{
    "cells": [
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# SolasAI Disparity Calculations"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "import solas_disparity as sd\n",
                "import pandas as pd"
            ]
        },
        {
            "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": [
                "Some predictions were created using a tree model run on an HMDA dataset."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
                "label = \"Interest Rate\"\n",
                "data = pd.read_parquet(\"hmda_test.parquet\")"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Store commonly reused function arguments."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [],
            "source": [
                "protected_groups = [\"Black\", \"Asian\", \"Native American\", \"Hispanic\", \"Female\"]\n",
                "reference_groups = [\"White\", \"White\", \"White\", \"Non-Hispanic\", \"Male\"]\n",
                "groups = sd.pgrg_ordered(\n",
                "    protected_groups=protected_groups,\n",
                "    reference_groups=reference_groups,\n",
                ")\n",
                "reused_arguments = dict(\n",
                "    group_data=data[groups],\n",
                "    protected_groups=protected_groups,\n",
                "    reference_groups=reference_groups,\n",
                "    group_categories=[\"Race\", \"Race\", \"Race\", \"Ethnicity\", \"Sex\"],\n",
                "    sample_weight=None,\n",
                ")\n",
                "binary_outcome = data[\"Prediction\"] <= data[\"Prediction\"].quantile(0.5)\n",
                "binary_label = data[label] <= data[label].quantile(0.5)"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Adverse Impact Ratio (AIR)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [],
            "source": [
                "air = sd.adverse_impact_ratio(\n",
                "    outcome=binary_outcome,\n",
                "    air_threshold=0.8,\n",
                "    percent_difference_threshold=0.0,\n",
                "    **reused_arguments,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "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                           │ Hispanic                                                            │\n",
                            "│ Affected Reference                        │ Non-Hispanic                                                        │\n",
                            "│ Affected Categories                       │ Ethnicity                                                           │\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                           │ Hispanic                                                            │\n",
                            "│ Affected Reference                        │ Non-Hispanic                                                        │\n",
                            "│ Affected Categories                       │ Ethnicity                                                           │\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: 13.68%, Race: 13.56%, Sex: 46.88%"
                        ],
                        "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>340.0</td>\n",
                            "      <td>141.0</td>\n",
                            "      <td>41.47%</td>\n",
                            "      <td>9.70%</td>\n",
                            "      <td>0.810</td>\n",
                            "      <td>0.001</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>327.0</td>\n",
                            "      <td>243.0</td>\n",
                            "      <td>74.31%</td>\n",
                            "      <td>-23.14%</td>\n",
                            "      <td>1.452</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>20.0</td>\n",
                            "      <td>9.0</td>\n",
                            "      <td>45.00%</td>\n",
                            "      <td>6.17%</td>\n",
                            "      <td>0.879</td>\n",
                            "      <td>0.657</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>3,623.0</td>\n",
                            "      <td>1,854.0</td>\n",
                            "      <td>51.17%</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>508.0</td>\n",
                            "      <td>167.0</td>\n",
                            "      <td>32.87%</td>\n",
                            "      <td>21.54%</td>\n",
                            "      <td>0.604</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "      <td>109.4</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>2,072.0</td>\n",
                            "      <td>54.41%</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>1,034.0</td>\n",
                            "      <td>414.0</td>\n",
                            "      <td>40.04%</td>\n",
                            "      <td>9.78%</td>\n",
                            "      <td>0.804</td>\n",
                            "      <td>0.000</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>1,622.0</td>\n",
                            "      <td>808.0</td>\n",
                            "      <td>49.82%</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "air"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Standardized Mean Difference (SMD)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [],
            "source": [
                "smd = sd.standardized_mean_difference(\n",
                "    outcome=data[\"Prediction\"],\n",
                "    label=data[label],\n",
                "    smd_threshold=30,\n",
                "    lower_score_favorable=True,\n",
                "    **reused_arguments,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Standardized Mean Difference"
                        ],
                        "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",
                            "│ SMD Threshold                      │ 30.0                                                                       │\n",
                            "│ SMD Denominator                    │ population                                                                 │\n",
                            "│ Lower Score Favorable              │ True                                                                       │\n",
                            "│ Affected Groups                    │ Hispanic                                                                   │\n",
                            "│ Affected Reference                 │ Non-Hispanic                                                               │\n",
                            "│ Affected Categories                │ Ethnicity                                                                  │\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",
                            "│ SMD Threshold                      │ 30.0                                                                       │\n",
                            "│ SMD Denominator                    │ population                                                                 │\n",
                            "│ Lower Score Favorable              │ True                                                                       │\n",
                            "│ Affected Groups                    │ Hispanic                                                                   │\n",
                            "│ Affected Reference                 │ Non-Hispanic                                                               │\n",
                            "│ Affected Categories                │ Ethnicity                                                                  │\n",
                            "└────────────────────────────────────┴────────────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Standardized Mean Difference Summary Table"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "\\* Percent Missing: Ethnicity: 13.68%, Race: 13.56%, Sex: 46.88%"
                        ],
                        "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>Average Label</th>\n",
                            "      <th>Average Outcome</th>\n",
                            "      <th>Difference in Average Outcome</th>\n",
                            "      <th>Std. Dev. of Outcomes</th>\n",
                            "      <th>SMD</th>\n",
                            "      <th>P-Values</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000653</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>27.559</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>0.04</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>-0.001668</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>-70.355</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000655</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>27.617</td>\n",
                            "      <td>0.199</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td></td>\n",
                            "      <td>0.00</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>508.0</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.001032</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>43.545</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td></td>\n",
                            "      <td>0.00</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>1,034.0</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000395</td>\n",
                            "      <td>0.00</td>\n",
                            "      <td>16.655</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td></td>\n",
                            "      <td>0.00</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "smd"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Adverse Impact Ratio by Quantile"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 10,
            "metadata": {},
            "outputs": [],
            "source": [
                "airq = sd.adverse_impact_ratio_by_quantile(\n",
                "    outcome=data[\"Prediction\"],\n",
                "    air_threshold=0.8,\n",
                "    percent_difference_threshold=0.0,\n",
                "    quantiles=[decile / 10 for decile in range(1, 11)],\n",
                "    lower_score_favorable=True,\n",
                "    **reused_arguments,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Adverse Impact Ratio By Quantile"
                        ],
                        "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",
                            "│ Lower Score Favorable                     │ True                                                                │\n",
                            "│ Affected Groups                           │ Black, Hispanic, Female                                             │\n",
                            "│ Affected Reference                        │ White, Non-Hispanic, Male                                           │\n",
                            "│ Affected Categories                       │ Race, Ethnicity, Sex                                                │\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",
                            "│ Lower Score Favorable                     │ True                                                                │\n",
                            "│ Affected Groups                           │ Black, Hispanic, Female                                             │\n",
                            "│ Affected Reference                        │ White, Non-Hispanic, Male                                           │\n",
                            "│ Affected Categories                       │ Race, Ethnicity, Sex                                                │\n",
                            "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Adverse Impact Ratio By Quantile Summary Table"
                        ],
                        "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>Quantile</th>\n",
                            "      <th>Reference Group</th>\n",
                            "      <th>Group Category</th>\n",
                            "      <th>Quantile Cutoff</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",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>13.0</td>\n",
                            "      <td>3.82%</td>\n",
                            "      <td>4.93%</td>\n",
                            "      <td>0.437</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>91.0</td>\n",
                            "      <td>27.83%</td>\n",
                            "      <td>-19.08%</td>\n",
                            "      <td>3.181</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>1.0</td>\n",
                            "      <td>5.00%</td>\n",
                            "      <td>3.75%</td>\n",
                            "      <td>0.571</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>317.0</td>\n",
                            "      <td>8.75%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>15.0</td>\n",
                            "      <td>2.95%</td>\n",
                            "      <td>7.79%</td>\n",
                            "      <td>0.275</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>409.0</td>\n",
                            "      <td>10.74%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>66.0</td>\n",
                            "      <td>6.38%</td>\n",
                            "      <td>3.05%</td>\n",
                            "      <td>0.677</td>\n",
                            "      <td>0.006</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>10.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.044761</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>153.0</td>\n",
                            "      <td>9.43%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>37.0</td>\n",
                            "      <td>10.88%</td>\n",
                            "      <td>9.85%</td>\n",
                            "      <td>0.525</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>132.0</td>\n",
                            "      <td>40.37%</td>\n",
                            "      <td>-19.64%</td>\n",
                            "      <td>1.947</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>2.0</td>\n",
                            "      <td>10.00%</td>\n",
                            "      <td>10.73%</td>\n",
                            "      <td>0.482</td>\n",
                            "      <td>0.403</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>751.0</td>\n",
                            "      <td>20.73%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>42.0</td>\n",
                            "      <td>8.27%</td>\n",
                            "      <td>14.95%</td>\n",
                            "      <td>0.356</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>884.0</td>\n",
                            "      <td>23.21%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>155.0</td>\n",
                            "      <td>14.99%</td>\n",
                            "      <td>4.92%</td>\n",
                            "      <td>0.753</td>\n",
                            "      <td>0.002</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>20.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.045863</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>323.0</td>\n",
                            "      <td>19.91%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>62.0</td>\n",
                            "      <td>18.24%</td>\n",
                            "      <td>11.30%</td>\n",
                            "      <td>0.617</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>175.0</td>\n",
                            "      <td>53.52%</td>\n",
                            "      <td>-23.98%</td>\n",
                            "      <td>1.812</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>4.0</td>\n",
                            "      <td>20.00%</td>\n",
                            "      <td>9.53%</td>\n",
                            "      <td>0.677</td>\n",
                            "      <td>0.464</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,070.0</td>\n",
                            "      <td>29.53%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>69.0</td>\n",
                            "      <td>13.58%</td>\n",
                            "      <td>19.14%</td>\n",
                            "      <td>0.415</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>1,246.0</td>\n",
                            "      <td>32.72%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>225.0</td>\n",
                            "      <td>21.76%</td>\n",
                            "      <td>5.74%</td>\n",
                            "      <td>0.791</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>30.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.046427</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>446.0</td>\n",
                            "      <td>27.50%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>103.0</td>\n",
                            "      <td>30.29%</td>\n",
                            "      <td>16.38%</td>\n",
                            "      <td>0.649</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>238.0</td>\n",
                            "      <td>72.78%</td>\n",
                            "      <td>-26.11%</td>\n",
                            "      <td>1.559</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>8.0</td>\n",
                            "      <td>40.00%</td>\n",
                            "      <td>6.67%</td>\n",
                            "      <td>0.857</td>\n",
                            "      <td>0.656</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,691.0</td>\n",
                            "      <td>46.67%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>139.0</td>\n",
                            "      <td>27.36%</td>\n",
                            "      <td>22.30%</td>\n",
                            "      <td>0.551</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>1,891.0</td>\n",
                            "      <td>49.66%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>380.0</td>\n",
                            "      <td>36.75%</td>\n",
                            "      <td>7.27%</td>\n",
                            "      <td>0.835</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>40.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.046703</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>714.0</td>\n",
                            "      <td>44.02%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>141.0</td>\n",
                            "      <td>41.47%</td>\n",
                            "      <td>9.70%</td>\n",
                            "      <td>0.810</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>243.0</td>\n",
                            "      <td>74.31%</td>\n",
                            "      <td>-23.14%</td>\n",
                            "      <td>1.452</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>9.0</td>\n",
                            "      <td>45.00%</td>\n",
                            "      <td>6.17%</td>\n",
                            "      <td>0.879</td>\n",
                            "      <td>0.657</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,854.0</td>\n",
                            "      <td>51.17%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>167.0</td>\n",
                            "      <td>32.87%</td>\n",
                            "      <td>21.54%</td>\n",
                            "      <td>0.604</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>2,072.0</td>\n",
                            "      <td>54.41%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>414.0</td>\n",
                            "      <td>40.04%</td>\n",
                            "      <td>9.78%</td>\n",
                            "      <td>0.804</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>50.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.047009</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>808.0</td>\n",
                            "      <td>49.82%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>161.0</td>\n",
                            "      <td>47.35%</td>\n",
                            "      <td>13.62%</td>\n",
                            "      <td>0.777</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>260.0</td>\n",
                            "      <td>79.51%</td>\n",
                            "      <td>-18.54%</td>\n",
                            "      <td>1.304</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>55.00%</td>\n",
                            "      <td>5.97%</td>\n",
                            "      <td>0.902</td>\n",
                            "      <td>0.648</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>2,209.0</td>\n",
                            "      <td>60.97%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>214.0</td>\n",
                            "      <td>42.13%</td>\n",
                            "      <td>21.53%</td>\n",
                            "      <td>0.662</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>2,424.0</td>\n",
                            "      <td>63.66%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>520.0</td>\n",
                            "      <td>50.29%</td>\n",
                            "      <td>7.60%</td>\n",
                            "      <td>0.869</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>60.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.047266</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>939.0</td>\n",
                            "      <td>57.89%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>248.0</td>\n",
                            "      <td>72.94%</td>\n",
                            "      <td>7.96%</td>\n",
                            "      <td>0.902</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>308.0</td>\n",
                            "      <td>94.19%</td>\n",
                            "      <td>-13.29%</td>\n",
                            "      <td>1.164</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>14.0</td>\n",
                            "      <td>70.00%</td>\n",
                            "      <td>10.90%</td>\n",
                            "      <td>0.865</td>\n",
                            "      <td>0.250</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>2,931.0</td>\n",
                            "      <td>80.90%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>364.0</td>\n",
                            "      <td>71.65%</td>\n",
                            "      <td>10.83%</td>\n",
                            "      <td>0.869</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>3,141.0</td>\n",
                            "      <td>82.48%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>765.0</td>\n",
                            "      <td>73.98%</td>\n",
                            "      <td>4.44%</td>\n",
                            "      <td>0.943</td>\n",
                            "      <td>0.010</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>80.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.048018</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>1,272.0</td>\n",
                            "      <td>78.42%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>288.0</td>\n",
                            "      <td>84.71%</td>\n",
                            "      <td>5.41%</td>\n",
                            "      <td>0.940</td>\n",
                            "      <td>0.003</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>321.0</td>\n",
                            "      <td>98.17%</td>\n",
                            "      <td>-8.05%</td>\n",
                            "      <td>1.089</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>17.0</td>\n",
                            "      <td>85.00%</td>\n",
                            "      <td>5.12%</td>\n",
                            "      <td>0.943</td>\n",
                            "      <td>0.441</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>3,265.0</td>\n",
                            "      <td>90.12%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>428.0</td>\n",
                            "      <td>84.25%</td>\n",
                            "      <td>6.85%</td>\n",
                            "      <td>0.925</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>3,469.0</td>\n",
                            "      <td>91.10%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>887.0</td>\n",
                            "      <td>85.78%</td>\n",
                            "      <td>4.04%</td>\n",
                            "      <td>0.955</td>\n",
                            "      <td>0.002</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>90.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.048694</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>1,457.0</td>\n",
                            "      <td>89.83%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>100.0%</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0.058530</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>100.00%</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "airq"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Odds Ratio"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [],
            "source": [
                "odds_ratio = sd.odds_ratio(\n",
                "    outcome=binary_outcome,\n",
                "    odds_ratio_threshold=0.68,\n",
                "    percent_difference_threshold=0.0,\n",
                "    **reused_arguments,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Odds 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",
                            "│ Odds Ratio Threshold                      │ 0.68                                                                │\n",
                            "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
                            "│ Lower Score Favorable                     │ True                                                                │\n",
                            "│ Affected Groups                           │ Black, Hispanic, Female                                             │\n",
                            "│ Affected Reference                        │ White, Non-Hispanic, Male                                           │\n",
                            "│ Affected Categories                       │ Race, Ethnicity, Sex                                                │\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",
                            "│ Odds Ratio Threshold                      │ 0.68                                                                │\n",
                            "│ Percent Difference Threshold              │ 0.0                                                                 │\n",
                            "│ Lower Score Favorable                     │ True                                                                │\n",
                            "│ Affected Groups                           │ Black, Hispanic, Female                                             │\n",
                            "│ Affected Reference                        │ White, Non-Hispanic, Male                                           │\n",
                            "│ Affected Categories                       │ Race, Ethnicity, Sex                                                │\n",
                            "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Odds Ratio Summary Table"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "\\* Percent Missing: Ethnicity: 13.68%, Race: 13.56%, Sex: 46.88%"
                        ],
                        "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>Odds</th>\n",
                            "      <th>Percent Difference Favorable</th>\n",
                            "      <th>Odds Ratio</th>\n",
                            "      <th>P-Values</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>141.0</td>\n",
                            "      <td>41.47%</td>\n",
                            "      <td>0.708543</td>\n",
                            "      <td>9.70%</td>\n",
                            "      <td>0.676058</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>243.0</td>\n",
                            "      <td>74.31%</td>\n",
                            "      <td>2.892857</td>\n",
                            "      <td>-23.14%</td>\n",
                            "      <td>2.760229</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>9.0</td>\n",
                            "      <td>45.00%</td>\n",
                            "      <td>0.818182</td>\n",
                            "      <td>6.17%</td>\n",
                            "      <td>0.780671</td>\n",
                            "      <td>0.657</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,854.0</td>\n",
                            "      <td>51.17%</td>\n",
                            "      <td>1.048050</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>508.0</td>\n",
                            "      <td>167.0</td>\n",
                            "      <td>32.87%</td>\n",
                            "      <td>0.489736</td>\n",
                            "      <td>21.54%</td>\n",
                            "      <td>0.410319</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>2,072.0</td>\n",
                            "      <td>54.41%</td>\n",
                            "      <td>1.193548</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>1,034.0</td>\n",
                            "      <td>414.0</td>\n",
                            "      <td>40.04%</td>\n",
                            "      <td>0.667742</td>\n",
                            "      <td>9.78%</td>\n",
                            "      <td>0.672700</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>808.0</td>\n",
                            "      <td>49.82%</td>\n",
                            "      <td>0.992629</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "odds_ratio"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Categorical Adverse Impact Ratio"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Generate an example categorical outcome.\n",
                "categorical_outcome = pd.qcut(data[\"Prediction\"], q=[0.0, 0.25, 0.5, 0.75, 1.0])\n",
                "categories = categorical_outcome.cat.categories.to_series()\n",
                "categories = pd.Series([\"Best\", \"Great\", \"Good\", \"Bad\"], index=categories.index)\n",
                "categorical_outcome.replace(categories.to_dict(), inplace=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 15,
            "metadata": {},
            "outputs": [],
            "source": [
                "cair = sd.categorical_adverse_impact_ratio(\n",
                "    outcome=categorical_outcome,\n",
                "    category_order=list(reversed(categories.tolist())),\n",
                "    air_threshold=0.8,\n",
                "    percent_difference_threshold=0.0,\n",
                "    **reused_arguments,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Categorical 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",
                            "│ Affected Groups                           │ Asian                                                               │\n",
                            "│ Affected Reference                        │ White                                                               │\n",
                            "│ Affected Categories                       │ Race                                                                │\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",
                            "│ Affected Groups                           │ Asian                                                               │\n",
                            "│ Affected Reference                        │ White                                                               │\n",
                            "│ Affected Categories                       │ Race                                                                │\n",
                            "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Categorical Adverse Impact Ratio Summary Table"
                        ],
                        "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>Category</th>\n",
                            "      <th>Reference Group</th>\n",
                            "      <th>Group Category</th>\n",
                            "      <th>Ordinal</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",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>92.0</td>\n",
                            "      <td>27.06%</td>\n",
                            "      <td>-7.96%</td>\n",
                            "      <td>1.417</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>19.0</td>\n",
                            "      <td>5.81%</td>\n",
                            "      <td>13.29%</td>\n",
                            "      <td>0.304</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>6.0</td>\n",
                            "      <td>30.00%</td>\n",
                            "      <td>-10.90%</td>\n",
                            "      <td>1.571</td>\n",
                            "      <td>0.250</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>0</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>692.0</td>\n",
                            "      <td>19.10%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>144.0</td>\n",
                            "      <td>28.35%</td>\n",
                            "      <td>-10.83%</td>\n",
                            "      <td>1.618</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>0</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>667.0</td>\n",
                            "      <td>17.52%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>269.0</td>\n",
                            "      <td>26.02%</td>\n",
                            "      <td>-4.44%</td>\n",
                            "      <td>1.206</td>\n",
                            "      <td>0.010</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>Bad</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>0</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>350.0</td>\n",
                            "      <td>21.58%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>Good</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>199.0</td>\n",
                            "      <td>58.53%</td>\n",
                            "      <td>-9.70%</td>\n",
                            "      <td>1.199</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>Good</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>84.0</td>\n",
                            "      <td>25.69%</td>\n",
                            "      <td>23.14%</td>\n",
                            "      <td>0.526</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>Good</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>11.0</td>\n",
                            "      <td>55.00%</td>\n",
                            "      <td>-6.17%</td>\n",
                            "      <td>1.126</td>\n",
                            "      <td>0.657</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>Good</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,769.0</td>\n",
                            "      <td>48.83%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>Good</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>341.0</td>\n",
                            "      <td>67.13%</td>\n",
                            "      <td>-21.54%</td>\n",
                            "      <td>1.472</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Good</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>1,736.0</td>\n",
                            "      <td>45.59%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>Good</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>620.0</td>\n",
                            "      <td>59.96%</td>\n",
                            "      <td>-9.78%</td>\n",
                            "      <td>1.195</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>Good</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>814.0</td>\n",
                            "      <td>50.18%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>Great</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>2</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>296.0</td>\n",
                            "      <td>87.06%</td>\n",
                            "      <td>-10.88%</td>\n",
                            "      <td>1.143</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>Great</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>2</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>182.0</td>\n",
                            "      <td>55.66%</td>\n",
                            "      <td>20.52%</td>\n",
                            "      <td>0.731</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>Great</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>2</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>18.0</td>\n",
                            "      <td>90.00%</td>\n",
                            "      <td>-13.82%</td>\n",
                            "      <td>1.181</td>\n",
                            "      <td>0.191</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>Great</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>2</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>2,760.0</td>\n",
                            "      <td>76.18%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>Great</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>2</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>461.0</td>\n",
                            "      <td>90.75%</td>\n",
                            "      <td>-17.22%</td>\n",
                            "      <td>1.234</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Great</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>2</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>2,800.0</td>\n",
                            "      <td>73.53%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>Great</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>2</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>859.0</td>\n",
                            "      <td>83.08%</td>\n",
                            "      <td>-5.21%</td>\n",
                            "      <td>1.067</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>Great</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>2</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>1,263.0</td>\n",
                            "      <td>77.87%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>Best</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>Best</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>Best</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>Best</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>Best</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Best</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>Best</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>3</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td>100.00%</td>\n",
                            "      <td>0.00%</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>1.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>Best</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>3</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>100.00%</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "cair"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Residual Standardized Mean Difference"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 17,
            "metadata": {},
            "outputs": [],
            "source": [
                "rsmd = sd.residual_standardized_mean_difference(\n",
                "    prediction=data[\"Prediction\"],\n",
                "    label=data[label],\n",
                "    residual_smd_threshold=30,\n",
                "    lower_score_favorable=True,\n",
                "    **reused_arguments,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Residual Standardized Mean Difference"
                        ],
                        "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",
                            "│ Residual SMD Threshold                │ 30.0                                                                    │\n",
                            "│ Residual SMD Denominator              │ population                                                              │\n",
                            "│ Lower Score Favorable                 │ True                                                                    │\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",
                            "│ Residual SMD Threshold                │ 30.0                                                                    │\n",
                            "│ Residual SMD Denominator              │ population                                                              │\n",
                            "│ Lower Score Favorable                 │ True                                                                    │\n",
                            "│ Affected Groups                       │                                                                         │\n",
                            "│ Affected Reference                    │                                                                         │\n",
                            "│ Affected Categories                   │                                                                         │\n",
                            "└───────────────────────────────────────┴─────────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Residual Standardized Mean Difference Summary Table"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "\\* Percent Missing: Ethnicity: 13.68%, Race: 13.56%, Sex: 46.88%"
                        ],
                        "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>Average Prediction</th>\n",
                            "      <th>Average Label</th>\n",
                            "      <th>Average Residual</th>\n",
                            "      <th>Difference in Average Residual</th>\n",
                            "      <th>Std. Dev. of Residuals</th>\n",
                            "      <th>Residual SMD</th>\n",
                            "      <th>P-Values</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>0.047486</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000472</td>\n",
                            "      <td>0.000265</td>\n",
                            "      <td>0.004864</td>\n",
                            "      <td>5.445415</td>\n",
                            "      <td>0.337</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>0.045165</td>\n",
                            "      <td>0.04</td>\n",
                            "      <td>-0.000769</td>\n",
                            "      <td>-0.000976</td>\n",
                            "      <td>0.004864</td>\n",
                            "      <td>-20.069038</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>0.047487</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000628</td>\n",
                            "      <td>0.000421</td>\n",
                            "      <td>0.004864</td>\n",
                            "      <td>8.646488</td>\n",
                            "      <td>0.699</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>0.046833</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000207</td>\n",
                            "      <td></td>\n",
                            "      <td>0.004864</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>508.0</td>\n",
                            "      <td>0.047667</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.001097</td>\n",
                            "      <td>0.001046</td>\n",
                            "      <td>0.004864</td>\n",
                            "      <td>21.513889</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>0.046634</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000051</td>\n",
                            "      <td></td>\n",
                            "      <td>0.004864</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>1,034.0</td>\n",
                            "      <td>0.047268</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000435</td>\n",
                            "      <td>0.000246</td>\n",
                            "      <td>0.004864</td>\n",
                            "      <td>5.047722</td>\n",
                            "      <td>0.229</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>0.046873</td>\n",
                            "      <td>0.05</td>\n",
                            "      <td>0.000189</td>\n",
                            "      <td></td>\n",
                            "      <td>0.004864</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "rsmd"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Segmented Adverse Impact Ratio"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 19,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Generate example income segments.\n",
                "segments = pd.qcut(data[\"Income\"], q=[0.0, 1 / 3, 2 / 3, 1.0])\n",
                "categories = segments.cat.categories.to_series()\n",
                "categories = pd.Series(\n",
                "    [\"Low Income\", \"Mid Income\", \"High Income\"], index=categories.index\n",
                ")\n",
                "segments.replace(categories.to_dict(), inplace=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "metadata": {},
            "outputs": [],
            "source": [
                "sair = sd.segmented_adverse_impact_ratio(\n",
                "    outcome=binary_outcome,\n",
                "    air_threshold=0.8,\n",
                "    percent_difference_threshold=0.0,\n",
                "    fdr_threshold=0.2,\n",
                "    segment=segments,\n",
                "    **reused_arguments,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 21,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Segmented 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",
                            "│ FDR Threshold                             │ 0.2                                                                 │\n",
                            "│ Affected Groups                           │ Hispanic                                                            │\n",
                            "│ Affected Reference                        │ Non-Hispanic                                                        │\n",
                            "│ Affected Categories                       │ Ethnicity                                                           │\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",
                            "│ FDR Threshold                             │ 0.2                                                                 │\n",
                            "│ Affected Groups                           │ Hispanic                                                            │\n",
                            "│ Affected Reference                        │ Non-Hispanic                                                        │\n",
                            "│ Affected Categories                       │ Ethnicity                                                           │\n",
                            "└───────────────────────────────────────────┴─────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Segmented Adverse Impact Ratio Summary Table"
                        ],
                        "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>Segment</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>BH Critical Value</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>Low Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,510</td>\n",
                            "      <td>11.80%</td>\n",
                            "      <td>151.0</td>\n",
                            "      <td>33.0</td>\n",
                            "      <td>21.85%</td>\n",
                            "      <td>4.18%</td>\n",
                            "      <td>0.839</td>\n",
                            "      <td>0.280</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,414</td>\n",
                            "      <td>13.14%</td>\n",
                            "      <td>117.0</td>\n",
                            "      <td>56.0</td>\n",
                            "      <td>47.86%</td>\n",
                            "      <td>5.02%</td>\n",
                            "      <td>0.905</td>\n",
                            "      <td>0.332</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,398</td>\n",
                            "      <td>15.78%</td>\n",
                            "      <td>72.0</td>\n",
                            "      <td>52.0</td>\n",
                            "      <td>72.22%</td>\n",
                            "      <td>5.15%</td>\n",
                            "      <td>0.933</td>\n",
                            "      <td>0.313</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>Low Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,510</td>\n",
                            "      <td>11.80%</td>\n",
                            "      <td>69.0</td>\n",
                            "      <td>22.0</td>\n",
                            "      <td>31.88%</td>\n",
                            "      <td>-5.85%</td>\n",
                            "      <td>1.225</td>\n",
                            "      <td>0.325</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,414</td>\n",
                            "      <td>13.14%</td>\n",
                            "      <td>89.0</td>\n",
                            "      <td>67.0</td>\n",
                            "      <td>75.28%</td>\n",
                            "      <td>-22.39%</td>\n",
                            "      <td>1.423</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,398</td>\n",
                            "      <td>15.78%</td>\n",
                            "      <td>169.0</td>\n",
                            "      <td>154.0</td>\n",
                            "      <td>91.12%</td>\n",
                            "      <td>-13.75%</td>\n",
                            "      <td>1.178</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>Low Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,510</td>\n",
                            "      <td>11.80%</td>\n",
                            "      <td>9.0</td>\n",
                            "      <td>3.0</td>\n",
                            "      <td>33.33%</td>\n",
                            "      <td>-7.30%</td>\n",
                            "      <td>1.280</td>\n",
                            "      <td>0.704</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,414</td>\n",
                            "      <td>13.14%</td>\n",
                            "      <td>6.0</td>\n",
                            "      <td>4.0</td>\n",
                            "      <td>66.67%</td>\n",
                            "      <td>-13.78%</td>\n",
                            "      <td>1.261</td>\n",
                            "      <td>0.690</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,398</td>\n",
                            "      <td>15.78%</td>\n",
                            "      <td>5.0</td>\n",
                            "      <td>2.0</td>\n",
                            "      <td>40.00%</td>\n",
                            "      <td>37.37%</td>\n",
                            "      <td>0.517</td>\n",
                            "      <td>0.081</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>Low Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,510</td>\n",
                            "      <td>11.80%</td>\n",
                            "      <td>1,279.0</td>\n",
                            "      <td>333.0</td>\n",
                            "      <td>26.04%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,414</td>\n",
                            "      <td>13.14%</td>\n",
                            "      <td>1,195.0</td>\n",
                            "      <td>632.0</td>\n",
                            "      <td>52.89%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>1,398</td>\n",
                            "      <td>15.78%</td>\n",
                            "      <td>1,149.0</td>\n",
                            "      <td>889.0</td>\n",
                            "      <td>77.37%</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>Low Income</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1,527</td>\n",
                            "      <td>10.81%</td>\n",
                            "      <td>242.0</td>\n",
                            "      <td>40.0</td>\n",
                            "      <td>16.53%</td>\n",
                            "      <td>10.86%</td>\n",
                            "      <td>0.603</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td></td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1,393</td>\n",
                            "      <td>14.43%</td>\n",
                            "      <td>183.0</td>\n",
                            "      <td>74.0</td>\n",
                            "      <td>40.44%</td>\n",
                            "      <td>16.09%</td>\n",
                            "      <td>0.715</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td></td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1,396</td>\n",
                            "      <td>15.90%</td>\n",
                            "      <td>83.0</td>\n",
                            "      <td>53.0</td>\n",
                            "      <td>63.86%</td>\n",
                            "      <td>15.05%</td>\n",
                            "      <td>0.809</td>\n",
                            "      <td>0.002</td>\n",
                            "      <td></td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Low Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1,527</td>\n",
                            "      <td>10.81%</td>\n",
                            "      <td>1,285.0</td>\n",
                            "      <td>352.0</td>\n",
                            "      <td>27.39%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1,393</td>\n",
                            "      <td>14.43%</td>\n",
                            "      <td>1,210.0</td>\n",
                            "      <td>684.0</td>\n",
                            "      <td>56.53%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>1,396</td>\n",
                            "      <td>15.90%</td>\n",
                            "      <td>1,313.0</td>\n",
                            "      <td>1,036.0</td>\n",
                            "      <td>78.90%</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>Low Income</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,263</td>\n",
                            "      <td>26.23%</td>\n",
                            "      <td>528.0</td>\n",
                            "      <td>107.0</td>\n",
                            "      <td>20.27%</td>\n",
                            "      <td>6.27%</td>\n",
                            "      <td>0.764</td>\n",
                            "      <td>0.012</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>815</td>\n",
                            "      <td>49.94%</td>\n",
                            "      <td>316.0</td>\n",
                            "      <td>160.0</td>\n",
                            "      <td>50.63%</td>\n",
                            "      <td>8.89%</td>\n",
                            "      <td>0.851</td>\n",
                            "      <td>0.016</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>578</td>\n",
                            "      <td>65.18%</td>\n",
                            "      <td>190.0</td>\n",
                            "      <td>147.0</td>\n",
                            "      <td>77.37%</td>\n",
                            "      <td>4.07%</td>\n",
                            "      <td>0.950</td>\n",
                            "      <td>0.268</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>Low Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,263</td>\n",
                            "      <td>26.23%</td>\n",
                            "      <td>735.0</td>\n",
                            "      <td>195.0</td>\n",
                            "      <td>26.53%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>Mid Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>815</td>\n",
                            "      <td>49.94%</td>\n",
                            "      <td>499.0</td>\n",
                            "      <td>297.0</td>\n",
                            "      <td>59.52%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td>High Income</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>578</td>\n",
                            "      <td>65.18%</td>\n",
                            "      <td>388.0</td>\n",
                            "      <td>316.0</td>\n",
                            "      <td>81.44%</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>CMH Test</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.899</td>\n",
                            "      <td>0.071</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>Breslow-Day Test</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.976</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>CMH Test</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>1.241</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>Breslow-Day Test</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.058</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>CMH Test</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.959</td>\n",
                            "      <td>0.849</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>Breslow-Day Test</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>4,322</td>\n",
                            "      <td>13.56%</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.107</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>CMH Test</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.710</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td></td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Hispanic</td>\n",
                            "      <td>Breslow-Day Test</td>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>4,316</td>\n",
                            "      <td>13.68%</td>\n",
                            "      <td>508.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.929</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>CMH Test</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.857</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Female</td>\n",
                            "      <td>Breslow-Day Test</td>\n",
                            "      <td>Male</td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>2,656</td>\n",
                            "      <td>46.88%</td>\n",
                            "      <td>1,034.0</td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td></td>\n",
                            "      <td>0.906</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "sair"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Custom Disparity Metric"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "We can recreate the AIR as an example of how custom disparity metrics\n",
                "can be used. Many more advanced disparity metrics can benefit from the\n",
                "framework and additional validation provided by the custom disparity\n",
                "metric interface."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 24,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Define a function for calculating perecent favorable.\n",
                "def percent_favorable(outcome, sample_weight):\n",
                "    return (outcome.mul(sample_weight, axis=0)).sum(\n",
                "        axis=0, min_count=1\n",
                "    ) / sample_weight.sum(axis=0, min_count=1)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Custom Disparity Metric"
                        ],
                        "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",
                            "│ Metric                              │ percent_favorable                                                         │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Affected Groups                     │ Black, Hispanic, Female                                                   │\n",
                            "│ Affected Reference                  │ White, Non-Hispanic, Male                                                 │\n",
                            "│ Affected Categories                 │ Race, Ethnicity, Sex                                                      │\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",
                            "│ Metric                              │ percent_favorable                                                         │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Affected Groups                     │ Black, Hispanic, Female                                                   │\n",
                            "│ Affected Reference                  │ White, Non-Hispanic, Male                                                 │\n",
                            "│ Affected Categories                 │ Race, Ethnicity, Sex                                                      │\n",
                            "└─────────────────────────────────────┴───────────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Custom Disparity Metric Summary Table"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "\\* Percent Missing: Ethnicity: 13.68%, Race: 13.56%, Sex: 46.88%"
                        ],
                        "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>PERCENT FAVORABLE</th>\n",
                            "      <th>Difference</th>\n",
                            "      <th>Ratio</th>\n",
                            "      <th>P-Values</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>0.414706</td>\n",
                            "      <td>0.097</td>\n",
                            "      <td>0.810</td>\n",
                            "      <td>0.001</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>0.743119</td>\n",
                            "      <td>-0.231</td>\n",
                            "      <td>1.452</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>0.450000</td>\n",
                            "      <td>0.062</td>\n",
                            "      <td>0.879</td>\n",
                            "      <td>0.657</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>0.511731</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>508.0</td>\n",
                            "      <td>0.328740</td>\n",
                            "      <td>0.215</td>\n",
                            "      <td>0.604</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>0.544118</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>1,034.0</td>\n",
                            "      <td>0.400387</td>\n",
                            "      <td>0.098</td>\n",
                            "      <td>0.804</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>0.498150</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "custom_air = sd.custom_disparity_metric(\n",
                "    outcome=binary_outcome,\n",
                "    metric=percent_favorable,\n",
                "    difference_calculation=sd.types.DifferenceCalculation.REFERENCE_MINUS_PROTECTED,\n",
                "    difference_threshold_significance=lambda value: value > 0.0,\n",
                "    ratio_calculation=sd.types.RatioCalculation.PROTECTED_OVER_REFERENCE,\n",
                "    ratio_threshold_significance=lambda value: value < 1.0,\n",
                "    statistical_significance_test=sd.types.StatSigTest.FISHERS_OR_CHI_SQUARED,\n",
                "    **reused_arguments,\n",
                ")\n",
                "\n",
                "custom_air"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Note that the values from the custom disparity metric summary correspond\n",
                "to those of the original AIR calculation."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "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>PERCENT FAVORABLE</th>\n",
                            "      <th>Percent Favorable</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Group</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>Black</th>\n",
                            "      <td>0.414706</td>\n",
                            "      <td>0.414706</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Asian</th>\n",
                            "      <td>0.743119</td>\n",
                            "      <td>0.743119</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Native American</th>\n",
                            "      <td>0.450000</td>\n",
                            "      <td>0.450000</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>White</th>\n",
                            "      <td>0.511731</td>\n",
                            "      <td>0.511731</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Hispanic</th>\n",
                            "      <td>0.328740</td>\n",
                            "      <td>0.328740</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Non-Hispanic</th>\n",
                            "      <td>0.544118</td>\n",
                            "      <td>0.544118</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Female</th>\n",
                            "      <td>0.400387</td>\n",
                            "      <td>0.400387</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Male</th>\n",
                            "      <td>0.498150</td>\n",
                            "      <td>0.498150</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                 PERCENT FAVORABLE  Percent Favorable\n",
                            "Group                                                \n",
                            "Black                     0.414706           0.414706\n",
                            "Asian                     0.743119           0.743119\n",
                            "Native American           0.450000           0.450000\n",
                            "White                     0.511731           0.511731\n",
                            "Hispanic                  0.328740           0.328740\n",
                            "Non-Hispanic              0.544118           0.544118\n",
                            "Female                    0.400387           0.400387\n",
                            "Male                      0.498150           0.498150"
                        ]
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "pd.concat(\n",
                "    (\n",
                "        custom_air.summary_table[\"PERCENT FAVORABLE\"],\n",
                "        air.summary_table[sd.const.PERCENT_FAVORABLE],\n",
                "    ),\n",
                "    axis=1,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "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>Ratio</th>\n",
                            "      <th>AIR</th>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Group</th>\n",
                            "      <th></th>\n",
                            "      <th></th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>Black</th>\n",
                            "      <td>0.810399</td>\n",
                            "      <td>0.810399</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Asian</th>\n",
                            "      <td>1.452169</td>\n",
                            "      <td>1.452169</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Native American</th>\n",
                            "      <td>0.879369</td>\n",
                            "      <td>0.879369</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>White</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Hispanic</th>\n",
                            "      <td>0.604171</td>\n",
                            "      <td>0.604171</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Non-Hispanic</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Female</th>\n",
                            "      <td>0.803747</td>\n",
                            "      <td>0.803747</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>Male</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "                    Ratio       AIR\n",
                            "Group                              \n",
                            "Black            0.810399  0.810399\n",
                            "Asian            1.452169  1.452169\n",
                            "Native American  0.879369  0.879369\n",
                            "White                 NaN       NaN\n",
                            "Hispanic         0.604171  0.604171\n",
                            "Non-Hispanic          NaN       NaN\n",
                            "Female           0.803747  0.803747\n",
                            "Male                  NaN       NaN"
                        ]
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "pd.concat(\n",
                "    (\n",
                "        custom_air.summary_table[sd.const.RATIO],\n",
                "        air.summary_table[sd.const.AIR_VALUES],\n",
                "    ),\n",
                "    axis=1,\n",
                ")"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Confusion Matrix Metrics"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "SolasAI-provides ready-made implementations of confusion matrix metrics\n",
                "by wrapping around the custom disparity metric."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(<cyfunction false_discovery_rate at 0x7f44c7e735f0>,\n",
                            " <cyfunction false_negative_rate at 0x7f44c7c01ad0>,\n",
                            " <cyfunction false_positive_rate at 0x7f44c7c15040>,\n",
                            " <cyfunction precision at 0x7f44c7ba6930>,\n",
                            " <cyfunction true_negative_rate at 0x7f44c7b7b520>,\n",
                            " <cyfunction true_positive_rate at 0x7f44c7b85a00>)"
                        ]
                    },
                    "execution_count": 28,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "(\n",
                "    sd.false_discovery_rate,\n",
                "    sd.false_negative_rate,\n",
                "    sd.false_positive_rate,\n",
                "    sd.precision,\n",
                "    sd.true_negative_rate,\n",
                "    sd.true_positive_rate,\n",
                ")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 29,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Precision"
                        ],
                        "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",
                            "│ Metric                              │ precision_score                                                           │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Difference Threshold                │ 0.0                                                                       │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Ratio Threshold                     │ 1.0                                                                       │\n",
                            "│ Affected Groups                     │ Black, Hispanic, Female                                                   │\n",
                            "│ Affected Reference                  │ White, Non-Hispanic, Male                                                 │\n",
                            "│ Affected Categories                 │ Race, Ethnicity, Sex                                                      │\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",
                            "│ Metric                              │ precision_score                                                           │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Difference Threshold                │ 0.0                                                                       │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Ratio Threshold                     │ 1.0                                                                       │\n",
                            "│ Affected Groups                     │ Black, Hispanic, Female                                                   │\n",
                            "│ Affected Reference                  │ White, Non-Hispanic, Male                                                 │\n",
                            "│ Affected Categories                 │ Race, Ethnicity, Sex                                                      │\n",
                            "└─────────────────────────────────────┴───────────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Precision Summary Table"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "\\* Percent Missing: Ethnicity: 0.00%, Race: 0.00%, Sex: 0.00%"
                        ],
                        "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>Total Label</th>\n",
                            "      <th>Average Label</th>\n",
                            "      <th>PRECISION SCORE</th>\n",
                            "      <th>Difference</th>\n",
                            "      <th>Ratio</th>\n",
                            "      <th>P-Values</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>139.0</td>\n",
                            "      <td>0.41</td>\n",
                            "      <td>0.511</td>\n",
                            "      <td>0.105</td>\n",
                            "      <td>0.829</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>210.0</td>\n",
                            "      <td>0.64</td>\n",
                            "      <td>0.724</td>\n",
                            "      <td>-0.108</td>\n",
                            "      <td>1.176</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>10.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.556</td>\n",
                            "      <td>0.060</td>\n",
                            "      <td>0.902</td>\n",
                            "      <td></td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,809.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.616</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>508.0</td>\n",
                            "      <td>200.0</td>\n",
                            "      <td>0.39</td>\n",
                            "      <td>0.563</td>\n",
                            "      <td>0.069</td>\n",
                            "      <td>0.891</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>1,976.0</td>\n",
                            "      <td>0.52</td>\n",
                            "      <td>0.632</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>1,034.0</td>\n",
                            "      <td>451.0</td>\n",
                            "      <td>0.44</td>\n",
                            "      <td>0.560</td>\n",
                            "      <td>0.063</td>\n",
                            "      <td>0.898</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>803.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.624</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "precision_arguments = reused_arguments.copy()\n",
                "precision_arguments[\"group_data\"] = precision_arguments[\"group_data\"].fillna(0.0)\n",
                "precision = sd.precision(\n",
                "    outcome=binary_outcome,\n",
                "    label=binary_label,\n",
                "    ratio_threshold=1.0,\n",
                "    difference_threshold=0.0,\n",
                "    **precision_arguments,\n",
                ")\n",
                "precision"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "``sd.precision`` is essentially a convience wrapper for the\n",
                "following call to the custom disparity metric."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Custom Disparity Metric"
                        ],
                        "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",
                            "│ Metric                              │ precision                                                                 │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Affected Groups                     │ Black, Native American, Hispanic, Female                                  │\n",
                            "│ Affected Reference                  │ White, White, Non-Hispanic, Male                                          │\n",
                            "│ Affected Categories                 │ Race, Race, Ethnicity, Sex                                                │\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",
                            "│ Metric                              │ precision                                                                 │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Affected Groups                     │ Black, Native American, Hispanic, Female                                  │\n",
                            "│ Affected Reference                  │ White, White, Non-Hispanic, Male                                          │\n",
                            "│ Affected Categories                 │ Race, Race, Ethnicity, Sex                                                │\n",
                            "└─────────────────────────────────────┴───────────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Custom Disparity Metric Summary Table"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "\\* Percent Missing: Ethnicity: 0.00%, Race: 0.00%, Sex: 0.00%"
                        ],
                        "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>Total Label</th>\n",
                            "      <th>Average Label</th>\n",
                            "      <th>PRECISION</th>\n",
                            "      <th>Difference</th>\n",
                            "      <th>Ratio</th>\n",
                            "      <th>P-Values</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>139.0</td>\n",
                            "      <td>0.41</td>\n",
                            "      <td>0.510638</td>\n",
                            "      <td>0.105</td>\n",
                            "      <td>0.829</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>210.0</td>\n",
                            "      <td>0.64</td>\n",
                            "      <td>0.724280</td>\n",
                            "      <td>-0.108</td>\n",
                            "      <td>1.176</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>10.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.555556</td>\n",
                            "      <td>0.060</td>\n",
                            "      <td>0.902</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,809.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.615965</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>508.0</td>\n",
                            "      <td>200.0</td>\n",
                            "      <td>0.39</td>\n",
                            "      <td>0.562874</td>\n",
                            "      <td>0.069</td>\n",
                            "      <td>0.891</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>1,976.0</td>\n",
                            "      <td>0.52</td>\n",
                            "      <td>0.631757</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>1,034.0</td>\n",
                            "      <td>451.0</td>\n",
                            "      <td>0.44</td>\n",
                            "      <td>0.560386</td>\n",
                            "      <td>0.063</td>\n",
                            "      <td>0.898</td>\n",
                            "      <td>0.000</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>803.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.623762</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": {
                        "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAH0CAYAAADFQEl4AAAgAElEQVR4XuydB1RURxuGX6UICoJd1NhbNPbeu6Kx9xp7jd2YaDRRYzf23nvvvfdeY+y9oKKISlEQkOJ/vuGHgALuZSm7yzvn5Ki7M/fOfWaW7MM330yiz58/fwYLCZAACZAACZAACZAACZAACcQBgUQUkDigzFuQAAmQAAmQAAmQAAmQAAkoAhQQTgQSIAESIAESIAESIAESIIE4I0ABiTPUvBEJkAAJkAAJkAAJkAAJkAAFhHOABEiABEiABEiABEiABEggzghQQOIMNW9EAiRAAiRAAiRAAiRAAiRAAeEcIAESIAESIAESIAESIAESiDMCFJA4Q80bkQAJkAAJkAAJkAAJkAAJUEA4B0iABEiABEiABEiABEiABOKMAAUkzlDzRiRAAiRAAiRAAiRAAiRAAhQQzgESIAESIAESIAESIAESIIE4I0ABiTPUvBEJkAAJkAAJkAAJkAAJkAAFhHOABEiABEiABEiABEiABEggzghQQOIMNW9EAiRAAiRAAiRAAiRAAiRAAeEcIAESIAESIAESIAESIAESiDMCFJA4Q80bkQAJkAAJkAAJkAAJkAAJUEA4B0iABEiABEiABEiABEiABOKMAAUkzlDzRiRAAiRAAiRAAiRAAiRAAhQQzgESIAESIAESIAESIAESIIE4I0ABiTPUvBEJkAAJkAAJkAAJkAAJkAAFhHOABEiABEiABEiABEiABEggzghQQOIMNW9EAiRAAiRAAiRAAiRAAiRAAeEcIAESIAESIAESIAESIAESiDMCFJA4Q80bkQAJkAAJkAAJkAAJkAAJUEA4B0iABEiABEiABEiABEiABOKMAAUkzlDzRiRAAiRAAiRAAiRAAiRAAhQQzgESIAESIAESIAESIAESIIE4I0ABiTPUvBEJkAAJkAAJkAAJkAAJkAAFhHOABEiABEiABEiABEiABEggzghQQOIMNW9EAiRAAiRAAiRAAiRAAiRAAeEcIAESIAESIAESIAESIAESiDMCFJA4Q80bkQAJkAAJkAAJkAAJkAAJUEA4B0iABEiABEiABEiABEiABOKMAAUkzlDzRiRAAiRAAiRAAiRAAiRAAhQQzgESIAESIAESIAESIAESIIE4I0ABiTPUvBEJkAAJkAAJkAAJkAAJkAAFhHOABEiABEiABEiABEiABEggzghQQOIMNW9EAiRAAiRAAiRAAiRAAiRgFALSpUsXLFmyJNxomZubI2PGjGjQoAFGjRoFe3t79f7q1avRrl27r0bW1tYWOXPmRKNGjfDrr78iSZIkqs6LFy/w3XffRTkTypUrh9OnT4erc/LkScycORNnzpyBm5sbUqdOjUKFCqF79+6qTyHl33//RZEiRTBv3jz06NEj9PWbN29i8uTJOH78OFxcXJA4cWLkyJEDLVq0wODBg0P7F1l7udCxY8cwe/ZsnDt3Dm/fvoU8Y+HChdGhQwe0adNGXTOkhHApVaoUzp49G+49qbN//37Url0bDx48UJxYSIAESIAESIAESIAESCA2CBiNgCxbtgxHjhwJZeDl5YXz589j2rRpyJs3Ly5evAgzM7NQARE5KFCgQGh9d3d3HDp0CPPnz0fXrl2xYMGCcALy008/oVWrVhEyTpEiBeSLe0gZO3Yshg8fjpIlS0LaicC8fPkS69atg4hJ//79Vb+kRCQQ169fR8WKFVX/+vTpg2zZssHb2xsHDhxQUlK/fn1s2bIl0vbyxrBhwzBu3DiULVtWyUamTJmUCO3atQtbt27Fjz/+iG3btsHCwkJdRwRE+pooUSLMmTMnnAxRQGLjo8VrkgAJkAAJkAAJkAAJRETAaARk+fLlCAgI+OoZRDT69euHffv2wdHRMVRATp06hfLly39Vv1evXioa8eTJE2TNmjU0AjJ69GglFd8qBw8eRK1atdCpUycsXrxYfaEPW/r27YtZs2YpWapatWqEAiJRkrVr18LV1RXW1tbh2k+YMAHr169XEYn06dNH2F4Eo0mTJpB7zZgx46suL126FJ07d8bvv/8OkaUQAZHIkMjXpk2bcO/ePaRNmza0LSMg3xp5vk8CJEACJEACJEACJBATBIxeQK5cuYLixYuryIZ8sQ9ZahSZgKxZswZt27ZVy5cqV66sWUCqVauGa9eu4fnz51/JgwyIj4+Pel8iJiInEUVAOnbsqKITr169ivAaYQc2ovZybWn76NGj0AjHl5NBllPJ8rDXr1+re4RwefbsmeJVs2ZNrFq1igISE58iXoMESIAESIAESIAESEBnAkYvIBs3blR5E7J8Sb5Uf0tAQpYuPX36FFmyZAkVkBEjRmDIkCERgrO0tFQ5E58+fVJ5FnK/lStX6gQ5IoHYsWMHGjZsqJZwDR06FNWrV4eNjU2E1/uy/YcPH1S+i0iMRGAiK7JkTaI0R48eRZUqVUK5eHh4YOfOnWo5Vsh7cg1GQHQaTlYiARIgARIgARIgARLQk4BRCYivr2/o4378+BEXLlxAt27dkCxZMvzzzz8QUQgREEnuluTxkCJfvPfu3YuePXuiRo0a2L59u3pLlyR0yR0RSXB2dla5FiNHjoQIiy4lsiRyWVImMiS5I5JQX7RoURWREbmRv4eUL9vL0inJeZGlVbLEKrIiSfMVKlTAihUrlGyEcJFcGBEYkRJJfpdojXCjgOgymqxDAiRAAiRAAiRAAiSgLwGjEZAvd8EKeXBJ5pb3QnZuimwXLKkvotK6dWtMnz4dSZMmDScgsnxLdo+KqOTLlw/JkydXsiA7b/35559q5y1dSlS7WAUGBqrdtSRxXZaMnThxQkVZJH9j0aJFES7hun//PvLkyYO///4bv/zyS6RdCMlVkUiN5H58KSB37txRu3aJSIkIUUB0GU3WIQESIAESIAESIAES0JeA0QiIRAxk16uQIlGDDBkyhEuklvdCvmjL8iT5gi1Fvug3bdpURQ4kmhG2hERAdElClyR4EZF69ephw4YNOrGPSkC+vIBEaSSxXHIzZBesxo0bf5VDIrtl2dnZqeVVCxcujLQPkhMj0R6RG4mEfCkg0lAiKCJjsiWwiA234dVpSFmJBEiABEiABEiABEhADwJGJSAR7YL15bNHlgMieQ9yPodsvyvLtkKKFgGRNnXq1FEJ7LIUKnPmzF+hF9mRqIIIQvbs2SNMQpczOyRBPVWqVF+1lyVSKVOmDN3BKiKBkaiP3N/JyQlWVlYRDr9swyvnfcgyKznzJCIBkWVs+fPnx/fff4+BAweqpWk8B0SPTxObkgAJkAAJkAAJkAAJfJNAghEQISFb10oERH7jHyIPWgVEcktke12JFshOVpI/EbbIIYJylsfhw4chO2Z9KRAiGLKMK7IoiiSGSzs5q0O2DI5IQPbs2YO6detCDmiUKMiXWwGHyEbYqE5EAiL93r17t+qLnF0i0RAKyDc/M6xAAiRAAiRAAiRAAiSgB4EEJSCSwyH5HLL7lORISAkRkKgOIpR6krQdcnq65F/IaeoSOZBzNeQEc4k0yJd8yekQAZGIgpSIBGLq1KkYNGiQkpj27durxPaQpHo5wFAiI7K9sOy4FdkSLslBkWR4eRbZEUt29Hr37p3a4Wrz5s1o1qyZOmtEDmeUEpmAyHtyOrxIjb+/PwVEjw8Tm5IACZAACZAACZAACXybQIISEMEhhxBKZCFkKZYuu2BJOzn3Q0QhpMjyJpEFEQ45gVwO9ZPzNSQxPOzuW5EJhOzIJXka8r4cSCjb/MqSLYlsiNzIMqzIBCakD5LfIQcxynkfIh8iLLKDlkRGmjdvHi4yEpWAyNkgImaSX8IIyLc/NKxBAiRAAiRAAiRAAiQQfQJGISDRfzy2JAESIAESIAESIAESIAESMCQCFBBDGg32hQRIgARIgARIgARIgARMnAAFxMQHmI9HAiRAAiRAAiRAAiRAAoZEgAJiSKPBvpAACZAACZAACZAACZCAiROggJj4APPxSIAESIAESIAESIAESMCQCFBADGk02BcSIAESIAESIAESIAESMHECFBATH2A+HgmQAAmQAAmQAAmQAAkYEgEKiCGNBvtCAiRAAiRAAiRAAiRAAiZOgAJi4gPMxyMBEiABEiABEiABEiABQyJAATGk0WBfSIAESIAESIAESIAESMDECVBATHyA+XgkQAIkQAIkQAIkQAIkYEgEKCCGNBrsCwmQAAmQAAmQAAmQAAmYOAEKiIkPMB+PBEiABEiABEiABEiABAyJAAXEkEaDfSEBEiABEiABEiABEiABEydAATHxAebjkQAJkAAJkAAJkAAJkIAhEaCAGNJosC8kQAIkQAIkQAIkQAIkYOIEKCAmPsB8PBIgARIgARIgARIgARIwJAIUEEMaDfaFBEiABEiABEiABEiABEycAAXExAeYj0cCJEACJEACJEACJEAChkSAAmJIo8G+kAAJkAAJkAAJkAAJkICJE6CAmPgA8/FIgARIgARIgARIgARIwJAIUEAMaTTYFxIgARIgARIgARIgARIwcQIUEBMfYD4eCZAACZAACZAACZAACRgSAQqIIY0G+0ICJEACJEACJEACJEACJk6AAmLiA8zHIwESIAESIAESIAESIAFDIkABMaTRYF9IgARIgARIgARIgARIwMQJUEBMfID5eCRAAiRAAiRAAiRAAiRgSAQoIIY0GuwLCZAACZAACZAACZAACZg4AQqIiQ8wH48ESIAESIAESIAESIAEDIkABcSQRoN9IQESIAESIAESIAESIAETJ0ABMfEB5uORAAmQAAmQAAmQAAmQgCERoIAY0miwLyRAAiRAAiRAAiRAAiRg4gQoICY+wHw8EiABEiABEiABEiABEjAkAhQQQxoN9oUESIAESIAESIAESIAETJwABcTEB5iPRwIkQAIkQAIkQAIkQAKGRIACYkijwb6QAAmQAAmQAAmQAAmQgIkToICY+ADz8UiABEiABEiABEiABEjAkAhQQAxpNNgXEiABEiABEiABEiABEjBxAhQQEx9gPh4JkAAJkAAJkAAJkAAJGBIBCoghjQb7QgIkQAIkQAIkQAIkQAImToACYuIDzMcjARIgARIgARIgARIgAUMiQAExpNFgX0iABEiABEiABEiABEjAxAlQQEx8gPl4JEACJEACJEACJEACJGBIBCgghjQa7AsJkAAJkAAJkAAJkAAJmDgBCoiJDzAfjwRIgARIgARIgARIgAQMiQAFxJBGg30hARIgARIgARIgARIgARMnQAEx8QHm45EACZAACZAACZAACZCAIRGggBjSaLAvJEACJEACJEACJEACJGDiBCggJj7AfDwSIAESIAESIAESIAESMCQCFBBDGg32hQRIgARIgARIgARIgARMnAAFRM8BfvnOR88rsDkJkAAJkAAJkAAJxB+BDKms4+/mvHOCJEAB0XPYKSB6AmRzEiABEiABEiCBeCVAAYlX/Any5hQQPYedAqInQDYnARIgARIgARKIVwIUkHjFnyBvTgHRc9gpIHoCZHMSIAESIAESIIF4JUABiVf8CfLmFBA9h50CoidANicBEiABEiABEohXAhSQeMWfIG9OAdFz2CkgegJkcxIgARIgARIggXglQAGJV/wJ8uYUED2HnQKiJ0A2JwESIAESIAESiFcCFJB4xZ8gb04B0XPYKSB6AmRzEiABEiABEiCBeCVAAYlX/Any5hQQPYedAqInQDYnARIgARIgARKIVwIUkHjFnyBvTgHRc9gpIHoCZHMSIAESIAESIIF4JUABiVf8CfLmFBA9h50CoidANicBEiABEiABEohXAhSQeMWfIG9OAdFz2CkgegJkcxIgARIgARIggXglEBMC0rBhQ+zevRvm5ubqWRIlSoT06dOjdevW+Ouvv2BmZhavz8ibGxYBCoie40EB0RMgm5MACZAACZAACcQrgZgSkLx582LChAnqWT5//ozbt2+jXr166Nu3L/r37x+vz8ibGxYBCoie40EB0RMgm5MACZAACZAACcQrgdgQkJAH+vnnn+Hp6YnVq1erl/7++28sWrQIvr6+SJMmDebOnYtSpUqp944cOYIBAwbg9evXyJo1K2bNmoWSJUvC398fv//+O7Zv367+Xrx4cSxevBj29vbxyo03jz4BCkj02amWFBA9AbI5CZAACZAACZBAvBKIDQGRCMjVq1dRv359/PHHH+jevTvOnDmDOnXq4N69e2p51qhRo7B161Zcu3YN7969Q7Zs2ZRkVK1aFStXrsTQoUPh5OSkoip79uzBwYMHYWtrqyIqLi4u2LhxY7xy482jT4ACEn12FJBosAsK+ow9p67D9e2HaLQ2jia2yazRqHphWJhzvatxjBh7SQIkQAIJm0BMCcj+/fthZWWlYEqEI126dBg0aJASBikiJe/fv4ednZ3698WLF1G5cmV8/PgR69atw5gxY3Dr1i31XlBQkJKS1KlTI1euXEpi2rdvr957/Pgx8uTJA29vb1haWibswTPSp6eA6DlwjIBoA/jRxw+DJm+GZbJk2hoaUW1f748Y1fNHpE8d/AOWhQRIgARIgAQMmUBMCUjYHJClS5eGCoW1tbV6fBGNYcOG4dSpUyop3cfHB/fv31eyIsutNmzYgNOnT3+FKlWqVLCwsAiVG6ng4eGBGzdu4LvvvjNktOxbJAQoIHpODQqINoAiIL/P3AGblCm0NTSi2l7uHhjSqSYFxIjGjF0lARIggYRMIDYERKIdkr/h6OiI0aNHK7y//fYbLl++jJ07dyJZsmRqSVa1atWUgMhyquHDhyshkSLtJRoikQ7J+RgxYgQaN26ckIfJpJ6dAqLncFJAtAGkgGjjxdokQAIkQAIkENsEYkNApM8hgiGRCllG1a5dOyRNmhQLFiyAl5eXyguRqIcspZJoSObMmbFq1So0aNAAmzZtQp8+ffDixQuMHz8ex48fV9v8SjRl7969OHToEKZNmxbbaHj9WCJAAdETLAVEG0AKiDZerE0CJEACJEACsU0gtgRE+i3ngEgux4EDB1SyeZs2bRAYGKjyQ6ZOnaryOmRnq7t37+LkyZPo0aMH3rx5o5ZWzZkzB2XKlIGfn59aurVjxw6FIm3atJg5cyaKFSsW22h4/VgiQAHREywFRBtACog2XqxNAiRAAiRAArFNICYEJLb7yOubFgGjEhDZ9aBz587KoLNkyYLZs2ejXLly4UZE6uTLly/ca2LWsrawSZMmkB0aBg8ejJcvX6JEiRJYvny52gpOysSJE9V+1GLass5Q7DrkRM/Ihp0Cou0DoY+ABPh/wtltc+D84CrMLaxQoFJj5C3l+FUHNv/dHR/fvwt9XXbSkHql63fDs9sXcXn/Cvh8cEdKh2wo26gX7NJkDHeNq4fX4d6F/Wg5bIW2h/t/beaARAsbG5EACZAACcQTAQpIPIFPwLc1KgGRrdpkXaBs5yZr/0RGnj59qnZGiKxIGK9s2bJqq7fEiRMjZ86cKvlJ5GPkyJFqL2pZZyhrC7t27ap2ZrCxsUGjRo3QsGFDyAE6URUKiLZPjz4CImLg8foZKjTvDx8vD+ydPwQ1Oo5AyvRZo+zE3gVDUbRmW9imTIft0/uievs/kDZzHvxzeC3eON2FY9cxoe093zjjyKqx+OTjTQHRNrSsTQIkQAIkYKQEKCBGOnBG3G2jERBXV1clD25ubqFRCVn7N2XKFLWHdGRFpKJIkSLo1auXEo0lS5aoKIgUOZlT1iDKnwMHDlTrDYcMGaLe27Vrl7q2iAkFJOZmuD4Csm1ab5Rr0kfJg5SLe5bCIok1ilRvFWkHH/17As9uX0CV1r/C2/MtXJ/dQ7YCwVGzdy8f4/CKMWgxdGlo+/2LhiNPqVq4sGsxBSTmhp1XIgESIAESMGACFBADHhwT7ZrRCIjspNCzZ09cv349dChatmypTsvs1q1bhMMjy7GqV6+utnSTpVRjx47F69ev1dKqkCLLryTpSSIdcv2QLd4kGUquLUu1KCAxN/v1EZAVw5ug5e/LkSSprerQ3Qv78frJLVRqOSjCDgYFBUKkpVq7YbBPm+mrOjdObMW7l49QudVg9d7DK0fx8tE1lPyxM7ZP70MBiblh55VIgARIgAQMmAAFxIAHx0S7ZjQCIkuuZH/oCxcuhA5Fp06dULBgQfTv3z/C4ZGoh2z7NmDAAPW+7KAQEBCgcj1CSvbs2bF161YlIHL92rVrq7eePXumri0H3URVPvoFmOjUiJ3H8vbxQ/9JW2H9/1NQdb1LUGAAlg1rgvajN8LcIolq9uCfY3h68xxq/PR7hJd5fP00Hl09gRrth331/ov7/+Ds9vn4sfs4JLNLDV/v99g97zf82GM8EiVKjK3T+qD18OjlgHh7vseYno5wSGuv6+OxnpESSBQH/Q4I+gzzxHFxpzh4GAO6xWcD6gu7QgLxTSBpEvP47gLvn8AIGI2AnD17VkU6bt68GTpEzZs3R82aNdGlS5evhk1EQ5ZX3b59W/0pZdy4cSqiIcnrIUW2cpNri6zI9Zs2bareknZybdl/Oqri/uFTApsy+j2uCMjgqduQ1F77l/PlEgEZugxWyZKrTtw5vxeuTndRqcXACDt1aMVo5CxSBdkKlg/3vizL+vfoBtRo/weSp3JQ753aPBNps+RFnhI1lYxsm94HraKZhO7t6Yk/u9aCQxrtz6gfXbaOawJx8SVWDuNKlIgCEtNjS6IxTZTXM2YCKWwtjbn77LsREjAaAXn79q3a+UqSyuUQGykS3VixYoVKMv+yHDt2TJ24KcnnIUUiHTNmzMCJEyfUS87OzsidO3doDkiKFCkwatQo9d6aNWvUtQ8ePBjlsDIJXdus12cJ1vYZfVGqblc45Cigbnpm62zYpEiHQlWafdWJgE9+WD+uPZoPWQJLq2Sh70s+yD+H1qBmp1FIavvfaexrR7dF4sRmqt5nfIaf9wclOo0GzA5d8qXrk3IXLF1JsR4JkAAJkIAhEIiLJVgffT4hMChI58dNamUJM7PEOtdnReMiYDQCIlgln0MSzocOHapOzpQlUw8ePICZmRnWrl2LatWqhUY75HAbyReRbXZDyocPHyBLrqRtxYoV1W5acvqmiMbp06fV4TiSa2Jra6vuJVGRjh07UkBicE7rIyDXjm6E67O7qNLmN3xwe419i4bhxx4TYZc6AySqkSFHIVjbBkcd3jk/wtHVE9Dst0Whvffz8cL2Gf1Qp/s42KYIjopFVCQCwhyQGBx0XooESIAESMCgCcS2gLzz8MaQmTsQoOOqdV8/f9Qu/z1+qlfSoLmxc9EnYFQCIlvudujQQZ0DkiNHDixYsCD0FExJJt+8eTPKlw9ebiO7WllaWmLChAnh6EguieSMuLi4qMiJCEqqVKlUncmTJ6sIiSzfatWqlfq3bN0bVWEERNvk00dAAgP8cUbOAbn3j9r9qnC1lshZtIrqwPqx7ZWYpMsafAaMnPdx/fgm1O31d2gHH1w+jNNbZ8PMLPxa1+ZDloYu65LKFBBtY8raJEACJEACxk0gtgXk2Ss39JuyHUGWwStYvlV8fHxR5QcHDO1c61tV+b6REjAqATFExhQQbaOij4Bou1P81eYSrPhjzzuTAAmQAAloJ2BqAiK/jA45/012Qc2TJ4/aCdXRMfjwYnt7e5VTnCnT1ztkfouenBEn+cJt27b9VlW+HwUBCoie04MCog0gBUQbL9YmARIgARIggdgmYIoC0qNHDyUJgYGBWLVqlVp2H3KWHAUktmfUt69PAfk2oyhrUEC0AaSAaOPF2iRAAiRAAiQQ2wRMWUCE3adPn5AkSRK1E6qDg0O4CMjixYsxadIk+Pv7q82ORFbkYGqRFckDPn/+vFqqP23aNNSqVQthIyBysHXv3r3VbqqyqyqL7gQoILqzirAmBUQbQAqINl6sTQIkQAIkQAKxTcCUBUTyeufPn4+VK1eG7owaEgERKRHZkMOns2bNqo51kCVbUr9r165q19Xp06fj0qVL6mgGyR+WQ7BlCVaJEiWUkOzZswf58+eP7SEyuetTQPQcUgqINoAUEG28WJsESIAESIAEYpuAKQrI1atXYWFhAV9fX1hZWSkBqV+/vkIZdgmWj48PrK2t1euyo6psTiRHMGTMmBG7d+9GkSJF1Hvu7u6Q4xokAlKjRg3MmzdPRUXk7yzaCVBAtDML14ICog0gBUQbL9YmARIgARIggdgmYIoCEpIDIoe53rlzB40aNcKUKVNQt27dUAERyRgzZgx27dqlDnwVycicOTMOHz6soh9yKLVERsIWERA5T06uK0c3MPoRvdlJAYket9BWFBBtACkg2nixNgmQAAmQAAnENgFTFpAQdnIEQ1BQEGbOnBkqIBcuXFAHUJ86dQp2dnYqSiL/iYCIiGzZskUttZJy7949JSMtWrRA4cKF1Q5aslRLckRk2RaLNgIUEG28vqpNAdEGkAKijRdrkwAJkAAJkEBsEzB1AXFyclL5Gr/++is6deoUKiDbt2/HgQMHVATEw8MDzZs3x/v375VUyGHUfn5+WLhwoTp/TpZaOTs7h+aAyA5bsq1vmTJlMGLEiNgeIpO7PgVEzyGlgGgDSAHRxou1SYAESIAESCC2CZiigIScAyJLq2QXK0kwF1GQf4dNQq9Xr56SD0lG/+uvv9RSrXbt2mH48OFo3769io6kSZNGRU4kET3sLljPnz9H0aJFlcTInyy6E6CA6M4qwpoUEG0AKSDaeLE2CZAACZAACcQ2gbgQkN6TtiLQIjjZ+1vF18cP1QpmwO9dgw8OZDE9AhQQPceUAqINIAVEGy/WJgESIAESIIHYJhDbAuL10Q/DZu/Ce69POj2Kf0AgWtQqjHqVCuhUn5WMjwAFRM8xo4BoA0gB0caLtUmABEiABEggtgnEtoDEdv95feMjQAHRc8woINoAUkC08WJtEiABEiABEohtAhSQ2CbM639JgAKi55yggGgDSAHRxou1SaAmkbQAACAASURBVIAESIAESCC2CVBAYpswr08BieE5QAHRBpQCoo0Xa5MACZAACZBAbBOggMQ2YV6fAhLDc4ACog0oBUQbL9YmARIgARIggdgmQAGJbcK8PgUkhucABUQbUAqINl6sTQIkQAIkQAKxTSAuBOSjzycEBgXp/ChJrSxhZpZY5/qsaFwEmAOi53hRQLQBpIBo48XaJEACJEACJBDbBGJbQN55eGP4nF0ICkqk06P4+vmjeuncaFe3pE71Wcn4CFBA9BwzCog2gBQQbbxYmwRIgARIgARim0BsC8izV274fc5eWCe30+lRvLx9UCRrCvzasYZO9VnJ+AhQQPQcMwqINoAUEG28WJsESIAESIAEYpuAqQrIL7/8gqVLl+LatWv47rvvvonRxsYGd+/eRaZMmb5ZlxX0I0AB0Y8fKCDaAFJAtPFibRIgARIgARKIbQKmKCCBgYHInz8/2rdvr/ANHTr0mxhdXV2ROnVqJE7M3JNvwtKzAgVET4AUEG0AKSDaeLE2CZAACZAACcQ2AVMUkD179mDjxo0YN24catWqhZs3b4ZinDx5MhYsWIDPnz8jXbp0WLVqFbJnz46wEZDFixdj0qRJ8Pf3R5YsWVQdiaI0bNgQRYsWxeXLl/Hy5Uv12ubNm2FmZhbbw2RS16eA6DmcFBBtACkg2nixNgmQAAmQAAnENgFTFJBmzZqhe/fuqF69OqpVq4YJEyagRIkSePv2LXLnzg0nJyfY2tpixYoV8Pb2Rq9evUIFJEmSJEosZDlW1qxZ0aVLF5ibm2P+/Plo2rQp3NzccPDgQSUdBQsWxJQpU1CzZs3YHiaTuj4FRM/hpIBoA0gB0caLtUmABLQTCAgIxKPnb7Q3NKIWWTKkhFUSSyPqMbtqyARMTUDc3d1RrFgxPHz4UC2nWrZsGf755x/MmjULPj4+yJAhA8aPHw+RlFSpUoUOTdgIiNSztrZW761duxbLly9X0iECUr58efTv31+916hRI9SvXx8dO3Y05CE2uL5RQPQcEgqINoAUEG28WJsESEA7gVPnr+HJuZ3IZG+aSyLeegUiac6KqFu7knY4bEECERAwNQGZO3cuBg4cCCsrK/W0stTK0tJSLZmysLDAlStXVETk0KFDKF68OObNm4dcuXKFRkAyZsyIMWPGYNeuXUiUKBFEaDJnzozDhw8rAXF0dFRRESlf/psTTDcCFBDdOEVaiwKiDSAFRBsv1iYBEtBO4Oipy8j4aCkKO3zW3tgIWjx2T4TLNg1Qr24tI+gtu2gMBExNQEqVKoUZM2agdOnSofgbNGigohSSwxFS/Pz8MGLECNy4cQOSMxISAblw4QJGjRqFU6dOwc7ODitXrlT/UUBibjZTQPRkSQHRBpACoo0Xa5MACWgnQAHRziyht5DfkAcG6n5KtzHyMjePPCJoSgJy584dlfPh7OysohchRZZhSUTj119/VcnlsqxKIiSytEqSyHfv3h0qINu3b8eBAwdUfQ8PDzRv3hzv37/H+fPnGQGJoclPAdETJAVEG0AKiDZerE0CJKCdAAVEO7OE3mLH9hN4evshLMxMc/vVD34BaNu9KTKmSxHhUJuSgAwZMkRJgySMhy2SfC7LqJ4+faryP7Zu3aoSy9OnT49FixYhX7584ZLQ69Wrp64jyeh//fWXyvVo164dnjx5wiVYMfADgwKiJ0QKiDaAFBBtvFibBEhAOwEKiHZmCb3FyoWb4ej+ECmTmKaAnHUPRLJmrVDsh6zxJiC/ztgNK1tbnaaa90cfFMueGkM6c2cpnYAZYSUKiJ6DRgHRBpACoo0Xa5MACWgnoI+AHLnmjD/XXYaLuw+KZE+FOd3LI5198E44YcuLt17oNf8Mrj5+i6zpbDG3R3kUyJISbh98MWjpeZy49QpJk5ijW83v0bfeD6rpPWcPDFxyHjefuSFDyqQY1ao4ahbRfuIyc0C0z4lvtaCAfD3Hv8VMy/teH/0wcv5efPD+pFMzf/9ANK5eEHUrBn92WEyPAAVEzzGlgGgDSAHRxou1Aa+Pvliz+wJgmvnEaneW79KnwI+VC3G4Y4hAdAXk/cdPKDZwK9YOqooi2VNj4pZrePjKE8v6Vf6qZz/+tR+1i2VC91r5sOnsY5y57YI5Pcqjx9xTSGZljvE/lcRLt4+o8ecerBlYFSVzp0XZ33agbeVc6On4PY7eeImOM47j7twWSlS0FAqIFlq61aWAxK6A6DYKrJWQCFBA9BxtCog2gBQQbbxYG7j/1AXLdp5HxoxpTRJHYFAQXF+6Ymy/xib5fPHxUNEVkO0XnmL18QfY/FsN1W0Rkjy9NuDpotZIYvFfAq+T6wf8OHo/rs9oisSJ/0tylTa7LzmhVO60SGMX/IWu6cRDaFQ6G1qUz66u3aZSLliYBy/zydJlLY6NqYvs6ZNrwkQB0YRLp8oUEAqIThOFlWKMAAVET5QUEG0AKSDaeLF2sICsO3AFWbM4mCQO2XnH6YkzRvVuYJLPFx8PFV0BmbL9Ot54+mBC+1Kh3c7bawN2/+GInA52oa/tufwMCw/cUUuvjt94iWzpbDGpQ2nkzvBfHYlsXbz/Bp1nn8DeP2sjcxqbcCiuPHqD9tOP49/pTWCuMfGZAhLzs0ofAZl39jRWXbmETwGBcMz7PUbWqg3zxF/nkpx9+gTD9+2By4f3KJk5C2Y0bAI7Kys8ePsGv+/djTuvXZDONjmGVauBqrlyh3vIc05P0XLVchzp0Rs5U6fWDCC+c0A0d5gNTJ6AUQnI48eP0blzZ1y7dg1ZsmTB7NmzUa5cua8G6dOnT+jZs6faVi158uTqsJk2bdqoevv378fgwYPVYTQlSpRQ26/JDghSJk6cCDm8RvaFbty4MWbOnKl2SIiqUEC0fUYoINp4sTYFhHNAO4HoCsiYjf8gIPAzRrYqFnrTIv23YOWAKiq/I6SsPfkQg5edx7pfqqFCvvSYt+821p58hNMT6qsqEjnJ2WM9LM3N8HeHUmhVMWe4h5AIStOJhzGhfUlUK5hR8wNSQDQj+2aD6AqIiMGQ3TuxqX0nJLO0RLdN61EzT160L14y3D09fXxQbf4czGrcFEUyZsL4I4eQK3UatC1WHDUWzEWLwkXQuWRpnHz8CD23bMSVAYNhbWGhruEXEIBGyxbD1csL69t1oIB8czRZwRgIGJWAVK5cGXKQTN++fdXplSIjsp2anGoZtvz555+4desWVq1ahYcPH6JTp044ffq0EoucOXNi586dSj5GjhyJe/fuYdOmTTh+/Di6du2qDp2Rg2hkuzU5rObnn3+mgMTgTKaAxCDMBHIpRkASyEDH4GNGV0Cm7riuks8ndfgvApK75wbsH1E73DIpWWb197brODGunuq1LKPL2HENbs9uhpS2/528/PDVe7SfcRxDmhRG/ZJZVN1bz9zw0/TjGNu2BByLfhetp6aARAtblI2iKyB/7NsDh+R26FWuvLr+4fv3sOjCOWxo1yHc/TZeu4rTjx9jZqMm4V4PCArChn//QfNCRWBhFrzM74e/x2N35+7ImjJYeqedPK5yxfbdvYN5TZpTQGJ++HnFeCBgNALi6uqq5MHNzS00KlGsWDFMmTIFIiZhi0RHRFBy5w4fwhTRWLJkiYqCSPH09ES6dOnUnwMHDlR7Pcv+0VLk8Bm5tohJVIUREG2zlgKijRdrMwLCOaCdQHQFZNclJyzYf0ctuZLy0s0bJX/ZjqeLWoVbJnXDyQ1tpx7FtRlNVT0RkAwd1uD+vObYedEJjcpkg41V8C/GJmz+F67vfTC1Uxk8df2AJhMOYW738iiVJ/o5TRQQ7XPiWy2iKyBt1qxE22IlUDvv9+oWD9++RavVK3Cp/6Bwtxx1cD8CAgPx2O0dnNzcUDJLFoyqVQe2SZKEq/evszN6bNmA0737q2Vcj9+9Q4/NG7CzczfUW7LQqAXko88n9VnRtSS1soSZxuWJul6b9eKfgNEIyJkzZ9SyquvXr4dSa9myJapWrYpu3bqFviaHxjg4OKjlVLJEy9raGqNHj0b9+vUxduxYvH79Wi2tCimy/OrkyZMq0iHXl6VXUu7evauuLUu1oipvPHzjfxSNqAfePn4YMn07kqaI+DAkI3qUSLvq7eGB4Z1rIn0ae8N6nPC5sobVtyh6c++JC9bsu4ysWTJo7vM7V2dsWTgBr549QIrU6VG//UBkzVPwq+sEBPhj+9LJuHHxGKysk8GxZQ8UKVdL1fN6746N80bD080VAyauDtf2zP6NOLZzFQID/FGicj3UbtUr3Mm7unRY5YA8fYExfRtGWF1+8xn2NF9drpnQ6xw5eRkOD5agsIO2rdM++PijcP8tWNa3EsrmTYffVlzER78AzOtZHvdfekKWTtUoHLxtbvkhO9Gzdj60rpgD8w/cwcbTj1VCefU/96DyDxkwtGkheHh/Ukut2lTKiS418qLumAPoUj0PGpaO+CwGXcdNBOSKbUM0qB88Rw2maMNtMN2WjiybL+eAPNB8DkijZUvQt0JFVMmZSz2Ps6cnai6Yh1u/Bv8yM6QM3LEdl148w4a27ZEyWTL0274V6Wxs8Zdj7dA6z9zd0Xbtaox2rI1KOYKX7bVctQI/l6uACtmzo/r8uZjfNHoRkNPugUjevDVKFIh47qWxD47cxVZ55+GNkfP3ANDtf0Q+vv6oUjIX2tQpEVtd4nXjmYDRCIhENIYPH44LFy6EIpOlVQULFkT//v1DX5MlWRIpkVMrhw4divPnz6sTK+/cuYM5c+YgICBAyUlIyZ49uzoNUwRErl+7dvAPg2fPnqlri9BEVT4F6G7z8TzWBnF7EZA+4zfDys7AvpzHIB2f954Y+3MdZEhrYM9opF8O7jx+hcU7LiBbVu0CMndUL+QvXhEVajfD/euXsGH+WAyfvQ1mX+R27d+4EC7Pn6B17xF46/JC1es9aj4CAwMwc3hX5CtaDneunsXgyWtCZ8qDm5exefEk9PxjNswtLLF6xh+o26Y3MmXPo2k2BSehv8DUwcG/Tf+yBAQGaU5S1tQBE6y8/9hFpLi9EEUyaJ/0R6+/VOLh6uGDUrnTYF6v8khla4Ulh+5h+/mn2PVH8Jf+xy7v0X76CRXVyJvJHnN6lFNJ6LLsasDic7ju5KZ+g92gVBZMbF8Szm7eKNBnS7jdtOQ6y/pWRL3/L8/SdSgeuSXCDfvGaN6kjq5N4qaebt8t46YvGu8yd+Z61Hh7DymT/LfbmS6XaLlKIiDFUDdfflX9/htXtFq1ClcGho+A/LFvLxInSoRR/xeOC05OGLpnN472Cl7mffu1C7ps2ICRtRxRM0/wz5CN//6rckJmNw5etlVl7hwsai4CkkaXroWrc9otCOl+aovShbJH2Nby/zuzab6wjg2evXLDmCWHkCadbn1//+EjsqWxxsB21XS8A6sZGwGjEZCzZ8+qSMfNmzdDGTdv3hw1a9ZEly5dQl9zd3dHypQplTjY2QXvSFKtWjX06tVL5XtIREMiIyElbdq0kGvL+3L9pk2DvwTcvn1bXfvFixdRjimXYGmb8lyCpY0Xa0d/CZaXpzumDGqB4Qv2wswseDOJ2cM7o06b3sj+fZFwaCf1a4JOQ6YhtUPmcK/7+X6El4cbPni6Yceyv9FvwqrQ9zfNH4MsuQugZFX9dq/iLlgxP8ujuwQr5nsSO1fkEqyY5xrdJVgjDuxTO1kNrFRFdWr7jevYfP0aVrdpF66TSy6cV5IxpX5wpPPiMyeMPLAPe7v2QHDkYxWm1m+I4t/99zNIEtovPX8Gs0TBO2q5+3xEcisrTK7XENW+2CXrW0TiexesuBaQ8uXL4+PHj7h8+TIS/39HMvlemDVr1m/+Yllyh11cXCDXkPay2kZei6mye/dutG3bVm161Lp165i6bITXkZxmWdGTKZP2A09jtWMSC/ss8X0jKG/fvlU7X7158wZJkyZVPc6VKxdWrFiBsmXLhnuCFClS4OrVq2qiSREBkcT1wMBAzJgxAydOnFCvOzs7qzyRkBwQaTdq1Cj13po1a9S1Dx48GCUdCoi2yUMB0caLtaMvIE73r2PHsinoO35FKMb1s0cge76i4aTBx/sDxvduAMeWPXHu0FZYWFqiRtOu+L5ocFKplKf35FrhBWTW7x1QoHQ13Lx4HH4+3ihaoTaqNGyvecgoIJqRfbMBBeSbiFjhCwLRFZBLz56h344t2NK+s9oFS3JC2hUvoZLKZdtdOytr5E+fXu1gVXPBXKxr214lkfffsRUZktthWPWaanvddsVK4Mf/R1EiGxzZLctYk9DjQ0CeP3+OYcOGhS7T11VApk2bpjYtkpxgWTUj7VJHY+vjyMZRftEtS/x37NiBAwcOxOpnUfKnpe8hEharN9N4caMREHmu6tWrq4RzWVq1YcMGtWTqwYMHMDMzw9q1a5VoSFK5yIaY7/z585WISCRDoh+SDyJLrqRtxYoVVT1vb28lGrJLlmzVK7kmtra26l4SFenYsSMFROOkiqo6BSQGYSaQS0V3F6wHNy7h0OZF6DVqYSipLQvHI33mHCjn2Dz0Nfc3rzBlUEtUb9IZleq3w/OHt7Bs0iAMmLQGyVME77cfkYBM6t8UGbPlQbPuw+Hr441FY/vAsUUP5C9RSdPIUEA04dKpMgVEJ0ysFIZAdAVELrHw3FksvXQesqNVg/wFlFTIcqveWzcjT5q06FOhorrT0Qf3MXz/Hvj6B6B8tuwYV6cu3Hw+osLsGUjy/yhtSJdktyw5UyRsoYDoPmUleiHf3/744w+1BF9WxHwpIIsXL8akSZPg7++vfsEtO6fKd8pmzZqp3VWlfZMmTVQERH55Ld89w+YhFypUSC3pl1+C9+7dWy35F2H5/fffw63MCdtr2UipTJkyKiqRP39+HD58GBkyBC8vlp1XCxQooO4hq33at2+PJEmS4OjRoxCZku+u8v779+8jvZ88p9xfNlF68uSJ+k4cEgFZvXo1ZJdYOapCvkvLpkxy/Yg4yKZM0p+iRYuqKJCsHpLX5HgL+c4dE8WoBETyOzp06KDOAcmRIwcWLFgA2QlLiiSTC5iQsJvUk+iFwJ88eTLq1QveLlFySSRnRMJrMmnkHJBUqVKp96SeTDKZQK1atVL//pY1MgKibRpSQLTxYm19IiA3sH3ppHDLptbN+gM5fyiJElWCfx5I8fF+j9Hd6+DPhfthlTT4sLgl4/qhVPVG+KFk8A57EQnIzN/bo3qTLshXrELwl4tty+H93h312g/QNGwUEE24dKpMAdEJEyuFIaCPgBgDyIS4BEvkYN26dbC0tMTUqVPDCYisppEv1PLlXFbLyFJ+OfdNfnEtMiFLliQCErIES5bly5J9+aV2tmzZ1Jf74sWLq++ScracrNKRX2aL5Mjr27ZtQ+HChb+aGpKL/OrVK4wZM0ZtkGRlZaXaS5HIiPzyfM+ePWrJ1/fff68kQURE6ooEzJs3T32Hjex+Eu0QcZJnl++vIUuw5Hut9OvSpUvInDmzkgs5R0+Os4iMg/RHhEm+S4t0SF60iI38Uj8milEJSEw8cExfgwKijSgFRBsv1o6+gHh/8IBEKYbN3Q3LJME7vEiko2n3YSp3I2z5q1tt9Bm7FCnSBJ+2LgJSplbTULmISEBWTRuq3i9WMTgRWAREZObHtn01DRsFRBMunSpTQHTCxEoUkFACGVJZx+p8iI8lWHIIdb58+dR/cqSC/KI6bA6Ij4+PWhkjRVbRyC+k5ct2RAIiQtCuXTv1Jb5fv35KaERKJHogEQz5BXhIOoAIhaQKhCzpDwu2ZMmSWLlyJfLmzaskRs62C4mqyBd+iUzI/SVlQIRIdm4V8ZG0gI0bN6plW1HdTwREjpGQKIuUEAHZt28fJPdE2ksR0RGpkAhIZBykP/JL/ZCNnuR8PNlR9lsrg3SdSBQQXUlFUo8Cog0gBUQbL9aOvoAokRjfXyWcV6rfFtfPH8WhTYswaMo6fPL1wc1Lx1G8Ul2FeNfK6fD/5IsGHX/BK6cHWDphIAb+vRY2dsHbRUckILcuncDR7cvReegMBAUGYuHoXqjTpg/yFgmfk/atMaSAfIuQ9vcpINqZJfQWjICYpoDIF+hZs2Zh7969KhoSIiCS/ixRCPmyLtucywZGEhmQJVGRCYh8eZdjHI4cOaKW8cvyrho1aijZkM2PRBikSP6IbJIkK2rCFhEWiSKIFIQULy8vXLx4US11ki/8devWVSt9pEi/Pnz4oOqvX78esoRKJCKq+4mAnDt3TuVIhxUQESy5v0hW2BIVB+mP7CIbstHTl//W92cGBURPghQQbQApINp4sbZ+AiL5HZsXjsMrp4dIlS4DGnYajIzZ8uLNSyfMGPITxqwM3pDik58vtiwcC8kbsUmeArVb98b3Rcvh1uUT2DBnlDqFOCgwAGbmFkjt8B36jgtObJeox9mDm2FuboGSVeujaqOoc8YiGk8KSMzPcgpIzDM19StSQExXQGT5keRrSN6wnPcmy6S2bNmiIhSnTp1S+SESlZD/ohIQEQuJPkiuR4UKFdQuqSIdcvSDREAiWnIV9nPz22+/QTY7CjnwWt6ThHcnJydMnz5dZwGJ6n4iINI/qRNWQCSyI88sy7ukiHCFyE9kHCggBv5TjwKibYAoINp4sbZ+AmIM/CggMT9KFJCYZ2rqV6SAmK6AyNyVL+CysZDkToiAyHEMsgOVREDk3xKxkORu+fI+YMAAtTRJlnB9uQ2v5Af7+vqqZVGShyxl4MCBakmT5GeI7IhoSL0SJf47RFGWVEmERaInsvwqpMgyrNKlS6tdWSXZXZcISFT3i0xAJJoiSe+y0ZLcX+4lOdQSXYmMAwXEwH/qUUC0DRAFRBsv1qaAcA5oJ0AB0c4sobeggJi2gMj8lvyFkydPKuGQJHTZnEj+LknYcni15DhInockWcvuV3Xq1MEvv/wS7hwQObha3pNIiey8KkWWScmyLdlNVYosW5LlVyFLsuS1/fv3q1wKSXr/skjkRJaDyfIoXQQkqvtFJiCSVL9p0yYlSyJJstOr5K+IdEXGQeSIS7AM+CcjBUTb4FBAtPFibQoI54B2AhQQ7cwSegsKSOwLyMgFB5AqTUqdptp7r4/Ind4Wg9pX16k+KxkfAeaA6DlmFBBtACkg2nixNgWEc0A7AQqIdmYJvQUFJHYFxOujH8Ys2g9vn086TbVP/oFoUKUA6pTPr1N9VjI+AhQQPceMAqINIAVEGy/WpoBwDmgnQAHRziyht6CAxK6AJPT5xef/mgAFRM9ZQQHRBpACoo0Xa1NAOAe0E6CAaGeW0FtQQCggCf0zENfPTwHRkzgFRBtACog2XqxNAeEc0E6AAqKdWUJvQQGhgCT0z0BcPz8FRE/iFBBtACkg2nixNgUkqjlw+OgZvHNxNtlp8jmxOerWrwObpMEn2etaKCC6kmK9EAIUEAoIPw1xS4ACoidvCog2gBQQbbxYmwIS1RyYNXkiOiTdDTMz05wph97lRL7W45Era3pND0gBiRiX36cA7Nt5DJ+8fDTxNKbKtqnsUevHCkicOJGmblNAKCCaJgwr602AAqInQgqINoAUEG28WJsCEtUcmD/tbwyw3wxzExWQ7a7fI3PTCRSQLybBY/dEuGzTAPXq1tL0I+LVW0/sHTMTNT86aWpnTJW3psiNbiP7w9raUlO3KSCxLyAffT4hMChI53FJamUJM7PEOtdnReMiQAHRc7woINoAUkAi53XzwQscv3APFhbm2qAaSW1fP3+0qF0C6dPYaerx/acuWHfgCrJmcdDUzlgq63MSOgUk4lFmBCRiLiIgJybORtOPj43l46G5n4vt8qH9H30oIF+QO+seiGTNWqHYD1kjZJohVewKyDsPb4xdvB+JoJtQ+Pj6o0LR7GhVp7jmOcAGxkGAAqLnOFFAtAGkgETOa8mWU7j93A3JkyfTBtVIar9554kfS+VC7YoFNfWYAhI5LgoIBUTLh4kCEjktRkBiV0CevXLD1NXHkS1bRp2mrLunF1JaJUbfNlV0qs9KxkeAAqLnmFFAtAGkgETOa/n2M3js6gV7OxttUI2k9us37qhSKAscKxTQ1GMKCAWEOSDh54A+S7AYAYn480QBMR0B6du3Lz59+oT58+eHDna1atWQK1eucK9VqlQJbdu2xYEDB+Do6IguXbp8NTlsbGxw9+5dZMqUCcuXL0eHDh00/f+LlSMnQAHRc3ZQQLQBpIBQQCgg4edAfCzB8vUPQt9Nzjhy1wvJLBOhf9U06FQ21VeTc8RuF8w6/gZmYRJ6j/XPgR8yWONTQBAGbH6JHdc9YWtlhpE/pkeLYvb4/Pkz/tr7GqsuuiMw6DNqfm+LaU0zIqmlbksvwnaCOSAR/7yggET+c5RLsCJmE99LsOIyArJ7924MGDAADx48UDD8/PyQM2dOJEuWTMmEFB8fH9jb2+PevXv45ZdfIhUQV1dXpE6dGokSJYKDgwNcXFy0felh7UgJUED0nBwUEG0AKSAUEApI/AvI+AOvccfFFwtafwfXDwGoOfMRtnbPhvwO4be77b/JGfkcrNCt/NdyMnbfa9x57YuFrb/D47d++Hm9Mw70yY6N/3hg2Tk3bOmaFRZmidBiiROq5rHBL9XTavthAYACYjgC8szLC79euoDbHh7IlDQZ/ipaDMXTpPmqgw0PH8Qtd3eE7EGV3NISlxs0UvWcvb0x6OJ5XHNzQ5ZkNphSqjTyp0gB38BADL10ESddXsHa3Bw98+ZDm5w5Nc8XaUABoYB4eXkhZcqUePjwITJnzoxjx45hxowZSjaOHDmCDBky4NChQ+jVq5eSlKZNmyJfvnzqtcePH6NChQpYv349zM3NERIB6dOnD3bs2KHq7du3D97e3ujZsyecnZ1haWmJpUuXomTJktGaswm1EQVEz5GngGgDSAGhgFBA4l9ASk68jzktMqFE1qSqM7/veAVbq8QYWitdhL+RggAAIABJREFUuM51WvUMtb63RYviKb6auPlH38WO7tmQM22ScO9devoRVhaJUCBj8JKOqUdcce+1n5IdrYUCYjgC0uLoEdTImBEdc+XGqdcu+PXSRZypWx8WicNHtqru3Y355Sogt93Xm000O3oYNTJkRMfcebDd6SnOu7oqCZl28wbue3piWqnSeOPniyZHDmNlxcrIa2+vdcpQQCIhlpAiIIKgYsWK6NSpk1oy9ccff8DW1lbJRuXKldGmTRsMHToUnp6emDt3rhIQEYn9+/fDwsIChQoVUq/XqFEjVECsrKzUMixfX18V5S1atCh69OiB7t2749KlS6hfvz6ePXum2rPoRoACohunSGtRQLQBpIDEjoBcPLgB/57aiUB/f+QqXB5Vm/2MxF8cDvHk9iVsmzs83OuVGndHkUoNcHL7Ylw5shmJwnyZaPPrbKTJmB3v3Vyxf+UkuDjdg32aDHD8aTDSZtL+20nmgEQ89vGxBCvV4Jt4MCovUiYN3nFtydl3OPvIG0vaZQ7XySYLnyDoM/DwjZ/6jXaHMikxsFpaePgEIveIO/irbnosOPMO1haJMdwxHer8kDxc+2dun9BmmRMGVU+LhoW07X4mF6KAGIaAvPX1RaU9u3CtUROY//9nxI8H92N44SIokza8tBbfsQ27atSCQ9JguQ0pEkFpfvQIztarj8SJwp/RUX3fHkwqUQpFU6dW1cf8+w+SmVtgwA/a8sWkLSMgEc+ZhCYgY8aMURGPVatWoXz58pg6dSru37+P48ePY/HixShVqhSGDBmCRo0aKQEpW7YsBg4cqODJayIUHTt2jFBA5DqFCxfGhw8fYPb//8+WKFECkyZNQpUqTJrX9VshBURXUpHUo4BoA0gBiXkBeX7/Gg6unYaWA6fCIok1diwciZwFyyqxCFvuXj6GB/+eRr0uf3zViUPrpiN1hmxftZGK66cNRI4CZVC0SiPcuXQEL+5fVxKitVBADENA/AM/QwTk9cT8ShykrLvkjp033mNdpyzhOjn5sCvsrM3wU6kUePTmE5oseoqJDR1QKJM1Co+7h2GO6TCoWhpccvqIxguf4tJvueFgF/wbwCrTHuLKcx90L59KtdF6MBwFJPJPWFzngFx+8wbDrlzCAcc6oZ3qffYMyqZLh9Y5wv8yIvfmjaji4IArb98ijZUVBhcohKoZMuDAixdY/uAeMtvY4rSLCzLb2GB0seLImTw5cm7agMv1G8I+SXA0bfXDB7j45g1mlimr9ccMBSQSYglNQC5cuIDGjRsr6ciSJQtev36t8jckMnL16lWkTZtW/VvyQERAwiahh/13yBKssBGQixcvKmGRiEhIkSVZEjVp1qyZ5jmbUBtQQPQceQqINoAUkJgXkCMbZsE2RRqUrNlSXfzRjXO4fGQzWvSfEu5m107vhovTfdRqE/xbnrBl99JxyP5DSeQrWT3c655vX2HDtEHoOnp1uOiItlEPrk0BiZhafEVA7o/Ii1Q2wRGQRWfe4cLTj1jcJuplUhMOvsZLD3+MquuALMNv4/nYfEpQpNSb9xhdyqZCgzCRjjcfAvDb9pdIlcwcfzfOoHnaMAISMbK4FpBTLi6YfOM6dtSoGdqhwRcvIK+dPTrnyRP6WtDnz/j14gXUzZwZ5dOlx5GXLzHgwjkcqf0jTr92wfArl7G0QiWUTZsWi+/fw+Ynj7G7pqMSkHtNmsHKPHg+bnn6BPtfPMei8hU1zxlGQCJGltAEJDAwEGnSpFFRiW3btmHPnj0KjOyEJcuvFi1ahHPnzqnXtArIixcvUKBAAbi7u2uen2zwHwEKiJ6zgQKiDSAFJHJe0d2Gd9Os31C4Qj219EqKm8szbJw5GD3GbQh3M1mmdf/qSQQG+MPHyxPZ8pdAlaa9YGmVFFvm/I7Pn4Pg7uqsltoULP+jEpqH187gn+PbYZ/aAU53/1F/VmvRBynTh1+qo8ssoIAYjoCU+fuBikpUzBW85XOfjS+QJaXlV4ni5x57o2hmayQxD46UjN7rAk+fQExukhGZh93CqUG5VLsQAelRITUkqJI1lSVypwtOaD/5wAsDtrzElSG5dZkm4epQQAxDQK68fYMhly7hUO3/IiC9zp5GxfQOaJk9R5Tj2urYEbTIngNWZmaYeesW9tZyVPXlROy8WzbhYv2GKLFzu/oz5f8jICsfPMCVd28wozQjIJo/NJE0SGgCEiIWEgH56aef1E5XUmSr3cuXL6slVn/99ZfOAiI5JKlSpVJ5I7KbVrFixdQ1W7VqhTdv3qBfv35YsGCByjVh0Y0ABUQ3TpHWooBoA0gBiXkBWTelP0o7tka2/ME7cEjOxspx3dB78vZwN5PlVy7P7qN41aZKNvYsH49U6TOrfJEL+9ciSVIb/FDGER5vnLF1zjBUadYLfr7eOLJ+Fhr1HI3vchfGlaNbcOv8QbQftlDbwDMCEimv+IiATDrkqiIeqztkxtN3n1BnzmMc7psDOdIkUcJgn9QMBTNao8r0h2ob3d9qpMUTt0+oO/cJZjbPiBp5bTF420v4fArC9KYZcc3ZB40WPMGVIXkw+8RbXHf2wYr2mWFlngi/bH0JT58g9W+thQJiGALi5ueHsrt24GrDxmqXKimSEzK1VGkUS/3fTlgfAwJw18MjNJdD6jU5cgidc+dBFhtbdDt9Cmfq1Q8VkDxbNuGfBo0gyekjihRTS7qk/HbpIr5Llgy98+XXOmW4BIsCEkpAhEASxSVJvHjx4BPVV69ejXbt2uHEiRNqOVaIqHxrCZYst6pZs6a6liSrp0iRQiWgP3/+XO2WJdv+yr9ZdCdAAdGdVYQ1KSDaAFJAYl5ANs8agoLl6yB3keAfpu9eOWHz7CHoPnZdlIPz4sF1HFw7FZ1GLP+q3rm9q/DB4y2y5SuB8/tWo93Q4AOdgoICMXNAPXQfux7WNuETjr81ExgBiZhQfAiIX0AQ+m50xqG7H2CbxAxDaqZFqxLBO111XPkM3ztY4dcaafHQ1Q/9NjvjhrMP7K3N0LNiavWflI+fgtBz3QscvfcBaWzNMba+A2rnTw5vvyAlJwfvfIDkm5TIkhQzmmVERnvtu8NQQCKeM3G9BEt60fr4UZRJkxa9vs+HXc+fqSVZJ+rUhVnixGpHq3Lp0sM8USKU371T7YJVIX16HHv1En3PncWxOnWR2soKjvv3oVOePGiWNRuWPbiPbU5PVcL6rFs3ceXdW8wvWx7PvL3R4tgRbK1WA9mi8dtkLsGKeM4YQgRk4vKjyJI5/bf+V6He93jvjbQ2FujfrqpO9VnJ+AhQQPQcMwqINoAUkJgXkKOb5sDK2gZl67ZXF5dE8VvnD6FpnwnhbiZiksTaBjb2wWc6yJKqY5vnocPwRXB+dBPpMueGuUXwcprTu5bB76MXCpSrjR0LRqockBABmdG/LnpN3Iwk1sk0DT4FxHAERNPAxWNlCojhCMhzby/8ckHOAXFX0YzxxUugQMqUqoOy89W8suVRIk0aHH3pjLHX/oWrjw8yJUuG4YWLotz/IxtOXh/Q6+wZyI5Ysk2v7HyVI3ly+Mk5IJcv4firl2r3q/4//IAmWbNFa+ZRQAxTQLw++mHS8kPw/uiv07h+8g9A3Yr5UatcPp3qs5LxEaCA6DlmFBBtACkgMS8gIg+ynKrVwOkqn2PTzF9RuGJ9/FCmlsoH8XznopZnndy+CG9fPkXdzsPxOSgIu5eMQZpMOVCxYRes+buPqlPGsQ083r3CpumDUaPNABUBWTmuO4pWaYz8pWvi6vFtuH3xCNr+NkfbwHMJVqS84iMConnw4qkBBcRwBCSepoDm21JADFNANA8kG5g8AQqInkNMAdEGkAIS8wIiV7x0eBOuHtumlkjlLV4FlRp1U7tWXTu1WyWeN+s7Cf5+Pji8fiYe37oIMzNztbVu5aY9YGFpBbfXL3B43XS4vngEq6Q2astdkQ4pHm9eYteSMZAdsVI5ZEGttoOQMp32Q+UYAWEERNtPC54DEhmv+FiCpXXs4qs+BYQCEl9zj/fVRoACoo3XV7UpINoAUkBiR0C0jUL81KaAUEC0zjxGQBgB0TpnKCAUEK1zhvXjhwAFRE/uFBBtACkgFBDHCtpON77/1AXrDlxB1iwO2iabkdTmEqzIB4oCQgHR+jGmgFBAtM4Z1o8fAhQQPblTQLQBpIBQQCgg4ecABYQCou2nKMAlWJETo4BQQLR+nlg/fghQQPTkHpWAeLz/iM+f9byBATe3SWoJC4vgPeF1LRQQCggFhAKi688LRkAYAdF1roTUo4BQQLTOGdaPHwJGJSCPHz9G586dce3aNWTJkgWzZ89GuXLlviJXunRpXL16FYkSyZnOgL29PVxcXNTf5QCZwYMH4+XLlyhRogSWL1+O9OmD96WeOHEi5s6dCz8/PzRu3BgzZ85UB8xEVSITkMfPXTFs1k5YWSeJn5GN5bsGBASiTIEs6NWyiqY7UUAoIBQQCoiuPzQoIBQQXecKBSRqUvF9DojWcWR90ydgVAJSuXJlNGjQAH379sWhQ4eUjDx9+hQWFuEPuMqbNy+2bNmC/PnDn6Lq6emJnDlzYufOnUo+Ro4ciXv37mHTpk04fvw4unbtilOnTsHGxgaNGjVCw4YN8fPPP0dLQC7ffIKpG07Dzl7bYW3GMuX8PvkjZ6pk+L1bbU1dpoBQQCggFBBdf2hQQCggus4VCggFROtcYf34JWA0AuLq6qrkwc3NLTQqUaxYMUyZMgUiJmGLg4MDLl26hEyZMoV7XURjyZIlKgoiRYQkXbp06s+BAwfiu+++w5AhQ9R7u3btUtcWMYmqRBYBoYBETI0CQgGhgFBAdP3fHgWEAqLrXKGAUEC0zhXWj18CRiMgZ86cQc+ePXH9+vVQYi1btkTVqlXRrVu3cBStra1Rp04dSBtZXjVu3Dj177Fjx+L169dqaVVIkfdPnjypIh1yfVl6JeXu3bvq2rJUiwLyNQFGQCKfFV7uHhjSqSbSp7bT9Olevv0MHrt6wd7ORlM7Y6nMbXgjHikmoUc+gykgFBCtP9+YAxIxMS7B0jqTWD+2CRiNgMiSq+HDh+PChQuhTDp16oSCBQuif//+oa8FBQWppVktWrRA9erVsXv3brRr1w537tzBvHnzEBAQoHI9Qkr27NmxdetWJSBy/dq1g5cUPXv2TF3bw8MjyjHw8w+K8P1z1x5h1NJjSG7CS7DyOyTHuL71NM1Rbx8/9J2wGVZ29praGVPlj56eGPtzHWRIq+0Z5204iXsvPWFvb5oC4uLqjjolc6B+lUKahvPO41dYsuMCsmXNoKmdsVQOFpAXmDK4aYRdDgz6DLPEwflsX5ZJY8ajt+0GmJsZy9Nq6+fOt/nwQ7spyJtD2xbM+49dRMrbC1Ekg2nuAvLILRFu2DdGsyZ1NAF1dvXArpHT0ejDQ03tjKnyipT50XvCICS1ttTU7Xkz16PGu3tImcQ0P0yn3YKQtl1blC6UPUIuSSwSa+LFyiSgLwGjEZCzZ8+qSMfNmzdDn7l58+aoWbMmunTpEiUHiWRIHckXkYiGJK+HlLRp00Ku3atXL3X9pk2DvwTcvn1bXfvFixdRXvvd+08Rvn/xxmNMWnMyWgLi+ugabh1bA98P7rDPkB1FfuwJK5vwX2YDA/yx5++fkNjsv/yX9LmKoXijfqo/fh/f459dc+D73g1Vuv4dro8vbp3Btf2LUeTHHsiQt1S05pBEQHKnscGInj9qai8C8uu0bUhmr+3LuaabxHNlb09P/NGlFtKn0faMS7aexsNX701aQGoUzYY6lQpqGqF7T1ywau8lZDPlc0CeOmNsv0YRcvmMz0iEiAVk1t+T0M9uo8kKyPY33yNni7+RO1vwRiG6lsMnLyH9/cUo7GCaAiLb8P5j2wgNGzjqikTVe/XGA4fGzUQT78ea2hlT5aX2+dF5VD/NArJ03kbUcnuAlElM84v4GfdA2LVsgxIFskU4nKmSaxM2Y5oT7KthEjAaAXn79q3a+erNmzdImjSpopkrVy6sWLECZcuWDaXr7e2NGzduQHbCCinly5dXUZLEiRNjxowZOHHihHrL2dkZuXPnDs0BSZEiBUaNGqXeW7Nmjbr2wYMHoxy5mM4B8ff9iCPz+6Nks19g75AD905thpfbK5Ro9F+URzrk6+WB44t/hWP/hV/1L8DPB6dW/ol0OYvg9cOr4QTk0cU9ePfsLny93JGzdD29BIRJ6BFPDS7BipgLl2BFzIVLsCL/EcslWBGz4Tkgkc8ZLsGKmA2XYBnml/CE3CujERAZJFlSJQnnQ4cOxYYNG9SSqQcPHsDMzAxr165FtWrVVIJ6tmzZ1C5YNWrUwL59+9C6dWu125XkhsiSK2lbsWJFtZuWCIuIxunTp9GmTRuVN2Jra6vuJVGRjh07xqmAvLxzHk7Xj6FMi6HqviIkB2b2QO2BS2Bm/l+0w+vdS5zfNAnVe0z/WkA++cLPywO+3h64vn9JOAHxfP0UydNmwbn145C1SHUKSCx8+ikgFBAt04oCQgHRMl+kLgWEAqJ1zlBAtBJj/dgmYFQCIkuoOnTooM4ByZEjBxYsWADZCUuKJJNv3rwZEu3Ys2dP6FkfWbNmxdSpU1VCuRTJJZFoiJwLIpETOQckVapU6r3JkyerCInkibRq1Ur9W6ImUZWYjoDcP7MNft6eKFCzQ+htRUDKtR0Bm5T/rYN2f/kQF7dMgW2qjHj/5jns0mZBgVodw9V59/zuVwISctGz68ZSQGLp00UBoYBomVoUEAqIlvlCAYmaFiMgEfOhgGj9lLF+bBMwKgGJbRjRuX5MC8idExvwOSgQ+aq0Du3O4Xn9UKLxANilyxr62oe3L/D40n5kL1kbSe3S4N7pLXB9+C8qd/kvwZ4CEp0R1b8NBYQComUWUUAoIFrmCwWEAqJ1vkh9Ckh0qLFNbBKggOhJN6YF5P7Z7So/o2DN/5Z+7Z/RHRXajUKylJEnYgYFBmDvlI6o3nMmrGxTqKeigOg5uNFsTgGhgGiZOhQQCoiW+UIBoYBonS8UkOgQY5vYJkAB0ZNwTAvIq3sXVWSjXNs/Vc98Prjh6IKBKgckceL/tgcUSfH39YZt6uDDFmVXrL2TO6Bm33lIkjT49HUKiJ6DG83mFBAKiJapQwGhgGiZLxQQCojW+UIBiQ4xtoltAhQQPQnHtIDIDlay5KpYw75Ilfl73Dy0AgGffFG0Xi+VkC6CkrlQZbx+9K/K7yjX5k9YJ0+Fe6c3482TG6jQfnToE1FA9BzcaDangFBAtEwdCggFRMt8oYBQQLTOFwpIdIixTWwToIDoSTimBUS64/rkOm4dXqW22k2ZKTeK1O0BS2tbfHjnjOOLfkW9IWtUrx+c24EnVw4iKMAfdg7ZUKhWFyS1T4NX9y7hyo5ZAD4jKDAQic3MYZPKAZU7T8SJZb/jw5sXCAoKQKJEidV/IjcZvv9v22JdkPAk9MgpUUAoILp8hkLqUEAoIFrmCwWEAqJ1vsSVgDRs2BA7d+6McPMeJycnZMyYUe1CKjuUpkyZEj169ICNjY3a8EdLkQ2JZLdTf39/tfNpZEU2LcqUKRPGjBmjDrG2sLBA0aJFtdyKdWORAAVET7ixISB6dilOmlNAKCBaJxrPAYmYGAWEAqL1s8RteCMnxl2wImYTF0noIiB58+bFhAkTIh0gOb9NjkfImTNntAUkMDBQnQknu59GVTw9PdUxDSI5Xbt2RalSpb55cLXWzyLrR58ABST67FRLCkhtTQQ/+vjh95k7YJMyOFHeFAsjIBGPKgWEAqL1886DCCMmRgGhgGj9LBmCgDRt2lSd0SbnsY0bNw7Hjh1TEYyXL1/i/Pnz6gy2xYsXo0KFCup8NjkQunDhwrh+/ToeP36M3r17q/PbvoyAHDlyBAMGDMDr168hRy/MmjULJUuWVMc2SAQkefLkGDlyJOzs7NS5cHLeW8uWLdWRDFKCgoLw3XffYcmSJXB0dNSKlvWjSeCbAhKVyYa955AhQ6LZBeNuRgGhgHw5gykgFBAtP9UYAYmcFgWEAqLlsyR1GQGJmJghCIicsSbLoOQA6ZAIyK5du3D06FHkyZMHw4YNw8mTJ3Hq1CmsW7cOP/30kzokWiIXV69eRenSpeHh4aFEI2QJlkQ55O/bt29X572tXLlSHVYtS766dOkSugRLzogTIZHX5Ay5OXPmKLGRcvz4cSUmz58/VxETlrgh8E0BqVu3boQ9SZQokZpIZ8+eVZPh8+fPcdNjA7sLBYQCQgHR7UPJCAgjILrNlP9qUUAoIFrnDAUkfgVk9+7dX+Vl1KxZU+WGRCQgkschkQcp+/fvR/fu3ZU8rF+/Hn/++Sfu37+v3vPz84OVlZWKhMj3zxAB2bRpk8rxuHXrlqon0Yx3794hderU6NixY4QCItLi4OCgZKd48eLRXgqmdW6yfngC3xSQyIA9fPhQhcMuXryoBr9Xr14Jki0FhAJCAdHto08BoYDoNlMoIN/ixCVYkROigMSvgESVAxKRgIRNQj98+LCKUrx48UIJiCSnX758WT1Q2LaybCtEQObNm6cS2yVS8mUJm4QeNgIi9dq0aaOWZM2cOVPJiCzjKliw4Lc+enw/BgloFhBfX1+MHz8ekyZNUmvo5M80adLEYJeM61IUEAoIBUS3zywFhAKi20yhgHyLEwWEAvKtOfLl+4a6BEtfAdm6dSuGDx8eGimR1TgSDZElXZJ4HrIL1pcCIrLTqlUrLF++XLWXJV4scUtAk4Ds2bMHffr0UQk9c+fORdmyZeO2twZ4NwoIBYQCotsHkwLyP/bOBM7G6v/jX/vOMLIV2cuSCC34KWuWyq6ELCFLhJIthbJTtuxClrJkrUhlX7IVJZGtZCxj3w2G/+tz/J/p0r1zzjPPzDP33vmc12tezNzzbJ/zPc897/P9fs8hgJhZCgFEpxMBhACisxF/BBCERyVPnlw2b94spUuX/k/oU0w8IJcvX5ZcuXLJzJkzpVatWoKQLIxT4UXxzAFBfkj16tWlW7duShrcC7woyPlAfSSxs7irgBGAIB4PqwVgxYL+/ftLhw4dmKjz/+1EACGAEEDMXloEEAKImaUQQHQ6EUAIIDobiS8A8bUPCJLDkejdpEkTlTCOsSTyO5x6QBCOhVwO7CmCpXmxmhUSzJ955pmoVbCQJoBQrbfffltq1qypIAWlT58+aslgwErWrFntSsr6DhXQAgjCrQYMGCB16tRR8XhspHsVJ4AQQAggZm8hAggBxMxSCCA6nQggBBCdjcQHgNi9p/iuP3nyZEHS/JIlS+L7VhLk9bUAgtUGsGMllky7ceOGz9Wudu7cmSAFJIAQQAggZl2fAEIAMbMUAohOJwIIAURnIwSQ6BU6fvy4lC1bVuWAlC9f3q6crB8LCmgBBCsRmBQkpCfEQgAhgBBAzHo+AYQAYmYpBBCdTgQQAojORgggvhX64IMP1F4gCMmyckLs6sn6zhXQAojzSwT3GQggBBACiFkfJ4AQQMwshQCi04kAQgDR2QgBxK5CrO+2AsYAgv0+kLhz8OBBtQkM1np++eWXE/y6yQQQAggBxOy1RQAhgJhZCgFEpxMBhACisxECiF2FWN9tBYwABMuTjRw5UgoUKCCPPfaYygPBOsv79++XXr16qY0IE2ohgBBACCBmvZ8AQgAxsxQCiE4nAggBRGcjBBC7CrG+2wpoAQRrK7/++uuCJdTuz/P46quv1EYuqANvSEIsBBACCAHErOcTQAggZpZCANHpRAAhgOhshABiVyHWd1sBLYBgLWVs3vL+++97vTd4P1asWCEbNmxw+9794noEEAIIAcSsKxJACCBmlkIA0elEACGA6GzEHwHkzPkrcuPmLeNbDw1JI8mTJTWuz4qBpYAWQNKkSaPgokSJEl6f7Ndff1VLmV26dCmwnjyW7pYAQgAhgJh1JgIIAcTMUgggOp0IIAQQnY34G4CEn70kk0cMllA5ZXTrlyJEcj/5gjRo+JJRfVYKPAW0AJI6dWr56aeffCab79mzR0qVKiVXr14NvKePhTsmgBBACCBmHYkAQgAxsxQCiE4nAggBRGcj/gYgR46flR/G95AGGX8yuvV/LqeQ7bk6y2vNGhvVZ6XAU0ALIMWLF1c5IB07dvT6dKNHj5Zp06bJL7/8EnhPHwt3TAAhgBBAzDoSAYQAYmYpBBCdTgQQAojORhI6gJQrV07atm0rTZo0iZICG2Y/99xzcv78edm+fbvKaz5w4IBdKWNc/4UXXlDX9LynGJ8sCA7UAghWv8KmLatWrZKSJUve88gbN26U559/XgYMGCBvvfVWEMhh/xEIIAQQAohZvyGAEEDMLIUAotOJAEIA0dkIASR6ALl165YCkcyZM9uVMsb1cb2UKVOqHxYRLYBERkZKgwYNZPHixQo2ihQpIrdv3xbkfvz4449q9avZs2dLkiRJEqSeBBACCAHErOsTQAggZpZCANHpRAAhgOhshABi7gFZuXKl2hX9+vXrkjx5chk2bJjUqFFDatasqca8u3btkrNnz6pUhAkTJkiyZMlk8+bN0qFDB7l48aKkSJFCxo4dKxUqVBB4WZo3b66O3bZtmxw5ckRGjRqlxs+eHhBEDSG66MSJE5I/f36ZPn265M2b126zBnR9LYDg6bDvx7x586I2IsTfsBEhXEm1atUKaAGc3jwBhABCADHrRQQQAoiZpRBAdDoRQAggOhshgJgDSNGiRRVYIGwLk+tjxoyRyZMnS+3ateXUqVOydu1aSZw4sfoc6QjYfgIw0r17d2ncuLHMmjVL7Ye3d+9e2b17tzz++ONqddgqVarIggUL5OOPP5ZNmzbdAyAFCxaUQYMGSb169WTo0KEqygjHJKRiBCAJSRC7z0oAIYAQQMx6DQGEAGJmKQQQnU4EEAKIzkYIIOUUDHiGOyHsygq98swBqVy5sgAI3nnnnXu+doicAAAgAElEQVS8EAAQ5Ix07txZyYntKM6cOSOffvqp3LhxQ5ImTarA5NixY5I7d271N1wToIJwKxR4T1566SX5+++/owDkqaeektKlS8u5c+ckUaJEcvPmTYmIiJC0adPabdaArq8FELiHTEq2bNlMqgVdHQIIAYQAYtatCSAEEDNLIYDodCKAEEB0NkIAKac8FfAwWOX3339Xv9+fhI5xLjwYixYtkpCQEBk+fLja/w4Agh+EVKHg7z///LPMmTNHeTYQWgXoQKoCQq8ANwCQatWqydGjR9Uxnr9bIViAnYYNG8pff/1ltxmDqr4WQEBnJgVhWgmxEEAIIAQQs55PACGAmFkKAUSnEwGEAKKzEQKIeQiWp1ZLliyR1157TeV8AFbgzYBnBKVnz55qzzt4QuDx2LFjhxQqVEjleSB/wxRAypQpI1hhFiAEDwog5tChQyq1ISEVLYDATWVSsBdIQiwEEAIIAcSs5xNACCBmlkIA0elEACGA6GyEAGIGIMj5qFixovJoPPTQQwoEnnjiCQUgdevWVUniq1evVnIibAqrwgI6kHAOLwcS0nv37i1DhgyRK1euyMGDB7UeECzDi+T2bt26Ke8KPCnLly9nDohdo07o9QkgBBACiNlbgABCADGzFAKITicCCAFEZyMEEDMAwT4g2MsOCeHwYKRKlUqFY9WpU0eFXyFcav369Qo2sJLVxIkT1aqvzZo1U8npWbNmVVtR9O3bV+Vx4Fy6ECwACEKz8G9YWJgCmhkzZkiePHnsNmtA19d6QPB0M2fOlAIFCsjTTz+tHvaHH35QS5ZduHBBESKWLHNjGV6QKZYtQ1LPww8/rJY9K1u2rM8GQLLQI488oowJG9KgYJUBUCeShkCzWPrMyl8BwY4bN04ZEZ4LmywiySi6QgAhgBBAzN6BBBACiJmlEEB0OhFACCA6G/FHAFk8qodUT/ez0a0fu5pcDuZrJy1bNjWqHxeVACD169fnxoFxIa4Y7AMyfvx4BRtffvmlyuRH1j4oDf9H2NXAgQPVJoSIjYvrgtUIsOxvp06d5Pvvv1cwgiQeuMC8Fbi2QKhYKg0AAmDCestLly5V8AFi3bdvn1peeM2aNdK6dWtFuliJwKJfrPNMAPmvAhE3bkr+0DTSqw0BhABi1vMJIAQQM0shgOh0IoAQQHQ24m8Acu36DZn31Tdy6/pVo1u/cTNSnnymtJR8vJBR/bioRACJC1X/PafWA4I4tR49ekjTpncpFO4nxKthNQEkqGPwjoE8fo/LEh4eruABcXmWVwI7s48YMUItk3Z/AVDgvrC+M34AILjXqVOnRsXZAUjgPsO/Xbt2lZw5c6pnRVm2bJk6N85DACGA2LHty+fOS4+WVSVb5gx2DpPpizfKofDLEpIhOJfiI4AQQGx1CBFZHF5IctUfLAVy21tlcdX67fLgwc+kePbgXByFAEIAsduXNp2LlDQNGknJorm9HpojNJXdUwZ9fQJI3DaxFkCwKyQy/K0wJWy6gkQdhCuhYG1jhDlhB8m4LBs3bpR27dqpTWKsgo0QkTzUpk2bey6NFQXgnZk7d65ar9kCEMTpnTx5UoVWWQXPtW7dOrWjJc6P0CsUbCiDcyNUiwBCALFj2wQQ72oRQAggdvoR6hJAvCtGACGA2O1LBBC7irF+XCugBRCEI+3fv1+yZ8+u7iVXrlwq9wIhWCjIy8Cuj1iaLC4LQq7ee+892bJlS9RlWrZsqXajtDaJsT7o16+f2tgFuR9vvvlmFIBgpQIkGVnwhPpYOm3hwoUKQHB+rP2MAujCua3NZHw925mLEV4/2vrbIRk6e72kD0kfl7LE27kRglXwgTTyQbuatu7hyrUIefeTxZImJMTWcYFU+cqF89Kn1fOS7QF7zzh14QY5cPyShIQEpwfkRPg5qfJEbqnxbDFbzbnv8AmZ+e12yfPw3XdQsJXIyNvy919hMvCt2l4f7fYdkcQ+VkMfPWyovJVhviRNEmyq3H2exacKSYGXh0rBPPY8ID+s2yZZ/5wa1B6Qn9PVkdq1nrfV8MdPnZfvB46RelcO2ToukCp/FlJYXu/3lqROldzWbX82fr48f3a/ZEqR2NZxgVJ547lIyfDKq1L6Me+JzqHpUwTKo/A+g0QBLYCUKFFChSW9/PLLKhwJA3SEQ6VLl05JMG/ePMGAP65DsLCNPTwdWDnAKtjIpWrVqtKqVauov/35559q7eZt27apHTA9AQT5KvBoAKCskiVLFsG527dvr86PhCOUPXv2qHNbm8n4au/rNyK9fvTTroPSb9qaoAaQItnSyYBOL9rqCgCQTkO+klQZ7A3ObV0knitfvXBBPmpfXXJksfeME+atk31hF4MaQKo/mVdeqvC4rRb649Bx+WzpVsnzcA5bx7lROTYCfO4CyFEZ/s6/G2Z53ntk5B1JksQ7gQwbMEg6ppsXtACy9HRhKdx0uDya1x58frd6q4TumSwlcsRGC7lhSfaucfBsIvk1Y11pUNdeDl5Y+Hn5uu9IqXP5oL0LBlDtGZmKSIdBXW0DyITRc6XKmX2SKUVw0vyGc5GSpUkTeerxvF5bM2Xy4HzuADLdBHerWgCZPHmy2oQFy4/9+OOPaumxjz/+WAmFhG0sI4bkbXgP4rKcPn1arXx16tQpSZ06tboUVubC0mXY1MUquDd4PhA6hgLPDHJG4OFAWBbyV5CYjoLlz7DEmpUDkjFjRgVTKLNnz1bnXrlyZbSPxVWw7H0BXr0WIb1GL5G0mTLGpbnE67kZguVdfoZgeddFAcjhMOn3Zi3bdjvhk2HSJWRB0AIIQ7C8mwRDsHx3lSkZCkuzPh0llU0PyOeTFki1cweC1gPCECzbr1ceEMcKaAEE18dStfB+IJcCK1BZg3usQoVQJyR2+1qJKjbvv3LlyirhHCtuIb8D0IPwMCwBPGfOHKlUqZJKKvcsnh4QwAhCrnBs+fLl1bNg4xiAxoYNGwT5Lcg1gXcH14JXpEWLFgQQLwpwFSzfZkEAIYDYee8RQHyrRQAhgNjpS6hLAPGumD8AyJnzV+TGzVvGTRoakkaSJ4t+KwTjk7Gi3ylgBCC+7joyMtKV/T+s62PJXSyti31A8uXLp1bkwkpYKEgmx06W5cqV8wkg+AC5JMgZwe6W8JwArkJDQ9Uxw4cPVx4S5Ik0atRI/Z44cfTxoPSA0ANyf/8ggBBA7LzpCSAEEDv2grr0gPhWjADinwASfvaSTPl0vGROZpYvfPH6bXm4WHlpUNdenpPdvsT68aeAFkAwUDcp1ipZJnWDqQ4BhABCADHr0QzB8q4TAYQAYtaD/q1FACGA2LWZ+PaAHDl+Vn6cNVJezn3U6Nb/uZBItqaqKU0b312ZlCX4FNACCPb6MCl37gRnsp/u2QkgBBACiK6X3P2cAEIAMbOUf2sxBMu7YgQQAojdvpTQAATRMFevXpXt27dHRbJgVdPcuXNrVze1oy2ug33mkA9tlZ07d6p0AVwP18eWEQcOHLBzWkd1X3jhBXVNz3tydMI4OlgLIBDPpCDBOyEWAggBhABi1vMJIAQQM0shgOh0IoAQQHQ2cv/nCRFA/vnnH8H2C9ZecfEBIAjpx3UzZ85st8liXB/Xwyqw+PHnogUQf755f7g3AggBhABi1hMJIAQQM0shgOh0IoAQQHQ2QgAppxYR6tOnj/zxxx+SIUMGBQKeHpD58+erlU+Rz4wFjCZMmCCPPvqoYAf0J554QnkvsHVDzpw5VY4xFjy6v9jxgGBV1bfffltt3I3FnIYNGyY1atSQmjVrSpEiRVR+89mzZ9UedLgXLO60efNmtYrrxYsXJUWKFGobiQoVKgi8LMiJxrHYdgJ71yGHGSvWenpAfvnlF8GCUUinyJ8/v8p7xmJM/lCMAASi/Pbbb/9x52B5XpAllrJNqIUAQgAhgJj1fgIIAcTMUgggOp0IIAQQnY0QQMqpTae/+OILNdjHFg2eAIIB+2OPPaYG7xjDfvrppzJz5kz56aef1H5wAAEAA6ADQDBixAi1N5wTAMFKsgALQMuvv/4qY8aMEWx1AeDBFhPYIgILH+Hzjh07qsWQcO3u3burVVpnzZqltpnYu3ev2hMPm4CvWLFCqlSpogAJz4h97TwBBM82aNAgtT/e0KFDZdWqVeoYfyhaAIFIWC3q1VdflUmTJkXdM5bfhSDr1q1Tu5Njj46EWAggBBACiFnPJ4AQQMwshQCi04kAQgDR2QgBpJwMHjxYChcurH6wlQQWS7I8INg+YsmSJbJ06VIlFbwSadKkkXPnzknLli0VBGDFVJQ6derISy+95HVbBtQDDHiGOyHsygq98swBwfYOAAJM3nt6IQAgyBmxrvf+++/LmTNnFBTduHFD7WUHMIE3BvePv+GauDagCgWOAtzj33//HQUgTz31lJQuXVo9E/K5MW6PiIiQtGnT2jWfOKmvBRBQExLMFy5c+J8bwN/xwHBdTZkyJU5u0N9PSgAhgBBAzHopAYQAYmYpBBCdTgQQAojORgggdwEEg3R4Gr799lvlDbEABJ/9+eef8tlnn0VJhT3gELLUo0cPqVatmrRq1Up9Bo8Ifi9RooQKcULBvnPYUw7nh6cCY2Wr/P777+r3+5PQEQYFD8aiRYskJCREbfVQvXp15QHBD0KqUPD3n3/+We1vB88GQqsAHQgVQ+gV4AYAgns6evTuqmKev1seEMBOw4YNBVtY+GPRAgjgAo1WsWJFr/cPFxV2Qgd1JcRCACGAEEDMej4BhABiZikEEJ1OBBACiM5GCCD/AggG7AhXwubV7dq1U2Awbdo0Wbx4sfKCoFgeEHyG3BFvANKsWTPlmUBBPkbGjBkVgMRkFSxc97XXXlOhXoAVnAeeERRsto2Ns+EJATDt2LFDChUqpPI84DkxBRBELxUvXlw9LzwogJhDhw6pPBd/KFoAQewciAsuLG8F1IUVsNB4CbEQQAggBBCznk8AIYCYWQoBRKcTAYQAorMRAsi/AAItMFnevn17OX36tBqQw3Ng5YAgOXvkyJEq0gdpBZbH434PiPW7p7amAIJ0Bkzkw6Px0EMPKRBAojsApG7duipJfPXq1erUCJv64IMPFHQg4Rz3ioR0rOiFvJYrV67IwYMHtR4QLMOL5PZu3bop7wo8KcuXLw+cHJA8efKoTH00iLcyY8YM6d+/vxIjIRYCCAGEAGLW8wkgBBAzSyGA6HQigBBAdDZCALkXQKAHUgYAGFbeBEKhMNCHZwArXU2cOFF5GOICQLAPCLwuSAiHByNVqlQqHAv5JQi/QrjU+vXrFWwgzAv3ggR4eF2QnI5opAEDBkjfvn1VHgfOpQvBAoDASYB/w8LCFNBgzI5xvT8UrQekS5cuKkkHiTRwN3kWrLEMUmvatKmClIRYCCAEEAKIWc8ngBBAzCyFAKLTiQBCANHZiD8CyNKpn0iNHMeMbv3YxUSyP7SatGjqffLb6CQBUgkAAujx940DY1tOLYDAPfTMM88oNxHi3ODOAb0hLAuEhtWvNmzYoBJqEmIhgBBACCBmPZ8AQgAxsxQCiE4nAggBRGcj/gYg167fkHmLf5BbEdeMbv3GzVvy5JMlpGSx4N/mgQASjUlgCS+4fhAfZyWb58uXTxo0aKCSZfxlSS8jq47lSgQQAggBxKxTEUAIIGaWQgDR6UQAIYDobMTfAMTu/Sak+gQQw9a+ffu2Wk8YPywiBBACCAHE7E1AACGAmFkKAUSnEwGEAKKzEQKIXYVY320FtCFYWI8Ymf9WiBWy71OnTh0FICdPnlQbEv7www9u37tfXI8AQgAhgJh1RQIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK6AFEHg69u/fL1imDAXhVsj/sH7HBifIqMemhAmxEEAIIAQQs55PACGAmFkKAUSnEwGEAKKzEQKIXYVY320FCCAOFSeAEEAIIGadiABCADGzFAKITicCCAFEZyMEELsKsb7bChBAHCpOACGAEEDMOhEBhABiZikEEJ1OBBACiM5GCCB2FWJ9txUggDhUnABCACGAmHUiAggBxMxSCCA6nQggBBCdjRBA7CrE+m4rQABxqDgBhABCADHrRAQQAoiZpRBAdDoRQAggOhshgNhViPXdVsAIQHr06CGhoaHq3nr37i1du3aN+h17hAwcOJBJ6Pe13Pbdh+XjuRskQ0h6t9vUletF3Lgp+UPTSK82BBACiJnJEUAIIGaWQgDR6UQAIYDobIQAYlch1ndbAS2APProo0b3tHfvXqN6wVaJHhACCAHErFcTQAggZpZCANHpRAAhgOhshABiVyHWd1sBLYC4fUOBdj0CCAGEAGLWawkgBBAzSyGA6HQigBBAdDZCALGrEOu7rUCsAMj169clZcqUbt+7X1yPAEIAIYCYdUUCCAHEzFIIIDqdCCAEEJ2NEEDsKsT6biugBRBsMDhp0iRZsGCB3LhxQ+rUqSNvvfVW1E7oW7dulWbNmskff/zh9r37xfUIIAQQAohZVySAEEDMLIUAotOJAEIA0dkIAcSuQqzvtgJaAPn4449V4nnz5s0lefLkMmPGDOnYsaO8//770q9fPxkyZIg0adJEpk2b5va9+8X1CCAEEAKIWVckgBBAzCyFAKLTiQBCANHZCAHErkKs77YCWgBBEvq7774rLVu2VPe2du1aqVWrluTOnVsuXLigvCNVqlRx+7795noEEAIIAcSsOxJACCBmlkIA0elEACGA6GyEAGJXIdZ3WwEtgMDrsWfPHsmfP7+6t9u3b0uqVKnk9ddfl2HDhkmaNGncvme/uh4BhABCADHrkgQQAoiZpRBAdDoRQAggOhshgNhViPXdVkALIIkSJZJ//vlHHnrooah7S5s2rezcuTMKSty+aX+6HgGEAEIAMeuRBBACiJmlEEB0OhFACCA6GyGA2FWI9d1WgADiUHECCAGEAGLWiQggBBAzSyGA6HQigBBAdDZCALGrEOu7rYARgHzzzTeSJUuWqHsrX768zJkz5x6vSKlSpdy+d7+4HgGEAEIAMeuKBBACiJmlEEB0OhFACCA6GyGA2FWI9d1WwAhATG4Ky/UmxEIAIYAQQMx6PgGEAGJmKQQQnU4EEAKIzkbiA0Bu3rwpffr0kfnz56tFipIlSyaYnB4+fLg88sgjdm+Z9YNcAS2AnDhxwkiCbNmyGdULtkoEEAIIAcSsVxNACCBmlkIA0elEACGA6GwkPgDkgw8+kFWrVsm8efMke/bscuXKFenZs6esWLFC9u7dK4kTJ7Z726wfxApoAcR6dpAtVsBKkSJFvMlx6NAhtfrWrl275OGHH5axY8dK2bJl/3M/v/zyi3To0EF+//13yZkzp6LvatWqqXroCN26dZNjx45J6dKlZfr06WLBE/Y0GTdunEREREjdunVl9OjRkjRp0miflwBCACGAmL0SCCAEEDNLIYDodCKAEEB0NhIfAIKNqgsVKiQDBw6MujzGU0ePHpW8efOqDay3bNkinTt3ljNnzqg6AwYMkAYNGsju3bsF4f07duyQPHnyyL59++Spp56Sn376SbAdBEvwKaAFkLNnz0rr1q1l2bJlCkCw58fUqVMlR44crqvx3HPPqT1IOnXqJN9//72Ckb/++ku5+ayCUDDsUQKjbty4sXz99ddqo8STJ08qsMBywkuXLlXw0bdvX2XkcBeuWbNGPef69esFq3yhI9WuXVuBTHSFAEIAIYCYvQoIIAQQM0shgOh0IoAQQHQ2Eh8A8sUXX6g94zBGe+GFF9Q4K2XKlFG3gvEkYOLTTz9V0IFJ5ZIlS8qGDRukSJEiMnToUFm9erV8++23UrFiRTXeA6ywBKcCWgBp27at2nwQA3oY0uDBgyVdunSCxHQ3S3h4uIIHGLDllYDhjhgxQgAmVrl27ZoCitdeey3qb9i3BHuZbN++XcETvCAoiFHMmjWr+rdr167KW9KjRw/1GYAL5waYEED+q0DEjZuSPzSN9GpDACGAmL0JCCAEEDNLIYDodCKAEEB0NhIfAIJrAiYQWYJx45EjRxRI9OvXT5588kmZNWuWyhE5fPhw1O1hrIZJ4/79+6tJbnhBEJWCsd6PP/6ovCYswamAFkBy5cqlBu3WbudwpcFYENvnZjjWxo0bpV27dvLrr79GtcQrr7yijLtNmzZeWwdhY1OmTJGJEyfKzz//LIMGDVKeEIRWWQWGvm7dOuXpwPkReoWCeEWcG6FaBBACiJ3uf/nceenRsqpky5zBzmEyffFGORR+WUIypLV1XKBUJoAQQOza6uLwQpKr/mApkNtejuGq9dvlwYOfSfHswbk4CgGEAGK3L206FylpGjSSkkVzez00R2gqu6fU1g8LC1Nh7SNHjpT9+/fLV199pULgPXOGr1+/LvXq1VNeERRMIDds2FAWLlyoIlFYglcBLYAgaQi0ipwLq6ROnVrF6yGmz62CkKv33ntPxQ9aBa6+YsWKeXXRwYOBECpsoAhDhrekd+/ecuvWLUGuh1XwDPgcAILzV69+d0Yf5I5znz9/PtpHvBV52+vnm3celPenrpb0IendksjV68ADUjRHehn81ou2rnvlWoR0GLRAUmYIsXVcIFW+duGCDHqzhuTIYu8Zx81dJ38cvSAZQ4ITQI6Hn5MXnsontSo+bqs59xw8LlOWbJE8ud0P+7R1ozGsHBl5W/4+fFQ+ebe+1zPcihRJmsT7yQd/OEg6pJ3r8/MY3pLfHLb0dGEp1myEFMqX3dY9LV+1VTL+PklK5AhOADl4NpH8lrGuvFy/hi1dwsLPy5L3R0rtSwdsHRdIlWeEFpG3hrwtqVMlt3Xb40Z9KZVO75PQFD46m62z+V/lDWdvS7bXmsjTxb2P25ImcZYgfuPGDZk7d64adyFKxrNkyJBBJaZfvXpVkKjuOZHsWQ8T2yVKlJCXX35ZnQsbXmO8yRKcCmgBxF92Qt+0aZPydAB8rAJKrlq1qrRq1cpr60RGRqoVGZADsm3bNuX+g0cDyetWwf4mOHf79u3V+evXvzsIQMgWzg2PT3Ql/HyE14+3/XZIhn+xPqgBpEDmNNKnbU1bPQMA0mPkYkmT0d7g3NZF4rnylfPnpffrz0u2B+w942eLNsjBE5ckJEgB5ET4Oan8RG6p/r9itlpo3+ETMmf5dnk4t71BqK2LxGNlAMiRw2HyYafatu/i0+FDpXPI/KAFkMWnCkmeBkOlYB57HpAf12+T7PunBrUHZEfaOlLrpedt2czxU+dl1eAxUu/KIVvHBVLlqRkKS4u+b9kGkGkT5ku1c/slUwpnA3F/1WrjuUhJ3/BVKfVYHq+3mCXE+QJDTzzxhEpCx8I/WAULIfGTJ09W0PHnn39K8uTJJV++fGoshoWBABzI8UD0CY7FOAxhWBMmTJCmTZsKwMVzvOav2vK+YqZAwADI6dOnlRfm1KlTUURcoEABmTFjhpQpUybq6RFi9cMPP6gEdKs8++yzysBh/KNGjVKxiShwDxYsWDAqByRjxowqVhFl9uzZ6twrV66MVlkmoTMH5H4DYQiW9y7DECzvutz1gIRJvzdr2X6LT/hkmHQJWRC8AMIQLK82wRAs311lSobC0qxPR0ll0wPy+aQFUu3cgaAFEDdCsJCr26tXL/nuu+/k4sWLkiRJErVSKRb8QRQKCla16tKlixrLYYIbk77IMUa+B6JasHpp+vTp1SpZhQsXVrBipQDYfkHyAL9WwAhA4ALzTAQCtd7/t8uXL8f5g1auXFklnGNdabjnEDKFuEIYOXZmr1Spkvo/CBurMdSoUUO5+pDUhMQoAAxCrnAs/oaVGvAsAA18DmhBrgnch7gWaLxFixYEEC8KMAndt1kQQAggdl6GBBDfajEHxLs2BBACiJ13DOq6ASB274n1E7YCWgD58ssvjRRCQnhcFyy527x5c7UPCCADyeUWVSOpacGCBVKuXDm1ytW7776r8jjg1ejevbtgNS8U5JLA5YcNFuE5wWoNoaGh6jO4DeEhQZ5Io0aN1O+6jXPoAaEHhB4Qs55PDwg9IGaW8m8tAggBxK7N0APiXTECiF1LYv24VkALIHF9A4F+fgIIAYQAYtaLCSAEEDNLIYDodKIHhB4QnY3c/zkBxK5irB/XChBAHCpMACGAEEDMOhEBhABiZikEEJ1OBBACiM5GCCB2FWJ9txUggDhUnABCACGAmHUiAggBxMxSCCA6nQggBBCdjRBA7CrE+m4rQABxqDgBhABCADHrRAQQAoiZpRBAdDoRQAggOhshgNhViPXdVoAA4lBxAggBhABi1okIIAQQM0shgOh0IoAQQHQ2QgCxqxDru60AAcSh4gQQAggBxKwTEUAIIGaWQgDR6UQAIYDobMQfAeSfE+fk2vUbxreeM1smSZUymXF9VgwsBQggDtuLAEIAIYCYdSICCAHEzFIIIDqdCCAEEJ2N+BuAHD91QSb2HS8P3bxkdOvnb9yWLM+Wk9da1DSqz0qBpwABxGGbEUAIIAQQs05EACGAmFkKAUSnEwGEAKKzEX8DkCPHz8qyPiPkxcuHjW497GZi2Vu5jrRoV8+oPisFngIEEIdtRgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORhI6gGBT6s2bN0uiRInukSIsLEyyZs1qVz7j+jt37pT69evLgQMHjI9JqBUJIA5bngBCACGAmHUiAggBxMxSCCA6nQggBBCdjRBAyknbtm2lSZMmdqVyVJ8AYi4fAcRcK681CSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHEN4BcvHhR3nzzTfnpp5/k1q1b0qtXL2nVqpWSLCQkRPr16ycrVqyQvXv3yogRI2T58uXq//Cm4P9p0qRR3pUOHToIzpUiRQoZO3asVKhQQTwBJLrr2G2/YKxPAHHYqgQQAggBxKwTEUAIIGaWQgDR6UQAIYDobIQA4htAOnfuLKdPn5YZM2bI+fPnpVSpUrJo0SIpXvctP74AACAASURBVLy4ZM6cWbp16ybdu3eXyZMnS6dOneS3336T/PnzS6VKlaRNmzby8ssvS7FixVSdxo0by6xZs+Sjjz5SkOIJINFdx277BWN9AojDViWAEEAIIGadiABCADGzFAKITicCCAFEZyMEkHKye/duSZkyZZQU8FwcPHhQcuTIIQsWLJAyZcqozwAcqVOnVp4PAMiaNWukaNGi8uOPP0rHjh1lz549ql7r1q2lcOHC0qVLF7lx44YkTZpUEidOLMeOHZPcuXOrv3kCSHTXsdt+wVifAOKwVQkgBBACiFknIoAQQMwshQCi04kAQgDR2QgBpJw0atRI6tX7dxUtwEKWLFkUbGTKlEkBBEpERIQ0bNhQRo0apQBk+/btCigAIu+88476HQU5JfCE4G8AGNQHdERGRirwQDiXJ4BEdx277ReM9QkgDluVAEIAIYCYdSICCAHEzFIIIDqdCCAEEJ2NEEB8h2ABIgAQCLm6v5gAyGuvvaYAZceOHVKoUCE5cuSI5M2b9z8AEt117LZfMNYngDhsVQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIA8Q0gXbt2latXr8r48eMVNCCXA96S0qVLG3lAqlWrphLOjx49KsmSJZPevXvLkCFD5MqVK7Jv376oZXiju47d9gvG+gQQh61KACGAEEDMOhEBhABiZikEEJ1OBBACiM5GCCC+AeTSpUtqFawNGzYomQAUCKdCSJaJBwQhWM2aNZO1a9eqPUUGDBggffv2VaFcSFy39gGJ7jp22y8Y6xNAHLYqAYQAQgAx60QEEAKImaUQQHQ6EUAIIDob8UcAmdl9mJS7/I/RrZ+8lUjOVXlJ3ujYwKg+KwWeAgQQh21GACGAEEDMOhEBhABiZikEEJ1OBBACiM5G/A1Abt6KlG8WrpZrl68a3fqtyNtSqtzjUqhQHqP6rBR4ChBAHLYZAYQAQgAx60QEEAKImaUQQHQ6EUAIIDob8TcAsXu/rB/8ChBAHLYxAYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxF/BJB/TpyTa9dvGN96zmyZJFXKZMb1WTGwFCCAOGwvAggBhABi1okIIAQQM0shgOh0IoAQQHQ24m8AcvzUBZkyYqbkTHLT6NbPX78loSUel6ZNqhrVZ6XAU4AA4rDNCCAEEAKIWScigBBAzCyFAKLTiQBCANHZiL8ByJHjZ2X5qGlSO7XZPiBHr0bKb4+UkuYtXrD7qKwfIAoQQBw2FAGEAEIAMetEBBACiJmlEEB0OhFACCA6G0noAFKuXDkJCwuTQ4cOSaJEiaLk+OKLL+TVV1+V77//XipXruxTxp07d0r9+vXlwIEDdqVmfUMFCCCGQvmqRgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORggg5eTIkSMC4ChbtmyUHLVq1ZItW7bIrFmzCCB2jSiW6xNAHApKACGAEEDMOhEBhABiZikEEJ1OBBACiM5GCCDlJH/+/JI6dWoZN26ckuP8+fNSvHhxyZ07t7z33nsKQDZv3iwdOnSQixcvSooUKWTs2LFSoUIF8fSA4LM333xTfvrpJ7l165b06tVLWrVqZbcJWP8+BQggDk2CAEIAIYCYdSICCAHEzFIIIDqdCCAEEJ2NEEDKSbdu3aRt27bKE5IsWTKZOnWq7Nq1S3799dcoAClWrJh0795dGjdurLwiH330kezdu/ceAOncubOcPn1aZsyYoSCmVKlSsmjRIgUzLDFXgAASc+3UkQQQAggBxKwTEUAIIGaWQgDR6UQAIYDobIQAUk4GDx4sgwYNknbt2skLL7ygPB4ffvih9OzZMwpAbty4IUmTJpXEiRPLsWPHlHcEf/P0gOTIkUMWLFggZcqUUbICbOBZ6devn91mYH0PBQIKQJBM9PrrryuCffjhh5WrzDO2z3quP/74Q9544w1V78EHH5Rhw4ZJzZo11ccrVqxQxgNDK126tEyfPl2yZcumPhsyZIhy1UVEREjdunVl9OjRyjCjKwQQAggBxOydSgAhgJhZCgFEpxMBhACisxECyF0A+fvvv+Xrr7+WTz75RJCYjqTy5557LgpAABajRo1S0BEZGanAA2FWngAC2MiUKVPUeBBjxIYNG6rjWGKuQEABCIwGCUSdOnVSKxgARv766y/lWvMsRYsWVZ/BbbZy5Uq1ksHJkyfl5s2bKiZw6dKlCj769u0r+/btk/nz58uaNWukdevWsn79ekmbNq3UqVNHateurWIDCSD/VSDixk3JH5pGerUhgBBAzF5ABBACiJmlEEB0OhFACCA6GyGA3AWQEiVKSJ48eVSY1ZkzZ2TgwIFRAILwK3g8duzYIYUKFVKhWnnz5v0PgGDcCFBhyJVdq4u+fsAASHh4uIKHs2fPRlFoyZIlZcSIEcqYrAJyRZxfy5Yto8AkQ4YMysB++eUX9Rm8ICgXLlyQrFmzqn+7du0qOXPmlB49eqjPli1bps4NMCGAEEDsdLvL585Lj5ZVJVvmDHYOk+mLN8qh8MsSkiGtreMCpTIBhABi11YXhxeSXPUHS4Hcd73UpmXV+u3y4MHPpHj2O6aHBFQ9AggBxK7BbjoXKWkaNJKSRXN7PTRHaCq7p7RV3+19QODtAIDg30aNGqmxHCakH3vssSgAQfQLEs6PHj2qxou9e/dWkTBXrlxRk9PWMrwYH169elXGjx+v4AQwg3NiIpsl5goEDIBs3LhRxfEhecgqr7zyilSsWFHatGnjU4GtW7dKvXr15PDhw8qw4AlBaJVVYIDr1q1Tng6cH6FXKEhCwrkRqkUAIYDY6WIEEO9qEUAIIHb6EeoSQLwrRgAhgNjtSwkZQDChjLyP3bt3K9k8Q7CaNWsma9euVZPRAwYMUJExCLGaPHlyFIBcunRJrYK1YcMGdXy1atVU+JUuRN9uGyW0+gEDIAi5wrJpWL/ZKvBywIWGUCtvBdDx/PPPy5gxY9S/oFvQK0DEKnC3LVy4UAEIzl+9+t2QIrjicG6seBBduXDlptePt/x6SAbNXCvpQ9IHpU0hBOvRrGmlX3t7u5ReuRYhb49YJKlD7HkHAknEK+cvSN83qkn2B0Js3fbkBetl//FLEhISnB6QE+Hn5PmSeaTmc8Vs6bL38An5/Ottkjt3dlvHBUrlyMjb8vfhozK4y93Jj/vL7Tsiif/dR+uej0cOGSKd0s+TpEkC5Wnt3eeS04Wl4CtD5ZG89tr++zXbJMufU4LXA3I2kfySobbUrW0vBPbYqfPy3Yejpe6Vg/YaIoBqT8tYRNp82FlSp0pu666njJsnVc/8KZlSBGdn2nguUjI2aixPFsvjVZcMae4NZbclnkFleEDmDJkizya7YlBb5OS1SAkvVlratKllVJ+VAk+BgAGQTZs2KU+HRbCQGklAVatW9boeMzwl8GYg8ejFF19ULYPYP3g0kLxulSxZsgjO3b59e3V+uNxQ9uzZo84N11x05cr1W14//unXQ/LR9DVBDSCFs6WVD9+8q61pAYB0GbpQUgUzgFy4KB+2rSbZs9gDkInz18ufxy4GNYBUK51XXrQLIIeOy7Rl2yRPUANImAx92zuA3Lkj4rGR7z1dbcSgwdIxXfACyNLTheXRxsPkUZsA8t3qrZJ57xQpkSNIQ7DOJpKdIXWkfh2bABJ+Xr7tP0rqXA5eAJmeqYi0G9DFNoBMGjtPqpzZF9QAEtq4iTzlA0DSpIx+wR3T73hf9W7eipRvlm2Qa1euGZ3qVuRtKf10UXn0kVxG9Vkp8BQIGADBGsxY+erUqVNq+TOUAgUKqHWZraXRLPmxWhbgAZ95rpIFTwfcZnC3oYSFhUnBggWjckAyZswYtaza7Nmz1fGIGYyucBUse1+AV69FSK/RSyRtpoyB11sM75ghWN6FYgiWd13uekDCpN+b9mf6JnwyTLqELAhaDwhDsLzbDEOwfL+Mp2QoLM36dJRUNj0gn09aINXOHZBMKRIbvukDq1p8h2AFllq8WzcUCBgAgRhYwxmxe4jlmzt3rgqZ2r9/vyRJkkTmzJkjlSpVUnF8SCqCR6NBgwb3aIg4PoRc4djy5cur1bSQbATQQGwfNqJBrkm6dOnUtXCOFi1aEEC8KMBVsHybBQGEAGLn5U0A8a0WAYQAYqcvoS4BxLtiBBC7lsT6ca1AQAEIltxt3ry52t8jX758MnHiRMFKWChIJscyadj3A5CRIkWKe7T74osv1NK6yCVBzsiJEyeU5wT7gISGhqq6w4cPVx4S5IlghQP8js1poiv0gNADcr99EEAIIHZe3AQQAogde0FdekB8K0YAIYDY7U+sHz8KBBSAxI9E0V+VAEIAIYCY9UyGYHnXiQBCADHrQf/WIoAQQOzaDD0gdhVj/bhWgADiUGECCAGEAGLWiQggBBAzS/m3FkOwvCtGACGA2O1LBBC7irF+XCtAAHGoMAGEAEIAMetEBBACiJmlEEB0OhFACCA6G7n/cwKIXcVYP64VIIA4VJgAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQggDhUngBBACCBmnYgAQgAxsxQCiE4nAggBRGcjBBC7CrG+2woQQBwqTgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORgggdhVifbcVIIA4VJwAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQggDhUngBBACCBmnYgAQgAxsxQCiE4nAggBRGcjBBC7CrG+2woQQBwqTgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORgggdhVifbcVIIA4VJwAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQggDhUngBBACCBmnYgAQgAxsxQCiE4nAggBRGcjBBC7CrG+2woQQBwqTgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORgggdhVifbcVIIA4VJwAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQIKQA4dOiSvv/667Nq1Sx5++GEZO3aslC1b1qtmc+bMkTfeeEOmTZsm9evXj6qzYsUK6datmxw7dkxKly4t06dPl2zZsqnPhwwZIuPGjZOIiAipW7eujB49WpImTRptmxBACCAEELPXFgGEAGJmKQQQnU4EEAKIzkYIIHYVYn23FQgoAHnuueekVq1a0qlTJ/n+++8VjPz111+SLFmye3T7+OOPZd26dXL8+HEFGxaAXLhwQfLnzy9Lly5V8NG3b1/Zt2+fzJ8/X9asWSOtW7eW9evXS9q0aaVOnTpSu3Zt6dChAwHEiwIRN25K/tA00qsNAYQAYvbaIoAQQMwshQCi04kAQgDR2QgBxK5CrO+2AgEDIOHh4Qoezp49G+WVKFmypIwYMUIAJp5l586d8vjjj0uVKlWkbdu2UQAC0Jg6darAC4ICIMmaNav6t2vXrpIzZ07p0aOH+mzZsmXq3ACT6Ao9IAQQAojZa4sAQgAxsxQCiE4nAggBRGcjBBC7CrG+2woEDIBs3LhR2rVrJ7/++muURq+88opUrFhR2rRp41W3ypUr3wMgAwYMkJMnT6rQKqsg/AreEng6cH6EXqHs3btXnRuhWgSQ/ypAD4hvq7h87rz0aFlVsmXOYKs/T1+8UQ6FX5aQDGltHRcolQkgBBC7tro4vJDkqj9YCuS+GyZrWlat3y4PHvxMime/Y3pIQNUjgBBA7BrspnORkqZBIylZNLfXQ3OEprJ7StanAo4UCBgAQcjVe++9J1u2bIl64JYtW0qxYsWkc+fORgDSu3dvuXXrlsr1sErevHll4cKFCkBw/urV787oHzlyRJ37/Pnz0Qp8+473L7hNOw9Kn8mrJH1IekcN5K8HA0Aey5FehnR5ydYtXrkWIe0HLJCUGewNzm1dJJ4rX714QYZ0rCk5soTYupNPv1wne45ekIwhcQMgiWzdTexXPh5+Vl58Or/Urvi4rZPvOXhcJi3eInkezm7ruECpHBl5W478FSYju/+bq+Z57xE3b0uKZIm9Ps6AfgOlQ9q5kjRJoDytvftcerqwlGjxsRTKZ6/tv/1xi4T8PllK5AhOADl4VmR3xnrySoMatgQNCz8vC9/7RGpfOmDruECq/HloUek87G1JnSq5rdseO/JLqXRqn4SmDM7OtPFspGRr1lSeKZ7Xqy6JE8X3N4St5mLlIFAgYABk06ZNytOxe/fuKNkbNmwoVatWlVatWhkByMCBA5VHA8nrVsmSJYvg3O3bt1fnt/JF9uzZo8599OjRaJv5+JlrXj/ftvuwfDx3g2QIYgBBDkjvGOaApMmUMQi6j/dHgAekpx96QOJ7KEYPiHd7AYD8fThM+r1Zy3afmPDJMOkSsiBoAYQeEO8mQQ+I764yNUNhadano6SyCSAzJi2Q6ucOSKYU3mHfduf0swM2/r8HpJQPD0h2ekD8rMWC/3YCBkBOnz6tVr46deqUpE6dWrVMgQIFZMaMGVKmTBkjAIGnY9SoUbJ27VpVPywsTAoWLBiVA5IxY0bp16+f+mz27Nnq3CtXrozWCpgDwhyQ+w2EIVjeuwwBhABi9yuVAEIAsWszU2IIIJ9PWiDVghhAGIJl15JYP64VCBgAgRDI6UDCec+ePWXu3LkqZGr//v2SJEkSwbK7lSpVUknlVrk/B+TSpUuCkCscW758ebWa1pUrVxRobNiwQRo3bizINUmXLp26FrwiLVq0IIB4UYA5IL7NggBCALHz4qYHxLdaBBACiJ2+hLoEEO+KEUDsWhLrx7UCAQUgWHK3efPmah+QfPnyycSJEwUrYaEgmXzBggVSrlw59bfff/9dbt68qeAkceLEMnPmTGnQoIFavhc5IydOnFCeE+wDEhoaqs4xfPhw5SFBnkijRo3U7zg2ukIPCD0g9ICYvaboAaEHxMxS/q1FACGA2LUZAggBxK7NsH78KBBQABI/EkV/VQIIAYQAYtYzCSAEEDNLIYDodGIOiG+FCCAEEF3/4ef+oQABxGE7EEAIIAQQs05EACGAmFkKAUSnEwGEAKKzkfs/ZwiWXcVYP64VIIA4VJgAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQggDhUngBBACCBmnYgAQgAxsxQCiE4nAggBRGcjBBC7CrG+2woQQBwqTgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORgggdhVifbcVIIA4VJwAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQggDhUngBBACCBmnYgAQgAxsxQCiE4nAggBRGcjBBC7CrG+2woQQBwqTgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORgggdhVifbcVIIA4VJwAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQggDhUngBBACCBmnYgAQgAxsxQCiE4nAggBRGcjBBC7CrG+2woQQBwqTgAhgBBAzDoRAYQAYmYpBBCdTgQQAojORgggdhVifbcVIIA4VJwAQgAhgJh1IgIIAcTMUgggOp0IIAQQnY0QQOwqxPpuK0AAcag4AYQAQgAx60QEEAKImaUQQHQ6EUAIIDobIYDYVYj13VaAAOJQcQIIAYQAYtaJCCAEEDNLIYDodCKAEEB0NkIAsasQ67utAAHEoeIEEAIIAcSsExFACCBmlkIA0elEACGA6GyEAGJXIdZ3WwECiEPFCSAEEAKIWScigBBAzCyFAKLTiQBCANHZCAHErkKs77YCBBCHihNACCAEELNORAAhgJhZCgFEpxMBhACisxECiF2FWN9tBQIKQA4dOiSvv/667Nq1Sx5++GEZO3aslC1b9j+aRVdvxYoV0q1bNzl27JiULl1apk+fLtmyZVPnGDJkiIwbN04iIiKkbt26Mnr0aEmaNGm0bUIAIYAQQMxeWwQQAoiZpRBAdDoRQAggOhshgNhViPXdViCgAOS5556TWrVqSadOneT7779XMPLXX39JsmTJ7tHNV72rV69K/vz5ZenSpQo++vbtK/v27ZP58+fLmjVrpHXr1rJ+/XpJmzat1KlTR2rXri0dOnQggHhRIOLGTckfmkZ6tSGAEEDMXlsEEAKImaUQQHQ6EUAIIDobIYDYVYj13VYgYAAkPDxcwcPZs2ejvBIlS5aUESNGCIDDKtHVO3XqlEydOlXgBUG5cOGCZM2aVf3btWtXyZkzp/To0UN9tmzZMnVugEl0hR4QAggBxOy1RQAhgJhZCgFEpxMBhACisxECiF2FWN9tBQIGQDZu3Cjt2rWTX3/9NUqjV155RSpWrCht2rSJ+lt09QAgJ0+eVKFVVkH41bp165SnA+dH6BXK3r171bkRqkUA+a8C9ID4torL585Lj5ZVJVvmDLb68/TFG+VQ+GUJyZDW1nGBUpkAQgCxa6uLwwtJrvqDpUDuu2GypmXV+u3y4MHPpHj2O6aHBFQ9AggBxK7BbjoXKWkaNJKSRXN7PTRHaCq7p2R9KuBIgYABEIRcvffee7Jly5aoB27ZsqUUK1ZMOnfuHPW36OoBQG7duqVyPaySN29eWbhwoQIQnL969bsz+keOHFHnPn/+fIwE3rTzoHQcvlTSpE0do+P9/aCbt25JqXxZZEzPBrZu9cq1CGnS83OJTJrc1nGBVPnOzQiZ2Luh5MgSYuu2R3z+g2z87aikSxucXwRnzl+SFtVLSr2qJWzpsufgcflwykrJ+kBGW8cFSuXI27fl6sXLMrV/E9u3/G6P/vLc7e8lSWLbhwbEAdsv5ZK6XYdLoXzZbd3v0pWbJWzNZ1IgNDgBJOySyJ0CtaR54xdt6RIWfl4+7TJMSl7+x9ZxgVR5U2h++fDTHpI6lb3vmEGDPpecf++T9MmDszPtvnRbKrz1ujxTPG8gNSfvNYgVCBgA2bRpk/J07N69O6o5GjZsKFWrVpVWrVpF/S26egjPgkcDyetWyZIli+CY9u3bq/PXr19ffbRnzx517qNHjwZx8/PRqAAVoAJUgApQASpABaiAuwoEDICcPn1arXwFL0bq1He9nZF4OAAAIABJREFUCgUKFJAZM2ZImTJlolSLrt6JEydk1KhRsnbtWlU/LCxMChYsGJUDkjFjRunXr5/6bPbs2ercK1eu9NkiM2fOlDt37shrr73mbqvxalSACiQYBapUqaIW3WChAiYKIEwZ313IYWShAiYKcCxjohLrxLYCAQMgePDKlSurhPOePXvK3LlzVcjU/v37JUmSJDJnzhypVKmSSir3VQ+rYCHkCseWL19eraZ15coV9bLesGGDNG7cWJBDki5dOnUOeEVatGhBAIltq+P5qAAVMFaAAGIsFSuKqDxJAghNwY4CBBA7arFubCkQUACCJXebN2+u9gHJly+fTJw4UbASFgqSyRcsWCDlypVTS/P6qoeZROSMwBsCzwn2AQkNDVXnGD58uPKQIE+kUaNG6vfEiX3Hg7LTxpYZ8jxUgAr4UoAAQtuwowABxI5arAsFOJahHcSHAgEFIPEhUHTXdKPT/vDDDyoXJXny5JIoUSJJnz69vPjiiyqPJWXKlDJy5EiVFzNlypQYyRMSEqKOf+ihh2J0vD8chM0oL1++rMBUV7Zv3y5YPe3AgQO6qgH3OeAbXj48owXOWEQhd+7c2sUUoAegHOcIRo3g7bxx44ZMmDAhql3hMUUYp+ffnn32WWnSpInajPSnn36SWbNm/ccOsE8QVslDn8EEBiY74rI4BRBMqGCvpH/++eeefo53B5YZX7x4sZpswTPF9D0Sk+f31DEmx7t5DLztn3zyido3yvNdWbx4cWUD+NdfSmwDCL6DYIOINPAsb7zxhnz66adx+tjYiwt5meiTgVbwLkWYNzZGxne3Vb744gt59dVXVVglIi18Faff7Xb0cmMsY+d+WDdhKEAAcdDObnRavPzbtm0bNWA+fvy42oyxXr160r179wQPIL///rva2R5fjr169ZJnnnkm2hbFYAyD8syZMztoef88FF94GGT27t07amlqUwDB4AqDbuyDE4waff3119KlSxcVsomCZ8W+QmnSpFEDb5Rr164JgByDTNT3BSBYzAL2g0FF9uzZFbjFZXEDQACuaHdMcLhVLB2j8zK7dS+66wBA8L7HZAfCfa2SUADE8ztIp1Vsfh7oAILVNAEcsBur4Psbq3licoMAEpvWwnMFmgIEEActFh8AgtvFQPvMmTMqBM1zlgR7nDRr1kzBSmRkpBpwYeYX5ZdfflE7x2OwhIEXZu2QD+PpAcHAFbNnS5YsiTb0zIFksX4o4KNw4cLKG4T9XMaPH6+ugdluPC9WOLt9+7YKt8PsLoDF8oBEpxd0GTRokHzzzTdy8OBBadq0qdLdnwsABDlLffr0kT/++EMyZMigYMvTAwINhg4dKjdv3lSLOsCGMShv0KCBmiXH8YBbaIRwRORbee698/jjj6tlrKHnm2++qQbpGLhCG8/V6PxNJ3jIMmXKpPpGrly5ZPXq1er5ABs//vij5MiRQ81IIu8LesDDiHww6LR582b1OUI8oZk1c9+xY0fVV2B/y5cvV/lk2EsIs57wWH722Wfy5JNPOpbCDQDx9IDg/3i3YIEN5NTBRvCuAKxhII5FPDARgv5lvV+82RU2dsX7CZ5J6IgVBdEOX3311T06wqOAwdj777+v+i3y/LBhbIoUKRxrF1snwHOjfyC3wgIRnNsTQObPn68WMcG7F7rBs/boo49Gq0Fs3Z/neeLCAxIdgADW0f9h/3ny5FED7gceeEA9N7530KaAfPQhrDSJtsX7qW/fvirvEu9n9CVsEIx3SYUKFdS7OmnSpOIJIL6uExcaxsY58T7Gdy0WzRk3bpw6Jd7HsBm8k2FTABC8X7ANwMWLF5XN490DDTy/2/FZXL5v3RjLxIamPEdwKUAAcdCebnTa+z0gmOGuWbOmDBw4UF544YV7XlIYDGAWd/Lkycrti4ER/sWLH6t9YUCNwSUGoKtWrVIvfAtAMNjCwBKDeAywAqHgix7PhS85fFkVKlRI/vzzT/USx2Bx0qRJ8t1336mB1Lvvvit16tRRn1kAEp1emOHGYLR///6CmVoMps6ePasGYf5a8IWHNsQAAAPgjz/++B4AwQpyeA4MBvAFCGCAbhgo4csNA0F4QKwQLCxFjWWqoS8GFocPH5ZSpUopiAX4YcU5DMjwpYq/L1q0yK9CUe5vJyw8gb2DEDIFSMNiE4ANDHgxEAJsXbhwQQ0WMAj44IMP1OIUsCt8Ds0GDBgQBSCAXmh2/fp1ZWNPPPGE8lYiNGXbtm3y0ksvqf2EMHB1UtwEkMGDB6s+9ffffyt90L4YWKIv4F2BZ4ONwZaQh4eBJGzNl11BRwzKEeaJQTnsDF4WvL8skMOgE/YDzQCHGHRixhi26C8Fg8VUqVLJI488ItAI9woPmAUggNvHHntM/R36ITQJ3w8A9Og0iIvncxNAANvYLwtAj3+x8hb6DN4FeG70F9gI2hz9B+HEgIv169crGMFn2IcL8Lljxw7Vj2AL0BvvaQtAMCD3dZ240DA2zon3Md6TeCdY7wHAF4AcbWQBCJ4L0Qx4xwDEP/roI/WO9gQQ5K3G5fvWjbFMbGjKcwSXAgQQB+3pRqcFgDz//PNqMICXM2ZC4ML9/PPP1Uvd8yWFATl+MCBAwRchPB2YjSpdurScO3dOfWliNhIhKBgAYFCBc7399tsqHvzBBx90oIi7h3777bfq3r/88kt1YXh/MOgDZOFLEF9gmMlFrD8Giyie+Q2+9MLsPgAE2lux3Vii+eeff1YDcX8t+MLD4AjgiR+0JxZn8PSAAFAxkEJBKAnsA0tNewMQeAvg+cGA4K233lJAAyjBAMLyCFhLYOOLFjN91jLW/qgRvtjh8UC/hVZ4HgArdMIzPfXUU2rQC1DF4Am6LF26VD0KvCVofwzIrYGzJ4DgPLCVS5cuRcXKo88B9jF4clJiC0DwHvAMd0LYVcWKFe/JARkzZoxqW0xWwCtmLdCB+8e7AlpZfQJAh5lr1PNlV9AR/Qh5Jr50hPcIs9vwJqHgvhBS6W8eELQ3Bo0AViy9Dpi1AASDZ9y/ZS+AUkxW4J2Ld5QvDZzYha9j4wJAqlWr9p+wVQA62gmTPdZy9ZanEdCKdy/sBZ+j/O9//1P2gv27AC54RwH4UaCX9Y4GmOCdBa+KBSCwCV/XcQr4cdEGOKf1PkZfgmcUE4bweHz44YdqssMCEHiIMBGEvol9yvDs+Jvnd3tcv2/dGMvElc48b+AqQABx0HZudNr7PSB4ESNeH1/omG3zfElhZgUvNYRH4IWGLyJ8MQBI8NLH6mD3FwwqMHAE4OzcuTNqcOpAFtcOxTMBQvCsKJhJxYDKGgQgJAIzkZjBB5RgcIUZN8sD4ksvfHEAQDB7CRc6yv2/u/aQNi5kfeHhXzwrtIE3xAIQACwG4cuWLVMgisERZpxhY74ABIOq0aNHqzAlDDjhOcCAGDaDWV9LewAt2gMDdX8tiLuuW7eugg6EUiEED94cPBdsBN4e/I4+gYGzZw6I5+/eAGTr1q0qLM0zQRmDMHhTMEB3UmILQABQyFmxCjylGDjfn4SOvwFkEZIG+ERYI5L1oQveEbAnFAyoAPytW7f2aVcmOgKEAbaAYX8teK9aAIL3Ro0aNdS7BLaD+4Y3GXaFsDur4J0Ku8JnvmzJ8qDh3LG16W1cAAjaGKFCngUTYGhfeIk9c+oAFWhPhNphwsdqV4DbO++8o+zGCgUGsOA9hAkw3DfeJ/iewvsImlsAgoG5r+t42rQ/2Y/1PoY3EYCN7238DRM70MICEIAV3puADkyKoY/hu8zzuz2u37dujGX8qW14L/6hAAHEQTu40WnvBxDcLkJeMCOPPBDMrlmrYCEEoGvXrlF7lyBuG59j9gQzdTgOsyx40SE0C/HJGFRgAIIZYMx2+vMA0rOp8Czw8OBL2/L44KWNL/TffvtNeX2sAp1efvll5UnCbLQFIL70CgYAgRbI18CXHGbfoBcGBPBQIPwB+SGwDWt21heAACxgPxhAYQYTemOQADDDF6c/rf6j68r4coddwCuBEBHk96BgcI0ZSQzIrUGWycDZ0wMCXWBPGEzFdoktALG7ChbaHrPc6E/QCu8KvI8AJShY8AEDR8CsL7sy0RGTJLBNqz2gIQamCOvyl+IJILgnhKJhAA4wB3QANPAetbw4lgcE/Q5eM18AAgjGxAA0RIhabJS4ABBfOSAIGcJ7wPJwed4/2t4EQOAVwcQaPCZ4t8CzhO8uTwDBeX1dJzY0i4tzWABSokQJ5TlHmBW+ixB+aAEIwq8A9IB+hHoiVAvPfj+AxPX71o2xTFxozHMGtgIEEAft50anvR9A8GLCjDRAATMrnrMkmIVCzgP2RsHMN740sOki3OdFihRR8aiIf8exCHvwzAHBDAsGUNgBHi9Hfy+YlUUyrBV+Zd0vwrCsvWGQs4EBFAq+1KABns0CkOj0CnQPCJ4ZAzvE7iN2GAMhDAhgH/CA4Hd4LBDSh8ERFixAyAtmvu9fhhd74mBABQ8BBgkoAF0MGtAOsEl8uaIewo78uWBJT8xUI4QGs7EoyIXBM2M2H7OsKCYDZ8xwA9ox44twG9gdzgkdkCOBsDXohXpOipsAAi0AaPBKALAwe42BH2ZwASDoX3h/YGEGvC8wkYHPfdmViY4YfKNvIg8NkyLon9DS33JALA8I2hLti3tGn0F/wvvCygHBYBHvZeQ2IKcuOg2c2IWvY90EEAAUBtGY1MCEELzymNSAB9YUQNAnLZiF5wTeJdgA3kWWBwR9wNd14kLD2Dinp0ca7wSEo+GdDDuxAAQhspgUwwQGQsmwEAxyrKwQNmtyMa7ft26MZWJDU54juBQggDhoTzc6rec+ILhVzPbjyxlfcJjh9gQQhHsg3hRfhlg3HbMtSHpDeAhi0/E3xN5ipgWzcpiV8VwFC6FLGDThC8zpoMmBrEaHPv3002pDSXxReRbMxCHMCICFFZ3wLIhTxsAYM5VI7rMAJDq9oHEgh2BZmmBQjUEQgAODJuwhg/9jdhmDbeQ7IM8DyaEIU8OXPwbRnnulYCCFz2CLyKdBgT3Ba4JcGxRALgamVkiWUSPGQyUAAcAcAyVrJh+zuNAAQIuQGlMAgbcNuuFcgHl4JTEzDk8DdADU4XenxU0AgT5of7Q5ngEDJHiGEK+PdwXi8gEngHtAJ1bvic6uEOqmC2WDjgiXxCALMIs4eXhk/TUHxGpP5BDBAwTvBzyB8KphwgMeZvQvaInZ7GAGEGgBzxU8iMgDgmcV30kYfJsCCFYqxIQActPQJ/GOwkQZQAYAbO0D4us6TvtXXB3vCSCAVGgEoEDxDMEC1OPdAw8YkvaxOhi8j0hKtwAkrt+3boxl4kpnnjdwFSCAOGg7dloH4vFQKkAFjBRwCiBGFzGoFAyblho8ZsBXiW0PSMALwgfQKsCxjFYiVogDBQggDkRlp3UgHg+lAlTASAECiJFMrPT/ChBAaAp2FeBYxq5irB8bChBAHKjITutAPB5KBaiAkQIEECOZWIkAQhuIoQIcy8RQOB7mSAECiAP52GkdiMdDqQAVMFLAXwDE6GZZKd4VoAck3psg4G6AY5mAa7KguGECiINmZKd1IB4PpQJUwEgBAoiRTKxEDwhtIIYKcCwTQ+F4mCMFCCAO5GOndSAeD6UCVMBIAQKIkUysRAChDcRQAY5lYigcD3OkAAHEgXzstA7E46FUgAoYKUAAMZKJlQggtIEYKsCxTAyF42GOFCCAOJCPndaBeDzUWAFsAoc9XLBvyauvvmp8XEwqpk2bVu2Vgr0ZWPxDAQJI7LUD9hnBhm92d4WPvTvwfqYXXnhB7b2Dfu60MAfEqYIJ73iOZRJem/vDExNAHLQCO60D8XiosQLYiKtixYqyZMkSteN0XJbw8HC1kWXixInj8jI8tw0FCCA2xNJUNQGQq1evqg0R06dPH3sX1pwJm4Nip3X8OC0EEKcKJrzjOZZJeG3uD09MAHHQCuy0DsTjoUYKYMfpZ555RnklihQponYjz5Ejhzq2du3a8thjj6nd3rFjLnbUxe7Rq1atUjO8c+fOVZ9fvHhR7VqOHakxsMJu1q1atVLnwM7F+H3EiBFy+PBhtRuv5QHBDuHvv/++2tkZO/dOnTpVnR+7VA8dOlRu3rwp2Oka/QA7P+N+nnjiCdm+fbscO3ZM/W3BggVqJ3qWmCtAAIm5dvcfaQIgw4cPV30Adr5y5Uq12/n169clefLkMmzYMKlRo4bUrFlT9cddu3apXeGLFSsmEyZMUN4VX/0DO4SjPvrN0aNH5fLly/LVV1+pPuTpAfHV70xVIICYKsV6lgIcy9AW4kMBAogD1dlpHYjHQ40U+PTTT+X48ePy0UcfyYcffqhmSLt166aOhWcEs7XffPONHDhwQAoVKqQgASCCuoCA8ePHS+fOneX06dMyY8YMwUxrqVKlZNGiRVK8eHHl7WjRooUMGTJEeT2sECwM1FBv27ZtkitXLgUXZcuWlddff12BBQZouXPnViCTNGlSNfjC/WAwhkEboAODMoBN1apVjZ6VlbwrQACJPcuwCyBFixZVtl2uXDkF+mPGjJHJkyer/nDq1ClZu3at6jf4vGPHjlK5cmWf/WPs2LHSr18/NVkA0MekALwsAwcOjAIQnMdbv+vRo4exCAQQY6lY8f8V4FiGphAfChBAHKjOTutAPB5qpMCTTz4pn3/+uTz66KPKQ1GrVi01ELIABJ4JDGQiIyMVCJw8eVKyZMkis2fPlnnz5qmwLXhM4IkoU6aMOg4Akzp1ajUYAoAsW7ZMeVlQLABZvny5IPcEx6MAdAAV8IBcu3ZNUqVKpf4+Z84cmT59uoIOAAgGUAAelDp16shLL72kAIcl5goQQGKu3f1HWgDywAMP3BNmCPtGmOPixYvF0wMCoChYsKC88847kjdv3qjTAUDQ9yxbh6fwzJkzggkDX/0DAAIPJq6BMmrUKPn555/VxIDlAcGxvvqdqQoEEFOlWM9SgGMZ2kJ8KEAAcaA6O60D8XioVoE9e/YoLwKgwCoI29i6dasKdcKAHwOX5s2bq48TJUokly5dUvW//PJLQSgHBjOAjUyZMilAQYmIiJCGDRuqARAAZPPmzVKgQIF7AARggesDLjzLnTt3lDcG0ILrnTt3TnlIMLDC/VSrVi0qvOv+37UPzApeFSCAxJ5hWACCgX/27NmjTgyvxo4dO/4DICdOnFD2Do9hSEiIgpPq1asrDwh+rL6Hv+OcAH9f/QMAgjBI9EsUz98tAIHX0lu/s6MAAcSOWqwLBTiWoR3EhwIEEAeqs9M6EI+HahXo3r27ZMyYUTzDLz755BP5+++/BfHkpgCSP39+5QFByNX9BQCCQRHqoFgeEHg0EJ+O8C4UgIYFP/CcrF+/XuWPwDuDHwKItjljXIEAEmPp/nOg3RAszxPAG/jaa6+pMMN69eopbx88Iyg9e/ZU8F+hQgXlWfTWP0wABPlW3vodwh5NCwHEVCnWsxTgWIa2EB8KEEAcqM5O60A8HhqtAgipgmfhxx9/VOFXVkEY1tNPPy1hYWFq2U4TD0jXrl1VCBXyQTAAA9g0atRISpcurTwg3gAE3g0k2W7cuFFdH9cqWbKkAhSsxAUPCPJJ4ElBkjvOQQ9I3Bg1AST2dLUDIKNHj1ZhWYB3LEt96NAh5XkEgNStW1fgHVm9erW6OfSlDz74QIVA+uofJgACgPHW75gDEns2wDP9VwGOZWgV8aEAAcSB6uy0DsTjodEqsGLFChVfjmTv+ws8GQjzQHiUCYBgZhZ5Ihs2bFCnQpgUwq8QkuULQDDgmj9/vgBeMGhDLDxW9wFsvPjiiwo+MCvbv39/levRtGlTlaPCEKzYN2wCSOxpagdAYO/Tpk2TQYMGqT6AvCf0O9g7wq+QGwJPB1a0ev7552XixIkKTnz1D6x2pQvBwj4g3vodcq9MCz0gpkqxHj0gtIH4VIAA4kB9AogD8XgoFaACRgoQQIxkcrUSAAQev9jYODC2b5wAEtuKBv/5OJYJ/jb2xyckgDhoFXZaB+LxUCpABYwUIIAYyeRqJQKIq3LzYnGsAMcycSwwT+9VAQKIA8Ngp3UgHg+lAlTASAECiJFMrlYigLgqNy8WxwpwLBPHAvP0BJDYtgF22thWlOejAlTgfgUIILQJOwowBMuOWqwLBTiWoR3EhwL0gDhQnZ3WgXg8lApQASMFCCBGMrHS/ytAAKEp2FWAYxm7irF+bChAAHGgIjutA/F4KBWgAkYKEECMZGIlAghtIIYKcCwTQ+F4mCMFCCAO5GOndSAeD6UCVMBIAQKIkUysRAChDcRQAY5lYigcD3OkAAHEgXzstA7EC4JDsft31apVJXny5FFPkz17dmnXrp28++67QfCEfAR/UCAYAAS7hm/evFmwwaVnef/99wU/6EvY8BL7z9y/oaVJG2zfvl1tlnngwAGf1bFnDupg6VzP65mcP5DqJNQQLOxRhH2Lli9fLhERERIaGiodOnSQTp06BVLzxcu9ciwTL7In+IsSQByYADutA/GC4FAMYtq2bXvPoGf37t1qMz5sSlazZs0geEo+QnwrECwAgr7ia9+MWrVqSc+ePeXpp5+OEYBgo0BsjomNNX0VfJ4yZUr143m9+G7f2L5+QgWQN954Q65fvy7jxo2TNGnSyG+//SboOzNmzFAbRbL4VoBjGVpHfChAAHGgOjutA/GC4FBvAILHwiALs7nvvfeenDx5Upo1a6YgJTIyUrp06RI1Izdr1iw1+3vjxg157rnnZOrUqYIdj7/++mvp1auXXLlyRfLkySNffPGFPPDAA0GgGB8hJgoEO4AMGTJEPvjgA3nwwQdl2LBhMmfOHClcuLB8//33cujQIfnf//4nX375pSRNmlRCQkLUzuTffPONHDx4UJo2bar6iqcHBLuRt2jRQu06jlnwTz75RA1ALQ9IWFhY1PVefvllWbp0qWDiwColS5ZU/RKQEogloQJI2bJlpWPHjsrLZZW//vpLvTsBJHv37lXeabQ/vNafffaZPPnkkzJixAhZt26dLFmyRB0GrzaWWW7fvn0gNn+M7pljmRjJxoMcKkAAcSAgO60D8YLg0PsB5M6dO7Jz507lAcGAqUKFCgo2rl27JpMnT1aDKQys8C+go1SpUrJt2zbJlSuX+sLDFygGVMWKFZPVq1erf/HluGHDBlm0aFEQKMZHiIkCwQ4g0KRo0aIyZcqUKA8IBokrVqyQZMmSyeOPP65mtaEDPBwYGPbv31/Cw8MlZ86cAuD4448/okKwWrduLalTp5aRI0eq/oUB5YkTJ5RnxQrBsq4H2EDY5KpVq1R/++eff+Sxxx5TEweYDAjEklABBO/K4cOHS48ePRRwYhLIKng3P/HEE8pjDU8J7OKll16SI0eOSOLEiRWIAGzxrh48eLBs3LhR/T2hFI5lEkpL+9dzEkActAc7rQPxguBQAAi+6NKlS6eeBnHHmG3r16+fmoFFgdcDP1aeSMGCBWX69Ony+++/K0+HNet29epVSZIkiQoXWLBggaxcuVIdf/nyZcmUKZPyhmAwxpLwFAgWANm6det/BnV79uyRvHnz/gdAypQpo+L5UerUqaMGi+hTABD0u+LFi6vPMmbMKD///LOcOXMmCkDgSUHfKlGihKpz7tw5Vc8zB8QTeDAoxecYgI4ePVp27Nih+mGgloQKIGgvTNR8/vnnagInbdq0CjbgIYO3DDZz6dIl9Z5FKV26tAwdOlRNFMGG4KlGKN+8efMUhCakwrFMQmpt/3lWAoiDtmCndSBeEBx6vwcEYSHweGA21gKOXbt2qVCs48ePqxASDA4AF5s2bRIMvgAjngVfiJjd9Yxlv3DhgqqLmVqWhKdAsABIdDkg93tA4EVs1aqVamzPpHT0C4RW5c+fX31m/Y78DisJHd4P9JfcuXPfYyy+AGTt2rVq8IlwnYoVK8o777wjNWrUCFhDS8gAYjXa7du3lRcDNtSyZUsFGYBaLHJgFUzqwLPWoEED9Sd4oAEnCMdKaIVjmYTW4v7xvAQQB+3ATutAvCA41FsOSPny5dVsLQYxKJhJw0yu5RHBbC9m6P7880/56quvVCw7CmZp4e3AYAgekMWLFweBQnyE2FCAAFJfhTViMGkCIAhpRN/CDDfKvn37FIzUq1fvPyFYSHpHeA6OGT9+vBqsIvwrkL2NCRFA4EGG5wIg6bnSGsKpEBaL0Cy8i/Ge9VbwHsbkD5LYkf+T0BYQ4VgmNt7UPIddBQggdhXzqM9O60C8IDjUG4DAs4Evr/3796vBEn6+++47Qaw5kskxCzx37lz1ZVikSBE1S4dYZczeog5ABbHo69evF4RrIVYZwDJmzJggUIyPEBMFEgKAIFwKIVAAjfuX4bXrAUGOCMIhJ02aJPBAQj9AhWcOiOf10CaYMAD4V65cWeWiBHJJiACCMFe8L1999VWVA4Kkc4Rdoc0BrliOF+9XtHOjRo3k1KlT8tZbb6nVCpHrgTwjhG8BQBo2bKgWJcA5EkrhWCahtLR/PScBxEF7sNM6EC8IDvW1ChYSyuHqHzt2rHLxY2AFEMHqWIhVx2pXiIfHD7wjiDu2Bj5IfMVsHJYkRUJkhgwZVDIt9lFgSZgKBAuAeNsHpFKlSgrQEXaIGeiBAweqEBgnIVgIWcRMOCAeOVnI60AiumcIluf1EDaJHAAMUBEeCb0DuSREAEF7ATiw/xLaHe/ULFmyKDsAkMArAq8zckKw0ADCYbEiIX4HlKA+3rMoABbk9WH1tIRSOJZJKC3tX89JAHHQHuy0DsTjoVSAChgpEAwAYvSg8VgJOVpYlQ6rIllJyvF4O44unVABxJFoCfxgjmUSuAHE0+MTQBwIz07rQDweSgWogJECBBAjmWJcCTkgmPXGnjtYMCLQCwEk0FvQ/fvnWMZ9zXlFEQKIAytgp3UgHg+lAlTASAECiJFMMaqE/T6wPwQS1pHp7Q88AAAGzUlEQVSjlSpVqhidx58OIoD4U2sExr1wLBMY7RRsd0kAcdCiWHkDs2cJKVnNgVw8lApQgRgocPr06XuWZY7BKXhIAlLg5s2bat8g7BrPQgVMFOBYxkQl1oltBQggsa0oz0cFqAAVoAJUgApQASpABaiATwUIIDQOKkAFqAAVoAJUgApQASpABVxTgADimtS8EBWgAlSAClABKkAFqAAVoAIEENoAFaACVIAKUAEqQAWoABWgAq4pQABxTWpeiApQASpABagAFaACVIAKUAECCG2AClABKkAFqAAVoAJUgApQAdcUIIC4JjUvRAWoABWgAlSAClABKkAFqAABhDZABagAFaACVIAKUAEqQAWogGsKEEBck5oXogJUgApQASpABagAFaACVIAAQhugAlSAClABKkAFqAAVoAJUwDUFCCCuSc0LUQEqQAWoABWgAlSAClABKkAAoQ1QASpABagAFaACVIAKUAEq4JoCBBDXpOaFqAAVoAJUgApQASpABagAFSCA0AaoABWgAnGkwJ49e2TAgAGyatUqOXv2rGTJkkWeffZZ6dOnjzzyyCNxdFWelgpQASpABaiAfytAAPHv9uHdUQEqEKAKbN68WSpXrixlypSR7t27S44cOeSff/6RMWPGyMaNG2Xr1q1SoECBAH063jYVoAJUgApQgZgrQACJuXY8kgpQASrgU4HixYtL9uzZ5dtvv5VEiRJF1btz54706NFDXnzxRSlXrpxcvHhRMmTIINOmTZNu3bpJ3759pUOHDjJu3DgFK0ePHpV8+fLJ22+/LU2bNlXnadu2rVy+fFlmzZoVdd7MmTPLlClTpHbt2lK0aFF59dVXZf369bJv3z7BNSdNmiRVqlRhi1EBKkAFqAAViHcFCCDx3gS8ASpABYJNgUOHDiloQOhVhQoVon2869evS6pUqaRq1aoyevRoeeihh2T58uXSunVr+frrr+XJJ5+UxYsXyyuvvCJbtmyRUqVKaQEE8IOQr7Vr10qePHlk+vTp8uabb8qJEyckbdq0wSY3n4cKUAEqQAUCTAECSIA1GG+XClAB/1fghx9+UN4GDPizZs1qBCAzZ86UJv/X3h2DpBZHcRw/1KZL4SxBNARNjSG6uFgI0dAWCCWBTroIToJTopGDjY3RULQ3BE4SREigIDg7BDUFidAQ54CX96D33oX7Sr19/1v4/3vv/3NbfvzPue7t2dzNzU3rEanX685aLeXSMKM9Jf86AdEAEo1G7QRFx/v7uywsLMj5+blsb29PPyB3iAACCCDgawECiK8fL5tDAIFJCNze3lr/x2AwsN4PHdr3oQ3o47G6uiqdTkfGJyCtVks2NjbsYw0fWnJ1eHjozN/f35fRaGQhwk0ASaVSks/nnfXLy8tW4pXJZCZBwjURQAABBBBwBAgg/DMggAAC/1lA+zbC4bCVUiUSCfv2t7c30dIsHVpapWVRvV7PCSD39/dWXvW3AKL9IldXV58GkFAoJGdnZ9YDoicg2gNSKBScnS0tLUmxWLS1DAQQQAABBCYpQACZpD7XRgAB3wpEIhGZm5uTZrMp8/Pzv+1Tw8fR0dEfA8jW1pasrKxYT8h4aAlWLBazdblczprTNYzoeH19tUb26+trJ4Bo74g2nusYDoeyuLho85PJpG/N2RgCCCCAwGwIEEBm4zlxlwggMGMCj4+PVnK1trYmpVLJmsGfn59F+0NOTk7shKLRaHx6AqJB4uDgQG5ubmR9fV0uLy9FS6ra7ba94er09FSq1ap0u10JBoP2uyK1Wk0uLi6cAPLy8mKBQ09DKpWKfa49KYFAYMYkuV0EEEAAAb8JEED89kTZDwIITI1Av9+Xcrks2hOi4UMbwfVkQsugxicR4x6QX0uwdAN60qElVU9PT9YTon/H43Hbm76CVwPMw8ODNblns1k5Pj62a+3u7lro2NnZkbu7O3sTlv4Aon7XeP3UAHEjCCCAAAI/UoAA8iMfO5tGAAE/C2gASafT9updBgIIIIAAAtMmQACZtifC/SCAAAIeBQggHgFZjgACCCDwpQIEkC/l5csRQACB7xcggHy/OVdEAAEEEHAvQABxb8VMBBBAAAEEEEAAAQQQ8ChAAPEIyHIEEEAAAQQQQAABBBBwL0AAcW/FTAQQQAABBBBAAAEEEPAoQADxCMhyBBBAAAEEEEAAAQQQcC9AAHFvxUwEEEAAAQQQQAABBBDwKEAA8QjIcgQQQAABBBBAAAEEEHAvQABxb8VMBBBAAAEEEEAAAQQQ8ChAAPEIyHIEEEAAAQQQQAABBBBwL0AAcW/FTAQQQAABBBBAAAEEEPAo8AGWRlgcyofyygAAAABJRU5ErkJggg=="
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "def precision(y_true, y_pred, sample_weight):\n",
                "    from sklearn.metrics import confusion_matrix\n",
                "\n",
                "    tn, fp, fn, tp = confusion_matrix(\n",
                "        y_true=y_true,\n",
                "        y_pred=y_pred,\n",
                "        sample_weight=sample_weight,\n",
                "    ).ravel()\n",
                "\n",
                "    return tp / (tp + fp)\n",
                "\n",
                "\n",
                "sd.custom_disparity_metric(\n",
                "    outcome=binary_outcome,\n",
                "    metric=precision,\n",
                "    label=binary_label,\n",
                "    difference_calculation=sd.types.DifferenceCalculation.REFERENCE_MINUS_PROTECTED,\n",
                "    difference_threshold_significance=lambda difference: difference > 0.0,\n",
                "    ratio_calculation=sd.types.RatioCalculation.PROTECTED_OVER_REFERENCE,\n",
                "    ratio_threshold_significance=lambda ratio: ratio < 1.0,\n",
                "    statistical_significance_test=sd.types.StatSigTest.BOOTSTRAPPING,\n",
                "    p_value_threshold=0.05,\n",
                "    **precision_arguments,\n",
                ")"
            ]
        },
        {
            "attachments": {},
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Additionally, statistical significance can be set to ``None`` for custom\n",
                "disparity metrics, causing statistical significance calculations to be\n",
                "skipped."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 31,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/markdown": [
                            "## Disparity Calculation: Custom Disparity Metric"
                        ],
                        "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",
                            "│ Metric                              │ precision                                                                 │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Affected Groups                     │ Black, Native American, Hispanic, Female                                  │\n",
                            "│ Affected Reference                  │ White, White, Non-Hispanic, Male                                          │\n",
                            "│ Affected Categories                 │ Race, Race, Ethnicity, Sex                                                │\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",
                            "│ Metric                              │ precision                                                                 │\n",
                            "│ Difference Calculation              │ reference_minus_protected                                                 │\n",
                            "│ Ratio Calculation                   │ protected_over_reference                                                  │\n",
                            "│ Affected Groups                     │ Black, Native American, Hispanic, Female                                  │\n",
                            "│ Affected Reference                  │ White, White, Non-Hispanic, Male                                          │\n",
                            "│ Affected Categories                 │ Race, Race, Ethnicity, Sex                                                │\n",
                            "└─────────────────────────────────────┴───────────────────────────────────────────────────────────────────────────┘\n"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "## Custom Disparity Metric Summary Table"
                        ],
                        "text/plain": [
                            "<IPython.core.display.Markdown object>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "text/markdown": [
                            "\\* Percent Missing: Ethnicity: 0.00%, Race: 0.00%, Sex: 0.00%"
                        ],
                        "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>Total Label</th>\n",
                            "      <th>Average Label</th>\n",
                            "      <th>PRECISION</th>\n",
                            "      <th>Difference</th>\n",
                            "      <th>Ratio</th>\n",
                            "      <th>Practically Significant</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <td>Black</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>340.0</td>\n",
                            "      <td>139.0</td>\n",
                            "      <td>0.41</td>\n",
                            "      <td>0.510638</td>\n",
                            "      <td>0.105</td>\n",
                            "      <td>0.829</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Asian</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>327.0</td>\n",
                            "      <td>210.0</td>\n",
                            "      <td>0.64</td>\n",
                            "      <td>0.724280</td>\n",
                            "      <td>-0.108</td>\n",
                            "      <td>1.176</td>\n",
                            "      <td>No</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Native American</td>\n",
                            "      <td>White</td>\n",
                            "      <td>Race</td>\n",
                            "      <td>20.0</td>\n",
                            "      <td>10.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.555556</td>\n",
                            "      <td>0.060</td>\n",
                            "      <td>0.902</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>White</td>\n",
                            "      <td></td>\n",
                            "      <td>Race</td>\n",
                            "      <td>3,623.0</td>\n",
                            "      <td>1,809.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.615965</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>508.0</td>\n",
                            "      <td>200.0</td>\n",
                            "      <td>0.39</td>\n",
                            "      <td>0.562874</td>\n",
                            "      <td>0.069</td>\n",
                            "      <td>0.891</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Non-Hispanic</td>\n",
                            "      <td></td>\n",
                            "      <td>Ethnicity</td>\n",
                            "      <td>3,808.0</td>\n",
                            "      <td>1,976.0</td>\n",
                            "      <td>0.52</td>\n",
                            "      <td>0.631757</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>1,034.0</td>\n",
                            "      <td>451.0</td>\n",
                            "      <td>0.44</td>\n",
                            "      <td>0.560386</td>\n",
                            "      <td>0.063</td>\n",
                            "      <td>0.898</td>\n",
                            "      <td>Yes</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <td>Male</td>\n",
                            "      <td></td>\n",
                            "      <td>Sex</td>\n",
                            "      <td>1,622.0</td>\n",
                            "      <td>803.0</td>\n",
                            "      <td>0.50</td>\n",
                            "      <td>0.623762</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": {
                        "image/png": "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"
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "sd.custom_disparity_metric(\n",
                "    outcome=binary_outcome,\n",
                "    metric=precision,\n",
                "    label=binary_label,\n",
                "    difference_calculation=sd.types.DifferenceCalculation.REFERENCE_MINUS_PROTECTED,\n",
                "    difference_threshold_significance=lambda difference: difference > 0.0,\n",
                "    ratio_calculation=sd.types.RatioCalculation.PROTECTED_OVER_REFERENCE,\n",
                "    ratio_threshold_significance=lambda ratio: ratio < 1.0,\n",
                "    statistical_significance_test=None,\n",
                "    p_value_threshold=0.05,\n",
                "    **precision_arguments,\n",
                ")"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.8.13 ('.venv': poetry)",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.8.13"
        },
        "vscode": {
            "interpreter": {
                "hash": "c7f58fa8a5cb6293260375d5a8f9a36ec50b86cdb24939052bb9f031284f2b9c"
            }
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}
