{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028999,
     "end_time": "2020-09-17T22:26:04.188184",
     "exception": false,
     "start_time": "2020-09-17T22:26:04.159185",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Set Summary Info\n",
    "\n",
    "This notebook provides summary information and descriptive statistics for our data sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.030022,
     "end_time": "2020-09-17T22:26:04.249214",
     "exception": false,
     "start_time": "2020-09-17T22:26:04.219192",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:04.314215Z",
     "iopub.status.busy": "2020-09-17T22:26:04.313213Z",
     "iopub.status.idle": "2020-09-17T22:26:04.316215Z",
     "shell.execute_reply": "2020-09-17T22:26:04.316215Z"
    },
    "papermill": {
     "duration": 0.038002,
     "end_time": "2020-09-17T22:26:04.316215",
     "exception": false,
     "start_time": "2020-09-17T22:26:04.278213",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:04.374213Z",
     "iopub.status.busy": "2020-09-17T22:26:04.374213Z",
     "iopub.status.idle": "2020-09-17T22:26:04.378212Z",
     "shell.execute_reply": "2020-09-17T22:26:04.378212Z"
    },
    "papermill": {
     "duration": 0.033997,
     "end_time": "2020-09-17T22:26:04.379211",
     "exception": false,
     "start_time": "2020-09-17T22:26:04.345214",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:04.432211Z",
     "iopub.status.busy": "2020-09-17T22:26:04.431210Z",
     "iopub.status.idle": "2020-09-17T22:26:06.150182Z",
     "shell.execute_reply": "2020-09-17T22:26:06.151181Z"
    },
    "papermill": {
     "duration": 1.746968,
     "end_time": "2020-09-17T22:26:06.151181",
     "exception": false,
     "start_time": "2020-09-17T22:26:04.404213",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import itertools as it\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.auto import tqdm\n",
    "import plotnine as p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:06.204180Z",
     "iopub.status.busy": "2020-09-17T22:26:06.203195Z",
     "iopub.status.idle": "2020-09-17T22:26:06.401177Z",
     "shell.execute_reply": "2020-09-17T22:26:06.400176Z"
    },
    "papermill": {
     "duration": 0.224987,
     "end_time": "2020-09-17T22:26:06.401177",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.176190",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import bookgender.datatools as dt\n",
    "from bookgender.nbutils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:06.457175Z",
     "iopub.status.busy": "2020-09-17T22:26:06.457175Z",
     "iopub.status.idle": "2020-09-17T22:26:06.462176Z",
     "shell.execute_reply": "2020-09-17T22:26:06.462176Z"
    },
    "papermill": {
     "duration": 0.033991,
     "end_time": "2020-09-17T22:26:06.463176",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.429185",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def eprint(*args):\n",
    "    print(*args, file=sys.stderr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:06.521177Z",
     "iopub.status.busy": "2020-09-17T22:26:06.520176Z",
     "iopub.status.idle": "2020-09-17T22:26:06.527176Z",
     "shell.execute_reply": "2020-09-17T22:26:06.527176Z"
    },
    "papermill": {
     "duration": 0.036999,
     "end_time": "2020-09-17T22:26:06.527176",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.490177",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using figure dir figures\\DataSummary\n"
     ]
    }
   ],
   "source": [
    "fig_dir = init_figs('DataSummary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:06.585175Z",
     "iopub.status.busy": "2020-09-17T22:26:06.584175Z",
     "iopub.status.idle": "2020-09-17T22:26:06.589175Z",
     "shell.execute_reply": "2020-09-17T22:26:06.589175Z"
    },
    "papermill": {
     "duration": 0.035984,
     "end_time": "2020-09-17T22:26:06.589175",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.553191",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def lbl_pct(fs):\n",
    "    return ['{:.0f}%'.format(f*100) for f in fs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.029016,
     "end_time": "2020-09-17T22:26:06.645191",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.616175",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Function to make plots:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025999,
     "end_time": "2020-09-17T22:26:06.700173",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.674174",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Load Data Files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.025999,
     "end_time": "2020-09-17T22:26:06.753172",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.727173",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Load book author gender info:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:06.807203Z",
     "iopub.status.busy": "2020-09-17T22:26:06.807203Z",
     "iopub.status.idle": "2020-09-17T22:26:06.811170Z",
     "shell.execute_reply": "2020-09-17T22:26:06.811170Z"
    },
    "papermill": {
     "duration": 0.031986,
     "end_time": "2020-09-17T22:26:06.811170",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.779184",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "datasets =  sorted(list(dt.datasets.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:06.869201Z",
     "iopub.status.busy": "2020-09-17T22:26:06.869201Z",
     "iopub.status.idle": "2020-09-17T22:26:08.264145Z",
     "shell.execute_reply": "2020-09-17T22:26:08.263146Z"
    },
    "papermill": {
     "duration": 1.423974,
     "end_time": "2020-09-17T22:26:08.264145",
     "exception": false,
     "start_time": "2020-09-17T22:26:06.840171",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12234574 entries, 0 to 12234573\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype   \n",
      "---  ------  -----   \n",
      " 0   item    int64   \n",
      " 1   gender  category\n",
      "dtypes: category(1), int64(1)\n",
      "memory usage: 105.0 MB\n"
     ]
    }
   ],
   "source": [
    "book_gender = pd.read_parquet('data/author-gender.parquet')\n",
    "book_gender['gender'] = book_gender['gender'].astype('category')\n",
    "book_gender.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:08.323143Z",
     "iopub.status.busy": "2020-09-17T22:26:08.323143Z",
     "iopub.status.idle": "2020-09-17T22:26:10.755101Z",
     "shell.execute_reply": "2020-09-17T22:26:10.755101Z"
    },
    "papermill": {
     "duration": 2.462941,
     "end_time": "2020-09-17T22:26:10.755101",
     "exception": false,
     "start_time": "2020-09-17T22:26:08.292160",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12234574 entries, 0 to 12234573\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Dtype   \n",
      "---  ------  -----   \n",
      " 0   item    int64   \n",
      " 1   gender  category\n",
      "dtypes: category(1), int64(1)\n",
      "memory usage: 105.0 MB\n"
     ]
    }
   ],
   "source": [
    "book_gender = pd.read_csv('data/author-gender.csv.gz', dtype={'gender': 'category'})\n",
    "book_gender.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.026033,
     "end_time": "2020-09-17T22:26:10.810134",
     "exception": false,
     "start_time": "2020-09-17T22:26:10.784101",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Book gender will be more useful if we index it, and it's basically now a series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:10.870115Z",
     "iopub.status.busy": "2020-09-17T22:26:10.869117Z",
     "iopub.status.idle": "2020-09-17T22:26:10.925097Z",
     "shell.execute_reply": "2020-09-17T22:26:10.925097Z"
    },
    "papermill": {
     "duration": 0.085985,
     "end_time": "2020-09-17T22:26:10.925097",
     "exception": false,
     "start_time": "2020-09-17T22:26:10.839112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item\n",
       "0                     male\n",
       "1           no-viaf-author\n",
       "2                     male\n",
       "3            no-loc-author\n",
       "4                     male\n",
       "                 ...      \n",
       "12234569     no-loc-author\n",
       "12234570     no-loc-author\n",
       "12234571     no-loc-author\n",
       "12234572     no-loc-author\n",
       "12234573     no-loc-author\n",
       "Name: gender, Length: 12234574, dtype: category\n",
       "Categories (6, object): ['ambiguous', 'female', 'male', 'no-loc-author', 'no-viaf-author', 'unknown']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_gender = book_gender.set_index('item')['gender']\n",
    "book_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.033998,
     "end_time": "2020-09-17T22:26:10.989097",
     "exception": false,
     "start_time": "2020-09-17T22:26:10.955099",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Load the Library of Congress book list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:11.051095Z",
     "iopub.status.busy": "2020-09-17T22:26:11.050097Z",
     "iopub.status.idle": "2020-09-17T22:26:11.738118Z",
     "shell.execute_reply": "2020-09-17T22:26:11.737085Z"
    },
    "papermill": {
     "duration": 0.722021,
     "end_time": "2020-09-17T22:26:11.738118",
     "exception": false,
     "start_time": "2020-09-17T22:26:11.016097",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5267327 entries, 0 to 5267326\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Dtype\n",
      "---  ------  -----\n",
      " 0   item    int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 40.2 MB\n"
     ]
    }
   ],
   "source": [
    "loc_books = pd.read_csv('data/loc-books.csv.gz')\n",
    "loc_books.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.028,
     "end_time": "2020-09-17T22:26:11.792113",
     "exception": false,
     "start_time": "2020-09-17T22:26:11.764113",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Load rating data sets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:11.853082Z",
     "iopub.status.busy": "2020-09-17T22:26:11.853082Z",
     "iopub.status.idle": "2020-09-17T22:26:17.064030Z",
     "shell.execute_reply": "2020-09-17T22:26:17.064030Z"
    },
    "papermill": {
     "duration": 5.243948,
     "end_time": "2020-09-17T22:26:17.065030",
     "exception": false,
     "start_time": "2020-09-17T22:26:11.821082",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading ratings for AZ\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading ratings for BX-E\n",
      "loading ratings for BX-I\n",
      "loading ratings for GR-E\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading ratings for GR-I\n"
     ]
    }
   ],
   "source": [
    "ratings = {}\n",
    "for ds in datasets:\n",
    "    eprint('loading ratings for', ds)\n",
    "    ratings[ds] = pd.read_parquet(f'data/{ds}/ratings.parquet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032014,
     "end_time": "2020-09-17T22:26:17.133048",
     "exception": false,
     "start_time": "2020-09-17T22:26:17.101034",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Fill and Expand Gender\n",
    "\n",
    "For later computations, we want to upgrade the book-gender frame so it has the following properties:\n",
    "\n",
    "* All available books have a gender record\n",
    "* Both full status and simplified unlink status are available for each book\n",
    "\n",
    "This will simplify combining other records with the book gender data later.\n",
    "\n",
    "Let's start by making a huge array of all available book IDs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:17.194026Z",
     "iopub.status.busy": "2020-09-17T22:26:17.194026Z",
     "iopub.status.idle": "2020-09-17T22:26:23.903911Z",
     "shell.execute_reply": "2020-09-17T22:26:23.904909Z"
    },
    "papermill": {
     "duration": 6.741883,
     "end_time": "2020-09-17T22:26:23.904909",
     "exception": false,
     "start_time": "2020-09-17T22:26:17.163026",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7387475,)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_lists = [loc_books['item'].unique()]\n",
    "for rdf in ratings.values():\n",
    "    item_lists.append(rdf['item'].unique())\n",
    "all_item_ids = np.unique(np.concatenate(item_lists))\n",
    "all_item_ids.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.033008,
     "end_time": "2020-09-17T22:26:23.969916",
     "exception": false,
     "start_time": "2020-09-17T22:26:23.936908",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "How does that compare to the book gender frame?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:24.035907Z",
     "iopub.status.busy": "2020-09-17T22:26:24.034907Z",
     "iopub.status.idle": "2020-09-17T22:26:24.073906Z",
     "shell.execute_reply": "2020-09-17T22:26:24.073906Z"
    },
    "papermill": {
     "duration": 0.073998,
     "end_time": "2020-09-17T22:26:24.074906",
     "exception": false,
     "start_time": "2020-09-17T22:26:24.000908",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12234574"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_gender.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.031999,
     "end_time": "2020-09-17T22:26:24.138904",
     "exception": false,
     "start_time": "2020-09-17T22:26:24.106905",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Add a category to `gender` for no-matching-book, and put an order on the categories (we're also going to make `book_gender` refer to the series, to simplify code):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:24.201917Z",
     "iopub.status.busy": "2020-09-17T22:26:24.201917Z",
     "iopub.status.idle": "2020-09-17T22:26:24.244903Z",
     "shell.execute_reply": "2020-09-17T22:26:24.244903Z"
    },
    "papermill": {
     "duration": 0.075999,
     "end_time": "2020-09-17T22:26:24.244903",
     "exception": false,
     "start_time": "2020-09-17T22:26:24.168904",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "book_gender.cat.add_categories(['no-book'], inplace=True)\n",
    "book_gender.cat.reorder_categories(['no-book', 'no-loc-author', 'no-viaf-author',\n",
    "                                    'unknown', 'ambiguous', 'female', 'male'],\n",
    "                                   inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.030002,
     "end_time": "2020-09-17T22:26:24.304904",
     "exception": false,
     "start_time": "2020-09-17T22:26:24.274902",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Reindex to match our list of book IDs, and fill in the missing value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:24.363940Z",
     "iopub.status.busy": "2020-09-17T22:26:24.363940Z",
     "iopub.status.idle": "2020-09-17T22:26:25.074901Z",
     "shell.execute_reply": "2020-09-17T22:26:25.075889Z"
    },
    "papermill": {
     "duration": 0.741986,
     "end_time": "2020-09-17T22:26:25.075889",
     "exception": false,
     "start_time": "2020-09-17T22:26:24.333903",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "item\n",
       "0                      male\n",
       "1            no-viaf-author\n",
       "2                      male\n",
       "3             no-loc-author\n",
       "4                      male\n",
       "                  ...      \n",
       "924482338           no-book\n",
       "924482339           no-book\n",
       "924482340           no-book\n",
       "924482341           no-book\n",
       "924482342           no-book\n",
       "Name: gender, Length: 7387475, dtype: category\n",
       "Categories (7, object): ['no-book', 'no-loc-author', 'no-viaf-author', 'unknown', 'ambiguous', 'female', 'male']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_gender = book_gender.reindex(all_item_ids, fill_value='no-book')\n",
    "book_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.030994,
     "end_time": "2020-09-17T22:26:25.139889",
     "exception": false,
     "start_time": "2020-09-17T22:26:25.108895",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now the index should be both monotonic and unique - this should simplify later use. Double-check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:25.198886Z",
     "iopub.status.busy": "2020-09-17T22:26:25.197886Z",
     "iopub.status.idle": "2020-09-17T22:26:25.814875Z",
     "shell.execute_reply": "2020-09-17T22:26:25.815876Z"
    },
    "papermill": {
     "duration": 0.647957,
     "end_time": "2020-09-17T22:26:25.815876",
     "exception": false,
     "start_time": "2020-09-17T22:26:25.167919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_gender.index.is_unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:25.886874Z",
     "iopub.status.busy": "2020-09-17T22:26:25.886874Z",
     "iopub.status.idle": "2020-09-17T22:26:25.910884Z",
     "shell.execute_reply": "2020-09-17T22:26:25.910884Z"
    },
    "papermill": {
     "duration": 0.06101,
     "end_time": "2020-09-17T22:26:25.910884",
     "exception": false,
     "start_time": "2020-09-17T22:26:25.849874",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_gender.index.is_monotonic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.029991,
     "end_time": "2020-09-17T22:26:25.973880",
     "exception": false,
     "start_time": "2020-09-17T22:26:25.943889",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's quick look at a histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:26.042872Z",
     "iopub.status.busy": "2020-09-17T22:26:26.039871Z",
     "iopub.status.idle": "2020-09-17T22:26:26.324871Z",
     "shell.execute_reply": "2020-09-17T22:26:26.325867Z"
    },
    "papermill": {
     "duration": 0.320995,
     "end_time": "2020-09-17T22:26:26.325867",
     "exception": false,
     "start_time": "2020-09-17T22:26:26.004872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(book_gender)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032997,
     "end_time": "2020-09-17T22:26:26.392865",
     "exception": false,
     "start_time": "2020-09-17T22:26:26.359868",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Ok. Last thing we need to do here is create a simplified column that collapsed our various types of link failure into 'unlinked'.  We'll put this in the `gender` column, and make the existing series `gender_status`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:26.465865Z",
     "iopub.status.busy": "2020-09-17T22:26:26.465865Z",
     "iopub.status.idle": "2020-09-17T22:26:26.542890Z",
     "shell.execute_reply": "2020-09-17T22:26:26.542890Z"
    },
    "papermill": {
     "duration": 0.119025,
     "end_time": "2020-09-17T22:26:26.542890",
     "exception": false,
     "start_time": "2020-09-17T22:26:26.423865",
     "status": "completed"
    },
    "tags": []
   },
   "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>gender_status</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>no-viaf-author</td>\n",
       "      <td>unlinked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>no-loc-author</td>\n",
       "      <td>unlinked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924482338</th>\n",
       "      <td>no-book</td>\n",
       "      <td>unlinked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924482339</th>\n",
       "      <td>no-book</td>\n",
       "      <td>unlinked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924482340</th>\n",
       "      <td>no-book</td>\n",
       "      <td>unlinked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924482341</th>\n",
       "      <td>no-book</td>\n",
       "      <td>unlinked</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924482342</th>\n",
       "      <td>no-book</td>\n",
       "      <td>unlinked</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7387475 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            gender_status    gender\n",
       "item                               \n",
       "0                    male      male\n",
       "1          no-viaf-author  unlinked\n",
       "2                    male      male\n",
       "3           no-loc-author  unlinked\n",
       "4                    male      male\n",
       "...                   ...       ...\n",
       "924482338         no-book  unlinked\n",
       "924482339         no-book  unlinked\n",
       "924482340         no-book  unlinked\n",
       "924482341         no-book  unlinked\n",
       "924482342         no-book  unlinked\n",
       "\n",
       "[7387475 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_gender = pd.DataFrame({\n",
    "    'gender_status': book_gender,\n",
    "    'gender': book_gender.cat.rename_categories({\n",
    "        'no-book': 'unlinked'\n",
    "    }).cat.remove_categories([\n",
    "        'no-loc-author', 'no-viaf-author'\n",
    "    ]).fillna('unlinked')\n",
    "})\n",
    "book_gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.035014,
     "end_time": "2020-09-17T22:26:26.616876",
     "exception": false,
     "start_time": "2020-09-17T22:26:26.581862",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And see that histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:26.702865Z",
     "iopub.status.busy": "2020-09-17T22:26:26.701865Z",
     "iopub.status.idle": "2020-09-17T22:26:26.938868Z",
     "shell.execute_reply": "2020-09-17T22:26:26.937874Z"
    },
    "papermill": {
     "duration": 0.279005,
     "end_time": "2020-09-17T22:26:26.938868",
     "exception": false,
     "start_time": "2020-09-17T22:26:26.659863",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='gender', ylabel='count'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9wIlD1TOA7WpX1RyzLzawLzawLzbYrvsiVRvtkpckNcQriyWpcQaBJDXOIBgjyRFJzu+nXzFueIyp2m/FYy1IcsPW3HdoSU5Ictpc1zGXNtUHSb472/XMpiQnJ7kpyRcHWv8Hk5wyxLq3Z9vyfjEURx/djKo6j43PdpKoqufOdQ0D+0/AkVV161wXomE1sUUw+VN3klP6TyPfSfLxJFcl+WGSfzfmvr//RJjkrH6QvO8muSXJsWPaPyvJ95P8cZJDklycZEWSC9cPn9HPX5nkCuDtAz71ybVtSz+8LMkVSeZP1Q/9acCfSHJDkuuT/Ek//4wkr+inv5bkzH76zUk+2td1U5L/nmRVkn9M8vAZfu7n9n+HVf2V6iS5r3/eK5L83ySL+764ZX29vX2TfDPdAIofGFnnff3vh/TPcVWS85NcMNInP04yv59elOQ7/fRj+5quS3Jlkmf08x/0KbnvywVJHpnk//SvmxvW9+1QkiwF/hg4L8n7kpyZ5Or+tX1M3+aE/jl8PcmtSd6R5D/3ba5M8ti+3Vv6+65M8tUkjxjzeE/q+3hFkkuTPHXI57et+r/JD5L8j/7v8cUkL0pyeZIf9a+lxf3/yPf7308Zs55Hjuvb2dZEEGzGrlW1GHgX8IHNtAXYCzgcOBr469EFSZ4LLKUbQ+k24FTg2Ko6BDgT+Fjf9PPAyVV16Ew8gRkyZT8keRXwHuCoqlp/mfy4fng18EzgQOBFwCfShd8lwPpw2ZtuEEL6+1/aTy8ETq+q/YG7gNfM3FMD4E3932ERcHKSxwGPBL7Tz78X+CjwYuBVwIdH7rsYeEP/3F6bZPIVoq8GFgBPB/4DMJ2/64eA71fVM4D3Amdvpv1Lgdur6sCqOgD45jQeY6tV1VvpLu58AV0/fauqntXf/kSSR/ZNDwD+PV0ffQy4v6oOAq4Aju/bnFNVz6qqA4GbgDePechlwEn93+IU4IxhntmM+jfAZ4BnAE+l64fD6ep/L/AD4Hl9f7wf+Msx63gfU/ftrHHXEJzT/15B98+8OedW1e+AG5M8fmT+v6V7Mb+kqm5PcgDdP8lF6cZC2QVYm+TRwB5VdXF/v7+lG4V1rk3VDy+ge/N8SVXdMzJ/XD8cDnypqn4L/DTJxcCz6N7s35Vu9Nkbgcf0AXEocDLwOODWqrp2ihpmwsl9oEF3NftC4NdseEO9HvhVVf0myfWTHv+iqroTIMk5/fNcPrL8cODLfX/8JMm3p1HP4fRhV1XfSvK4/rUxleuBTyb5OHB+VV26ibYz7SXAK0a2VHYD9uunv11V9wL3Jrkb+PpIvc/opw9I8lFgD2B3umuLfi/J7sBzgS9nw7hBDxvgecy0W6vqeoAkq4B/qqoaef08GvhCkoV0Q+fMG7OOqfr2pqGLH9VKEDzAg7d+dhuZ/lX/+7dMrz9+NTI9OlbS2n69B9F9kgqwavKn/iR7MGY8pVmyNf1wC90ugifz4De/cf0wdvSvqvqXJI+h+1R7CfBY4HXAfVV1b//pfHR9vwVmbNdQkiPotlAOrar7+90zuwG/qQ0X0vxufQ1V9bsko30w+e81nTGz1hvt89H+nmqcrbF/o6r6YZJD6C7A/Ksk/1hVHx6zjiEEeE1VPWiwxyTP5sF/t9+N3P4dG15HZwGvrKqVSU4Ajpi0/ocAd1XVM2e06uFt7rl/hC4oX5VkAfCdMesY27ezrZVdQz8F/rD/1PUwut0ZM+0u4GXAX/ZvPDcDE0kOBUgyL8n+VXUXcHeSw/v7vWGAWqayNf3wz3S7Ps5Osv9m2l4C/EmSXZJMAM8DruqXXUG32+kSui2EU9iwW2hojwZ+0YfAU4HnbOH9X9zv03848Epg8vdEXga8Jt2xgsfz4De6HwOH9NOju7suof/b96+XO/otrh8DB/fzDwae2E//Ed1ul/8JfHJ9m1lyIXBS+o/rSQ7awvs/im5reB5jXu/98741yWv79SfJgdtY8/bg0cC/9NMnTNFmW/t2RjQRBFX1G7p9vt8DzqfbdzfE4/wUeDndF/IcBBwLfDzJSuBaus1f6IbSOD3dweJfDlHLFPVtVT/0n1beQLfp/qRNNP0acB2wEvgW8F+q6if9skvpjkOsBq6h2yqYrSD4JrBrkuvoPqVduYX3v4xuF961wFeravmk5V+lG0DxBuC/0fXv3f2yDwGfSXIp3ZbOeh8EFvU1/TXwxpF1PTbJtcDbgB/2858OXNXPfx/d8YzZ8hG63RrXpTvZ4CNbeP+/oOuTi5j6NfcG4M39/8oqNv7ukh3Rf6XberucbtfwONvatzPCISakGZBk96q6r9/NdRVw2EgIStu1Vo4RSEM7vz/+81DgI4aAdiRuEUhS45o4RiBJmppBIEmNMwgkqXEGgTSgdOMybTQmlbQ9MQik7cikK5qlWeGLTuol+Qu6C5tuA+6gG/Poa3QXCE7Qfa/2W6rqB0nOAu6hG4dpT7qL577SXyF6KvBC4FZGhpLoh4j4NN14O3cAJ1TV2n7Ii+8Ch9ENef6pwZ+sNMIgkOiGiKYbAuIguv+La+iCYBnw1qr6UT+2zhl0b/KwYQTWp9K9gX+FbuTSp9BdCfx4ukH2zuyHVzgVOKaq1qUbRvpjwJv6de1RVc8f/IlKYxgEUudw4B+q6pcASb5ON+DbpkbFHDcC6/PYMALr7Um+1c9/CmNGox1Z1/+e+ackTY9BIHXGjQa6uVExpxqJdtxVmmNHox3xr5utUBqIB4ulzmXAy5Ps1o+P/zK6YwJbOirmJcBx/Qise9F9nwNMMRrtIM9E2kIGgQRU1dV0+/lX0n1Jz3K6EUS3dFTMrwE/ovtils8BF/fr/zVTj0YrzSnHGpJ6IyOIPoLuk/2SqrpmruuShuYxAmmDZf3Xae4GfMEQUCvcIpCkxnmMQJIaZxBIUuMMAklqnEEgSY0zCCSpcf8f96lAr36URfIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(book_gender['gender'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041017,
     "end_time": "2020-09-17T22:26:27.030872",
     "exception": false,
     "start_time": "2020-09-17T22:26:26.989855",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Basic Data Set Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:27.097853Z",
     "iopub.status.busy": "2020-09-17T22:26:27.097853Z",
     "iopub.status.idle": "2020-09-17T22:26:37.041681Z",
     "shell.execute_reply": "2020-09-17T22:26:37.041681Z"
    },
    "papermill": {
     "duration": 9.978807,
     "end_time": "2020-09-17T22:26:37.041681",
     "exception": false,
     "start_time": "2020-09-17T22:26:27.062874",
     "status": "completed"
    },
    "tags": []
   },
   "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>Users</th>\n",
       "      <th>Items</th>\n",
       "      <th>Pairs</th>\n",
       "      <th>Density</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>8026324</td>\n",
       "      <td>2268142</td>\n",
       "      <td>22460535</td>\n",
       "      <td>0.000001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-E</th>\n",
       "      <td>77805</td>\n",
       "      <td>151670</td>\n",
       "      <td>427283</td>\n",
       "      <td>0.000036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <td>105283</td>\n",
       "      <td>279501</td>\n",
       "      <td>1129814</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-E</th>\n",
       "      <td>808782</td>\n",
       "      <td>1080777</td>\n",
       "      <td>86537566</td>\n",
       "      <td>0.000099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <td>870011</td>\n",
       "      <td>1096636</td>\n",
       "      <td>188943278</td>\n",
       "      <td>0.000198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Users    Items      Pairs   Density\n",
       "AZ    8026324  2268142   22460535  0.000001\n",
       "BX-E    77805   151670     427283  0.000036\n",
       "BX-I   105283   279501    1129814  0.000038\n",
       "GR-E   808782  1080777   86537566  0.000099\n",
       "GR-I   870011  1096636  188943278  0.000198"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_summary = pd.DataFrame.from_dict(dict(\n",
    "    (n, {'Users': f['user'].nunique(), 'Items': f['item'].nunique(), 'Pairs': len(f)})\n",
    "    for (n, f) in ratings.items()\n",
    "), orient='index')\n",
    "ds_summary['Density'] = ds_summary['Pairs'] / (ds_summary['Users'] * ds_summary['Items'])\n",
    "ds_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:37.117710Z",
     "iopub.status.busy": "2020-09-17T22:26:37.116712Z",
     "iopub.status.idle": "2020-09-17T22:26:37.137680Z",
     "shell.execute_reply": "2020-09-17T22:26:37.137680Z"
    },
    "papermill": {
     "duration": 0.061983,
     "end_time": "2020-09-17T22:26:37.137680",
     "exception": false,
     "start_time": "2020-09-17T22:26:37.075697",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "{} &     Users &     Items &       Pairs & Density \\\\\n",
      "\\midrule\n",
      "AZ   & 8,026,324 & 2,268,142 &  22,460,535 & 0.0001\\% \\\\\n",
      "BX-E &    77,805 &   151,670 &     427,283 & 0.0036\\% \\\\\n",
      "BX-I &   105,283 &   279,501 &   1,129,814 & 0.0038\\% \\\\\n",
      "GR-E &   808,782 & 1,080,777 &  86,537,566 & 0.0099\\% \\\\\n",
      "GR-I &   870,011 & 1,096,636 & 188,943,278 & 0.0198\\% \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def pct_fmt(p):\n",
    "    return '{:.4f}%'.format(p * 100)\n",
    "def n_fmt(n):\n",
    "    return '{:,d}'.format(n)\n",
    "print(ds_summary.to_latex(formatters={\n",
    "    'Users': n_fmt,\n",
    "    'Items': n_fmt,\n",
    "    'Pairs': n_fmt,\n",
    "    'Density': pct_fmt\n",
    "}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.032001,
     "end_time": "2020-09-17T22:26:37.201679",
     "exception": false,
     "start_time": "2020-09-17T22:26:37.169678",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.030003,
     "end_time": "2020-09-17T22:26:37.264709",
     "exception": false,
     "start_time": "2020-09-17T22:26:37.234706",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "What is the rating distribution for explicit-feedback data sets?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:37.328700Z",
     "iopub.status.busy": "2020-09-17T22:26:37.328700Z",
     "iopub.status.idle": "2020-09-17T22:26:37.337676Z",
     "shell.execute_reply": "2020-09-17T22:26:37.337676Z"
    },
    "papermill": {
     "duration": 0.042969,
     "end_time": "2020-09-17T22:26:37.337676",
     "exception": false,
     "start_time": "2020-09-17T22:26:37.294707",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AZ', 'BX-E', 'GR-E']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_re = re.compile(r'^\\w\\w(-E|$)')\n",
    "[ds for ds in ratings.keys() if exp_re.match(ds)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:37.449704Z",
     "iopub.status.busy": "2020-09-17T22:26:37.400701Z",
     "iopub.status.idle": "2020-09-17T22:26:39.489655Z",
     "shell.execute_reply": "2020-09-17T22:26:39.489655Z"
    },
    "papermill": {
     "duration": 2.12198,
     "end_time": "2020-09-17T22:26:39.489655",
     "exception": false,
     "start_time": "2020-09-17T22:26:37.367675",
     "status": "completed"
    },
    "tags": []
   },
   "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>rating</th>\n",
       "      <th>count</th>\n",
       "      <th>Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1115069</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.5</td>\n",
       "      <td>254</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>976839</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.5</td>\n",
       "      <td>453</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1918050</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating    count Set\n",
       "0     1.0  1115069  AZ\n",
       "1     1.5      254  AZ\n",
       "2     2.0   976839  AZ\n",
       "3     2.5      453  AZ\n",
       "4     3.0  1918050  AZ"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_rate_stats = pd.concat(\n",
    "    (rates.groupby('rating').item.count().reset_index(name='count').assign(Set=ds)\n",
    "     for (ds, rates) in ratings.items() if exp_re.match(ds))\n",
    ", ignore_index=True)\n",
    "exp_rate_stats.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:39.565670Z",
     "iopub.status.busy": "2020-09-17T22:26:39.564670Z",
     "iopub.status.idle": "2020-09-17T22:26:39.971630Z",
     "shell.execute_reply": "2020-09-17T22:26:39.971630Z"
    },
    "papermill": {
     "duration": 0.451994,
     "end_time": "2020-09-17T22:26:39.971630",
     "exception": false,
     "start_time": "2020-09-17T22:26:39.519636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\seaborn\\axisgrid.py:723: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16900b49c48>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(col='Set', data=exp_rate_stats, sharex=False, sharey=False)\n",
    "grid.map(sns.barplot, 'rating', 'count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:40.049628Z",
     "iopub.status.busy": "2020-09-17T22:26:40.048644Z",
     "iopub.status.idle": "2020-09-17T22:26:50.228195Z",
     "shell.execute_reply": "2020-09-17T22:26:50.228195Z"
    },
    "papermill": {
     "duration": 10.220566,
     "end_time": "2020-09-17T22:26:50.228195",
     "exception": false,
     "start_time": "2020-09-17T22:26:40.007629",
     "status": "completed"
    },
    "tags": []
   },
   "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>user</th>\n",
       "      <th>AvgRating</th>\n",
       "      <th>Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user  AvgRating Set\n",
       "0     1   5.000000  AZ\n",
       "1     2   5.000000  AZ\n",
       "2     3   5.000000  AZ\n",
       "3     4   4.666667  AZ\n",
       "4     5   4.000000  AZ"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_means = pd.concat(\n",
    "    (rates.groupby('user').rating.mean().reset_index(name='AvgRating').assign(Set=ds)\n",
    "     for (ds, rates) in ratings.items() if exp_re.match(ds))\n",
    ", ignore_index=True)\n",
    "user_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:50.490190Z",
     "iopub.status.busy": "2020-09-17T22:26:50.489190Z",
     "iopub.status.idle": "2020-09-17T22:26:55.602705Z",
     "shell.execute_reply": "2020-09-17T22:26:55.603714Z"
    },
    "papermill": {
     "duration": 5.334519,
     "end_time": "2020-09-17T22:26:55.603714",
     "exception": false,
     "start_time": "2020-09-17T22:26:50.269195",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16902362788>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(col='Set', data=user_means, sharey=False, sharex=False)\n",
    "grid.map(sns.distplot, 'AvgRating')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:26:55.675737Z",
     "iopub.status.busy": "2020-09-17T22:26:55.675737Z",
     "iopub.status.idle": "2020-09-17T22:27:04.140081Z",
     "shell.execute_reply": "2020-09-17T22:27:04.140081Z"
    },
    "papermill": {
     "duration": 8.503308,
     "end_time": "2020-09-17T22:27:04.140081",
     "exception": false,
     "start_time": "2020-09-17T22:26:55.636773",
     "status": "completed"
    },
    "tags": []
   },
   "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>item</th>\n",
       "      <th>AvgRating</th>\n",
       "      <th>Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.523077</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>4.348315</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>4.368421</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>3.968750</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item  AvgRating Set\n",
       "0     0   4.250000  AZ\n",
       "1     2   4.523077  AZ\n",
       "2     5   4.348315  AZ\n",
       "3     8   4.368421  AZ\n",
       "4    11   3.968750  AZ"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_means = pd.concat(\n",
    "    (rates.groupby('item').rating.mean().reset_index(name='AvgRating').assign(Set=ds)\n",
    "     for (ds, rates) in ratings.items() if exp_re.match(ds))\n",
    ", ignore_index=True)\n",
    "item_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:27:04.291082Z",
     "iopub.status.busy": "2020-09-17T22:27:04.290080Z",
     "iopub.status.idle": "2020-09-17T22:27:06.737039Z",
     "shell.execute_reply": "2020-09-17T22:27:06.736036Z"
    },
    "papermill": {
     "duration": 2.554959,
     "end_time": "2020-09-17T22:27:06.737039",
     "exception": false,
     "start_time": "2020-09-17T22:27:04.182080",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x169023a5488>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(col='Set', data=item_means, sharey=False, sharex=False)\n",
    "grid.map(sns.distplot, 'AvgRating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.038985,
     "end_time": "2020-09-17T22:27:06.820033",
     "exception": false,
     "start_time": "2020-09-17T22:27:06.781048",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Count and Integrate\n",
    "\n",
    "Now that we have the data loaded, we need to do a few things:\n",
    "\n",
    "1. Connect with gender info\n",
    "2. Count books (or ratings) by gender. All kinds of unlinked gender are mapped to `unlinked`.\n",
    "3. Integrate into a single set of lists\n",
    "\n",
    "To start, we'll define a helper function for summarizing a frame of interactions by gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:27:06.908053Z",
     "iopub.status.busy": "2020-09-17T22:27:06.908053Z",
     "iopub.status.idle": "2020-09-17T22:27:06.910037Z",
     "shell.execute_reply": "2020-09-17T22:27:06.910037Z"
    },
    "papermill": {
     "duration": 0.049999,
     "end_time": "2020-09-17T22:27:06.911036",
     "exception": false,
     "start_time": "2020-09-17T22:27:06.861037",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def summarize_by_gender(rate_frame, gender_col='gender'):\n",
    "    # count ratings per book\n",
    "    i_counts = rate_frame['item'].value_counts().to_frame(name='ratings')\n",
    "    # join with gender\n",
    "    books = i_counts.join(book_gender)\n",
    "    # count by gender\n",
    "    counts = books.groupby(gender_col)['ratings'].agg(['count', 'sum'])\n",
    "    counts.rename(columns={\n",
    "        'count': 'Books',\n",
    "        'sum': 'Ratings'\n",
    "    }, inplace=True)\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.040998,
     "end_time": "2020-09-17T22:27:06.999030",
     "exception": false,
     "start_time": "2020-09-17T22:27:06.958032",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's see the function in action:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:27:07.082029Z",
     "iopub.status.busy": "2020-09-17T22:27:07.081030Z",
     "iopub.status.idle": "2020-09-17T22:27:07.134028Z",
     "shell.execute_reply": "2020-09-17T22:27:07.134028Z"
    },
    "papermill": {
     "duration": 0.092961,
     "end_time": "2020-09-17T22:27:07.134028",
     "exception": false,
     "start_time": "2020-09-17T22:27:07.041067",
     "status": "completed"
    },
    "tags": []
   },
   "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>Books</th>\n",
       "      <th>Ratings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>unlinked</th>\n",
       "      <td>36757</td>\n",
       "      <td>45524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>16900</td>\n",
       "      <td>27434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ambiguous</th>\n",
       "      <td>4014</td>\n",
       "      <td>30444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>38093</td>\n",
       "      <td>138827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>55906</td>\n",
       "      <td>185054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Books  Ratings\n",
       "gender                   \n",
       "unlinked   36757    45524\n",
       "unknown    16900    27434\n",
       "ambiguous   4014    30444\n",
       "female     38093   138827\n",
       "male       55906   185054"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summarize_by_gender(ratings['BX-E'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.044019,
     "end_time": "2020-09-17T22:27:07.225047",
     "exception": false,
     "start_time": "2020-09-17T22:27:07.181028",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now build up a full frame of everything:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:27:07.321026Z",
     "iopub.status.busy": "2020-09-17T22:27:07.320029Z",
     "iopub.status.idle": "2020-09-17T22:27:52.013291Z",
     "shell.execute_reply": "2020-09-17T22:27:52.013291Z"
    },
    "papermill": {
     "duration": 44.738247,
     "end_time": "2020-09-17T22:27:52.013291",
     "exception": false,
     "start_time": "2020-09-17T22:27:07.275044",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing LOC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing AZ\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing BX-E\n",
      "summarizing BX-I\n",
      "summarizing GR-E\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing GR-I\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 30 entries, ('LOC', 'unlinked') to ('GR-I', 'male')\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   Books    30 non-null     int64  \n",
      " 1   Ratings  30 non-null     int64  \n",
      " 2   ratings  0 non-null      float64\n",
      "dtypes: float64(1), int64(2)\n",
      "memory usage: 1.3+ KB\n"
     ]
    }
   ],
   "source": [
    "eprint('summarizing LOC')\n",
    "summaries = {'LOC':  summarize_by_gender(loc_books).assign(ratings=np.nan) }\n",
    "for ds, f in ratings.items():\n",
    "    eprint('summarizing', ds)\n",
    "    summaries[ds] = summarize_by_gender(f)\n",
    "gender_stats = pd.concat(summaries, names=['DataSet'])\n",
    "gender_stats.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:27:52.099292Z",
     "iopub.status.busy": "2020-09-17T22:27:52.097322Z",
     "iopub.status.idle": "2020-09-17T22:28:34.742522Z",
     "shell.execute_reply": "2020-09-17T22:28:34.742522Z"
    },
    "papermill": {
     "duration": 42.68723,
     "end_time": "2020-09-17T22:28:34.743520",
     "exception": false,
     "start_time": "2020-09-17T22:27:52.056290",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing LOC\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing AZ\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing BX-E\n",
      "summarizing BX-I\n",
      "summarizing GR-E\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing GR-I\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "MultiIndex: 42 entries, ('LOC', 'no-book') to ('GR-I', 'male')\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Books    42 non-null     int64\n",
      " 1   Ratings  42 non-null     int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 1.4+ KB\n"
     ]
    }
   ],
   "source": [
    "eprint('summarizing LOC')\n",
    "fsums = {'LOC': summarize_by_gender(loc_books, 'gender_status')}\n",
    "for ds, f in ratings.items():\n",
    "    eprint('summarizing', ds)\n",
    "    fsums[ds] = summarize_by_gender(f, 'gender_status')\n",
    "full_stats = pd.concat(fsums, names=['DataSet'])\n",
    "full_stats.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:34.825513Z",
     "iopub.status.busy": "2020-09-17T22:28:34.824515Z",
     "iopub.status.idle": "2020-09-17T22:28:34.838483Z",
     "shell.execute_reply": "2020-09-17T22:28:34.838483Z"
    },
    "papermill": {
     "duration": 0.057976,
     "end_time": "2020-09-17T22:28:34.838483",
     "exception": false,
     "start_time": "2020-09-17T22:28:34.780507",
     "status": "completed"
    },
    "tags": []
   },
   "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>gender_status</th>\n",
       "      <th>no-book</th>\n",
       "      <th>no-loc-author</th>\n",
       "      <th>no-viaf-author</th>\n",
       "      <th>unknown</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LOC</th>\n",
       "      <td>0</td>\n",
       "      <td>842755</td>\n",
       "      <td>297405</td>\n",
       "      <td>1214597</td>\n",
       "      <td>53232</td>\n",
       "      <td>664221</td>\n",
       "      <td>2195117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>934847</td>\n",
       "      <td>226764</td>\n",
       "      <td>157072</td>\n",
       "      <td>236219</td>\n",
       "      <td>17056</td>\n",
       "      <td>215556</td>\n",
       "      <td>480628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-E</th>\n",
       "      <td>22674</td>\n",
       "      <td>6456</td>\n",
       "      <td>7627</td>\n",
       "      <td>16900</td>\n",
       "      <td>4014</td>\n",
       "      <td>38093</td>\n",
       "      <td>55906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <td>45560</td>\n",
       "      <td>12906</td>\n",
       "      <td>15631</td>\n",
       "      <td>34614</td>\n",
       "      <td>6770</td>\n",
       "      <td>67087</td>\n",
       "      <td>96933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-E</th>\n",
       "      <td>0</td>\n",
       "      <td>487495</td>\n",
       "      <td>35267</td>\n",
       "      <td>95887</td>\n",
       "      <td>11087</td>\n",
       "      <td>174996</td>\n",
       "      <td>276045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <td>0</td>\n",
       "      <td>495592</td>\n",
       "      <td>36106</td>\n",
       "      <td>97667</td>\n",
       "      <td>11189</td>\n",
       "      <td>176640</td>\n",
       "      <td>279442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender_status  no-book  no-loc-author  no-viaf-author  unknown  ambiguous  \\\n",
       "DataSet                                                                     \n",
       "LOC                  0         842755          297405  1214597      53232   \n",
       "AZ              934847         226764          157072   236219      17056   \n",
       "BX-E             22674           6456            7627    16900       4014   \n",
       "BX-I             45560          12906           15631    34614       6770   \n",
       "GR-E                 0         487495           35267    95887      11087   \n",
       "GR-I                 0         495592           36106    97667      11189   \n",
       "\n",
       "gender_status  female     male  \n",
       "DataSet                         \n",
       "LOC            664221  2195117  \n",
       "AZ             215556   480628  \n",
       "BX-E            38093    55906  \n",
       "BX-I            67087    96933  \n",
       "GR-E           174996   276045  \n",
       "GR-I           176640   279442  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_counts = full_stats['Books'].unstack()\n",
    "book_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:34.920481Z",
     "iopub.status.busy": "2020-09-17T22:28:34.919482Z",
     "iopub.status.idle": "2020-09-17T22:28:34.933497Z",
     "shell.execute_reply": "2020-09-17T22:28:34.934482Z"
    },
    "papermill": {
     "duration": 0.059966,
     "end_time": "2020-09-17T22:28:34.934482",
     "exception": false,
     "start_time": "2020-09-17T22:28:34.874516",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataSet\n",
       "LOC     1140160\n",
       "AZ      1318683\n",
       "BX-E      36757\n",
       "BX-I      74097\n",
       "GR-E     522762\n",
       "GR-I     531698\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_counts[['no-book', 'no-loc-author', 'no-viaf-author']].sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:35.019487Z",
     "iopub.status.busy": "2020-09-17T22:28:35.012518Z",
     "iopub.status.idle": "2020-09-17T22:28:35.027481Z",
     "shell.execute_reply": "2020-09-17T22:28:35.027481Z"
    },
    "papermill": {
     "duration": 0.053969,
     "end_time": "2020-09-17T22:28:35.028480",
     "exception": false,
     "start_time": "2020-09-17T22:28:34.974511",
     "status": "completed"
    },
    "tags": []
   },
   "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>gender_status</th>\n",
       "      <th>no-book</th>\n",
       "      <th>no-loc-author</th>\n",
       "      <th>no-viaf-author</th>\n",
       "      <th>unknown</th>\n",
       "      <th>ambiguous</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LOC</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.159997</td>\n",
       "      <td>0.056462</td>\n",
       "      <td>0.230591</td>\n",
       "      <td>0.010106</td>\n",
       "      <td>0.126102</td>\n",
       "      <td>0.416742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.412164</td>\n",
       "      <td>0.099978</td>\n",
       "      <td>0.069251</td>\n",
       "      <td>0.104146</td>\n",
       "      <td>0.007520</td>\n",
       "      <td>0.095036</td>\n",
       "      <td>0.211904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-E</th>\n",
       "      <td>0.149496</td>\n",
       "      <td>0.042566</td>\n",
       "      <td>0.050287</td>\n",
       "      <td>0.111426</td>\n",
       "      <td>0.026465</td>\n",
       "      <td>0.251157</td>\n",
       "      <td>0.368603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <td>0.163005</td>\n",
       "      <td>0.046175</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.123842</td>\n",
       "      <td>0.024222</td>\n",
       "      <td>0.240024</td>\n",
       "      <td>0.346807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-E</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.451060</td>\n",
       "      <td>0.032631</td>\n",
       "      <td>0.088720</td>\n",
       "      <td>0.010258</td>\n",
       "      <td>0.161917</td>\n",
       "      <td>0.255413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.451920</td>\n",
       "      <td>0.032924</td>\n",
       "      <td>0.089061</td>\n",
       "      <td>0.010203</td>\n",
       "      <td>0.161074</td>\n",
       "      <td>0.254817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender_status   no-book  no-loc-author  no-viaf-author   unknown  ambiguous  \\\n",
       "DataSet                                                                       \n",
       "LOC            0.000000       0.159997        0.056462  0.230591   0.010106   \n",
       "AZ             0.412164       0.099978        0.069251  0.104146   0.007520   \n",
       "BX-E           0.149496       0.042566        0.050287  0.111426   0.026465   \n",
       "BX-I           0.163005       0.046175        0.055925  0.123842   0.024222   \n",
       "GR-E           0.000000       0.451060        0.032631  0.088720   0.010258   \n",
       "GR-I           0.000000       0.451920        0.032924  0.089061   0.010203   \n",
       "\n",
       "gender_status    female      male  \n",
       "DataSet                            \n",
       "LOC            0.126102  0.416742  \n",
       "AZ             0.095036  0.211904  \n",
       "BX-E           0.251157  0.368603  \n",
       "BX-I           0.240024  0.346807  \n",
       "GR-E           0.161917  0.255413  \n",
       "GR-I           0.161074  0.254817  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "book_fracs = book_counts.divide(book_counts.sum(axis=1), axis=0)\n",
    "book_fracs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:35.103482Z",
     "iopub.status.busy": "2020-09-17T22:28:35.103482Z",
     "iopub.status.idle": "2020-09-17T22:28:35.107484Z",
     "shell.execute_reply": "2020-09-17T22:28:35.107484Z"
    },
    "papermill": {
     "duration": 0.043964,
     "end_time": "2020-09-17T22:28:35.108478",
     "exception": false,
     "start_time": "2020-09-17T22:28:35.064514",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# book_counts.divide(book_counts.sum(axis=1), axis=0) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:35.193477Z",
     "iopub.status.busy": "2020-09-17T22:28:35.192477Z",
     "iopub.status.idle": "2020-09-17T22:28:35.204477Z",
     "shell.execute_reply": "2020-09-17T22:28:35.204477Z"
    },
    "papermill": {
     "duration": 0.058997,
     "end_time": "2020-09-17T22:28:35.204477",
     "exception": false,
     "start_time": "2020-09-17T22:28:35.145480",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrrrrr}\n",
      "\\toprule\n",
      "gender\\_status &  no-book &  no-loc-author &  no-viaf-author &  unknown &  ambiguous &  female &  male \\\\\n",
      "DataSet &          &                &                 &          &            &         &       \\\\\n",
      "\\midrule\n",
      "LOC     &     0.0\\% &          16.0\\% &            5.6\\% &    23.1\\% &       1.0\\% &   12.6\\% & 41.7\\% \\\\\n",
      "AZ      &    41.2\\% &          10.0\\% &            6.9\\% &    10.4\\% &       0.8\\% &    9.5\\% & 21.2\\% \\\\\n",
      "BX-E    &    14.9\\% &           4.3\\% &            5.0\\% &    11.1\\% &       2.6\\% &   25.1\\% & 36.9\\% \\\\\n",
      "BX-I    &    16.3\\% &           4.6\\% &            5.6\\% &    12.4\\% &       2.4\\% &   24.0\\% & 34.7\\% \\\\\n",
      "GR-E    &     0.0\\% &          45.1\\% &            3.3\\% &     8.9\\% &       1.0\\% &   16.2\\% & 25.5\\% \\\\\n",
      "GR-I    &     0.0\\% &          45.2\\% &            3.3\\% &     8.9\\% &       1.0\\% &   16.1\\% & 25.5\\% \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print((book_counts.divide(book_counts.sum(axis=1), axis=0) * 100).to_latex(float_format='%.1f%%'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.038002,
     "end_time": "2020-09-17T22:28:35.279508",
     "exception": false,
     "start_time": "2020-09-17T22:28:35.241506",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "To facilitate plotting, we need to do a few more transformations:\n",
    "\n",
    "1. Shift into a tall format with a `Scope`\n",
    "2. Convert counts to percents\n",
    "3. Drop the LOC Ratings, because it is meaningless"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:35.362505Z",
     "iopub.status.busy": "2020-09-17T22:28:35.362505Z",
     "iopub.status.idle": "2020-09-17T22:28:35.393492Z",
     "shell.execute_reply": "2020-09-17T22:28:35.393492Z"
    },
    "papermill": {
     "duration": 0.075014,
     "end_time": "2020-09-17T22:28:35.393492",
     "exception": false,
     "start_time": "2020-09-17T22:28:35.318478",
     "status": "completed"
    },
    "tags": []
   },
   "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>DataSet</th>\n",
       "      <th>Scope</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Count</th>\n",
       "      <th>Fraction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnL</td>\n",
       "      <td>1318683.0</td>\n",
       "      <td>0.581393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnK</td>\n",
       "      <td>236219.0</td>\n",
       "      <td>0.104146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Books</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>17056.0</td>\n",
       "      <td>0.007520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Books</td>\n",
       "      <td>F</td>\n",
       "      <td>215556.0</td>\n",
       "      <td>0.095036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Books</td>\n",
       "      <td>M</td>\n",
       "      <td>480628.0</td>\n",
       "      <td>0.211904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnL</td>\n",
       "      <td>8984419.0</td>\n",
       "      <td>0.400009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnK</td>\n",
       "      <td>2069787.0</td>\n",
       "      <td>0.092152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>535239.0</td>\n",
       "      <td>0.023830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>F</td>\n",
       "      <td>4313858.0</td>\n",
       "      <td>0.192064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AZ</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>M</td>\n",
       "      <td>6557232.0</td>\n",
       "      <td>0.291945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnL</td>\n",
       "      <td>36757.0</td>\n",
       "      <td>0.242349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnK</td>\n",
       "      <td>16900.0</td>\n",
       "      <td>0.111426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>4014.0</td>\n",
       "      <td>0.026465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>F</td>\n",
       "      <td>38093.0</td>\n",
       "      <td>0.251157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>M</td>\n",
       "      <td>55906.0</td>\n",
       "      <td>0.368603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnL</td>\n",
       "      <td>45524.0</td>\n",
       "      <td>0.106543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnK</td>\n",
       "      <td>27434.0</td>\n",
       "      <td>0.064206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>30444.0</td>\n",
       "      <td>0.071250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>F</td>\n",
       "      <td>138827.0</td>\n",
       "      <td>0.324906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>BX-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>M</td>\n",
       "      <td>185054.0</td>\n",
       "      <td>0.433095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnL</td>\n",
       "      <td>74097.0</td>\n",
       "      <td>0.265105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnK</td>\n",
       "      <td>34614.0</td>\n",
       "      <td>0.123842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>6770.0</td>\n",
       "      <td>0.024222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>F</td>\n",
       "      <td>67087.0</td>\n",
       "      <td>0.240024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>M</td>\n",
       "      <td>96933.0</td>\n",
       "      <td>0.346807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnL</td>\n",
       "      <td>116297.0</td>\n",
       "      <td>0.102935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnK</td>\n",
       "      <td>77892.0</td>\n",
       "      <td>0.068942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>76294.0</td>\n",
       "      <td>0.067528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>F</td>\n",
       "      <td>391728.0</td>\n",
       "      <td>0.346719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>BX-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>M</td>\n",
       "      <td>467603.0</td>\n",
       "      <td>0.413876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnL</td>\n",
       "      <td>522762.0</td>\n",
       "      <td>0.483691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnK</td>\n",
       "      <td>95887.0</td>\n",
       "      <td>0.088720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>11087.0</td>\n",
       "      <td>0.010258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>F</td>\n",
       "      <td>174996.0</td>\n",
       "      <td>0.161917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Books</td>\n",
       "      <td>M</td>\n",
       "      <td>276045.0</td>\n",
       "      <td>0.255413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnL</td>\n",
       "      <td>8605504.0</td>\n",
       "      <td>0.099442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnK</td>\n",
       "      <td>3598667.0</td>\n",
       "      <td>0.041585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>8346632.0</td>\n",
       "      <td>0.096451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>F</td>\n",
       "      <td>32348714.0</td>\n",
       "      <td>0.373811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>GR-E</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>M</td>\n",
       "      <td>33638049.0</td>\n",
       "      <td>0.388710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnL</td>\n",
       "      <td>531698.0</td>\n",
       "      <td>0.484845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnK</td>\n",
       "      <td>97667.0</td>\n",
       "      <td>0.089061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>11189.0</td>\n",
       "      <td>0.010203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>F</td>\n",
       "      <td>176640.0</td>\n",
       "      <td>0.161074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Books</td>\n",
       "      <td>M</td>\n",
       "      <td>279442.0</td>\n",
       "      <td>0.254817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnL</td>\n",
       "      <td>26200849.0</td>\n",
       "      <td>0.138670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>UnK</td>\n",
       "      <td>9640560.0</td>\n",
       "      <td>0.051024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>13491116.0</td>\n",
       "      <td>0.071403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>F</td>\n",
       "      <td>71070367.0</td>\n",
       "      <td>0.376147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>GR-I</td>\n",
       "      <td>Ratings</td>\n",
       "      <td>M</td>\n",
       "      <td>68540386.0</td>\n",
       "      <td>0.362756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>LOC</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnL</td>\n",
       "      <td>1140160.0</td>\n",
       "      <td>0.216459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>LOC</td>\n",
       "      <td>Books</td>\n",
       "      <td>UnK</td>\n",
       "      <td>1214597.0</td>\n",
       "      <td>0.230591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>LOC</td>\n",
       "      <td>Books</td>\n",
       "      <td>Amb.</td>\n",
       "      <td>53232.0</td>\n",
       "      <td>0.010106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>LOC</td>\n",
       "      <td>Books</td>\n",
       "      <td>F</td>\n",
       "      <td>664221.0</td>\n",
       "      <td>0.126102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>LOC</td>\n",
       "      <td>Books</td>\n",
       "      <td>M</td>\n",
       "      <td>2195117.0</td>\n",
       "      <td>0.416742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DataSet    Scope Gender       Count  Fraction\n",
       "0       AZ    Books    UnL   1318683.0  0.581393\n",
       "1       AZ    Books    UnK    236219.0  0.104146\n",
       "2       AZ    Books   Amb.     17056.0  0.007520\n",
       "3       AZ    Books      F    215556.0  0.095036\n",
       "4       AZ    Books      M    480628.0  0.211904\n",
       "5       AZ  Ratings    UnL   8984419.0  0.400009\n",
       "6       AZ  Ratings    UnK   2069787.0  0.092152\n",
       "7       AZ  Ratings   Amb.    535239.0  0.023830\n",
       "8       AZ  Ratings      F   4313858.0  0.192064\n",
       "9       AZ  Ratings      M   6557232.0  0.291945\n",
       "10    BX-E    Books    UnL     36757.0  0.242349\n",
       "11    BX-E    Books    UnK     16900.0  0.111426\n",
       "12    BX-E    Books   Amb.      4014.0  0.026465\n",
       "13    BX-E    Books      F     38093.0  0.251157\n",
       "14    BX-E    Books      M     55906.0  0.368603\n",
       "15    BX-E  Ratings    UnL     45524.0  0.106543\n",
       "16    BX-E  Ratings    UnK     27434.0  0.064206\n",
       "17    BX-E  Ratings   Amb.     30444.0  0.071250\n",
       "18    BX-E  Ratings      F    138827.0  0.324906\n",
       "19    BX-E  Ratings      M    185054.0  0.433095\n",
       "20    BX-I    Books    UnL     74097.0  0.265105\n",
       "21    BX-I    Books    UnK     34614.0  0.123842\n",
       "22    BX-I    Books   Amb.      6770.0  0.024222\n",
       "23    BX-I    Books      F     67087.0  0.240024\n",
       "24    BX-I    Books      M     96933.0  0.346807\n",
       "25    BX-I  Ratings    UnL    116297.0  0.102935\n",
       "26    BX-I  Ratings    UnK     77892.0  0.068942\n",
       "27    BX-I  Ratings   Amb.     76294.0  0.067528\n",
       "28    BX-I  Ratings      F    391728.0  0.346719\n",
       "29    BX-I  Ratings      M    467603.0  0.413876\n",
       "30    GR-E    Books    UnL    522762.0  0.483691\n",
       "31    GR-E    Books    UnK     95887.0  0.088720\n",
       "32    GR-E    Books   Amb.     11087.0  0.010258\n",
       "33    GR-E    Books      F    174996.0  0.161917\n",
       "34    GR-E    Books      M    276045.0  0.255413\n",
       "35    GR-E  Ratings    UnL   8605504.0  0.099442\n",
       "36    GR-E  Ratings    UnK   3598667.0  0.041585\n",
       "37    GR-E  Ratings   Amb.   8346632.0  0.096451\n",
       "38    GR-E  Ratings      F  32348714.0  0.373811\n",
       "39    GR-E  Ratings      M  33638049.0  0.388710\n",
       "40    GR-I    Books    UnL    531698.0  0.484845\n",
       "41    GR-I    Books    UnK     97667.0  0.089061\n",
       "42    GR-I    Books   Amb.     11189.0  0.010203\n",
       "43    GR-I    Books      F    176640.0  0.161074\n",
       "44    GR-I    Books      M    279442.0  0.254817\n",
       "45    GR-I  Ratings    UnL  26200849.0  0.138670\n",
       "46    GR-I  Ratings    UnK   9640560.0  0.051024\n",
       "47    GR-I  Ratings   Amb.  13491116.0  0.071403\n",
       "48    GR-I  Ratings      F  71070367.0  0.376147\n",
       "49    GR-I  Ratings      M  68540386.0  0.362756\n",
       "50     LOC    Books    UnL   1140160.0  0.216459\n",
       "51     LOC    Books    UnK   1214597.0  0.230591\n",
       "52     LOC    Books   Amb.     53232.0  0.010106\n",
       "53     LOC    Books      F    664221.0  0.126102\n",
       "54     LOC    Books      M   2195117.0  0.416742"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_tall = pd.DataFrame({'Count': gender_stats.stack()})\n",
    "gs_tall.index.rename(['DataSet', 'Gender', 'Scope'], inplace=True)\n",
    "gs_tall = gs_tall.reorder_levels(['DataSet', 'Scope', 'Gender']).sort_index()\n",
    "gs_tall['Fraction'] = gs_tall['Count'] / gs_tall.groupby(level=['DataSet', 'Scope'])['Count'].sum()\n",
    "gs_tall.drop(('LOC', 'Ratings'), inplace=True)\n",
    "gs_tall.sort_index(inplace=True)\n",
    "gs_tall.reset_index(inplace=True)\n",
    "gs_tall['Gender'].cat.rename_categories({\n",
    "    'female': 'F',\n",
    "    'male': 'M',\n",
    "    'ambiguous': 'Amb.',\n",
    "    'unknown': 'UnK',\n",
    "    'unlinked': 'UnL'\n",
    "}, inplace=True)\n",
    "gs_tall['Gender'].cat.reorder_categories([\n",
    "    'F',\n",
    "    'M',\n",
    "    'Amb.',\n",
    "    'UnK',\n",
    "    'UnL'\n",
    "], inplace=True)\n",
    "gs_tall['DataSet'] = gs_tall['DataSet'].astype('category')\n",
    "gs_tall['DataSet'].cat.reorder_categories(['LOC', 'AZ', 'BX-I', 'BX-E', 'GR-I', 'GR-E'], inplace=True)\n",
    "gs_tall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.058565,
     "end_time": "2020-09-17T22:28:35.492053",
     "exception": false,
     "start_time": "2020-09-17T22:28:35.433488",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Finally, we can plot it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:35.580089Z",
     "iopub.status.busy": "2020-09-17T22:28:35.579081Z",
     "iopub.status.idle": "2020-09-17T22:28:36.353037Z",
     "shell.execute_reply": "2020-09-17T22:28:36.352052Z"
    },
    "papermill": {
     "duration": 0.823955,
     "end_time": "2020-09-17T22:28:36.353037",
     "exception": false,
     "start_time": "2020-09-17T22:28:35.529082",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1690246f088>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 649.5x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='Gender', y='Fraction', col='DataSet', col_wrap=2, hue='Scope',\n",
    "            data=gs_tall.reset_index(),\n",
    "            kind='bar', sharey=False, height=2, aspect=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.038974,
     "end_time": "2020-09-17T22:28:36.432036",
     "exception": false,
     "start_time": "2020-09-17T22:28:36.393062",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Manual plotting logic for the paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:36.524046Z",
     "iopub.status.busy": "2020-09-17T22:28:36.522033Z",
     "iopub.status.idle": "2020-09-17T22:28:41.515963Z",
     "shell.execute_reply": "2020-09-17T22:28:41.515963Z"
    },
    "papermill": {
     "duration": 5.044928,
     "end_time": "2020-09-17T22:28:41.515963",
     "exception": false,
     "start_time": "2020-09-17T22:28:36.471035",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Research\\book-rec-fairness\\bookgender\\nbutils.py:39: UserWarning: file has suffix, ignoring\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 7 x 4.5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\link-stats.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 7 x 4.5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\link-stats.png\n"
     ]
    },
    {
     "data": {
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zO53leya1PBVJS0sLZjaxoaFhXrGP1dbWVtfS0jKrvr5+3+rq6or6ZqHYy0Ox527FihWjgLs6W79o0SLq6uqU691Q7OVR4tgL/kVi6dKllPJ57y7f01Q8FUl7ezvLli2bN3To0E4r10J59913GwCWLl36QmNj44piH6+QFHt5KPbcddeEn0qllOs5UOzlUcmxA7S1tZU09lxO2YE6jIuIiIjkRcWTiIiISB5UPImIiIjkQcWTiIiISB5UPElRmVm1mT1qZp8zs6vM7MzM9TvssMPJP/vZz9h6661vNrPJZnaamd1oZsPM7EgzG1Gm0EVERDqk4kmK7RTgT0AKuC175auvvnrjT37yE/r16/cGcCcwBPgt8BlgAjDezKyE8YqIiHRJxZMUjZkdALwCvAOsA5Z2tF1LSwvt7e21IYQlwNPAaGAHYAHwGrBTSQIWEZFeK+NMxp5mdrOZ/dLMtspYv5eZ3RAvbyjmmQyN8yTFdCiwhqj42Ru4tKON7rrrLnbaaac7AEIIj5rZM8AhwHLgfaB/acIVEZFeLH0m4yLgaMCA/wJ+Gq8/Gvgh0Rfww/n4mYytzWxqKMDUKhVZPDnnGoCXgAu999Odc6OBXwPDiVorjvfez3POVQEPArsB93rvz44fvztwqvd+Yln+gA1ECOE0ADM7AXg5c128bN60adNeX7BgAQ899NAbGauPBm4AJhIVYOeVJGAREemVMs5kbAGEEMLSePnQzO1CCO1m9m/gS0RnMsYSnfVYwIdnMl5c33gqsngCrgbmAjjnDLgfuMB7f79z7qvAfUSV5zggACOAfzjnhnvvFwJTgB+UI/ANUQhhesbdqZnLmpubGw466KDs7a+Ob06Pf0REZMOWeSYjaWaDiFqeMruDtJtZFbA58O9insmouOLJOeeANmB2vGgnAO/9/fHvGc65Kc65nYFGYK73vt059xow3Dm3E/C6976pDOFXvOHTzi3WTNLLC7mzhSdepk7mIiJ9RNaZjFeAy4j6bf88fSYDuBu4BqgFzo4fWpQzGRVVPDnnNgUuAPYHTo8XbwPMz9p0PrAj8CYw0TnXL76/ELgSuMM5NwuYA5zhvS9WQSAiIiIFknEm4x8Zi9/MuP33rO2Lciajooon4EbgR977FVEDFAADgNVZ27UCA733s51zSaKK9A5gP+BxYDKwJ3BtvOxPxQi2qqqqrrm5uaEY+85Sn/7d3NxcgsP1fnreu6XYc1RVVVXX3t7e7TZ6zXVLsZdHJceeVrLYc8l3qKDiyTl3HLDEe//7rFWriAqoTA3ASgDv/Unx4/sBs4h63B/svV/pnJsN7EoRiqfq6mpqampmFXq/3VhY4uP1ZgU9DdiNSn7eFXs3amtraW1t7XR9//79IXpvKSX938pDsZfYyvO3hALHPvDStztd112+p1VM8UR0vvKzzrlvxPc3BtqBR4Bts7YdxcdP5U0iGoRxVcYyo0hjXbW1tdHe3r5vbW3tC8XYf5Z6ohfXcKClyMcqZVGyPjYpwTFK+bwXmmLP0Zo1a0bTRXHU2tpKVVVVX8z1QlPs5aHPh4/r9POhu3xPq5jiyXt/cOZ959xFwIJ4qILnnXPjvfcPOOe+BmwEPJexbQNRi9M4os7mQ5xzdcAewEPFirm9vX1VY2PjimLtPy2jObOlFMerBHreu6bYc9fU1LSqu22U691T7OVRybEXS1fPQy75DhVUPHVjAnCbc+46oBkY773PPGl5FnCV9z4F4Jy7DHgVeAF4rNTBioiISOWq2OLJe39Rxu15RB3AO9v2gqz704BpxYpNRERE+i7NbSciIiKSBxVPIiIiInlQ8SQiIiKSBxVPIiIiInlQ8SQiIiKSBxVPIiIiInmo2KEKRESkcpnZPsCXgS2BvwGfIRqh+roQwsJ4m8OIBjP+FNFk8EcD2wE/JpqXdE4IYUGpYxdR8SQiIiUXQngaeNrMdgX+AgwjmgHibGBKvM0jwCNmdjgwGhgC/Jao0JoAbG1mU0MIoQx/gmzAVDyJiEhZmJkRzVt6EHAh8BqwadY2tcD+wBlEXU3GAkuBBfH2OwEvlixoEVQ8iYhIGZhZFfBT4NYQwlzgybgVqjpjm42BS4BLQghrgEfN7BngEKJTfO8D/UsevGzwVDyJiEg5XAjsCtSb2QvACKABON/MTgDmAd8HaoBzzGxmCMET9Xu6gajF6lDgvNKHLhs6FU9FUlVVRUNDw6hUKlX0Yw0ePLiupaWF+vr60alUKqcZofu6VCo1ptjHqOTnXbHnbtCgQaOWLVvW6fqamhrq6uqU693Ijn3t2rUzgBkZm7wQ/96eD0/DXZ25j1QqNWbt2rVP8uGpuheBoj/3fel5L3c8vUFXnw/d5Xuaiqciqa+vp6Gh4a5SHKu6uprBgwcDzCrF8SrEM8U+QCU/74o9dw0NDXT1ZrrFFlsAKNe7odjLo5JjL6JOPx+6y/c0FU9F0tLSgplNbGhomFfsY7W1tdW1tLTMqq+v37e6urrY3yyKXpQUyNhiH6DEz3tBKfbcrVixYhRdFEeLFi2irq6uL+Z6QbW1tdW9/f2BBf8A3/qW95XrXdDnQ4c6fc10l+9pKp6KpL29nWXLls0bOnTos8U+1rvvvtsAsHTp0hcaGxtXFPt4laCmpkbPexcUe+66+xaaSqWU6zlIx15oyvWuVXLsxdLVayaXVifQCOMiIiIieVHLk0gf08HIzZsDWwFTQwgvAUyePHlYW1sbf/3rX7/673//+w4zOw2N3CwikhO1PIn0MSGEp0MIPwJ+AaRCCBcANxJNcwHAu+++O+DII4/MfFj2yM3j4wEMRUQki4onkT4oY+Tmh8zsbKIxdX6fXn/33Xe/VVtbm/mQp4mmv9iBj47cLCIiWVQ8ifQx8cjNPyMauXlxCOEK4AjgtM4eE0J4FLgZWEkZR242s33M7BIzu93MRpvZVWZ2ZuY2p5122tDrrruOUaNGnW5mdWZ2mpndaGbDzOxIMxtR6rj7olz+F2Z2mJlNMbO7zGwz/S9kQ6HiSaTvSY/cfJKZnWNmFwOXAQ+b2QlmtlcnjzsauAd4m2jk5pdKEm2GrFOOXwJuy97m//7v/47ZeOONaW9v7w8YOuVYFLn8L0IIj8SnhR+k44l79b+QPkkdxkX6mBDChV2sngXQ3NzcsPfee/Pss89+MMJzCCE9mvP0+KcsMk45XgYMzF6/du3abSdOnMiDDz748EsvvXQM0SlHTRZbBN39L+JtNHGvbHDU8iQivUb2KcdOtllVU1PDgAEDWoHa3nLKsa/J8X+xMXAp8cS9+l/IhqJiWp6cc9XAWcB3iOJuBiZ5759zzo0Gfg0MJ/q2c7z3fp5zroqoOXk34F7v/dnxvnYHTvXeTyz9XyKSv+HTzg1F2vXyQu5s4YmXre8pmszJYp8HnkivSE8Wu+2229537rnnnvT888+fBpwbr94gJ4s1s4HAxcBCYA5wFNH/9LoQwsJ4m8OIrrT8FHA60XOVy7AU3f4v0MS9soGqmOIJGAD0A8Z675c55yYAdzjndgLuBy7w3t/vnPsqcB/R+fdxQCCarfsfzrnh3vuFwBTgB+X4I0Skc52ccpwar5sO0SlHgKlTp/5XesTk3nLKsQzqiPoi7Q8cDpwJtAFnE73PEUJ4BHjEzA6n435JW5vZ1BDCRwr0XP4XwOzsDTbg/4VsQCqmePLerwB+mrHoEeBOYOd4/f3x7xnOuSnOuZ2BRmCu977dOfcaMDwutl733jeV9i8Q2TBtQK1mJRdCeMfM0qfFbiRqLXoN2DRzO/VLEimsiimeOvBZ4Hng08D8rHXzgR2BN4GJzrl+8f2FwJVELVaziJq5z/DeF+vNXUSkJEIIc4GzzWxXMt7b435JlxD3SwIeNbNngEPool9SMYremYXeoUiZVGTx5JwbQNR8fDawGbA6a5NWYKD3frZzLkl0bv4OovP7jwOTgT2Ba+NlfypGnFVVVXXpUwxFVp/+3dzcXILD9X563sujRM97UXQWe1VVVV17e3uXjy1nrn/+858f+Nxzz9Xuu+++Oz377LNH9+/fv+Giiy766bBhw04eMmTI/Nra2u+EEKo32mijCxobG/88Z86c/9tkk01OvPHGG3/1wx/+8BurV68+/Pzzz7+oUv93yvVuVXLsRdHVayaXfIcKLJ7iVqTfALd773/vnDuCqD9Upgaiqz3w3p+U8bhZROf4D/ber3TOzSbqEFnw4qm6upqamppZhd5vNxaW+Hi9WUFP6XRDz/uHSvm8F1qHsdfW1tLa2trpg/r37w/xEBAl9MFr7u67707fvDhj/aRjjjmmo8d9H+DVV18FuPzFFz84U3dywSMsHeV6bio59kLr9DXTXb6nVVTx5JyrAR4AnvTe/0+8eC6wbdamo/j4qbxJRH2kVmUsM4o0XENbWxvt7e371tbWvlCM/WepJ0qM4UBLkY9VKR+Om5TgGHreP66j572SY2fNmjWj6aI4am1tpaqqqmS5vvL8LQv+ITjw0rf73P+twEqZ64Wm96mP6/Q1012+p1VM8RQPVXAP8Iz3/oNvWN77uc65VufceO/9A865rwEbAc9lPLaBqMVpHNGVKEOcc3VEl+8+VKyY29vbV6WvBiqmjKbYllIcrxLoeS+PSn4eOou9qalpVUfLM5Uh1wuqL/7fCqmSc72SYy+Wrp6HXPIdKqh4Ar5BdCnubs65b2UsP4eoMLrNOXcd0fhP4733mSctzwKu8t6nAJxzlwGvAi8Aj5UieBEREekbKqZ48t7fDdzdxSZ7dvHYC7LuTwOmFSg0ERER2YBoepYKYGYD0zOaZ810vmN6myVLljBq1KjTzOw8zTQvIiJSPCqeKkN6FOHsmc73SW9w3333UVVV1Up05aFmmhcRESkSFU8VIITwDtFowMBHZjr/oLP7P//5T84///zpRFcjpmeaHw3swEdHERYREZH1oOKpwnQ20/mAAQMYNWpUimiAUM00LyIiUiQV02FcPpA90znbbLPNW7/4xS/4+te/fhWQAtId5DW7uYhIBTGzgUQDni4kGhD6HGAd8PMQwlsZ221CNOPNwcCxwHbAj4lmzZgTQlhQ0sA3MCqeKkScCFM7Wpceav6NN974r8zxKzS7uYhIxUn3cd0fOIGoeDLgv4CfZmx3KvDH+HZ2H9etzWxqCEHzthaJiicRkSIzs/2Ao4hOoV8XQlhoZj8gmrB8NTAF+BZqPdjghRDeMbN0F4tNQghLAcxsaHobMzsWeJho7EOI+riOJeobu4AP+7h+MP9Otk5ek4cRDR79KeB0orMXek12QMVTGRRjtvJYQYfGX3jiZbo6T6QwDgfOJJrh4GyiYikAS4hOyaxGrQfycUvNbBBRy9PSjOX7ExXenwPWhhAuNbNngEPIvY/rx16TIYRHgEfM7HCiC470muyEiicRkeK7kai/4mvApgAhhBsAzGx/YDw9aD2QPm86cBnRxV0/N7MTgHkhhBMAzOwioguDIP8+rh97Tcb7rCUqzs6Ij6vXZAdUPImIFFkIYS5wtpntysffd5cDw0MIj/ag9UD6oKw+ridnrHoza7uLMm7n1ce1o9ekmW0MXAJcEkJYA+g12QkNVSAiUmRmtrWZ/RQ4Dqgys73M7GQzu5SoU/D/izc9mmgC9LeJWg9eKke80vd19JoEbgK2AM4xMxdvqtdkB9TyVCRVVVU0NDSMSqVS5Q6lx1Kp1Jhyx9BTpYh98ODBdS0tLdTX149OpVI5zcTd1/XF18ygQYNGLVu2rNPH1dTUUFdX12Wur127FmBG1uK/xz8A26ZSKdauXfskH54WeRH4yH4HDx5ct7LbvyR/ffH/VkilzPWt77zgmULv89lDT/9Y7J28Jq/OvJNKpcZ095qsRF29ZrrL9zQVT0VSX19PQ0PDXeWOYz0VPIlLqOixV1dXM3jwYIBZxT5WBelzr5mGhga6ejPdYostADrM9a3vvKCjxetlZsH3CPTB/1shVXquV3LsRdLpa6a7fE9T8VQkLS0tmNnEhoaGeR2srpQ3qrEdLKvk2Auqra2trqWlZVZ9ff2+1dXVxW55quTnvZJjZ8WKFaPopDgCWLRoEXV1dcr18lGud+PN725c6F2y9S3v98nXTHf5nqbiqUja29tZtmzZvKFDhz5b7lh6qqamRrF34d13320AWLp06QuZg5NuyPria6a7b6GpVEq5XkbK9fLoq6+ZXFqdQB3GRURERPKi4klEREQkDyqeREREesDM9jGzS8zsdjPbMWP5Jmb2jJkNM7PTzOzG+PaRZjaijCFLgajPk4iISA+EEJ4Gno4HmtwHeCVedSqatLdPU8uTiIhID5mZEU2J8lB8Pz1pb2u8ydNE88TtwEenOJEKppYnkW6Y2UDgYmBhCGFqvGyvjTfe+Nvjx49nyJAh9VdfffW30ezjIr2KmY0EziGafPnnIYS3zOxQ4MtAPfAz4DB6mLtmVgX8FLg1hLA4XlyISXull1PLk0j36oDbspYd/cQTT5x5zDHHcOedd36FjzfNj4+/kYpI+ZxAVDz9CDgxXrYDcB3wB6KpSNYndy8EdgVOMrMTzGyvEMIJIYRziU7bZU7aqylO+hC1PIl0I4Twjpl97JvilltuGfr168fatWu3IGqa1+zjIr3LJiGEpQBmNjReNgO4Mr79TaIvRz3K3RDChV2suyjjdl6T9krv12eKJ+fcaODXwHCiBDgemA88COwG3Ou9PzvednfgVO/9xPJEK31A+9tvv23Lly+npqbmnRCCZh8X6X2WmtkgwIiKI4DTgcOJWqCODyH8Urkr+eoTxZNzzoD7gQu89/c7574K3AecAgRgBPAP59xw7/1CYArwg3LFK5XLzE4A5gF377ffflccccQRHH744b+LVx8N3EDUefRQ4LzyRCkisenAZURdVP5qZnsBfwMuBxqI8hVyyN2V528JsHx+AYPbdvo6ndqvUH2ieCK+csF7f3/8e4ZzbgpR57+53vt259xrwHDn3E7A6977prJFKxUn7kA6NXNZc3PzPOB7wLJ4GzXNi/QiIYQ3gZOzFs/uYDvlruSlrxRP2xCdoss0H1gL7O6c60d09cNConPddzjnZgFzgDO89xpvYwMwfNq5xfo/Ly/kzhaeeJm+jYqI9GJ9pXgaAKzOWtYKLAaSRKdZ7iBqiXocmAzsCVwbL/tTMYKqqqqqa25ubijGvktBsZeHYi+PzmKvqqqqa29v7/KxyvXy6Sj2PR67pqBfaABmFnqH9L3nvVJ0FXsu+Q5gfWGQU+fcEcCJ3vvDMpbNIOokfk98vx8wi+hS1Hu89591zp0E1Hnv/6eQ8TQ1NY2prq5+pqamppC7FZEyaW1tBRg7cuTIj8zG3tTUNKZ///7PlCcqESmGzvI9U19peZoLbJu1bBQfPZU3CbgTWJWxzCjSWFdtbW20t7fvW1tb+0Ix9p+lnuiU5HCgpQTHKyTFXh6KPUdr1qwZTfTFq0Otra1UVVUp17un2MtDseehu3xP6xPFk/d+rnOu1Tk33nv/gHPua8BGwHMAzrkGohancUAbMMQ5VwfsQTykfjG0t7evamxsXFGs/ac1Nzenb7aU4niFpNjLQ7HnrqmpaVV32yjXu6fYy0Ox5yeXfIc+UjzFJgC3OeeuA5qB8d779InLs4CrvPcpAOfcZcCrwAvAY+UIVkRERCpTn+jz1Ns0NTWNAZ4BjiE6pVhUVVVVdbW1tbPWrFmzb3t7e05Vc2+h2MtDsedle6JT/h32eUK5nhPFXh6KPW+d5nsmFU9F0NTUtBXRG+mAcsciIgWxGth+5MiRb2UuVK6L9Ekd5nsmFU9FEr+pDit3HCJSEIs7eyNVrov0OZ3me5qKJxEREZE8FOUyfREREZG+SsWTiIiISB5UPImIiIjkQcWTiIiISB5UPImIiIjkIe/iyczOMbPdM+6fYWbLzex5M9uxsOGJiIiI9C55D1VgZm8Ch4YQXjWznYA/A0cAnwX2DyEcUPgwRURERHqHnhRPrUBDCCFlZr8GFoQQfmZmdUAyhDCoCHGKiIiI9Ao9mRj4beAzZrYC+Bqwbby8FrACxSVSVolE4gmiOY5Wx4teB84Efgo8nkwmr4u3qwbmAD9IJpNPZ+1jBDA7mUxuXqKwRSQPBczzJqLPxrTZyWRyQlGDl7LqSfF0GfAUUaF0QQhhcbz8c8BLhQpMpBc4PplMPppIJAyYBNwBHAr8JZFIPJBMJt8BTiV6o3y6i/2ISO9ViDx/N5lMjihJtNIr5N1hPITwa2BH4DMhhP/OWPUCMLFQgYn0FslkMgD3AiOTyeRC4ErgykQisSXwPeCccsYnIutPeS756MnVdv8LLAkhvJG5PITwL+A9M/uRmU01s30LFaRIOSUSiY2A4wAfL7oe2C6+f34ymVxerthEpDCU55KPnozz9CVgajw0wTwzu8rMBsbrbgc+D/QDvJkdWqhARcrgtkQisQBoIXpTPQ8gmUy2AzOI+vs9XrboRKQQCpHnmyYSiQUZP2cUM2Apv570eQLYGrgIWAecDFwLnAjsD4wIIbxnZn8FzgJ+V4A4Rcohsy/EF4E/JxKJ0cAA4FvAdUR9AE8CSCQS9wJ7xY/9Mh92QhWR3qsQea4+TxuYnhZPE0II7wGY2dPA/Hj5wPRy4FHg6vWMT6Ts4r4QjycSicXATsB/AZcCdwIvJBKJvZLJ5MeuromvwhGRCqA8l3z05LTdQmCzjPufAAaaWQOAmaX32UpUuYtUvEQisScwAkgAWwLTksnkWqKrcH4Z95cQkQqmPJdc9aTl6WbgcTO7H2gDxgOPEQ1TkCQa++kh4HDgtcKEKVIWtyUSiVaiYTkWA8cQtaYeFX9LJZlMPpZIJL4PnAL8omyRikhPFSLPN437TaVpnKc+Lu8RxgHM7HDgQKA/8IcQwt1mNgJYBfwJGAYMBMaHEP6vcOGKiIiIlFePiqcud2hWA+xMNG3LkoLuXERERKTMetryNALYBWjIXhdCuH29oxIRERHppXoyMfC3gBuBN4jGxcgUQgh7FCi2DjnnjiUaAmEo8KL3/hDn3Gjg18BwYAFwvPd+nnOuCngQ2A2413t/dryP3YFTvfcaEV1ERETy0pMO4z8Cjgoh/KbQwXTHOXcc8G3Aee8XxMsMuB+4wHt/v3Puq8B9wGhgHBCIrp74h3NuuPd+ITAF+EGp4xcREZHK15OhCrYAytUJ/EfAsenCKbYTgPf+/vj3DMCcczsDjcBc73070ZV/w51zhwCve++bShq5iIiI9Ak9aXn6G9FpsKcKHEuXnHPDgRrgm8657wJrgKnACj4cpDNtPtHkxW8CE51z/eL76cke73DOzQLmAGd47wvbax5oamraiuiqQxGpfItHjhz5VkcrlOsifU6n+Z7Wk+Lpv4E7zOwWotac1syVIYT/7cE+czGcqNVrGbAD8ElgFnADH58GoxUY6L2f7ZxLAvOAO4D9iOYomgzsSTStzH5EwysUTPxmOpcSDhLav39/Wltbu9+wF1Ls5aHY87K6qalp++w3VOV6fhR7eSj2vHWY75l6UjxdG//+bgfrAvCpHuwzF21EHcRviu+/7pz7DdHYUtlvXA3ASgDv/UkAcevTLGACcLD3fqVzbjawKwUunoBh/fv3L/no6v379y/1IQtGsZeHYs9Na2vrAKLWpew3U+V6nhR7eSj23HWR7x/Iu3gKIWy9PkGthzeJ+jBlqiFqZdo2a/koPn4qbxLRHEWrMpYZPev31a3W1laqqqr2ra2tfaEY+89ST3RKcjgfvwKyt1Ps5aHYc7RmzZrRRF+8OqRcz5liLw/Fnofu8j2tpxMDl5z3folz7hnn3Bne+/92zo0ADgWuAE5yzo333j/gnPsasBHwXPqxzrkGohancUQtWEOcc3XAHkRTyRRFe3v7qsbGxhXF2n9ac3Nz+mZLKY5XSIq9PBR77pqamlZ1t41yvXuKvTwUe35yyXfIo3gys/8OIZxhZtOITs91KITwrVz32QPfB251zp1K1IJ0qvf+defcBOA259x1QDMwPr7CLu0s4CrvfQrAOXcZ8CrwAtG8fCIiIiI5yaflaWH8e3ExAsmF934R8OUOls8j6gDe2eMuyLo/DZhW8ABFRESkz8u5eAoh/CL+fVbxwhERERHp3fLuLG1mHZ53NLP+ZpZc/5BEREREeq+eXGk2sJPljV2sExEREekT8ukw/iRQCwQz+3vW6ipgO6KhAERERET6rHw6jB8IfJHo0v7rs9atA94MITxdqMBEREREeqN8Ooy/D/yvmX0zhPCbIsYkIiIi0mv1ZITx35jZYGAbYOMO1nc7MqeIiIhIpcq7eDKzY4BbgDVEQ6cvBYYQTc77NDkMay4iIiJSqXpytd1FwMQQwiDAQgjDiCbQ+z+iokpERESkz+pJ8bQF8Nv4dquZbRxCeA84A7iyYJGJiIiI9EI9KZ7eAD4Z314I7BzfXgZsVoCYRERERHqtvPs8AbcCnwJeB+4F7jCzXwP7Ak8WMLaKVlNTQ11d3ahUKlX0Yw0ePLiupaWF+vr60alUKqcZoXsLxV4eij13gwYNGrVs2bJO1yvXc6PYy0Ox56e7fE+zEEKPD2JmGwHnAnsBTcAlIYR3e7zDPqKpqWnMVltt9Uy54xCRwnjrrbcAxo4cOfLZzOXKdZG+p7N8z9STlqcPhBDWAZek75vZ5uuzv75k0aJF1NXVTWxoaJhX7GO1tbXVtbS0zKqvr9+3urq6or5ZKPbyUOy5W7FixSjgrs7WK9dzo9jLQ7Hnp7t8T8ureDKzrwEHE40oPj2EMCdebsB/AT8DBuUZa5+USqVYtmzZvKFDh3ZauRbKu+++2wCwdOnSFxobGzucuLm3Uuzlodhz110TvnI9N4q9PBR7fnI5ZQd5dBg3s+8B04AUsBHwmJl91sx2B54BJgET8o5UREREpILk0/I0CfhmCOFRADP7X6J57gYAlwNTQwhrCx+iiIj0NWZWTTTszUyiAZe3IvoceSlefySwEzASOIvoy/l2wI+B/YA5IYQFJQ9chPyKp6356OjhjxMNTfDJEMLbBY1KRET6ulOAPwEzQwhzzOyzwB7ASwAhhAeBB83sm8AORDNZ/Bb4DFEhtbWZTQ3rc9WTSA/lUzwNDCGsTt8JIaw2s6DCSURE8mFmBwCvEA26jJmdDXwR+G7WdlcC2wLHATXAWKIpwRYArxG1TL1YqrhF0vIpnszMruhuWQjh7PUPSzZUGU35FwNHAgtDCFOzttmEqKn/YOBY1JQvUmkOJZofdSdg7xDC98zsOqK8Pz29UQjhLDPbDvhWCOEXZvYMcAiwHHgf6F/60EXyK55uAzbNYZnI+kg35aeIXl/7d7DNqcAf49tqyhepMCGE0wDM7ARgMzO7GGggOk13AjAPGA0MJ5o79er4oUcDNwATiQqw80oauEgs5+IphHBiMQMRyWrKX0fUPJ+9zbHAw8Dh8aKnUVO+SEUKIUzvYHG6b+3sDrZPF1HT4x+RslivQTJFCuwjTfnApR1ssz+wI/A5YG0I4VI15YuISClVZPHknGsguiLjQu/9dOfcaODXRE28C4DjvffznHNVwIPAbsC93vuz48fvDpzqvZ9Ylj9AOpTVlP9y5rp0U34I4YT4/kXAzfFqNeWLVICV528JsHx+Afe57fR1VsDdieQk50Eye5mrgbkAzjkD7gcu995vRtRacV+83TggACOA/Z1zw+PlU4CflDJgyV0IYXoIYU4IYUG6s3i8bHbGNheFEBbHt68OIaTibSZnXhUqIiJSaHkXT2a2ZTECyZVzzgFtfHg+fCcA7/398e8ZgDnndgYagbne+3aivjDDnXOHAK9775tKHryIiIhUvJ6ctnvNzIaV49u9c25T4AKifi/py1m3AbJbgecT9Yt5E5jonOsX318IXAnc4ZybBcwBzvDe68qsMlJTvoiIVJIeFU9Eo73OKXAsubgR+JH3fkXUAAVE08NkF3KtwEDv/WznXJLostc7iMYBehyYDOwJXBsv+1Mxgq2qqqprbm5uKMa+s9Snfzc3N5fgcAVV3/0m+dPz3i3FnqOqqqq69vb2brfpa6+5vffee9/FixePa2tr23K77ba75s033zwA2GjcuHH33HLLLf8G2GWXXY5YuXLl7mbW+t3vfvcX06ZNO3bt2rXbHH744VNmzpw5btSoUc/dfffdb2XFXlB97XkvAsWeh1zyHXpWPJ0IXBuP/PoXsgqXYrVIOeeOA5Z473+ftWoVUQGVqQFYCeC9Pyl+fD+iS2AnAAd771c652YDu1KE4ql///7w0elsSmFhiY/Xmy0v4bEq+XlX7N2ora2ltbW10/V9Ndf/8pe/ADBnzhzmzp37zdbWVpYuXcqJJ554UXqbAw44gCuvvJJ//etfzJ49+4wTTzyRXXfdlbq6um8tX76cMWPGEELArKgNwcr13Cj2HHSX72k9KZ5mEM1p9zBRZ+w0i+9v1IN95mIi8Fnn3Dfi+xsD7cAjRMP3ZxrFx0/lTQLuJCq20owidZpvbW2lqqpq39ra2heKsf8s9UQvruFASwmOV0jp2AttkyLsM1tfeN4VezfWrFkzmi6Ko76c66NGjZrc2tq670YbbbTosMMOu2jo0KGpffbZZ/Lrr7/+M4C///3ve2677bYHV1dXLwGqGhoa5t566627VFdXL1uzZs2I55577ukZM2a89eijj76Ccr1cFHseusv3tJ4UT/v24DHrzXt/cOZ959xFwIJ4qILnnXPjvfcPOOe+RlTAPZexbQNRi9M4os7mQ5xzdUSTUD5UrJjb29tXNTY2rijW/tMymjNbEonEbsAXiGcoJ7oq8bfAj0MIcwDMbDKwJVERPIeogMxpihMz+2Ih91+spthSP++lOF4hKfbcNTU1repum3LkeimOt2LFiilmNqCtre3iBx988D2icdQ+yK9//vOffwD+YGaHA6uXL1/+mJk9STTuWl1ra+vSRYsWtTc2Nq5QrpeHYs9PLvkOPSieQgj/yj+copsA3Oacuw5oBsbHV9ilnQVc5b1PATjnLgNeBV4AHit1sMUUQpgJzMyYofxAsk5LpkfpNbMLiVrjTiXHKU6KvX8R6R3M7Ciiq5kbiMbL+wWwlqjbxglEfUnXAl8F+gHnxw/VuGvS5/VokEwz+yTRHGQ7Ep2qmwdcE0J4s4Cxdcl7f1HG7XlEHcA72/aCrPvTgGnFiq3cMmYov4uMmcuztmkANg4hLDGzvKY4Kfb+RaT8Qgj38eGYefDRUxmZg9g+m/U4TaEifV5Pxnk6gOhDbyjwO+D/gMHA8/E6KbMQwhXAEcAYolNs44HvZW32PaJR2QkhPEo0WvdKcpjipNj7FxER6c160vJ0BfD9EMLdmQvN7PfxujGFCEx6JrupPYQwKz3dSUZT+7PAyBDC6xkPzampvdj7F5HcZfVB/DVwEFF/w8tDCK/E2/yA6CzBaqLZFb5FDn0ch087t+Cn1WcWeociZdKT4ml7oivusnng1vULR9ZXB03tmTOXZ47N9f2sbXJqai/2/kUkd1l9EHcIIfzIzHYF9iE6pQ5R14olwDqiAmoI6oMosl56UjwtIkq6v2Ut35nKHkdCRKTiZPRB/K5FAypNBC5Lrw8h3BBvtz/RKXb1QRRZTz0pnn4B3G9mF/Nhso0hutJCk+2WSammOFFTvkjvEkK4wsyuAy4mGsfu1vSk2VmWA8NDCI+a2TNEwwmoD6JID/RkqIJrzWwR0dxyo4Aaokv+Tw8hPFDg+EREpBNZfRB3Iiqe6s3s+XiTecAuRH2iGoBz4+XqgyiyHno0VEEI4UGicT9ERKRMOuqD2IHZHTxOfRBF1kNRpiYRERER6at61PIkIiLlUar+jSLSObU8iYiIiORBxZOIiIhIHvI+bWdm5wAzQwj/iO+fQTREwZvAxPSothu6mpoa6urqRqVSqaIfa/DgwXUri7DfVCpVsaPFlyL2wYMH17W0tFBfXz86lUrlNBN3b1HK2E8++eTRTz311OdaWlo2O/HEE6f/7ne/+1x7e3vVaaed9ttjjjlmMcA+++zzjXfeeedT1dXV799yyy2/OvPMM93ixYtHXH311TfcdNNNYw855JBXJ02atKjUsQMMGjRo1LJlyzpdr1wvL+V61xR7frrL97Se9Hk6GXgEwMx2An5ENKv2Z4GrAc1vB2yxxRYQTZxbdNXVReu69kyxdlwCRY+9urqawYMHw0cnTK0IpYz9xhtvBOC5557jmWeeOfSYY45hyZIlHHDAAf+V3ubYY49l0aJFbLTRRuy1117HfulLX2KPPfZg4MCBXx8wYACtra2EEDCzkj/vDQ0NdPVmqlwvO+V6FxR7frrL97SeZOLmQHrOstOAq0IIT5jZP4BzerC/PmnRokXU1dVNbGhomFfsY7W1tdVRnBfX2A6WVcqbbEexF1RbW1tdS0vLrPr6+n2rq6sr6htdqWNPpVJ885vfPLWtra32iiuuuHn06NGp/fff//hXXnnleoCTTz4ZgOOOO273ww47bOjgwYOXe++3r6ura1m8eHGira3t2ffee2/RJZdc8nqpY1+xYsUouiiOusr1zFa34cOHv/z222/vNGDAgCWnnHLKfemWtIceemjw5MmTz9xll11mPvLII4/vueeeR3fW6qZc75ByvQuKPT/d5Xua5TudkZnNB44CVgD/ALYNISw2syHAmyGETXoQb5/S1NQ0huiNZ+zIkSOfLfbxmpubG1aev+XyQu+3ZCOM//nKQu+yJFcPNTc3NxCN0LxJY2PjimIfr5BKGbuZVQE/Be4GjiWaQPx94OIQwplZ2+4G7BhCuM3MNiUaBfuTRNNBLQ8h/K3Uz3tX+Zxrrsfzze1LNFjlWuCyEMLyeN0IogGHB4YQHjSzKUR/70rgh/HtqSGEoFz/OOV61xR7fnLN6Z50GL8MeAp4CbgkYxqAz8XLpBtmto+ZXWJmt5vZjmZWbWaPxh8c6W32MrMbzOyXZtZgZqeZ2Y1mNszMjozfcEUqwYXArsBJwD+Jpni6GphuZifEr/WTzexS4ATg/8WPOxq4B3ibaBTsinx/yZhv7q4QwhnAr4AfpNeHEBYQFZNpTwOjgR346NxzIr1ajp9th5nZFDO7y8w2q9TPtp5Mz/JrM5sJVIUQ3shY9QLRG4R0I4TwNPB0xuznBwN/ytrsaKJvnaOBw9FM6FKhQggXZi36dcbtl+PffXIU7IxWt8z55pYTTWvVIc09J5Uql8+2EMIjwCNmdjjR51tFfrbl3fJkZp8PISzIKpwAFgLfLUxYfV/Gt9H3gFeAd7K3CSG0A/8GGtG3UZFK9EGrm5mdbWaXAT8DfpVudevkcX2i1U02PLl8tplZLbA/8Gcq9LOtJx3G7zOzU0II96cXmNm2RP0ZDPhxoYLrqzK/jQLfA9YQvVj2BubEm7XH220O/FvfRqVSFaPvzN8PPqXQuyyKDlrdMk3P2O6JrMdVfKubbHhy+Wwzs42BS4i6/awBKvKzrSd9nr4AXGpmpwOY2SSiJ+UxYM8CxtaXZfYBeSGEcC7wAHBzxrfRu4FrgEnAw/Hj9G1URER6q1w+224CtgDOMTMXP67iPtt60udprpntDcwws+/G+/hSCOEvBY+uj+ro22gIYXp8c07G4r9nbaNvoyIVpChXrBV6hyIFkuNnW5/o35hT8WRmA7IWrQS+AlwPLAGeS28TQlhd0AhFRESk19uQTtHn2vK0EujoSUmPr3FyfDsAGxUgrj5N30ZFREQqV67F09ZFjSIHzrlq4CzgO0RxNwOTvPfPOedGE13+PJyot/7x3vt5zrkq4EFgN+Be7/3Z8b52B0713mtoBREREclLTh3GQwj/yvWniLEOAPoBY733nyQaZO8O55wB9wOXe+83Ay4F7osfM46oNWwEsL9zbni8fArRZMYiIiIieenJ1XaY2SfN7L/jkUP/z8x+YWZFbZ3y3q/w3v/Ue78sXvQI0ZQGO8fr749/zwDMObcz0fhIc7337URjRwx3zh0CvO69bypmvCIiItI39WSQzAOAF4GhwO+A/wMGA8/H60rls8DzwKeB+Vnr5gM7Am8Cuzjn+sX3FwLnAy8552Y55/4nbrkSERERyUlPBsm8Avh+COHuzIVm9vt43ZhCBNYV59wAYCpwNrAZkH2FXysw0Hs/2zmXBOYBdwD7AY8Dk4nGpLo2XpY9NUpBVFVV1cUTG1Ykxd6t+vTv5ubmEhyuoCo59rSSxF5VVVXX3t7e7TbKl/JQrnerkmNPK1nsueQ79Kx42h6Y0cFyTzSqaFHFrUi/AW733v/eOXcEUX+oTA1EVwjivT8p43GziObOOdh7v9I5N5toQK+CF0/9+/cnPl4lK/js7SVUytgXlvBYhabYu1FbW0tra2un65XrZadcz41iz0F3+Z7Wk+JpEdEEfn/LWr4zRf4DnXM1RKOVPum9/5948Vxg26xNR/HxU3mTgDuBVRnLjB72++pOa2srVVVV+9bW1r7QwepKeaPapINllRx7odUTveaHAy0lOF4hlTL2Yr1mSvK8r1mzZjRdFEfK9bJTrndNuZ6H7vI9rSfF0y+A+83sYqK+TxCdqjufIl7BFg9VcA/wjPf+4vRy7/1c51yrc2689/4B59zXiMaaei7jsQ1ELU7jgDZgiHOuDtgDeKhYMbe3t69qbGxcUaz9F5ti71pGM3JLpT1XlRx7hpLE3tTUtKq7bZTr5aNc71olx56hZLHnku/Qs+lZrjWzRcDpRC08NURF1OkhhAfy3V8evgEcDuzmnPtWxvJziAqj25xz1xGN/zQ+vsIu7SzgKu99CsA5dxnwKvAC0Zx8IiIiIjnpScsTIYQHiQafLBnv/d1Ek+V2ptNJib33F2TdnwZMK1BoIiIisgHpUfEEYGY7EV3+H4C5IYQXu3mIiIiISMXLu3gys6FELUCfBV4n6nT9aTP7KzAxhLC4sCGKiJkNBC4m6vj5V+DLwJbA5SGEVzK224RoqsODgWOB7YAfEw3JMSeEsKCkgYuI9EE9udLsOuBdoDGEMDaEMAZIxMuuLWRwIvKBOuA2gBDC0yGEHxFdvLFP1nanAn+Mbw8Bfkt0dewEYLyZaVBYEZH11JPi6cvAGSGElekFIYQW4EzgkEIFJiIfCiG8AyxN34+LoIlkXC1qZscCDxMNEgvwNDAa2IFowuzXgJ1KErCI9IiZDTSzq8zszMzbWdt8wszuNrMj4/unmdmNZjbMzI40sxFlCX4D0pM+TyH+yWZA98Nyish6MbMq4KfArVmnyfcn6of4OWBtCOFSM3uG6EvNcuB9oH+p4y2mrNOZvyG6+nYd8PMQwlvxNkcSFY0jia68nYBOZ0rvlW5l3j/rdqYBwO3AwPh+divz1mY2NYTQ0Wd1xcox3ycTdWnYDJhD1EhU8HzvScvT/wJXmVl6yHfMbBDRKYSH1zcgEenWhUQj459kZifEP3uFEE4IIZxLdNru5njbo4nGR3sbOBR4qSwRF88HpzOBE4jeTH8EnJjeIITwYAjhQqK5OHdApzOlF8tsZc5ucc7YZgHRl6G0DaWVOZd8vzqEcCbwT6KBsYuS7z1pefohUcX7jpn9K142gmjOuO+sb0Ai0rH4DXNqDttdlHH76vjm9PinTwkhvGNm6da0TUIIS+GDC1s+YGZXEs1EcBzR2HRjiT6UFvDhB42uGJaKFEJ4tK+3MkNe+d4AbBxCWGJmT1OEfO/JIJnvAV8xs22JBsnsB7wQQnh9fQIREVlPS+NWcCPr23oI4Swz2w74VgjhFxvCB430PWZ2AjAvhDC7g9VHAzcQ9YU8FDivhKGVQ6f5DnwP+DUUr7Ds8ThPIYT5wHwzqyM65ygiRTJ82rkF77vw94NPKfQuy206cBlRd4Sfpz9oiE5nDAeGAemWuA3tg0YqSGYrc9bt6RnbPJH1mD7dytyB6XSc788CI7MadAqe7zkXT3GRdAVRa9O9IYRbzGw6UTP4OjN7FDgu3YwmIlIKWaczT85Y9Wb8+2Pf0jfADxqRPiGHfAf4ftZjCp7v+bQ8XU3U4epO4Pj4UshBwNZEp+7+G/g5H/1jRDYo8fn4HxJNTn17CKHZzA4lGuKjHvgZcBi98GqvTmI/CtiLaPiDy4Bv0wtjFykHMzsO2BnYGEiFEE4zs08AVwEPhRAeNLPTyCNn1MpcGfIpng4EDg0hvGxmvwdeAbYPIfwLwMxOB/5UhBgrUk1NDXV1daNSqVS5Q+mxVCo1ptwx9FQpYh88eHBdS0sL9fX1o1Op1CqAvfba64g1a9ZsvGrVqkEXXHDBjqlUavPRo0cf+NWvfvXxP//5zztsvfXWn//rX/+6w6hRo16ur68//LHHHjuqsbHx5dWrV99eU1NT7JA/orq6utvYE4nEhPnz50+5/vrrh8+YMeP0xYsXJ3pD7CvP3xJg+fwC7nPrW94f29HyQYMGjVq2bFmnj1Oul1e5ch1g7dq1LwMvjxs37usHHnjgs6lUasxVV121xZNPPvn0ypUrt06lUmO22267sud7dq4DzJs3r/bYY489qr29veq000777THHHLMY4MknnxzonLvxjjvu+OGUKVO+vHjx4hFXX331DTfddNPYQw455NVJkyYtKlnglDbXoft8T8uneNqcqJc6QBPRt9M3Mta/AWyRx/76tC222ALgrnLHsZ6eKXcA66HosVdXVzN48GCAWellu+22G+eeey79+vVj6tSp3z7qqKO46667OPfccyfX19dz1VVX8eSTT/Lss88yePBgNt10Uz73uc/tP3fu3Mk777xzsUP+iFxj/+lPf/q1YcOGcdhhh7Hjjjv2itiLpMPXTENDA129mSrXy64suZ62bt06xo4dy09+8hMAJk2axI477sjixdEQbFOnTi17znQU+1NPPcUxxxzDkiVLOOCAA/4rvfyJJ57g5JNPZq+99nr8S1/6EnvssQcDBw78+oABA2htbSWEQB8Y2aPT10x3+Z6WT/FUHUJIAYQQ1plZCCF8MChmCKFdY6V8aNGiRdTV1U1saGiY18HqSnmj6qg6r+TYC6qtra2upaVlVn19/b7V1dWrAB577LEfOOduGz58+NoHH3xw0sUXX3zVIYcccv5LL73088svv/xTBx100K5PPfXUg++///6ga6+9dp/FixdvsXDhwpeHDh268pprrnm5i8MV/HlfunQp3cW+9957s/fee/O1r33tCwMGDHj/9NNP/2tviL1IOnzNrFixYhRdFEfK9bIrS66nOecOHDp06DLgH+llt91229h333130Ne//vXHDz74YPLMmaLnOsAVV1xx5mWXXXbr6NGjU/vvv//xr7zyyvUHHnjgl3fffffXf/e7331xn332uW/+/Pnbe++3r6ura1m8eHGira3t2ffee2/RJZdc0tnV9RX/muku39PyKZ7MzL5PdFlg+v6kPB6/QUmlUixbtmze0KFDny13LD1VU1Oj2Lvw7rvvNgAsXbr0hcbGxhUAr7/++iUHHXTQBcBa4O/9+vXrB/xu0KBBE4AG4IaampoXxo8fP5moD9HEV199dVfgvF/+8perix1zpra2tlxiXwt8lahf4/kPPPBA6A2xF0Nnr5nuvoUq18urXLme9oc//OEHIYRvZw4jcOeddzYAw9KxlTtnsnMdYMGCBQsmTJjwDNGl+66mpubZP//5z6f8+c9/HgjseMQRR+wVz1KwKdFl/qvfeOONVx955JHlV1xxRcW+XqDr10wurU6QX/E0C/hGxv0/A+M72EZkgxVCeIOM0W5jFXG1VyexQ3Tpb+Z2vS52kXIJIXw7/j09Y9kTWdv0xpz5FdHMIGuBl9KzFACY2UV8dJYCDeuRJefiKYSwXxHjEBER6bWK0XF52+nrytbVpYsvSxvULAU91eNBMkUqlZntQzR0wJbA5SGEV+LlmwAzgYOBY8nh8uJKfkOt5NhFctFRrsenob5N1AXlGqLRqDX8huRFxZNscEIITwNPm9muwD5Ew24AnEo0qS5sILOUi/RlneT6iUTjlg0jKqD6fK7ri1LhVZU7AJFyiK8MnQg8FN8/FniY6E0VNpxZykX6tOxcJ5ol42bgAeAYlOvSAzkVT2Z2rZkNiW83FDckkeIysyqikb5vDSEsjhfvD3wTOAD4XgjhUaI32JVo8liRitRJrq8CUkRflGqV69ITuZ62Ox44K769lGiATJFKdSGwK1BvZs8TXV58AugqE5E+5mO5DtwB3BivvyD+rVyXvORaPC0ATjOz3xG1gm7Ph+M9fUQI4dUCxSZSFCGEC7tYd1HGbV1lIlLBusj1v2dtp1yXvORaPH0P+CVwERD4sINttkCZWqWcc6OBXwPDiYq944H5wIPAbsC93vuz4213B0713k8sR6wiIiJSuXIqnkIIs4maPjGz+SGEbYsaVZ6ccwbcD1zgvb/fOfdV4D7gFKKCbgTwD+fccO/9QmAK8INyxSvlUYzZymcWeocist6U61JsPbna7uiCR7H+dgLw3t8f/55BdFpxP2Cu976d6AqK4c65Q4DXvfdNZYpVREREKlje4zyFEOaY2UCisTJ2JGrZmQdMCyGs6PLBxbMN0Sm6TPOJhp3f3TnXjyjWhcCVwB3OuVnAHOAM732fGc9DREREiivv4ikebOwx4FWiGZQNOBz4kZkdHEJ4rrAh5mQAkD3JYiuwGEjy4RUW+wGPA5OBPYFr42V/KkZQVVVVdc3NzRU7tINiLw/FXh6dxV5VVVXX3t7e5WOV6+Wj2Mujr8aeS74DWL6DqJrZLOD/ZVydkF4+GTgyhDAurx0WgHPuCOBE7/1hGctmEHUSvye+349o4uIJwD3e+886504C6rz3/1PIeJqamsb079//mULuU0TKp7W1FWDsyJEjPzJJsnJdpO/pLN8z9WR6lt2IxsHINg34eQ/2VwhzgexO7KP46Km8ScCdRAOkpRlFGmW9tbWVqqqqfWtra18oxv6z1BOdkhwOtJTgeIWk2MtDsedozZo1o4m+eHVIuZ4zxV4eij0P3eV7Wk+Kp6VEkyxmj+f0SWBJD/a33rz3c51zrc658d77B5xzXyMaMuE5AOdcA1GL0zigDRjinKsD9uDDIfsLrr29fVVjY2PR+4E1Nzenb7aU4niFpNjLQ7HnrqmpaVV32yjXu6fYy0Ox5yeXfIeetbr8GrjHzA40s83in0OAu4HrerC/QpkAnOmce4doVNnx8VV2EI2OfpX3PhV3Dr+MqPgbRtR/S0RERCQnPenzZMAZwOnA5kRX2zUD14QQrix4hBWoqalpDFFn+mOITikWVVVVVV1tbe2sNWvW7Nve3p5T1dxbKPbyUOx52Z7olH+HfZ5QrudEsZeHYs9bp/meKe/i6SMPNqsnmlhxcbcbb0Campq2InojHVDuWESkIFYD248cOfKtzIXKdZE+qcN8z7RexZN0Ln5THVbuOESkIBZ39kaqXBfpczrN9zQVTyIiIiJ5KMpl+iIiIiJ9lYonERERkTz0ZJwnzKwW+BbRuElDgGXA34FbQgiVNgiXiIiISM561OfJzO4lmi9uBrACGAx8E9g5hHBgQSMUERER6UVyLp7MbCZwZgjhWTObC+wVQliesX4rYE4I4RPFCVVERESk/PI5bXcZcKuZvUI0j91TZjYfWEnU8rQd0cCZIiIiIn1WXqft4tHFTwB+BDwC3B+vWg7MDyG0FTpAERERkd4kr6vtQmQa8BlgMXAX0eS6KpxERERkg5Bz8WRmQ8zsp2b2MHAv0B/4KrAt8JKZHVmcEEVERER6j3w6jP8ZmAk8EC/6BnBgCGEfM9uOqE/U5iGEzxYlUpESSyQSuxOdoh4LtAMp4LZkMjklkUgsAGriZeuA/wXOSCaTazvYzwnAl5LJ5IQShS4iOShwjl8PvJux+KpkMnlVMeOX8snntN1ngBtCCK+GEF4FfgmMAgghvBZCOBw4qwgxipRcIpHYn+iLwq+ArZPJ5CeB3fnwywPA/slkcgSwMzACmFziMEWkh4qQ448kk8kRGT9XFSVw6RXyudruEuAvZjYHMGAX4MeZG4QQnipcaCJldR1wUjKZfCy9IJlMLgWWZm+YTCZXJRKJR4ExJYxPRNaPclx6LOfiKYTwP2Z2K/BpIBB1El9ZtMhEyiSRSIwENs18U+1m+82ACcANRQ1MRApCOS7rK6/pWeJBMZ8pUiwivcVmwMLMBYlE4p9E+TIomUwOihc/nkgk1gEJ4NhkMnlPSaMUkZ4qRo4fFveTSpuYTCafLlzI0pv0aG47kT5uGdCYuSCZTH46kUhsDLRmLN4/mUzOSyQSjwMbpxdmvIGuSCaTOxc5VhHJ3zIKn+OP6KKQDUde4zyJbCDmAesSicS+OW7/I+Ci+I2XjA6jKpxEeifluKwXFU8iWZLJZDtwHnBbIpHYP5FIpPOkrpPtZwMvAP9VohBFZD0ox2V9qXgS6UAymZwOnAn8HFiSSCTeAp4DftPJQy4AzkkkEoNKEqCIrJci5PhhiURiQcbPqQUOWXqRvOa2ExEREdnQqeVJREREJA8Vd7Wdc+5YopHMhwIveu8Pcc6NBn4NDAcWAMd77+c556qAB4HdgHu992fH+9gdONV7P7Ecf4OIiIhUropqeXLOHQd8B3De+0RcOBlwP3C5934z4FLgvvgh44gG9BwB7O+cGx4vnwL8pKTBi4iISJ9QUcUT0eWix3rvF2Qs2wnAe39//HsGYM65nYnG8ZjrvW8HXgOGO+cOAV733jeVNHIRERHpEyrmtF3calQDfNM5911gDTAVWAHMz9p8PrAj8CYw0TnXL76/ELgSuMM5NwuYA5zhvS94r/mmpqatgGGF3q+IlMXikSNHvtXRCuW6SJ/Tab6nVUzxRNSfaQuikWF3AD4JzCKaa2h11ratwEDv/WznXJJoQLQ7gP2Ax4lmxt4TuDZe9qdCBhq/mc4FBhRyv13p378/ra2t3W/YCyn28lDseVnd1NS0ffYbqnI9P4q9PBR73jrM90yVVDy1EXUQvym+/7pz7jfAKj7+xtUArATw3p8EELc+zSKa3PFg7/1K59xsYFcKXDwRfQsdYGbfNbPXCrzvjzGzAcCjG2200ZdCCNmFZK+m2MtDsecuhLBdCOEWorzOfjNVrudIsZeHYs9PN/n+gUoqnt4kay4iotN4rcC2WctH8fFTeZOAO4mKrTSjSP2++vfvD3BLMfbdmX79+j1ayuMVkmIvD8Wem66++SrX86PYy0Ox5y6Xlq6KKZ6890ucc884587w3v+3c24EcChwBXCSc2689/4B59zXgI2IRooFwDnXQNTiNI6oBWuIc64O2AN4qBjxtra2UlVVtW9tbe0Lxdh/lnqi/lzDgZYSHK+QFHt5KPYcrVmzZjRRq3WHlOs5U+zlodjz0F2+p1VM8RT7PnCrc+5UohakU733rzvnJgC3OeeuA5qB8fEVdmlnAVd571MAzrnLgFeJ5ip6rFjBtre3r2psbFxRrP2nNTc3p2+2lOJ4haTYy0Ox566pqWlVd9so17un2MtDsecnl3yHCiuevPeLgC93sHweUQfwzh53Qdb9acC0ggcoIiIifV6ljfMkIiIiUlYqnkRERETyoOJJREREJA8qnkRERETyoOJJREREJA8qnkRERETyoOJJREREJA8qnkRERETyoOJJRIrKzKrN7FEz29PMbjWzW8xs74z1PzCzG8xsqpltYmanmdmNZjbMzI40sxFlDF9E8tBdvsfbbGJmz8Q5XpH5ruJJRIrtFOBPwL7Ag8DtwMiM9QFYAqwEVgNDgN8CnyGak3K8mVkpAxaRHusu3wFOBf4Y367IfK+o6VlEpLKY2QHAK8AWwMvABUA/4Lj0NiGEG+Jt9wfGA08DY4GlwALgNWAn4MUShi4iecol383sWOBh4PB4UUXmu4qnIqmpqaGurm5UKpUq+rEGDx5c19LSQn19/ehUKpXTpIa9hWIvj1LFvvXWW5+40UYbrX3vvfc+3b9///fuuuuuqevWrbOTTz75slQqdX3mtj/84Q83f/HFF0f+8Y9//O2MGTP+eu211+6zePHiATvuuOPwoUOHDkqlUtWljD1t0KBBo5YtW9bpeuV6bhR7eZQy9lzyvbGxcfyAAQO++J///GeXrbbaatizzz77687yvRzPe3f5nmYhhOJHs4Fpamoas9VWWz1T7jhEeovbb7+dt956iyVLlgDw5S9/mUWLFrHddtvxwgsv8NZbb9HS0sIll1zCwIEDufbaazn55JO55557eP7557n44osZMGBA2eJ/6623AMaOHDny2czlynWRj+sq3/fcc08AfvaznzFp0iSGDRtWMfmeScVTETQ1NY2pqal5pq6ubmJDQ8O8Yh+vra2trqWlZVZ9ff2+1dXVFfWtSLGXh2LP3YoVK0YtW7bsLjopnpTruVHs5aHY89NVvmfSabsiSaVSLFu2bN7QoUM7ffIL5d13320AWLp06QuNjY0rin28QlLs5aHYc9ddE75yPTeKvTwUe35yOWUHKp5EpMiGTzu34M3bfz/4lELvUkTW04aU6xqqQERERCQPKp5ERERE8qDiSURERCQPKp5ERERE8qDiSURERCQPKp5ERERE8qDiSURERCQPFTnOk3OuAXgJuNB7P905Nxr4NTCcaGLB473385xzVUSzOu8G3Ou9Pzt+/O7Aqd77iWX5A0RERKRiVWrL09XAXADnnAH3A5d77zcDLgXui7cbBwRgBLC/c254vHwK8JNSBiwiIh8ysy+a2RQzu83MPm9mt5rZLWa2d8Y2R5nZL8zsUjNrMLPTzOxGMxtmZkea2Ygy/gmyAau44sk554A2YHa8aCcA7/398e8ZgDnndgYagbne+3bgNWC4c+4Q4HXvfVPJgxcREQBCCDNDCBcANwJ7EZ0luB0YmbHZgcDpwK3AkcAQ4LfAZ4AJwHgzs1LGLQIVdtrOObcpcAGwP1FCAWwDzM/adD6wI/AmMNE51y++vxC4ErjDOTcLmAOc4b3vM7Mjm9kXgS8AWwFvAPXAxkAqhHBavM2mwLcBA64BvgdsB/wY2A+YE0JYUOrYRSQ/ueR7vN0mwEzgYOBYekm+m9nZwBeB7wLXAf2A4zI2mQZcAiwm+rL/NDAWWErUReM1oi/QL5YsaBEqr+XpRuBH3vvMCQIHAKuztmsFBnrvZwNJYB7wMNEbxePAZODLwOB4WZ+R9W1uYQjhTOBl4JaMzU4keo4GEBVQ+jYnUoFyzHeAU4E/xrd7Tb6HEK4AjgBOIyrqJsS30+ufDiGcT/RF+KUQwqPAzcBKYDnwPtC/1HGLVEzLk3PuOGCJ9/73WatWERUBmRqIkgvv/Unx4/sBs4iS82Dv/Urn3GxgV+BPxYi5qqqqrrm5uaEY+85Sn/7d3NzMqFGjJldXV++73377nfL4448P2njjjXdpamq6Ox1LbW3tTj/72c/OuPPOO0e+/fbb3xk4cOBzy5Yt26O6unrZmjVrmgcNGvTWsGHD9mpubn6llLGPGDFi38WLF49ra2vbcrvttrvmzTffPADYaNy4cffccsst/wYYM2bMV1euXLlDKpX61EEHHfTjWbNmfX3t2rXbHH744VNmzpw5btSoUc/dfffdb5Ug7o/E3tzcXKJDFkwlx55Wktirqqrq2tvbu92mHLkOdJvvo0ePPuozn/nMH958883DnHMDn3zyyXLl+0di32WXXY5YvXr1Dv3792/YaKONlqxbt+4KgGHDhj06bNiwk4cMGTK/pqYm9a9//evLAwYM6PePf/zjp83NzQ2bbLLJiTfeeOOvfvjDH35j9erVh59//vkXleC5r+R8qeTY00oWey75DhVUPAETgc86574R398YaAceAbbN2nYUHz+VNwm4k6jYSjOK1PrWv39/iIq1UloIMG/ePFpbW7n88stfff3115k+fTrASemNjj76aCZMmHDM3nvvzcyZM/nOd77DkiVLmDlzJgsXLmTMmDH/VV9f38khihf7X/7yFwDmzJnD3Llzv9na2srSpUs58cQTL0pv9OyzzwLw8MMPM3To0G+MHDmSXXfdlbq6um8tX76cMWPGEEKgxF+kF5byYAWm2LtRW1tLa2trp+vLmevQfb5/4Qtf4BOf+AT9+/dnq622Ovdvf/tbufN9IcDzzz/f2fqTOll+OsCrr74KcPmLL35wpu7kwoXWLeVLeZQs9u7yPa1iiifv/cGZ951zFwEL4qEKnnfOjffeP+Cc+xqwEfBcxrYNRC1O44g6mw9xztUBewAPFSPe1tZWqqqq9q2trX2hGPvPUk/04hq+yy67HLh69eod2tvbGzbffPOHb7/99qPfeOONHwLsvPPORw8ZMmS+mYVtt932BIADDzzw4u985zvvjBs37uQbb7zxV1OmTPnG9ddfP/r888+/aMyYMd2/ggoYO9AyatSoya2trftutNFGiw477LCLhg4dmtpnn30mv/766z9LP2CbbbaZkkqlPv29733vpBkzZuxx66237hJ/ix7x3HPPPT1jxoy3Hn300VK1mn0QewmOV0iljH15kfZbkud9zZo1o+miOCpXrgMt6dabrvL9iSeemAOw7bbbnvfpT3/6JuC9MuV7/crztyz4h+DAS9/epND77IByPTcVnevQfb6nVUzx1I0JwG3OueuAZmB8fIVd2lnAVd77FIBz7jLgVeAF4LFiBdXe3r6qsbFxRfdbrp+M5syW//znP9OzVj+avrF48eIbM5Z/5FTlsmXLrgCYMGFC5jZFlxl7Y2PjihUrVkwxswFtbW0XP/jgg+8R9Wkg83lctWrVZDPb7vrrrx8fQviFmT0JHALUtba2Ll20aFF7qZ/3UhyvkCo59gwlib2pqWlVd9uUI9cbGxtX5JHvrFy58rz07XLke7FOuyjXu1bJsWcoWey55DtUcPHkvb8o4/Y8YM8utr0g6/40oqs4+oyV528JsDz7XOX62Hb6upKe+zKzo4iunGkgumz5F8Ba4FozO4Go4/9oom8hw4jG+wI4GriB6NTuocB5iIiIFEnFFk/S94QQ7uPDAU7ho02nL8e/Z5MlhJAuoqbHPyJ9Vl/4oiRS6SptqAIRERGRslLLk5SdvkmLiEglUcuTiIiISB5UPImIiIjkQcWTiIiISB5UPImIiIjkQcWTiIiISB5UPImIiIjkQcWTiIiISB5UPImIiIjkQYNkFklNTQ11dXWjUqlU0Y81ePDgupVF2G8qlRpThN1+RKXH3tLSQn19/ehUKpXTZJK9RSXHDlBdXV2y2AcNGjRq2bJlna5Xruem0mOv1Hyp5NihtLkO3ed7moqnItliiy0A7irFsaqri/ZvfKZYO06r9NgHDx4MH52DryJUcuxASWNvaGigqzdT5XpuKj32Ss2XSo4dSpvr0H2+p6l4KpJFixZRV1c3saGhYV6xj9XW1lZHcV5cY7MXnHzyyaOfeuqpz7W0tGw2fPjwl99+++2dBgwYsOSUU065b9KkSYsA9tlnn2+88847n6qurn7/lltu+dWZZ57pFi9ePOLqq6++4aabbhp7yCGHvJretpSxF1pbW1tdS0vLrPr6+n2rq6sr6htdiWMv+Ifb0qVLKdXzvmLFilF0URwp15XrvZlyPT/d5XuahRBKEM6GpampaQzRi2jsyJEjny328ZqbmxtWnr/l8kLvt6v54cxsV2BfYCtgLXBZCGF5vG4SsAWwDrgE+AnwN2Al8MP49tQQQihH7IXS3NzcACwHNmlsbFxR7OMVUiljHz7t3IK/yfz94FOgRM97V/msXFeu93bK9fzkmtNqeZK8mZkBE4neRBeb2UjgB8ClACGEG+Lt9gfGA08TfTtcCiwAXgN2Al4sefAikjPlukjHdLWd5MXMqoCfAbeGEBbHi5cDNR1svhyoCSE8CtxM9G10OfA+0L8E4YpIDynXRTqnlifJ14XArkC9mTUDQ4AG4BIzOwGYB+xC1MTfAJwbP+5o4Aaib7GHAueVNGoRyZdyXaQTKp4kLyGEC7tYPT3+PbuDx12dsc307PUi0rso10U6p+JJ8lKMDoEzC71DEVlvynWRzqnPk4iIiEgeVDyJiIiI5EHFk4iIiEgeKqbPk3OuGjgL+A5R3M3AJO/9c8650cCvgeFEY4sc772f55yrAh4EdgPu9d6fHe9rd+BU7/3E0v8lIiIiUskqqeVpANAPGOu9/yRwNXCHc86A+4HLvfebEQ3edl/8mHFAAEYA+zvnhsfLpxCNhCsiIiKSl4opnrz3K7z3P/XeL4sXPQKMAnaO198f/54BmHNuZ6ARmOu9byca6Xa4c+4Q4HXvfVOp/wYRERGpfBVz2q4DnwWeBz4NzM9aNx/YEXgTmOic6xffXwhcSdRiNQuYA5zhvdcEfyIiIpKTiiyenHMDgKnA2cBmwOqsTVqBgd772c65JNFIuHcA+wGPA5OBPYFr42V/KkacVVVVdfGkjMVWX4ydlij2oijx817f3NxcgsMVVCXHnlaS2Kuqqura29u73Ua5Xh7K9W5VcuxpJYs9l3yHCiye4lak3wC3e+9/75w7gqg/VKYGormV8N6flPG4WcAE4GDv/Urn3Gyi6QcKXjz179+f+Hgfs2rVKi6//HIaGxs5/vjjmT59OuvWrePII49k8803B2Dx4sVceOGFHHLIIXzlK1/h5ptvpqmpiXPOOYe//OUvjB49mi233LLQYWcr+AzoJVTK2BeW8FiFpti7UVtbS2tra6fr1zfXf//73/P888/z1ltvceGFF/Kb3/xGuZ4f5XpuFHsOusv3tIoqnpxzNcADwJPe+/+JF88Fts3adBQfP5U3CbgTWJWxzChSv6/W1laqqqr2ra2tfSF73TnnnLPpa6+9tsXChQs/f91117WsW7duwLp164a8+uqrN99www3/ATjllFO2ampq2vavf/1r3Ve+8pUZU6dO/dEmm2wy56mnnlqVTCa/t/HGG8+ZPXv2NYMGDYLom0UxXlybdLCsUt5kO4q90NLP+3CgpQTHK6RSxl6s10xJnvc1a9aMppPiCNY/1w866CAOOuggxo4d+5XDDjts9XvvvfdZ5XpelOtdU67nobt8T6uY4ikequAe4Bnv/cXp5d77uc65VufceO/9A865rwEbAc9lPLaBqMVpHNAGDHHO1QF7AA8VK+b29vZVjY2NK7KXP/TQQyvMbB2wBhgJXAKsnTFjxtkPP/zw+QBPPPHEy2Y2DKhqbGxcsWrVqj+tWrVqLLAU+GcqlXpxxx13HBFCeLFYzZkdxV4pShF7xvPeUmnPVSXHnqEksTc1Na3qbpv1yXUAM6sF9gbOAN5XrudOud61So49Q8lizyXfoYKKJ+AbwOHAbs65b2UsP4eoMLrNOXcd0fhP4+Mr7NLOAq7y3qcAnHOXAa8CLwCPlSL4LqwC1gLvEw3F0KEQwqNm9gxwCFF1/z7QvyQRikghdJjrZrYxUVF1SQhhDaBcF+nlKqZ48t7fDdzdxSZ7dvHYC7LuTwOmFSi09fUr4BdEb6rXmtkJwLwQwsdmKweOBm4AJgKHAueVKkiR3sjMBgIXE52WeBb4ArAVMDWE8FK8zVHAXkQXklwGfBvYDvgx0QUjc0IIC0oQboe5DnwfqAHOMbOZIQSPcl2kV6uY4qmvid+sp8Z3T8xY9XLGNk9kPebq+Ob0+EdkQ1cH3AbsH0KYCcw0s88SnZJ/Kd7mQOC7wKeAI4EhwG+BzxC1Wm9tZlNDCEUZsiSHXP/YFyXlukjvpuJJRCpWCOEdM/vglJaZnQ18kahYSptGdFpsMdEFIk8D6T5FC4gG0N0JeLE0UYtIpVPxVAbDp51b8G+4Mwu9Q5EKFEK4wsyuIzqVd3q87GngaTM7HFgdQnisVH2KlOsifZOKJxHpE+K+TTsRjfP2YEaforXAV4k6aaevcFOfIhHpMRVPIlLRsvoU3ZexKnOslmezHqM+RSLSYyqeREREpGKY2X7AUUSn3a8LISws9VW1Kp5EpOKsPH9LgOXZ0wisj22nr7MC7k42AJ18iG9K9MFtwDXA9yjP0Bh92eHAmUSDXp8NTKHEV9WqeBKpIGY2lmiqoXZgWgjhL71oHCORDU1HH+InEuXiMKICqkcf4sr1Lt0IXEh0peym8bKSXlWr4kmksnwKeJBo4uuRwF8o8zhGIhuwjj7ERwE/ALYBjqHnH+LK9U6EEOYCZ5vZrsR1TKmvqlXxJFJZ/gTcSnTl2HHxMo1jJFIGHX2IE03DkyJqHapdj6m1lOudMLOtgROIrqydb2Z7UeKralU8iRSQmR0H7AxsDKRCCKeZ2SeAq4CHQggPmtlp9Lyp/VzgWKLTAecA55dzHCORDVknH+J3ELVIAaSnBuvJh7hyvRMhhDeJWvyyleyqWhVPRVJTU0NdXd2oVCpV7lB6LJVKjSl3DD1VitgHDx5c19LSQn19/ehUKrUKYO3atS8DL48bN+7rBx544LOpVGrMVVddtcWTTz759MqVK7dOpVJjtttuux1GjRr1cn19/eGPPfbYUY2NjS+vXr369pqamm6Pefjhh//rueeeuwlg8803bzrppJOO79+/f+qxxx77/DbbbFPz/PPPX5dKpcaMGTPmmw8//PADEydO3Prtt98+4aGHHrou8znpKPYNXWevmUGDBo1atmxZp49TrpdXuXIdYO3atQAzOnjIDfHvLVKp1BZr1659kg9bhF4Eun29KNcj1dXVJY29u3xPU/FUJFtssQXAXeWOYz09U+4A1kPRY6+urmbw4MHw0fGEWLduHWPHjuUnP/kJAJMmTWLHHXdk8eLFAEydOpVnn32WwYMHs+mmm/K5z31u/7lz507eeeeduz3mfffd1+HyK6+8Mn3zRIDZs2cDnPn444+nl38zl9g3cB2+ZhoaGujqzVS5XnZly/ViUq5Hlp29BcsKHPvWt7zf6bru8j1NxVORLFq0iLq6uokNDQ3zOlhdKW9UYztYVsmxF1RbW1tdS0vLrPr6+n2rq6s/+FbknDtw6NChy4B/pJfddtttY999991BX//61x8/+OCDef/99wdde+21+yxevHiLhQsXvjx06NCV11xzzcsdHadUsX/jG9/43OzZsw8eMGDAklNOOeW+SZMmLXrooYcGT548+cxddtll5iOPPPL4nnvuefTixYtHXH311TfcdNNNYw855JBXJ02atKibw1X0a2bFihWj6KI4Uq6XXUly/e3vDyx48bH1Le+X7X2qSCr+NdNdvqepeCqSVCrFsmXL5g0dOvTZ7rfunWpqahR7F959990GgKVLl77Q2Ni4Ir38D3/4ww9CCN9OTw8SQph95513NgDD0nGNHz9+MtGlxhNfffXVXYHzfvnLX67u7FjFmCPt7wef8pHYH3744S2JpzM59dRTn5o8efLyCRMmjACufuyxxwbW1NQ8+9xzzx0OTD/iiCPagT0fe+wxTj311P/tC1f3dPaa6e5bqHK9vEqZ64XWUeylyPUNXVevmVxanSDqrS8iBRRC+Hb8e3oIYXZ8+4kQwoMZ21wdQkjF20wOIXRaOJVKCGFGCOEM4FdEl1qnpz7JbON+GhgN7MBHr+4RkQpiZtVm9qiZ7RbfP8zMppjZXWa2mZmdZmY3mtkwMzvSzEaUOeReRcWTiGRbDnTYez2E8ChwM9HYMxvM1T0ifdApRMMhABBCeCSEcAHR2FKj+fgYUuPNTKPwx3TaTmQ99KVpQszsG8AYosuuk2a2V7rlLEvRxk4RkeIzswOAV4AtspbXAvsDZxA1rmxwY0jlSsWTiAAQQrgfuL+D5U9k3S/a2CkiUhKHAmuIiqG9gTlmtjHRAJyXhBDWAD0d3HODoNN2IiIiG5AQwmkhhHOBB/hwcM+biFqizjEzF296NHAP8DZRwfVSOeLtjdTyJBscMxsIXAwsDCFMjZftRTQFQno03z4/4WZfOuUo0hHletdCCNMz7n7sFL1amTunlifZENUBt2UtOxr4IVFn6MNRZ0mRvkC5LkWhlifZ4IQQ3jGzj527DyG0m9m/gS+xgU64KdKXKNcjamUuvD5TPDnnRgO/BoYTJcDxwHyiyy53A+713p8db7s7cKr3fmJ5opVeqN3MqoDNgX+vx0zoItK7KddlvfWJ03bOOSO6Suhy7/1mwKXAfcA4IAAjgP2dc8Pjh0wBflKGUKWXMbMT4j4QdwPXAJOAh+PV6iwp0kco16WQ+krL004A3vv7498znHNTiDr/zfXetzvnXgOGO+d2Al733jeVLVopu7hD6NSsxX/P2kadJUUqnHJdiqGvFE/bEJ2iyzQfWAvs7pzrB+wILASuBO5wzs0C5gBneO8rfl4uERERKY2+UjwNALLnBmsFFgNJoslO7yBqiXocmAzsCVwbL/sTRVBVVVXX3NxclAklS6Gvxb7HY9csL/RxZhZ6h/S9571SdBZ7VVVVXXt7e5ePVa6Xj3K9PPpq7LnkO4D1gcnQcc4dAZzovT8sY9kMok7i98T3+wGziC5Fvcd7/1nn3ElAnff+fwoZT1NT05j+/fs/U8h9ikj5tLa2AowdOXLkR2ZjV66L9D2d5XumvtLyNBfYNmvZKD56Km8ScCewKmOZUaRO862trVRVVe1bW1v7QjH2n6We6JTkcKClBMcrJMVeHoo9R2vWrBlN9MWrQ8r1nCn28lDseegu39P6RPHkvZ/rnGt1zo333j/gnPsasBHwHIBzroGoxWkc0AYMcc7VAXsADxUrrvb29lWNjY0rirX/tObm5vTNllIcr5AUe3ko9tw1NTWt6m4b5Xr3FHt5KPb85JLv0EeKp9gE4Dbn3HVAMzDee58+cXkWcJX3PgXgnLsMeBV4AXisHMGKiIhIZeoTfZ56m6ampjHAM8AxRKcUi6qqqqqutrZ21po1a/Ztb2/PqWruLRR7eSj2vGxPdMq/wz5PKNdzotjLQ7HnrdN8z6TiqQiampq2InojHVDuWESkIFYD248cOfKtzIXKdZE+qcN8z6TiqUjiN9Vh5Y5DRApicWdvpMp1kT6n03xPU/EkIiIikoc+MbediIiISKmoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIpMOdccM5tXO44RKS4lOsbrupyByDrxzn3BLA90Jqx+HPe+4XliahrcbyfARq992uz1j0PbO6937wMoaVjmAnM9N5fXK4Y1odzbgHwJe/9vIxl+wGXee/36uaxJ8SPnRDfHwD8CbjOe39HsWLOimEBBYq/r6m0XIfene/KdeX6+lDx1Dcc771/tNxB5KEZ+ArwUHqBc25XIFW2iKIYtgZqgaOcc5d478N67G59Hlt2zrmNgPuB/1eqN1PJSaXlOvTCfFeuf0i53jMqnqQcbgNOIOPNNL5/P3BGGeJJOxG4G/gC8HngCfjg29Ak4F3gMGAN8C3ga8CRQDsw2XvvM/b1fefcD4F+wO3ABev5Br3enHMjgHuBB4j+nlrgLu/9OR1sfgPwuvf+itJF2LU845feozfmu3L9Q8r1HlCfJymHR4A9nHOfAHDO1RB9M/1tuQJyzlUBE4jeUKcTvWFmOhLw3vutgCuBPwIve+8/CZwEXJO1/VbANsAOwEHA14sWfH72AKq89yOBnYCjnXO7Z27gnLsAGAScXvrwutVt/NLr9Kp8V65/SLnecyqe+obbnHML4p+/ljuYHKwFHgSOju8fBszko305Su0A4Fnv/VLgUWAf51xDxvp/ee8fi2/PJGqqn55xfyvnXGZL7nXe++C9XwncTPQ39gbtwFUA8d/6N2DbjPWfB74DNHzskb1Dd/H3dZWW69D78l25HlGurwcVT33D8d77EfHPZ8sdTI7STfkQNaFPL1skkW8DX3HOLQMWA8OBozLW/yfjdhuwLN00771vA4yPngZ/N+uxmxUh5o60d7N8ifc+s69JG1CTcf+fwHbAUOCUwofXrfWNv6+rxFyH3pXvyvWIcn09qHiSsvDe/wPo55w7GBjpvX+6XLE454YAuwMN3vtB3vtBwL58tDk/3z4Mn8i4vTnw7/UKMndvE30YZNoS+Fd8u7u/I+m9f5/otMb5zrmdChxfd9Y3fumFeku+K9c/Qrm+HlQ8STndRtTB8q4yxzEReNh7/8E3Ie/934Chzrnte7jPyc45c84NBL4L/AbAOXe2c66YnWTvAn7snBsWH284cBZwZz478d6/AZwK3FPicWwKEr/0Sr0h35XrWZTrPaPiScrpTmAw0RtqOX2LqPNotjv5eGfSXMwD3gReAl4Bfgekr84ZSfTtqlhuIeqI+5Rz7s342Fd473+X74689/cAfwemFjbELq1v/OOdc+9n/Py4aJFKvnpDvivXO6Bcz5+F0KtbxkRERER6FbU8iYiIiORBxZOIiIhIHlQ8iYiIiORBxZOIiIhIHlQ8iYiIiORBxZOIiIhIHlQ8iYiIiORBxZOIiIhIHlQ8iYjkwcxOMLM55Y5DRMpHxZOIVCwz62dmZ5rZM2a23MxWmtlcM5tS7thEpO+qLncAIiI9YWYDgCeAJcAPgOeBGmAboKFsgYlIn6eWJxGpVD8FVgCHhhBmhxDeDyG0hBCeDSE8AWBmVWZ2qZn9x8yWmNmdZjY4vQMzW2Bmh5nZE2bWamZvmtm3Mw9iZsfG27Wa2e+BRNb6/mZ2k5ktM7N/m9k1ZrZxxvp2M/u0mT1pZu+b2bhiPikiUnwqnkSkUh0PXBZCaO9im1OBzwJ7AJ8G1gE3ZG1zHXAhMBQ4A7jRzD4FYGY7EM3+fibwCeAq4PSsx18ObAJsB+wCjIr3l2bAPcAl8TH+mvNfKCK9koUQyh2DiEhezGwI0em6TUMIi+NlXwL+L2OzrYGngK+EEJ6Pt/kk8E9gQAghZWYLgFtDCFMy9v0acF4I4SEz+29geAjhqIz1NwB7hBB2M7M64N14m/fi9Z8HbgshjIjvB+D0EMIvivBUiEgZqM+TiFSifvHvAekFIYRHATOzgUALUE90iu05M8t+/BbAW/HteVnrWviwz9SngdlZ6+cStWQBjAT6A0uyjpH9rfTprv8cEakkOm0nIpXoP0RFzvZdbJN+f0uEECzr562M7Vq7OVb2acG1Gbctvl+Vtf/s99bV3RxDRCqIiicRqThxP6ffAOd0sdlyoBnYaz0O9Qbwmaxln864/U+iAmrX9TiGiFQYFU8iUqnOA7Y1s/vMbKyZ1ZpZf2DHjG0uB6aa2d7xVXEjzewbeRxjGnCkmX3VzOrM7AvACemVIYRVRB3ObzGzneJj7Ghmh63/nycivZWKJxGpSCGEZmB3oo7jvwFWAv8G7iS6um0ZcC3R1XL3ErVEzSKPVqIQwovAJOAa4D1gCvCjrM3OBf4E/CE+5m+BT/XojxKRiqCr7URERETyoJYnERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTyoeBIRERHJg4onERERkTz8fyeoK1p8JWvSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371939947189592)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_plot(gs_tall, p.aes('Gender', 'Fraction', fill='Scope'),\n",
    "    p.geom_bar(stat='identity', position='dodge'),\n",
    "    p.geom_text(p.aes(label='Fraction*100'), format_string='{:.1f}%', size=5, \n",
    "                position=p.position_dodge(width=1), va='bottom'),\n",
    "    p.facet_wrap('~DataSet', ncol=2),\n",
    "    p.scale_fill_brewer('qual', 'Dark2'),\n",
    "    p.scale_y_continuous(labels=lbl_pct),\n",
    "    p.ylab('% of Books or Ratings'),\n",
    "    legend_position='top', legend_title=p.element_blank(),\n",
    "    file='link-stats.pdf', width=7, height=4.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.041997,
     "end_time": "2020-09-17T22:28:41.598945",
     "exception": false,
     "start_time": "2020-09-17T22:28:41.556948",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Known-gender books:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:41.693943Z",
     "iopub.status.busy": "2020-09-17T22:28:41.686944Z",
     "iopub.status.idle": "2020-09-17T22:28:41.700954Z",
     "shell.execute_reply": "2020-09-17T22:28:41.700954Z"
    },
    "papermill": {
     "duration": 0.060009,
     "end_time": "2020-09-17T22:28:41.700954",
     "exception": false,
     "start_time": "2020-09-17T22:28:41.640945",
     "status": "completed"
    },
    "tags": []
   },
   "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>gender_status</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LOC</th>\n",
       "      <td>0.767701</td>\n",
       "      <td>0.232299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.690375</td>\n",
       "      <td>0.309625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <td>0.590983</td>\n",
       "      <td>0.409017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <td>0.612701</td>\n",
       "      <td>0.387299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender_status      male    female\n",
       "DataSet                          \n",
       "LOC            0.767701  0.232299\n",
       "AZ             0.690375  0.309625\n",
       "BX-I           0.590983  0.409017\n",
       "GR-I           0.612701  0.387299"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_bc = book_counts[['male', 'female']]\n",
    "k_bf = k_bc.divide(k_bc.sum(axis=1), axis=0)\n",
    "k_bf = k_bf.loc[['LOC', 'AZ', 'BX-I', 'GR-I']]\n",
    "k_bf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:41.788941Z",
     "iopub.status.busy": "2020-09-17T22:28:41.787942Z",
     "iopub.status.idle": "2020-09-17T22:28:41.796942Z",
     "shell.execute_reply": "2020-09-17T22:28:41.795942Z"
    },
    "papermill": {
     "duration": 0.054999,
     "end_time": "2020-09-17T22:28:41.796942",
     "exception": false,
     "start_time": "2020-09-17T22:28:41.741943",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrr}\n",
      "\\toprule\n",
      "gender\\_status &  male &  female \\\\\n",
      "DataSet &       &         \\\\\n",
      "\\midrule\n",
      "LOC     & 76.8\\% &   23.2\\% \\\\\n",
      "AZ      & 69.0\\% &   31.0\\% \\\\\n",
      "BX-I    & 59.1\\% &   40.9\\% \\\\\n",
      "GR-I    & 61.3\\% &   38.7\\% \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print((k_bf * 100).to_latex(float_format='%.1f%%'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:41.885940Z",
     "iopub.status.busy": "2020-09-17T22:28:41.882940Z",
     "iopub.status.idle": "2020-09-17T22:28:41.888941Z",
     "shell.execute_reply": "2020-09-17T22:28:41.889951Z"
    },
    "papermill": {
     "duration": 0.051007,
     "end_time": "2020-09-17T22:28:41.889951",
     "exception": false,
     "start_time": "2020-09-17T22:28:41.838944",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "k_bf.columns = k_bf.columns.astype('str')\n",
    "k_bft = k_bf.reset_index().melt(id_vars='DataSet', var_name='gender')\n",
    "k_bft['gender'] = k_bft.gender.astype('category').cat.reorder_categories(['male', 'female'])\n",
    "k_bft['DataSet'] = k_bft.DataSet.astype('category').cat.reorder_categories(['LOC', 'AZ', 'BX-I', 'GR-I'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:42.005965Z",
     "iopub.status.busy": "2020-09-17T22:28:41.994938Z",
     "iopub.status.idle": "2020-09-17T22:28:42.577955Z",
     "shell.execute_reply": "2020-09-17T22:28:42.577955Z"
    },
    "papermill": {
     "duration": 0.637015,
     "end_time": "2020-09-17T22:28:42.577955",
     "exception": false,
     "start_time": "2020-09-17T22:28:41.940940",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Research\\book-rec-fairness\\bookgender\\nbutils.py:39: UserWarning: file has suffix, ignoring\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 4 x 2.5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\frac-known-books.pdf\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 4 x 2.5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\frac-known-books.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371939936570724)>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_plot(k_bft, p.aes('DataSet', 'value', fill='gender'),\n",
    "          p.geom_bar(stat='identity'),\n",
    "          p.scale_fill_brewer('qual', 'Dark2'),\n",
    "          p.labs(x='Data Set', y='% of Books', fill='Gender'),\n",
    "          p.scale_y_continuous(labels=lbl_pct),\n",
    "          file='frac-known-books.pdf', width=4, height=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.047975,
     "end_time": "2020-09-17T22:28:42.679930",
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     "start_time": "2020-09-17T22:28:42.631955",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And do that again for ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:42.774959Z",
     "iopub.status.busy": "2020-09-17T22:28:42.773957Z",
     "iopub.status.idle": "2020-09-17T22:28:42.783933Z",
     "shell.execute_reply": "2020-09-17T22:28:42.783933Z"
    },
    "papermill": {
     "duration": 0.061005,
     "end_time": "2020-09-17T22:28:42.783933",
     "exception": false,
     "start_time": "2020-09-17T22:28:42.722928",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "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>gender_status</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DataSet</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>0.603181</td>\n",
       "      <td>0.396819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-E</th>\n",
       "      <td>0.571364</td>\n",
       "      <td>0.428636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BX-I</th>\n",
       "      <td>0.544148</td>\n",
       "      <td>0.455852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-E</th>\n",
       "      <td>0.509770</td>\n",
       "      <td>0.490230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GR-I</th>\n",
       "      <td>0.490939</td>\n",
       "      <td>0.509061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender_status      male    female\n",
       "DataSet                          \n",
       "AZ             0.603181  0.396819\n",
       "BX-E           0.571364  0.428636\n",
       "BX-I           0.544148  0.455852\n",
       "GR-E           0.509770  0.490230\n",
       "GR-I           0.490939  0.509061"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_counts = full_stats['Ratings'].unstack()\n",
    "k_rc = rate_counts[['male', 'female']]\n",
    "k_rf = k_rc.divide(k_rc.sum(axis=1), axis=0)\n",
    "k_rf = k_rf.loc[datasets]\n",
    "k_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:42.872922Z",
     "iopub.status.busy": "2020-09-17T22:28:42.868923Z",
     "iopub.status.idle": "2020-09-17T22:28:42.893922Z",
     "shell.execute_reply": "2020-09-17T22:28:42.894922Z"
    },
    "papermill": {
     "duration": 0.069967,
     "end_time": "2020-09-17T22:28:42.894922",
     "exception": false,
     "start_time": "2020-09-17T22:28:42.824955",
     "status": "completed"
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "{} & \\multicolumn{2}{l}{Books} & \\multicolumn{2}{l}{Ratings} \\\\\n",
      "gender\\_status & female &  male &  female &  male \\\\\n",
      "DataSet &        &       &         &       \\\\\n",
      "\\midrule\n",
      "LOC     &  23.2\\% & 76.8\\% &   23.2\\% & 76.8\\% \\\\\n",
      "AZ      &  31.0\\% & 69.0\\% &   39.7\\% & 60.3\\% \\\\\n",
      "BX-E    &  40.5\\% & 59.5\\% &   42.9\\% & 57.1\\% \\\\\n",
      "BX-I    &  40.9\\% & 59.1\\% &   45.6\\% & 54.4\\% \\\\\n",
      "GR-E    &  38.8\\% & 61.2\\% &   49.0\\% & 51.0\\% \\\\\n",
      "GR-I    &  38.7\\% & 61.3\\% &   50.9\\% & 49.1\\% \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "all_cts = full_stats.reorder_levels([1,0]).loc[['male', 'female']].reorder_levels([1,0]).unstack()\n",
    "all_cts.sort_index(axis=1, inplace=True)\n",
    "print(all_cts.divide(all_cts.sum(axis=1, level=0), axis=0, level=0).to_latex(float_format=lambda f: '{:.1f}%'.format(f*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:43.013954Z",
     "iopub.status.busy": "2020-09-17T22:28:43.007921Z",
     "iopub.status.idle": "2020-09-17T22:28:43.019925Z",
     "shell.execute_reply": "2020-09-17T22:28:43.019925Z"
    },
    "papermill": {
     "duration": 0.063003,
     "end_time": "2020-09-17T22:28:43.019925",
     "exception": false,
     "start_time": "2020-09-17T22:28:42.956922",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "k_rf.columns = k_rf.columns.astype('str')\n",
    "k_rft = k_rf.reset_index().melt(id_vars='DataSet', var_name='gender')\n",
    "k_rft['gender'] = k_rft.gender.astype('category').cat.reorder_categories(['male', 'female'])\n",
    "k_rft['DataSet'] = k_rft.DataSet.astype('category').cat.reorder_categories(datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:43.114922Z",
     "iopub.status.busy": "2020-09-17T22:28:43.113920Z",
     "iopub.status.idle": "2020-09-17T22:28:43.781907Z",
     "shell.execute_reply": "2020-09-17T22:28:43.781907Z"
    },
    "papermill": {
     "duration": 0.717984,
     "end_time": "2020-09-17T22:28:43.781907",
     "exception": false,
     "start_time": "2020-09-17T22:28:43.063923",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Research\\book-rec-fairness\\bookgender\\nbutils.py:39: UserWarning: file has suffix, ignoring\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 4 x 2.5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\frac-known-rates.pdf\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 4 x 2.5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\frac-known-rates.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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NpZQnAUTE04FPt5sPA54aEVMnKn8w5fHUScqp13ct4+cnO7855fFhwN7Axr5VGJsd2PfzTTtDEIbBvnTjfwOX0rxJ66bsLsA+Q6hLkiSpZj+iuVjuscA3tjN2T+D3SimXbWfcPdN43jLl8U+38Fw3llKWbec8UzPinDXIBXRvB15fStmjlPLgKX8MwpIkSTuolNKjmXw8vf0/8tuygmaJwo66iSZ895saelcAB0XEfkN4vjlhkDB8EPD+YRciSZKk+3kdsBH4YkQ8s72Lwy7tHSAe2TfurcCLIuKlEbFXRExExC9HxLEzfL53ASdHxG9FxB4R8SjgnVPGfBa4DrgsIg5vv5TjFyLihRGx24Cvs1ODhOFv06wXliRJ0oiUUu4CfoXmXsNvAG6jWbZwC30Xx5VSrgSOA04EVtGsFX4/zdremTzfpTTB+j009zD+O5q7VNzeN6YHPJ1m/fFVwFqai/zSAC9xThj01moXRcTZwI1MuVVGKeX6YRQmSZJUu1LKeuDP2z/bGnclcOU29v/c1W6llPcB75uy7e00S2L7XTplzGqa27G9dCvP9Qaa8L5TGCQMXw0EcMkW9hVglx2qSJIkSZolg9xn2LArSZKkeWGQNcOSJEnSvDDtmeGIeHkp5V0R8fptjSulvGnHy5IkSZJGbybLJA5q/37MKAqRJEmSZtu0w3Ap5bT27+NGVo0kSVLl9r/gVVO/BW4obn/hWT93RwkNsGY4Iv59K9t3j4h/2/GSJEmSpNkxyAV0j97K9j2Bw3agFkmSJGlWzeQCuo/R3Ee4RMTlU3aPAUcCnxlibZIkSdJIzeQCulcC/wv4bZqvZO53H3AFzdcFSpIkSTuFmVxAdyvwnogYK6WcN7qSJEmStLOJiALsXkq5t+taZmKQb6A7DyAi9gF228L+24ZQlyRJkjRyMw7DEfEbwMXAgzZvol1LDEwCjxxadZIkSdIIDXI3iXcCf04ThgOYoLl47nPAHw6vNEmSJI1aRPwwIl4UEd+JiB9ExMkR8fyIuCkiVkXEG/vGHh0R10TErRHx3Yj4na2cc2lE/ENE3BIRX4mII2btBc3QIGH4IOC9pZSfAD8FNpRSrgNOA943xNokSZI0evsCB5dSHg48FfhL4CnAMuAI4JSIOLAdexvwvFLKAcBLga1dR3Yp8MlSyoHAqcBHI2LGKxJmwyBF3U7TtNuAVTTLIr7VPj5wG8eNVErpn4CHA5vaTVfmnE9JKR0OfBjYH7gVeEHOeWVKaQz4OPAE4LKc8xnteY4ETss5nzDLL0GSJKkr5wOUUm6IiDuBD5VSesAdEXE9zWToLaWU/+g75vPAQyJi11LKTzdvjIj9gENKKR9oz/mliLgXOBT45iy9nmkbJAx/HHgcTfi9Ejg/It4F/BrwjeGVNmP7AEfknO/cvCGlFMDlwJk558tTSs8EPgocDhxNs875AOCrKaX9c863A28GTp7t4iVJkjp0R9/Pm4D/mvJ4IUBEPIdmpne/vv1TVxrsDzwoIm7t27Yn8OBhFTtMg9xN4jV9D18DnAO8jubiuT8YTlkDeSDw4ynbHgOQc768/ftTKaU3p5QOA5YCN+SceymlG4H9U0qPAW7OOU/OZuGSJE31+X8+u+sS5qcXntV1BXNV2d6AiHgYzQzysaWU6yJiHNjSbdRWAbeXUg4eco0jsUNrN0op6+i7aC4idt/higbQzgDvCXwzpbSYZtnGGcAjgJumDL8JeBRwC3BCSmnX9vHtwNnAxSml5cC1wOk55+3+ckiSJFVgMbAGuD4igq38n/RSyvci4raIOKmU8mGAiDiolPLdWax12mYUhiPi8TTLIe4DLu+/p3BEPAN4Fx2sG845l5TSfsBaYBfgRODTwGuBe6YMXw/smXO+JqX0fWAlza3ijgGuppn6fyJwbrvtC6OoeWxsbNGqVasmRnFuzYzvQ3fsfXfsfbfsf3cG7f3Y2NiiXq837HJ2KqWUb0VEplkNcCfwHuDmrQx/HnBuRJxJk80maS7Km3OmHYYj4reBS2huobYQeHVEHA3cTRMcfwU4cxRFTkfOeU374ybggymlU2mWQuwxZegETc3knF8C0M4OLweOB56Wc747pXQN8FhGEIYXLFjAwoULlw/7vBrYXV0XUDF73x173y37352Bej8+Ps769euHXcucUEqJKY8PmPL4mL6fTwFO6dt9/pbOU0r5AfC7Qy51JGYyM3wG8OJSykcAIuJE4O9oAudlwLJSytQ1u13aBfh74KQp25fx80snXkYT9Nf1bQsGu/Xcdm3atIler/fk8fHxFTM81A/P0XjANMbY+9GYTu/B/o+Cve+Wnzvdme7v/v1s2LDhcJqJM80zMwnDh9CEy80+QXPLsieWUr461KpmKKX0UGCvnPOK9vFJNPdAXgmsTykdl3P+WErpWTQh+et9x07QzAgfTTOrvFdKaRFwFM1rHIler7du6dKla7Y/UqPm+9Ade98de9+t6fR/6qyNhmPQ3/3Jycl12x+lndFMwvADSik/+wUqpayJiNJ1EG6NA+9vQ/G9NLd4e2bO+b6U0vHARSml82iubjwu59y/6OeVwDk5540AKaWzgOuBFcBVs/gaJEmSNMtmEoYjIl5Ks3ygf9vL+geVUt4zlMpmIOf8HZqL3ra0b+XW9rX7z5zy+ALggqEWKEk7KW/vNSLe3kuaM2YShpcDz5my7Z+B4/oeF5orCyVJkqQ5b9phuP9KQkmSJGk+2KEv3ZAkSdJw3f7Cs2L7ozQsI7l1mCRJkrQzMAxLkiSpWtNaJhER5wL/t5TyXxEx0X+LNUmSJA3PTSfuUkZx3kMuvM/lF1sw3ZnhFwD3tD//ZES1SJIkSbNquhfQ3Qr8SUT8A829hQ/l/vcb/plSyvVDqk2SJEkaqemG4T8C3gu8geZewt/eyrhC83XHkiRJ0pw3rTBcSrkGeCxARNxUSjlkpFVJkiRJs2CQ+ww/b+hVSNI2+JXAI+DXAUtqRcQDgcuBQ4CPlFJePeLnK8DupZR7R/k80zXjMFxKuTYi9gReCDyKZmnESuAC7zIhSZK003kxcHsp5dcjorrlrjO+z3BEPBb4LvA7wDpgPfBs4DvtPkmSJO08DgGuBSil3NdxLbNukC/d+GvgraWUY0opp5dS/rSUcgzwVuBdQ61OkiRJIxMRbwaOA94UEbdGxEREXBoRt0TENyPi2L6xP4yIF0XEdyLiBxFxckQ8PyJuiohVEfHGvrFHR8Q17Tm/GxG/s5XnXxoR/9A+31ci4oiRv+gpBgnDTwA+vIXtFwCP37FyJEmSNFtKKWcCnwReW0o5gGbS89ZSyoHAs4ALI2JJO3xf4OBSysOBpwJ/CTwFWAYcAZwSEQe2Y28Dntee86XAeVsp4VLgk+3znQp8NCIGuaZtYIOE4Z8AD93C9ocBP96xciRJktSFiBijmSV+K0ApZRK4Dji6b9j57b4bgDuBD5VSeqWUO4DrgYPa/f9RSvlue8zngYdExK5Tnm8/4JBSygfaY74E3AscOppXuGWDJO8PA38bEa8A/r3d9jjg7Ww99UuSJGluezCwO3B9xM++W20P4Iq+MXf0/bwJ+K8pjxcCRMRzaGZ69+vbP3USdn/gQRFxa9+2Pds6Zs0gYfj1wF3ARcBDaO4msQp4VynF+x9JkiTtnO4E7gEeXkrZtJUxZXsniYiH0cwgH1tKuS4ixmlmfKdaRXMXi4MHLXgYZrxMojTeUUpZCjwA2LeU8lCDsCRJ0s6rvZPE3wFntksmiIj/ERG7zfBUi4E1NDPMAZy8lef7HnBbRJy0eVtEHDRQ8TtghxYol1LWAmuHVIs0Z/mlDyPiFz9I0lzzcpoL477TBuIfAL/Flmd2t6iU8q2IyMAkzWzze4CbtzL8ecC5EXEmsEt7zFMGL3/mZvVqPUmSJM0tpZQT+35eA/zhVsbFlMcHTHl8TN/PpwCn9O0+f0vnKaX8APjdgQofkkHuJiFJkiTNC4ZhSZIkVWugZRLtVYEn0dx3bi9gNfAV4APtOmJJkiRpzht0zfBFwErgbJqrBR8I/B7wCeDXh1OaJEmSNFrTDsMR8XngFaWU64DDgZeUUu7q238HcO3wS5QkSZJGYyYzw2cBH4qIbwMXAF+MiJuAu2lmhh8J/OnwS5QkSZJGY9phuJTy2Yj4R+BE4LXAlcDl7e67gJu28W0lkiRJmoZDLrwvtj9KwzKju0m03z53AfBompsoXwochUFYkiRJO6Fph+GI2Csi3hgRnwQuA3YHngkcAnwzIjq9YbIkSZI0UzNZM3wF8HngNe3j5wDvK6X8akQ8EjgrIk4vpfzysItUw68EHhG/EliSpGrNJAw/GvjdUsqPACLivbRfs1dKuRF4dkQ8afglSpIkSaMxkzD8VuDfIuJaIIAjgNf1DyilfHF4pUmSJEmjNZO7SbwzIj4EPBwoNBfN3T2yyiRJkqQRm9E30LVfsvG1EdUiSZIkzaoZ3VpNkiRJmk9mNDOs4RgbG2NiYmLZxo0buy5FwMaNGx/XdQ21svfdsffdsv/dGbT3S5YsWbZ69eohV6O5wDDcgcWLFzMxMXFp13XoZ1z60x173x173y37352Bej8xMYFheH4yDHdg7dq1RMQJExMTK2d4qB+eo/H4aYyx96Mxnd6D/R8Fe98tP3e6M93f/ftZs2bNMppv3tU8YxjuQK/XY/Xq1Sv33nvv67quRbBw4ULfh47Y++7Y+27Z/+4M2ntnhecvL6CTJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmq1oKuCxiGlNIC4JXAi2le0yrgZTnnr6eU/gl4OLCpHX5lzvmUlNIY8HHgCcBlOecz2nMdCZyWcz5hll+GJEmSZtm8CMPAHsCuwONzzqtTSscDFwOPBvYBjsg53znlmKOBAhwAfDWltH/O+XbgzcDJs1a5JEmSOjMvwnDOeQ3wxr5NVwKXpJQCeCDw4y0cthS4IefcSyndCOyfUnoMcHPOeXLkRUuSJKlz8yIMb8EvA99of94T+GZKaTHwLeCMnPO3gVuAE1JKuwKPAm4HzgYuTiktB64FTs85l9kuXpIkSbNj3oXhlNIewDtoQm9JKe0HrAV2AU4EPp1SemTO+ZqU0veBlTRLKo4BrgZOBZ4InNtu+8Io6hwbG1u0atWqiVGcWzPj+9Ade98de98t+9+dQXs/Nja2qNfrDbsczQHzKgy3s7xXAH+Tc/4s/GwJBTQX0H0wpXQqcBjw5ZzzS/qOWw4cDzwt53x3Suka4LGMIAwvWLCAhQsXLh/2eTWwu7ouoGL2vjv2vlv2vzsD9X58fJz169cPuxbNAfMmDKeUFgIfA/4l5/zObQzdBbh7yraXAZcA6/q2BSO69dymTZvo9XpPHh8fXzHDQ/3wHI0HTGOMvR+N6fQe7P8o2Ptu+bnTnen+7t/Phg0bDqeZONM8My/CcHtrtb8FvpZzfkvf9ocCe+WcV7SPTwJ+SrM0YvOYCZoZ4aNpZo/3SiktAo4CPjGqmnu93rqlS5eu2f7I/3bTqIqp3HTeB3s/GtP9Z8D+D5+975afO92Z6b97N5ucnFy3/VHaGc2LMAw8B3g28IQ28G72XuC321B8L81Fdc/MOd/XN+aVwDk5540AKaWzgOuBFcBVs1C7JEmSOjIvwnDO+SPAR7ay+y+2c+yZUx5fAFwwpNIkSZI0h/l1zJIkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVa0HXBdRobGyMiYmJZRs3buy6FAEbN258XNc11Mred8fed8v+d2fQ3i9ZsmTZ6tWrh1yN5gLDcAcWL17MxMTEpV3XoZ/5WtcFVMzed8fed8v+d2eg3k9MTGAYnp8Mwx1Yu3YtEXHCxMTEyhke6ofnaDx+GmPs/WhMp/dg/0fB3nfLz53uTPd3/37WrFmzDHAiax4yDHeg1+uxevXqlXvvvfd1XdciWLhwoe9DR+x9d+x9t+x/dwbtvbPC85cX0EmSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFXLMCxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFVrQdcFzIaU0uHAh4H9gVuBFwA3AR8HngBclnM+ox17JHBazvmEbqqVJEnSbJn3M8MppQAuB/4i57wv8Dbgo8DRQAEOAI5NKe3fHvJm4PUdlCpJkqRZNu/DMPAYgJzz5e3fnwICOAa4IefcA24E9k8p/SZwc855sqNaJUmSNItqWCbxCJolEf1uAn4KHJlS2hV4FHA7cDZwcUppOXAtcHrOucxmsZIkSZo9NYThPYB7pmxbD9wJfB9YCVxMM1N8NXAq8ETg3HbbF0ZR1NjY2KJVq1ZNjOLcmhnfh+7Y++7Y+27Z/+4M2vuxsbFFvV5v2OVoDqghDK+jCcT9JoC7c84vAWhnh5cDxwNPyznfnVK6BngsIwjDCxYsYOHChctnetyeb/vesEtR467tDbD3I7Pd3oP9HxF73y0/d7ozrd/9qcbHx1m/fv2wa9EcUEMYvgE4ZMq2Zdx/6cTLgEtogvNmwYjWVG/atIler/fk8fHxFaM4/xywmGbZyf7A2o5rqY2975b97469704Vvd+wYcPhNBNnmmfmfRjOOd+QUlqfUjou5/yxlNKzgF2ArwOklCZoZoSPBjYBe6WUFgFHAZ8YVV29Xm/d0qVL14zq/F1atWrV5h/XztfXOFfZ+27Z/+7Y++7U0vvJycl12x+lndG8D8Ot44GLUkrnAauA49q7SAC8Ejgn57wRIKV0FnA9sAK4qotiJUmSNDuqCMM555U0F8Vtad+ZUx5fAFwwG3VJkiSpWzXcZ1iSJEnaIsOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStRZ0XUDFDp2cnOy6hpEYGxtbND4+zoYNGw6fnJxc13U9NbH33bL/3bH33amo94d2XYBGI0opXddQlcnJyV8AbgD26LoWSZI0I/cAhx588MG3dV2Ihscw3IE2ED+o6zokSdKM3GkQnn8Mw5IkSaqWF9BJkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWJIkSdUyDEuSJKlahmFJkiRVyzAsSZKkahmGJUmSVC3DsCRJkqplGJYkSVK1DMOSJEmqlmFYkiRJ1TIMS5IkqVqGYUmSJFVrQdcFaOeXUvo88Pmc81v6tp0FHN83bAGwH7B/zvn7s1ziTi+l9E/AocA97aabgVcAbwSuzjmf145bAFwLnJxz/tcp5zgAmAS+17f5mpxz//ukrRjie3BNzvkhs1T2Ti+ldCTwWuDxQA/YCFyUc35zSulWYGG77T7g/wGn55x/uoXznAi8G/hR3+Zzcs7njLL++WDI78HT/czRXOPMsHZISulAYBx4bkopNm/POb8q53zA5j/Ah4DLDcI75AU55wOBg4BPARcDpwBnpJT2bcecRhO2/nXLp+BH/e+L/1KasWG8B5qmlNKxwMeADwIH5pwfBhzZbtvs2PYz5jDgAODUbZzyyim//+eMpPB5ZATvgTTnGIa1o14IfAS4EfifWxqQUloGvAQ/IIci51yAy4CDc863A2cDZ6eUHgr8EfBnXdZXA9+DWXMe8JKc89/nnDcB5Jx/knNeOXVgznkd8Blg2SzXON/5HmjeMwxrYCmlMZqlEB8BLgRO2sKYAM4HXp9z/uGsFjhPpZR2Af4AyO2mdwOPbB+/Jud8V1e11cL3YPRSSgcDD845XzXN8fvSfB59bqSFVcT3QLVwzbB2xK8B1+Wcf5JS+gzw1ymliZzzmr4xL6L5j64PdFLh/HJRSmk9sA/NTPyzAHLOvZTSp2jW9F29nXM8uF3jt9m5Oee/HEGt89Uw3gNNz77A7f0bUkrfofn31pKc85J289Uppftorkn4/Zzz327jnM+Y8vt/gstZtmkU74E05xiGtSNeBPzvlNLq9vHuwHNpg29KaR/gLcBT2v+trB3zgpzzZ9rZ9qcC/5xSOhzYg2ZW/jzgLJolKaSULgN+qT32f9Fc+PWjdm2fBjOM90DTsxpY2r8h5/zwlNJuwPq+zcfmnFemlK4Gdtu8sS/0rsk5H9b+fKXr5GdkNcN/D6Q5xzCsgaSU9qK5iGIi59xrtz0ROIf/ngU+B3h/zvmGLmqcr9r/sLg6pXQn8BiaC7jeBlwCrEgp/VLO+efuEtHeyUBD4HswK1YC96WUnpxzXj6N8a8FLk8pfSTnfK//0TcUvgeqgmuGNagTgE9uDsIAOecvA3unlA5NKT2N5jY8b+2qwPms/Q+PA2j+t+RDgQvaWxmdBry3XdOqEfI9GK32s+XVNEtTjm2vUQBYtJXx1wAraP7DREPge6BaODOsQZ0E/OEWtl/S7juCZr3ZjSml/v3Htx+YmrnN61UDuBN4PvDXwHM3L0PJOV+VUnop8HLgr7Zwjqlrhr3P8MwM4z3QNOWcL0wprQX+HHhE+zPAFVs55EzgcymlD+ScV29h/9Q1w95neDtG8B5Ic06U4lJOSZIk1cllEpIkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqpZhWNJOLyIujIjS/tkUEbdHxAURcdAMz/OmiPitHajjIRFxSUT8Z0TcExE3RcTrZnD81wZ9bknSYAzDkuaLvyylBPAA4Bnttmsj4tHTOTgiAviNHazhEqAHHAY8CDge+NI0n/8XgYft4PNLkmbIMCxpXimlrCulfL2U8kLgY8B7N++LiEdExBXtzO1dEZEj4kHt7q8ATwSubGeYX9Ee8/yI+FpE3N3OOL9yG0//S8B7Syn/WUq5p5RyXSnl6r7nPzAiroqI9RHxnYh4Sbv9l4AvA3v3zXA/aCvPIUkaIsOwpPns7cCTImLf9vEa4ALgkcAjgCXAqwBKKUcC/wE8o5QSpZR3tMfcBrwIeDDNTO9btjHb/A3gDyJil6k72pnnK4B/BPYBfgd4bUQ8rZRyDc1s9o/b545Syp079MolSdNiGJY0n90C/BQ4AKCdsc2llNWllDtowuk2l1GUUpaXUr5RSllfSvkisHIbx5wEPAm4MSL+bMrs7rHt+d5RSllbSlkBfAj4vR14fZKkHWQYljRvlVJ6wH3tHyLiqIj4dETcERE94J3Abts6R0S8PCK+2S6TKDTrgbd4TCnlJuBw4E9oQvEtEXFcu/sXgcP7lkEU4A3AL+zwC5UkDcwwLGneioiH0wTXyYjYE/gMcC1NYF0AvHw7x58AnAmcDvyP9gK967Z1TCmlV0q5spTyDOCPgQ9FxAIggH/sWwax+c9Td/BlSpJ2gGFY0nx2BvDZUspPgGXA7sDrSyk/aGeNHzFl/H3c/3Pxl4FPlFI+W0pZ24baA2bw/J8DFgN7AtfTzAxvbSZ66nNLkmaBH7yS5pWI2C0iHh8RFwEJOLnddRuwC/BrEbEoIp4FHDfl8FuAp0fEgojYHZgEjoqIfSNiH+A8YOFWnndJRDwvIh4aEbtGxH7Am4AvllJW0wTj7wHnR8R+EbE4In41Io5qT3ErsCQifqU9/ucuwpMkDZ9hWNJ8cXq7Dnc1cBlwF/DYUsokQHvB3MuAi4AfAS8AXjzlHK8BngKsbfedD9xME4r/nWZ296Nbef4x4IU0yyjuprlV2n00d42glFKAZwKLaC7C+yHwHppZY0op36NZQ/z3wA9o7jghSRqxaD6fJUmSpPo4MyxJkqRqGYYlSZJULcOwJEmSqmUYliRJUrUMw5IkSaqWYViSJEnVMgxLkiSpWoZhSZIkVcswLEmSpGoZhiVJklQtw7AkSZKqZRiWJElStQzDkiRJqtb/B5Irbh/zXJ6sAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371939882777772)>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_plot(k_rft, p.aes('DataSet', 'value', fill='gender'),\n",
    "          p.geom_bar(stat='identity'),\n",
    "          p.scale_fill_brewer('qual', 'Dark2'),\n",
    "          p.scale_y_continuous(labels=lbl_pct),\n",
    "          p.labs(x='Data Set', y='% of Ratings', fill='Gender'),\n",
    "          file='frac-known-rates.pdf', width=4, height=2.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045998,
     "end_time": "2020-09-17T22:28:43.870936",
     "exception": false,
     "start_time": "2020-09-17T22:28:43.824938",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Popularity and Gender Distributions\n",
    "\n",
    "We now want to look at popularity and assorted distributions.\n",
    "\n",
    "We will start by computing item statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:43.981904Z",
     "iopub.status.busy": "2020-09-17T22:28:43.981904Z",
     "iopub.status.idle": "2020-09-17T22:28:58.574683Z",
     "shell.execute_reply": "2020-09-17T22:28:58.573651Z"
    },
    "papermill": {
     "duration": 14.650778,
     "end_time": "2020-09-17T22:28:58.574683",
     "exception": false,
     "start_time": "2020-09-17T22:28:43.923905",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing  AZ\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing  BX-I\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "summarizing  GR-I\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item</th>\n",
       "      <th>nratings</th>\n",
       "      <th>gender_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>PopRank</th>\n",
       "      <th>PopQ</th>\n",
       "      <th>Set</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>0.652857</td>\n",
       "      <td>65</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>65</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>0.977629</td>\n",
       "      <td>98</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>89</td>\n",
       "      <td>no-loc-author</td>\n",
       "      <td>unlinked</td>\n",
       "      <td>0.984913</td>\n",
       "      <td>98</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>0.911341</td>\n",
       "      <td>91</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>0.948883</td>\n",
       "      <td>95</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item  nratings  gender_status    gender   PopRank  PopQ Set\n",
       "0     0         4           male      male  0.652857    65  AZ\n",
       "1     2        65           male      male  0.977629    98  AZ\n",
       "2     5        89  no-loc-author  unlinked  0.984913    98  AZ\n",
       "3     8        19           male      male  0.911341    91  AZ\n",
       "4    11        32           male      male  0.948883    95  AZ"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def _ds_stats(ds, df):\n",
    "    eprint('summarizing ', ds)\n",
    "    stats = df.groupby('item').user.count().reset_index(name='nratings')\n",
    "    stats = stats.join(book_gender, on='item')\n",
    "    stats['PopRank'] = stats['nratings'].rank()\n",
    "    stats['PopRank'] = stats['PopRank'] / stats['PopRank'].max()\n",
    "    stats['PopQ'] = (stats['PopRank'] * 100).round().astype('i4')\n",
    "    stats['Set'] = ds\n",
    "    return stats\n",
    "item_stats = pd.concat(_ds_stats(ds, df) for (ds, df) in ratings.items() if not ds.endswith('-E'))\n",
    "item_stats['Set'] = item_stats['Set'].astype('category')\n",
    "item_stats.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.04598,
     "end_time": "2020-09-17T22:28:58.664649",
     "exception": false,
     "start_time": "2020-09-17T22:28:58.618669",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Compute rating count histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:28:58.754648Z",
     "iopub.status.busy": "2020-09-17T22:28:58.754648Z",
     "iopub.status.idle": "2020-09-17T22:28:59.986627Z",
     "shell.execute_reply": "2020-09-17T22:28:59.985625Z"
    },
    "papermill": {
     "duration": 1.277979,
     "end_time": "2020-09-17T22:28:59.986627",
     "exception": false,
     "start_time": "2020-09-17T22:28:58.708648",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\pandas\\core\\series.py:726: RuntimeWarning: divide by zero encountered in log10\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371939881977216)>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nr_hist = item_stats.groupby(['Set', 'nratings'])['item'].count().reset_index(name='items')\n",
    "make_plot(nr_hist, p.aes(x='nratings', y='items', color='Set'),\n",
    "          p.geom_point(),\n",
    "          p.scale_x_log10(),\n",
    "          p.scale_y_log10())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045997,
     "end_time": "2020-09-17T22:29:00.083624",
     "exception": false,
     "start_time": "2020-09-17T22:29:00.037627",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let's look at rating count per book by gender resolution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:00.213661Z",
     "iopub.status.busy": "2020-09-17T22:29:00.211661Z",
     "iopub.status.idle": "2020-09-17T22:29:00.487617Z",
     "shell.execute_reply": "2020-09-17T22:29:00.486637Z"
    },
    "papermill": {
     "duration": 0.325995,
     "end_time": "2020-09-17T22:29:00.487617",
     "exception": false,
     "start_time": "2020-09-17T22:29:00.161622",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{lrrrr}\n",
      "\\toprule\n",
      "gender & \\multicolumn{2}{l}{female} & \\multicolumn{2}{l}{male} \\\\\n",
      "{} &   mean & median &   mean & median \\\\\n",
      "Set  &        &        &        &        \\\\\n",
      "\\midrule\n",
      "AZ   &  20.01 &      4 &  13.64 &      3 \\\\\n",
      "BX-I &   5.84 &      2 &   4.82 &      1 \\\\\n",
      "GR-I & 402.35 &     34 & 245.28 &     20 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "rate_rates = item_stats.groupby(['Set', 'gender'])['nratings'].agg(['mean', 'median'])\n",
    "rr_stat = rate_rates.unstack().swaplevel(axis=1).loc[:, ['male', 'female']].sort_index(axis=1)\n",
    "print(rr_stat.to_latex(float_format='%.2f'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.043991,
     "end_time": "2020-09-17T22:29:00.576648",
     "exception": false,
     "start_time": "2020-09-17T22:29:00.532657",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now compute gender histograms by percentile so we can stack:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:00.672647Z",
     "iopub.status.busy": "2020-09-17T22:29:00.671645Z",
     "iopub.status.idle": "2020-09-17T22:29:00.973607Z",
     "shell.execute_reply": "2020-09-17T22:29:00.974607Z"
    },
    "papermill": {
     "duration": 0.35196,
     "end_time": "2020-09-17T22:29:00.974607",
     "exception": false,
     "start_time": "2020-09-17T22:29:00.622647",
     "status": "completed"
    },
    "tags": []
   },
   "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>gender</th>\n",
       "      <th>male</th>\n",
       "      <th>unlinked</th>\n",
       "      <th>female</th>\n",
       "      <th>unknown</th>\n",
       "      <th>ambiguous</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Set</th>\n",
       "      <th>PopQ</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AZ</th>\n",
       "      <th>18</th>\n",
       "      <td>0.174433</td>\n",
       "      <td>0.667347</td>\n",
       "      <td>0.067153</td>\n",
       "      <td>0.085216</td>\n",
       "      <td>0.005852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>0.207661</td>\n",
       "      <td>0.593931</td>\n",
       "      <td>0.085052</td>\n",
       "      <td>0.106450</td>\n",
       "      <td>0.006905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.219571</td>\n",
       "      <td>0.567501</td>\n",
       "      <td>0.091894</td>\n",
       "      <td>0.114128</td>\n",
       "      <td>0.006906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>0.225784</td>\n",
       "      <td>0.554707</td>\n",
       "      <td>0.095220</td>\n",
       "      <td>0.116672</td>\n",
       "      <td>0.007617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.229021</td>\n",
       "      <td>0.542932</td>\n",
       "      <td>0.100422</td>\n",
       "      <td>0.120130</td>\n",
       "      <td>0.007495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender        male  unlinked    female   unknown  ambiguous\n",
       "Set PopQ                                                   \n",
       "AZ  18    0.174433  0.667347  0.067153  0.085216   0.005852\n",
       "    45    0.207661  0.593931  0.085052  0.106450   0.006905\n",
       "    57    0.219571  0.567501  0.091894  0.114128   0.006906\n",
       "    65    0.225784  0.554707  0.095220  0.116672   0.007617\n",
       "    71    0.229021  0.542932  0.100422  0.120130   0.007495"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_g = item_stats.groupby(['Set', 'PopQ', 'gender'], observed=True)['item'].count().unstack()\n",
    "pop_g.fillna(0, inplace=True)\n",
    "pop_g = pop_g.divide(pop_g.sum(axis=1), axis=0)\n",
    "pop_g.sort_index(inplace=True)\n",
    "pop_g.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.046963,
     "end_time": "2020-09-17T22:29:01.073608",
     "exception": false,
     "start_time": "2020-09-17T22:29:01.026645",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Propagate to percentile 0, so we can plot the whole width:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:01.171637Z",
     "iopub.status.busy": "2020-09-17T22:29:01.171637Z",
     "iopub.status.idle": "2020-09-17T22:29:01.189604Z",
     "shell.execute_reply": "2020-09-17T22:29:01.190604Z"
    },
    "papermill": {
     "duration": 0.068968,
     "end_time": "2020-09-17T22:29:01.190604",
     "exception": false,
     "start_time": "2020-09-17T22:29:01.121636",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for ds in pop_g.index.levels[0].categories:\n",
    "    dspg = pop_g.loc[ds, :]\n",
    "    pop_g.loc[(ds, 0), :] = dspg.iloc[0, :]\n",
    "pop_g.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.045011,
     "end_time": "2020-09-17T22:29:01.281634",
     "exception": false,
     "start_time": "2020-09-17T22:29:01.236623",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Stack for plotting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:01.379601Z",
     "iopub.status.busy": "2020-09-17T22:29:01.375632Z",
     "iopub.status.idle": "2020-09-17T22:29:01.392603Z",
     "shell.execute_reply": "2020-09-17T22:29:01.392603Z"
    },
    "papermill": {
     "duration": 0.06497,
     "end_time": "2020-09-17T22:29:01.392603",
     "exception": false,
     "start_time": "2020-09-17T22:29:01.327633",
     "status": "completed"
    },
    "tags": []
   },
   "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>Set</th>\n",
       "      <th>PopQ</th>\n",
       "      <th>gender</th>\n",
       "      <th>items</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AZ</td>\n",
       "      <td>0</td>\n",
       "      <td>male</td>\n",
       "      <td>0.174433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AZ</td>\n",
       "      <td>0</td>\n",
       "      <td>unlinked</td>\n",
       "      <td>0.667347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>0</td>\n",
       "      <td>female</td>\n",
       "      <td>0.067153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0.085216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AZ</td>\n",
       "      <td>0</td>\n",
       "      <td>ambiguous</td>\n",
       "      <td>0.005852</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Set  PopQ     gender     items\n",
       "0  AZ     0       male  0.174433\n",
       "1  AZ     0   unlinked  0.667347\n",
       "2  AZ     0     female  0.067153\n",
       "3  AZ     0    unknown  0.085216\n",
       "4  AZ     0  ambiguous  0.005852"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop_g = pop_g.stack().reset_index(name='items')\n",
    "pop_g.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:01.492640Z",
     "iopub.status.busy": "2020-09-17T22:29:01.492640Z",
     "iopub.status.idle": "2020-09-17T22:29:01.502408Z",
     "shell.execute_reply": "2020-09-17T22:29:01.501389Z"
    },
    "papermill": {
     "duration": 0.062808,
     "end_time": "2020-09-17T22:29:01.502408",
     "exception": false,
     "start_time": "2020-09-17T22:29:01.439600",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "pop_g['gender'].cat.reorder_categories([\n",
    "    'male', 'female', 'ambiguous',\n",
    "    'unknown', 'unlinked'\n",
    "], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.046998,
     "end_time": "2020-09-17T22:29:01.595382",
     "exception": false,
     "start_time": "2020-09-17T22:29:01.548384",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "And make an area plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-09-17T22:29:01.706398Z",
     "iopub.status.busy": "2020-09-17T22:29:01.700378Z",
     "iopub.status.idle": "2020-09-17T22:29:04.771326Z",
     "shell.execute_reply": "2020-09-17T22:29:04.770326Z"
    },
    "papermill": {
     "duration": 3.128942,
     "end_time": "2020-09-17T22:29:04.771326",
     "exception": false,
     "start_time": "2020-09-17T22:29:01.642384",
     "status": "completed"
    },
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 8 x 5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\gender-by-pop.pdf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:729: PlotnineWarning: Saving 8 x 5 in image.\n",
      "C:\\Users\\michaelekstrand\\Anaconda3\\envs\\bookfair\\lib\\site-packages\\plotnine\\ggplot.py:730: PlotnineWarning: Filename: figures\\DataSummary\\gender-by-pop.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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8FJXUFXcP0mLuQdE4kDz3Nct8TdNAkzwkycO8rQEJgaYSaFtCTVygiVc8tv1Q0yDQxA80CQJNwoC05GtS8jUt+2TlI93uN8asHDnsn/GD3ftLlcnHa335Iz39tZGe/uFDpcqWY312Q1D9+M9u2PEG4Hnbt2///hKEa8xhrZqed2PMytGpztEH9D01uWRB7hwUR1e8BLwZJWxo0UBo5UIrgzQXslwkTSEr5h5I3hB0SpBURBOHpEKxeqrgEhEvFfxMIJOcDMhEdXaSciaA5HiaZb7kOtQfXjU9PvUdP09SX5M80JRAEwk1IdC2CzX1isfEC0iCUBMv0CQMNAn9ogFRCkjLnmYVj9z+RhuzgBxs6EmTDT1pwhnTk7x03y4A6n5wxz9tOb3xzY3bnpc5F3Y5TGOOyv5hMMasOlLUH+8X6H9q44I1Dp5NcRm4JtDSaQ9t9g1rMUE5ySHJhTRH0kzIMiFPhTQR0QaQCpo4mW0wSDEXAT8R8VJUMkE6jQQtKhl1qhyh4kmeeZqlvqaZR6a+pnmgqQaaEJCor5n4muJr6nxS52sqQfHc8zV1vqZ+57nvaxb4pL6nWeBrFnqkgadZ6JOVbHiSWe160uTS//TEQ/z4Ew8duLd/6J7Pn3rOmfvL1VO6HZcxh2PJuzHGPEedOwk1oDY7QRk4SoPhcK9P/FsVrwVeE8KmQluhnQvtHNIcEhWyHMkyIc+FPIM8E0kSQZtSzNDPRCR1kHUaD0UjAi8T8VLwMsHLwGUyWxq1aDjkgmaikqMS+K60rre0vT41c1+WJHWPPPM0yxy5eprlPhmeZuqTqq+p+JqpT+KKIUyJCzSRQFMvKO5C+AGp72viF3ci0tAnDX1NS75moU9atsaEWSwO1l80cfClF9713eTu/qFvfvSsHVclzrN1PcyyYsl7F0yecoi80XqMPG90O5alIp5X8nsqZ6T1xk7NsuMvI3By36Z+GpSCdqXmJ6VeLwuqi/t9xnTHbEUjirsMwJyJy7BIjYbDyTIONtGUYdVgBkh09kdIc8g6dyGyXCTPiyFL2mlMpJlAS0imRbRoRCBzGxJpMXTJSyFIRbysaDxI9nQDQnNRQcidauKKxkPqyDJfs9wrHtXXVH1SPM0oGhKZeJpKUGxzvqYuIBVfU8/TzAs0dR6p72vm+aS+r2ngaeb7ZIGnWeBpWiqR9CzCL9R0mUCwY+LgS//wtm/u+uQZ5+2+af3Wq7odkzGzLHnvgtsuuwFme+bWnjOW/BuVBJgSdXXJvRkv8xpeFra9JEyCpJT57bKG7YoE7YoLW1U/aFVLYatSCtqVapCUa3671OvlgfW8GHNkHkmOFHMY+p7xjs57XMTGRGcidAukpfgt8NtKMXRJi8ZDMefh6UZEngl50ZCQLBPSRKAhQiZI6pAMJBVxnUaFn4q4zhwIPxUpuap31kQrfSITbQaazJS03SjnzaSqM1lPPiM9ed3vzephTz5dreXTvTWd6S9pu/+YJ2OWBQ/d9oad92175ejO78UXvGim2/EYA5a8m7VACIBBlXxQXU7uJyQ0T+wYRQNgUtTVXe41XOY3vDRo+WmY+kkpC9oVDdoVCVoVF7aqQdiqhkG7Ug7alVogtdO8ut/0c9/qnhuziDrDl6pAdW5poKfXWGDhGxFjAJyi0FJkLKc0lUppOpGBZtuRNJ0k407S3b60Go7pGSfjTUepLZC6XHPR3NesHWjSCjRJS9pKy9qikje1og2vmje8nrxeruXTtVo+01vNGwMBqd1NXGJD7ebzr3vk7gd2n/aCbodijCXvxhyXogEwpJIPZS4n8xOS0gmNeiqjtCkaANMu96Zd5je9NGj7aSn126UsSMoatCoStqsubFWCoFUNw1alHLQrVT8p14Kk1Odyv7Q4J2iMeS4ESgKbHWz2FcqqkMOxGgXF3QKpK+G0EjYyqTUzaGUi7URI245k3Eky6sl0PZSJaSeHZhylllPJnGaBpjMlbTXLeTMpaTsvaUsqeZOyNr2KNr1y3gwr2gxLeatS1mbF1zRUBBXJFVFF8oo2e0NNepfkF2WMec4seTdmqQghsF4lX3+SDQBA2qJuxuHPeLnf9LKg7WVh4qel1E9KeZCUCVoVgnbZBa2KH7QqQdAql/xWqRy0SxW/Vao59ZZkKXFjzLF17hY8VRnp6cXWOk8yOEoDoJU7N5F71enE1RqpI0lE8raTrOWEuuAOCF7TiWsJeRPSVBAPMqdkHmQ+5A0hmfTyyV5pHtqYHWgPt0fDTa09g/3JgQ0BuZVN7JBa32C3YzAGLHk3ZoXRUCULM7KBzGt1xgOcGCcBgVQJKONTIcgr+FrCz8r4aQk/DfGTEl4rwG+HeM0Qr+njNTo/0z4us2IfxiwDJZfrRpfrc//H3AnaXxtu1gaY6D2HuxT2pRl1yekNoc9PqNDCkeM0xyPH04xA21TTOpX2BNXWONXGASrNsYU4t2XHHz77Vd2OwRiw5N2YNSfXhJZO0GLi6Y1C8dfguP8iSMupN+3Ubzgt7gD4xR2AzE/KeSnrrfZ6W87PxhsPejN+O2hVAr9dDoNWqey3SlW/Xao5dfb3x5jlIldkrEllrEkF2Pz0O0nmyb5W4MaaLmynTjQPXdX1l8+vT7buG8vy1qjbHMwI5RmhOlOl1+vN9l/eumviksmbL1hNPffp6ENfZvgyS+BN19k/nsaYk6ClXNJSLmnx8kh3AAY558iHYAZkSvJiErCX+U0vDRM/CVM/KedBu6JhMQHYC9qV2UnAlaBdrgbtco+flPoEdxL3HowxJyDwMh2uZtnwM2bJHmqxEc4/3AcS4bF7qjtmPrTu4gOnpY8/dM30t8/pzetblibcxaPTk4e6HYMxYMm7MaZbhCpoVb1sU+ZlZEEbOMFKbMo0yKTL3UxRBjRoeWnQ9pNSFiTlLGiXCVpVF7aqXtiqBH5SDoN2KfTTcsVPwoqXhlU/CWvWCDBm4QTKhZdOp1wyzb7HS8N8qvc6Wh77KBYUzmYfS9qaGMzG6luSJ93WdM/A5vTJU8tWRtOYY7Lk3Rizcgk10FruZfBUA+AkKE1gWtQ1RF3D5a7lMr/tMj/xsiD10jDzk1D9pISflAiSsgvaZd9Pyn6QlEK/XQ79pFT20lLVp7TJawS5Q2xigFnTBDae1so3ntpqtxWezKGlxQrASSYkM85vHAg2ye5wS++d5cu3tZ30O80e788nd52SjCbb2zsHtyZ7zvbJrMyuMXNY8m6MMUIZKKvkKDm5B5xsQ6DgUBrMNghy13C513K5VzQI0jD10zD3006DoF0mSEqen5Q9v13yg6Qc+km55Celsp+EFT8tVb006BFEjvnNxiwzAqHAKQ6eUW+/mivr04zzGtnspiennezZXepJHitdsOmu3gvPVshq+fQtF7fuaV/SvPsiW+DKGEvejTFmcQgVoKKSoy4nJ31ux1OUYlzRjKiblty1igaB3/Yyv+2lQealYe4nJe38iJcFzk9C56Wh89LA99PQ97LQ99Ig9LIg9FI/cFlQ8rKg7DK/YpOITTcJbOrJddM5jYxzGhkKkw3Hg0+USo27K1ee/t3KVZWevH7Ljta9rbPaj5yyLhs/VYrp9sasKfaH2hhjVgJB6KweqpKvf7pB0Fq471BSoAE0RaWFuraoJKKu7XIvfeon83OX+bmX+bmXBhpKb09fedsVzYlDt9HMZ7w0dH4ael4a+p1GQ+ClQeCKBkPoFQ2GipcFtlKoOSKBvmrOFec2Ms5tZGTwyJNhaeahyhX93+u/anMO06G2dw5mY2OnJKNyefOOi23MvFkLLHk3xhhTEHygF+hVUSB7apRDdoyP7uEu6Od5J/R9z7yb0BSVpuReuxhe5CVeFqQuCzIvDdRPg9xLS/hJKH5acn5SckFS8v3iJ/SSMPTTUtlPw7KXBhUvDWwi8irjwZlb2/mZW9s5Cu1UeOyQL2OjpfX+vaWNp91Sudzfku698Yenv3nqUDZ2+lLHNzw8fBvw18AnRkdHV2exe7MsrJrkPYqibwBnwVP3pr8Ux/Hboii6BPgrYBvwKPDTcRzfH0WRA74AXAF8Jo7jd3WOcyXw9jiOr1viUzDGmLWluJtQA2rFfAOKiccLdXylKGGk0hB1TaeuKblLXOa3vcxPO5OR82K4USidCckuSMrOT0pBp2FQ8pNS6KdhxU9KZS8Lal4WVBYqRHNyBMJAuXBTomxKUi6vQ8Nx2209G3s+0f+fT+3J6ze/bPpb4ZnJY5cuYVg/D7weuGV4ePhW4C+BfxkdHT3iErnGnIxVk7wDG4FL4zg+MLshiiIBPge8O47jz0VR9Grgs8AlwNUUU2dOB26JomhbHMe7gPcCb13q4I0xxiwwIQRCRAeUjKebBc9xqJGSAzOCzFTYsLGdTN7jpcG0n5TaQbucha0qpVZNwmaPX2rWwrBZrYatWk/YqvT5SblPrBLRoqjkPO8lkwkpPHhPrdL+cu+PXtabT33vP0x+aXggn9y22N8/Ojr6feD7wK8NDw9PAjnwweHh4c8CHxsdHX10sWMwa8NqSt7XAQfnbdsBEMfx5zqPfx9F0XujKLoY2ArcF8dxHkXRA8C2KIp2AA/FcTyylIEbY4xZQQQH9CjaM8M+CLkwDZu0mDr2Z4vEf1zUTXiZP+UnpZmgVW2VmrW83OiV8kxfUJ7pq5QbfT2lRs+6oF1ZZ8n+ifHhnEum03N2TKe7v9dbc38z8Pqhc9sPfePf1L/+Qp+8tERhpKOjo68fHh7up+iN/8Tw8HBrdHT0h5fo+80qtiqS904Pew9wVxRFvcDdwLuAs4EH5+3+IHAhsBO4LoqisPN6F/B+4IYoim4EbgXeEcex3e4yxhizMIrEf0AlH0hdmzRo06xOHTntL+YF1EHqRclR13C53/SyIPGSIPXTUuYnpTxsVSk1e/xSoycsNXoqpWZPT9iqDQTt8sBaLTHqYOsLp5Kt58/Iv/7LurMv/YvB7Xt/ePqbT17QeuCqJfh6GR4e9oEXAy+jmEvyf5bge80asCqS9ziONYqiYWCKYqH2nwH+EfhNnr1kYwPoieP4piiKRoH7gRuAa4CvAtcDzwc+2Nn29cU/A2OMMeYwinkBPaA96mVkXkZGQkLj+D6vZCATLvcmvSyou9xLVVRUcqeiDtQT9dqlRs90tT6Y9Uxs8Hsmhnprk0Mby62eDYt6bktkINMX/8cDrf239vr7/m/Py6/6Zu3qB140c9PEjubdVzg4/rsazguOZ7fh4eGrgDJFfvGvwJ+Ojo7eeFLBG3MYqyJ5B4jjeLLzNAU+GkXR9RRDY+aXIusD6p3PvBmg0/t+I/Ba4BVxHNejKLoJuAxL3o0xxqxUggc6mHvpYO4dea2BJJyh3r+PfcP3P7XNEVLRQcrpOsrNAXomhujbs57q3j6crqzOfIENV06lGy6o+Hy/FJ77Dbma7w9dzWWMcNGer+Hnx16Hob31nNcd7f3h4eHfAn4SOAT8MvDZ0dHR+oKcgDFzrJrk/TA84MvAG+dtP49nD6V5C/AJYHrONuFEWuTGGGPMKpLTZlr2Mh3shYBi4Mc2ENzMIGdV/QP+7eFEdcZlnnOZLy73xMt81zO+cbB3fOOZbhkO16nta3I10HL84I4ev/3N0pmXf3vd6U+8bvzzMpSNnXq0z4a7H/w0Z7/4aAl8L/Dq0dHRBxY0aGPmWRXJexRFpwCDcRzf2Xn9RqBNccuqEUXRtXEcfz6KotdQJPW3z/lsH0WP+9UUvfaDURTVgKuALy7piRhjjDHLnJJXD/IgrOcy1h9xp4Plmb6Hhp48o7V513lbesc3bl9OdfdLORc/fzLlCtLH7qn5T3yi/z+f9+OT8T2npLsvPOKH8iw5xmEvo6hgd9jkfXh4+PdGR0f/60kHbUzHqkjegRLw4U4S3wTuAF4dx3EWRdFrgb+JouhPgd3AtXEc53M++07gA3EcJwBRFL0PuBe4E/jKEp6DMcYYszoIQ83a5NDomXcyeuadoLRc7j1SavQc6Jnc0O4/tDUYOLBtU8/k+jO6WU3Hg9Munk5Pq2T6zS/2RVf+m+lv3Hxh6/6TndB6BfDHncWafmV0dHTfvPdfC1jybp4zUbViKktpZGTk8pvTX7+t23EYY4wxXafMeGm4szY1eHDg4DYZevL0DX1jm7c7Pb7JoQvpvop34+09/ouvaN7+ry+e+d4PzX+/3bfx47tf9qY3AM/bvn379+e/Pzw8/AhwLvAbwC8C7x4dHf3o3PdHR0fPXMRTMGvEaul5N8YYY8xKI1SzoH3h5OBeJgf38vjZt4LS9NLwwZ7J9QeGnjzdX793+7ZaffC0xQ7lvEZ2dSp8+9aey39oyvV840fqX73mRI8xOjqaAL89PDz8aeAjw8PDPwn84ujo6P3H+Kgxx82Sd2OMMcYsH0I5C9oXTgztZmJoN49c8B1Q2V+e6R0Z3H9qa8Pus4cGDg6fu9C98wJy0Uz24kzkX++tnv3SU0tP3HJB68ErT+wQhdHR0QeBlw4PD/8c8LXh4eEPU0z7NeY5s+TdGGOMMcub6IZmbXLD7trd7D79blAaflK+t//QlvGNu8+uDj15xjlBUu5/zl8D7pLp9AWJ8L3/W3v5ucPJntH+fGr4OD/+rHHIo6Ojfzk8PBwDf0xRvtqY58ySd2OMMcasLEIlDZuXHNy8k4Obd4KSha3abaeMXNYa3nnxZV4eVE7+0HhX1NPznyh57U/1/8T4L4x9bKNHfjy95q893MbR0dH9wOuHh4ffcbIxGTOX1TE3xhhjzMomeO3y9PNGLvz2i2585Z+nt7z0U98+sOmRO0/+cPS/bLz9QNuFF/5d3yu/czyfGR0dvfkY7+852XiMmct63o0xxhizegi99f79L7nr+V/CS4N7t9/74smtj1581YmWpFyX6UuGW9kdu0rbXnpX6YKbzuXAYkVszAmxnndjjDHGrEqZn1zw4MXfeME3X/lnj+4896Zv53LMhZae4SUTSa+oJl+r/dD5o6Vt08f+hDGLz3rejTHGGLOqqZed+ei53zvz8XNuO7hu4rTHhkd29AyOnnLMVV892P6ilt76r2V3xS2DLz3lZFdvMmYhWfJujDHGmDUhl3To4MDI0MHnjVC6speN7QvZ+Oh2+u5df8TPnDaTXvFwX5XhTX2vghPquDdmUdiwGWOMMcasOa18iif8m7jtrE/yr6/86CP7Tx2577A7pjkv2t+4e/TJyS8vcYjGHJb1vBtjjDFmTWt702fefemX8S8o/eC8O/5tvmHv9kvnvl9pZRede6jxTTjpCpTGLBjreTfGGGOMAdKwdfHdV3350u/+8Mdumj+5dXMj6+1WXMbMZcm7McYYY8wczdrkC2655pO3KPqsVVON6TZL3o0xxhhj5pnpHXvRHS/63zd2Ow5j5rPk3RhjjDHmMMbXj7703sv+6ZvdjsOYuSx5N8YYY4w5gidPeeClj5z3nW91Ow5jZlnybowxxhhzFI+dfcuLHr3glslux2EMrJJSkVEU+cA7gTdRnNNu4C1xHN8eRdE3gLOAtLP7l+I4flsURQ74AnAF8Jk4jt/VOdaVwNvjOL5uiU/DGGOMMcuR4O3f+FDfpm7HYQyrJHkHqkAIPC+O4/Eoil4L3ABcBGwELo3j+MC8z1wNKHA6cEsURdviON4FvBd465JFbowxxhhjzHFaFcl7HMeTwG/P2fQl4BNRFAmwDjh4mI9tBe6L4ziPougBYFsURTuAh+I4Hln0oI0xxhhjjDlBqyJ5P4wXAnd0nvcAd0VR1AvcDbwrjuN7gJ3AdVEUhcCFwC7g/cANURTdCNwKvCOOY6vxaowxxhhjloVVN2E1iqIq8L+A3+gk3sPADmA78LfAP0ZRVInj+CZgFLgf+DvgGuCrwPXAj1H02F+zxOEbY4wxZhkSvKDbMRgDq6znvdOL/rfAx+M4/md4akgNFBNWPxpF0fXAxcD34jh+85zP3Qi8FnhFHMf1KIpuAi4Dvr7Ep2GMMcaYZWYouOB15N2OwphV1PMeRVEAfB74VhzHf3iUXT2gPm/bW4BPANNztgmr6PdjjDHGmJN3MLn3092OwRhYJclpp1Tkp4Hb4jj+3TnbT4mi6JI5r98ItCmGysxu66Pocf8QcAAYjKKoBlw1dz9jjDHGrF1KlnQ7BmNg9Qyb+QngPwBXdBL0WX8B/HgURacATYpJrK+O4zibs887gQ/EcZwARFH0PuBe4E7gK0sQuzHGGGOMMcdFVK2YylIaGRm5/Ob012/rdhzGGGOMOX5V2fLxi7zr3wA8b/v27d/vdjxm7VoVw2aMMcYYY4xZCyx5N8YYY4wxZoWw5N0YY4wxxpgVwpJ3Y4wxxhhjVghL3o0xxhhjjFkhLHk3xhhjjDFmhbDk3RhjjDHGmBXCkndjjDHGmGMo+/2D3Y7BGLDk3RhjjDHmmDbWzntVt2MwBix5N8YYY4w5pn3T93+52zEYA5a8G2OMMcYcUzOdONTtGIwBS96NMcYYY4xZMSx5N8YYY4wxZoWw5N0YY4wxxpgVwpJ3Y4wxxhhjVghL3o0xxhhjjFkhLHk3xhhjjDFmhfC7HcBSiKLoEuCvgG3Ao8BPAw8CXwCuAD4Tx/G7OvteCbw9juPruhOtMcYYY4wxh7fqe96jKBLgc8D/jON4E/D/Ap8FrgYUOB344SiKtnU+8l7gv3UhVGOMMcYYY45q1SfvwA6AOI4/13n8e0CAa4D74jjOgQeAbVEU/SjwUBzHI12K1RhjjDHGmCNaC8NmzqYYIjPXg0AbuDKKohC4ENgFvB+4IYqiG4FbgXfEcaxLGawxxhhjjDFHshaS9yowM29bAzgAjAL3AzdQ9MR/FbgeeD7wwc62ry90QIPe+Uxlo7ekTI8v9LGXq8DVeteVznjBWGvnTUk+PdXteJaK4LwB76yXj2cPf03Js27Hs1Tsetv1Xgvseq+t612W9U92OwZjYG0k79MUCfxcfUA9juM3A3R6328EXgu8Io7jehRFNwGXsQjJ+47qLwJcudDHXRF8XtDtELrk5d0OoCvseq8tdr3XlrV3vf9to9HodgzGrInk/T7gnHnbzuOZQ2neAnyCItGfJSzinIB2u/0jYRh+d7GOv9wMDAxc4nnejVmW/dD4+Pid3Y5nCfVSDMnaBqyZHiq73na91wi73mvoerfb7RcC/9TtOIxZ9cl7HMf3RVHUiKLo2jiOPx9F0WsAD7gdIIqiPooe96uBFBiMoqgGXAV8cbHiUtWZrVu3Ti7W8ZebJEmmAZxz02vpvHfv3j37dGotnbddb7vea4Fd77V1vXfu3Dl/CK45CSJSphi+/DJV/UaXw1mR1kK1GSiS81+LouhJ4L8D13aqzAC8E/hAHMdJZ3Lq+4B7gfXAV7oSrTHGGGPMIhGRa0XkyyKyW0RaIrJfRL4nIr/Q7djMsa36nneAOI7vp5iEerj33j3v9ceAjy12TKp67sjIyPSx91wdBgYGzuvr62NycvK88fHxboezZJxztVKpRKvVusSu9+pn19uu91qwVq+3iJzb7RieKxHxgM9TlNH+b8AbgTFgHXARMN614MxxE1WrhLiURkZGTqUYhz9/Eq0xxhhjlrcZ4Pzt27c/3u1AToaI/AbwJuAKVT10jH2vBd4DbAceo6jC92faSRxFRIEfBn4VeBnF7+b/Ar8899gi8nbg7cAWivznt4AvMWfYjIgMAR8AXgXkwL8Cb1fVRzrvvwcYBv4P8D+B04BLVfW+k/5lrGBroud9Odm+ffvjIyMj51MMyzHGGGPMynFgpSbuHW8Ffuc4EvdrgD8DXgd8F7gA+ALQBD46Z9ePA78A/GeKidt/C/wP4Jc6x/nPwO8CP0WR2J8NfOgwX/kFYAQ4v/Mdvw58RUR2qGqzs88PUxQgeQ1FY2LNlv6xnndjjDHGmFVORNYBh4Dnqer352z/NvDi2deqKiLyVeBLqvqBOfu9E3iNqr6481qB/66qvzNnn3cB16nqJZ3X3wX+VVV/bc4+Pwz8C52edxG5miLp36yqaWcfB0wAP66q/7fT8/5fgTNU9amZ4mvVWpmwaowxxhizloWdx3zuRlV9iaoK8KNzNl8M/JGI6OwP8PvAGfOOefe815NA/5zX5wE3z9vnrnmvLwaGgGTOd2VAz7zve9AS94Il78YYY4wxq99+ismplx3Hvj3A61RV5v1snbff8ZTPnD/Eo32Y73rgMN8lqvrhOfutmUnhx2LJuzHGGGPMKqeqOfBJ4B0icqyiGXdSrH/zXD3IsxsL5x3mu84UkeEF+L41wSasdkGn4oxNWDXGGGNWlpU+YfW3gG8A3xaR3wa+TVEesh+YWwrzfwCfF5G7gc9SLGJ5IVBV1a+ewPf9CfDnIvId4GsUw2D+cN4+/wx8H/iMiPwy8ACwkWKC6qfnTFg1HZa8L7GRkZFTK5XKY43G2pokHQQBW7ZsYc+ePSRJ0u1wllSlUsGu99ph19uu92q3lq83MDMyMrJiS0Wq6oSIvIiidON7KKq3lIE6sJNOJRlV/VKnVORvAX9EMQb9EeC9J/h9n+z0qP85sHnOMU6Zs08uIj8C/B7F4phDFBNrvwN8+iRPdVVbcdVmoih6CUVJoV+P4/ivO9suAf6KokzRo8BPdxZmOuJ7URS5znGuAD4Tx/G7OvtfCbw9juPrFiP+kZGRyyuVym3NZvPn5872Xu06i3p8cnJy8rrx8fH7ux3PUuks4nJjq9X6oTzP18x4Pbvedr3XArvea+t6i8jlqvoR4Hnbt29fM/9+m+VnRfW8R1H0JuDNwB1ztgnwOeDdcRx/LoqiV1Pc4rnkaO9RjOVS4HTgliiKtsVxvIuiRfjWxT4XEXngzDPPXDP/55/tnenr67t/aGhozZz37t27+wBKpdKdW7dunex2PEvFrrdd77XArvfaut47d+6srbQOT7M6rbQJqw8ALwf2ztm2AyCO4891Hv8ekCiKLj7Ge1uB++I4zjvH3RZF0Y8CD8VxPLJE52OMMcYYY8xxW1E973EcfwsgiqK5m8+mmM0814MUEyvaR3lvJ3BdFEVh5/Uu4P3ADVEU3QjcCrwjjuNFaWaLSHW212YtGBgYqHmeR5Zltf3796+Z8wZ6Zx9371475Wntetv1XiPseq+h630cFVqMWRIrKnk/girPrjPaoKgb2jzSe3Ec3xRF0ShwP3ADcA3wVeB64PnABzvbvr7QAX/t0E6Af1ro4y5rTy/EfGMXo+imXd0OYEnZ9bbrvbbY9V4DTi8PsEXK3Q7DmFWRvE9TJPBz9VHMnG4d5T3iOH4zQKf3/UbgtcAr4jiuR1F0E0Vt0gVP3r819thCH9IYY4wxi+jh4MCnf2rDRa/rdhzGrIbk/T6KUkdznUcxPGbmKO/N9RbgEzxz9S5h5c0JMMYYY8wiSNEVWxfzzd/61KIMAf7Q1a+XxTiuOboVn5zGcXwf0Iii6FqAKIpeA3jA7Ud7b/bzURT1UfS4fwg4AAxGUVQDrqIYUmOMMcYYY8yysBp63qFIvv8miqI/BXYD13aqyBzrPYB3Ah+I4zgBiKLofcC9FMv1fmWpTsAYY4wxxphjWXGLNK10IyMjl//+7u/d1u04jDHGGHP8NgTVj//shh1vYAUu0mTDZlaXFT9sxhhjjDHGmLXCkndjjDHGGLOsiIiKWG3Ow7Hk3RhjjDHGmBXCkndjjDHGGGNWCEvejTHGGGPMghCRvSLycyLysIjsEZG3ishPisiDIrJbRH57zr5Xi8hNIvKoiDwiIv/xCMfcKiL/ICI7ReRmEbl0yU5oGbLk3RhjjDHGLJRNwHZVPQt4OfAHwMsoFsm8FHibiJzR2fdx4PWqejrwS8CfHuGYnwT+TlXPAK4HPisiq6Xc+Qmz5N0YY4wxxiykDwGo6n0UC2D+parmqrqPYi2dMzvvP6aqj3Q+8zVgs4iEcw8kIsPAOar6kc5nvgs0gfOX5EyWoTXbajHGGGOMOV59YXmw2zGsIPvmPE+BQ/NeBwAi8hMUPenDc96f37G8DVgvIo/O2dYDbFioYFcaS96NMcYYY47hrIFNr8LWtTxex/xNichpFD30P6yq3xeREkWP+ny7gV2qun2BY1yxbNiMMcYYY8wxPDz+5Je7HcMq0wtMAveKiABvPdxOqvoE8LiIvHF2m4icuTQhLk+WvBtjjDHGHMNku3no2HuZ46WqdwMxMALcAUwDDx1h99cDP9apNvM48JdLEuQyZcNmjDHGGGPMglBVmff69Hmvr5nz/G3A2+a8/aHDHUdV9wD/aYFDXbFOuOddRP4fEblyzut3iMiEiNwhIhcubHjGGGOMMcaYWSczbOYXKW5tICI7gN8EXg18FvjjhQvNGGOMMcYYM9fJJO+beXpM0q8AH1DVbwB/AlyxQHEZY4wxxhhj5jmZMe9PABeJyCTwGuCczvYSIEf6kDHGGGOMMea5OZnk/X3AtykS9Xer6oHO9pcAdy1UYCciiqI3AL8zb/NG4BeANwFnUSwKAPClOI7fFkWRA75AcbfgM3Ecv6tzrCuBt8dxfN2SBG+MMcYYY8xxOuHkXVX/SkS+Brg5S9oC3Al0JeGN4/jjwMdnX0dRVKYY2vPPwG8Al8ZxfGDex66mWETgdOCWKIq2xXG8C3gvR6g1aowxxhhjTDedcPIuIi9V1W8e5q1dwG8Dv/Wco3rufgr4lziO90VRtA44eJh9tgL3xXGcR1H0ALAtiqIdwENxHI8sZbDGGGOMMYvlQ1e/3oY1ryInM2zmsyLyX1T1c7MbROQc4FMUQ2m6mrxHUSTA24Gf7DzvAe6KoqgXuBt4VxzH9wA7geuiKAqBCykaH+8Hboii6EbgVuAdcRzbYsjGGGOMMWZZOJlqMy8D/l8R+VUAEXkLRaL7FeD5CxjbyfoR4GAcx7d3Eu9hYAewHfhb4B+jKKrEcXwTMArcD/wdcA3wVeB64MeAdZ1txhhjjFnjfCTodgzGwMmNeb9PRF4E/L2I/HznGD+iqt9Z8OhOzq8wp958HMeTnacp8NEoiq4HLga+F8fxmwE6ve83Aq8FXhHHcT2KopuAy4CvL2XwxhhjjFl+zqoNva7bMZys7A9/blFGEXi/+pc2HKcLjqvnXUSqc3+AOvAqikmq/wzcPue9romi6CKK0pV/d5TdPIr453oL8Ak6i091CCd3Z8IYY4wxq8zD0wc/3e0YjIHj73mvU1RmmW+2xfWLnedKkRx3y68CfxbHcQYQRdEpwGAcx3d2Xr8RaFMMlaGzrY+ix/1qit75wSiKasBVwBeXNnxjjDHGLEcpmnQ7BmPg+JP3MxY1igUQRdFGikWj3jFncwn4cCeJbwJ3AK+eTe473gl8II7jpHOc9wH3UtxV+MriR26MMcYYY8zxOa7kXVUfW+xAnqs4jvcBg/O2PcwxJtHGcfzuea8/BnxswQM0xhhjjDHmOTqpMd0icpqI/IGI/JOI/KOI/JGILPveeWOMMcYYs7hEZJ2I/F8ReUxEfm8Jvk9FpLzY37NcnHDyLiL/BvgBMAT8A/CPFGUV7+i8Z4wxxhhj1q43AbtU9TSWx+Kdq8rJLNL0+8Avqeqn5m4UkX/uvHf5QgRmjDHGGGNWpHMo1gBCVbNj7GtO0Mkk7+cDf3+Y7THwl88tnLXhzYfGaNcP3qFpa6LbsSwVF5R6qgObnzczvve2PGnNL9W5iolXGtjyktb4nm/Dc/8DpqCZCKk4SZ0jE3GpOFJxkjlxiTgycS514lJxkonzUieSivNScS5z4mXivFTEdd7zMnF+JsX2TCQonkuQifNVpJvVo4wxxqwwIvJe4Frgx0XknRRr6/wF8CKK6oVvV9WvdvbdC/wm8F+BGvC7wATw34Ae4COq+t87+14NvB/YDOTAO1X1fx/m+7cCHwEuAPYDv6CqdyzW+XbDySTve4CLgO/N234xsOs5R7QGXDLyfYBLuxzG0tv7GMDzuh3Gkju4B+Al3Q7jZGhR/jUDEopSqqkWr1NFUhVyLZ5nxXPJcpFcg1LIwMZz0okD9+Vpu56LaCaSdR519jETp5kIs49p8SiZc6Qiao0UY4xZWVT13SJyCnCTqv5/IvIx4FFVvU5EtgPfEJEdqjoObAK2q+pZInI+cDvwSeA8YD1wv4j8taruBB4HXq+qj4jIK4C/Bp6VvHc+/ylVfaWIvBD4rIhcqKrpIp/6kjmZ5P2PgM+JyO9SjH2HYqjMb1C0lIwxq4QU6zf4zPlb8fRyejpn9Qd95koQWQp7d0Jxp27ZO+lGCmS5SJ6LZDmiuXOSDQ5fkY/vvTlDE2ukGGPWMhFxFL3wGwFUdUREvk+xts6XOrt9qPPefSJyAPhLVc2BfSJyL3AmsHNe5cOvAZtFJFTV9pzvGwbOUdWPdI75XRFpUvxbdNdinutSOuHkXVU/KCJ7KBZEOg8IKJL4X1XVzy9wfMYYs+hOupFyOHsehmKRt2VnwRop8+6kaFipeUNbL22OPfn9JG3VT6aRkjrnMnEuEXGpcy4V51LnvFSclzrnJ3MaJ6k4L3Pip+KCTMTPisdAi0TBGLN8bAAqwL0iT/1VrQJ/O2effXOep8Chea8DABH5CeB6YHjO+/P/P78NWC8ij87Z1tOJY9U4mZ53VPULwBcWOBZjjDGLaEEbKXM1pmHiAHS5YIEW42CTzk+qTz1KplI85k89dn4gLxomLs9EZn80K15rVjQ6yJzTtGiQkDoniTgyz/OCgS0vmZza/50EyVPnJC0aIzLbAEmKBojrNEK8VMRLnes0QsRPnfPT4m6JnzhXSp23ZsrdmTXhADADnHWUYSvH/GsjIqdR9ND/sKp+X0RKFItvzrebosrN9pMNeCU4qeQdQER2ABdS/NLvU9UfHOMjxhhjzKKRoheu1Pk5TMPkBBslx+PJJ6CYiLcgFFJ1bjp33kzqvGbi+a225ydNz89m/CCf8QOtez71IHBTXuBP+YFf94LSpO+V675fmXZ+LXUuXKh4zNP6wvLgsfcyc6lqJiL/G3i3iPy2quYisgUYU9XDJd9H0gtMUvTgC/DWI3zfEyLyuIi8UVX/CkBEzlTVR57ruSwnJ5y8i8gQ8CnghcBDFH8fzxKR7wLXqeqBhQ3RGGOMWRsEfMnzfpfn/T4JJ9MNr56PhmXyoEwWlkmCkMQPafsBba/4Z18BFUE7TRwVUGTO9sNs086jgKqQC0z4Pg/5Ifc6pfH0sIjjNqRweZZzTrPBlukJ6mGZu3r6+bbvMXHih1tUZw1setWCN/7Whv8C/AHwcGcM/B7gVRy+5/ywVPVuEYmBEYre/D+nyEEP5/XAB0Xk3YDX+czLTj785edket7/lKL0zlZVrQOISC9FGaAPAq9buPCMMcYYcyIkS5FGHdeo49O5DbGIfohiyFLmB3snStVDu2t9rYdrfcHDtd51j5UqW3LnfIDeJJm4YHpy9IKp8Zkz6uOldY36Ri9LN8091iBwKvBjkDVLlUcf7h8a+/tNpw7vLle2LPJpHNPD409++ZL+vld1O46VQFV/Zs7zSeDnj7CfzHt9+rzX18x5/jbgbXPe/tDhjqOqe4D/dFKBrxAnk7z/GMVM3qdqdavqlIj8GnD/gkVmjDHGmBVBwPlpsnUondg6ND3Bjs52hSQR97CnWvbQbUD/cR7Pq7Qa23fs28VF+3blh8LyzZ8/9ZzwB+s2XLpoJ3EMk+3moWPvZcziO5nkXTn8qEGhmCxkjDHGGINAEGp+1nM8hhtqN6/6xYd/QMu5+/9l82n7D4XlUm/aTnqTdtaTJnk5S7lp/Zb+Owc2XIqcxPgdY1aQk0ne/w/wARF5s6pOAYjIAEX9979buNCMMcYYY55WyvPzXrl753mHe++S8QNkyOg9A0MPxcPbT9td7TljqeMbHh7++jF2kdHR0WuWIhazep1M8v7LwMeBJ0VktmD+6cBXgTctUFzGGGOMMSfEQ4cvHj8wfPH4AZrOu+9AqXJof7mS7inXvD2VnupotTa4t9Jz+iKGcB7wMzw9QuFvgJ/uPFfghkX8brNGnMwiTYeAV4nIORT/kYbAnap6pFm/xhhjjDFLqpxn529r1NnWqHMZ+5/ansPe0WrPw99dv9W7eWjz+TN+MLCAX9sYHR39yuyL4eHhxujo6D/PeT29gN9l1qiTrvOuqg8CD4pIjWKFPmOMMcaYZc3B5lNm6ptPefxBrn38wWzaD+644fTzuWthJsPOX/Fz0/DwsDc6Omp5klkwx528d5L036fobf+Mqn5ERP4aeAOQicg/AW9Q1bFFifQYoij6BnAWxVK6AF+K4/htURRdAvwVxZK5jwI/Hcfx/VEUOYpVYq8APhPH8bs6x7kSeHscx9ct8SkYY4wxZgkJeD1pcukvPfwDDoXlm//i7IvXj1Z7zzzcvk71eIpy7B8eHn7+6Ojo94aHh19OUYXvvwwPD/8N8FqOXJt8UXm/+pc2iXcVOZGe9z8GLgI+Afy0iJwODABnUAyd+QPg94BfXNgQj9tG4NI4jp9aJCqKIgE+B7w7juPPRVH0auCzwCXA1RTjz04HbomiaFscx7uA93KElbuMMcYYszoNtptX/cY9N6c7a/03/tX2C88eajXHLh7fv//8yUP+xubM1qxvo+zZePGxDvObwJeGh4cfA9ZTlMH/PeC3gUeAn1rUkzBrwokk7/8WeGVnlat/Bu4BzlfVxwBE5FeBY82yXkzrgIPztu0AiOP4c53Hv4+i6L1RFF0MbAXui+M4j6LoAWBbFEU7gIfiOB5ZysCNMcYY030C/pnTEz/0uz/4DsAzFobKVb91HIf4Y4rCHruAu0ZHR6eAnwQYHh7eSrHQ5Y8vaNBmzTmR5H0z8EDn+QjFkrOPzHn/Eeb9h75UOj3sPcBdURT1AncD7wLOBh6ct/uDwIXATuC6KIrCzutdwPuBG6IouhG4FXhHHMe2GLIxxhhjjsdPUKw4vwv4FWBqeHhYKO7ov5Ni+PGS+8O/uXVRcplf/ekrbDhOF8yfWHE0vqomAKqaFQ9Pj//qPO/KRewk2MMUPe3bgb8F/hGoAjPzdm8APXEc3wSMUoxH+zvgGopyl9dTrCK7rrPNGGOMMWud84Jj7TI6OnrX6OjoSyhykBuHh4f/K/Bd4ErgitHR0T9b5CjNGnAiPe8iIr/E0wm6iMhbFiGmkxLH8WTnaQp8NIqi6ymGxlTn7doH1DufeTNAp/f9RorJJK+I47geRdFNwGV0dyiQMcYYY5aB9tZzXncCu3+KosDHb1BMUv3N0dHR/Uf/iDHH50SS9xspbgfN+iZw7WH2WS484MvAG+dtP49nD6V5C8VE3Ln1V4UTuzNx3D596puZaSSPZJmumXqvvucqvT2ls6bqrYfTLG90O56lIoLXUwsvqE+371VdiJKqqoGmSahJUtJ2VtKWlrWplbzhVfKGq+TNsKzNsJI3SuW8VS1po1bSdo+HnnRZWGOMMRDufvDTnP3iYybww8PD/45i1flvUVS6eznwL8PDwx8F/sjKRprn6rj/QVfVaxYxjuckiqJTgME4ju/svH4j0KYYEtOIoujaOI4/H0XRayiS+tvnfLaPosf9aope+8EoimrAVcAXFyPefdMOKJ3ZnUFGXZLD1KQC4Vlr6ryBmWmA8gULdd6NkzmOakPQKUGnPbKGr2kr0LQdajspaSsv5628ok0p501X1YZXyRthRRulct6qlLVZLWurFmq7VxapQWuMMcteniXH2mV4ePiLFFXsfnZ0dPTmzua/HR4e/hfgf1LMp7ts0WI0a8Jq6Y0rAR/uJPFN4A7g1XEcZ1EUvRb4myiK/hTYDVwbx/HcWq3vBD4Qx3ECEEXR+4B7gTuBr7AIrm1lMJ0+Rqbzx+OvWuK7stdbOiObau3UNG92O54lIzh6g3OZSh5AOZ4awceSp9BKnbQSIW2L5C0HTSc0nXhNR9h0ErZEyi1Hte2kty30qUhFkYoCOR6JhJzw7Q9VBeqCTjnyGU+zhk/aCjRNStpOSnkrL2tTy9qUHi/rGxioXiYHdt1Xbk/lZW1WytqqlvJWT0jSswC/B2OMWY6+CfzH0dHRZ0wQ7VSdecvw8PALuxPWyiMiClRUde3kDMdJin+PzVIZGRm5vPbFPbd1Ow6ztihMK0wpTGfCTCq0UpGkLSRtJ3lLoOnEdRoAQctJqemk2nJU2yI9idCHyMLcOygmvNcFrXtPNQKSdqhJO8zbWUlbeUWb2rkL4Cp5M5gdClTRZrWct2qhtnoDsvKCxGOMMceh3bfx47tf9qY3AM/bvn3794+1//Dw8KdHR0dPZJz8olmJ1WaOlrx35lw+oapfWqzvX85WS8+7MeYoBGoCNQBPIVQo1iib+3hkCjkwqVDPYSYTGqnQTEWStiNriWjLiTYdrukkSEKv3+8LLzhYbz/RUIK2ozctVmkGEQ/oV6Q/xZGKT4tSMeHEO4GTKqpfTTnyaUc+42vW9LVoBBTzAZp5JW9JWRtSzZteRRtBOW+WOsOBqmVt1kra7vPIj1lBwhhjTsILuh3AKnDYf6BU9c+XOpDl5LiSdxH5IPDfVfWQiPSp6uQxP2SMWTU6Y937Bfod4CuUZhsAWefxGVI41AI4ZXaLFnNKphSmcpgpkn/aiZN2W8jaTrQpIk2Haznxm0Xvf7nlqLREetqOvlyk9HRQEgCDOd5gjkcqAXASnfGqLWDSkc94ZDO+Zq2nGwGtvKytvJw3paJNV8kbXkUbQTVvlsrarJTzp+cDOPREmh7GmNVvzQ5tEJFfBN4G9AL7KVaW3UhRIGQ/8O+BFkVRkdcA/4mik+h6VY3nHOqXROSXgRD4OPBuVVUReR/QVNX3iMgg8NfApRRVBj1gv6puFJFvAO9T1X/qxPUjwK/PzuMUkdcAv0NRifAR4M2q+lDnvUeBH1HV++ec0wtU9WdE5CLgwxRrIHnAH6rqHy/Qr++Yjrfn/acpxoYDjHFi/WPGGIMUf2/WCaybbQCgQH5CdwBawGQO07kwk0IzddLujP/PWg5tOpGWE7/h8FtOwmYx/r/WdtLTGf//zL97RYNgQ47XmQ8AUDnxE1SdFtHp3rLb2Gy07/M1bQSatENtpyVt5+W8mZe1KZW84ara9Mt5M6xqIywaAM1qSds9obZ7pEvrZRhjFtyPdTuALvoe8BlVHReR3wPeDXyIIkn/UVV9q4j8AvAvwC+q6jtE5N9RJMRzk/dTKRbcrAFfo5jT+IV53/VfgcdVNRKRHRSVBs84VoAich7FirgvVdVHReRVQCwiO1Q1PcbHfxv4sKr+tYhUKdYGWjLHm7w/CvyKiPwDRX338znCPzCqeu8CxWaMMc8gxeT0DR5s8BQCgOz4k//OXjNaDAGazoRGJrQSkVan9z9rCdJpADw9AdhJpeWotkT6EqEHkWdX3RGpKVKbbAISnt+W8MRPsJiENCnotCOf9jRrBqSt2UZAuZgUTNEIaLpK3vA7DYBy5elJwb0B6fz1LYwxS2B4ePi0OS8b814DMDo6+tgShtQVqnr7nJdfo0h2AR5T1a/M2a4Uveazr08VEX9O8vynOlssQeTDFD3285P3iygaBqjqXVJ0yGylWOX2aK4FPq2qj3Y++2UR+W/A84F/PcZn7wH+s4jcqKqP8OwFQRfV8Sbvv0Cx3O97KH7R9xxhP8V65Y0xy5hAVTqLt3md3v/KiY3/V54e/z+3AZC0PVE2lF48daj9rWYxCdhrOgmbQqnlpNpyUm0LvamTw1fcKSYF9ynSl+HIxKdN6bC7Hj1ITSkmBU97ZEUjQNNW8NT6AM2srC2p5E2paMNV8kZYzZtBWRuVsrYq5bxZLWm7zyc7iS83Zk37LkXn5pH+mAiwZenCWXpSdG78JkVy3EcxnvHRztv75uyaAuOd5BxVTaX4G+h33oNiiA1zPrvpMF95P/CjIhIDl1P8jvceIby5HS/beHY+OwoMH8dnfxt4M/BlEXmYYrjPziN8bsEdV/KuqrOrjSIiD6rqOYsalTHGLFOdYS19An3PGP6DFncBRhtsKNaNOKLOTIG5E4CbidBMRJKkmABM0wmtzgTg4odykfxLT8vRm4sceYB/MTRoQJGBYlJwQOtkTla1zTMmBaetQNNWqO0k1HY2Ox+gJ8h61/VXLufA7ntK7cm0UswHqJS11VPSVq+zRcLMGjE6Orq12zEsA9dR9JC/QlX3iMgreLrn/UTnAWwEpjrPN3P4pPx3gNuAx+mUBJ/Tc5/yzFx385zneykS+LmGO8c46me1qJr25yLyFxTDdj4FLFkZ0JP5g/r6BY/CGGPWECnuUD5j/P/RJwA/mxYL0U3mUM+FmQyaiUg7cSSd+v/aKsp/uqbDb4qUm05KbUe19fT4/6NX2hEJgaEcb2h2UvARCy7XgfKZFx52znCxSFinPGjW8DVrBZq0Qm1nJW2n5bxJWZsMZBPB+uxg71A2tqEnr2+y8f/GrEj9wM5O4l4BfvY5HOt6EbmeYsz7zwO/e5h9LgO+q6rXHea9h4B/R9FD3gv8HDC72NbngK+IyIdUdaQz5n2AYrz+7GdfAdwtIpuA19EZTiMilwJ3qWomIndTjOVfMiecvKvqrSLSQ3ExLqT4V+Z+4GNWhcYYY5aGFNUX1nuwfnb8f1k7RT1PYPw/RfWf2eE/jUQkSYS0VYz/p+VEmk78piNoOim1nJRbQk/LSU8i9B52/P+zgn1qkbANs5OCG8eaFKzacuR7yto61JvXp9dlY/n69GCwITvYO5iNbarl0xssuTdmWfoE8B9E5DFgD/C/gF87iePcD+wE7qKoWvMxnjmZddZBIOp8XwY8Bvymqn4H+D3gkyKyq7PfH1BUuEFV7xGRtwP/0GlkPAK8WosyxAC/Dnyss88uismtV3Te+4/Al0QkBQ4Av3QS53fSTniRJhG5jGLl0XspblMIxRijCyhukdx+lI+vebZIkzFmFVEVGniuJT3huqSVPpHmOpW6ov5/W0TbDpoi0nJ4TZGgKQRNodwUyi2lljg52cm1rUDyg1VpT/TqdGNdNq6D6aFgKNlfG0gOrevNpgYX9EzNmpdsOevLo1f9xKs4zkWalpOVuEjT8eiUifwuRdJ9vxTriFwL/JqqXnH0T69cJzNs5o+B/zG/nmXntsafcIyxnsYYY1YNEaVKmlcZbxLAKSe84pUAoQehR172aZc8ZnzHlINDquxPMg62U/TZneylRN3WCS1vnaDMLjcE4fbifgTge0Jv2dEbZPS5Fn15nb5kjL7mPvqmRqk1Djznkzdriz989qu6HYN5lq1AD09PhHUUncm7j/iJVeBkkvcrgFceZvvHKG5PGGOMMcdHgVYGrQw31aZMUZpiEJhTYy/JPTnQ9t34jCeNSV90zHPBmKN3zDHUEumbf9g0U8amM8aAosJoCRgCzoIqSJVm4PIDVVqTfVpvrssmdDA9GAwmB3rWtQ8M1fKZ/sU/ebOSpKMPfZnhyyyBX0ZU9e7OpNFbOlVulKIn/he6G9niOpnkfYxi1cT59dxPoxhPZIwxxiykwGW6pZxlW2YT+9PnvKkwmcHeppNDdU9a477IId9Vxn0ZmvRl6+Eq8yiU27nb1qbCOBUedxsgPKvoua8BqnWPbG8lb4715VONwWxM16cHSuuzgwOD2fimijaXdFEW0306PXmo2zGYZ1PV3+XwE1lXrZNJ3v8K+LSI/Brwg862y4HfB/50oQJbzfZd9ATpzNS9mqVTx957dXB+UCv3DlzUnBq/O0+T6W7Hs2REXNAzcGVSH78F1fw5H04dQbtcCtqVUtCuVP2kVAuSUq/LfavHbdYsgT4f+npypSdXNifQKdszW5f/yUTYN+NkcsqXZMx3/pgvPeO+bJx2svkIi271ZPhn1b0e6l4Pu4N5pblVp3yyfZt7dbuOH/jXwfRQuj49UF6fHeofzMY2l7U1sNjnbYxZm04mef9vwATwNxQ1L5VibNGfqOr7FzC2VeueM78IxZisteiibgfQJVcu6tG1KBso6qZd7jVc5jW8LEi8NGz7SSkL2mWCdkWDdsULWxUvaFfDoFUuBe1KOUjKNb9drvlp2CscR+UQY1aQTkWaTaGyKcyUgUw5pfV0O1qhncOetuPgtJP6hO/yMV9KY77rn/RlS8vJ4XvYRXpT/N5ddcDf8uJR/1nJ/YRP+mQlb4735ZPNoaLnvrI+OzQwmI1tKmnbhuUYY07KyZSKVIqyP/+rUzOzpKo288eYbpKibKBKvj5zOZmfkBy5IvfhKQpMoVJ36mYk92a8LGh7adD2kzD1k3IetMsatCsubFdc0KoEQbsShK1K2U8q1aBdrvlJ2OvlwZEXDzJmmZFiuuxplZzTKrmyPs2e8X5nSM6eppPxuifNcV8Y8111zJfBKV+2ZkWJucMcWPpTgv4pL2DK62U0mLdoo+qkT7qnmjeK5D4dk43Z/vKWZO/mgXziFCuDaYw5kue06p2qTvH0ylfGmJVMEKAX0d6cDLyMLGif+HGUtojXqLrB/nYy/ShZPuHSoOWnYeInpcxPSnnQrkjYqkjQrvhBqxKE7WopaJcrQbtc8ZNyr5+EPYLzFvwcjTlBxxqSo2WPtJ3dMyMyMeVLuzMkp3fcl/XTTrYcsQ6+SF9K0DfpBUx6fewK5iz0qDoTkD7el00e3JTuT4bT3dUtyd4tA/nENkvqzcnodklHs7BsyWpjzMISQiULp/P94HE6HhA2aZ3ocZQ6SF1yN+1yr+llftNLg8RLS0mQlPLOUCCCdsULWhU/bFXDoF0uB+1KJUjKNT8p9XhZcLI1xI05JgGRZkYIF4YcdUjOgWknMxO+y8Y7Q3ImfNnccnL4WvQi1YTgvIP+EAf9Ie7lvM4BdSYgeawvmzq4Kd2XDad7KpbUG7P2rIrkPYoiH3gn8CaKc9oNvCWO49ujKPoGcBaQdnb/UhzHb4uiyAFfoCh9+Zk4jt/VOdaVwNvjOD7cMrvGmKUi9ID2qJeRnfxdgBSYEnV1yV3D5X7Dy/z2nLkAOjsUqJgPUA2CdqUctMqlICnXiqFApT67C2BOxokOyTkUiOwN3OCB0J2RitSefUCpJoTnP53Un985kE4HJI93kvp0ON1d25I8uWUgnxi2pN4A7H3/txdlkabN73yJ/ffVBasieQeqFAW+nhfH8XgURa8FbqCYHLkRuDSO4/nj8q+mmGx7OnBLFEXb4jjeBbwXeOuSRW6MWTyCD6xTydepy8lJn2rFnxBlBmRKcjfjcm/GZX7Ly4K2n4SJn5TyIClr0K5I0K4GtWDrizjYvCNsVwK/GApUDZJyr5cFz07GzJp2uCE5F5ChkOfw6LQnew4E0t4berUnA3dqw5ONhz+Q1I6S1D/Wn00e2pjuz4rhN09u6s8nhx1qDVJjVqiTSt5FpAS8kSIBHgTGgZuBj3TGwS+pOI4ngd+es+lLwCeiKBJgHYevP78VuC+O4zyKogeAbVEU7QAeiuN4ZNGDNsasHEIVtDr3LkBytP3P4NJnbVMyYEpUpiX3OkOBglZxFyDM/aScBe2yFPMBql5nMnBYzAWoVPyk1OMnpV6n3gkvYmpWFgHnwel9mZ7elylnNouhOAr7m47HD/muvjd04ZOh2zzuyWlHGVdfSwgvOOCv54C/fm5SnzjyXRVtHujPJqfXZwfYlO6vbEj3D63Lxod9cis9a8wydrI9738D3A+8H5ikSJBfB3wR+LcLE9pz8kLgjs7zHuCuKIp6gbuBd8VxfA+wE7guiqIQuBDYRXE+N0RRdCNwK/COOI4X5VaTMWaNETxgQEUH1KWduwAnPBMAlEYxF0DqTr2GK+YCtP00TP2klPntMmExF8AFraoftiuzQ4E6cwHKNS8NegSx290rjMCGSs6G4XbOcPuphH46EXZOejL2ZOjYE7rBg4E784hVcABEghzvtGmpnTbtas+sYa+aCzpa1ta+vmyyvj47lG1M91U2ZgcGh9JDwwGpzSMxpsuOO3kXka8Bv6aq3wcuAd6sqhNz3t9HkfB2VRRFVYpSlu+K41ijKBqmqIjjAT8D/GMURefGcXxTFEWjFI2QG4BrgK8C1wPPBz7Y2fb1pT4HY4w5IqECWlFPN2R0yoKWGid6lBxlStRNi3ozLveaXhq0vCxM55UFlaBV8cN22Q9alTBoV0qdoUA1Pyn1euqFi3GK5vgJ1ELlovVpMab+wpkMhSwXdtY92XcgdMmewPU8GbhTjzhB9hkHFKfIcEMqww1X4clgE/fM9tgDQr6v5rLJbf2c5R/adft5U3dWh5M95y7mOS4bzu56meXhRHre3wf8pYjcA3wM+LaIPAjUKXrezwV+deFDPH6dXvS/BT4ex/E/w1NDaqCYsPrRKIquBy4GvhfH8ZvnfO5G4LXAK+I4rkdRdBNwGZa8G2NWH4fQp5L3KTm5l5AGJ7guAMWUAp8yvpbxtFQ8ZiX8vISfhnhpCT8Jn/rxWgF+K8Rr+vjNEL/p4zUCnM2pXFACnqec0Z/qGf1pxvZOacu87NGu+bQCx4wv1AXGFQ7lyliakx/HdVDcxnruNt4/BsgZl93ddwbrq8qO7CEu2Hsjfn7UAWUrWnvrOa/rdgyrnYgoUFHVE/+DtIYcd/Kuqv8sIv+Xovf6NynGlX+u8/YE8KCqntRcsIUQRVEAfB74VhzHf3iUXT2KBsdcbwE+AUzP2SaArTZpjDFHoKQk1Emk/nRNkxOfBqkodVFX97JgImxXJ8vT/e1qfZDa5FClNjXYX51atyFIS7Yi6XPkmhnlZkYZmP/LVEgV9iWOgw1Ppqc8SSY88cd9Vxn3ZLDuyaa8mO/2LAdmhK9zDt9Yd/bBM5LH7vuh+re39+dTWw6370oW7n7w05z9YkvgTded0Jj3zuqqHxORTwPvAD4J/DHwZ11O3H3g08BtcRz/7pztpwCDcRzf2Xn9RqBNMVRmdp8+ih73qyl65wejKKoBV1GM4TfGGLN4BKFXJe9NXWtLGrSYqY1xaOOjz9xLmXSZvydoVybKjd5GbWqQ2uT6sDY11F+tr9tYatXWdyX6VULAF9haytlaypWBRDkFmLsgFbAvEfa3y36Wbapd/ORY4/sPeKyf8t2pxT4y9Ehw+kseGTgt68unbnrJzHdLZ7cfuaxrJ7XQ8mz13lYwK8qJjHkfpBgPfkln093Aq4FfBO4SkXer6hcWPsTj8hPAfwCu6CTos/4C+PFOEt+kmMT66jiO5xbbfSfwgTiOE4Aoit4H3AvcCXxlCWI3xhhzLEJf7qd9LX+KVnWKiaHdz3xfmZHc2xskpUOlRm+jWl+X1aaGgp7J9X21qcH1pUbvJuEIVVnMMXXqxW8MlY1hI4VHJ+iHy8+GbNKT79zUFwwdDFwx9l3Em/T6XvB/el+Bp+nIjuY9uy5p3r19IJ/cdvRvMavB/KEvIvI+oKmq7xGR9wAt4EUU+WQdeIuqfuMwx/ld4AzgJymGa38H+CmKEt/7gJ9R1bs6+74E+CNgA/Ak8DZVvVlE/gX4Q1X9PyIiwB7gf6nq/+p87ibgXcDLjzeu5UCKzvTj2FHkm8DXKIamQJEw/1tVfbGInEsxJn6zqr5wUSJdJUZGRi6/Of3127odhzHGrClKIur2+kl4MGz21KvTA2ltasjvmVhfq9YHByvT/VucTcB9TmYct97SGwSjJe+Sw+6gerCmMzs3J0/Wz0geq56WPHFaTz69aYnDPGntvo0f3/2yN70BeN727du/3+14TsRSLtJ0HMn7fwFeoKoPisi1wHtU9cK5nwVeD1wH/KiqtkXkr4EXAz+kqntE5J3ANar6ShEZAn4AvFJV7xCRK4C/o6gk+FagT1V/XUSeD/x/wJiqvlyKhdBGKdYD+o2jxbXcnMiwmYuA/6Sq+wFE5C+AtwGo6gPAf+i0fIwxxpjlRQhU8lOSUvOUpNRkuv8A+3n46feVHGSvlwb7S83aVGWmP6lNDkltan21Njm4rjq9brMttHV01ZwrXjqR0Jbkrtt7/JmRsncVMqckqcjQtNSGRkpnMlI6s9ik+Z6evP7E1nTvzBntx3p8TYMZV2nNuGoy4ypZS0r5lY3vnz6UjZ3epdMyC+9Lqvpg5/k/Ap+a9/41FEn3y1V17tLan1LVPXM+9wud5z8GfEtV7wBQ1VtF5DvAqyiKjvxBZ79/D3wY+E0R6aWoLHhzp3FwPHEtGyeSvP8P4DsicivF7bNLgd+au4OqfnvhQjPGGGOWiOBAN2dBe/NM0Gamd4yDmx595j7KIS8LngxalcnKTH+zWh/yBvLtLwn3uAdrU+s2BEllXVdiX2ZCZcfzp1KumEpH7qn5e++pelepyGHLLKq4LVNe35YHvD4eKJ1z2OM9EJ6dbE33fPOVU1+5qKrNoUUN3iyG+cPV9s55nvLsXPQPgJnOz9E+N/vf1DZg3jg6RoFh4LPAuVKse/AqiuHeL6JYk+hSihLhxxvXsnEi1Wb+UET+EjiLYuLKg6o6v2qLMcYYszoJg5mfDGZ+QrM2ydiGJxjlDthOkXUqdZd7e4N2ZazU6G3Uptbltan1YW1yqK9WH9wQNmsb19LiWB5sv3g63X7RdLr7kC8je0PH7pK38aAvZx4pmT8skWB3sPWlH1n3M5PntR/85svr37wqIDvyIlSm2zKemV9uBh6Z8/pYQ3j+HfAB4D0U1Q2P9bm9FKW95xoGblPVVERuBn4cQFUfE5F/AH4UOJti3uPxxrVsnGi1mQnAxmsbY4wx8wk9uZed1arUaVXqTA7ueeb7SkvU7fGLSbXT1fpAVpsa8nsm1/dUpwbXlxt9m526Zdvbd7IcbF2f6tb1acZFxSJSzVR4cNyTg3tDJ7tL3qZDRUJ/9HMX6bu/dO5LHwjP3rsp3XfLue2HwnNbD59T0eaxF58yS+kh4BXA/xaRsyh6vP/kBD5/EHgzcIeI/ONxjOr4B+B9InJFZ8jMFcBLKAqqQDF05p0UJc6hKEbyHqAXWFFzF2atuj8SxhhjzLIklFTy05NS4/Sk1KA+sO+Z7yuJ5N7OsFXdX6uva/SOb3L9h7b09Uxs3Fpq1TZ0J+iFJ1AOlAs3pMqGNGNHkdA3EuH+lpN6W0jbTrKmQMMTeaDin9vw5KmJrSpu895g8+a9wWa+WX2J+qQPbE327j2rPVLZ3t653YbWdN0vA38mIn9EUb3vA5zgujmqekhE3gR8XEQuPca++0TkJ4CPdioj7qWYo3mos8vXgf9JZ4y8qo6JyD7gAVXNDnvQZe64q82YhWHVZowxxpwwZcJLw13lRu9Yz8SGtG9sc7lvbPNQz9TQqS73D7t40mqh0B7z5ebv9QZbxgK3/Vj7O812DmVjo2e2d+pZ7Ue2rc8OnbEQcVi1mWc7XLUZs/is590YY4xZ7oT+LGj3TwcHme47yJOndNYaVHJR90TQruyr1gfqveObpG9sc0/f+KYt5UbfqljlVCAcTPUlPzLW1objlpt7g3D3kcpRArl4Z+z315+x31/P96pXgur+vnzqkdOSJ5rntB7esDXdc65DT2gt4IaUxu6v7ZiyGclmObDk3RhjjFmpBKeSn9IuT5/SLk8zvn706feUaZf5T5QbvYdqU0PtvrHNYc/Ehr5yo3eg1OgZ8vJgRU36FJBqzpXXFOUo7+6Uo7zyGeUoD/tB2TDp9W24y7uQu8oXgupkTz79wGnJ4zPntx7cuDXdc64cYVhHhmt/s/aS795VuuCSvsDvfdminJkxJ8aSd2OMMWY1Emq5n5430zvGTO8Y+7c+PO9tN+M0GPez0nSQVBqlVi0tNXu0PNMrpeneUnm6t1ya6ekrzVT7Xe4vqwWsQuWi50+lXDGT7bq/Nzjwg0AuOuaE11kifXWv58p7vAu4p3wBItT7pPnE1uzJ6TObIz2nNHaeWtJ29Qe9l/3g26WrNiTqXgpQqwQ2MdYsC5a8G2OMMWuQklczaVUzv0XLn6R+lH74QKqUpI+S9hJmPZSSGmGrRnVsHX0PDuE1upNOeJluu3C8ve3CWsDEhir3aM6jjeSEjqFKz4SWz5+Q07ivchpShVrJo97MLp5bPHB4U9+r4MSObcxisOTdGGOMMUeV6AyJzlBnbzHApNT56QNOIyulvTvXHTplfOMT5/Su273tTKfe8ddxXwjTCf3TE7wIeL4nu3ZX/b13ldzwuJMTHvevCvXms4uQjD45+eXhdZVXLUS4xjwXlrwbY4wx5rnwWv7UWXs33svejffC5cyEreoPhp48fWrzExds6j+09VxBTqhU4HMKJtNtp0wl27ZNkU968p1v9wdbJnz3nCvOTDeSQ7CipgmYVcqSd2OMMcYsHKHaLs88b89p97LntHtBGfOT0q6gXZ4uNXva5Zl+rdYH/Mr0QLUyPdBfnulbH6SlvoUPA9ef6Yt+7FA7G/PlW9/uD86oe27bQn/PSmAlHVcXS96NMcYYs3iEdWnYWpeGLRo9E8Dos/dRpkXdfi8NJ8JWZbrU6E0qM/1Up9aFG/ecvb3U7Nl48l+PN5jq1f/+YLu9P5Bvfrs/vLDpZP1zOCNjusqSd2OMMcZ0l1BTyWtp2CQNm8z0jjHWeevhi27M/bR014bdZx06ZeTy02v1wdNO7isINyb60v9woDW5s+y+eXNv8PxcpLyAZ2HMkrDk3RhjjDHLl+DSoLVjz2n3sOe0e3CZ91C1Pvhk39jmbODAtt7+Q1u2lpu9m4//cPSd2cxfenqzNfqDmv/ovVXvRcesFb/CffqBaxdlhdXXnfv5Vf17W64seTfGGGPMipF72dn1/v1n1/v3s/v0u4qNyli50ffght1ntbY8fsGptfrQ6cc6joPhS6fT4Yum0wdGKt6T91T9C5qeDacxy9+aSN6jKLoE+CtgG/Ao8NPAg8AXgCuAz8Rx/K7OvlcCb4/j+LruRGuMMcaYEyKsa1Ynn//EWd/nibO+j+Syqza5YWf/oS3aP7al1je2aXN5pn+r8Owedh/OPbeRnXtOI0sbjlvvr/rthyreZZmIlZYxy9KqT96jKBLgc8C74zj+XBRFrwY+C/wXQIHTgVuiKNoWx/Eu4L3AW7sVrzHGGGOeG3W6rT6wb1t9YB+j3NnZyIyXBY+u23fq/lNHnrelf2zLOXM/I+BXc664vJ5yWT2d3B/Izbf2BqeM++7MbpyDMUey6pN3YAdAHMef6zz+fRRF7wWuAe6L4ziPougBYFsURTuAh+I4HulatMYYY4xZeEI185MLDmwd4cDWESR3j607sO3RU0aeN7hu/ykXzq1FL9C3MdGX/tihNk3h9h/U/NaBqsu7Gb4xs9ZC8n42xRCZuR4E2sCVURSFwIXALuD9wA1RFN0I3Aq8I47jRZnkYYwxxpjuUZefdmjj46cd2vg4KPt7Jjc8sPXRHf7mXeft8LKgNrtfWbnsqnpK5sn9zW4GvAaIiAIVVT3uX7WIXAO8T1VfsNTf3S1rIXmvAjPztjWAAxTFZu8HbqDoif8qcD3wfOCDnW1fX6I4jTHGGNMNwoZ6//4ND17yNR68+GutsF27o3diY73/4LA/cGB4qGd8w2niglK3w1xLROQjwHtU9TALA6xtayF5n6ZI4OfqA+pxHL8ZoNP7fiPwWuAVcRzXoyi6CbiMRUjeL6m8baEPaYwxxpiFUaLKpQwAp0EGTKqjlg++Dhs4s2RU9ee7HcNytRaS9/uAc+ZtO49nDqV5C/AJikR/lgCORTDgn0W73f6RMAy/uxjHX44GBgYu8TzvxizLfmh8fPzObsezhHophmRtA6a6HMuSsett13uNsOu9hq53u91+IWT/1O04lrv5w09E5H1AU1XfIyLvAVrAi4BLgDrwFlX9xmGOcxPw66r6DRH5BvDnwK8DG4AR4CdVdde8zwTAPwGfVdUPi8iLKEZSDAJ3AG9S1YNS1PV/D/AzQAr8GaycptmqT97jOL4viqJGFEXXxnH8+SiKXgN4wO0AURT1UfS4X01xAQejKKoBVwFfXKy4VHVm69atk4t1/OUmSZJpAOfc9Fo67927d88+nVpL523X2673WmDXe21d7507d84fgmtOzjuBF6jqgyJyLUXifOFxfO7twDWqOikifwr8V55dHfCjwHc6ifsA8Hngx1T1ThH5f4A/oEjYfxx4DUUDYgL4Qxapw3YxrJhAn6PXAr8WRdGTwH8Hro3jeLaF9U7gA3EcJ53Jqe8D7gXWA1/pSrTGGGOMMavTl1R1dvTDP/Ls0RFH8hFVnW0s/tP8z4nIbwGo6rs7m14JfFNVZ+8OfRj4953nEfAXqjquqkqR1K8Yq77nHSCO4/spJqEe7r13z3v9MeBjSxGXMcYYY8wqN7+jeO+c5ynHn4vO/1ww5/XZFL3wn52zbRvwShF5dG4sIhICm4Ddc7bPPfaytyaS9+VIVc8dGRmZPvaeq8PAwMB5fX19TE5Onjc+Pt7tcJaMc65WKpVotVqX2PVe/ex62/VeC9bq9RaRc7sdwwqR8cz8cjPwyJzXJ1uC+2if20cxjv4WEfk/qvrPFMn5F1T15+bvLCL7KBL4WYMnGVNXWPK+9A40Go0Z4CPdDmQpjY+P0/kj/8kuh7Kk8jyn0WhAUc1ozbDrbdd7LbDrvbaudzG6ghmKUtPmyB4CXgH8bxE5C3gV8CeL/J0TqjomIj8NfFpELgP+AfgdEblMVW/vTFI9TVUfBb4E/D8i8hlVnQJWVBlAS96X2Pbt2x8fGRk5n2JMvTHGGGNWjgPbt29/vNtBLHO/DPyZiPwRxRzCD7BEcyxV9V9F5Abgw6r6H0XkPwN/KiJbOzF8imKi6xcoJqveLSLTFBUHV0w9eem0JI0xxhhjzCr06QeuXZRk73Xnfl4W47jm6NZKtRljjDHGGGNWPEvejTHGGGOMWSEseTfGGGOMMWaFsAmrXTAyMnIqNmHVGGOMWWlswqrpuhWXvEdR9BKKWcK/HsfxX3e2XQL8FUVB/keBn+4szHTE96Iocp3jXAF8Jo7jd3X2vxJ4exzH1y1G/CMjI6dWKpXHOuXF1owgCNiyZQt79uwhSZJuh7OkKpUKdr3XDrvedr1Xu7V8vYGZkZGR8y2BN920opL3KIreBLwZuGPONgE+B7w7juPPRVH0aooVti452nvA1RQF/08HbomiaFscx7uA91Ks0rVY1gOIyM+r6vcX8XuWlVqtdh7wyVqtdt34+Pj93Y5nqTjnasCNzrkfyvN8zSziYtfbrvdaYNd7bV1vEblcVT9C8e+4Je+ma1ZU8g48ALwc+OCcbTsA4jj+XOfx76Moem8URRfP7nCE97YC98VxnEdR9ACwLYqiHcBDcRyPLPaJiMgDZ5555ppJ3md7Z/r6+u4fGhpaM+e9e/fuPoBSqXTn1q1bJ7sdz1Kx623Xey2w6722rvfOnTtrK7W8tpV0XF1WVPIex/G3AKIomrv5bODBebs+CFwItI/y3k7guiiKws7rXcD7gRuiKLoRuBV4RxzHK/P/qcYYY4wxZtVZUcn7EVQpliueqwH0AM0jvRfH8U1RFI0C9wM3ANcAXwWuB55P0bt/DfD1xQhaRKqzvTZrwcDAQM3zPLIsq+3fv3/NnDfQO/u4e/furgaylOx62/VeI+x6r6HrLSLVbsdgDKyO5H2aIoGfqw+oA62jvEccx28G6PS+3wi8FnhFHMf1KIpuAi5jEZL39458E+CfFvq4K8SN3Q6gS3Z1O4Auseu9ttj1XlvW1PV+ybpTeUFlc7fDMGZVJO/3AefM23YexfCYmaO8N9dbgE9QNARmCYtUBz/HRuIYY4wxK8lD9QOffkFl8+u6HYcxK36RpjiO7wMaURRdCxBF0WsAD7j9aO/Nfj6Koj6KHvcPAQeAwSiKasBVFENqjDHGGLPGpeiaq4tplqfV0PMORfL9N1EU/SmwG7g2juP8ON4DeCfwgTiOE4Aoit4H3AvcCXxlqU7AGGOMMcaYY5GVWvZopRoZGbn893d/77Zux2GMMcaY47chqH78ZzfseAPwvO3bt6+ZEplm+Vnxw2aMMcYYY4xZKyx5N8YYY4wxZoWw5N0YY4wxxpgVwpJ3Y4wxxhhjVghL3o0xxhhjjFkhLHk3xhhjjDFmhbDk3RhjjDHGmBXCkndjjDHGmGPoC8uD3Y7BGLDk3RhjjDHmmM4a2PSqbsdgDFjybowxxhhzTA+PP/nlbsdgDFjybowxxhhzTJPt5qFux2AMgN/tANainxsfp1U/dEeetiaW6jsTcUyEpeBQWC6Nh6XqRFDqmwzCgZbn15YqBmOMMcYY89xY8t4Fz3voNoBLuxwGAApNhfHEucmW86YbftCc8oNkMijpWFiSsbAcjIWl8nhYrk0EYd9kUFqXOlfqdtzGGGOMMWuRJe9rnEBZYHMpzzeX8py+NGHTMT6jMJ0jY4lzk03Pm5nxg9aUH6YTYYmxoOQ6CX9lPCz1TASl/noQDOTi7L81Y4wxxpjnyBIqc8IEah5a8/KMcp4xkLSB6SPur6CIq2e9gz1Ja3qkofmhaS9oTQVhNh6WGA/L3qGwFI6H5cp4UOqZCMKBaT8YQESW7qyMMcYYY5Y/S97NohMQNO/xJw/gw/YKbB+kBY0jf0YhUxjPRCbazqs3PL9R94P2jB/k035A3Q+oB6Ff9wO/7gdh3Q9L075fmfGDasPze2wsvzHGGGNWI0vezbIk4AkMOdWhIEupZSnr283j/rxCRjG8p56JzKTONdrOtVrOS5qen874fj7jFY2A6SD0pvzAn/aDoO4HpbofVmZ8vzbjBbWm5/XYHQBjjDHGLBerInmPougNwO/M27wR+AXgTcBZQNrZ/qU4jt8WRZEDvgBcAXwmjuN3dY51JfD2OI6vW5LgzaIQ8IA+D+3zVAmznGp24sdRUGBKYToTN506aSbitZqe12p6ftrw/HzaD7TuB1IPAjflh0HdD4N6EJTrflCe8YKeJChtqjvL/40xxhjz3K2K5D2O448DH599HUVRGXgI+GfgN4BL4zg+MO9jV1MkZqcDt0RRtC2O413Ae4G3Lma8tw+/gvbU5A80SSYX83vmCjSRwWysZ102vr4vn9rk0FVx7RebgAC9Ar1Oc4IMKmT0pcf86DMo5MDU7J2AxLlm4rxW0/PaTc/Ppj0/m/YDqQchdT/wp/zQrwdBOBmUqlN+UKv7YX/T93sX4xyNMcYYs3Ks1gTup4B/ieN4XxRF64CDh9lnK3BfHMd5FEUPANuiKNoBPBTH8chiBje8exiyrWejnERf8ElrpcK+MZEndnk81HRZO3GttO0aZK4RBEyWhrJDlaF0bGAgn9hU0nbfEsa26kmxIFq/h/bP3gkgSyE5/mMopAoTuchUIm667XmNhue3p/0grftBPhmEMhmU3EQQhhNBqTQZhNWpIOyZ9oO+luf3LNrJGWOMMWbJrLrkPYoiAd4O/GTneQ9wVxRFvcDdwLviOL4H2AlcF0VRCFwI7ALeD9wQRdGNwK3AO+I41oWOUVIFqCz0cY+hJ1CGAlV6cihyycpTYSjD9RyebDrGHnGyZ9rXdtu1k7ZrkbiGB9PBUD5WGswO9Q5m4+t78vpGh3pLfA5rmoA/Ow/A14xKntGftI/rswpJJ/GfyUQaqbhW4ly75byk1Rn+M+P7OuMFTPuBV/cDN+0HwbQfhNN+UJrx/HLD96sNz6+1nFezeQDGGGNMd6y65B34EeBgHMe3A0RRNAxMUYyB/hngH6MoOjeO45uiKBoF7gduAK4BvgpcDzwf+GBn29eXOP6uEOjxoKeaQzVX1qcAYeenF4W2yhn72iKH9no8Wg/koaZLk5Zr54lrukxmgj6d8IfSQ+XB9NBAfzaxsURivb3LhEAgsN6p4qtSIudk7/t0hgBN5yJTifMmm54/M+MHrckgTMeDko6FZW+y2jOQ9K2/4LGktfeQ71UaftC/oCdkjDFLzEeCbsdgDKzO5P1XgD+efRHH8ey48hT4aBRF1wMXA9+L4/jNAJ3e9xuB1wKviOO4HkXRTcBlrJHk/VgEQlG2lVW3lXNYlyhF730ZKKMygJZOISt7TPc6DvhCy1MSl5G4hJQWoc7Qm4zT1z5I/8yT9Db241jwGxtmkXWGAPV4qj1elm4pZykD7SZbD7/7ZgB1HllYJSlVaAVlMs9DkWccdO5rBRAhE0fieSSu+Gk5j7ZztJyj5TyaztEUoeEcDREaIsw4YRqYECG1icLGmAVyVm3odd2OwRhYZcl7FEUXAecAf3eU3TygPm/bW4BP8MyVhoQiSTHHQRSkmeGaGQHFWKVnKkGpSpoPPd7UM/c94aQ5FZI3vTRrukQT13SezHiD2Zg3mB6sDqZjA/3ZxMaQtLr0Z2MWmuQZfnMKvzm1pOPFFBoqMpWKm06dm2k5r9X0/GTGD7JpP8in/ICm50vT813Tea7leV7TeZ6nqmGe5YHmWZhnGuSqQfFawzwnyHMAfbzaEzzYO7BhV6Xn1Nw565UzZhV7ePrgp19Y3WIJvOm6VZW8A78K/FkcxxlAFEWnAINxHN/Zef1GoE0xVIbOtj6KHverKXrnB6MoqgFXAV9c2vBXuVaGD6f2wKk9KJuAon1UAkoo/dM5W/a2HGMHnDz+SCCPTjvNm147b7uWZNL0+vIp6c8n/YFsotKXT/X2ZlPrqtoY6lSFMeYZBCqiWgk1I8wzqicyQ/g4XNGZCq/QTsQ9dLBcPbR3+Nzn35U1v/9wT9+mA+Xq8IJ+oTGma1J0Yf+AGHOSVk3yHkXRRuA1wDvmbC4BH+4k8U3gDuDVs8l9xzuBD8RxnHSO8z7gXuBO4CuLH7mZJVDzYPvT4+5nh9QEQIDSkyjr92fCWFuYHnOyZ9ST3XUHLZfnbZfmbZdIKm1XYUb6sinpzyfC/myy2pdP9ffm9aFQbRy+WXgCYaj52VsadbY8fBuXweUACpMt540K6pxq6JRA0JKAn4vUE3H1tvNmGp7Xrgf/f3t3HifHXd6J//PU1fcxt0YtyZJlHbZly5blEx8yEA4HivtYyAJhSUicEI4sLEuOXzZkE7KwBDYEgpMlgEkMhhAovCHmSIx827JlY1uSLcmSLLWOOXv67rqe3x9VY7dGM6ORNDM90/O8Xy/Z01Xfqnqqq2vm6W899S3DGTai/kA0rg1E47GBaDxd1I207vuO4Xt2xPPsiO85Ec9zI77nRj3XjXieH/E9P+E6fk+jhqxd11Ouk4h6boYAdVSPHD2QzNR3Zbriz6Y7VhWMaN+8vjHMvLY8tkdh5v3JzLozvToR8dxyT712Iuq7dtTznKjnOhHPc8eMSOxYLNE9YkT7mOjUG+eZOe65YwSGS4rmkWK4RPrZ3mgd9dxSb7163CNSa6oWbShqrKFqCVdRjJTPMJz60bRjl9NOoxLzXGcoEksOROK9Rd3omm6bHY368TWVsfy6UqG8ujymZJxGrKwZjeFI1D0RTdCJaDw2FIkl6qoaqatapKGoEVtRo65CuuH7DcP36obv2Ybn2yXdSI0Zkd6z2T8hxOJCzFJzPJ/279+/JfH9Y4+1Oo52Fj5YadQDRhxCsaFQraqSU1GIqiqjoXh+Q3HhkK0o1EDKLyPjFdWMX4xkvGIy5Zc6En61S4UvZRCirTBQZaDGoLpPaHik2D6R7RP5PsjziHyfyLcVxS3phlswohg2osqIETVcRVGydt3OOrabsRtIuTY5isJHYik6mu7sGUl3XriP2E24buHaoaPPXTV0HMvqlfUK0B1uu1ZVtX2HEumRJzt6IrsyXauGI7FTb5Vg5s2FwSdfl3++srxWuZyAKUvnGHB8ouM1VRtWmFXd95Iqc5qAbPigtlPaA6gz0AjeA2p4RA0nePpyo65qbkXT/bqq+l2NutZp1zNRz+0f34dJ1sfTXfVjoOERnbAVtUDMqspsKGCDmA0FSBAwa0PyMsBVVXvq4e7+ws+WrdrQ/EVtebV84Lqhoy9cPjIQT7p2t6OopZqq1Uq6bo/pEa+uaoh4HiK+R4bvke77BAC2ovh1VeO6qlFNVTEQjevPJ7PdR+LJ8xxFjcxW7ItFjx7/5q/3XPIeAFesXbv28VbHI5YuSd7nmSTvC0uQyGDIJYzZROWaCruikF9WSakpPoLefJdccpQYV/20X0LGK+pZfyyW8YqZtF/qljHxhQhwMIaRMtMyNh8YLmvGoRcSqdLudKe+tjxmbx4dvEAFr5jjUNsaA35F1Z4aicSKuWp5zWy/nwz4XqpLKfnO08NEwwUjihEjSiNG1BiOROMD0XjnUCS2nIna6r4xSd7FQtE2ZTOLyb5tO1BvDD3h+fWx+dqm6hqcGuvV06N9HcliT053ojJ0H8KaaGClwVhphGPg95w0Ao4KQAUj4gPJYQ+9o46CYl2hyhGFxko6HSmrrNQVn+tBkk9Jrrhpv4isN6Zn/bFkJsKXpkv5sgydKdrdZL3d01GArrRrd20aG8amscmepSfOBgFK0nM3J6ulOVu/VhpGB7CpAwBw6p8yBmyPKF/W9OHBSLyyN92h7uzo6c/HkufLcyKEODeSvLfA4fT9AHDZfG93MLf3pReMYc2JHI3WUmOJYreXHl1mpAp9XclS1wrV02WElwnC4RF7NKBH84GYz+g4ZZhLDQzNZcQGXeoesYlKVY1K+WQUT7j202WFqaZ43FA8RYXtpvyyk/GLyHoFPeuNJTJ+MZv2Sj26jLAjhFjkCDA05jVZx16TdWysKxdwy9EDYKAwphv79qWy5b2pjujheKrjWCyRk6dACzFzkrwvVYQu12h0lY0GypkhnFgZDsDDYDAd153IsWg1U06N9fipwrJYutDXEy91rFBYlTrwaYRPQe03GP0GM5I2A0eqyAHXvNRKBSM25iM2ZCs9hbpCtTGFRg9rNFaM0ImKwmpN9RWHPD/OtXrGL7pZr6B0eqPRDq+QznrF7jjXulq3l0IIcXYIyGYde+vWkQFsHRl4cboPDDZU9YStqA0G2CdiBvkekT8YidnPpjv1PenOvnw8uXrSm5SFWEIkeRcnIxCIlzmR+jInUkep4wSAp4N5DI9YeUG3YwOxcraaGuvhdGFZMlXo7Y1VsjlCe9U3ziUCMiqQiTX34tsnt2EoDiMx4FJiuK4sr1RUqj6vUrWo02hJxeGq6sMhz49xrZ72S06HV6AObzTa6RVSGW+sO8HVbhlCUwixGChAT8zzemLeqY9+7m3UcHFxBADAQL2hqM8ejyWGn0114JlsV9fBRGatqyhL7gZasXRJ8i5mjqAy+avsaGWVHa1grDv/0jxGg3z1iNGID8fLHbVUoVdJF5alUmO9y6K11LLWBb14EaATkDMYOcNjpD1G/4Q2DHIY8QGX4iN1ZVmpolLtgEq1ok5jRZWO1lSfbPL8GGrVpFdxkn4FSb+sJP2ykfIrsYRfTiT8aibG9Q4FLL1ZQogFjYBo1PcuWl0pYnWliFcfPwQGHFtR9hyOpwZ2dvbqOzt6z5/3YVGFmEeSvIvZQYiw6q1txEtrG/ESRntfeGkeo6z78aFu2rDaH27siA9lGunCsnRyrDtn2PHO1gW9+M0swYfLiJ1wKTbqUHfVVsipK1QfUFCrajRWVWmoSog2FMBWfDjkQ4NjR7nRiPtVL0j4K2rKL+tJvxxP+pVkPEz4pWdfCNFqBOgR3994QXls4wXlMbzthb1wiQ6diMYPu6Sw4fuKzp6q+b6mMquOojg1VXfKmu6WdJ1HjJiyo7MvdySRWtvqfRFiJiR5F3OPkHTUavIYdgI92Iqe5llKSffig9F6ppQsdbrJQq+WHO1OJwqdvZpjJFoXdPsI6/CDBJ+BhM/AKTfbnoSZ9DIrRtlT0lVXobqtklNXyBvSYL+gUKFGKBcVDJZVTzGURq3TKzi97oDaZx9PddvH+6J+Q24+E0K0jMZ8Xq5WOW/qFo2TXr3q+CF4qjZwJNWZf6SrT3sw03l+XdVO+huUNqLS2SQWBEneRUsx/JStllN2ooxiIg80FdhElAyS3IO4041YJYv4WAbGWBx6IQK9oINYSuznCBEjRR6nFI+hA4gBmHJs0UQMXjKDavp8DKqE3T5jFB5IZ2TVOjr8IrKNQXRUjiAzdhCa787fngghxAypntt7XmGg97zCAN6q6hhasR4/7+rDdjW4wHhBtu910/d7CDE/JHkXC1bDH0MDYxjW9gWZ48nZo6ewNqb5kTHdjVWNRsKO1lJ+tJJRYpW0ES2n49FKKmNU41m5kXaOVRyoFQcpACkALz4NJqLCJeVwWckOjWid/nPqxuhQBtmK6rNOXk2D5+jwPJ0dz2DHN+Cw4duIcIMifoMiXFeSbjmScQuplFvoiLAtQ2gKIeYFeQ56Dj2Ddx56Bm/RjMMP960c2GukD6E7/eZWxyaEJO9isVJ9cjtt1e201QoqkaHJHzTO8AA6rnraqGZHS5FGohGtprxYJavGytlovJJNRavpLqOR6JIkf5Y1PGjAymzwD2vCyRw8nn6EQQ2ftLoPzfcpxh7IcQmeS/BdIt9RwEcVUnbpVCxGlVJJRcJV2DXYHov5tWrKL9lZr6hk/YLR4RUSGa/YmfLLPSp8o3U7LYRoN7prr7w+v3/lVaXSM0dvvrDV4QghybtocwQV4GWe5izzNAeNeAnFjuOntmO4AJ0Ik/xypJ6oR2tpjpWzarySjcQqmXS0mukyGvEugjwd8FwQECVgOQCo45eg+cX/NE84BQNVHwSH4lxVE35Z7fdGVHIOGIpdVKlRVmG7RCUN7lDCr472uEON5e4xY7lzvKfbG14lib0QQojFTpJ3IQCAoAHc72lO/4tJPqZI8pkGVU8f0e1oKVJP2rFKluPlDi1R7kjGyh0d0Wq6V/W16PzvRPsjIK4Ca1QGoi6j0x1P8l8aG5oB1wNQU6IY01byiH6e/1ycaiMqFTyFKwm/ciJI6o/ry9zjnQQoNum2TYYT/NM9nV0lwg09wrZusB0J/jlRnZ2Yzk5ChtUUQkwnl8sRgI0AsgBO5PP551sbkWgnkrwLcSYIGoj7PcXu93Qb9UQRY11HT23HGFU8bVB3YuVO3rDFGy3dFy9nOV7uiMfLHdloJdOtu5Ep7wEVZ4+ArAZkUz4jZTNW2P6L8xgYdcgoV9ScN6qtsJ/UlVGXoIbLjX8TUHyQ4hLIJcAl8l2C6xIaHhF7BNcLLtW4BpzBlF8e7faGvX73hNHrDvR0c/EC3au3YteFEAtALpd7L4A/B6ACOApgRS6XGwLwoXw+//OWBifagiTvQswFQoevuR0NrYRj2AHEcf0pbRhl8tVBzTVGjXq8Fq1l3Hg5S/FyZzRe7kjHy9luw453tyD6tkVAh8HoMFxGh8s4H/7pF5oCA54P1OtKolrUUtUhbY33bExplGIaqp53LOrXj2a9sUqfO4DVzgsd/c6xC3R4sVncHSHEApPL5d4E4I8BvCOfz9/XNP2VAP4+l8u9PZ/PP9qyAEVbkORdiFYhJFn1ko5aW+NEaqhkhjE8sQ2jSr56XHeio9FashIvd3Ki2KUni92peLmzJ1JL9kkNfmsQoKrAqoSPVQnbR78NoOoBBQcMGDYpelntpGG9238ksskbTFDNU/wjHV7hRL97wkt7JS3hVyJJvxKLczWZ9CvZCNuzcjWGAd+BXjXgyHj7QsyvTwL4jebEHQDy+fzPcrncrQA+BeBNLYlMtA1J3oVYyAhxVr3zbbUCO1pBsePEyfMZNrFyXHMiQ5FashKvdPiJYpeaLHanEqXOrmg13U9QpD57nhHQFWF0RVxGl+thfS2oyfeBSlVJ6aN62q0q5BUUatQ0+HWFvKpCblGFrZA3HPdrhaw31ujwRhHnmhb3q3rcr0XiXItF/XoixvWUwXaq+Qm3w2rHgV2RjYf3GecbRUquVwFO+2O/vLb2SOwC+/nL5Gm4QsyLCwD8xxTz7gbw1XmMRbSptkneTdO8B8FJM/4EmB9ZlvUh0zQ3A/gaguGnDwJ4r2VZe0zTVAB8D8BWAN+2LOsT4XquBPARy7LePc+7IMSZIxhM/ionUlvlRGooZweBXNN8hkdMedWNDBmNeFm3I55ux32jESejHlcjjYRu1BMRoxGP641YUrdjac0zpLd2jijAyqTPK5ONKUfTcX2gXFMMGtP6eERbpg4ppNY10uoK6Q0FEVuhiE2I+kGNfpHAZQBqwoN+ft2jGws+pzwbAGIFLe4/nnyl/uMkHVlrH9h/ef3JZT3u0HlSviPEnCnl8/kXT/BcLrc3n8+vA4B8Pu/ncjlv6kWFmJm2Sd4B9AK4zLKsofEJpmkSgDsB/JFlWXeapvkGAN8BsBnADQj++K0G8KhpmissyzoC4NMAfme+gxdiThBUJs65Rj3nGjO8iZLhABgjXykpvlZVPb2mOYatOVHXaMSCxL+RUOLljliq0NsXq2RzMkb+7CBAU4E1SR9rkraPnD11WwbqABo+YBOgKcBKND0jCwA6XL7+5QUHLrB3X2wV3ZU8v7OmIKLAPxzza8e7vNFqv3tcWe4e6+xzB1dG2J7saQkncaBVBrTuw8e0ZSNJv2Kss/dfOtUQnD7I3W+s+eWI2lGL+1Ut7tciMa5FY349kfGL/Qp4yr9BHhQnry/fo7Gr9bqDazR4LRnBqUHG2LDaeaygZoodXiHb7Q6tlC8/Yjq5XK4LQDl8qTVNzwBwWhKUaCvtlLx3AKeUDF8CAJZl3Rn+/4emaX7aNM1LEYwzvduyLN80zWcBrDBN8xIAey3L2j+fgQuxoBB0AN2s+t2easPTbdjRytTtGXXFVw8b9eRwotTZSI8uU7PFXH+y3LtWq+jzFvZSQ0AUQHQmNVEasG5jzVu3seaBgcEGYbCgGdUBo189oOc6H49SzlEoTewfi3EtH/drdQ7vpWAQMREaFInUKNrPoH6DsazL8Z26QrG7k6+o9Sml3VfEjm0+7/C9dQL7eyLrf/lkdJM3rHZenPG4I+Uy1XRK1BUkG0QZV6EEmOsG23t6vOHh1fYL2nnOof6KkqjsiawfOayvSFUpttFgrPQB1SXoKvwDSb98vNcdbCjwUaMY1ZWIYpOhOaTr/kvlYcThyEERtutJv+KkvSJn/TE1643FdHY0Bb6qsK+o8InASllJVApqplZQMk5RTStlJaFXlHjGgd4Pog40P9+Z2R//8pPhsh3tyL7seJGftKHHXagdADIELqjwRiJsl+J+tZb0y17Cr1LSr6hJv2Ik/Eoswg2jTtFGTYnVqxR3q0rMbZDhq/BIZ5c0dklnVwGAMTXllpQUl5WEXlNiMY1d+/rqg5F19vOXz+ZnSsyKRwEMNL0ea/q5D8A35jcc0Y6IefLLt4tJ2MNeBHAIwRPanwbwCQRjrL7PsqzXN7X9HoB/BnAAwB8CeDOCk+1XAdwB4HYAvwZgB4DftyxrVt+g/fv3b3nE/eRjs7lOIRYqYrWgOtrhaDVdSI31eOnR/li60NcTrWT6NM9ItDo+8RIGhmxCvqxSqaZQMIJ+0/iZEZ8p43I6wlhOQRICAPCAAwNJfeRQV/SKAzWnxszc7fALF1bdE8ttf4UKrJ1kW7YHHBrT6MRxQ+EjEbV3RKMLNEZ5ZcN7dnXdr3U7/jIVWE8A+cDxBuFoSaPysKbAJZDGYBVglQE1/EPGADEBDGIfoIYCpaqQXlMpVlMoXlco5RDiLiHBRC9+71GY7bTLh7tcf6jb4VqH66sRn6M6I6YykkowVneSgeM2YbCsUrmgKX5Rp2hH1rhaHWzcF3d9jvkc0RgRl9CoK9SoKuRVVKKKSkZdIb2uINogitkKJRxCTGfUdOaa4aNhMDcUhldXKFZTkKorlHEVSgGA4XMh7fLxrOsXOlxu+AQ8ndA2eeSNbKk9ceLK2uNbW3VlYimx073fPHrzB94D4Iq1a9c+3up4xNLVFj3vlmWxaZo5ACUE46q+D8CPAfwBgOqE5jUAScuyHjJNMw9gD4KEfRuAnwP4MICrAfx1OG2qG0+EEKfB5GVdw8uWjUGUs4M4dt6uppmoESsFxdeKmhupana0YTTibqSe5EgtqUZrKSNSTcUjtWQyUk9kZFz8uUVAd4TRHXEZUz3hdjIqsKa/7KzpLzu4SqUSfHYUxkYEnSdTbcvQgHVdLq/rcj1cXPXAwe/qFAFXTWyvAMtijGUxh9HrzE7JMAMNADUOyo66KPiSccoXjQlxr4gyVkRdRrfrBYVLJRfAyUPBRhhI+Iyu07+P036mGbARxJdF8O9F62teo6jRvieSl2ce7bqi3O2N7Ixy3Yv6dcS4QVG/Rjq7iq3ovgODg/9rFGEbK+0jyZxzZFWC652nC1A0UdTTXkrM5XLPY/qbwymfz6+etZjEktQWyTsAWJZVDH90Afy9aZofRlAaE5/QNI2wFs2yrA8CgGmaBoDtAN4J4NWWZZVN03wIwOWQ5F2IuUGIMfkxT7H7Pc1GI1rCNMU5IKjQkYTBCeheHLqbgO7GYDRi0Bsx6PUo9GoMRjkCvRyBUZ20DFvMIcXj3rNdlk79XT2nCIgAiCzkIXgIMBD8m2xeJOPy1TcVHHhJDSOZ7u6KQigBGPUZox5Q833EFQVxBYgqhCgBJQb+rbERTISYQeg0HGSpggjbiPp1RL0qom4F3ZXD6KhM8gC6Jcxevv4/zaDZa5p+ZgD/DuAVE14LcU7aJnmfhArgLgDvnzB9I4DnJky7FcC3gJNyBwIgN+EJsUAwPNgYg01jwW+u8d9eU42Nw6gTq4OaaxSMRqIaqaWcWCVD8XKHHi93pGLlTEekluxRZChNscipZRc9ZRc9M2x/HTBW0Wjv4YjSeD6irshrmfNOaRTdAjXqvbDMHTx8YWNP9ILGvvURdlKzG/niYhx97g6se9m0CXw+nz8pv8jlcn7ztFwuJzesinPWFsm7aZorAXRalvVk+Pr9CC437gFQM03zbZZlfdc0zTciSOp3Ni2bRtDjfgOCXvtO0zQTCC7dfn9ed0QIMXsIUSZvpWPUVjpGDZXU0KltGB5Ax1RXHzYasVK0lrZjlSwUTyPioE82+D8BwIvTwATFVyk11ptJFfpyhh2T8gOxaBCQSbq89ULXw4UVb3zkorIPlH1C1QMaRY2Kz8W01JFI35X5xDLjZ/GbHB3Org6vMHweDSZWG8Ut3QNP1w24S6fW3vdmnHjncrmrAfxZ8GOuL5/Pn8jlcjEAMlSkOGdtkbwjuPx5W5jE1wE8AeANlmV5pmm+E8A3TNP8EoCjAN5mWVbzM9E/DuALlmU5AGCa5mcA7ALwJIIHKggh2hVBBbjf0+3+mm6jlhzDaM/hM18PY0hzIkfjlc5yj7PpOuOw+nhmtK83Ws3k5Am4YqFrGrmoW2VABxB1GL2OAwZKZZUeez6q+oci2nkDas+FA9RLj3oAuq7VM97Yg7eUftLb6w1Ne7/AUpHL5TYD+FMAlwH4/wD8XwDfzeVy30ZQUvOvrYtOtIu2GG1mMZHRZoRYQhhV1dMPxcrZoexwzu8cOK8jM7L8fHkQllisGCh7ujLgLEucf3ys8djTGnpLKuU6vdGHfrV8d67TK5xagtMmZjLaTC6XGwDwvwD8n3w+b4fT3gHglQiqAb6Yz+fdyZYVYqbapeddCCEWHkLc05wLy9lgtJ0ja58AGD4xHQGTA5BPgA8mJlYasWpmLD26zMsO5ZKZ0eW5aC21bKab8sl3q6mRw2Mdx06MdeYbvurx8oOXdHcMrbxIHqIlZgsBSc3xk9rhEtYAV6wG3BGNHrg/k119e+ady3q9oXtfXv7Fsj5vcF2rY22RC/L5fLF5Qj6f/w6CB0QKMSskeRdCiPlEUJh4xfhwjM3XPsuZQZQzgzi6+imEM2sInsjoAuSB4RHgAeSCyaNgms/EGiveShDWAFgzvr7B5fsAxmCi2P3c8kOb1L78+g26E+uYt309Rwzm+S478hW30YhWhlytUfc01/ZU2/U0x/FV12OaeKVaUaN61xZl1NurN6Ix3YkmVVdP+Ipn+6rX8FXX9lTH9hXX8RXfY8XzfMXzWfE9Jp9VT9dUV9dVVzcUT49orhHTnEh6MX3ZIkDrcvn61w/bzrBGD9yf6brg29m3LtfZ3r2pvnvwytpjl8S4sWg+c+eqOXHP5XIH8vn8munaC3E2JHkXQoiFihADEAtecNNDk05N/KdZR08lM9Sz99J7sPfSewCmQd2O5GOVbDFdWMaZ4eWp1FhvX7SaXj5douyqdrnYcfxgofvI6FjnUb+SGk7rdqy2Zs+1sZ5jF1w22bKFzqO7D2x8YKCaLCQUX/UUT/UUT/NUT2PdjnOskqFYJWvEKplktJrONmLl4mjPC8Oj3Ye5khrp9DT7fAAV3Y4dTha7S9mhFVp2eEWnHanUC135YrHjuFJLjna4mr0STDXNNQaitVQxXur0UmO9WqLUlY6XO7qi1VQfNY0qZBvV4UJX/oXR7sPFYudxtREtxz3NzviK1wVCFkBuhkdo3MReZh3A2T2EjOEDGCFfGVN8rax6WiOcQwifHqs7kUZffgP3H9q0PtJIzHSQmTlFgN7t8o3msG2PqXT/zqSW3hnbfOPO6KWNjF988KLGHm99Y9/qrF9c0epY55HUJYs5ITXv80xq3oUQCxKjpnja4Ug9MaK5EccxahFXs2Oe6iZZ8bIAsqDJHz6jsnFibXlbX9fDK4fIh3dgw0O7T6zYk/NVb2GUTjA8MJ1QPX3U05xuEPedfqFFgMGqq+/uHFw92HVidYzJR9Cz77Gv+D6TzyAGj+eQBCRKnbHuY2svVfj0Dxw6Vy7w3N6YevyphHb5+NNiif0jnd7owQvs57G5/vRFsUX0oKgzfcJqLpd7Ip/PXzb3kYmlRpL3eSbJuxCijXlghKP4iAWLUYxVss/kDl7C/S9cvElzI+nm2T55NrGiz1bJEgPlskpPH4oozv6Yen5FVYIrG8x2xi8+fnV1h7rB3rtFAS/oz82ZJu9CzBUpmxFCCDFb1GkfDC8WBkK6lixcu2/Tvdh38b02sXKIiWMARwHEQTDIUw+s2r/lyOrnrrpK8bXIuW0OyZTH12yqethU9eABB4Z05YWnEmr3gJG55iepV+CnfPPx85wX9txUuf+CxVxak8vl3nu6Nvl8/hvzEYtoX5K8CyGEEEsVwWDyTxnekVVvzaH1j645tG7HYN+R9bvWPb1ts+5Es7OxSRVY0+f4a/oKPlw4z+6NqSeeTmiXHTRWbzuon+dl/OKDN1e2J89zjlwyG9ubZ68+zXwCIMm7OCeSvAshhBBicsQ9J1Y+e9OJlc/VYk5mj+7E7KjToWdp5YX+iP1s7761PZFq4qzr1jVgw4U1b8PGulcbi2k7H4+r2eNq5tofpF+PmOLuv6q+s3xpacelClp/TYcS6dPuZz6ff9d8xCKWNql5n2dS8y6EEKJdaBTF2sLNWHb/BVDc2Rnh0u+O4VDawI5KAw4DfWkVN5f+A31Dz8zK+s8Wb30tDuUuB6TmXbTYohlLVgghhBALi8t1PJv5Me577W1HT6zeOyvZtTJUw5rnx/DWgXrh5pr3WGHMLX6bb/R+0PWWx+tKpDwb2zgbbn7vXa3athDNJHkXQgghxDnxqLF816X/evH9v/L3jw73HnhyNtZJjGx/ybnizYMN59Ky88Ah7rnstuz7qo9FN98/G+s/U1wpjrRiu0JMJDXvQgghhJgVdqxy5S+vsUC+crA3v/7g+Xuu3RCtpfvPZZ0EdG2qejdsqHm7tmd0/77EdS/bFdl4/zvGvr/ZgJOcrdiFWCwkeRdCCCHErGLFX31i5Z7VJ1bs8Y1GfEfuwOZ67sDmS3Q3kjnbdeqMi15ecHhUc+/9aUfHFV/t/PWBtxZ/mO93T2yYzdiFWOgkeRdCCCHE3CAodrS69cCFD+LAxgftSD35yPKDl9hnm8gTQJ0u3/Dmocbu/9cZ6bgz/aZlW+pP3HtD9aEb5iJ8IRYiSd6FEEIIMfcIRiNWvurFRL6WemTl85e7yw9tukz19PiZrEpnXGgON47/PGsceDx2+Q3PG2seeFfhu5t1uIm5Cl+IhUJuWBVCCCHE/CIYjXjpqn2btl+3/ZYv+w9vu/2B4yv27GCwP9NVKMCyVxbs8y6oug8V1Ox1t3W+78iYksrPZdhCLASSvAshhBCidQjJanrkut1b7t76i1/9m0MHNjx0r0+eM7NFEb+y7F59TdG+xyV9w9ez7zYO6qt+OdchC9FKbVE2Y5qmBuDjAD6AYJ+OArjVsqydpmneA+ACAG7Y/EeWZX3INE0FwPcAbAXwbcuyPhGu60oAH7Es693zvBtCCCHEksaqt+bghofXHFz3yLHcoUueW7vr+q2qp09bCkMAnV/3t6W8xvafZo3rf5i6JXNV7bF7r609KnXwoi21S897HIAB4ArLss4D8EUAt4fzegFcZlnW6vDfh8LpNwBgAKsBvMI0zRXh9E8D+ON5i1wIIYQQJ1O4P7/mlzdtv+XL9QPrH7pvJov0OHzj60fsRxSAH4lvveHO9Bu3N8gYm+tQhZhvbZG8W5ZVtCzrf1iWVQgn/QjARtM0CUAHgOFJFlsOYLdlWT6AZwGsME3ztQD2Wpa1fz7iFkIIIcQ0CF0HNz58/X2vvm1nJTly6HTNUx5f86ahxm7d57Fjev+Nf9vxfv9niW33OFBr8xGuEPOhLZL3SVwL4Inw5ySAp0zTPGSa5v8zTfPicPoBAJeZpmkAuBjAEQCfCttuN03z82HyL4QQQogWciK1yx+5+fbe3Zf95B6ffHe6thHGZW8aagzEPT4Ooo5nohdu+0rnBwr3x66+1wdNu6wQi0HbJe+macYBfA7ApyzLYgA5AJcAWAvgXwD82DTNmGVZDwHIA9gD4AcAtgH4OYAPA7gFQY/9tnkOXwghhBCTIcSOr9q97b7X/u2Bke4ju6drqgHrzOGGsqHmPgwATEr/jviWG77c9Rv5XZENj5/V9hVVP6vlhJhlxMytjmHWhL3oPwJwt2VZn5+izVMAPmBZ1sMTltsO4J0A7rAs61rTND8IIDHVes7W/v37tzzifvKx2VynEEIIsbQQcuWrseb+LdAb0+fUjU4DD8VU5J2X8p1c0sW2kZ+gu3TaSpwXlS+6CUPrXgYAV6xdu/bsvgAIMQvapufdNE0dwHcB3HuahFsFUJ4w7VYA3wJQaZpGaKP3RwghhGgfjHzyIdz/K3977NiqXdN2iEVGbNyYr7mvGLPv030uAkC+rOEfjdfaP0y/7r4G6aWZbNE4+twdsxG5EOeqLZLTcKjIOwA8ZlnWnzVNX2ma5uam1+8HYCMolRmflkbQ4/5VAEMAOk3TTAC4qrmdEEIIIRYWVvz+PZf99IqHXv6NBx2tMeXIMgRofQ3/+rcONWqr696OcKpxUF95/W0d7x8bVjsOnHZj/szGnhdirrXFOO8A3g7gTQC2hgn6uK8AeLNpmisB1BHcxPoGy7K8pjYfB/AFy7IcADBN8zMAdgF4EsDd8xC7EEIIIc5BLVm49v7X3Hbo8vveejxT6N8wVTsC+q4rOn3rau72f88aWz2iuE/Kim9l3jH0luIPd61wj100n3ELcTbaquZ9MZCadyGEEGKOMGpr9lz72Oq9V11/uqYecOA/skZtwFCChJ25fEv5J8+ts5/fMll7O937zaM3f+A9kJp30WJtUTYjhBBCCAFC7MCFD16/44Zv3+srbmO6piqw5hUFe/2VRecXwbKU/NfkqzY9Ed304LzEKsRZkuRdCCGEEG2l1HHihvtec9vzpfTAtA9dJEBbV/duuqji3h9MIOMX8euvvi9+zfZ5CVSIsyDJuxBCCCHajqc5F+646Y4Vuy/76S8Yvjdd280Vd+sy23sKAECkPBa7/MZfxK/7xbwEKsQZkuRdCCGEEO2JEDm+atdN9772q7vLqaEpR5QhIHJzwVmW8Pz8+LQnYptvuif+MkngxYIjybsQQggh2pqn25se3faP/bsv++k9rmpPfNYLAICAnl8dtssa84vPfHkydulN98SvlwReLCiSvAshhBCi/RGix1ft2nbvLV/hx1/23e2l9OAp9fAasOGWYftpNA3F92Tskpv+I3GDJPBiwZDkXQghhBBLByE11nX0xh3b/mntfa++beeJ3J4dzbOTPl99fdE56YbVX0Y33fSz9Csn7bEXYr5J8i6EEEKIJcmJ1C7fdcXdV+y/7MGTnr+yquFfv8znk2rky4m+VfMbnRCTk+RdCCGEEEsZvbDqkSuOvHx38zT1Zo/WqPTShFxf+nXzHZgQk5HkXQghhBBL3t7kT/D09T9+sQeeRut4WdV7saQmf6J4V2siE+JkkrwLIYQQQgAY7Hzuih033nEvg30AyJWcSzsdfy8AVGrOSGujEyIgybsQQgghRKiUHbhhx03/9AAAEGC8omA7xDztQ56EmE+SvAshhBBCNClnhq7fd9G92wFAZ1x0Zcm9r9UxCTFOknchhBBCiAkOr338mtGuI7sAYG3du6rf82WoSLEgSPIuhBBCCDERwXjy2u+nHb02SkDssjEn0eqQhAAkeRdCCCGEmBQrvOKRbf+4j8FMAJ1+CSHmniTvQgghhBBTsGOVK3dffvf207cUYn5orQ5gPpimuRnA1wCsAHAQwHsBPAfgewC2Avi2ZVmfCNteCeAjlmW9uzXRCiGEEGIhObHi2esT6sqnu/HKVociRPv3vJumSQDuBPCXlmX1AfhzAN8BcAMABrAawCtM01wRLvJpAH/cglCFEEIIsRAR1OO5Z1KtDkMIYAkk7wAuAQDLsu4M//9DBHVr2wDstizLB/AsgBWmab4WwF7Lsva3KFYhhBBCCCGmtBTKZtYhKJFp9hwAG8CVpmkaAC4GcATAZwHcbprmdgA7APy+ZVk8n8EKIYQQQggxlaWQvMcBVCdMqwEYApAHsAfA7Qh64n8O4MMArgbw1+G0/5jtgFLKatT8gd0u6ktmzFidovGUsfzikn30GYfrE49H2yIoalJZsaXsH3mc4S+ZJ/TJ8ZbjvRTI8V5ax9ug7GirYxACWBrJewVBAt8sDaBsWdYHASDsfd8O4J0AXm1ZVtk0zYcAXI45SN63JD4KABfO9noXBQMXtzqEFtnS6gBaQo730iLHe2lZesf7ylqt1uoYhFgSyftuAOsnTNuIk0tpbgXwLQSJ/jjCHN4TYNv2awzDeHCu1r/QZLPZzaqqbvc878ZCofBkq+OZRykEJVkrAJRaHMu8keMtx3uJkOO9hI63bdvXAvi3VschRNsn75Zl7TZNs2aa5tssy/quaZpvBKAC2AkApmmmEfS43wDABdBpmmYCwFUAvj9XcTFzdfny5cW5Wv9C4zhOBQAURakspf0+evTo+I+lpbTfcrzleC8FcryX1vE+cODAkikREgtb2yfvoXcC+IZpml8CcBTA28JRZgDg4wC+YFmWAwCmaX4GwC4ATwK4uxXBCiGEEEIIMZklkbxblrUHwU2ok837owmv/wHAP8x1TMy8Yf/+/ZXTt2wP2Wx2YzqdRrFY3FgoFFodzrxRFCURiUTQaDQ2y/Fuf3K85XgvBUv1eBPRhlbHIAQAELOMhDif9u/fvwpBHf7Em2iFEEIIsbBVAVy4du3aF1odiFi6JHlvgTCB7251HEIIIYQ4I0OSuItWk+RdCCGEEEKIRWLOhkIUQgghhBBCzC5J3oUQQgghhFgkJHkXQgghhBBikZDkXQghhBBCiEVCknchhBBCCCEWCUnehRBCCCGEWCQkeRdCCCGEEGKRkORdCCGEEEKIRUKSdyGEEEIIIRYJSd6FEEIIIYRYJCR5F0IIIYQQYpGQ5F0IIYQQQohFQpJ3IYQQQgghFglJ3oUQQgghhFgkJHkXQgghhBBikZDkXQghhBBCiEVCknchhBBCCCEWCUnehRBCCCGEWCS0VgewlJimuRnA1wCsAHAQwHsty9rT0qDErDBNUwPwcQAfQHBeHQVwq2VZO03TvAfABQDcsPmPLMv6UEsCFbNmquMq53n7MU3zPQD+dMLkXgC/ieCcl/O7DZimeT2A7wH4pGVZXw+nTXk+y7kuWkWS93limiYBuBPAH1mWdadpmm8A8B0Am1sbmZglcQAGgCssyyqYpvlOALcD2ITgj/xllmUNtTJAMetOOa5ynrcny7K+CeCb469N04wC2AvgJwA+BTm/Fz3TND8A4IMAnmiaNuX5LOe6aCUpm5k/lwCAZVl3hv//IQAyTfPSlkYlZoVlWUXLsv6HZVmFcNKPAGwMf8F3ABhuWXBirkx2XOU8Xxr+M4CfWZY1ADm/28WzAF4O4HjTtOnOZznXRctIz/v8WQfguQnTngNwMYBfzn84Yo5di5d6cJIAnjJNMwXgaQCfsCzrmVYFJs5d+KXslOMKOc/bXnjsPwLg16b6HMj5vfhYlnUvAJim2Tx5uvPZnmaenOtiTknP+/yJA6hOmFZD8ItftBHTNOMAPgfgU5ZlMYAcgl6atQD+BcCPTdOMtTBEcY6mOq6Q83wpeA2AYcuydsr53famO5/lXBctIz3v86eC4GRvlgZQbkEsYo6Ypmkg+AP+TcuyfgIEJTXhbBfA35um+WEAlwJ4uDVRitkwxXFdDjnP291HAXxx/IWc321tur/bjWnmCTGnpOd9/uwGsH7CtI049bKbWKRM09QBfBfAvZZlfX6apirkF3w7UgHcBTnP25ZpmpsQHN8fTNNMzu/2Md3fbfmbLlpGkvd5YlnWbgA10zTfBgCmab4RwS/5na2MS8yOcKjIOwA8ZlnWnzVNXxkOJzb++v0IaiVlOLFF7DTHVc7z9vUxAH9jWZYHyPnd7qb7uy1/00UrSdnM/HongG+YpvklBOOAv82yLL/FMYnZ8XYAbwKwNfwDPu4rAN5smuZKAHUEN7G+YfyPv1i0IgBum+y4hsOEynneZkzT7AXwRgC/3zR5ys/BvAco5sp057Oc66IliJlbHYMQQgghhBBiBqRsRgghhBBCiEVCknchhBBCCCEWCUnehRBCCCGEWCQkeRdCCCGEEGKRkORdCCGEEEKIRUKSdyGEEEIIIRYJSd6FEEIIIYRYJCR5F0IIIYQQYpGQ5F0IIYQQQohFQpJ3IdoQEX2diD7X6jjaGRFtI6KhVschFgYiYiLa1Oo4hBDtT5J30TYmJqxE9KdE9Lp5juFPwj/iTEQlInqKiD5KRDSfccwGIvoYEf1V0+uzfj/DRHf8fWkQ0fNE9AUiys5awLOMiN5PRL87YdqjRHTVLK3/tUT0SyJSm6a9mYiKRPS+Sdq/ioieDt+/p4ho25nMn9D2i0T0kdnYj7k02TFoV0R0KxHd1eo4hBALnyTvoi2FyfKrWrT5f2ZmApAD8F8B/DcAiy4BYebPM/NHgVl7P4fD9yUD4C0AtgL4v+e4zrn0mokTmPlKZn7kXFdMRAqALwH4ODN7RKQS0V8C+EMAhyZpvxLA9wF8EUAfgM8C+AER9c9k/iT78WFm/sK57sc8OOUYtLGvAlhPRLe0OhAhxMImybtoV48AuBrAj8Le3v8KAES0hojuJqIaEe0jog+OLxD2Dj9IRL9NREeJqExE3yKiZUR0R/j6CBH91kwCYOYiM98N4PMAXuyxJqKXE9HjYQ/pISL6aPNyRHSQiF5HRA+EbfYS0Zua5r+PiHZMWOZ1RHRwqliI6NeI6LGmffj4hP3eHvbc7g3b6ET0OSL6+hTv58fD2DZM2M7XiOhLp3lf6sy8E8DHALw2TGRncmwmizFORF8mokEiqhLRw0SkhctcHh7PGhE9Q0RvnPAe3kFE/52IjoW93XcR0bJw/ncBvA3AX4f7+6Vw+tBUPdpEpBDRnxPRABENh5+djinehlsAEICfhK/jAGIAbgAwPEn7dwG4n5n/jpkLzPxNAP8O4H0znD8x1u8R0Z+EP6tE9FdEdDx8r54iol+dYrl7iOjXieinRFQPj8VriOit4c+VcN7ypmVi4TEaCo/Rj4no/Kb5G4no5xRcqRoJPwOZqY7BhHi2EdF9RPSb4ee6Fq4/19RGIaJPU3BO18P2W85wHffQqVdhXnwPJ4krTkRfoeBcrhLRDiLa2jT/60T0ASL6bPjZu42ZPQBfA/A7k61TCCHGSfIu2hIzX4mgB/P1zEzM/DkiIgD/AuCnAHoR9P7+ARG9umnRKwBcC+BSAJcBuAnAkwD+DcAyALciSCZWnEE4OgAfeLGH9F8QJPTdAN4J4CNE9K4Jy/wVguS2G8DnAHz7DLc50QsA/guAnnCbf0Yn1+duBPBHAN4AYBkzO80LT/J+fhbAv4brQrhvGgATwJ0zjEkfX/0Mj81kMf5vABcjOGZ9AH6XmV0iigP4EYLe7W4AvwXga0R0cdP63gRgFYDNANYBSAD483B/3wbgFwA+FO7vTK6cfCSM4yoAFwDwAHx5irZvAGAxM4fbKzHz7zFzZYr2lyP4AtXs4XBbM5k/nf8M4OUIPvvdCJLHp6dp/5cA/gJAF4B/AnAHgitLr0LwfqoA/rip/WcBXBjGsipc9130UrnQ3wJ4EMFxvxDAV5h57AyOwVYE5+mVAM4HwAD+rmn+xwC8EcCrEXxGvgfgbiJKn8E6zggzVwFsB3Adgvf03nA/m/13BL8XzkPw2QGAHwD4FSKKne22hRDtT5J3sZS8AgCY+XNhsvQkgrKN/9TURgfwCWYeYuZ9AO4CsJ+Zv8HMZWa2ABwBcNob04iok4jeAOCjAL4eTr4VwI+Z+VthDA8C+BMAH5+w+N8z80Nhm68CuB/Ae85yv8HM25n5CWauMfN9APZM2IceAL/HzLuYuTzD1X4LTck7guSnAeC+6RYioiQRXYPgC8rXwwR2JsfmpBiJKIngC8mtzLwvXO7RsO27AOxg5n9k5goz3wvghwh6cse5AD7GzAPMfALAbQiSuLP1MQAfZeaDzDyKIIF9KxHpk7S9Gqcm29Ppwqk98sMIEt6ZzJ/OeBJdCN+r7cx8SulOk+8z87+HXzS+DCAL4M+Z+QAzDwP4BoIvvgiP0W8C+G1mfp6ZhxCUkUXx0tUoFUAj/GyeYOYfzCDmZhEEx/EYMx8D8NsAXtPU+//7CMqTngq/FHwBwC4Av34G6zhjzHwHMx8NE/lv4tTfGSUAn2Tm0bANADwLoILgi5QQQkxKknexlFwEYDO9dOMkI0icVzW1qTLz8abXFQDPT1hPBUBymu28JVz3swgSul9n5u+E8zYi6Mlv9hiACyksHwntndBmJ4IewbNCRL8XlkOUw9guRZBAjauGpSxn4i4Ay4hoc/j6zQC+x8z+FO27wm0fQ1Df+08APhzOm+mxaY7xfAQ9l7sm2dZFAN4wYX3vm7C+Q8xca3pdAtDcGztjRJRCcI/DzqbtHQSgAZis7nwFgi+BC8HtAJ4BsJ+I/ieFpUPT2Nf08/iVgucnTBs/P9YC8Jl5z/jM8PPxBIIrJkDQa/9eInqIiN4VXsE5E6Xwy9f4+g8BGAVwfti7vgyTn3PNV2GmXMcZxgIAoKDU7mtEdJiIHACPI/iC0OyB8SsvTdtlBJ+Lc7nKJoRoc5K8i6WEAPw0vATf/O/lTW1qkyznnuF2/jlcbw8z38TMP2qaZ0wRFxBcqh83sbdWmTB/ovhUM4jo3QjKTX4fQD8HN40+PqHZZPs9LWZuIChBeEdY9vImAN+ZZpHh8H1JMfNmZv5CU3nO2RwbNYxjsveFAPzdJOt7/zTrOxfjv0tzk2zzhUnapwAUz2D9Awh615t1Axia4fwpMbPNzO9CUDrTD2APEd08zSL1SaZNdY4YwIs3PDcjhJ/n8CrLBgTlYR8H8EBY9jRTk13ZGD9fJibMp2x/BuuYynQx3o7g/X9F2O7SSdpUJ5kGAGM4yy+RQoilQZJ30c48nPwZ34Wgdzc6Rfv58CSALROmbQHw7IQkdOMkbcZ7PEs4ted/A6Z2LYJSh58wcyns2Vx9RlEHJr6fQFA68xYEZSAegtrls3E2x+Z5ABoRXTjF+q4+y1jGTba/k2LmMQBHAVwzw3WXESTwM/UoTq1fvxrBFZmZzD+tsBzp/QhGrPno6drP0B4EifJF4xPCK0ybw3nj2/aY+XsI9qETL40yM5NjECWi85rWvwbBiEbPI/jychSTn3N7ml5Ptw5g8nNu/TQxXQvgL5j5ufAL6rrT7EOzNILPhxBCTEqSd9HODiCoW9XCG8B+BuAwgK8SUY6IUkT0Mpqlcbtn6O8AvIqI3hNu/xoE5SGfndDuN4no+rA+/LcQJDXfDOc9CeACInpjOKrFjQhqv6eyH8BVRNRHRL0IbuKcrKfxdCa+n0BwU14UQfnLnVP0gs/EGR+bMGG+HcDfENFqIko09Rj/I4BOIvoMEfUQUZaIfoWIJn4pms4BAK8kosgMbyD8SwCfI6LrKBhhZS0RvX2KtkcBrDyDWP4JwTH8QLgv70Fwg+g3Zzh/SkR0Yxhzgoh6AFyCoOTnnDFzCcG9C18O349OAJ8BYCO4oRjhuXA+ERnhtjvw0nCZMzkGPoC/Cj83/Qjq8K2wfp0RfBn5X0R0KQWj2HwYQcnMP8xkHeH8xwG8O/ycdRDRHyIok5rKfgTnSoyIrgDwyZm+Z+F6j55BeyHEEiPJu2hnnwJwM4Jesw+Ef8jfgGBUkT0AjiP4Iz1d/fqsYuYDCIYJvBXAIIIyky8y87cmNP0cghFphhH0gr51PJHg4Eba30WQlIwA+J94abSKyXwVQQ39fgC/RNArPV15y1ROej/DWBhB4vgOzHyUmVOcw7H5MIJEcyeC9/MvwvVVEYwusgVBIngEwKdxZl9aPgNgOYACgD+dQfu/RvDl7NsISh+2IxgFZjLjQ2/OCDMPIBjJ50MATiAYqeTtzPz8TOafRgJBgj0EYDeCco4/mmlsM/ARADsQXJU5gqCE5HXMPF5qsxXAAwg+V98B8AfM/Fg4bybH4DCC0VweRdBT7gL4YNP8zyK4QvSvCN6btwN4DTMXzmAd/xvBebMznN8L4G+m2ecPIrgiVUBw/n0Qk5cbnYSI1iHo8d9xurZCiKWLzr6jTAgxFygYr/19zHxPi0OZEQrGoP88M69pdSyLBRG9GcEXtLXncLViyaNgzP2vM/PqVq5jtlDw/IVXM/MrWx2LEGLhkp53IcS5ejOCsb7FzFkIbrht1VOAxQIT3gvwXxCUtQkhxJTOdEguIYQYTzQMANsAvB4zGPdevISDB0n9DoDPENFPpxleUywdvwHgwFmMcy+EWGIkeRdCnI3lCOqjRwC8l5kXypjliwYz34VgrHwhwMHD2L7a6jiEEAuf1LwLIYQQQgixSEjNuxBCCCGEEIuEJO9CCCGEEEIsEpK8CyGEEEIIsUhI8i6EEEIIIcQiIcm7EEIIIYQQi4Qk70IIIYQQQiwSkrwLIYQQQgixSEjyLoQQQgghxCLx/wNjjEgTJFpWOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (-9223371939882010988)>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "make_plot(pop_g, p.aes(x='PopQ', y='items', fill='gender'),\n",
    "          p.geom_area(),\n",
    "          p.scale_fill_brewer('qual', 'Set2'),\n",
    "          p.scale_y_continuous(labels=lbl_pct),\n",
    "          p.scale_x_continuous(expand=(0,0)),\n",
    "          p.facet_grid('Set ~'),\n",
    "          p.labs(x='Item Popularity Percentile (100 is most popular)',\n",
    "                 y='% of Books',\n",
    "                 fill='Gender'),\n",
    "          file='gender-by-pop', width=8, height=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "papermill": {
     "duration": 0.050973,
     "end_time": "2020-09-17T22:29:04.873328",
     "exception": false,
     "start_time": "2020-09-17T22:29:04.822355",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  },
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