.853227</td>\n",
       "      <td>1.726213e-82</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Non-Hispanic</th>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17207</td>\n",
       "      <td>0.13965</td>\n",
       "      <td>15175.0</td>\n",
       "      <td>13943.0</td>\n",
       "      <td>0.918814</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10719</td>\n",
       "      <td>0.46405</td>\n",
       "      <td>4222.0</td>\n",
       "      <td>3719.0</td>\n",
       "      <td>0.880862</td>\n",
       "      <td>0.012781</td>\n",
       "      <td>0.985698</td>\n",
       "      <td>4.300645e-02</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10719</td>\n",
       "      <td>0.46405</td>\n",
       "      <td>6497.0</td>\n",
       "      <td>5806.0</td>\n",
       "      <td>0.893643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Reference Group Group Category  Observations  Percent Missing  \\\n",
       "Group                                                                           \n",
       "Black                     White           Race         17224          0.13880   \n",
       "Asian                     White           Race         17224          0.13880   \n",
       "Native American           White           Race         17224          0.13880   \n",
       "White                                     Race         17224          0.13880   \n",
       "Hispanic           Non-Hispanic      Ethnicity         17207          0.13965   \n",
       "Non-Hispanic                         Ethnicity         17207          0.13965   \n",
       "Female                     Male            Sex         10719          0.46405   \n",
       "Male                                       Sex         10719          0.46405   \n",
       "\n",
       "                   Total  Favorable  Percent Favorable  \\\n",
       "Group                                                    \n",
       "Black             1337.0     1065.0           0.796559   \n",
       "Asian             1286.0     1224.0           0.951788   \n",
       "Native American     94.0       81.0           0.861702   \n",
       "White            14461.0    13142.0           0.908789   \n",
       "Hispanic          2032.0     1593.0           0.783957   \n",
       "Non-Hispanic     15175.0    13943.0           0.918814   \n",
       "Female            4222.0     3719.0           0.880862   \n",
       "Male              6497.0     5806.0           0.893643   \n",
       "\n",
       "                 Percent Difference Favorable       AIR      P-Values  \\\n",
       "Group                                                                   \n",
       "Black                                0.112230  0.876506  1.242621e-38   \n",
       "Asian                               -0.042999  1.047315  2.389532e-08   \n",
       "Native American                      0.047087  0.948187  1.467790e-01   \n",
       "White                                     NaN       NaN           NaN   \n",
       "Hispanic                             0.134857  0.853227  1.726213e-82   \n",
       "Non-Hispanic                              NaN       NaN           NaN   \n",
       "Female                               0.012781  0.985698  4.300645e-02   \n",
       "Male                                      NaN       NaN           NaN   \n",
       "\n",
       "                Practically Significant  Shortfall  \n",
       "Group                                               \n",
       "Black                                No        NaN  \n",
       "Asian                                No        NaN  \n",
       "Native American                      No        NaN  \n",
       "White                                          NaN  \n",
       "Hispanic                             No        NaN  \n",
       "Non-Hispanic                                   NaN  \n",
       "Female                               No        NaN  \n",
       "Male                                           NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "air.summary_table"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can also be viewed as a styled Pandas dataframe by using the SolasAI `sd.ui.show` command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Observations</th>\n",
       "      <th>Percent Missing</th>\n",
       "      <th>Total</th>\n",
       "      <th>Favorable</th>\n",
       "      <th>Percent Favorable</th>\n",
       "      <th>Percent Difference Favorable</th>\n",
       "      <th>AIR</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,337.0</td>\n",
       "      <td>1,065.0</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>1,286.0</td>\n",
       "      <td>1,224.0</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>94.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>0.147</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.88%</td>\n",
       "      <td>14,461.0</td>\n",
       "      <td>13,142.0</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>2,032.0</td>\n",
       "      <td>1,593.0</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.000</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>13.97%</td>\n",
       "      <td>15,175.0</td>\n",
       "      <td>13,943.0</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>4,222.0</td>\n",
       "      <td>3,719.0</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>0.043</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.40%</td>\n",
       "      <td>6,497.0</td>\n",
       "      <td>5,806.0</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.ui.show(air.summary_table)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The user can generate plots for specific columns of the summary table by using the `plot` method of the results object.  Below, we show examples of plotting the `percent_favorable` and `air` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSZAAYkxQh6ABEiABEiABEiABEiABEhAKwEKiFZS7EcCJEACJEACJEACJEACJBBjAhSQGCPkAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSZAAYkxQh6ABEiABEiABEiABEiABEhAKwEKiFZS7EcCJEACJEACJEACJEACJBBjAhSQGCPkAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSZAAYkxQh6ABEiABEiABEiABEiABEhAKwEKiFZS7EcCJEACJEACJEACJEACJBBjAhSQGCPkAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSZAAYkxQh6ABEiABEiABEiABEiABEhAKwEKiFZS7EcCJEACJEACJEACJEACJBBjAhSQGCPkAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSZAAYkxQh6ABEiABEiABEiABEiABEhAKwEKiFZS7EcCJEACJEACJEACJEACJBBjAhSQGCPkAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSZAAYkxQh6ABEiABEiABEiABEiABEhAKwEKiFZS7EcCJEACJEACJEACJEACJBBjAhSQGCPkAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSZAAYkxQh6ABEiABEiABEiABEiABEhAKwEKiFZS7EcCJEACJEACJEACJEACJBBjAhSQGCPkAUiABEiABEiABEiABEiABLQSoIBoJcV+JEACJEACJEACJEACJEACMSYQawUkICAAQ4cOxfTp0+Hi4oIUKVKE3OyUKVMwf/58+Pr6omHDhpg9ezaMjIzg6OiITp064caNG8iYMSPmzp2L0qVLh4P0tX4HDhzAgAED8OrVKxQrVgwrV66EtbU1bt++jSFDhqhjyXWVLFlS/fz+/XtUrFgRJ0+eROLEiWM8IDwACZAACZAACZAACZAACcRnArFWQOrXr4+CBQti/PjxeP36dYiAnDhxAl26dMGpU6eQKFEiNGjQANK3Z8+eqFChAurVq4c//vgDhw8fVjLy5MkTGBsbhxnDyPp9/PgRWbNmxe7du5V8jB49Gg8ePMCWLVvQokULDBw4UF1H165dsX//fnVMOZfIiDzPRgIkQAIkQAIkQAIkQAIk8HUCsVZArl+/rgREIhuhBUREI3369Bg8eLC6sz179mDGjBnYvHmzkod3796p10grUqSIek6EQ9ckmhJZv7dv32LZsmWQKIg0iW6kTp1a/SuScfToUSRNmhTZsmXDo0ePVKSlf//+SnbYSIAESIAESIAESIAESIAEoiYQawVEd+lfCkiVKlXQvXt3lXol7f79+6hUqZKKUsjjN2/eDLnr5s2bq+ckYqFrZ86cibSfCMibN29USpeuSfqVvb09WrVqhX379ikByZMnj4qMyLHHjBmDBQsW4MOHD+rnwoULR02dPUiABEiABEiABEiABEjgFyUQ5wREajqGDx+OGjVqqCF79uwZ8ufPrwREHr9w4ULIUHbs2FE99+eff4Y8JtGKyPqJgEjtidSY6FrmzJmxfft2LFmyBLVq1UKSJEmwaNEiVK5cGbdu3YK5uTmkj/zeunVrSIrY19ort0+/6FTjbZMACZAACZAACcRGAmmTm8fGy+I1xWMCcU5AqlatqiIajRs3VsNy9+5dyGOSgiWPS7G4rjVt2lQ917lz55DHzp49G2k/Sc+S4nMpXte1VKlSQV5jYWGBHj16KEEZN26cqi8R2ZDaj8mTJyNv3rywtbWFk5MTBSQev2F4ayRAAiRAAiQQ3whQQOLbiMb++4lzAiJF35IGJelO0tatW4dVq1Zh/fr1auUriWIkTJhQPSe1GvJcqVKlQkbC1dU10n5SazJr1iy1opW0ly9fInv27KoGRFdXIo/36dNH1YRIilfNmjXVSl25c+dWtSUODg4UkNg/73mFJEACJEACJEAC/ydAAeFU0DeBOCcgp0+fVvUYUsshy95K6pNEJjp06KB+loJzWS5306ZNKtVKisUNDQ2VoNjZ2ami8sj6ySpYkk4lry1Xrpxa4crb21tJjK5JjYkUnh86dEg9JD9LWpj0lxSt8+fPU0D0PYt5PhIgARIgARIggW8mQAH5ZnR84TcSiJUC4ubmhnTp0qlbkr0+TE1N1c9Pnz5VAiERB4lUSDqUpEDJ7wYGBmrJ3fbt26vVqbJkyaJqNWQlLGlSTL5161aUKVPmq/2kRkRqRiQaIpET2QckefLkIXhFYiRFK1euXOoxqUFp166dupYRI0aolK+vNdaAfONM5ctIgARIgARIgAR+CAEKyA/ByoN+hUCsFJD4PGIUkPg8urw3EiABEiABEoh7BCggcW/M4voVU0D0PIIUED0D5+lIgARIgARIgAS+SoACwgmibwIUED0Tp4DoGThPRwIkQAIkQAIkQAHhHIhVBCggeh4OCoiegfN0JEACJEACJEACFBDOgVhFgAKi5+GggOgZOE9HAiRAAiRAAiRAAeEciFUEKCB6Hg4KiJ6B83QkQAIkQAIkQAIUEM6BWEWAAqLn4aCA6Bk4T0cCJEACJEACJPDDBcTKygqyn5psiyBNNnCWLREGDhyo9m9jI4HQBCggep4PFBA9A+fpSIAESIAESIAE9CIgGzduRPXq1dW5ZH+0AwcOoHHjxjh79iwKFy7MUSCBEAIUED1PBgqInoHzdCRAAiRAAiRAAnoXEN0J8+TJg759+6Jz58749OkTunfvDnt7e/j5+aFAgQJYsWIFUqVKpbrPnDkT//zzD/z9/VG8eHEsXrxYPefs7IxevXrh5s2b6rnWrVtj7NixIdEWDm/cI0AB0fOYUUD0DJynIwESIAESIAES0LuAiChs374dbdu2xbVr15A7d25MmDAB+/btw4kTJ5AgQQLUrFkTuXLlwqxZs3Do0CF06NABFy5cQNq0adG1a1d4enpi8+bNqFy5ckg/kRj5vU2bNujRowdHNo4SoIDoeeAoINqB33nsjDNXHsHQMDifND41I8MEaFq9GMzNTOLTbfFeSIAESIAE4iCB77ERodSASNqV1H5I8/LyQpEiRVSkolq1auoxkRJfX18kSpRI/T516lQlIyIl3bp1U6+dN2+eek5EQ/qLhKRPnx5PnjxBxowZ1XPLly/HypUrVSSFLW4SoIDoedwoINqBz1t/HNecXsPC3Fz7i+JIT6/3H9C/rR1yZUkbR66Yl0kCJEACJBBfCXwvAQldAyKRD0mzksd0zcHBAcOHD4f8K7Lx+vVr5MyZU9WKNGrUSEU5xo8fHwbzrVu3kD9//hD5kCdFdJInT44bN27E1yGJ9/dFAdHzEFNAtANftPkk7r3yQCKLhNpfFEd6eri5o2eTMhSQODJevEwSIAESiM8EfoSAvHr1Cjly5MDOnTthZ2en8JUoUUL9PG7cOBgaGqqUrFOnTikBkXQqEQup+5AmERQ5RsqUKZEsWTK4ubmpf9niBwEKiJ7HkQKiHTgFRDsr9iQBEiABEiCBbyXwIwRErmXixIlYvXq1Kh43MTFRqVSSdtWiRQs4OTmhQYMGKh3r9OnTOHr0KJo3b47z58/D1tYWffr0UcXnW7duVTUfUpQuxwsKClLHkDoRibKwxU0CFBA9jxsFRDtwCoh2VuxJAiRAAiRAAt9K4EcJiNR7SFqVrIA1dOhQrFu3Tv1raWmJvHnzqpWt6tatiyZNmmDhwoWq/kMkIzAwUNWPLF26FGnSpFGREOkrKVdSvF6oUCEsWLAAKVKk+NZb5ut+MgEKiJ4HgAKiHTgFRDsr9iQBEiABEiCBbyXwPQTkW8/N1/2aBCggeh53Coh24FEJyOfPn3H9yAY8vHIUAX4+yFG8GopWDw7HXj6wCrdP7QqzRnjtHtORLE2mcBfg9soRx9dNQbrshVGyXreQ5985O+HcroX49MEDxqbmKFqjPdJlKwj3109x5eAa1S9/hcZIlTGn+tnPxxv7lwxHza4TVf+vNdaAaJ8H7EkCJEACJPBjCVBAfixfHj08AQqInmcFBUQ78KgExPHGKdw8vgXVO4+DyMj+pcNRyK45bPOXwdkd85HUOhNylaz51RO6PL2Hc7sWIal1RhibJgwjIDv+6YWC/z+e6wsHHFw2Ek2HLMeZbXORr3xDmCVMjDM75qNqh1HqHOf3LEGqDDmQuUC5KG+SAhIlInYgARIgARLQEwEKiJ5A8zQhBCggep4MFBDtwKMSkNPb5sAqpQ3ylmugDnrv/D44P76FSq0G4cSG6UifsyiyFKrw1RN6ujnDPJEV7p7Zg48f3EMERIRm1bCGaD5sFcwsLNUx1o9rjZrdJuPkphlKekzNE2Hb9N/RqP9CSLTk0r4VqNZprKYbpIBowsROJEACJEACeiBAAdEDZJ4iDAEKiJ4nBAVEO/DoCsjj6ydx234H6v0xE4dWjFFREU/XlwASqPSs/BUaRXryG8c2hxEQ6Xhg6QhkzFNSRVFeO97Gqa2z0ajfAuxdMBBV2o+EiXki7JjZSz12YMlwFKzcAg8uHIC/7ycUqtwCydNlifR8FBDt84A9SYAESIAEfiwBCsiP5cujhydAAdHzrKCAaAcelYA43rCHiEP1LuNhYGCI4+un4qOnGxr+NR83jm+BibkFshetAk/XVzi0cixK1O6MTHlLRngBEQmI+5tn2L9kGD4HBSHQ3w8VWgxAhtzFVV2ITY6iMDGzwIOLB5A2q9SFPIGhsSkSJ0uNtFkLwH7TP6jRdQIFRPtwsycJ/FACr1w84OPr/0PP8bMOnjKZJRJbmP6s0/O88YAABSQeDGIcuwUKiJ4HjAKiHXhUAiJicO3IBoiISJqUTY4iePHwKmp3nxruJNePboT3ezeUbthTk4AE+Pth58zeKFn/d6TLVgjv375UMlLr98kwNDJRdSOfgwJRqEpLVRMisnFi43QUrdZW1ZNsmdoFTQYuoYBoH272JIEfRuCN63vsXLcSGS28ftg5ftaBP/l9xgfLXGjbOvII78+6Np437hCggMSdsYovV0oB0fNIUkC0A49KQL480q2T2/HB/Q1K1e+ON0/uIoVNNhgaGatuVw6thd8n7zBF5qFf/2UERFbGOrJqHJoNWRHS7eDy0chaqEKYupILe5YgZYacyFygLA6vHItiNdrDKnUGbJ3WDY0HLKKAaB9u9iSBH0bg5Rt3XNk5H80yvfhh5/hZB/bwATa7l0ab9q1/1iXwvPGAgD4E5IO3T7RIJbYwi1Z/do5bBCggeh4vCoh24FEJyLO7F3D/wgFUbDkQPt7v1RK4kiYlK1HtmT9ARUQKVmyqpESek+iHTfbCqlBd0rOSp80ccjFfCojvJy9smdIZ1TqPQ0qbbPjo+Q67Zv+Jqh1Hh7zunfOT/xeej1HHubhvBVJnzInUmfIoeandYxoFRPtwf3NPXz9/+PgGfPPrY/MLTUwMYW5qEpsvMU5cGwUkTgwTL/InEvjRAuLwzAV9p+9AQnNtUuHl/Qkz/qqHnJnT/EQqPPWPJEAB+ZF0Izg2BUQ78KgEJCgwAKe3z8XzuxdhaGyilszNWaK6OoGkTJ3dOR/vXjmpYvHcpesgT+k66rkTG6YhaeqMKFCpKS78uwwPLuxHUFAQgM+qliR7sar4rW5XPLt3EVcPrUOgvy8SGBggd+m6IceX40iRuvSzSpVeHdfL4y1ObZmpakbk2JK6FVljEbr2eRBVz/GL/oW3T/wUEDNjAwzqVANmpsGRPLZvI0AB+TZufNWvQ+BHC8jpqw4YsugIkiW30gT13bv3GN+pIsoXy66pPzvFPQIUED2PGQVEO/CoBET7kWJfTwrI9xuTobO2I0eO8BtMfr8z/LwjPa+KT6wAACAASURBVHZ4jgEdqoGpCDEbAwpIzPjx1fGfQHwSkICAABgbG8PU1BQJEiSAmZkZSpQogblz5yJr1qy4fv06GjduDAcHh28aWCsrK9y+fRs2Njbf9Hq+KJgABUTPM4ECoh04BUQ7q1+5JwXkVx59bfeuRUDuPXdHvxXnceeZO2xTJ8b8bmWQO0NSdYKAwCCM23wVc/fewcMFzZA8ccRpJDvPP8GU7dfhFxCEjCkT4e9OJZEpVWK1JPj4zdew68ITdbxSOVNjcrsSSGhqhGFrLsLd2w9GBgkwu2vpkBv6Z/ctmBoZoEfNPF+9SdaAaJsD7PV1AvFRQJ4/f64k4ePHj+jbty+ePXuG/fv3U0BiyZuBAqLngaCAaAdOAdHO6lfuSQH5lUdf271HJSAiCCUH7sLv1XOhTcVsWHH0IZYdvo9zU+urE7T++xjyZUyG6Ttv4v78phEKyKt33ig9aDdOTKiNjKkSY/6+Ozh8/SV2DK2KnReeYPae2/h3RHWYGhug/ayTKGCbDPWKZ8LoDVewrl8l/LH4DNpWyo6iWVPipZs32s86gf2jasDI0IACom2Y2SsGBOKzgAiWQ4cOoXfv3njw4EEYAZH0a3n8wIEDkMhJxYoVsXTpUhgZGeHatWvo1KkTXr9+rSInK1euRObMmRE6AjJs2DDcvHkTu3btgoHB19+rMRieePlSCoieh5UCoh04BUQ7q1+5JwXkVx59bfcelYDIF/6SA3fi6dKWKmVDhCRXz83YMaQqcqVPiltP3ykBSdlmdaQCcvb+awxYeQFnJtdTFyURlQaTDuH+/GaYuv0G3D74YEq7Euq5JYfu4fwDFzQulRn2d5wxqW1xTNtxA+lTJELzslnQYdYJdK6aE6VzWUd5g4yARIkoWh3uOb6KVv+41DlbhtQwMjKM8JLjs4B4eXnh999/R5o0aTBt2rQwArJ9+3aMHDkSV65cUe/7okWLYvjw4WjevDmyZ8+OSZMmoVGjRpg6dSqOHTumREUnIGfOnMGUKVNgb2+PRIkSxaWpECuulQKi52GggGgHTgHRzupX7hmVgHzy9sTulX/j0e3LMDYxQY0WvZD/t0oK2TuXl9i+ZDKcnznAKoU16rbri4zZ84fD6fzUAbtWzoCX5zuYmiVE9eY9kC1fMdXv5vljOLZjOQIC/JEsZRrU7zgAyVKlw/Wzh3Hn0gkEBQaiYZfBsEgcXHz59OEt2P+7Dm3+mhzlsLEGJEpEmjpoEZBSg3bhyZIWSkCkFeu3A6NbFEGtohlCzvE1AfH86Ifi/Xdg04DKyJ8pGWbsugmHV55Y2KMsRE76LT+vIiAWpsZoO/M46hTPiNRJzHH4+gtM6/AbJm65huxpkyC5pRk2nnqMSvnTYvs5J2RPa4VxrYpGep8UEE1TQFOna/ee4fy67cicRNtKTZoOGks6vfLyg61deVQoV+CXERBLS0v1fhYByZYtm4pSiFR8WQPi4+Oj6kSkde3aFZkyZUKTJk1QrFgxuLu7q2P4+/vD19dXiYYIyOrVq9GvXz+cOHEC6dKliyWjHLcugwKi5/GigGgHTgHRzupX7hmVgOxcPg3+fj5o0HkwnJ8+woopf+GPSatglTw1lkzojVyFy6BUtcZwuHUJ25dOxoB/tsLQyCgM0pmDWqNSg45KXF443sfyyX0xePZ2fProhVmD26LX+GVIliotTu/biAc3zqPTkJmYNbgNek9ciRtnD8PT3RXl67RGUFAgFo/riabdRyhJiapRQKIipO35qAREl4LVpWpOdKycA7suPEX3hacwr1sZNCxpq0lApNOWM47oueg0EpkZq/oOEQ6pAZHWf8V5rD3xCMaGBiiUOQW2Dq4Mtw++6DD7BPaPrIEmU45gfOti6LXoDBb2KIOOs0/i2Pjaqi6lUUlblMsT8XKkFBBtc0BLrws3HiNw20YUTxb/Vp27894frmWqwc4uYpmNzxEQSa06fPgw2rdvr+TjzZs3IUXoIhgiEpJGJWlXT548Qa9evVC1alU0bdpU/f5lEwFJmDAhEidOrI5nbm6uZXqxzxcEKCB6nhIUEO3AKSDaWf3KPaMSkOl/NUXzXmNhkzmnwrRhzghkylEQ+UpUwox+zTB80T4YGgYLx9zhnVCzVS9kzvXfEsry5XR42/IYNn8PEiZOovqN/70Wuo2cDy9PdxVd6TN5tXr89XNHLJ/8J/6avgGLx/XAHxNX4cn9G7hivw+Nug7BuUPb8NHrPewadtQ0ZBQQTZii7BSVgMgB7j5zx8BVF/Dc1Qs1i2TAZYe3GNy4IOzy/yeKX4uAyOtb/n0Me4ZXU6lUUnAuUQ2pI1l17CH2XnmGVX0qwMzEEMPXXsYnvwDM7FwKSw/fx77Lz1A+bxp8/gyYGBmgZM7UmLXnNlb2qYDlRx7A65M//qiTN8L7pIBEOfyaO1BANKOKdkd9LsOrWwVLV4Suu9iCBQuqdCup49CtgiX1H1KkvmjRIiUgHTt2VM+3bNkS0t/Dw0PVdvj5+cHR0RE5c+ZUEZCdO3eqWpHkyZNj1qxZ0ebBF3AVLL3PAQqIduQUEO2sfuWe0RWQHcumwtjEFPlKVMSuFTNUNETXNs4dhcy5C6N4peA8fl1bNrEPchcrj5JVGsLp3jVsXTwJf01fD39fH/w9oCXa9Z+GtJmy48Su1Xjr/Az1O/TH/FFd8OeUtXC4cxm3zh9D1SZdsHJaf9Ru8ydO7lmjUrnqtO0bIjURjSEF5PvMbC0CEvpMvv6BqgZE5CG11X9/3fyagCzYfxfXHF2xuGe5kENZt1uDK383xKBVF1C1oI0qMpd26ZELfl9wWj2na1KHItGQfSNr4MYTN7Xi1oo/KmDFkQf46BuAnrUiXg0rJgLi9MIFAQGfvw/kWHYUG+ukMDeL3iaeFJAfN4g/W0CkTqNmzZqqsNzb2ztEQERESpYsqaIgd+/eVX2k/mPy5MnIkycPBgwYoCInIhmyglboGhCJguTLlw/r1q1DhQoVfhy8eHpkRkD0PLAUEO3AKSDaWf3KPaMSEBEOEYWGXYfg3ZsXWD75L+QsVAp5ilXA4a1L0GPM4hB82xZPgnWGLChdvWkYpG9eOKl0rc9BgfD380WL3mNV6pa062cOYeviCUooTEzN0WX4HJVeNXd4R3QcPBMXju5AYqvkePrgFvIUKw/7vevRuOtQPHt0W8lKlcadIx0+Csj3mdlaBKTqqL3oVz8/KhdIh2nbb+KKoyu2DKwc5gK+FBCp+9h98SlaV8iG47deoe+yczg2rhaSJTZTv3eZa6+K1idvu46HL99j+R/l1apWEzZfw70X7lj7V3AtkrSOc06iU+UcqvDc3csXjaccxtFxtTFk9UVVD1KlYMR7DnyrgDx+9gYHZi5HJuP4t4mnu18gEpUsg/pNw45fVLOJAhIVoW9//mcIiOwDIk0iGLa2thgzZowSj9A1IGfPnkXbtm1VGpUUoNepU0cJh9R4yMpXrVu3xsuXL5ErVy6sWrVKHSf0Kli7d+9Gnz59VAqXpGSxaSdAAdHO6rv0pIBox0gB0c7qV+4ZlYDoitCfProN6/SZYWGZFIkskyJnodLYuXwq+kxeE4JP0rOy5i2OYhXrhDwmwiH1HPU69Ee2fMXh6vxMyYikYPn5+mDN34PRedgcJE1hjdsXj+PQliUq8vHs4S0c27kSSZKlQsEy1XD24Ba06TsJU/9sjIEzt8Ll5RPsWz8X7QdMp4D84AmsRUBkNSoRiLfvP6mlcOf/XgbWSRPi3Qcf5Om9RV2hr3+QWkZX2o1ZjeHh7YfSg3bh7Zq26rG/d93E+pPBm5slsTBRxeOlclrjwyd/DFx5HhcfvVXPZbG2xD+dSiJdcgv1+8nbzlhv74BFPcqGkJix8yZO3XVGUgtTLO1dDoaRLPH5rQJy9/FLPJ85G3ZBrj+Yvv4P/9zPANfK10fD1rWidXItAvLv3TuYaX8CfoGBSG9lhUk16yBD0qRqBaV/7E9g841r+Ojnj1aFi2BQpfACJP2mnTiGzdevIfBzECpmzYYJNWrD3Di47mT5xfOYf+a0On6zgoUw1K4KPvr746/dO2BkYIASGTKhbdHgBTCktd+wDn3LV0CBtF+vKYsNNSAD5x9C0mTBaaxRNXf395jUtTJ3Qo8KVBx+ngKi58GjgGgHTgHRzupX7hmVgHzJRtKgCpetgSx5iigZGDb/X5iYBq+AMqNfczTuNgwZs+cLedmrp4+wevoADJ6zM+QxKWQXqfj44T1eON5Dsx6jQp4b0b4i+v+9SYmHNFln/r/C87SY2qcRBs7ahrevnmL/hnlo229qpMPHCMj3mdlaBOT7nEn/R6GAhGf+owTktacnqiyej72duyGDVVIsOX8Oxx0eYX3rtth95zbmnrbHhtbtEPT5M5qvXYW+5Sqgdu6wqXMbr1/FhqtXsLJ5KxgbGqLT5g0oa5sFvcqUxZknThi27191DFMjI/TavlUJyA3nl/jk74/OJUqi5tJF2NWhs3rt/vv3YP/YAZNq/fcHk8hm4M8WkNeunhg6Z4/mtLiPn/wwoVctpE0VvHogW/wjQAHR85hSQLQDp4BoZ/Ur94xKQPZvmK+KzO0adcTj25exbYnUb2xQKVPLJv2pCs7L122tltM9vGUJ+s3YAAMDQ7WMrkiKkZGxkoZOQ2fBJnMutaLVnGHt0WHg3/D+8F5FUXqOXapqOR7duoRN80ZjyLxdIYXt549sV6JSqUEHNUzzR3ZRUQ+nB9fx7NEd1GjRgwLygycwBSQ8YEZAwjOJKgJy4elTjDiwF4e6Bb9nH7i4oNW61bjctz8G7tmFrClSomvJUuq51Zcv4ewTJyxsHDad8+qLFzAzNkLu1MF7vEi0w8HtLf6u20BFOYraZEDLwkXCXNy4wwdRLH0GVM+ZCy3WrsL0OvWRLGFCNF61HGtbtkHShAmjfAf9bAGJ8gLZ4ZcjQAHR85BTQLQDp4BoZ/Ur94xKQN6/c8GGOaPw+rkDkiRLjYadB4bs9eH+1hlbF0+E7PORPHVatYdHOtvg1bIm9qyLln+MR6Yc+XHv6hkc3roY/r6+SGBgoGpEStgF75ItheeyypU0s4SJ1CpatjkLqt+9P3hg5dT+Kl3LyDi4IPbRrYs4tmMFDI2MVbRFlgOOrDEC8n1mNgWEAqJlJkUlIJ4+Pqi0YK6KXuSxtsbc06fw+J0rZtZrGE5Adt6+hUXnzmB/l98jPfULDw903boJvUqXRc1cuVFjyUIVMdl37y68fH3ROH9B9C5bDhOOHELBtOlQK3ceJR3zGjbB6ssXYWNlBUc3NzxyfYs6ufOiUf6I9/iQC6CAaJkB7KNPAhQQfdIGQAHRDpwCEp7VB29fuHl4aocYh3qamZrAOoW2/ODQtxWVgMQhBOEulQLyfUaPAvJtAnLXwx3Dr1yGq48PEhkZY0iBgihrba3qHabduom9z5+pA5dImQpjCheB+Rf758hzC+7dxRqHR/ALCkJ1GxuMLlRE1TK89/PDiCuXcerNa5gZGmJYgUKonSF408ex167Cw88PRgkSYGrx4N3jpc27ewcmhobokiP4jwSRtR+VgiXn23nrJvrt2QkLE1MkNDHG5jYdVA3Irtu3MOe0PTa2aa/ur/u2zXjz4QOOde8V4WXWXb4EN169RLuixTG6Wg0YJEiA0nNmIm+aNPinbgMlIE3XrFR1JJLGecP5Ff4sWx6N/i8gA//dhT/LVcCOWzcxtXZd1FuxFBtbt0Oi/xdef3lSCsj3+SzhUb4fAQrI92Op6UgUEE2YVCcKSHhWf686hNfvvGFkZKgdZBzp6e/rh4EdqyJlMstoXTEFJFq4fsnOFJDww64lBavy/r34M08+JQY337mh1YnjuFC3Po47v8KCe/ewpZIdTOTL9tkzyJc0KXrnCbtXyTmXNxh86SK2VKoMCyMjdD1zClXT2aBdtuwYevkSPgUEYEqx4rjr4YE2J4/jYPUa+BQQiEk3rmNp2XIYePECWmTJgkLJU+DVR290P3MG2+wqqy/4P0NA7ru8QedNG5RkSPRBIhXTTxzDkd97qsv5++RxVQsi6VEVs2TDicePsKND5KvcuXp7Y8yh/UhqnhBjq9dE9cUL8Ff5iqj6f8GafeokpM/IqtUx+uB+PHn3Dp1/K4lVly6q+pLzT5+oWpF2xYqj5/Yt6FKiFApGsis3BeSX/OiL1TdNAdHz8FBAtAOngIRnNWnpPnw2MYdEC+Jbe+Psgj9aVEC61EmjdWsUkGjh+iU7U0CiLyAS5ci8ZROu1muApP//q3rBHduw1a6Kiny4+fpgbOHgXbVXPXqIS2/fYm6p0mFOJBGONAkTokeu3OrxIy9fYsmD+9hUyQ5l/92DeaVKIX+y5Oq5HmdPq0hK2oQJcebNG4wuXASz79xGOgsLNMpki+5nTitx+S1V8OIOP0NAll04rwrCZ9dvFHL6bJPGwb5nH6SxDPuHk4Vnz+C5hzsm1Kwd5lKPOTxSBexZU6RQj0udyPD9e1WkpMvmjUo+mhQITuEUAfH49EkJiK4dkMJzx8eYWLM25BwJTUzUqlhSsN6tZCnkS5M2QjQUkKhmDZ/XNwEKiJ6JU0C0A6eAhGdFAQnPhAKi/T31q/akgERfQOQVLY4fU2lT8sX/vIsL+l88jxM1a+OKqyuGXrmkIhsJjYzw+5lTqG6THs0zZwlzolYnjqF11myoYZNePe7g6amOeale/XACIpEScyNDlE1tjePOzhhXpCim37qJrJaWSG5qhq1PHFHeOg12P3umHhtesFCk0/lHpWCdcnyMIXv3YE+nrqrwW37vvWObKkI/9ugh1l29rOoz3n38iOZrV2JugyYobGMDB1dXJSOy5O7kY0dw57Uz5jdqqqIXIw/sg9SWzG/URK1qNefUSaxv3Q4BQUFosmo5RlSphkrZgjew9PH3R+PVK1ThuZW5OY4+eqiuQVK4JKVrTcs2SGIWvKLfly02CMgHb59ofQQltoj4XqJ1EHaOtQQoIHoeGgqIduAUEAqIltlCAdFC6dfuQwH5NgF5+P49mh0/isDPn+ETGIh5JUuhSrrgDRGHX7mETY6OKh2qQLJkWF2ugqrPCN0aHj2M3rnzoOL//yr/0tsb1Q/ux62GjTHk8kV8DAjAtGIl8NTLC61PHodd2nTokyePSunaVqky2tqfwMiChdHv4nnM/K0kep49gz1VqmHYlcuomyEjSqeOeAGHHyUgcm9SeL7lxjV1m5ZmZhheuRpKZMwI/8BADNq7G4cfPICZsTH6lC2P1kWCI0Rrr1xW6VqyXO9HPz+MOrhfLd/rHxSIQuls1F4iugiKRD1WXLoAE0MjtCxUBH3KlQ9BKuleNkms0LxQYfVYYFAQem7fivc+n1A6U2a1lG9k7WcLiMMzFwyauRsWGqXig/cnTOpVGzkzp/m1P7zi8d1TQPQ8uBQQ7cApIBQQLbOFAqKF0q/dhwISfQER4aiyfx8mFC2KctZp4PjBE82OHVM1GPavnXHo5UssLF0GpgYGGHf9mhKUycWKhzlR6xPH0TJLFtRMH1xc/uj9eyUaUkeiK0KXaEoOqyRIYWqG5GZmGJS/gErpOvTyBcqktsZnAMayAV/KlJh/7546pxS1e/v74/f/p3Z9eXc/UkDi6jvpZwuI7IQ+Zd1pWKcOTrmLqr1xeYd+zUpyI8KoQMXh5ykgeh48Coh24BSQ6AuI5G2f3rMCD6/aqxfbZM2HSk17wtjEDIEB/jiycTYeXrOHqZkFytbvhFzF7CIckHuXjuHwhpmo3qY/shcqp/rcPn8QRzbMCtNfjtljyjZ4e7rh1K7l6rkS1VogbebgnG/fT97YPKs/mv05AyZmX1+rnjUg4YeCq2Bp/7z4Wk8KSPQF5I67OzqcOomLdYOXm5YmheINM9pi34tnqJQmLVpkyaoeF4noe+Ec7L/YEG/U1StIYmKCv/IGb+y58+kTbHVywtoKFcNdUHv7E6rWo06GjCHPOX/8iO5nT2Nrpcq47e6OxQ/uYX6pMljr8AgfAwLRNWfEq2FRQMKP968qIKVLl4aXlxdu3LgR5YfJ5cuX0bx5czg4OETZlx1iToACEnOG0ToCBUQ7LgpIeFZR1YA8uHoSlw5vQrO+f6t9JvYsHYfU6bPhtxqtcObfVXBzfoIa7QbB4+0rHFw7Hc3/mhmyP4XubJePbsULh1vw9nyHYpWbhAjIl1fz9P5VXDy4AU36TMO/yyeiWJWmME9kicPrZ6JRz4mq+7Et85DWNhdyFq0U5cBTQCggUU6Sb+xAAYm+gEiEotSeXVhfoRIKJE8Ol0+fVPrU6vIVsP/5c1XPMa9UaZWCJUvyPnzvgSVlyqnHn3t7qbQrKUzvc/4sttlVUatgtTp5HG2yZkNT28yYeOM6jBMkQN+8+XD6zWv0v3gBJ2vWhoWxccjFSsqV9JfCcw9fX7SxP6FSsEZfvaKiMpXSRlxwTQGhgAiBO3fuYMCAATA0NMTQoUNRsmTJr36CBAQEwMPDAyn+v0DAN37c8GUaCVBANIL6Xt0oINpJUkCiLyDn9q3BR6/3sGsavPb8tZO78PLxbdTuOAyLR7RC415TkCx1cA53ZM3lhQNSpsuCrXMGoUDZ2hEKSFBgINZM7o6a7YcgZTpb9XOTP6bCLGFiLBvdHp1Gr4TLi8c4uWMxmvSeomnQKSAUEE0T5Rs6UUCiLyDyClm1SgrBPwUGwDBBAnTMnkMVlXv5+6s9PK64uaoDZ06cGBOLFkPahBYqOrHvxXMlLtIW37+H5Q8fIuBzEOplyIhhBQupPS8kutHz3Bnc9/BQK2VNKVocRVOmDLnQM29eY7OTI2b9FryzuLQ5d+/g7Js3sDIxwdySpWAYyXK8FJDw4/0rRkBEPnLnzg0zMzPY29tjwYIFCoyfnx86deqEs2fPqj1WSpUqhaVLlyph0UVA5PHevXvjwIEDEDGpWLGi6mNkZAQrKytMmjQJe/fuxePHj9GmTRslOGzRI0ABiR6vGPemgGhHSAEJzyqqCMiLRzdxeOMsNO/7N4xNzbF7yRhkK1gG2QqWxaKhzVC2Xmdct98FI2NTlKrdHlnzR/4XoS2zB0YqIHfOH4LT3cuo3TH4Q3ftlJ5o2GOCEpCV4zuj46gV2DxrAErVaosbp/bAz+eT+jl1hmyRTgAKCAVE+6dD9HpSQL5NQKJHOfb0poBQQAIDA5E9e3Zcu3ZNSUOuXLnw8OFDmJqaYuvWrVi8eDEOHjyoNtUcOHAgGjRooJ7TCcj27dsxcuRIXLlyRfUpWrQohg8frp6XCEmPHj0wduxYuLi4IH369Hj37h0sLCxiz5sgDlwJBUTPg0QB0Q6cAhJ9AZFXSJ3H7XMHYGBgCOuMOdCo1yR4vXfDstHtULpWOxSv1gLOTvewbd4QdBixHImsIi4K/JqAiGTUaDcYqdMH54Af3TQHtnmKw9TcAjdP70WGnIXh+spJiU6SFNbImKMw9q2arGpBImsUEAqI9k+H6PWkgFBAtMyYCzceI3DbRhRP9l8amJbXxYU+v1oEZN++fVi9ejU2btyohqddu3aoW7cuGjVqhNOnTyuRWLRoEezs7FSERNqXNSA+Pj4hz3Xt2hWZMmVSkQ4RkCNHjqBgweD9WpImTYqrV6/C1tY2LkyFWHONFBA9DwUFRDtwCkj0BeTGqX/hcOMM6nYZCUNjE5zYtggB/r4oV78z5g1oiF7TdypJkLZl1gAUKFcX2QtFvHRjZALy+ulD7F89BR1GLAu5QBEckRBJzSpdpx0Orv0bzf6cjr0rJqJsvU5IkdYWS0a2QZexaygg2t8CYBF6NGB9pSsFhAKiZSZRQLRQ+rY++l4Fq2nTphAJkeiHNEmjqlSpEnbv3h38/78tWzBv3jwVIREpmTNnDu7duxcSAXF3d0e/fv1w8+ZNdYwnT56gV69eKgoiAnL+/HlkzRr8B7gvf/82Qr/eqyggeh5zCoh24BSQ6AvIzkWjkDlvCeQvXVO9+JXjXexfNQWdxqzC3P4N0GbIAiRJbh0iIIUqNkDW/P/lWIc+Y2QCcvbfVQjw90O5Bl0iHMxjW+b/v/C8IrbPH4byDboieZqMWDaqnbqOyBojIOHJUEC0f158rScFhAKiZSZRQLRQ+rY++hQQKSSX9KsXL17AxMQkREBsbGxw69YtpAxVa+Tm5oZmzZqhWrVqqs5Dl4Il9R8fP35UURIRkI4dOyJz5swUkG8b/ghfRQH5jjC1HIoCooVScB8KSPQF5PTu5XB7/Qx1Oo2AgaGhWpLX9dUT1O82Rq1I5e/niyrN+0AKzbfOGYyOo5YjYeKkkGV3M+QoBAvLpCEnjUxAdi4cgawFyyDvb9XCXeDbl444uX0xGveerJ47uX0R0mbOA5us+bFjwTC0HDCHAqL9LcAISDRYUUBaR4vW3ccv8XzmbNgFBReSx6fGGpDwo/krpWBJsfnJkydD0q90NCQNq0iRIupXqdkYNWqU+lnkIk+ePKhQoUKIgDRu3FitmiVRkLt376JmzZrqucmTJzMC8p0+LCgg3wmk1sNQQLSSooBERCqqInQ/n48qFUoiH9KSprZB5RZ9YJk0Ffz9fHBgzTQ8vXdFSUf5hl2RJV9wEfqCwU1V2la6LHmxZnIPtVyvpFMlMDBAggQJ1NK9OQoH78grK16VqdsRtrmLhbtESeuq1Kw3klsHbzzm+c4FB1ZPRVBQoFoKOFOu4J2BI2qMgISnwgiI9s+Lr/VkBCQ8HQpIeCaMgHyf91tER9FnBOS3337Dn3/+qYQhdNu5cyfGjx+P/fv3o0OHDiq9SpboLVasGJYvX4779++HCIiskNW2bVuYm5urAvQ6deqgffv2qq6kc+fOTMH6DlOFAvIdIEbnEBQQ7bQYAQnPKioB0U439vWkgFBAftSspIBQQLTMLQqIFkrf1kcEZMKqk0idKpmmA7i8dcegVmW4E7omWnGzU5wTEFkObdiwYaqASFq5sx0yPwAAIABJREFUcuVU8VDChAnh6Oio1naWHS8zZsyIuXPnQnbB/LJ9rZ+s+SxrR7969UpZ8cqVK2FtbY3bt29jyJAh6lChN7R5//69yhuUcF/ixImjnAUUkCgRhXSggFBAtMyWobO2I0eOTFq6xrk+jIB8nyGjgFBAtMwkCogWSt/W57WrJ0bO34uEZqaaDuD9yRdjutdA2lRWmvqzU9wjEOcERMRjypQpalMZWbNZ8vQkp09WJpD8vXr16uGPP/7A4cOHlYzIygXGoXZWlSGKrJ8UHMmqBrJKgsjH6NGj8eDBAyU7LVq0UGtFy2oHshybhPCkybkkT1Ce19IoIFooBfehgFBAtMwWCogWSr92HwoIBUTLO4ACooUS+5DA9yEQ5wRENn55+/atinpIkyiHrOk8e/ZsJQ9SWKRbdk3EZMaMGUo4dE02jYmsnxx32bJlaudLaRLdSJ06tfpXJOPo0aNqveds2bLh0aNHKtLSv39/JTtaGwVEKykKSESkmIIVngoFRPt76lftSQGhgGiZ+xQQLZTYhwS+D4E4JyAS+fj9999VBCRRokRo2LChWsM5Z86c6N69uyoq0jUpQJJ1nyVioWtnzpyJtJ8IyJs3b5TM6JqkX8m5WrVqpdaUFgGR1RIkMiLHHjNmDGTFhQ8fPqifCxcu/NWRoYBon7iMgIRnRQGhgGh/B7GnjgAFhAKi5d1AAdFCiX1I4PsQiHMCIrfdo0cPFamQ1CpJlTp48KCqwZA0rAsXLoSQkaXV8ufPr1ZD0DWJVkTWTwRENquRFC9dk3Wft2/fjiVLlqBWrVpIkiSJWhe6cuXKaj1pWSFB+sjvrVu3xokTJ746Mv4BQd9n5H6Bo/y95iiuOb1DIouE8e5u37t7YGjbCsibPV207m3YnN0IMDKFmWnw2ubxqb1xfouhHSsjQxptRYq6e+81aRNy5ogjO9AmiN6IOTx6jrE9a8EyUfBOvaGbfJYYGxlE74C/aO9nzu9wcsNsNMv4PN4RcP8EbPMsgx7dO0Tr3m49fIFbE6ejYkD8XIb3QdVGaNO5XrSYnL76CJ5r16JE8vi3E/ptD3942dVCrerFI2TCz5JoTRV2/g4E4pyALFy4ELKU2rZt22BmZoa//voLnz59UsujSaRDisV1TXbCrFq1qloyTddkabXI+kl6lhSfS1qXrqVKlQryGgsLCyU+Iijjxo1T9SUiG1L7IetC582bF7a2tnBycvrqsLh4+ET4vPyFbtOByzAxDt61Mz41X/8AdGlUBpaJzKN1Wws2nsCdlx7xUkA83rmjT7OyyJUlbbSYTFi8F0Em5vFUQFzwZ8uKsLH+by8SLXAG/7MN2eOKgHzWckf/9ZEi9MEdqyFxBALyGZ+RANE0muidPt70fvHGHZe2z0Mz2xfx5p50N+LxCdjsURrtO7aJ1r3JMrxOM2bF231AblRsgMZtakWLyfkbj+G/eQOKx0MBkX1A3MpVQxW78MunC6RUVuH/yBEteOxMAtEkEOcEpH79+ioS0aVL8C7M586dQ5s2bdSazLLylUQxZEUsaVKrsWrVKpQq9d9Oz66urpH2e/36NWbNmqWiKdJevnypdtOUGhBdXYk83qdPH1UTIilesjnN9OnTkTt3blVb4uDg8NUhiCwFa9uhy9hw7BYSJYqHf+338MLgNhVQokCWaE1PpmCFx8UUrPBMWAMSrbfVL9mZKVjhh537gIRnwhSsH/vx8ME74j/ARnbWxBaUoh87Ij/36HFOQGQJ3nv37mHz5s1KCiSdSqIeEhWRNCgpOJflcjdt2qSek2Jx2Whm/fr1sLOzU0XlkfWTVbAknUpeK8v7ygpX3t7eSmJ0TWpMpPD80KFD6iH5WZb6lf4iRiJCX2uRCciOo1ex1f4eLC0T/dwZ8QPO7v7uPfo2LU0BCcXWw80dPZuUiXYEhAJCAfkBb9F4f0gKCAVEyySngGih9G19HJ65YMS8fUisMRPC0+sjRnerjpyZ03zbCfmqWE8gzgmIFHv37NlTpUVJkwiF1GSkT59eLbkrqViyOlWWLFnU47ISljQpJt+6dSvKlCnz1X5SIyI1IxINkciJ7AOSPHnykIEUiZEUrVy5cqnHnj17hnbt2qnUrBEjRqiULwpIWAIUkPAzggISngk3IgzPhPuAfJ//h1JAKCBaZhIFRAulb+sjGxEu3nUF6W1SajrA81dv0almIW5EqIlW3OwU5wQkbmL+76oZAdE+gkzBCs+KEZDwTJiCFZZJQEAgjhw9iU/v32l/s8WhnslSWaN8hTLRumIKCAVEy4ShgGih9G199Ckg8gdhWaRIslgkrV7XJFNG/qgs/36tHTlyRK2samNjo1Ls79+/j6VLl37bjUfwKsmcWb58ufpjufzx/Ee1y5cvq1KBqEoDftT5ozouBSQqQt/5eQqIdqAUEAqIltlCAQlLydP7EzbOmYBmlvZa8MWpPp8/f8ZKz1r4c8jgaF03BYQComXCUEC0UPq2PvoWEEtLS1hZWUFkQmp0pWkVENnQWlL5f/vtN0hqvgiNHO97tMDAQLWVg2TOSJPz/Kgm1+3h4aE20I6NjQKi51H5VgF5dvMkbh5YFuZqgwL9Uf3PxQAS4NahFXjrdAuGxibIXakV0uUqGeGdOV7aj0fndiEoMAAZClRE7ootkSBBAvW7HP/VgwswMjFH7gotYJO3DAL8fHDt3wVIYGCA5OlzwbbIfylm5zdNQY6yjZE07deLy5mCFX4omIIVnglTsMIz+ZYULBGQ3YsmonuK4Dq1+NREQKa6NEKPAYOidVsUEAqIlglDAdFC6dv66FtAEidOjJkzZ6ptFGSrhi8FJCgoCL1791YbT8sX9YoVK6ooh2xePWrUKKRLlw7Tpk2Do6OjioDIHnC+vr4h+8S5ubkhQ4YMauVUZ2dntb+cLFxkYmKiohvFi0e83PHevXtVDfPEiRNRrVq1MCu3ijDJfnJyTXJOuZb9+/ern+V7mvwsK7LK7xGd79q1a2qFVhEcKSOYNGlSSAREPjsHDBiAdevWqWvs1auX+j0yDlJjLYs+yd52EkmR+5RojZQySF3192gUkO9BMRrH+FYB+fIUIhsiEqVaDseNA0sR6O+HgjW74v2bJzi3cRIqdp4Kc8v/alfk9W+f3FaSIa8xMDLGlV1zlIBYWdvivv0WfHB9gUJ1esDb/TWu712EMm1G4/kte3XsLMVr4uTyISjbbhwMDI3g/OAiXBxvokCN/5Y4jgwDBYQCouUtQgGhgEQ1TyggEXyW+ACb3UujTfvWUeEL8zxXwQqPiwISrSkUrc76FhDZo02EQb5Ajx07FnXr1g0TARExGTlyJK5cuQL5XClatKhauEhSlmRbBZERiYDoUrBk5dVmzZqpGmJpK1aswI4dO7Br1y51Dtkgu1u3brh06ZI6l9QHSxrYl61JkyaqnyyGJDXFso2D7GcnTSIVIgWDBg1Se89JCpnsNycrrEpf2UJCtpeI7HyyQbZcs1y73EfoFCwRD9lkW7aPkK0rZI+8LVu2KHmKjEPjxo3x7t07teiSSIe8RqQoqlpnrRODAqKV1Hfq9z0EJCgoEPbLh6Jw3Z6wTJUBRxb0QdH6fWCVJrO6yss7ZiF5+pywLVotzFVf3TMfyWxyIFMhu3B3c3heb5RsMRSJkoVdceL2kTXqWGlyFMPZ9eNRsNbvMEmYGGfWjkHJ5kNgYp44SjIUEApIlJMEAAWEAhLVPKGAUECimiPy/HM/A1wrXx8NW0dvHxAKiBa639ZH3wIi+8RJZEO+cIs83LlzB/v27QtTA+Lj46P2k5MmX+4zZcqEoUOHRigg8qVetnrYvXs3ChQooCRDZEAiHQULFoQskKSLDIhQTJ06VUVVQjd3d3e1MJLUZBgYGCiJuXr1KubMmRMiIHK9IkBHjx5VEZq7d++q5+QeJJVMVluN7HwpU6ZU1+Pl5aWOH1pAWrVqpSSrb9++6nienp4qmiLXHBkHERBZuEm3mXeDBg3UfXfoEL1NTyObMRSQb3svffOrvoeASFTCxfEGitTrra7jSwG5sX8JDI1MkbdK2zDXeWLZYJWaJWlWAb6fkD5fOWQv3QD+Pt44OKe7ioY4XT6o0rhylmsK62xFcOfYOiRNkwVpc/2G02tGK9FxunoICZOkhNc7ZxU1kWPKsSJrFBAKiJY3DAWEAhLVPKGAUECimiMUkIgJyUaErmWqwc6uaIQd0iaP3kbBWsYhdJ+fJSByDY0aNVJfzHPkyBEiICID/fr1g2ytIOlGEtmQtCSJgkQUAREBkS/vyZIlUxtgSzrS06dP1bYQsmKqFKzrmmzfMH/+fEi0I3STx+S1OumRzzNJh5L0JomWSAREpEFESEREitXld2kSYZFIiGz5ENn5ZHVWSeuSVDBpoQVE9qwTYZKVYkO3r3EQAalevXrIZt5f/h7dOfBlfwpITAlG8/XfQ0COLxmAwnV6Iol1JnX2G/slBcsXBWt1U+lTkoKVOmshFKgeNj3q8PzesLLOjEK1uyPA7xPOrB2L3BVbIElqWxxd+CdylGuCbCXrwf3VI5zfNBmVuszAuxcP4O78GDnKNAoWkAZ9VHqW/P7izmkUrNkN9quGo3TLETAyjfgDjAJCAdHyNqGA6E9ACk54gJfv/UNOGBj0GZ1KJce0hmmx/boHJh90gV/AZ2RMboKZTdLBNrlJpEN4ysELteY74fKgbMieOvivif8cfYulZ93gG/AZdfNZYmqDtDAyTIDNVz2w++Z7BAR+xrxmNkieyEj1v+DkjX+OvcXGTsGfaZE1CkgEnyVMwQoHhRGQ8PPkVxYQJycnlChRAuPHj1dREClGl+iCFJjLdg0iIB07dlT7wH1NQM6cOaOiAVI4vmbNGpWC9eLFC+TLlw/yRT6qJtcgq3JJmpSuScG7RBSk3kKLgEhqVWTnkz3xRBjkmr4UkLZt26rXSYqXNOkjm3ZLvUtkHCggUY1oHHs+pgLi4ewISaWq1HV6yJ37ffJSRejvXj6EZcoMME1oCRMLS1VIHrqdWDYIOco2QZrswX8BeXB6O/w+eqpC8gMzu6BG32UwNgveiV3SrTIVrgLr7EVx+/AqJTZZitWC05WDqr/rs7swNDRWaV6Xd85CluK1kDRt1ghHgwISwZcGbkQYDgoFJPw80VcRevW5jzGihjVsU5jgt6kPYf9XNmRKboK5J97i0L0P2N09OL3zy+YbEAS7WY/x2jMA+3rYKgERIem9+SUO9soMC1MDtFrxDLXyWqJrmeQoMfUhzvbPhi1XPeD8PgB97VJC5KfaXEcsaZX+q6Ij56aAUEC0/C+fAkIB0aVg6UhIapUUf0t0QwREvlyXLFlSRUEkzUkiBPLlXmoyChUqpAq45ct86GV45fNH0rAkBapFixbqP2mSViXRCvn97du36NOnjxIbKYTXNYmUSB2HRCekoFzXJA1rz549qlhei4DIeSI7n0RkIhMQufcJEybA3t5eFZ5LOtbatWtVTUdkHCggWj5t4lCfmArI/VNbVFF4nkqtIr1rWZ1KUqLS5Q67EtbFrTOUUGTIXz5EQPx9vJC3clvs/6czyneYhIRWwZsEiYDYFq0eIivymPODS3BxuqEiKw7n98DQ2FStinV552xkLVE7pAblywujgFBAtLxFKSA/R0A2XfHAvtueWNUuA8489ka/bS9xfmB2dTF3nX1Qd6ETHMYEb7z6ZZt08A0+fwZ23XyPNe0yKAGR16ezMsZfdqlU9/13PDHnhCs2dsqI6nMdlYCcdfTG2ovumN/cBotOu+GddwCGVEsd5TShgFBAopwkrAGJENGvHAERIFIXISlYUp8hAiKbWUtUQIrV5ct4nTp1VHrS6tWrVVqW1HDISlV+fn5h9gGRwnBJx3JxcUGiRIkU64cPH6rC8ufPn6toiqRqye+h2+DBg9WSuAsXLgzzuKurq1pNS14r1xdVCpYISGTn+1oERD47hw0bporbRc7kPiQa8jUOIihMwdLyiRNH+sRUQC5smYY0OYqHSITcttRpyMpUKjLhdBvX9i6E3e//wMjEDK5P78DYzAJJUmdSK1c9OLMdpVoMx+egQJxeOwZ57dqodK1bh1YiMMAP+at3wvvXT3B+0yRU6joDphZJFFmRntNrR6Nk86EwMU+E1w5X1bK/+aq0g/3K4aogXc4TUaOAUEC0vD0pIPoXEIk+FJ3yEJs6ZlTy8P5TIIpMfoitXTKhQDozTDvyFo9cfFV04svm4OKL1iuf4kTfrCj/j0OIgNRb6IROpZKhbv7gz46Hb3xQe4ETrg3JgYozHXBxUHaceOilUr1G1rRGo8VPMLVBGsw4+haJTQ1UGlgyi+DUrC8bBYQCouWzhBGQ8JRig4DM23YBNmm17Unx0tkN3eoV5U7oWiZ8HO3DGhA9D1xMBUSWws1VvjlSZSkQcuWfPN1UFMLz7TOYJ06OgjW7qNWupMnjlinTq2JzaZJ25XT5gFqGN2PBSqqWQ1qAvy+u/7sQLk43lXRIhEWK0HXtvv1mmFumUK+R9jkoSKVeSQF7ikx5kb1U/UhJUkAoIFreZhQQ/QuISMDmKx5hai8kItJ9w3MkNjVEQlMD7OuZOcLUqDoLHFWUo2L2RCq1ShcBqTL7MQZUSYWquYLTD567+6HUtEd4PjEPys54hF3dbbHszDtYWxrhnNNH1MtviZnHXLGghQ0uPPkIEZthNSKOhlBAKCBaPksoILFPQF67emLc4v1IaBZ5PVnoq/b+6IvhXasjbSorLUPOPnGQAAVEz4MWUwHR8+V+l9NRQCggWiYSBUT/AtJk6RO0KGqFhgWD/yd/x9kHzZc9wd4emZEhmQl23niPcfvf4NLAbDAw+C9vef0ldxx76IWl/4+MhBaQ+gud0L5kMtQvEBwBuf/aB/UXOeH+qFwq9WrqIRektTJG8yJWWHDKDRs6ZkTecfdxe0ROPHjjg6G7X2Nbl4iL0SkgFBAtnyUUkNgnIFrGjX1+LQIUED2PNwVEO/BFm0/i3isPJLIILoyPT407oYcfTQqIfgXko18Qso26h7sjcyKJefDOtvNPuuLq849Y2jpDyMWkGHAbN4blUHUdutZy+VOcc/KG4f+lRGo45BgLmtvg6EMvJDU3xNDqwVEMiahsuOSOnb/bhrw+KFThuRS75xl3H3dG5FTpWsP3vMbmzhQQrZ93HlwFKxwqCggFROv7h/1+HgEKiJ7ZU0C0A6eAhGc1aek+fDYxh5mptjC2dto/vycFRL8Ccv3FJ7Re8VRFHnTt2IMP6LPlJU72zarqMOT3jmufw2F0Lnj7BakldNuUSBbuQkNHQM45eqPzuuc41DsLEpkaQGpCOpdOhtbF/3vdkjNucPMOwOCqwZJS8R8HbOuaCWccvXHxyUeMqxN2Q1TdCRkBCT9HKCDhmVBAKCA///9ovIKoCFBAoiL0nZ+ngGgHSgGhgGiZLUNnbUeOHF/fP0LLcWJjnx+5DK+sfDX9qAuO9Qm7fPb0Iy5qhSppVuaGmFA3DUpnsVDRiRLTHsF9er6vCog8Ofv4W5VeJft9NC5shQl1rENSuNy8AtBoyRMc7J0ZpkYG6lhHH3zAlEMuMDFMgIUtbGCTNGLBpoBQQLS8TykgFBAt84R9fi4BCoie+VNAtAOngFBAtMwWCkhYSp7en7B70UR0T3FIC7441YcCQgHRMmEpIBQQLfOEfX4uAQqInvlTQLQDp4BQQLTMFgoIBSSqefLyjTuu7JyPZpmCdwiOT40pWOFHkwISOwXkg7dPtN56iS3MotWfneMWAQqInseLAqIdOAWEAqJltlBAKCBRzRMKSHhCdx+/xPOZs2EX5BoVvjj3PAUk9gmIwzMXjFt0AJaW2haVef/hI4Z2rIKcmSOuB4tzk5IXHI4ABUTPk4ICoh04BYQComW2UEAoIFHNEwoIBSSqOSLPX7jxGIHbNqJ4sv9WfNPyurjQJzZsRLj52B1kyWStCZfjszdoWDYnNyLURCtudqKA6HncKCDagVNAKCBaZgsFhAIS1TyhgFBAopojFJD/sXcm4DZW7/t/kHmeiUSGypzxK0OmVMqUIUKUmSKZMhYpw5cSKiKRITJ8y1RoEkmJiCQOmefOYDpm/+te/ff+7XPOPmc9+3j3ds7e97ouF8559rvf9173u/f6vM+z1kqvkSjRMRu3hUmgAKRs2bIyZMgQad26tTnfq1evSrZs2WTWrFnun12+fNn8bOfOnVKtWjXZvHmzFCsWc0GOX3/91cSHhYXJ9evXZdGiRdK2bdtEa8AXxlSAABJgRxBA9IITQAggGrcQQAggNp8QQAggNo8QQIIHQPr37y9RUVEyY8YM0+3r16+XFi1ayNNPPy3Tp083P/vmm2+kU6dOcvDgQcmVK5dXAAF0REZGmt//9ttvMnjwYPnqq680VmKMQgECiEIkJ0MIIHo1CSAEEI1bCCAEEJtPCCAEEJtHCCDBAyBr1qyRHj16yIEDB0y3v/baa3LlyhVZtmyZ7N271/xs6NChcvr0aQMpAIyRI0fK+++/L//884/07t3bZFBcGRDAR6lSpSQiIkKqVq0qX3/9taxcudLEXLx4UYoUKSKffvqp5M6dW2Mzxvx/BQggAbYCAUQvOAGEAKJxCwGEAGLzCQGEAGLzCAEkeAAkOjpasmfPLnv27JHChQtLzZo1ZeLEiaZ86vvvv5cCBQqYsqu+fftKq1atDICg1Gry5Mmyb98+QQkXYGP37t3uEqwlS5bIzJkzTQbk2LFjJua7774zf+PYGzdulP/9738amzGGAHJnPEAA0etOACGAaNxCACGA2HxCACGA2DxCAAkeAEFf1q9fX9q0aWP+FCpUSE6dOiXdunWT2rVrS5MmTQx0HD9+XHLmzGn+vXbtWqlQoYKxCeBl27ZtJhvimgPiCSAffvih4P94DdqFCxckR44cJhuSOnXwLWCguXcSE8MMSGJUu43XEED04hFACCAatxBACCA2nxBACCA2jxBAggtAxo0bJzt27JDnn39eJk2aJKtWrZJ58+aZrEXz5s1lxIgRpsQKLfYcENf/Mf/DG4CMHz9eRo0aZV7naphzgoxJ/vxcNlhzryGGAKJVyqE4AoheSAIIAUTjFgIIAcTmEwIIAcTmEQJIcAEIMhhNmzaVjh07SubMmWXAgAFy9OhRqVu3rgEQtDFjxiQKQAAyyIB8/vnnGlsxJh4FCCABtgYBRC84AYQAonELAYQAYvMJAYQAYvMIASS4AOTWrVuSN29eyZcvn1l+t1KlSsYCxYsXlwwZMsg777xjYARNkwH54osvZOzYsbJp0yYzeR1zPzZs2CAlSpSQLVu2yCeffCJTpkzR2Iwx/18BAkiArUAA0QtOACGAaNxCACGA2HxCACGA2DxCAAkuAEF/Yv7H6tWrJTw8XFKlSmUs0LlzZ1mwYIGZZJ42bVo1gJw8edKsgIU9RU6cOGFKurAsLya8Z82a1ZR51ahRQ2MzxhBA7owHCCB63QkgBBCNWwggBBCbTwggBBCbRwgg/geQ+Wt2SJFCeTVdIQePnJZn6pXmTugqtZJnEDMgAe43AohecAIIAUTjFgIIAcTmEwIIAcTmEQKIfwHk5NlzMuajNZIx3b9ZB1u7cOmKvNrpUbk7TzZbKH+fTBUggAS44wggesEJIAQQjVsIIAQQm08IIAQQm0cIIP4FEI3+jAktBQggAe5vAohecAIIAUTjFgIIAcTmEwIIAcTmEQIIAUTjEcY4pwABxDktVUcigKhkMkEEEAKIxi0EEAKIzScEEAKIzSMEEAKIxiOMcU6BeAFk//79MnfuXPn7779lzpw5cvPmTbPk2COPPOLcu4fgkQgg+k4ngBBANG4hgBBAbD4hgBBAbB4hgBBANB5hjHMKeAWQdevWSaNGjcyW9WvWrBGsp3zo0CEpV66cWee4ffv2zp1BiB2JAKLvcAIIAUTjFgIIAcTmEwIIAcTmEQIIAUTjEcY4p4BXAClZsqRgG3tASIoUKQyAoH3//ffy0ksvyc6dO507gxA7EgFE3+EEEAKIxi0EEAKIzScEEAKIzSMEEAKIxiOMcU4BrwCSPn16uXDhgtm4xRNArl27ZjZcuXTpknNnEGJHIoDoO5wAQgDRuIUAQgCx+YQAQgCxeYQAQgDReIQxzingFUCwVf3ixYulfPnyMQBkxYoV0rt3bzMvhC1xChBA9LoRQAggGrcQQAggNp8QQAggNo8QQAggGo8wxjkFvALIjBkzZPjw4dKlSxcZPXq02WL+999/N9vXT5gwQXr16uXcGYTYkQgg+g4ngBBANG4hgBBAbD4hgBBAbB4hgBBANB5hjHMKxLsK1qeffiozZ84UrIaFkqxixYpJz5495YknnnDu3UPwSAQQfacTQAggGrcQQAggNp8QQAggNo8QQAggGo8wxjkFuA+Ic1qqjkQAUclkggggBBCNWwggBBCbTwggBBCbRwggBBCNRxjjnAJuAJk6dar6qC+++KI6loExFSCA6B1BACGAaNxCACGA2HxCACGA2DxCACGAaDzCGOcUcAMIJpxr2/bt27WhjIulAAFEbwkCCAFE4xYCCAHE5hMCCAHE5hECCAFE4xHGOKcAS7Cc01J1JAKISiYTRAAhgGjcQgAhgNh8QgAhgNg8QgAhgGg8whjnFIgXQLAD+qpVq+To0aOSLl06KViwoDz99NPyn//8x7l3D8EjEUD0nU4AIYBo3EIAIYDYfEIAIYDYPEIAIYBoPMIY5xTwCiBjx46VYcOGSY0aNeTee+8174a9P3788UezDG/fvn2dO4MQOxIBRN/hBBACiMYtBBACiM0nBBACiM0jBBACiMYjjHFOAa8AUqBAAbMEb+wld5EV6dy5sxw5csS5MwixIxFA9B1OACGAaNxCACGA2HxCACGA2DxCACGAaDzCGOcU8AogWbNmlWPHjkmmTJlivNPVq1clb968EhER4dwZhNiRCCD6DieAEEC3gv+kAAAgAElEQVQ0biGAEEBsPiGAEEBsHiGAEEA0HmGMcwp4BZCXX35Z7rnnHnnllVckRYoU7nebNm2a/Pnnn/Luu+86dwYhdiQCiL7DCSAEEI1bCCAEEJtPCCAEEJtHCCAEEI1HGOOcAm4AadGiRYyjrlu3TnLmzCklSpQwk9D/+usvMyEdE9HnzJnj3BmE2JEIIPoOJ4AQQDRuIYAQQGw+IYAQQGweIYDcPoBky5ZNzp8/H+PBNXQtVqyY7Nmzx3TBBx98ID169DD/fuCBB8y84qeeekrTPe6Y2bNnm2kCGzduTPB1hQsXNnH169eXRYsWyaOPPio5cuTw6b0Y7D8F3ACCrIemXbt2Td577z1NKGO8KEAA0duCAEIA0biFAEIAsfmEAEIAsXmEAOIMgCxcuFAef/xxr3IfP35cqlat6p5HnFgAiY6OFvyxwcSZM2cEUwrSpEkjxYsXly+//NLAEFvSUMDnfUD++9//yoABA5LG2SfDsyCA6DuNAEIA0biFAEIAsfmEAEIAsXmEAOJfALl06ZLJeJw4cUKKFi1qYAALHXXr1k0ALVhpFb9HpiJ//vzy/PPPS/bs2eX06dPmNYAXTAN45JFHJHYGZNKkSfLOO+8IHpBXqVJFPvzwQ8mTJ4+4MiB43dKlS+W+++6ToUOHSq9eveSnn34S1wbcyM7g3ydPnhRkcdgCo0C8APLLL7/Itm3b5PLly+4zgQGQ/bh48WJgzi4I34UAou9UAggBROMWAggBxOYTAggBxOYRAoh/AQT6fv3119KxY0dTzo8G4MCqq//73/8kQ4YMplSqZs2a8sYbbxgw+eqrr2T79u0GRAAYy5Ytkw0bNsQAkLVr1xpY+fnnn+Xuu++Wrl27yrlz5+Szzz5zA0jt2rUlderUsm/fPpMBadOmjeTOnVsmT55szuP111+X3bt3m9ewBU4BrwCCjh40aJDcf//9pm6vdOnSZg5IoUKFTPajU6dOgTvDIHsnAoi+QwkgBBCNWwggBBCbTwggBBCbRwggzgAIMh0pU6aMIffo0aOlf//+XgEEGYn27dub+FdffdVkOzDPuHv37nLz5k2TzUDDNhCAi0OHDsUAEIDKXXfd5Z4agNIsZEKyZMkSL4AAWgAheKieNm1aefDBBwXVPb7ORdF4ijHxK+AVQLD54IIFC6R69eqSL18+k5bC0rvoaJBm7P1BAi3wd999ZyYxYangWrVqyfz5803a7MCBAwaOduzYYTZQnDp1qrmG2C2hOBA3IAvGrFy5sjE6NNi1a5cMHjzYHGrIkCFSrVo18++oqCipU6eOrF+/XjJnzmyVggBilcgdQAAhgGjcQgAhgNh8QgAhgNg8QgBxBkASmgPiLQPiOQkdG2AfPHhQ5s2bZwAEW0Hg92ier/UswWrevLkBCEBO7OYqwYqdAQHY4Hc4Nh60N2jQwIwnATJsgVPAK4Bg1asLFy6YzkCaChN50DAoxyoCf/zxR+DOMNY7AYRgNpj8P//5jwwcOFBKlixpzAqTNWnSRHr37i1YxQswAjMj9ebZ4osDuSM9t3z5cgMfSMsh87N48WJDy3ivXLlyGQpH/SIa3gswgt9rGgFEo9K/MQQQAojGLQQQAojNJwQQAojNIwSQ5AkgPXv2lOvXr7szJRi7YqyKFVzjAxD0NWAHY1mUgWGqASp/2AKrgFcAQce9/fbbJh2Fwf3HH39sVi5AXV3BggXN33eq4VwAF8jQeDZMVAI8hIeHuym2YsWKMnHiRAMmrpZQHEDro48+MnWHaMhuYONF/A3I+Oabb0wtIlZTQC0hMi1IK+J8tI0AolWKAOJNqTEzV8utNOklXdo0eiGTSeSpE6eld5vaUiBvdp/OmABCALEZhgBCALF5hADifwD54YcfzEPiU6dOmZWpYq+ClZgMCMZlrVu3ls2bN0uRIkWkT58+poxryZIlbgCpW7eueT9MPMfDZbSwsDApU6aMGdOtWrVKHnroIY1FGOOgAl4BBIN8ZA+QkkItHtJUmBy0c+dOAyCoxbtTDcsFo75v7969xkAowZoyZYo5N5Rl/f777+5TgylhPGQsXO3HH3+MNw4AghvDNTEJr0H5FW6atm3byurVq41ZS5UqZTIjOPbIkSPNutZY+xr/rlChQoLSEED0zmEGJK5WBJC4mhBACCC2TxUCCAHE5hECiDMA4m0fEGiLuRuYl4GHuchQrFy5Ul544YUY+4AkBkBwbCyO9NZbb8mNGzcED56x9wdW0vLcB6Rdu3by+eefy6hRo8wm22iY8B4ZGWnGj2yBVyDeVbCw4zkyIalSpTKprU2bNpnORMmRbe1lf14GVlAARHz77bemPAymwsoHjRo1Mik1rITgajB32bJlxXOPE2Qr4osDgCCVN27cOPcxsGwbVl6YMWOGPPnkk2ZN6enTp7uBLH369GZpNwAazuX7779P8PKvXLvp9feffvmrzFn7u2TJksmf8t2RY0eER8lrz9eWGhWK+/T+78z9RrYfDJdMGTP49LrkEBwVESmvPldbShcv4NPpDpu6XK7flTYoMyCnT5yRV5+vL4Xy+7ZRVO8xi+T+B4r4pGNyCQ4LOyIjezwpWTKli3PKN27eklQpU8T5edSFaJn3zuvSJfude1DkL31v3bol74S3lFdfG+bTWxw+ES4bFk6WVoWP+PS65BAcES2y7FwN6d79eZ9Od+e+o7LrrQlS9/pZn16XHIKPXE0pexo0l3admvh0uhu37ZPz8+dJ1Zwxy7Z9OkgSDd4VeU0u1HtSGj5WxesZpk0dc+J4Er0MR08LD5YrVaokffv2dfS4PJhOAa8AgsnbGLxjWbSk1pBewwoLrno9LMmGGkBAATIdmCzuaq1atTKTizp37uz+GUAqvjiUZ4HMcf2uhrWk8ZqMGTOa9wGgYIk4ZIgAG5j7MXbsWLNSGNJ/WMs6ofbPuatef71k7VZZ+P0fQQsgA5+tKdXK+7YB0PsLv5NdRyKCEkAiIiKl7zO1pGSxu326xd6YvlJupk4XlABy6uQZeaVtHSmYzzcAGThxidx/f2GfdEwuwQCQIZ2fkMxeAOSW3JIUEhdAzl2IliXvj5buOYMTQCacbSF9Xv13QRBtO3oyXH5eOlWeKfzv8p/B1CKjRRZH1ZDnOz3n02X9EXZM9k94R+rdDE4A+b1OM2nVoZFPmvy0PUyufvapVMkRfJOR/4i6JuGPPCEN6v9bghS75cwSfGW9CXU+KltQJYN5IKhsYQu8Al4BBJkFZBmQAUlqDRvOYF1orIKABgBxTTrHylfIYrjACXM1UEL28MMPuy/j7NmzZoUsb3FY7evdd981K1qhoQQNGmAOiOfqCIAgpBFh3oYNG5oUIubKYA4KysISaizB0juKJVhxtWIJVlxNWIIVU5NzF6Nl+fS3pEeutfqbLZlEIgMy/nRz6TlgkE9nzBKsuHLt3n9MjkyaHLQA8tsjTeXpdk/65JOfd+yXG0sXSpUcwZcBAYCcrfGY1KtXyasmd+e8/RIsn8S+g8GoWMFqqKhsqVev3h08k9B+63jngGA3SqzNjPKi2KtIIWV1pxogAdkGlGBhNSyUPWF/EqzhDFNhwjmWy8X5o9QKk8VRRoZJ6zAaJpXHF4dVsHC9eC3mlgBssOkiIMbVMMcEE8+xjjQa/o2lfhGPEi1MhCKAxFQAJVh9W1WXquWK+mQbAggBRGMYAggBxOYTAggBxOYR/J4AolGJMVTAGQW8AkiKFHHT+p5vh6dQd7JhxQKUQ2HDGcAEyq+wBweW3MUcEaxOVbRoUfNzTEhCw2RyrIpQo0aNBOMwRwRzRgA6yJwg05IzZ0735QJiUKIF+EE7fPiwdOjQwZRmDR8+3JR8EUAIILb7I/KfCOnVsoY8WNS3EixmQOIqSwAhgNjuNwIIAcTmEQJI6GRANF5gjP8V8AogWGYXS5bF17BPCFviFGAJll43ZkDiakUAIYDY7iCWYMVViABCALHdNwQQAojGI4xxToF4V8HCUreYZI2l07BJC+Y3uNZSdu7tQ+9IBBB9nxNACCAatzADwgyIzScEEAKIzSMEEAKIxiOMcU4BrwCCVQEwuRplSAUK/LtMKCZkY11lQAmW42VLnAIEEL1uBBACiMYtBBACiM0nBBACiM0jBBD/A8iJM1GabnDH5M+d1ad4BicvBbwCCCZyY1dILDebKdO/+1JgcxlM7kZGZMWKFcnrKpPQ2RJA9J1BACGAaNxCACGA2HxCACGA2DxCAPEvgOzef0I+m/qW5M+im0N84pzI091flbL3F9R0HWOSoQJeAQRrImMr+9hzPbBKFFacwlK2bIlTgACi140AQgDRuIUAQgCx+YQAQgCxeYQA4l8A2bgtTM4vfF5q5NdlQX46lUXSNp8lj1ROettBaLzEGLsCXgHknnvuMcvJusqvXIdBGVbVqlXl6NHg28zJLpUzEQQQvY4EEAKIxi0EEAKIzScEEAKIzSMEkOABEKxKiu0jjhw5IgUL/l8GBfvIYRrB559/bvZv27Nnj8ycOVNjDUdiUFGE9/Q8J0cOnEwP4hVAXnrpJQMgQ4cOlVKlSgmW3cUO46NHj5Zy5crJxx9/nEwv986fNgFE3wcEEAKIxi0EEAKIzScEEAKIzSMEkNACEFT0AFSyZMmisYYjMadPn5ZcuXJJypQpHTlecj+IVwBBxwwcOFBmzZpl9tpAQznWs88+a3YKd80LSe4XfyfOnwCiV50AQgDRuIUAQgCx+YQAQgCxeYQAEloA4pkBwb+xbxwetmOz6rlz55pNqTNmzGg2tF6/fr2ZltCpUyezQTUaMifjx48XrBh77733mtegeghZFuxFh5+jWujChQuydOlSE+OZAZk3b56MGDFCrl69ajbQ/uijjyRt2rQamwZNjFcAiYqKkqxZs5rOwEpYV65cMeVYsXdEDxoVAnghBBC92AQQAojGLQQQAojNJwQQAojNIwSQ0ASQsWPHSokSJcwCS9jQes6cOXLx4kWz2XW2bNmkW7duMm7cODlz5ozZ4PrPP/80++QBNlBOhVVhO3fuLHfddZdMmzbNbFQ9cuRIUzUEmHnxxRdNluWtt95yAwgyL5UqVZItW7aYedVNmzaV6tWry6uvvqqxadDEeAUQZDuwDG/r1q3lqaeekgwZMgTNBd/pCyGA6HuAAEIA0biFAEIAsfmEAEIAsXmEABJ8AJI7d+4Y5U6o7sF+dp5zQKZMmSJ33323jBkzRlq2bCk5c+Z0WwUAgjkj5cuXNz+rVauWYIoC4lAdlD79v5otWLBAZs+eLWvXrjUA8vXXX5v3QEPV0LZt2wzYuDIgX375paxcuVK++OILE4PzSpUqFTMgEGPdunUmZQRxkD5q3LixtGnTRh577DFmQTSfYgnEEED0AhJACCAatxBACCA2nxBACCA2jxBAgg9AMPDH/nWuNmPGDNm6dWucSej4GTIhGPsiM/HBBx9I8eLFTQZk+/bt7r3v8EAe4+EuXbqYOdHYkiJFihQSERFhMhkADwAI5lCjxArN8/8uAAGw7N6920BLKLd4d0KHKDdv3pRNmzbJsmXLzJ9z585J8+bNBZ3IljgFCCB63QggBBCNWwggBBCbTwggBBCbRwggwQcgvq6ChekGr732muzcuVNWrVplAARQAShBq1atmvTr189AB8qsNmzYYKYrfPLJJ+aPFkCQKcFDfrwHGgAGD/tR1hVKLUEAcQmBejgI9vbbb8vGjRvN3BC2xClAANHrRgAhgGjcQgAhgNh8QgAhgNg8QgAJTQDB/A1MJkdWAtMPkJVYsmSJKZECgHTo0MGUUe3fv1/KlCkjBw4cML9fs2aNyYBERkZKq1atzAN6ZD40GRAADFaY/fHHH+WBBx4w0x0qVqzIOSCumxTLhUFc1LGB6rBuMereILSrHk5zQzMmpgIEEL0jCCAEEI1bCCAEEJtPCCAEEJtHCCChCSBY/ap///6mygcTyfPly2eqfEqWLGkAZMiQIQZOwsPDZdCgQdKrVy8zIb1Ro0YGPpC1GDVqlDRr1kzat29vVruylWBhPL148WJ55ZVXzFLA9evXN6tqcRUsEalZs6YpvSpSpIi0aNHCQEeFChU09y9jLAoQQPQWIYAQQDRuIYAQQGw+IYAQQGweIYD4H0AOzekqVfKc03SFbD2TSfK3/fCO7oQOAMFqVtw4UNVlPgd5LcEC5SHb4ap78zwqUkZYLowtcQoQQPS6EUAIIBq3EEAIIDafEEAIIDaPEED8CyDhURdl3pyFkiGdbhO+i5evSdv2z0qu7Jk0XeeXGAKIX2R1HzTeOSCYjPP333/L5cuX3cHHjx832RBMlmFLnAIEEL1uBBACiMYtBBACiM0nBBACiM0jBBD/AohG/6QWQwDxb494BRAsRfbMM8+YmfmeDRsRosYNOzayJU4BAoheNwIIAUTjFgIIAcTmEwIIAcTmEQIIAUTjEcY4p4BXAClXrpyZ+Y8/mIizd+9eM6kGayNjy/pixYo5dwYhdiQCiL7DCSAEEI1bCCAEEJtPCCAEEJtHCCAEEI1HGOOcAl4BBLs7RkVFme3m8+TJI1gRC+23336Tvn37mp0h2RKnAAFErxsBhACicQsBhABi8wkBhABi8wgBhACi8QhjnFPAK4BgW/offvjBZDqwpBgmnmMVgBs3bkj27NnNesdsiVOAAKLXjQBCANG4hQBCALH5hABCALF5hABCANF4hDHOKeAVQLA28fz58+WPP/6QwYMHm2XIUI71008/mW3s8X+2xClAANHrRgAhgGjcQgAhgNh8QgAhgNg8QgDxP4CcOBOl6QZ3TP7cWX2KZ3DyUsArgCDTgY1YOnbsKFevXpXu3bubfUEKFy4sEydONDs2siVOAQKIXjcCCAFE4xYCCAHE5hMCCAHE5hECiH8BZPf+E7J41jTJn+0uTVfIicjr0uy5LlL2/oKqeAYlPwXiXYY3+V1K8jhjAoi+nwggBBCNWwggBBCbTwggBBCbRwgg/gWQjdvC5OI3Y6Vm4ZuarpBNh1NI6lqD7uhGhKoTZVCiFYgBINiG/q+//pKiRYu6D1i+fHlZsmQJV75KtMQxX0gA0QtJACGAaNxCACGA2HxCACGA2DxCAAkeALl+/bpg24jevXvLu+++6+76zz//XGbPni3424nmep8jR47E2C190qRJZrEmvA9Wjt2zZ4/MnDnTibdUHeOpp56S1q1bS7t27VTxdyooBoCkSJFC9u3bFwM2ACXbt2+X0qVL36lzDKr3JYDou5MAQgDRuIUAQgCx+YQAQgCxeYQAElwAkiVLFsFGgl9//bXZTgLtTgDIpUuXBKCC8wlUi4yMlHTp0pk/SbkRQALcOwQQveAEEAKIxi0EEAKIzScEEAKIzSMEkOACkMyZMwsyEcuWLZM1a9bEAZCbN2/KiBEjTIUPHr5XqlRJ3nvvPQMKAJcxY8bIqlWrZP/+/WYD7iFDhsSxkK8ZEGRDpk+fLrdu3ZK8efPK3Llz5b777pOMGTPKsGHDZP369XLixAnp1KmTyd6gIXMyfvx4uXbtmlmVFq+55557zLXt2LHD/Pzo0aNy4cIFWbp0qYnxzIDMmzfPXCfmc9euXdtsJJ42bVrN7eD3GAKI3yWO+QYEEL3gBBACiMYtBBACiM0nBBACiM0jBJDgAhDsZ3flyhWpUKGCjBo1Sho3bhwjA7Jw4UIDGdhmAgCAkqUiRYrI2LFjJVeuXNKzZ0/zOuyDhwF/eHi4ifNsvgAIjluiRAk5dOiQAI7mzJkjFy9eNO8D4OnWrZuMGzdOzpw5Y6ZB/Pnnn2YvPrw3SriwCFTnzp0FVUnTpk2TqVOnysiRI82qtICZF1980cDTW2+95QaQGjVqGLDasmWLFCpUSJo2bSrVq1eXV199VXM7+D2GAOJ3iQkgEeFR0rdVdala7v/mFmlkJ4AQQDQ+IYAQQGw+IYAQQGweIYAEF4Cg/AiAgLkYXbp0MdtKrF692j0HBFmNhx56SLDtBNq6detk0KBBsm3bNgMgKN3CHGg07H+HnwNQvAFI7ty5JWXKlO5foeyqbt26MeaATJkyRbDHHqCnZcuWkjNnTnc8AATn6Xq/WrVqyUsvvWTioqOjBTCFtmDBAnP+a9euNQCCc3TNZ8FcF5wjwMaVAcFrV65cKV988YV5Pc4rVapUSTcDAjLyFAYdgt3P8+XL5xarf//+mnuZMV4UYAZEbwsCCAFE4xYCCAHE5hMCCAHE5hECSHACCPq1efPmUqVKFbn//vvdAPL444/Ls88+K88995yxBva4a9KkiSlnAoBs3rzZPR/a9X8ADLIiaChrQvYCk90x8M+fP7/bYtjGAseLPQkdP0MmBLCDzMQHH3wgxYsXNxkQzLVGlgMNAIGMDcBp9OjRsmLFClMmFhERYTIZAA8ACM4RJVZonv93Acjx48dl9+7d5pqTYouRAXnggQdU54h0EFviFCCA6HUjgBBANG4hgBBAbD4hgBBAbB4hgAQvgPz9999StWpVM5gHRAAMsLk2Mg54wI6GrAI23gYkxAcggIzz58+beJRRYS4FAMTXVbBQGvbaa6/Jzp07zTwT12R5QAlatWrVpF+/fgY6UGa1YcMGyZo1q3zyySfmjxZAMO8D80LwHmgAGMwVQVlXUmjcByTAvUAA0QtOACGAaNxCACGA2HxCACGA2DxCAAleAEHfYhL5Z599ZlZ0BYBg8vmbb74pGzduNKtFtWrVSkqVKmUyHPEBSLFixWLYyJc5IJi/gcnkKKPC+yErgXNAiRQABECEMipMei9TpowcOHDA/B4T6JEBwcpWOMdz586ZzIcmA1KnTh1zTZjnggQD5rlgI/EkOQdEc4My5vYUIIDo9SOAEEA0biGAEEBsPiGAEEBsHiGABDeA4Mk/SrAqV65sAAQrUSG7sGjRIvNvTNgGAGCiuT8ABKtfYfoCVuXCRHJMa0CpFpYIBoAAkAAnmOyOqQ+9evUyE9IbNWpk4ANZC8BRs2bNzKpcWO3KVoKFfUAWL15s5rkAlurXr29W1UqSq2BpblDG3J4CBBC9fgQQAojGLQQQAojNJwQQAojNIwQQ/wPIkZXjpGpB3U7ovx5LIXkfHxgSO6EDQLCaVcGCBTU2DZoYlmAFuCsJIHrBCSAEEI1bCCAEEJtPCCAEEJtHCCD+BZDwqIsy/9PPJUPaVJqukIvR1+TZNs0kV/ZMqvjkHEQAUfQe0lTY9ARrE7MlTgECiF43AggBROMWAggBxOYTAggBxOYRAoh/AUSjf6jGEEA8eh5LgR08eDCOFzCDHpNw/vnnn1D1yW1fNwFELyEBhACicQsBhABi8wkBhABi8wgBhACi8QhjnFMgRgkWliHDn8mTJ7u3gfd8K8zK//bbb82EGLbEKUAA0etGACGAaNxCACGA2HxCACGA2DxCACGAaDzCGOcUiAEg2LBk/vz5Zjt4bGQSu2XIkEEwq75hw4bOnUGIHYkAou9wAggBROMWAggBxOYTAggBxOYRAggBROMRxjingNdJ6NggBcuTsTmvAAFErykBhACicQsBhABi8wkBhABi8wgBhACi8QhjnFMg3lWwduzYIdjxPDo6Os67dezY0bkzCLEjEUD0HU4AIYBo3EIAIYDYfEIAIYDYPEIAIYBoPMIY5xTwCiADBw6UiRMnCradx46NsVtYWJhzZxBiRyKA6DucAEIA0biFAEIAsfmEAEIAsXmEAEIA0XiEMc4p4BVAsBkKJpuXKFHCuXfikYwCBBC9EQggBBCNWwggBBCbTwggBBCbRwggBBCNRxjjnAJeAaRs2bLy+++/O/cuPJJbAQKI3gwEEAKIxi0EEAKIzScEEAKIzSMEEAKIxiOMcU4BrwDSp08fefTRR72uhOXcW4fmkQgg+n4ngBBANG4hgBBAbD4hgBBAbB4hgBBANB5hjHMKeAUQTDJfsmSJ2XSwQIECkiJFihjvuHLlSufOIMSORADRdzgBhACicQsBhABi8wkBhABi8wgBhACi8QhjnFPAK4D069dPUqdOHe+7jB071rkzCLEjEUD0HU4AIYBo3EIAIYDYfEIAIYDYPEIAIYBoPMIY5xSIdxle596CR/JUgACi9wMBhACicQsBhABi8wkBhABi8wgBhACi8QhjnFMgXgDZv3+/zJ07V/7++2+ZM2eO3Lx5UzZs2CCPPPKIc+8egkcigOg7nQBCANG4hQBCALH5hABCALF5hABCANF4hDHOKeAVQNatWyeNGjWS2rVry5o1a+TWrVty6NAhKVeunEyZMkXat2/v3BmE2JEIIPoOJ4AQQDRuIYAQQGw+IYAQQGweIYAQQDQeYYxzCngFkJIlS8q4ceMMhGACOgAE7fvvv5eXXnpJdu7c6dwZhNiRCCD6DieAEEA0biGAEEBsPiGAEEBsHiGAEEA0HmGMcwp4BZD06dPLhQsXJFWqVDEA5Nq1a5I1a1a5dOmSc2cQYkcigOg7nABCANG4hQBCALH5hABCALF5hABCANF4hDHOKeAVQIoXLy6LFy+W8uXLxwCQFStWSO/evc28ELbEKUAA0etGACGAaNxCACGA2HxCACGA2DxCACGAaDzCGOcU8AogM2bMkOHDh0uXLl1k9OjRMmnSJLMz+oIFC2TChAnSq1cv584gxI5EANF3OAGEAKJxCwGEAGLzCQGEAGLzCAGEAKLxCGOcUyDeVbA+/fRTmTlzpmA1LJRkYVPCnj17yhNPPOHcu9/mkV5//XWZNm2anDx50hzpwIED0qlTJ9mxY4fce++9MnXqVKlevXqcd0ko7quvvpIBAwbI8ePHpXLlyjJ79mzJly+f7Nq1SwYPHmyONWTIEKlWrZr5d1RUlNSpU0fWr18vmTNntl4RAcQqkTuAAEIA0biFAEIAsfmEAEIAsXmEAHL7AHLs2DEZOHCgmS985coVyZAhgzRs2NA8uM6UKZOmCxgTQgokuA/IjRs3zDwQNJgpbdq0SUAjlocAACAASURBVEaavXv3SuPGjSUyMtINIFi1q0mTJqZMDCt5AUYOHjwYZ1PF+OIwtwWgtXz5cgMfAJy//vrLlKO1adPG3Fi5cuWSrl27ypdffmm0wHsBRvB7TSOAaFT6N4YAQgDRuIUAQgCx+YQAQgCxeYQAcvsAgm0asFoqNqsGfOBB7jPPPCMlSpSQjz76SNMFjAkhBbwCCJbchWkwuH722WeNHG+++aZgDsiiRYtMduFOt7p160q3bt2kT58+BkBOnz5t4CE8PFzuuusuc3oVK1aUiRMnmuWEXS2huDNnzpibBFkQNGQ38ubNa/4GZHzzzTeSPXt2wRyZffv2mUxL//79DexoGwFEqxQBxJtSY2aulltp0ku6tGn0QiaTyFMnTkvvNrWlQN7sPp0xAYQAYjMMAYQAYvMIAeT2AQTjI4wRGzRo4Jb77NmzgofZGEuhYV85rLJ69epVM576+OOPpXTp0oLSf2zzsHXrVvPQGD8fP368/Pbbb5IuXTpN9zEmmSngFUAef/xxYxZ0vss0GLi/+uqrZrC/evXqO3qZMDBg4O233zbGxTn9+OOP0qNHDzNXxdVat24tABVkLFwtoTgAyKlTp2Ty5MnueJRf/fDDD9K2bVtz3bhhSpUqZTIjOPbIkSPlgw8+kPPnz5t/V6hQIUFtCCB66zADElcrAkhcTQggBBDbpwoBhABi8wgB5PYBpHv37rJy5Urp16+f1KtXz4zPUqZM6ZYem1mjSuXnn382D3KXLVtmtnZwVao8+eSTUrNmTencubOUKVPGPPSuVKmSpusYkwwV8AogqNU7cuSIGWx7NgzQ77vvPjPYvlPtn3/+kYcfftjsyg5juwAEWYhhw4YZY7vaCy+8IGXLlpWXX37Z/bOE4nB9169fN3Tuarhe3CSgc9wcWIZ4+vTpUr9+fbMfCubHIAb/b9eunal9TKhFX7nh9dcLv/pVPlm3U7JkCb46yYjwKBnR4RGpXqG4T7Z5d943sv1QhGTKmMGn1yWH4KjwCBnYvraULl7Ap9Md/t4KuXFX2iDNgJyRVzvWk3vy5/BJkz7jPpP77y/i02uSS3BY2GF5vceTkiVT3CeAN27cklSpUsS5lKgL0bJg0kjpkn1NcrlM9XliT6q3w1vIoBHD1K9B4JET4bJh4RR5pvARn16XHIIjokWWnqsh3bp19Ol0d+47KrvHTJS6N8769LrkEHzkakr589Gnpe0LTXw63R+37ZMLC+ZL1ZypfXpdcgjeFXlNztVtKA0fq+L1dNOn/bfc/nbazZs3ZeHChbJ06VLZuHGjyXI0a9bMVNDkz5/fgAWyIchuuFqhQoXMXFs80D1x4oRUqVLFwAf+Rhk8W/Aq4BVAChQoYOZBoITJs2Hw3rFjR8FEozvVnn/+eTOxHEZGas8FIJs2bTKZDkwWd7VWrVqZVCBiXS2hOGR5ULOIyeuulidPHsFrMmbMaCbhA1DeeOMNM78EsIG5H6h3xHkUKVLEukRx+PkrXqVbsnarfPrd7qAFkEFta0i18sV8ss17n34nu45EBiWAREZESN/WtaRksbt90uSNaavkRpp0QQsg/drVloL5fAOQAROXyv33F/ZJxzsW/O+eruoWFnZEhnZ+XDJ7ARDsD5siLn/IuQvRsvj90dI951r1+ySXQADIhLPN5eVX/10QRNuOngyXzUvfk2cKH7W/xIum9hfduYjIaJHFkTXkhc7tfTqJP8KOSdiESVIvSAHk97rN5JkOT/mkyU/bw+TKok+lShACyB+R1ySi9uPSoH5lr5rkyOz8HF88pB06dKgcPnxYtm/fLs2bNzfVK9myZXOfA/acQ+mVax4tVln98MMPzTgT4y+24FXAK4AgA4Cld5977jkzqAbV7t69W+bOnWtI9sUXX7xjimASuGuOB76MACG5c+c2GRHsW4IsBiY/oSHFh3ItZExcDfGYw+ItDqVc7777rlnRCg03ACZPYQ6I6z3xc8w7wZwQlHi5VnjA7vGYgxIWFpagNizB0luHJVhxtWIJVlxNWIIVU5NzF6Nl+fS3pEeu4ASQ8aebS88Bg/QfJPgsPxUhWz9/XwcgPh35zgdHXhb5LKK6tO/YzqeT2b3/mByZNFnq3QzODMhvjzSVp9s96ZMmP+/YLzeWLpQqOYIvA/JH1DU5W+MxqVfPe0nT3TlvrwQL2QuMnTAu8mzbtm0zD7Ojo6Nl0KBBgnGbZ5m7ZyyABWOqp556Ss6dOyfz58/3qf8YnLwUiHcVLOz5gbQYluHFSlhFixY1T/1btGiRZK7QMwOCk0IZFCacY7lcTIRCSRYmi+P8cT2oScSclvjisAoWyqnw2lq1aplJ+BcvXjQQ42qYY4KJ52vX/vvljn8jI4N4lGht3ryZABJLAZRg9W1VXaqWK+qTdwggBBCNYQggBBCbTwggcRUigMTVhABiu5Pi/z0e2D7wwAOmbApzQVA1gqoSbGuAB7OYf4uyrJYtW5pxEh4EY1Nr/B5jTayyitVHMde4adOmpnz+rbfeSlJjzsSrw1d6U8ArgKAECfMnXJmEpCpdbADBRCaUiGF1KgAT5mq4ysgwmXzJkiVSo0YNM+EpvjiUmWHOCLIhyJzgxsiZM6dbAkAM9HnwwQfNz5Ba7NChgynNwuaNnqs/eNONGRC9mwggBBCNWwggBBCbTwggBBCbR/B7AohGpfhjfv31VwMgW7ZskcuXL5sxJCad42cYg6HNmjXLrE6K+SGADjwwxiI/KNVCBgTl/2jIpgBW8NDX9drbOzu+Oqkp4BVAUNIEWkX5EZuzChBA9HoSQAggGrcQQAggNp8QQAggNo8QQG6vBEujL2OogKcCXgEEKxSgDKl9+/amJAlrMns2LouWeBMRQPTaEUAIIBq3EEAIIDafEEAIIDaPEEAIIBqPMMY5BbwCSApvS6t4vCcmEbElTgECiF43AggBROMWAggBxOYTAggBxOYRAggBROMRxjingFcAweoDadLEv9Myd6VMfAcQQPTaEUAIIBq3EEAIIDafEEAIIDaPEEAIIBqPMMY5BeJdBQubxWBpW6xSgL030AAmWbJkce7dQ/BIBBB9pxNACCAatxBACCA2nxBACCA2jxBACCAajzDGOQW8Agig49FHHzUrQWEZWpRcYeWohx56SNasWWN2qGRLnAIEEL1uBBACiMYtBBACiM0nBBACiM0jBBACiMYjjHFOAa8AUqdOHbNcLZZOwwZ8rjkf2K1y6dKlZgdwtsQpQADR60YAIYBo3EIAIYDYfEIAIYDYPEIAIYBoPMIY5xTwCiBYuzk8PFww1wMT0l0Acu3aNcFO5NgZnC1xChBA9LoRQAggGrcQQAggNp8QQAggNo8QQPwPIPsOnXaPJzX9UaJwXk0YY5KpAl4BpECBArJt2zaza7gngOzatUuQHTlz5kwyvdw7f9oEEH0fEEAIIBq3EEAIIDafEEAIIDaPEED8CyDb9xyRpa9PlkLpU2i6Qo5cuiGNhr8klUsXVsUzKPkp4BVAXnnlFdm6dauMGDFC6tevL9u3bze7UY4cOVIee+wxee+995LflSaRMyaA6DuCAEIA0biFAEIAsfmEAEIAsXmEAOJfANm4LUyOvzZK/pPxuqYrZMulVJJr+HB5pDI3xFYJlgyDvAJIdHS0vPzyy/LJJ5/I5cuXzWVlzpxZevToYSCEy/AmvqcJIHrtCCAEEI1bCCAEEJtPCCAEEJtHCCDBAyDXr183G2inSpUqRrc/8MADgkoef7ZJkyaZ95g5c6Y/3yYojh3vMry4uqtXr8rx48clffr0phyL7fYVIIDoNSSAEEA0biGAEEBsPiGAEEBsHiGABB+AHDlyRAoWLKjpesdiCCB6KeMACJbd/fXXXwUEWblyZe77oddSFUkAUclkggggBBCNWwggBBCbTwggBBCbRwggoQMge/bsMRU9x44dM5tuz5o1y2wvgekGHTt2lAYNGsgvv/xi5jtj9VdMOwgLC5NatWqZ/6MhwzF+/HjB4kz33nuvzJ07V+655x7xBJD43kfjxVCIiQEgu3fvlscff1xAjZh8njt3bvniiy/kP//5TyhoEZBrJIDoZSaAEEA0biGAEEBsPiGAEEBsHiGAhAaAYFXXChUqSPfu3aVbt26yZcsWady4sRw+fFj++usvKVeunNmE++GHH5a2bdvKjh07zJxovA5j4n379pnSLsAGAKNw4cLSuXNns2XFtGnT3AAyY8aMeN8H5WFsIjEA5KmnnjLCfvzxx5I2bVoZPHiwfPfdd7Jz505q5ZACBBC9kAQQAojGLQQQAojNJwQQAojNIwSQ4AMQAEPKlCndXY8NtocPHy7ly5eX8+fPu+eIoNoH2QzE165dW86ePWteg9h//vlH3n//ffP/4sWLy2effWY25cZcaUxPQFuwYIHMnj1b1q5d6waQgQMHxvs+WE2WLRaAYI+Pr776SipVqmS0uXDhginBioiIkKxZs1IvBxQggOhFJIAQQDRuIYAQQGw+IYAQQGweIYAEH4BgO4n8+fO7ux4LKO3du9dkNzznhmDqASDjwQcfFDyIP3jwoHkNNuPGOHjChAnm/5jEPm/ePKlYsaKMHj1aVqxYYaqFMEYuVKiQfP31124A6dq1a7zv07JlS40dgz4mRgYEQsaetJMpUyZTF1esWLGgFyMQF0gA0atMACGAaNxCACGA2HxCACGA2DxCAAk+APE2Cf3o0aNSpkwZAw2xG1av0gDIoUOHzIqwKNXCw3msGIs/ngACeInvfTReDIUYAkiAe5kAohecAEIA0biFAEIAsfmEAEIAsXmEABIaAIJ+Rgajf//+0qZNGzPRvE+fPjJ9+nQBWGgAZPPmzbJmzRqTAYmMjJRWrVrJuXPnBD/3nIQe3/tgWwu2WCVYyICsWrVK8uTJ49YGs/5R3+aZrnKVaFFA3xUggOg1I4AQQDRuIYAQQGw+IYAQQGweIYCEDoCgDAsT0JEhweTxvn37mv9rMyBY9apRo0YGPjAZfdSoUdKsWTNp3769FChQwL0PSHzvo/FiKMTEyYBoLhqrAbAlTgECiF43AggBROMWAggBxOYTAggBxOYRAoj/AWTb0NFSLr1uJ/Sd0amkzKih3AldY9xkGhMDQE6ePKm6jHz58qniGBRXAQKI3hUEEAKIxi0EEAKIzScEEAKIzSMEEP8CyMXoK7L4068lbeqYu5PH1y+Xr12XFs/Ul8wZ02m6jjHJUIEEd0JPhteT5E+ZAKLvIgIIAUTjFgIIAcTmEwIIAcTmEQKIfwFEoz9jQksBAkiA+5sAohecAEIA0biFAEIAsfmEAEIAsXmEAEIA0XiEMc4pQABxTkvVkQggKplMEAGEAKJxCwGEAGLzCQGEAGLzCAGEAKLxCGOcU4AA4pyWqiMRQFQyEUDikWnMzNVyK016SZc2jV7IZBJ56sRp6d2mthTIm92nMyaAEEBshiGAEEBsHiGAEEA0HmGMcwoQQJzTUnUkAohKJgIIAURtFAIIAcRmFgIIAcTmEQIIAUTjEcY4pwABxDktVUcigKhkIoAQQNRGIYAQQGxmIYAQQGweIYD4H0D2HTotvmzjUKJwXk23MSaZKkAACXDHEUD0gnMOSFytWIIVVxMCCAHE9qlCACGA2DxCAPEvgGzfc0Q+f3eu3JsltaYr5PC5K9KwVzupXLqwKp5ByU8BAkiA+4wAohecAEIA0biFAEIAsfmEAEIAsXmEAOJfANm4LUzOfjxbquVOq+kK+fnsVcnavj03IlSplTyDCCAB7jcCiF5wAggBROMWAggBxOYTAggBxOYRAkjwAMj169clderU0qFDB5k9e3aMru/WrZt8+OGHcu3aNbnrrrvitcWkSZNk165dMnPmTI11GJMIBQggiRDtdl5CANGrRwAhgGjcQgAhgNh8QgAhgNg8QgAJLgDJmjWr5MmTR3bv3i3p0/97bYCOkiVLypEjR+TChQsEEM1N4ccYAogfxfV2aAKIXnACCAFE4xYCCAHE5hMCCAHE5hECSHABSKZMmaRp06by9NNPS6tWrUz3r169Wj755BNZtGiROwOCDMf48ePN/++9916ZO3eu3HPPPeKZAdmzZ4/06NFDjh07JmnSpJFZs2ZJlSpVNJZiTAIKEEACbA8CiF5wAggBROMWAggBxOYTAggBxOYRAkhwAUi6dOlk6dKlBha++OIL0/3t27eXZs2aSfPmzQ1wREREGNgAYBQuXFg6d+5ssiLTpk1zA8iMGTOkQoUK0r17d0H51pYtW6Rx48Zy+PBhU+bFlngFCCCJ1y5RrySA6GUjgBBANG4hgBBAbD4hgBBAbB4hgAQfgFy6dEkKFSrkLsMqXry4hIWFmZIs1xyQ6Ohod4nWggULzJyRtWvXugFk4MCBUr58eTl//rykSpXK2Khy5coma1KnTh2NrRgTjwIEkABbgwCiF5wAQgDRuIUAQgCx+YQAQgCxeYQAEnwAgsnoXbp0kUqVKkmOHDlMCdbHH38sKVKkMAACoBg9erSsWLHC/AwZEQDL119/7QaQrl27ysMPPywFCxZ0W+jixYvy/vvvS8uWLTW2YgwBJGl4gACi7wcCCAFE4xYCCAHE5hMCCAHE5hECSHACyHfffSdvvvmmZMuWTQATDRo0cAMISrNGjhwpGzZsEExax/wQ/PEEkNdff13KlClj4ITNWQWYAXFWT+vRCCBWidwBBBACiMYtBBACiM0nBBACiM0jBJDgBJCbN2/K/fffb6Djzz//NFkPVwYEcz3WrFljMiCRkZFmsvq5c+dk8+bNMSahV6xYUfr37y9t2rSRM2fOSJ8+fWT69OmSOXNmja0YwwxI0vAAAUTfDwQQAojGLQQQAojNJwQQAojNIwSQ4AQQ9Gu/fv3kypUrMnXqVGMDF4Agq9GoUSMDH5iMPmrUKDNJHZPVCxQo4N4HZO/evWYCOpbvxST1vn37mv+z3Z4CzIDcnn4+v5oAopeMAEIA0biFAEIAsfmEAEIAsXmEAOJ/ANn5/iwpn123ctSOiKvyYLfnuRO6xrjJNIYAEuCOI4DoBSeAEEA0biGAEEBsPiGAEEBsHiGA+BdALkZfkSVL10u61P+uJGVrl69ek6efri2ZM6azhfL3yVQBAkiAO44AohecAEIA0biFAEIAsfmEAEIAsXmEAOJfANHoz5jQUoAAEuD+JoDoBSeAEEA0biGAEEBsPiGAEEBsHiGAEEA0HmGMcwoQQJzTUnUkAohKJhNEACGAaNxCACGA2HxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEkHhkGjNztdxKk17SpU2jFzKZRJ46cVp6t6ktBfJm9+mMCSAEEJthCCAEEJtHCCAEEI1HGOOcAgQQ57RUHYkAopKJAEIAURuFAEIAsZmFAEIAsXmEAEIA0XiEMc4pQABxTkvVkQggKpkIIAQQtVEIIAQQm1kIIAQQm0cIIAQQjUcY45wCBBDntFQdiQCikokAQgBRG4UAQgCxmYUAQgCxeYQAQgDReIQxzilAAHFOS9WRCCAqmQggBBC1UQggBBCbWQggBBCbRwggBBCNRxjjnAIEEOe0VB2JAKKSiQBCAFEbhQBCALGZhQBCALF5hABCANF4hDHOKUAAcU5L1ZEIICqZCCAEELVRCCAEEJtZCCAEEJtHCCAEEI1HGOOcAgQQ57RUHYkAopKJAEIAURuFAEIAsZmFAEIAsXmEAEIA0XiEMc4pQABxTkvVkQggKpkIIAQQtVEIIAQQm1kIIAQQm0cIIAQQjUcY45wCBBDntFQdiQCikokAQgBRG4UAQgCxmYUAQgCxeYQAQgDReIQxzilAAHFOS9WRCCAqmQggBBC1UQggBBCbWQggBBCbRwggBBCNRxjjnALJEkBGjRolH374oVy+fFmeeOIJmTZtmmTMmFEOHDggnTp1kh07dsi9994rU6dOlerVq8dRK6G4r776SgYMGCDHjx+XypUry+zZsyVfvnyya9cuGTx4sDnWkCFDpFq1aubfUVFRUqdOHVm/fr1kzpzZ2jMEEKtE7oDpn62XP49HSqaMGfQvSiaRkf9ESK+WNeTBonf7dMZjZq6WW2nSS7q0aXx6XXIIPnXitPRuU1sK5M3u0+kSQAggNsMQQAggNo8QQAggGo8wxjkFkh2ALFmyRIYOHSrfffedZM2aVZo2bSo1a9aUESNGSO3ataVJkybSu3dvWbdunYGRgwcPSurUqWMoFl/cpUuXpFixYrJ8+XIDH6+//rr89ddfsnjxYmnTpo0MHDhQcuXKJV27dpUvv/zSHBPvBRjB7zWNAKJR6d8YAkhcrQggcTUhgBBAbJ8qBBACiM0jBBACiMYjjHFOgWQHIFu2bJErV65IjRo1jArvvPOObN26Vd5++20DD+Hh4XLXXXeZ31WsWFEmTpxowMTVTp8+HW/cmTNn5KOPPhJkQdCQ3cibN6/5G5DxzTffSPbs2aV48eKyb98+k2np37+/gR1tI4BolSKAeFOKAEIAsd1B5y5Gy/Lpb0mPXGttocnu97du3ZLxp5tLzwGDfDp3AggBRGOYn3fslxtLF0qVHDEfWmpem9Rj/oi6JmdrPCb16lXyeqp35ySAJPU+DLbzS3YAErsDnnzySWnUqJGUKVNGevToIb///rs7pHXr1lK3bl2TsXC1H3/8Md44AMipU6dk8uTJ7niUX/3www/Stm1bWb16tQGQUqVKmcwIjj1y5Ej54IMP5Pz58+bfFSpUSNAjBBD9LcQMSFytCCAEENsdRACJqxABhABiu2/wewKIRiXGUAFnFEjWAIIBP+BgzZo1piRr2LBh8vPPP7uVeeGFF6Rs2bLy8ssvu3+GbEV8cQCQ69evy7hx49zx9913nyxbtkxmzJghgB2UfU2fPl3q168vO3fulPTp0wti8P927drJ999/n2DPXLx83evvF635Veau2yVZsmRypmeT0FEiwqNkWIdHpPpDxXw6q3fnfSu/Hw4Pzjkg4ZEysN0jUqp4AZ80ee39FXL9rnRBOwdkUMd6ck++HD5p8vK4xfLAA4V9ek1yCQ4LOyIjujWULJnSxTnlW7dEUqSIeyVRF6Ll03dHSdcca5LLZarPExmQt/9pIQOGD1W/BoFHTobLxkVT5JnCR316XXIIjogWWXquhnTt2sGn092176j8OfZtqXvjrE+vSw7BR66mlN2PPi3PPt/Yp9P98bcwubhgvlTNGYQZkMhrElXnCXnisSpeNcmY7t/KETYqECgFkiWA4EuoT58+snfvXgMHGTJkkE2bNplMByaLu1qrVq2kQYMG0rlzZ/fPEopDeRYmn2PyuqvlyZPHHBuT3Hv27GkA5Y033jDzSwAbmPsxduxYKV26tBQpUkT+/vvvBPsu6uI1r7//bO2vsuCbP4IWQAa3qyUP+wggUxZ8KzuPRAQtgPR79hEpVcy3SegjP1gpN1IHL4D0f66uzwDS77+L5f4HigTqMzOg7wMAGdblCa8AcvOWSMp4AOSzqW9ItyAFkIlnW8grQ4f41A8AkJ8WTw1aAFlyrrp07uwjgIQdk73j35Z6QQogu+o9La07NvLJJ5t+C5PohQuCFkAiaz8hjzWo7FWTrBmDD7p86nwGB1yBZAkg/fr1k6NHj8q8efPcE8zPnj1rVr5CFgNAgoa5GnPmzJGHH37YLWxCcSdPnpR3333XrGiFduzYMSlRooSZA+KaV4KfA34wJwQlXg0bNpQJEyZIyZIlzdySsLCwBDuRJVh6j7MEK65WLMGKqwknocfUhCVYcT3CEqy4muzef0yOTJos9W4GZwbkt0eaytPtntR/4bAEyyetGEwFbleBZAcggAOUVKHUKk2amEuRogwKE86xXO6iRYtMqRUmi6dKlUoWLFgg9erVM5PK44vDKlgop8Jra9WqZVa4unjxooEYV8McE0w8X7v23wme+DeW+kU8SrQ2b95MAImlAEqw+raqLlXLFfXJrwQQAojGMAQQAojNJwQQAojNI/g954BoVGIMFXBGgWQHIB06dJD58+fHyEhgUjhWwsKSux07djSrUxUtWtTM1cBKWGiYTI4lfLF6VkJxmCMCwEE2BJkT7AOSM2dOt9qAGJRoPfjgg+Znhw8fFpwTSrOGDx9uSr4SasyA6I1LACGAaNxCACGA2HxCACGA2DxCAOEqWBqPMMY5BZIdgDh36XfmSAQQve4EEAKIxi0EEAKIzScEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyWSCCCAEEI1bCCAEEJtPCCAEEJtHCCAEEI1HGOOcAgQQ57RUHYkAopKJABKPTJyEHlcYAggBxPapQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEs0HKGQAAIABJREFUEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU4BAohzWqqORABRyUQAIYCojUIAIYDYzEIAIYDYPEIAIYBoPMIY5xQggDinpepIBBCVTAQQAojaKAQQAojNLAQQAojNIwQQAojGI4xxTgECiHNaqo5EAFHJRAAhgKiNQgAhgNjMQgAhgNg8QgAhgGg8whjnFCCAOKel6kgEEJVMBBACiNooBBACiM0sBBACiM0jBBACiMYjjHFOAQKIc1qqjkQAUclEACGAqI1CACGA2MxCACGA2DxCACGAaDzCGOcUIIA4p6XqSAQQlUwEEAKI2igEEAKIzSwEEAKIzSMEEAKIxiOMcU6BoAKQAwcOSKdOnWTHjh1y7733ytSpU6V69epx1Eoo7quvvpIBAwbI8ePHpXLlyjJ79mzJly+f7Nq1SwYPHmyONWTIEKlWrZr5d1RUlNSpU0fWr18vmTNntvYMAcQqkTtg+mfr5c/jkZIpYwb9i5JJZOQ/EdKrZQ15sOjdPp3xmJmr5Vaa9JIubRqfXpccgk+dOC2929SWAnmz+3S6BBACiM0wBBACiM0jBBACiMYjjHFOgaACkNq1a0uTJk2kd+/esm7dOgMjBw8elNSpU8dQLL64S5cuSbFixWT58uUGPl5//XX566+/ZPHixdKmTRsZOHCg5MqVS7p27SpffvmlOSbeCzCC32saAUSj0r8xBJC4WhFA4mpCACGA2D5VCCAEEJtHCCAEEI1HGOOcAkEDIKdPnzbwEB4eLnfddZdRqGLFijJx4kQBcLhaQnFnzpyRjz76SJAFQUN2I2/evOZvQMY333wj2bNnl+LFi8u+fftMpqV///4GdrSNAKJVigDiTSkCCAHEdgeduxgty6e/JT1yrbWFJrvf37p1S8afbi49Bwzy6dwJIAQQjWF+3rFfbixdKFVyxHxoqXltUo/5I+qanK3xmNSrV8nrqd6dkwCS1Psw2M4vaADkxx9/lB49esjvv//u7qPWrVtL3bp1TcbC1RKKA4CcOnVKJk+e7I5H+dUPP/wgbdu2ldWrVxsAKVWqlMmM4NgjR46UDz74QM6fP2/+XaFChQQ9QgDR30LMgMTVigBCALHdQQSQuAoRQAggtvsGvyeAaFRiDBVwRoGgARBkIYYNGyY///yzW5kXXnhBypYtKy+//LL7ZwnFAUCuX78u48aNc8ffd999smzZMpkxY4Y8+eSTkjVrVpk+fbrUr19fdu7cKenTpxfE4P/t2rWT77//PlE9M+eLzTJ56WbJmCH4nkKcO3dRpvRrJDUrFvdJm9EzvpT1249I+vRpfXpdcgiOvnRJ3n65sZQpUcCn031p7Gdy9sI1SZP63yxfMLUL5y7IO/2bSaH8OXy6rPZD5kjOXL7NG/HpDe5gcPjZKJk6pKVkyZROfRZRF6Jl3GvD5ZHU//dZqH5xUg+8dUvWXa8nEyaO9OlMD58IlzlTJ0i1HKd9el1yCD5/RWRPmioyuP//PWjTnPfve4/KslfHS5kbkZrwZBVz+lpKkYZPSY8XW/h03hu27pMfp3wkJbMEXwbk7wvXpdDTjaTZUw/7pAmDqYC/FAgaANm0aZPJdGCyuKu1atVKGjRoIJ07d3b/LKE4lGdh8jkmr7tanjx5BK/JmDGj9OzZ0wDKG2+8YeaXADYw92Ps2LFSunRpKVKkiPz999/+6iselwpQASpABagAFaACVIAKJHsFggZAzp49a1a+QhYjQ4Z/V03CXI05c+bIww//H/EnFHfy5El59913zYpWaMeOHZMSJUqYOSCueSX4eZ8+fcycEJR4NWzYUCZMmCAlS5Y0c1DCwsLiNcXcuXMFNczPPfdcsjcOL4AKUIE7q8Cjjz7q0/yzO3u2fPc7oQBKkvEdiLmQbFQgPgU4NqE37oQCQQMgEA9lUJhwjuVyFy1aZEqyMFk8VapUsmDBAqlXr56ZVB5fHFbBQjkVXlurVi2zwtXFixfNB7ir4QMdE8/Xrv13gif+jaV+EY8Src2bNxNA7oST+Z5UIMQUIICEWIcn4nIJIIkQLQRfQgAJwU5PApccVACCJXc7duxoVqcqWrSomauBlbDQMJl8yZIlUqNGDbM0b3xxmCOCOSPIhiBzgn1AcubM6e4qQAxKtB588EHzs8OHD0uHDh1Madbw4cNNyRefMiQBZ/MUqECQK0AACfIOduDyCCAOiBgChyCAhEAnJ8FLDCoASYL6xjglf9/kgCDseZI2bVpJkSKFpEuXTqpWrWqACeVh27dvlxYtWiRYJpaQhk2bNjWvx2T75NawPDOgEfvEfPjhh9bTR1ndnj17ZObMmdbY5BKAjOA777xjVnArWLCg+7TLly9vQBt/x9fgLWQGsRocWqZMmYw+nsdJLjrEPk8sVIHNRVFSiXb16lXJli2bzJo1y/2zy5cvm59h4QlkWB9//PEYc8vwul9//dXEowwztl7+0OZ2AQQPY7p37x7jfsZnBLLIkZGRMa7HH+fv7ZhPPfWU0TCpf8ZAO2TM0ecpU6Y0lwLNChcubP5OKs1JAHF9v6CiwLM98MADMeZe+uPaJ02aZN4jOX0eu/TCA0p8vnq2bt26me+ha9euxSjvjq1doL5z/T028YcneMzkrwABJIB96O+b3PWBd+TIETMwxBdk3759TZYGGyeGMoAAwjCXB19gf/75p4GzhBq0g55ZsmQJoEP8+1YAEHgQJYMoSXQ1DYD89ttvZuDt2iMHCzZgU07X4Mu/Z+7fo6OMEt7ASndomAMG0H766adNFhUNewC5NjbF77wBCPyCwSd0ia2XP67A3wDieT3+OH9vx4R+uDdt92egzie+9wGA4HN26NCh7mXeQwVAXN8vgeyD5AogWDUTC9ns3r3brJiJBujAnFHoeOHCBQJIII3E90pSChBAAtgdgQYQXBrmqrz00kvmqbcngNy8edP8HANKDDTq1KljBueYbI9swfPPP2/ms6D8DE/NH3vsMfF8GoPXvfjii2aFMHzAJvVWpUoVWbhwoVliGfu3PPPMM+aUUY6HRQFQcnfjxg3zVBsDbc8MyE8//SS9evWSc+fOmewSYAZ6QU+U8mHuz5YtWwzoYREDaJUUGwAEGTLMaXKBCM7TE0C8XWulSpXM3jcREREmo/b111+bDAiWvMbvcN25c+c2l4wFGvBFi5Xh3nrrLfNeV65cETzZhjaxn54mBZ3WrFlj9hA6cOCAOZ3XXnvNnDOW3967d6/5GQaagC5ACgAEAwiUa+I1NWvWNN6CH/D0HvARW6+VK1eaLAvmlGG1vE8//dStWWI18DeAeGZ08DnSr18/QSYoTZo08t///tcswAHv41pR9orPDWSTpk2bZnyWmPvGMwMCHQF9uDeRwcVTZMzRSwoNAILPSJTd4oEGBpqeAILP1xEjRpiyX2SjcZ+899575oEGPkexXxT0xaqL99xzj4nzx73hjwxIfACCjCjuIyzeAo8gg4jPXdfnJMqTf/nlF7NQzJQpU4weyBZi/iT+j4bvoPHjx5tBOhaVwecU9PEEkPjeJyn4wvMc8L2Kz0n0Nx5mYFVONOwn9sknn5iMsisDEt91e37n+vO6/T02SWp9w/NJGgoQQALYD/6+yWNnQPB0BSUW+fPnNwMGTwDB4ApfkFu3bjUrc+ELEgNUDKC6dOliVhLDhz4G1vjiwCAAv8Pgq3LlymaQvWrVKjP4SOoNT5+wRPPGjRtlw4YNZnCMc0cDRGF+EK4dgIG9Yz766CMz0HSVYGFQNWjQIFN+NG/ePBk9erT5HUoCypUrZyAOg0EMIt5++20DZUmx4RoBB/fff7/RAH2LwZEngMR3rbg2fEm6MiCuEix4BV+sGIyhoQQF3gKUYMANLTJnzmx8g9IeAEpSa9HR0WaDUfQpzh9AgVWD0N9YartAgQJm1TtkE3GtuBYMsqAFBtrwwPvvv2+O4SrB8tQLsdD1u+++M3/j2PDi//73v9uSIpAAgmXGARYYeGNQiwEj7hEMkDCgRNYI2TD8Hg82sDx5Yu4bTwDBCoRjxoyR5s2bm0Hpt99+6/bfbQnnwItxnXiYAZDEYBv3vSeAAEhx7tj4Fku4wxcAT9x38A9gDVAH6HB5IqH5g4k95UABCL5DAFX4vkF5ET5bGjdubD4H8PAL9wg+ezGvEvcVgNX13YOHF67FYgAbrvsQD4PwQAy+cwEIPBff++BeTEoN38fI5C1dutTA2BdffGFOr3379tKsWTPjawAIHuzEd90uAIFm/rxuf49NklK/8FySjgIEkAD2hb9vcheA4CkbBpYAECxFjA8+fJnHLsHC00xXqQMG6Bh8YdCIARee2D700ENGHXxAYnCFD0MMerDzO7Ii+HdyaAMHDjRPTvHliC9KaIEBIFZEA0zgSTYyHliwwFVS5JkBwZwAfBHid3hiCZ3wMwAIBiKumm98qeJL99ChQ0lSFgAI+ht/AwaQ+QFweQJIfNcaH4AgewCvYDC9bds2M0DHU81nn33W7I0DP6EB+DCIdC1xndQEwsp4GDTjT6FCheTUqVNmIAWdMG8IZVXoe2QEMYDEQOqVV14xl4HBBPq9TJkyXgEEtd7Qz7VyHu7LHDlymGzI7QyanAAQeNiz3AmfIa7SK88MCPTBfYNyNc8sBD4ToJFrs1c81Pjnn3/M0+3E3DcuAEGmDQ868NmDzzIM1JCVAvgmhYb7HjCBTBj+AFTxIMM1BwSDTHx+ujyCzxg8xMA9Av/g9S7NXP5xQbyT1+cPAAEweJZewofIBOFz5Pz58+5MDvoP9zzi4REsgY+GWHgE0I6G76jPPvvM6IWHAa5SJZSJIuuF+8YFIPgsj+99kJVOSs0FICjnxWeKqwwL14vPSFynKwMS33W7AASZJH9et7/HJkmpX3guSUcBAkgA+8LfN3nsDAj+jy8+lAkBPjCock1Cxxc7SirwBYXBNUqRkA3A4BTZD3xY4svUs+HDEANIDOLxZC85ZD9QVoUPfwz6MJBBA3ihPAiDA3wB4In0/PnzzZNc/Axfcp4AgsEjyocwoMLxoCW0xeANcwGOHj1qjhv7/wG0luqtPAEEsIQSGpSPoATCNQk9vmuND0DwJYoV55Ahg6bQ58033zTHRrmFa8CIn6NUD089k2LD02xogkEgBjsAJmS7kLXAk0oMrDEgR4s9B8T1fwwQvGVAMAgbNWqUgRhXw5wT3GPITia2OQEgAC5cn6v98ccf5v+xJ6GjfwHrAE1Mxsf98cQTT5iHEviDzxg0/ByDbAweE3PfuAAEsAOYxedSUmwuAMHfyAahrAbZEBeA4HMBEO7a8wm+B8jisyI+/2AA7irfxGqLKNG53eYPAEH/evoWAItSRUC556IUAGxABhb/QL+6+vL11183n8fwChomseNewwMgeGzFihXmsxrfUfjsRsmnC0DwoCy+92nZsuXtyuXo610Agr+RKUaVAR48wCsff/yxG6yRBYvvul0AgvvBn9ft77GJo8LyYEGjAAEkgF3p75s8NoC4Lg0DIwyg8OTSBSAok8CTGUyyBYDgSTh+j0EqPvSRNsYTLDSk0PHFinkTOBa+ZJAWxxwRzw0aAyil+q0w+R7w4CodwgtRW46BJkDCsyH1j/khyBgBtPB/DKpx7RhA4IsUJQXQKbkDCK4bT/iRLcMEa5QI3H333fFea3wAAi9gIIynuZg7A4+j3ALlE8iAuJ7yqjvsDgViUOUaSKNkbMCAAWawCD+4BugoqUHzFUAwuIJ+n3/+uaNX5wSAJGYVLNwfGFijjAjaYBCOzAgaPICn4Pi8Scx94wIQDLbwWQMQwtN2wD/m22CwmhSaJ4DgswCex2cn5kDgnLHyEc4fZXtoeIoPbfA5Ep9/8BpkBtAw1wxZ59tt/gAQb3NAcK8gAwhoiN3wYEYDIMgcjxw50pRqYU4N5kngjyeAAF7ie5/b1crp13sCCB5k4MEM4B0QhXI7V2YP91N81+0CEGSQ/Hnd/h6bOK0tjxccChBAAtiP/r7JvQHIDz/8YJ5GY9CNJ1IuAMHfqGtHFgRPYhGDp7coK+jZs6cpd0DpCJ4KY6CDOnbXHBAskYknfHg9Juwm5QZoQmoeAy3PhsHR8uXLTZ02vvhxPUiDo84W/YSSCgAIBtB4Pb5gUS6Dych4Wg4t9+/fn2wzINACGR9ksTDYwVNHwGR814oBFLyBOR344vRchhcQC8CDjwCraNAWT/2hI2LhJRfoJkW/IKuHkjyU0QDG8LQSDeUSyAii5BAwogUQDCpcemHyOur8MbDCk0zUx2Ng5Zp4m1g9AgUgGMTi2gFRAE6AAO4TAAgm1yI7ggEWGh5a4DMBsJ6Y+8ZzDgi8CRBEdgUPEfAwwfNBQmJ1c+J1ngDiAgx8bqLMCAACrTDgRKknMgTI5uB6cE/EByCAdqdboAAE540MBkAUWTV8tmC+Fz4bABYaAMEDLZR04rMIGkIzzMvDzz0nocf3PnhwkJSaJ4BgUQLMvcNnJ7LOyHq4AAQP8+K7bs9J6P68bn+PTZJSv/Bcko4CBJAA9oW/b3LPfUBwWXhyiImPeLqCLz3POSAYSOIpJkpoMNhq1KiR+aLHwAgDBwzKMWBC/e7kyZPNExvPD0M8BcMgBB+c+DspNnyJ4ak+QCF2qQvAAgNifMnhqSWePOJLAbXbeMrmWYIFLZARwQAVgwr8HoCGNHpyLcFy9RcmzwJCAah4YhvftWJQgLp8PIk+ceJEDADBYAP64gnvG2+84bYCskco7cKXLwbymNyP/kiqDQMnlEdgYO1akQiDQpQT4ckuQE0LIBiUe+qFki7oA8jF010MqDCIvZ0WKABBvTq8DljHZww+M1AygrkL+EwAVOGzApCOEiIMOqFfYu4bTwDBk3M87MDDDwANVlTD51lSaLEBBOeEeUB44IPPHQAtPndRRoV/Ix4QhQnpwQogKMNCVhXfDfhsRfYH/9dmQLDqFb6HoB8mZQPW4DF8JmNeomsfkPjeJyn4wvMcPAEEP8fnLL43sIoimgtA8NkS33Vjcr5r7y1/Xre/xyZJrW94PklDAQJIAPuBN3kAxeZbUYEgV+B2AcQJeQK1UZoT5xqKx3AyAxKK+oXKNXNsEio9nbSukwASwP7gTR5AsflWVCDIFSCABHkHO3B5BBAHRAyBQ3BsEgKdnAQvkQASwE7hTR5AsflWVCDIFSCABHkHO3B5BBAHRAyBQ3BsEgKdnAQvkQASwE7hTR5AsflWVCDIFUgKABLkEif7yyOAJPsuDMgFcGwSEJn5JrEUIIAE0BK8yQMoNt+KCgS5AgSQIO9gBy6PAOKAiCFwCI5NQqCTk+AlEkAC2Cm8yQMoNt+KCgS5AgSQIO9gBy6PAOKAiCFwCI5NQqCTk+AlEkAC2Cm8yQMoNt+KCgS5AgSQIO9gBy6PAOKAiCFwCI5NQqCTk+AlEkAC2Cm8yQModgi9FfatwD4JTZo0MRv++bN57tPgz/fhse0KEEDsGtkisMs2NinFXieeDcsLY/dp7Bd0JzzvudGn7RoS+j0B5HbUC53XcmwSOn2dlK6UABLA3uBNHkCxQ+itsLFVVFSUzJw50+yyi52X/dWwSRiO78/38Ne5B9txCSC336MaALkTnj99+rTkypXLbCZ7O40Acjvqhc5rOTYJnb5OSldKAAlgb/AmD6DYIfRWVapUkYULF8q4ceOkbt268swzz5irx27b2OEcu5fv2bPH7ELetWtXsyM5QAU7urdt29bEYtdy7DSNnXrxxBe7NmM3a2RVypQpY343a9Ys8/PWrVubHapx7E6dOgl2/S5WrJjZ9fy+++6Tn376SXr16iXnzp0zu4cDkOrUqWPOZ8eOHXLt2jWza/aFCxdk6dKlgh2Q2XxXgADiu2axX6EBEM8MyIQJE8xO79jdPG/evILPdHgeO5wPGzZM1q9fLydOnDD3Re/eveXmzZvy0ksvyVdffWV2kcd9gAcF2CkcWZYKFSrIr7/+KsePHze7fy9ZssTcd64MCHYAHzBggMyfP1/SpEkjL774ovm/thFAtEqFdhzHJqHd/3fq6gkgAVSeN3kAxQ6Rt9q9e7eBio0bN8qGDRtk7NixsmrVKnP1GPi/+eabBjayZMkihQsXlgYNGpgBEGLxOvzu888/lyFDhsimTZskc+bM0qJFC1N+0qdPH/PvU6dOyZo1ayRDhgwxylFKlCghY8aMkebNm8v48ePl22+/NQOtsmXLyqBBgwzczJs3T0aPHm0ACOczcuRI2bVrlxm8YTCF8wL8sPmuAAHEd81uB0Aef/xxgecPHTpk7hNA+cWLF6Vnz56SLVs26datm3kIcObMGSlatKi5t37++WcZMWKEbN261UBLpUqVDKgA4nFvoXxy7dq1Bjpw30ycONHcoy4AAdBMnjxZvv/+e4mOjjYxixcvlmrVqqkungCikinkgzg2CXkL3BEBCCABlJ03eQDFDpG3GjhwoHkCizp2DHAwQAKMYICPAT8GLniqilazZk3zNLZVq1Zy7NgxKVmypCndevbZZ6V06dIGQtAAMAAKDH4wSKpYsaIMHjzY/M71NLhq1apSuXJliYiIkBQpUpisBrInGDgh44InvCgfwZNdgA9+hvPBE2cADxqyKdu2bTMDOTbfFSCA+K6ZNwABWKDcybOh7Aow7zkHBKCNLCKgu2XLlpIzZ073SwAguNfKly9vflarVi1zryHu8uXL7pJFQD/uB9xruLdq1Khh3gOtWbNm0rhxY3n++efdAAKQB7T07dvXxCCriGwLgEXTCCAalRjDsQk9cCcUIIAEUHXe5AEUOwTe6saNG1KoUCFTygQIQMNgBxmFV155xQz4Ud6B0ig0ZDX69+9vIMJVNoXXNmzYUH755Rcz6EHDcfPkyWOe2mKQ9Nhjj0mXLl3M71wAAtAByBw8eDCO0gAewAWgA8favn27KT/B+WzevNlkRdBi/z8EuszRSySA3L6cAGJ4G2WDng0QAN/HnoSOewJgsm7dOgMGH3zwgRQvXtxkQOBzwIXrPgFMAED69esnAAFAOe4XZP6QBcG9Bfjp3LmzeY3n/10ZEAAL7rOOHTsm6mIJIImSLeRexLFJyHV5krhgAkgAu4E3eQDFDoG3+vLLL81AH2VProZ5GRg8YTCkBRAMgJABcT2J9ZQu9iDJBSAPP/ywedqLJ8XIdAA2Dhw4IDly5DCDMAzUsDLX4cOHTYaGAOK8IQkgt6+pr3NAXO+IbN9rr70mO3fuNBlDAAiOBShBQ4kUwANZxEuXLpl5IwCQF154wdwPWgBBpgRzsFzzPjB3CqWQuM80jQCiUYkxHJvQA3dCAQJIAFXFFFsMAAAQyUlEQVTnTR5AsUPgrTDZHJNaUX7l2QAAy5cvlx9++EGVAUHsqFGjTAkJnrxiKV/XYCk+AMEk9FKlSpmBEZ7OAoQARJiki3PCQCl16tQydOhQUxePWnlMfmcGxDljEkBuX0tfAAQLLaA0ccGCBaakCplFZPtWrlxpAKRDhw7mPti/f7+BBgA5sh0uGMF8LWQbMf8DWRRNBgTzsjCPC/cyJrQDcJBB5ByQ2+97HuH/FODYhG64EwoQQAKoOm/yAIod5G+FzAPq0THYyZ8/f4yrRSYDAAEQ0ZRg4cUo28KACoMclJQAFnD8hAAEk8kBIphPgmwH5nIUKVLEDMTw5BfzUDB4wmpbeGKMnxNAnDMmAeT2tfQFQNq0aWNKGJctW2bur3z58sn/a+9cgK2c2jj+VC7ThUIKc2h0oVFMRpiuRCKKmmHGRBmFqWSUSM245hZCKhkVNWFiSjIiIUqKapKGEtGgUKMYlS5E3/wf39522dU5zrH22u/7WzNnzuxz3v2utX7Pu/Ze//U8z1rjxo3zXCoJEHkrJE6UWK7cDe0EJwHRo0cPq1q1qouHzp07u2CfNGmSC4n9hWBpFyyJeNUj0aOdtdgFq/x25w67E2BuwhNRCAIIkIDUGeQBYVMVBBJOAAESj4ElQCTIS0pK4mmUmeeeaGFAu2tRILA3AsxNeDYKQQABEpA6gzwgbKqCQMIJIEDiMTACJB5b0JKyE2BuUnZmvKP8BBAg5WdY6jswyEuNigshAIH9EECAxPOIIEDisQUtKTsB5iZlZ8Y7yk8AAVJ+hqW+A4O81Ki4EAIQQIDwDJSTACFY5QSYkrczN0mJoSPrJgIkoEEY5AFhUxUEEk4AD0jCDVwB3UOAVADEFNyCuUkKjBxhFxEgAY3CIA8Im6ogkHACCJCEG7gCuocAqQCIKbgFc5MUGDnCLiJAAhqFQR4QNlVBIOEEECAJN3AFdA8BUgEQU3AL5iYpMHKEXUSABDQKgzwg7Eiq0jkDHTp0sIMOOijbIp3b0adPHxs0aFAkraQZxUigmAWIxoXaX6VKlX+g37lzp+nnxRdftCuuuMK3t9V5GTrcsiylU6dOfuifzqrJV3RGjv7/5Zdf7lZfWeqI/do0CpBXXnnF7rnnHvvmm2/8+Tr99NNt5MiRfkYRJT8B5iY8GYUggAAJSJ1BHhB2JFXlO+gsM6F66qmn7KKLLoqkpTSj2AgUuwDp3bu3T/7zlaVLl9qQIUPsjTfe+NcCRId16vA+/eQrEjm6pnbt2pZbX7E9B/tqb9oEyJo1a+yUU07x5+bMM8+07du3+0GO8+fP90NQKQgQnoF4CCBAAtoCARIQdiRV7e2kZa3KNm7c2G677TZbv369nxKuydgff/xhAwYM8BOPVXRa8h133GG//fabnX322X5C+cEHH2wzZszwk5d//fVXX9mbPHmyHXnkkZH0mmaEIJBUAbJ582Zr0qSJ/fzzzz6JHDFihJ8gfvXVV9uECRNs165dJvF+/vnn+/+WLVtmv//+u3tItmzZYi+99JLVq1fPcj0g+cbRJ5984h4QiY/c+iRKBg8ebJdeeqmb8dVXX/UxqOuKraRNgLz11lt2ww032MqVK7Om2rZtm/3www9Wv359/9v999/vhzPu2LHDn5HHH3/cP0ebNm1qL7/8sp122mkuWPQZrcWi6tWrF5vZy9xe5iZlRsYbKoAAAqQCIJb2Fgzy0pJKznV7ChBNnj7++GMPKXnhhResXbt2Ljb0JTlu3DhbvXq1nXTSSf5boqN58+a2ePFiO+6446xLly7WqlUr6969u6/yvfvuu/5bpxy///77/uVJSQ+BpAoQWXDq1Kk2fvz4rAdE42Ds2LHWo0cP/60JpCaJo0ePtrvvvtsninXr1rV+/frZoYce6pPMjABp3bp13nHUvn37bAhWbn2PPvqoj6dp06b5w9SrVy9r1KiRi5JiK2kTIL/88ouLyRYtWrjd9Hl5yCGHZM02ffp0X7hZsGCB/10iUws7N954o39+Dh8+3N577z0Xvgrj6tixY7GZ/F+1l7nJv8LGm8pJAAFSToBleTuDvCy0knGtBIhWajNfglp1k6dCkyat6KrI66GfTJ7ICSecYBMnTrTly5e7p0MxzSpbt271mGZNvjRhevPNN/3vWvU9/PDDfRXvwAMPTAY4erFfAsUuQPbMjVKHu3XrZs8888w/BEjLli1t06ZNzkQej4svvthj/CVANMY0sVTRavZHH33kYyQjQCTu842jjAdEnsdcAfL9999bw4YNfdVc4/aoo47y8J3MCvp+DRPRBWkTIEIvuynn47XXXnNPyDnnnGPDhg2zZs2a+fMlT4dEiIqueeihh2zu3Ln+umvXrp4PVKNGDfcqp6UwN0mLpePqJwIkoD0Y5AFhR1LVnh4QfeHJ4/HZZ59lBYcmVArF0hfnAQccYJo0SFxolW7FihUuRnKLvjCHDh3qseuZopU/XasEd0o6CBS7ANlXDsieHpDcJPTcpHQJEIkDhVip5L7OCBAJinzjKDcJPbc+3UeeSXlbGjRoYDfffLMtWrSoKB+qNAqQXEP9+OOPLkaefPJJ+/bbb93jIVtKYKho4adOnTq2ZMkSf60QLgnj2bNnu3BJS2FukhZLx9VPBEhAezDIA8KOpKp8OSBt27b1FVxNbFROPvlku+mmm7IeEa20Tpo0yb744guPZ5doUVFMvLwdWq3ThCmz6htJV2lGYAIIkLW7CY69CRCFMuYbR8q9yuyCtacAUTikQnLkCVE+ycCBAwNbt2KqS5sAUbiqina+yhSFvVarVs1zeBRiJQ9I//79/wH4zz//NHna2rRpY/PmzfMFoMqVK1eMISK/C3OTyA2U0OYhQAIalkEeEHYkVeUTIPpi0+5Xq1atci+GfmbNmuXJj3L7a2VYW5BKmCieWbHuSljXZEnXKHRLuR/6klS4lr50JVhGjRoVSa9pRggCSRYgCjtU2IzGikIRy+MBkTcj3zjKzQHJra9SpUou9iU8lIC8cOFCz8EqxpI2AaJNCu69914XnAq5kvhUztADDzxgX3/9tc2cOdO9x3PmzHEviP4nr3PPnj1dzOozVZ+98p4pdFYJ7WkozE3SYOX4+ogACWgTBnlA2JFUtbddsJRQXlJS4l96Y8aM8S9ICRHtvLJx40bf7UqhAvqRd0RxyZowKTFXu2DJK6JtShXfXrNmTd8NSMm2lPQQKHYBsrdzQOThU+iTEoE1gVRYTHkEiMbUlClT/jGOcnNA1q1bl61PoZAq8lJqLGoBoFhL2gSI7JT5TJUdtQWzFm30+aoFHRVtUKCwVnk8tLmAPmtV5DWR2NTn8ldffeWJ7Mon0uukF+YmSbdwnP1DgAS0C4M8IGyqgkDCCRSzACkG08gTKU9j3759i6G5eduYRgFStMYqYMOZmxQQfoqrRoAEND6DPCBsqoJAwgkgQP47AytpXVuwagIvD2OxFgRIsVoubLuZm4TlTW1/EUCABHwSGOQBYVMVBBJOAAHy3xhYW7RqVy2FRioXoJgLAqSYrReu7cxNwrGmpr8JIEACPg06x0E7cqThZNWAWKkKAqkksGHDht22Yk4lBDq9TwI6IV7nA9WqVQtSENgrAeYmPByFIIAAKQR16oQABCAAAQhAAAIQgEBKCSBAUmp4ug0BCEAAAhCAAAQgAIFCEECAFII6dUIAAhCAAAQgAAEIQCClBBAgKTU83YYABCAAAQhAAAIQgEAhCCBACkGdOiEAAQhAAAIQgAAEIJBSAgiQlBqebkMAAhCAAAQgAAEIQKAQBBAghaBOnRCAAAQgAAEIQAACEEgpAQRISg1PtyEAAQhAAAIQgAAEIFAIAgiQQlCnTghAAAIQgAAEIAABCKSUAAIkpYan2xCAAAQgAAEIQAACECgEAQRIIahTJwQgAAEIQAACEIAABFJKAAGSUsPTbQhAAAIQgAAEIAABCBSCAAKkENSpEwIQgAAEIAABCEAAAiklgABJqeHpNgQgUDgCK1assPvuu8/eeecd++mnn6xOnTp21lln2e23324nnnhi4RpGzRCAAAQgAIEABBAgASBTBQQgAIEMgQ8++MDat29vLVu2tFtvvdWOOeYYW7NmjY0aNcrmz59vixYtskaNGgEMAhCAAAQgkFgCCJDEmpaOQQACMRJo1qyZHX300fb6669bpUqVsk3ctWuXDR482Dp37mytW7e2TZs2Wc2aNW3ChAl2yy232F133WXXX3+9jRkzxsXK2rVrrUGDBjZw4EDr3r2736d37962ZcsWe+6557L3rV27to0fP966dOliTZs2tW7dutm8efPs888/N9U5duxYO++882JERZsgAAEIQCChBBAgCTUs3YIABOIjsHr1ahcNCr1q167dPhu4fft2q1q1qnXo0MFGjhxpJSUlNnPmTLv22mttxowZdsYZZ9j06dPt8ssvt4ULF1rz5s33K0AkfhTyNXfuXDv++ONt4sSJ1q9fP1u3bp3VqFEjPmC0CAIQgAAEEkkAAZJIs9IpCEAgRgJvv/22exs04a9bt26pBMizzz5rV155pV/bsWNHzxEZMWJE9r0K5ZKYUU7J/jwgEiBt2rRxD4rKzp07rVatWvb888/bJZdcEiMy2gQBCEAAAgkkgABJoFHpEgQgECeB2bNne/7Hd99957kfKsr7UAJ6pjRu3Ng+/fRTy3hAFixYYC1atPB/S3wo5Oq6667LXt+zZ0/bsWOHi4jSCJCrrrrKBgwYkH1//fr1PcSrT58+cUKjVRCAAAQgkDgCCJDEmZQOQQACsRJQ3saxxx7roVQXXHCBN3Pr1q2m0CwVhVYpLGrlypVZAbJ48WIPr9qXAFG+yNSpU/MKkCOOOMKefvppzwGRB0Q5IIMGDcoiqlevng0ZMsTfS4EABCAAAQiEIIAACUGZOiAAAQj8n0CrVq2scuXKNmfOHKtSpcpuXCQ+hg0btlcBcuGFF1rDhg09JyRTFILVtm1bf1///v09OV1iRGXz5s2eyD5t2rSsAFHuiBLPVbZt22aHHXaYX9+pUydsBAEIQAACEAhCAAESBDOVQAACEPiLwLJlyzzkqkmTJnbnnXd6MviGDRtM+SGPPfaYeyhGjx6d1wMiIdGrVy+bNWuWnXrqqTZlyhRTSNXSpUt9h6snnnjCHn74YVu+fLlVr17dzxUZPny4TZ48OStANm7c6IJD3pAHH3zQ/6+clGrVqmEiCEAAAhCAQBACCJAgmKkEAhCAwN8EVq1aZUOHDjXlhEh8KBFcngmFQWU8EZkckNwQLN1Bng6FVK1fv95zQvT63HPP9ZtrC14JmCVLlniSe9++fe2RRx7xui677DIXHV27drUPP/zQd8LSAYi6V+b92AgCEIAABCAQggACJARl6oAABCAQAQEJkGuuuca33qVAAAIQgAAECkUAAVIo8tQLAQhAIDABBEhg4FQHAQhAAAJ5CSBAeDAgAAEIpIQAAiQlhqabEIAABCIngACJ3EA0DwIQgAAEIAABCEAAAkkigABJkjXpCwQgAAEIQAACEIAABCIngACJ3EA0DwIQgAAEIAABCEAAAkkigABJkjXpCwQgAAEIQAACEIAABCIngACJ3EA0DwIQgAAEIAABCEAAAkkigABJkjXpCwQgAAEIQAACEIAABCIngACJ3EA0DwIQgAAEIAABCEAAAkkigABJkjXpCwQgAAEIQAACEIAABCIngACJ3EA0DwIQgAAEIAABCEAAAkki8D8dvc4qqEGwzQAAAABJRU5ErkJggg=="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air.plot(column=\"Percent Favorable\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "air.plot(column=\"AIR\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results can also be exported. Examples of these commands are shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = Path(\".output\")\n",
    "output.mkdir(exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "air.to_excel(file_path=output / \"air_summary_table.xlsx\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility Functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`utils.pgrg_ordered` returns a unique and ordered list of protected AND reference groups.  This can be helpful when working with group data or with SolasAI results outside of SolasAI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Black',\n",
       " 'Asian',\n",
       " 'Native American',\n",
       " 'White',\n",
       " 'Hispanic',\n",
       " 'Non-Hispanic',\n",
       " 'Female',\n",
       " 'Male']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "protected_and_reference_groups = sd.utils.pgrg_ordered(\n",
    "    protected_groups=protected_groups,\n",
    "    reference_groups=reference_groups,\n",
    ")\n",
    "protected_and_reference_groups"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customizing Output\n",
    "\n",
    "A user can change column names used in all downstream results using the `solas_disparity.const` interface.  This can be very helpful when customizing output for a particular use case.  As an example, a lender might want to make the \"Percent Favorable\" column display as \"Loans Underwritten\" or \"Accepted Applications\", while an employer might want to make the \"Percent Favorable\" column be \"Job Offers\" or \"Promotions.\"\n",
    "\n",
    "Below is an example of how column names can be customized for the HMDA data use case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.const.FAVORABLE = \"Total Loan Offers\"\n",
    "sd.const.PERCENT_FAVORABLE = \"Loan Offer Percent\"\n",
    "sd.const.OBSERVATIONS = \"Obs. with Data\"\n",
    "sd.const.PERCENT_MISSING = \"Pct Obs. Missing Data\"\n",
    "sd.const.PERCENT_DIFFERENCE_FAVORABLE = \"Offer Percent Difference\"\n",
    "sd.const.AIR_VALUES = \"Adverse Impact Ratio\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Formatting in the table can also be changed, as occurs in the code below.  Here, `sd.ui.AUTO_FORMATTERS` is a dictionary that contains the formats for each of the attributes in the summary table.  When changing the formats, one specifies the key of the dictionary as the attribute from the const file (e.g., \"FAVORABLE\" when changing `sd.const.FAVORABLE`), and the value as desired python formatter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "sd.ui.AUTO_FORMATTERS[\"TOTAL\"] = \"0,.0f\"\n",
    "sd.ui.AUTO_FORMATTERS[\"FAVORABLE\"] = \"0,.0f\"\n",
    "sd.ui.AUTO_FORMATTERS[\"P_VALUES\"] = \"0.1%\"\n",
    "sd.ui.AUTO_FORMATTERS[\"PERCENT_MISSING\"] = \"0.1%\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "const_modified_air = sd.adverse_impact_ratio(\n",
    "    group_data=df,\n",
    "    protected_groups=protected_groups,\n",
    "    reference_groups=reference_groups,\n",
    "    group_categories=group_categories,\n",
    "    outcome=df[\"Low-Priced\"],\n",
    "    sample_weight=None,\n",
    "    air_threshold=0.80,\n",
    "    percent_difference_threshold=0.0,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The summary table with new variable names and formatted values is printed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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 class=\"dataframe disparity-table\" id=\"disp-output\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Group</th>\n",
       "      <th>Reference Group</th>\n",
       "      <th>Group Category</th>\n",
       "      <th>Obs. with Data</th>\n",
       "      <th>Pct Obs. Missing Data</th>\n",
       "      <th>Total</th>\n",
       "      <th>Total Loan Offers</th>\n",
       "      <th>Loan Offer Percent</th>\n",
       "      <th>Offer Percent Difference</th>\n",
       "      <th>Adverse Impact Ratio</th>\n",
       "      <th>P-Values</th>\n",
       "      <th>Practically Significant</th>\n",
       "      <th>Shortfall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Black</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>1,337</td>\n",
       "      <td>1,065</td>\n",
       "      <td>79.66%</td>\n",
       "      <td>11.22%</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.0%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Asian</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>1,286</td>\n",
       "      <td>1,224</td>\n",
       "      <td>95.18%</td>\n",
       "      <td>-4.30%</td>\n",
       "      <td>1.047</td>\n",
       "      <td>0.0%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Native American</td>\n",
       "      <td>White</td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>94</td>\n",
       "      <td>81</td>\n",
       "      <td>86.17%</td>\n",
       "      <td>4.71%</td>\n",
       "      <td>0.948</td>\n",
       "      <td>14.7%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>White</td>\n",
       "      <td></td>\n",
       "      <td>Race</td>\n",
       "      <td>17,224</td>\n",
       "      <td>13.9%</td>\n",
       "      <td>14,461</td>\n",
       "      <td>13,142</td>\n",
       "      <td>90.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hispanic</td>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>2,032</td>\n",
       "      <td>1,593</td>\n",
       "      <td>78.40%</td>\n",
       "      <td>13.49%</td>\n",
       "      <td>0.853</td>\n",
       "      <td>0.0%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Non-Hispanic</td>\n",
       "      <td></td>\n",
       "      <td>Ethnicity</td>\n",
       "      <td>17,207</td>\n",
       "      <td>14.0%</td>\n",
       "      <td>15,175</td>\n",
       "      <td>13,943</td>\n",
       "      <td>91.88%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.4%</td>\n",
       "      <td>4,222</td>\n",
       "      <td>3,719</td>\n",
       "      <td>88.09%</td>\n",
       "      <td>1.28%</td>\n",
       "      <td>0.986</td>\n",
       "      <td>4.3%</td>\n",
       "      <td>No</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Male</td>\n",
       "      <td></td>\n",
       "      <td>Sex</td>\n",
       "      <td>10,719</td>\n",
       "      <td>46.4%</td>\n",
       "      <td>6,497</td>\n",
       "      <td>5,806</td>\n",
       "      <td>89.36%</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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