Age X Disorder.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ages X Disorder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "Age_avg_onset = pd.read_excel('.\\Ages_Disorder\\AverageAgeOfOnset.xlsx', index_col=None)\n",
    "Disorder_Age = pd.read_excel('.\\Ages_Disorder\\Disorder_age.xlsx', index_col=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Average_age_onset_id', 'Disorder_id', 'Average_age_onset',\n",
       "       'Onset_class_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_avg_onset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6577, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Disorder_Age.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Disorder_id', 'Disorder_name', 'Disorder_id_2', 'Disorder_type',\n",
       "       'Disorder_type_id', 'Disorder_group', 'Disorder_group_id',\n",
       "       'Average_age_death', 'Average_age_death_id', 'Inheritance_type',\n",
       "       'Inheritance_type_id'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Disorder_Age.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Neonatal             2855\n",
       "Infancy              2305\n",
       "Childhood            1450\n",
       "Adult                1267\n",
       "All ages              832\n",
       "Adolescent            667\n",
       "Antenatal             621\n",
       "Elderly               245\n",
       "No data available      40\n",
       "Name: Average_age_onset, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_avg_onset.Average_age_onset.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No data available         1207\n",
       "normal life expectancy    1026\n",
       "any age                    447\n",
       "adult                      270\n",
       "early childhood            235\n",
       "infantile                  209\n",
       "adolescent                  97\n",
       "elderly                     64\n",
       "embryofetal                 21\n",
       "late childhood              20\n",
       "young Adult                 18\n",
       "stillbirth                  13\n",
       "Not yet documented           4\n",
       "Name: Average_age_death, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Disorder_Age.Average_age_death.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Autosomal recessive          1890\n",
       "Autosomal dominant           1660\n",
       "Not applicable                981\n",
       "Unknown                       252\n",
       "X-linked recessive            252\n",
       "Multigenic/multifactorial     171\n",
       "X-linked dominant              59\n",
       "Mitochondrial inheritance      20\n",
       "No data available              17\n",
       "Semi-dominant                   4\n",
       "Y-linked                        1\n",
       "Name: Inheritance_type, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Disorder_Age.Inheritance_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A disease should have just a type of Inheritance_type. In fact look below..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>Disorder_id</th>\n",
       "      <th>Inheritance_type</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Autosomal recessive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Autosomal dominant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Autosomal recessive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>Autosomal recessive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>Autosomal recessive</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5302</th>\n",
       "      <td>6571</td>\n",
       "      <td>Autosomal dominant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5303</th>\n",
       "      <td>6572</td>\n",
       "      <td>Autosomal dominant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5304</th>\n",
       "      <td>6573</td>\n",
       "      <td>Autosomal dominant</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5305</th>\n",
       "      <td>6575</td>\n",
       "      <td>Multigenic/multifactorial</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5306</th>\n",
       "      <td>6576</td>\n",
       "      <td>Multigenic/multifactorial</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5307 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Disorder_id           Inheritance_type  Time\n",
       "0               1        Autosomal recessive     1\n",
       "1               2         Autosomal dominant     1\n",
       "2               4        Autosomal recessive     1\n",
       "3               7        Autosomal recessive     1\n",
       "4               9        Autosomal recessive     1\n",
       "...           ...                        ...   ...\n",
       "5302         6571         Autosomal dominant     1\n",
       "5303         6572         Autosomal dominant     1\n",
       "5304         6573         Autosomal dominant     1\n",
       "5305         6575  Multigenic/multifactorial     1\n",
       "5306         6576  Multigenic/multifactorial     1\n",
       "\n",
       "[5307 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Disorder_Age.groupby([\"Disorder_id\", \"Inheritance_type\"]).size().reset_index(name=\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Age onset and of death per disease \n",
    "### ALS as example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Disorder_id</th>\n",
       "      <th>Average_age_death</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>any age</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>No data available</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>any age</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>No data available</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>No data available</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3626</th>\n",
       "      <td>6566</td>\n",
       "      <td>normal life expectancy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3627</th>\n",
       "      <td>6569</td>\n",
       "      <td>normal life expectancy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3628</th>\n",
       "      <td>6570</td>\n",
       "      <td>early childhood</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3629</th>\n",
       "      <td>6574</td>\n",
       "      <td>No data available</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3630</th>\n",
       "      <td>6577</td>\n",
       "      <td>infantile</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3631 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Disorder_id       Average_age_death  Time\n",
       "0               2                 any age     1\n",
       "1               3       No data available     1\n",
       "2               4                 any age     1\n",
       "3               5       No data available     1\n",
       "4               6       No data available     1\n",
       "...           ...                     ...   ...\n",
       "3626         6566  normal life expectancy     1\n",
       "3627         6569  normal life expectancy     1\n",
       "3628         6570         early childhood     1\n",
       "3629         6574       No data available     1\n",
       "3630         6577               infantile     1\n",
       "\n",
       "[3631 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Disorder_Age.groupby([\"Disorder_id\", \"Average_age_death\"]).size().reset_index(name=\"Time\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!! PROBLEM !! (but not a big deal) : one ID can have multiple Average_age_onset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disorder_id</th>\n",
       "      <th>Average_age_onset</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Infancy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Neonatal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>All ages</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Childhood</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Infancy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10277</th>\n",
       "      <td>6575</td>\n",
       "      <td>Neonatal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10278</th>\n",
       "      <td>6576</td>\n",
       "      <td>Infancy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10279</th>\n",
       "      <td>6576</td>\n",
       "      <td>Neonatal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10280</th>\n",
       "      <td>6577</td>\n",
       "      <td>Infancy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10281</th>\n",
       "      <td>6577</td>\n",
       "      <td>Neonatal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10282 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Disorder_id Average_age_onset  Time\n",
       "0                1           Infancy     1\n",
       "1                1          Neonatal     1\n",
       "2                2          All ages     1\n",
       "3                3         Childhood     1\n",
       "4                3           Infancy     1\n",
       "...            ...               ...   ...\n",
       "10277         6575          Neonatal     1\n",
       "10278         6576           Infancy     1\n",
       "10279         6576          Neonatal     1\n",
       "10280         6577           Infancy     1\n",
       "10281         6577          Neonatal     1\n",
       "\n",
       "[10282 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(Age_avg_onset.groupby([\"Disorder_id\", \"Average_age_onset\"]).size().reset_index(name=\"Time\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's merge everything in a table and let's make it clear removing IDs. All the info we want are in \"df\". We just need to filter it with the disease ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disorder_id_2</th>\n",
       "      <th>Disorder_name</th>\n",
       "      <th>Disorder_type</th>\n",
       "      <th>Disorder_group</th>\n",
       "      <th>Inheritance_type</th>\n",
       "      <th>Average_age_onset</th>\n",
       "      <th>Average_age_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17601</td>\n",
       "      <td>Multiple epiphyseal dysplasia, Al-Gazali type</td>\n",
       "      <td>Disease</td>\n",
       "      <td>Disorder</td>\n",
       "      <td>Autosomal recessive</td>\n",
       "      <td>Infancy</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17601</td>\n",
       "      <td>Multiple epiphyseal dysplasia, Al-Gazali type</td>\n",
       "      <td>Disease</td>\n",
       "      <td>Disorder</td>\n",
       "      <td>Autosomal recessive</td>\n",
       "      <td>Neonatal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Alexander disease</td>\n",
       "      <td>Disease</td>\n",
       "      <td>Disorder</td>\n",
       "      <td>Autosomal dominant</td>\n",
       "      <td>All ages</td>\n",
       "      <td>any age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17603</td>\n",
       "      <td>Multiple epiphyseal dysplasia, with miniepiphyses</td>\n",
       "      <td>Disease</td>\n",
       "      <td>Disorder</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Childhood</td>\n",
       "      <td>No data available</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17603</td>\n",
       "      <td>Multiple epiphyseal dysplasia, with miniepiphyses</td>\n",
       "      <td>Disease</td>\n",
       "      <td>Disorder</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Infancy</td>\n",
       "      <td>No data available</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Disorder_id_2                                      Disorder_name  \\\n",
       "0          17601      Multiple epiphyseal dysplasia, Al-Gazali type   \n",
       "1          17601      Multiple epiphyseal dysplasia, Al-Gazali type   \n",
       "2              2                                  Alexander disease   \n",
       "3          17603  Multiple epiphyseal dysplasia, with miniepiphyses   \n",
       "4          17603  Multiple epiphyseal dysplasia, with miniepiphyses   \n",
       "\n",
       "  Disorder_type Disorder_group     Inheritance_type Average_age_onset  \\\n",
       "0       Disease       Disorder  Autosomal recessive           Infancy   \n",
       "1       Disease       Disorder  Autosomal recessive          Neonatal   \n",
       "2       Disease       Disorder   Autosomal dominant          All ages   \n",
       "3       Disease       Disorder                  NaN         Childhood   \n",
       "4       Disease       Disorder                  NaN           Infancy   \n",
       "\n",
       "   Average_age_death  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2            any age  \n",
       "3  No data available  \n",
       "4  No data available  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = Disorder_Age.merge(Age_avg_onset[['Disorder_id', 'Average_age_onset']], on='Disorder_id', how='left')\n",
    "df = df[['Disorder_id_2', 'Disorder_name', 'Disorder_type', 'Disorder_group','Inheritance_type',\n",
    "         'Average_age_onset', 'Average_age_death']]\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to filter for the disease to return the info related to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disorder_id_2</th>\n",
       "      <th>Disorder_name</th>\n",
       "      <th>Disorder_type</th>\n",
       "      <th>Disorder_group</th>\n",
       "      <th>Inheritance_type</th>\n",
       "      <th>Average_age_onset</th>\n",
       "      <th>Average_age_death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>106</td>\n",
       "      <td>Amyotrophic lateral sclerosis</td>\n",
       "      <td>Disease</td>\n",
       "      <td>Disorder</td>\n",
       "      <td>Autosomal dominant</td>\n",
       "      <td>Adult</td>\n",
       "      <td>adult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Disorder_id_2                  Disorder_name Disorder_type  \\\n",
       "183            106  Amyotrophic lateral sclerosis       Disease   \n",
       "\n",
       "    Disorder_group    Inheritance_type Average_age_onset Average_age_death  \n",
       "183       Disorder  Autosomal dominant             Adult             adult  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Disorder_id_2'] == 106]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"df_age_disorder.xlsx\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only problem is that if a disease has multiple Average_age_onset there are multiple entries.\n",
    "\n",
    "Possible solution : We can take the \"lowest\"  Average_age_onset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inheritance type of the diseases in the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Inheritance_type</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autosomal recessive</td>\n",
       "      <td>1890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Autosomal dominant</td>\n",
       "      <td>1660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not applicable</td>\n",
       "      <td>981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>X-linked recessive</td>\n",
       "      <td>252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Multigenic/multifactorial</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>X-linked dominant</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mitochondrial inheritance</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No data available</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Semi-dominant</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Y-linked</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Inheritance_type  Count\n",
       "0         Autosomal recessive   1890\n",
       "1          Autosomal dominant   1660\n",
       "2              Not applicable    981\n",
       "3                     Unknown    252\n",
       "4          X-linked recessive    252\n",
       "5   Multigenic/multifactorial    171\n",
       "6           X-linked dominant     59\n",
       "7   Mitochondrial inheritance     20\n",
       "8           No data available     17\n",
       "9               Semi-dominant      4\n",
       "10                   Y-linked      1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Inheritance_df = pd.DataFrame(Disorder_Age.Inheritance_type.value_counts())\n",
    "Inheritance_df = Inheritance_df.reset_index()\n",
    "Inheritance_df = Inheritance_df.rename(columns = {'index':'Inheritance_type','Inheritance_type':'Count' })\n",
    "Inheritance_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x792 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing the required library\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# a4_dims = (11.7, 8.27)\n",
    "# fig, ax = pyplot.subplots(figsize=a4_dims)    \n",
    "plt.figure(figsize=(12,11))\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "j = sns.barplot(x = \"Count\", y = \"Inheritance_type\", data = Inheritance_df, palette = 'hls')\n",
    "\n",
    "for p in j.patches:\n",
    "    width = p.get_width()\n",
    "    plt.text(80+p.get_width(), p.get_y()+0.55*p.get_height(),\n",
    "             '{:1.2f}'.format(width),\n",
    "             ha='center', va='center')\n",
    "    \n",
    "plt.xlabel(\"Count of Rare Diseases\", size=14)\n",
    "plt.ylabel(\"Inheritance Type\", size=14) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average age on set of rare diseases in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average_age_onset</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Neonatal</td>\n",
       "      <td>2855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Infancy</td>\n",
       "      <td>2305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Childhood</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adult</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>All ages</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Adolescent</td>\n",
       "      <td>667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Antenatal</td>\n",
       "      <td>621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Elderly</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No data available</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Average_age_onset  Count\n",
       "0           Neonatal   2855\n",
       "1            Infancy   2305\n",
       "2          Childhood   1450\n",
       "3              Adult   1267\n",
       "4           All ages    832\n",
       "5         Adolescent    667\n",
       "6          Antenatal    621\n",
       "7            Elderly    245\n",
       "8  No data available     40"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_onset = pd.DataFrame(Age_avg_onset.Average_age_onset.value_counts())\n",
    "Age_onset = Age_onset.reset_index()\n",
    "Age_onset = Age_onset.rename(columns = {'index':'Average_age_onset','Average_age_onset':'Count' })\n",
    "Age_onset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ATZw4UQ899JAkaciQIRozZozq1q2rwMBAeXl5KTw8XLNnz5bD4VBeXp6OHDmisLAwzZ07VxUqVNATTzyhrKws1axZs8g7NIrbLjIyUlu3blV4eLi2bdum+++/311PAf6AiI0/gMuXLys2NlYPP/yw6tSp4+5xAACAB1q4cKHOnz+v+fPnu66b+OXTMX19fRUQEKDk5GRVqVJFcXFxio2NldPp1MiRI+Xv76/BgwcrPj5eW7dulbe3t6ZMmSJJWrZsmUJCQtSuXbtrbhcTE6OEhATFxMTI19dXs2bNcufTgD8Yi/OXiwvgUWw2m/7c/dqfFgEAAG59z8byEfkoGzabTZmZmTe1LReIAwAAADCC2AAAAABgBLEBAAAAwAhiAwAAAIARxAYAAAAAI4gNAAAAAEYQGwAAAACMIDYAAAAAGEFsAAAAADCC2AAAAABgBLEBAAAAwAhiAwAAAIARxAYAAAAAI4gNAAAAAEYQGwAAAACMIDYAAAAAGEFsAAAAADCC2AAAAABgBLEBAAAAwAhiAwAAAIARPu4eADfv2diq7h4BAAC4SX6BUz7eFnePAVwXZzYAAAA8EKEBT0BsAAAAADCC2AAAAABgBLEBAAAAwAhiAwAAAIARxAYAAAAAI4gNAAAAAEYQGwAAAACMIDYAAAAAGEFsAAAAADCC2AAAAABgBLEBAAAAwAhiAwDg0QoKne4eAQBQDB93D4Cbt/q/7e4eAQDcrsfDVnePAAAoBmc2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACOIDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACOIDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACNuq9g4fPiwBg8erLi4OD3++OOaO3eudu7cqZEjR1617uTJk/Xdd98pJSVFK1euvOr+Fi1aSJLGjBmjbdu2ldqMM2fOVGZmZqntDwAAAHAXH3cPUFbOnz+vUaNGKSUlRXXr1lVBQYFGjBihKlWqXHP98ePHl/GEAIDStG/fPs2cOVNpaWmuZRs2bNDy5cuVnp4uSUpOTtbevXsVGBgoSZo/f758fX0VHx+vM2fOKDAwUNOmTVOlSpWK7DsjI0OrVq2Sj4+Phg4dqjZt2ujy5cs33A4Abje3TWx88MEHioqKUt26dSVJ3t7emjZtmj799FOtXr1agwYN0tmzZ9WmTRsNGzZMcXFxSkpKcm1fUFCgxMREffXVV7rrrruUl5fnui89PV1Lly6V3W5XUlKSwsPDlZqaqo0bN8rHx0fNmjVTfHy8zp8/r/j4eNntdlfsPPTQQ/rHP/6hBQsWqFKlSrpy5YpCQ0PL+NkBgFvLkiVLtH79egUEBLiWHTx4UGvWrJHT6XQtO3DggJYuXVokCpYtW6awsDANGzZMGzdu1Pz58zVhwgTX/dnZ2UpLS9PatWvlcDgUGxurFi1aaOXKldfdDgBuR7fN26h+/PFH3XXXXUWWBQYGytfXVw6HQ/Pnz9eKFSu0fPnya26/bds2ORwOZWRkaPTo0bp06ZLrviZNmuiNN95Qnz59lJmZqS+//FLvvvuuVq1apVWrVunYsWPasmWLFixYoD//+c9asWKF5syZo/Hjx6uwsFAzZszQsmXL9Oqrr6pcuXLFHkN6erpsNptsNlvpPCkAcIsKCQlRSkqK63ZOTo5mzpypcePGuZYVFhbq2LFjmjhxonr37q01a9ZIkvbs2aOHH35YkhQdHa0dO3YU2ff+/fvVtGlT+fn5KSgoSCEhIcrKyrrhdgBwO7ptzmzUrFlTX3zxRZFlx48f1+7du9WgQQP5+flJknx8rv2UHD58WOHh4a591ahRw3VfkyZNJEmVK1fW5cuXdfToUd13333y9fWVJDVr1kyHDx/WkSNH1LVrV0lStWrVZLVadfbsWVmtVlWsWFGS1LRp02KPoVevXurVq5ckERwAcB0dO3bUiRMnJP18Znr8+PEaN26c/P39XetcvHhRffr0Uf/+/VVQUKC+ffvqnnvukd1uV1BQkKSfX5S6cOFCkX3/+v5f1rHb7TfcDgBuR7fNmY02bdrov//7v/Xtt99Kkq5cuaKpU6eqYsWKslgsN9w+NDRUn332mSTp1KlTOnXqlOu+/7t9aGio9u/fr/z8fDmdTu3evVv16tVT/fr19cknn7j2cf78eVWoUEEXLlzQ2bNnJUmff/55qRwvAOBnBw4c0LFjx5SUlKRRo0bpq6++0uTJkxUQEKC+ffsqICBAVqtVDz74oLKysmS1WpWbmytJys3NVXBwcJH9/fr+X9YJCgq64XYAcDu6bc5sWK1WTZ06VRMmTJDT6VRubq7atGlTJACup3379tqzZ4969OihmjVrus5EXEvDhg3VuXNnxcTEqLCwUPfff7/at2+vBx54QOPGjdM//vEPXb58WS+88IJ8fHw0ZcoUDRw4UOXLly/2zAoA4OaEh4dr48aNkqQTJ05o1KhRGj9+vI4cOaKRI0dq3bp1Kiws1N69e9WtWzedPXtWW7duVXh4uLZt26b777//qv3Nnj1bDodDeXl5OnLkiMLCwhQZGXnd7QDgdmRx/vpKOXgMm82mmJFvuHsMAHC7Hg9br7n8l7DIyMgodtmSJUv03nvvydfXV48++qhiYmJ06dIlJSQkKDs7W76+vpo1a5aqVKmiZcuWKSQkRO3atVNGRobS09PldDr15JNPqmPHjsVuBwCezmaz3fRXMxAbHorYAICfFRcbAIDS8Xti47a5ZgMAAABA2SI2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACOIDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACOIDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIH3cPgJvX42Gru0cAALcrKHTK28vi7jEAANfAmQ0AgEcjNADgj4vYAAAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARhAbAAAAAIwgNgAAAAAYQWwAAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBHEBgCgWIWFTnePAADwYD7uHgA3b+cXOe4eAcAt7sHGFd09AgDAg3FmAwAAAIARxAYAAAAAI4gNAAAAAEYQGwAAAACMIDYAAAAAGEFsAAAAADCC2AAAAABgBLEBAAAAwAhiAwAAAIARxAYAAAAAI4gNAAAAAEYQGwAAAACMIDYAAAAAGEFsAAAAADCC2AAAAABgBLEBAAAAwAhiAwAAAIARxAYAAAAAI4gNAAAAAEYQGwAAAACMIDYAAAAAGEFslKLFixerZcuWcjgcV923cuVKpaSkFLttSkqKVq5cKUlavny5sRkBAACAskJslKINGzaoS5cu2rhx4+/az4IFC0ppIgAAAMB9iI1SsmvXLoWEhKh3795asWKFJOmTTz6RzWZT//799V//9V+SpBMnTqhnz56u7Xr27KkTJ064bi9YsEDnzp1TUlJSmc4PAL/Fvn37FBcXJ0k6ePCgYmNjFRcXp4EDB+r06dOSpK1bt6pnz57q2bOnkpKS5HQ6tXjxYsXFxSkuLk6PPvqoWrRocdW+MzIyZLPZ1LNnT23ZskWSdPnyZQ0bNkyxsbF64okndPbs2bI7WADATSM2Ssnq1avVo0cPhYaGys/PT/v27dOUKVM0a9YsLVu2TLVr1y7RfoYOHary5ctfMzbS09Nls9lks9lKeXoAKLklS5ZowoQJrreMTp48WYmJiUpLS9Nf/vIXLVmyRHa7XTNmzNDChQuVkZGhWrVqKScnR4MHD1ZaWprS0tJUvXp1TZ06tci+s7OzlZaWplWrVunVV1/VSy+9pLy8PK1cuVJhYWF688039dhjj2n+/PnuOHQAwG9EbJSCc+fOadu2bXrjjTc0cOBA2e12LV++XKdOnVK9evUkSZGRkdfc1ul0lvhxevXqpczMTGVmZpbK3ABwM0JCQopcg/bSSy+pUaNGkqSCggL5+/vr008/VVhYmKZNm6bY2FhVrlxZlSpVcm3z/vvvKzg4WA8//HCRfe/fv19NmzaVn5+fgoKCFBISoqysLO3Zs8e1bnR0tHbs2FEGRwoA+L183D3ArWD9+vV6/PHHlZCQIEm6dOmS2rVrp3LlyunIkSOqX7++Pv/8c5UvX17+/v46c+aMCgoKlJubW+QtVL/4LQECAGWtY8eORf7uqlq1qiRp7969Wr58uVasWKEPP/xQu3bt0ltvvaU77rhDf/3rXxUREeF6AWbRokV66aWXrtq33W5XUFCQ63ZgYKDsdnuR5YGBgbpw4YLJQwQAlBJioxSsXr1a06dPd90OCAhQhw4dVL16dSUkJCgwMFCBgYEqX768qlSpohYtWqh79+4KCQlRnTp1rtpf/fr19eyzz2rmzJlleRgAcNM2bdqkBQsWaPHixapUqZIqVKige++9V1WqVJEkNWvWTAcPHlS9evX01VdfKTg4+Jp//1mtVuXm5rpu5+bmKigoqMjy3NxcBQcHl82BAQB+F2KjFKxfv/6qZb9cczFkyJCr7nvhhReuWjZs2DDXz2lpaaU3HAAY9vbbbys9PV1paWmqUKGCJOmee+7RoUOHdPbsWQUHB2vfvn2uD8f46KOPFB0dfc19hYeHa/bs2XI4HMrLy9ORI0cUFhamyMhIbd26VeHh4dq2bZvuv//+Mjs+AMDNIzYAADetoKBAkydPVo0aNVwvmjzwwAMaPny4Ro8erUGDBkmSOnXqpLCwMEnS119/fdWnUC1btkwhISFq166d4uLiFBsbK6fTqZEjR8rf318xMTFKSEhQTEyMfH19NWvWrLI9UADATbE4uUDAI9lsNj2X/Kq7xwBwi3uwcUV3jwAAcDObzXbTH1DEp1EBAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARhAbAAAAAIwgNgAAAAAYQWwAAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARhAbAAAAAIwgNgAAAAAYQWwAAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBE+JVlp3rx5GjhwoAICAoost9vteuWVV5SQkGBkOFzfg40runsEALe4wkKnvLws7h4DAOChio2Nr776StnZ2ZKkV155RWFhYQoKCrpqnZUrVxIbAHCLIjQAAL9HsbFx5swZ9e/f33V7+PDhV61zxx13aMCAAWYmAwAAAODRio2NqKgoZWVlSZLatm2rNWvWqFKlSmU2GAAAAADPVqILxDdv3qxKlSrp1KlT2rlzpy5fvqzTp0+bng0AAACABytRbFy8eFHDhw9Xq1atNGDAAGVnZysxMVG9evXSmTNnTM8IAAAAwAOVKDamTp2qnJwcffDBB/L395ckjRkzRhaLRcnJyUYHBAAAAOCZSvw2qrFjx6pWrVquZXXq1FFSUpK2b99ubDgAAAAAnqtEsXH58mX5+vpetTwvL09Op7PUhwIAAADg+UoUG+3atdOsWbN0/vx517JvvvlGL774olq3bm1qNgAAAAAerESxkZiYKF9fX0VFRenSpUt69NFH1alTJ1WoUEHjx483PSMAAAAAD1Ts92z8mtVqVUpKio4fP64jR44oPz9f9erVU/369U3PBwAAAMBDlejMhiR9/fXXqlChglq3bq077rhDy5cv19q1a03OBgAAAMCDlSg2MjMz9cgjj+iLL75QVlaWhgwZom+++UazZs3SvHnzTM8I4DZVWFjo7hEAAMDvUKK3US1atEjJycmKiorSlClT9Kc//UnLli3Tzp07lZCQoKefftr0nLiGo0eOuHsEwKhQ3qoJAIBHK9GZje+//14PPvigJGnLli1q27atJKl27do6d+6cuekAAAAAeKwSndm46667tH37dlWtWlXffvut2rVrJ0l66623FBoaanRAAAAAAJ6pRLExbNgwPfvssyooKFDXrl3VqFEjTZ06VYjkVSwAACAASURBVOnp6VyzAQAAAOCaShQbnTp1UvPmzXXq1Ck1atRIktSzZ08NGjRIlStXNjogAAAAAM9UotiQpIoVKyovL0/Hjx+XJPn6+urSpUs6fvy47rrrLmMDAgAAAPBMJYqNrVu3KjExUdnZ2UWWO51OWSwWHTx40MhwAAAAADxXiWJj5syZatq0qf72t7/JarWangkAAADALaBEsXH8+HHNmTOHT54CAAAAUGIl+p6NZs2aaf/+/aZnAQAAAHALKdGZjcjISD3//PPavHmzQkJC5OvrW+T+ESNGGBkOAAAAgOcqUWzs2LFD99xzj3JycpSTk1PkPovFYmQwAAAAAJ6tRLGRlpZmeg4AAAAAt5gbxsbZs2f17rvv6rPPPlNOTo4qVKigiIgIdenSRZUqVSqLGQEAAAB4oOteIL5p0yZ17NhRs2fPVk5OjsqXL69Tp05p3rx56tChgzZt2lRWcwIAAADwMMWe2di7d68SEhI0YsQI9evXTz4+/7uqw+HQ66+/rrFjx6pmzZqKiIgok2EBAAAAeI5iz2wsWrRI/+///T8NGjSoSGhIkr+/vwYPHqz+/ftr4cKFxocEAAAA4HmKjY19+/bpscceu+7GXbt21WeffVbqQwEAAADwfMXGhsPhkJ+f33U39vX1VUFBQakPBQAAAMDzFRsbDRs21ObNm6+78ebNm3X33XeX+lAAAAAAPF+xsdG/f3/NmTNHH3300TXvf//99zV37lwNHDjQ2HAAAAAAPFexn0bVsWNHHT16VIMGDVKTJk0UHh6u4OBgnT59Wv/+9791+PBhjR49Wq1bty7DcQEAAAB4iut+qd/QoUPVqlUrrV27Vp9//rnOnTun8uXLq1mzZpo+fboaNGhQVnMCAAAA8DA3/Abxxo0bq3HjxmUxyx/K4sWL9cYbb+iDDz6Qv7+/4uLilJSUpE2bNqly5cqKiYlx94gAAADAH9oNY+N2tWHDBnXp0kUbN26UzWZz9zgA/o8rV65ozJgxOnnypLy8vPTiiy/K6XQqMTFRTqdTd999txITE+Xt7a3XXntNGzdulCS1atVKTz/9dJF9HTt2TGPGjJHFYlGDBg00adIkeXl5KSMjQ6tWrZKPj4+GDh2qNm3auONQAQDwWMTGNezatUshISHq3bu34uPjbxgbP/zwg5KSkuRwOPTTTz/pqaeeUvv27bVlyxbNnTtXVqtV5cuXV8OGDTVs2DDNmjVLu3fvltPpVL9+/dS5c2etWLFCb731lry8vBQZGamEhIQyOlrAM23dulX5+flatWqVtm/frtmzZ6ugoECjRo3SAw88oDFjxrg+MW/9+vVavXq1LBaLYmNj1b59+yKfpDdlyhQ988wzioqK0sSJE/XBBx8oIiJCaWlpWrt2rRwOh2JjY9WiRYsbfiQ4AAD4X8TGNaxevVo9evRQaGio/Pz8tG/fvuuuf/ToUfXv319RUVHau3evUlJS1KZNGyUnJys9PV2VK1fW6NGjJf38C9KJEye0atUqORwO9ezZUy1atFBmZqYSExMVERGhN998U/n5+Vd9c3t6errS09ONHTfgSerVq6eCggIVFhbKbrfLx8dHs2fPlre3t/Ly8pSdna0777xT1atX19KlS+Xt7S1Jys/Pl7+/f5F9HThwQM2bN5ckRUdHa/v27fLy8lLTpk3l5+cnPz8/hYSEKCsrS+Hh4WV+rAAAeKpiY6Nt27ZauXKlqlWrpnnz5mngwIEKCAgoy9nc4ty5c9q2bZvOnj2rtLQ02e12LV++/LrbVKlSRQsWLNCaNWtksViUn5+vs2fPymq1qnLlypKkZs2a6fTp0zp06JAOHDiguLg4ST//4vPdd99pypQpSk1N1cyZMxURESGn03nV4/Tq1Uu9evWSJN7ahdveHXfcoZMnT6pz587KycnRwoUL5e3trZMnT6p///6yWq2qV6+efH19ValSJTmdTk2fPl2NGzdWvXr1iuzL6XTKYrFIkgIDA3XhwgXZ7XYFBQW51gkMDJTdbi/TYwQAwNMV+z0bP/30k7Zt26Zvv/1Wr7zyig4fPqzjx49f859byfr16/X4448rNTVVr776qjIyMrR9+3adPXu22G3mzJmjRx99VDNmzFBUVJScTqfuvPNO5ebmurb75exIaGiooqKilJaWptdff12dO3dW7dq1lZGRoeeff17Lly/XwYMH9emnn5bJ8QKe6rXXXlPLli31j3/8Q2+//bbGjBkjh8OhWrVq6f3331dMTIymTp0qSXI4HHr22WeVm5urSZMmXbUvL6///aswNzdXwcHBslqtys3NLbL81/EBAABurNgzG4MHD1ZiYqLr1b5fXlH/5RV3i8XiejXw4MGDZTBq2Vi9erWmT5/uuh0QEKAOHTpozZo1xW7TqVMnTZ48WYsWLVKNGjWUk5MjLy8vJSYm6oknnlBQUJAKCwtVp04dtW3bVh9//LFiY2N18eJFtW/fXlarVQ0bNlT37t1VsWJFVatWTffdd19ZHC7gsYKDg+Xr6ytJKl++vPLz8zVkyBBNmjRJdevWVWBgoLy8vOR0OvW3v/1NUVFRGjx48DX31bhxY+3atUtRUVHatm2bHnzwQYWHh2v27NlyOBzKy8vTkSNHFBYWVpaHCACAx7M4r/V+nf9x/vx5XbhwQe3atdPq1atVqVKla65Xq1YtYwN6skWLFql///7y8/PTs88+q5YtW+qxxx4rlX3bbDbNnDGjVPYF/FGF1q9f7H25ubkaN26csrOzdeXKFfXt21e1atXS9OnT5evrq4CAACUnJ2vfvn0aNWqUIiIiXNuOGjVKQUFBWr58uZKSkvT1118rMTFRV65cUWhoqJKTk+Xt7a2MjAylp6fL6XTqySefVMeOHcvisAEA+EOx2WzKzMy8qW2vGxu/+PjjjxUZGSkfHx/99NNPKiwsLDY88L+WL1+uNWvWqFy5cqpVq5amTJlSap9kQ2zgdnC92AAAAGXj98RGiT6Nqnnz5kpNTdXSpUuVk5Mj6ee3LcTGxmr48OE39cC3gz59+qhPnz7uHgMAAABwixLFxrx587RixQqNGDFCTZs2VWFhofbu3at58+bJ399fTz75pOk5AQAAAHiYEsVGenq6Jk+erLZt27qWNWrUSNWrV1dycjKxAQAAAOAqxX707a9dvHhRdevWvWp53bp1r/uRsAAAAABuXyWKjaZNmyo1NVUFBQWuZQUFBUpNTeXbdAEAAABcU4neRjV27Fj99a9/1YcffqjGjRtLkr744gsVFBRo6dKlRgcEAAAA4JlKFBv169fXu+++qw0bNujo0aPy9/dX69at9cgjj+iOO+4wPSMAAAAAD1Si2JCkihUrqm/fviZnAQAAAHALKdE1GwAAAADwWxEbAAAAAIwgNgAAAAAY8ZtiY8+ePVq7dq3sdrsOHTqkvLw8U3MBAAAA8HAlukD8zJkzGjJkiA4fPqy8vDw1b95cL7/8sg4dOqTU1FTVqVPH9JwAAAAAPEyJzmy8+OKLqlmzpnbu3Cl/f39J0owZM9SwYUMlJycbHRAAAACAZypRbOzYsUPDhg1TuXLlXMusVqtGjx6tvXv3GhsOAAAAgOcqUWx4eXnp0qVLVy3Pzs52nekAAAAAgF8rUWw88sgjSk5OVlZWliwWi+x2u7Zv365JkyapS5cupmcEAAAA4IFKdIF4fHy8XnrpJfXo0UNXrlxRt27d5O3trR49eig+Pt70jAAAAAA8UIliw8/PT2PGjNEzzzyj48ePq6CgQHfddZcCAwNNzwcAAADAQ5UoNnbv3n3Vsi+++EIWi0W+vr6qUqWKatasWerDAQAAAPBcJYqN8ePH68SJEyosLFT58uXldDp1/vx5WSwWeXt7Kz8/X/fdd59SUlJUtWpV0zPjf4TWr+/uEQCjCgsL5eX1m757FAAA/IGU6P/i3bp107333qt3331Xu3bt0scff6x//vOfatasmUaOHKkdO3aoWrVqfOcGgFJFaAAA4NlK9H/y119/Xc8//7zq1avnWnbXXXdp/PjxWrJkiSpWrKgRI0Zox44dxgYFAAAA4FlK/LLh2bNnr7msoKDAddtisZTOVAAAAAA8Xomu2ejevbsSEhI0fPhw3XvvvXI6nfr3v/+tefPmqVu3bsrJydGMGTPUvHlz0/MCAAAA8BAlio3Ro0crMDBQKSkp+vHHHyVJ1apVU58+fdSmTRv9+9//lo+PjyZOnGh0WAAAAACew+J0Op2/ZYOcnBxJ0saNG7Vu3Tp98cUXOnjwoJHhUDybzabMzEx3jwEAAIBb3O/5vbNEZzYkqaCgQFu3btVbb72lLVu2KD8/XxEREZo2bdpNPTAAAACAW9sNYyMrK0uZmZl65513lJOToypVqqigoEALFy5Uq1atymJGAAAAAB6o2Nh47bXXtG7dOh06dEh16tRRt27d1KFDB4WHh+uee+5RrVq1ynJOAAAAAB6m2NiYOnWq6tSpoxkzZqhLly58uRYAAACA36TYgpg5c6ZCQ0M1duxYNW/eXKNGjdKmTZtkt9vLcj4AAAAAHqrYMxuPPPKIHnnkEZ0/f17vvfee3nnnHT377LPy9vZWYWGhduzYoZCQEPn5+ZXlvAAAAAA8xA3fGxUcHKyePXvqjTfe0L/+9S+NGjVKTZo00eTJk/Xwww8rOTm5LOYEAAAA4GFK/NG3klS1alX1799f/fv317Fjx7RhwwZt2rTJ1GwAypCzsEAWL293jwEAAG4hvyk2fq1OnTp6+umn9fTTT5fmPPgNsvducfcIuIVUiWzj7hEAAMAtho+YAgAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARhAbAAAAAIwgNgAAAAAYQWwAAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARhAbAAAAAIwgNgAAAAAYQWwAAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARtzysbF48WK1bNlSDofjqvtWrlyplJSUYrdNSUnRypUrTY53lZ9++kkbNmwo08cEAAAATLjlY2PDhg3q0qWLNm7c6O5RSuTLL7/U5s2b3T0GAAAA8Lv5uHsAk3bt2qWQkBD17t1b8fHxstls+uSTT/T3v/9d5cuXl5eXlyIiIiRJqamp2rhxo3x8fNSsWTPFx8cX2desWbO0e/duOZ1O9evXT507d9aKFSv01ltvycvLS5GRkUpISNA333yjCRMm6MqVKypXrpxefvllORwOJSYmyuFwyN/fXy+++KIKCgo0evRoVa9eXcePH9e9996r559/XgsXLlRWVpbS09PVq1cvdzxtwDUtWrRImzdv1pUrVxQTE6O2bdtqwoQJOn/+vAoKCjR9+nTl5ubq73//u2ubzz77TK+88oqio6Ndy44dO6YxY8bIYrGoQYMGmjRpkry8vJSRkaFVq1bJx8dHQ4cOVZs2bdxxmAAAoBTd0rGxevVq9ejRQ6GhofLz89O+ffs0ZcoUzZo1S/Xq1dOkSZMk/Xw24d1333X9ojNs2DBt2bLFtZ+tW7fqxIkTWrVqlRwOh3r27KkWLVooMzNTiYmJioiI0Jtvvqn8/HxNmzZNgwcPVnR0tDZt2qQvvvhCa9asUVxcnFq1aqUdO3Zo5syZGjlypL755hu9+uqrCggIUPv27ZWdna0hQ4Zo1apV1wyN9PR0paenl9nzB/xi165d+vTTT7Vy5UpdunRJqampmjFjhrp27aouXbpo586dOnr0qFq3bq20tDRJ0rvvvquqVasWCQ1JmjJlip555hlFRUVp4sSJ+uCDDxQREaG0tDStXbtWDodDsbGxatGihfz8/NxxuAAAoJTcsrFx7tw5bdu2TWfPnlVaWprsdruWL1+uU6dOqV69epKkyMhIffvttzp69Kjuu+8++fr6SpKaNWumw4cPu/Z16NAhHThwQHFxcZKk/Px8fffdd5oyZYpSU1M1c+ZMRUREyOl06uuvv1bTpk0lSV26dJEk/f3vf9eiRYu0dOlSOZ1O1+OEhITIarVKkqpUqXLN60p+rVevXq4IsdlspfVUATf04YcfKiwsTE899ZTsdruee+45jR49Wg0bNlS/fv1Uq1YtjR8/3rX+xYsXlZKSouXLl1+1rwMHDqh58+aSpOjoaG3fvl1eXl5q2rSp/Pz85Ofnp5CQEGVlZSk8PLzMjhEAAJS+WzY21q9fr8cff1wJCQmSpEuXLqldu3YqV66cjhw5ovr16+vzzz9X+fLlFRoaqmXLlik/P1/e3t7avXu3HnvsMWVlZUmSQkNDFRUVpRdffFGFhYWaP3++ateurdmzZ+v555+Xv7+/Bg4cqE8//dS13z//+c9av369zp07p9DQUA0YMECRkZE6cuSIdu/eLUmyWCxXze3l5aXCwsKye6KAEsjJydF3332nhQsX6sSJExo6dKhOnjyp4OBgvfbaa5o3b56WLFmiESNGSJLWrFmjTp06qVKlSlfty+l0uv7bDwwM1IULF2S32xUUFORaJzAwUHa7vWwODgAAGHPLxsbq1as1ffp01+2AgAB16NBB1atXV0JCggIDAxUYGKjy5curYcOG6ty5s2JiYlRYWKj7779f7du3d8VG27Zt9fHHHys2NlYXL15U+/btZbVa1bBhQ3Xv3l0VK1ZUtWrVdN999+m5557TxIkTtWDBApUrV04zZsxQ69atlZSUJIfDocuXLxd5Bfj/CgkJ0aFDh/Taa6+pX79+pp8moEQqVKjgejtiaGio/P39VVBQoLZt20r6+c/Iyy+/7Fp/w4YNmjt37jX35eX1v59LkZubq+DgYFmtVuXm5hZZ/uv4AAAAnsnidDqd7h4Cv53NZtOiCcPcPQZuIVUii78ge8uWLXrjjTeUmpqqH3/8UX369FFYWJj+8pe/6LHHHtPrr7+uH374QQkJCbpw4YL69Omjt99++5r7GjJkiPr37++6ZuPBBx/UAw88oAEDBmjNmjXKy8tTjx499Pbbb8vf39/U4QIAgBKy2WzKzMy8qW1v2TMbAEpPmzZttHv3bnXv3l1Op1MTJ05UaGioJkyYoFWrVslqtWrWrFmSpK+//lq1atUqsv1XX32l5cuXKykpSQkJCUpMTNRLL72k0NBQdezYUd7e3oqLi1NsbKycTqdGjhxJaAAAcAvgzIaH4swGStv1zmwAAIDb1+85s3HLf6kfAAAAAPcgNgAAAAAYQWwAAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARhAbAAAAAIwgNgAAAAAYQWwAAAAAMILYAAAAAGAEsQEAAADACGIDAAAAgBHEBgAAAAAjiA0AAAAARhAbAAAAAIwgNgAAAAAYQWwAAAAAMILYAAAAAGCEj7sHwM2rEtnG3SPgFuIsLJDFy9vdYwAAgFsIZzYASBKhAQAASh2xAQAAAMAIYgMAAACAEcQGAAAAACOIDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACOIDeAW5yzId/cIAADgNuXj7gFw87I3LHX3CPAAVboOcvcIAADgNsWZDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACOIDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMAIYgMAAACAEcQGAAAAACOIDQAAAABGEBsAAAAAjCA2AAAAABhBbAAAAAAwgtgAAAAAYASxAQAAAMCI2zo2Fi9erJYtW8rhcFx3vfT0dF25cqVUHvOnn37Shg0brrtOixYtSuWxAAAAAHe6rWNjw4YN6tKlizZu3Hjd9RYtWqTCwsJSecwvv/xSmzdvLpV9AaVh0aJF6tWrl2w2m1avXq2DBw8qNjZWcXFxGjhwoE6fPu1a9+zZs+rQocM1A/3YsWOKiYlRbGysJk2a5Pozk5GRIZvNpp49e2rLli1ldlwAAMD9fNw9gLvs2rVLISEh6t27t+Lj42Wz2RQXF6e7775bhw8flt1u15w5c/TRRx8pOztbI0eO1Pz58zVr1izt3r1bTqdT/fr1U+fOnf9/e3ceVlW1/3H8wyCaoIJdyxxQMVFTicErFGZhGTmkSSpO+EvzOuQQaoUThorzkKWpaQ6FAzhg5k0rM284W5pjjjikaJpTChogZ/3+8HquKA5ZWyTfr+fpeTp7WPu79jqnzoe99j457leyZEmNGTNGO3bsUFpamsqXL69hw4Zp8uTJ2r17txISEuTn56fhw4fLZrPp/Pnz6t+/v/z9/XP71OABsmHDBv3444+aO3euLl26pOnTp2vx4sWKjo5W5cqVFR8fr6lTp6pPnz5atWqVxowZky18XGvYsGGKjIxUYGCgBgwYoBUrVsjX11dxcXFauHCh0tPT1bJlSwUHB8vFxeUe9xQAAOSGB/bKxvz589W0aVN5eXnJxcVFW7dulST5+Pho5syZCg4O1hdffKGmTZuqWLFieu+99/Tdd9/p6NGjio+P16effqrJkyfr/PnzOe6XmpqqwoULa8aMGYqPj9eWLVt04sQJderUSUFBQQoPD9f+/fsVFRWlmTNnqm3btkpMTLxlzQkJCQoLC1NYWJjl5wcPhtWrV8vb21tdunRRp06d9Nxzz2ns2LGqXLmyJCkrK0v58+eXJDk6OmrGjBlyd3fPsa2dO3eqRo0akqRatWpp7dq12rZtm/z8/OTi4qJChQrJ09NTu3fvvjedAwAAue6BvLLx22+/KSkpSWfOnFFcXJxSU1M1a9YsSdITTzwhSSpevPgNf8Hdu3evdu7cqYiICEnS5cuXdezYsRz3y58/v86cOaOePXuqYMGCunjx4g33fTzyyCOaOHGiChQooLS0NLm5ud2y7vDwcIWHh0sSgQN/ibNnz+rYsWOaPHmyjh49qs6dO+vLL7+UJG3evFmzZs3S7NmzJd3+XiJjjBwcHCRJrq6uunDhglJTU1WoUCH7Nq6urkpNTbWoNwAA4H7zQIaNzz//XK+++qqioqIkSZcuXdLzzz8vDw+PHLd3cHCQzWaTl5eXAgMDNXjwYNlsNk2cOFGlSpXKcZ+kpCQdP35c48aN05kzZ7R8+XIZY+To6Gifyz5kyBCNHj1a5cuX1wcffKCUlBRrOgzchLu7u/3qnpeXlz0kb9iwQZMmTdKUKVNUtGjRO2rL0fF/F0rT0tJUuHBhubm5KS0tLdvya8MHAAD4e3sgp1HNnz9fjRo1sr9+6KGH9OKLL+rw4cM5bl+9enV16NBBtWvXVsGCBdWyZUv7lYWbXY3w8fHRkSNH1KxZM3Xv3l2lS5fWyZMn5enpqb1792rmzJlq2LCh3njjDbVs2VKHDh3SyZMn//rOArcQEBCgVatWyRijEydO6NKlS0pKStKsWbMUFxen0qVL33FbTzzxhDZs2CDpStiuXr26fHx8tGnTJqWnp+vChQtKTk6Wt7e3Vd0BAAD3GQdjjMntIvDHhYWF6aO29XK7DOQBxV5uf8v1I0eO1IYNG2SMUY8ePdSrVy899thjKly4sCTpn//8p7p3727fvnbt2lq2bJny58+v/fv3a9asWYqJidHBgwcVHR2tzMxMeXl5KTY2Vk5OTpo3b54SEhJkjFHHjh0VGhpqaX8BAMBfKyws7Lb3Ft8MYSOPImzgTt0ubAAAANzKnwkbD+Q0KgAAAADWI2wAAAAAsARhAwAAAIAlCBsAAAAALEHYAAAAAGAJwgYAAAAASxA2AAAAAFiCsAEAAADAEoQNAAAAAJYgbAAAAACwBGEDAAAAgCUIGwAAAAAsQdgAAAAAYAnCBgAAAABLEDYAAAAAWIKwAQAAAMAShA0AAAAAliBsAAAAALAEYQMAAACAJQgbAAAAACzhnNsF4O4Ve7l9bpeAPMBkXZaDEx91AABw73FlA/ibI2gAAIDcQtgAAAAAYAnCBgAAAABLEDYAAAAAWIKwAQAAAMAShA0AAAAAliBsAAAAALAEYQMAAACAJQgbAAAAACxB2AAAAABgCcIGAAAAAEsQNgAAAABYgrABAAAAwBKEjTzL5HYBlrFdzsztEgAAAPAXcM7tAnC3HHTgvT65XYQlvHoMy+0SAAAA8BfgygYAAAAASxA2AAAAAFiCsAEAAADAEoQNAAAAAJYgbAAAAACwBGEDAAAAgCUIGwAAAAAsQdgAAAAAYAnCBgAAAABLEDYAAAAAWIKwAQAAAMAShA0AAAAAliBsAAAAALAEYQMAAACAJQgbAAAAACxB2AAAAABgCcIGAAAAAEsQNgAAAABYgrABAAAAwBKEDQAAAACWIGwAAAAAsIRzbheQF2zYsEGRkZF6/PHH7cs8PDx09uxZxcTEqHz58vblycnJiomJUVxc3B21HRwcrDVr1vzlNQMAAAC5jbBxh4KCgvTee+9lWxYREZFL1QAAAAD3P8LGX+DkyZN66623ZIxRsWLF7Ms3btyo9957T05OTipdurQGDRqkJUuWaOHChbLZbOrevbsk6cKFC2rcuLG++uorOTk5adSoUapatarq1q2bW126b2VmZqpv375KSUlRRkaGOnfurOeff16StGTJEs2aNUsJCQmSpNjYWG3evFmurq6SpIkTJ6pQoUL2tg4fPqzevXvLwcFBFSpU0LvvvitHR0fNmzdP8fHxcnZ2VufOnRUSEnLvOwoAAPA3QNi4Q+vXr892JePZZ5+1//uMGTPUoEEDNWvWTEuXLtXcuXNljFF0dLTmzJmjhx9+WOPGjdOiRYvk7OyswoULa9KkSfb9CxUqpICAAK1evVo1a9ZUUlKS3nzzzRtqSEhIsH+RflB9/vnncnd316hRo3T27Fk1btxYzz//vHbt2qUFCxbIGGPfdufOnfr4449VtGjRHNsaNmyYIiMjFRgY+phJcwAAIABJREFUqAEDBmjFihXy9fVVXFycFi5cqPT0dLVs2VLBwcFycXG5V10EAAD42yBs3KGcplF99913kqR9+/apUaNGkiR/f3/NnTtXZ86c0cmTJxUZGSlJ+v333xUcHCxPT0+VK1fuhvabNm2quLg42Ww2Pf300zl+uQ0PD1d4eLgkKSws7C/tX17x0ksvKTQ01P7ayclJZ8+e1ejRo9W3b19FR0dLkmw2mw4fPqwBAwbo1KlTatKkiZo0aZKtrZ07d6pGjRqSpFq1amnNmjVydHSUn5+fXFxc5OLiIk9PT+3evVs+Pj73rpMAAAB/E4SNv4CXl5d+/PFHVapUSdu3b5d05Qby4sWL26furFixQgULFtTx48fl6HjjQ8CqV6+uoUOHasGCBfaAghtdnRKVmpqq7t27680331S/fv3Ut29f5c+f377dxYsX1bp1a7Vt21ZZWVlq06aNqlatqkqVKtm3McbIwcHB3u6FCxeUmpqabaqVq6urUlNT71HvAAAA/l4IG3fo+mlU0pWrFZL05ptvqkePHlq6dKlKlSolSXJ0dFS/fv3UoUMHGWPk6uqqkSNH6vjx4zc9xssvv6wvv/xSFSpUsK4jfwPHjx9Xly5d1LJlS5UtW1aHDx9WTEyM0tPTtX//fg0ZMkS9e/dWmzZt9NBDD0m6cmVq9+7d2cLGtaEvLS1NhQsXlpubm9LS0rItvzZ8AAAA4M4RNu5AYGCg1q1bd8ttpkyZcsOymjVrqmbNmtmWXT/96drH3l6+fFlNmzb9E5X+/Z06dUrt2rXTgAED9NRTT0mSvvjiC0nS0aNH1bNnT/Xr10/Jycnq0aOHFi1aJJvNps2bN6tx48bZ2nriiSe0YcMGBQYGKikpSUFBQfLx8dG4ceOUnp6ujIwMJScny9vb+573EwAA4O+AsHGf6N27t86ePavx48fndin3tcmTJ+v8+fOaOHGiJk6cKEmaOnWqChQokG278uXL6+WXX1azZs2UL18+NWrUSBUqVND+/fs1a9YsxcTEKCoqStHR0Ro7dqy8vLwUGhoqJycnRUREqGXLljLGqEePHtmmZwEAAODOOZhrH9+DPCMsLEyjn6mY22VYwqvHsNwuAQAAAP8VFhamxMTEu9r3xjuVAQAAAOAvQNgAAAAAYAnCBgAAAABLEDYAAAAAWIKwAQAAAMAShA0AAAAAliBsAAAAALAEYQMAAACAJQgbAAAAACxB2AAAAABgCcIGAAAAAEsQNgAAAABYgrABAAAAwBKEDQAAAACWIGwAAAAAsARhAwAAAIAlCBsAAAAALEHYAAAAAGAJwgYAAAAASxA2AAAAAFjCObcLwN0y8uoxLLeLsITtcqYcnfPldhkAAAD4k7iykWc55HYBliFoAAAA/D0QNgAAAABYgrABAAAAwBKEDQAAAACWIGwAAAAAsARhAwAAAIAlCBsAAAAALEHYAAAAAGAJwgYAAAAASxA2AAAAAFiCsAEAAADAEoQNAAAAAJYgbORVxtx2E1tmxj0oBAAAAMiZc24XgLvk4KA17cJvuUnw9IR7VAwAAABwI65sAAAAALAEYQMAAACAJQgbAAAAACxB2AAAAABgCcIGAAAAAEsQNgAAAABYgrABAAAAwBKEDQAAAACWIGwAAAAAsARhAwAAAIAlCBsAAAAALEHYAAAAAGAJwgYAAAAASxA2AAAAAFiCsAEAAADAEoQNAAAAAJYgbAAAAACwBGEDAAAAgCUIGwAAAAAsQdgAAAAAYAnCBgAAAABL3JOwsWHDBlWvXl3Hjx+3Lxs9erQSExP/cFtz587V+PHjb7r+3LlzWrJkyV3VeTe6du0qSYqIiFBycvJNt6tdu7bS09OzLUtMTNTo0aMtrQ8AAADILffsyka+fPnUp08fGWMsPc6ePXv07bffWnqMa02YMOGeHQsAAADIS5zv1YGCgoJks9k0e/ZstW7dOtu66dOn64svvpCzs7OqV6+ut99+O9v6H374QUOHDlWRIkXk6OgoX19fSdKYMWO0Y8cOpaWlqXz58ho2bJgmT56s3bt3KyEhQX5+fho+fLhsNpvOnz+v/v37y9/f395uVlaWBgwYoF9++UVnz55VrVq11KVLF9WrV0+LFy9WwYIF9fHHH8vZ2VlPP/10jm0FBwdrzZo19jZ/+eUXxcTEKD09XefOnVOXLl30wgsvSJIGDBiglJQUPfzwwxoxYkS2PsbFxenf//63HBwcVK9ePbVp0+YvPf934vTp0woLC9P06dPl7Oys3r17y8HBQRUqVNC7774rR8f/ZVObzaaYmBjt2bNHLi4uio2NVZkyZXT48OFb7gcAAIAHxz39FhgTE6OZM2fq0KFD9mV79uzRsmXLFB8fr/j4eB0+fFgrV67Mtt+wYcM0ZswYzZgxQ6VKlZIkpaamqnDhwpoxY4bi4+O1ZcsWnThxQp06dVJQUJDCw8O1f/9+RUVFaebMmWrbtu0N07aOHz8uX19fTZs2TXPnztXcuXOVL18+vfjii/r6668lSUuXLlWjRo1u29ZVBw4cUNu2bTVjxgxFR0dr9uzZ9nUtWrTQrFmzVLJkSc2bN8++fP/+/Vq6dKnmzJmjOXPm6JtvvtGBAwf+1Ln+ozIzMzVgwAAVKFBA0pVzHhkZqTlz5sgYoxUrVmTb/ptvvlFGRoYSEhLUq1cvDR8+/I72AwAAwIPjnl3ZkCQPDw/17dtXvXv3tl9hOHDggJ588knly5dPklS9enXt27dPISEh9v1OnDihcuXKSZL8/f31888/K3/+/Dpz5ox69uypggUL6uLFi8rMzMx2vEceeUQTJ05UgQIFlJaWJjc3t2zr3d3dtX37dq1fv15ubm7KyMiQJDVt2lQxMTHy8vJS2bJl5eHhcdu2ripWrJgmTZqkBQsWyMHBQZcvX5Z0ZRrZ1Ssy/v7+WrNmjapVqyZJ2rt3r44dO6bXXntNkvTbb7/p559/lpeXV7a2ExISlJCQ8MdO+h0aMWKEmjdvrilTpkiSdu7cqRo1akiSatWqpTVr1qhOnTr27Tdt2qRnnnlGkuTr66sdO3bc0X4AAAB4cNzz+S21a9dWuXLltGjRIkmSl5eXtm3bpsuXL8sYo++//94eLK4qVqyY/ebr7du3S5KSkpJ0/PhxjR07Vj179tTvv/8uY4wcHR1ls9kkSUOGDFH37t01YsQIeXt733C/SGJiogoVKqQxY8aoXbt29jbKli0rY4w+/vhjNW3a9I7auur9999Xo0aNNGrUKAUGBtq3y8zM1K5duyRdmRZWoUIF+z5eXl56/PHH9emnnyouLk5hYWHy9va+oe3w8HAlJibe1Y31t5KYmKiiRYvaw4MkGWPk4OAgSXJ1ddWFCxey7ZOampotcDk5OdnH8Fb7AQAA4MFxT69sXNWvXz+tX79eklSxYkXVrVtXLVq0kM1mU0BAgP0eh6tGjRqlqKgoubq6ytXVVUWKFJGPj48mTpyoZs2aycXFRaVLl9bJkyfl6empvXv3aubMmWrYsKHeeOMNPfzwwypevLjOnj2brd2nnnpKPXv21KZNm/TQQw+pTJkyOnnypB599FE1adJE77//voKCgiTptm1d9dJLL2nIkCH66KOP9Nhjj9m3y5cvn+Li4nT48GGVKFFCvXr1sj81q1KlSnrqqafUokULZWRkyMfHR48++uhfes5vZeHChXJwcNC6deu0a9cuRUVF6cyZM/b1aWlpKly4cLZ93NzclJaWZn9ts9nk7Oyc7f6MnPYDAADAg8PBWP14KFgiLCxMvdzz3XKb4Ol/fMpVRESEYmJiNGrUKLVt21aBgYEaMGCAgoKCVK9ePft2X331lVauXKnhw4dry5YtmjBhgj7++GN16tTplvsBAAAgbwkLC7vrmTU8Jgg5ioqK0vjx4xUeHq7MzEyFhoZKkt555x0dO3ZMderUkYuLi5o3b65hw4apT58+t9wPAAAADx6ubORRVl3ZAAAAAK7FlQ0AAAAA9x3CBgAAAABLEDYAAAAAWIKwAQAAAMAShA0AAAAAliBsAAAAALAEYQMAAACAJQgbAAAAACxB2AAAAABgCcIGAAAAAEsQNgAAAABYgrABAAAAwBKEDQAAAACWIGwAAAAAsARhAwAAAIAlCBsAAAAALEHYAAAAAGAJwgYAAAAASxA2AAAAAFiCsAEAAADAEs65XQDukjEKnp5wy01smRlyzOdyjwoCAAAAsuPKRl7l4HDbTQgaAAAAyE2EDQAAAACWIGwAAAAAsARhAwAAAIAlCBsAAAAALEHYAAAAAGAJHn2bRx08eFBhYWG5XQbu0tmzZ+Xh4ZHbZeAuMHZ5G+OXtzF+eRdjl7elpKTc9b6EjTyqXLlySkxMzO0ycJfCwsIYvzyKscvbGL+8jfHLuxi7BxfTqAAAAABYgrABAAAAwBJOMTExMbldBO5O1apVc7sE/AmMX97F2OVtjF/exvjlXYzdg8nBGGNyuwgAAAAAfz9MowIAAABgCZ5GlcfYbDbFxMRoz549cnFxUWxsrMqUKZPbZSEHr7zyigoVKiRJKlWqlDp16qTevXvLwcFBFSpU0LvvvitHR0fNmzdP8fHxcnZ2VufOnRUSEpLLlT+4tm7dqtGjRysuLk6HDx++4/H6/fff9fbbb+v06dNydXXViBEjVLRo0dzuzgPn2vHbuXOnOnXqpLJly0qSWrRooXr16jF+96HMzEz17dtXKSkpysjIUOfOnfX444/z+csDchq74sWL89lDdgZ5yldffWWioqKMMcb8+OOPplOnTrlcEXLy+++/m0aNGmVb1rFjR7N+/XpjjDHR0dHm66+/NidPnjQNGjQw6enp5vz58/Z/x703ZcoU06BBA9O0aVNjzB8br+nTp5sPPvjAGGPMv//9bzN48OBc68eD6vrxmzdvnpk2bVq2bRi/+9OCBQtMbGysMcaYM2fOmGeffZbPXx6R09jx2cP1mEaVx2zatEnPPPOMJMnX11c7duzI5YqQk927d+vSpUtq166d2rRpoy1btmjnzp2qUaOGJKlWrVpau3attm3bJj8/P7m4uKhQoULy9PTU7t27c7n6B5Onp6fGjx9vf/1Hxuvaz2WtWrW0bt26XOnDg+z68duxY4f+85//qFWrVurbt69SU1MZv/vUSy+9pDfffNP+2snJic9fHpHT2PHZw/UIG3lMamqq3Nzc7K+dnJx0+fLlXKwIOSlQoIBef/11TZs2TQMHDtRbb70lY4wcHBwkSa6urrpw4YJSU1PtU62uLk9NTc2tsh9ooaGhcnb+38zSPzJe1y6/ui3urevHz8fHR++8845mz56t0qVL68MPP2T87lOurq5yc3NTamqqunfvrsjISD5/eUROY8dnD9cjbOQxbm5uSktLs7+22WzZ/geL+0O5cuXUsGFDOTg4qFy5cnJ3d9fp06ft69PS0lS4cOEbxjMtLS3bf5CRexwd//efx9uN17XLr26L3FWnTh37Yzbr1Kmjn376ifG7jx0/flxt2rRRo0aN9PLLL/P5y0OuHzs+e7geYSOP8ff3V1JSkiRpy5Yt8vb2zuWKkJMFCxZo+PDhkqQTJ04oNTVVwcHB2rBhgyQpKSlJ1atXl4+PjzZt2qT09HRduHBBycnJjOl94oknnrjj8fL399d3331n3zYgICA3S4ek119/Xdu2bZMkrVu3TlWqVGH87lOnTp1Su3bt9Pbbb6tJkyaS+PzlFTmNHZ89XI/f2chjrj6Nau/evTLGaOjQoSpfvnxul4XrZGRkqE+fPjp27JgcHBz01ltvycPDQ9HR0crMzJSXl5diY2Pl5OSkefPmKSEhQcYYdezYUaGhobld/gPr6NGj6tmzp+bNm6eDBw/e8XhdunRJUVFR+vXXX5UvXz6NGTNGxYoVy+3uPHCuHb+dO3dq8ODBypcvn/7xj39o8ODBcnNzY/zuQ7GxsVq2bJm8vLzsy/r166fY2Fg+f/e5nMYuMjJSo0aN4rMHO8IGAAAAAEswjQoAAACAJQgbAAAAACxB2AAAAABgCcIGAAAAAEsQNgAAAABYgrABANc5f/68RowYoeeff15PPvmkQkNDNWXKFGVmZt6T46elpSkxMfGu91+1apVq1qwpPz8/7d27N9u6o0ePqmLFitn+qVKlimrWrKnBgwcrIyPjz5Z/x66toVKlSgoICFDbtm3tz+i/tt7Dhw/fs7rupbVr16pixYqS8k5fT58+raVLl9pfV6xYUWvXrv3T7e7atUs//PDDXe9fq1atP/W5AWANfnoaAK5x7tw5hYeH6+GHH1ZsbKxKlSqln376SbGxsdq7d69Gjx5teQ0zZszQmjVrFBYWdlf7jx49Ws8884y6dOmi4sWL57hNQkKCSpYsKenK78Js3LhRAwYMkIeHh7p27XrXtf9R48aNU/Xq1WWz2XT69Gl98skn+r//+z8lJCTI29tbjz32mFavXq2iRYves5pyS17p6+jRo5WZmal69epJklavXq0iRYr86Xa7dOmizp07q3r16n+6LQD3D8IGAFxj9OjRypcvn2bMmKH8+fNLkkqXLi0PDw9FREQoIiJCTz75pKU1/NmfP7pw4YL8/PxUqlSpm27j4eGR7cezGjdurE2bNumbb765p2GjSJEi9joeffRRDR8+XEePHtXYsWM1efJkOTk5PTA/8pVX+nr9+zMv1Awg9zCNCgD+KyMjQ1988YVatWplDxpX1ahRQ5988om8vb0lSb/99puio6P19NNPy9/fX7169dK5c+ckSRs2bFDFihV1+fJl+/69e/fWW2+9JUkaP368evTooUGDBikgIEAhISH66KOPJEmJiYmaMGGCNm/ebJ9ec71bHbtixYpKSUlRdHS0IiIi/lD/XVxc5OTkZH+9cuVKNW7cWNWqVVNAQIAiIyOVmppq70OnTp0UERGhf/7zn0pKSlJGRoaGDBmioKAgBQYG6s0339SpU6f+UA0ODg5q2rSpVq9erUuXLt0wtejLL79UvXr1VK1aNb344otauHChfd8LFy4oKipKAQEBCg4OVnR0tL3e2/UnNTVVkZGRqlGjhvz9/dW1a1f9+uuv9n2/+eYb1a9fX08++aQaN26spKQk+7o9e/aoVatW8vX1VXBwsIYPH55t7K+Vmpqqnj17ys/PT6Ghodq5c6d93fV9TU5OVvv27eXn56dq1aqpRYsW2rdvn337HTt2qFmzZvLx8VHz5s31/vvv28c8MTFRLVq00IQJExQUFKSaNWtqyJAhstls9v0TExNVr149+fj4KCwsTBs2bLCv27Bhg8LCwuTj46PnnnvO/v4cP368Fi1apCVLlqh27dqSsk+junTpkgYNGqSgoCDVqFFD77zzjv0cnzx5Ut27d9c///lPVa1aVa+88oq+//57SVJERIRSUlLUv39/9e7dW5K0b98+tWnTRj4+PqpTp46mT5+eLejEx8fr2WefVUBAgL0+APcfwgYA/NfPP/+sixcvqlq1ajmuDwoK0kMPPSRJ6tq1q3bt2qXJkydr5syZOnjwoN555507Ptby5cvl5OSk+fPnKzw8XGPHjtX+/ftVr149tWvXTj4+Plq9enWO+97q2KtXr1bx4sXVu3dvjR8//o5qMcZo1apV+uyzzxQaGipJOnLkiLp166bmzZtr2bJlev/997V+/XrNnTvXvt/KlSsVGhqquLg4+fv7a+zYsdqyZYs++ugjxcXFyRijjh07/uErNY8//rgyMzNvuHfh9OnTeuutt/Taa6/pyy+/VMeOHdW/f38lJydLkvr27auzZ89q9uzZ+uijj3Tw4EH16dPnjvozbtw4paSkKC4uTvPmzdPp06c1bNgwSdLu3bv19ttv61//+peWLFmiZs2a2cdAkt5++215eXlpyZIlGjdunBYvXqwFCxbk2Ld3331XBw4c0KxZs9S/f3/NnDnzpmPyxhtvqESJElq8eLHi4+Nls9k0cuRISVeCVfv27VW5cmUtWrRIDRo00JQpU7K1sX37diUnJ2v27NmKjIzUrFmztGrVKklXgsagQYPUoUMHLV68WMHBwerQoYOOHTumrKwsde/eXSEhIVq6dKkGDBigDz/8UKtWrVK7du1Ut25dhYaG5tjHAQMGaN26dZowYYI+/fRT7du3T8OHD5ckvfPOO7p8+bLi4+P12WefqXjx4nr33XclXQkxV9+3/fr10++//6727dvL19dXn3/+ufr3769PPvlEs2bNknTlvqQhQ4aoR48eio+P15YtW3TixImbvaUA5CKmUQHAf50/f16SVKhQoVtut3v3bm3cuFFLly5V+fLlJV2ZflW3bt1sf3m+lUKFCql3795ycnJSp06dNGPGDO3YsUOPP/64ChYsKGdn5xynp9zu2BUqVJCTk5Pc3Nzk7u5+0+M3atRIDg4Okq5c0SlatKjatm2r119/XZKUlZWlfv36KTw8XJJUqlQpPf3009q/f7+9DXd3d7Vu3VrSlb9oz5o1S/PmzdMTTzwhSRo5cqQCAwO1adOmPzQP/+r5T0tLk5ubm335iRMnlJmZqUcffVQlS5bUq6++qhIlSugf//iHfv75Zy1fvlzr16+393vEiBGqXbu2jh8/ftv+pKSkqGDBgipVqpRcXV01cuRI+/th2rRpevXVV/XKK69Ikjw9PbVt2zbFxcVp6NChSklJ0XPPPacSJUqodOnSmjp1ao7n/sKFC1q2bJlmzJihKlWqSJI6d+6swYMH37DtpUuX1KRJE7Vs2VKurq6Srkx1u/oX/KVLl6pAgQKKjo6Ws7Ozypcvr82bN2e7GnP58mUNGjRIhQoVUvny5TV79mxt375dzz77rOLi4tSqVSt7n3r16qWNGzcqLi5OHTt21Llz5/Twww+rVKlSKlWqlGbOnKnSpUvL1dVVBQoU0OXLl2+4t+TChQtaunSppk6dah/vgQMHauPGjZKkkJAQvfjii3rsscckSa1atVL79u1ljJG7u7v9fVuoUCHNnz9fRYoUUc+ePSVJZcuWVWRkpD788ENFRERo/vz5ql+/vr3+IUOG6Nlnn731GwtAriBsAMB/eXh4SLoyTelWDhw4IFdXV/uXfUny8vJSkSJFlJycbG/nVkqWLJltypKrq+sdPe3qdseuUKHCbduQpEmTJqlEiRI6fvy4Bg0apMqVK6tjx472msqWLSsXFxdNmjRJ+/bt0759+7R//37Vr18/Wx+uOnLkiDIzM9WqVatsx0lPT9fBgwf/UNi4Ou3m2qAhSZUrV1aDBg3UoUMHeXp6KiQkRGFhYSpSpIg2b94sY4xCQkJuaO/QoUN66qmnbtmfDh06qEOHDnrqqacUGBioOnXq2L/IJicna+/evdmmbGVmZsrHx0eS1LNnT8XGxiohIUG1atVS/fr1VbVq1RvqOHjwoLKyslSpUiX7spy2k6SCBQuqZcuWWrx4sXbs2KEDBw7op59+soeYPXv2qHLlynJ2/t//xn19fbV8+XL7aw8Pj2zB2c3NzT69Kzk5WZ07d852TF9fXx04cEDu7u7q0KGDBg4cqEmTJikkJEQNGza87b0ZBw8e1OXLl+1BSpJ8fHzs56lFixZaunSpNm/erIMHD2rHjh2SrgTba/shXXmf79+/X35+fvZlNptNGRkZysjIUHJyspo2bWpfV7Ro0WzvRwD3D8IGAPyXp6en3N3dtX37dvsXpGtFRkaqQYMGcnFxyXH/rKws2Ww2+xWDa10/hz9fvnx3VePtjn2nSpQooTJlyqhMmTKaPHmyGjVqpBEjRig6OlrSlSsoLVq0UEhIiAICAvTaa6/pk08+ydbGtfe1ZGVlSZLi4uJuuDL0R5+utGfPHuXLl09ly5bN9pd6BwcHjRkzRu3atdO3336rb7/9VnPmzNHkyZOVlZWlggUL6rPPPruhvWLFit22P35+flq5cqW+/fZbfffddxo+fLiWLFmiuLg4ZWVl6fXXX7/h6WBXx6JVq1YKCQnRihUr9J///EdvvPGGOnfurG7duuXYv2unlV3/JfuqtLQ0NWnSREWKFNELL7ygBg0a6MCBA/apUk5OTjdMT7v+dU7vsavbFChQ4IZ1WVlZ9nHs1auXGjdurBUrVmjlypWKiIhQbGysXn311RzrlW7+3pSuBIV27drpt99+U7169VS7dm1lZmbe9GEEly9fVo0aNTRw4MAb1l09Z3fSXwC5j3s2AOC/nJycVL9+fc2aNeuG35tYv369li1bJg8PD3l5eSktLc1+r4Ak7d+/X6mpqSpXrpz9S8+1NycfPXr0juvIKaxcdbtj3w1PT09169ZNs2fP1pYtWyRJixcvtt+H0apVK/n4+Ojw4cM3vf+idOnScnJy0tmzZ+0hpmjRoho2bJhSUlL+UD2JiYmqXbv2DTfpJycna9iwYapSpYq6deumRYsWqXr16lq+fLnKlSunixcvKisry358SRo2bJhSU1Nv25+ZM2dq69atatiwocaMGaMpU6Zo48aNOnXqlMqVK6cjR47Y2y1TpowWL16s5cuXKz09XbGxsXJwcFBERISmTZumrl27Zvsdiqu8vLyUL18+bd++3b7s6n0f19u4caN++eUXxcXFqX379nr66ad17Ngxe70VKlTQnj177OFAUrabzW/Hy8tLW7duzbZs69atKleunH799VfFxMSoZMmS+te//qU5c+YoLCxMy5Ytk3Tz92epUqXk5OSkn376yb5s7dq1Cg0N1d69e/X9999r2rRp6ty5s5577jmdPHlSUs5PXytXrpwOHTqkkiVL2s/5rl27NHXqVDk6OqpChQrZzmNqaqqOHDlyx/0HcO8QNgDgGl27dlV6erratm2r9evX6+eff9aiRYsUGRmpsLAwBQQEyMvLSyEhIYqKitK2bdu0bds2+1OQKleurAoVKqhAgQKaMGGCjhw5ohkzZmT7AnY7BQsW1K+//prjl6fbHftutWnTRuXLl9egQYNks9nk7u6uvXv3autxjv+JAAAE40lEQVTWrTp06JCGDx+u7du333Sql5ubm5o2barBgwdr3bp1Sk5OVlRUlPbu3auyZcve9Li//fabfv31V504cUI7d+5Ur169tHPnTkVGRt6wbeHChZWQkKDx48fryJEjWr9+vfbs2aOqVauqfPnyeuaZZ/TOO+9o69at2r17t6KionT69Gk98sgjt+3PL7/8osGDB2vz5s06cuSIlixZohIlSsjDw8N+Q/rMmTN1+PBhzZ07V5MnT5anp6fy58+vzZs3a/DgwUpOTtaePXuUlJSUbSrRteeoYcOGGjJkiLZs2aL169dr4sSJOZ4Xd3d3Xbp0ScuXL9fRo0c1f/58zZ492x6C69evr4sXL2ro0KE6cOCA5s+fn2PAuZm2bdtqzpw5+uyzz3Tw4EGNGTNGu3btUrNmzVSkSBF98803GjJkiA4fPqxt27bphx9+sPepYMGCOnbs2A03ZLu5uSksLExDhw7Vli1b9NNPP2nUqFEKCgqSu7u7HB0dtXTpUqWkpOjLL7+0P8Dgap9cXV114MABnTt3Tg0bNlRGRob9AQBr1qzRoEGD7L/n0apVK3399deKj49XcnKy+vfvr/T09DvuP4B7yAAAsvnll19MdHS0qVWrlqlWrZqpW7eumTZtmsnIyLBvc/bsWdOzZ0/j5+dnqlevbqKiosy5c+fs6xcvXmyee+454+PjY3r27GlGjRplevXqZYwx5oMPPjDNmzfPdsyQkBAzb948Y4wxR44cMXXq1DE+Pj7m1KlTN9R3u2Nf29b1jhw5Yry9vc2hQ4duWLd27Vrj7e1t5s6da9LS0kz37t2Nn5+fCQwMNF27djUffPCBeeGFF27ah0uXLpmBAweawMBA4+vra9q1a2cOHDhw0/Ps7e1t/6dSpUomODjYdOvWzezevfum9SYlJZlGjRqZatWqmZo1a5r333/f2Gw2Y4wxp0+fNj179jT+/v4mICDAdOvWzZw8edIYY27bn4sXL5p+/fqZoKAgU61aNdO6detsdXzxxRembt26pkqVKubFF180ixYtsq87dOiQadeunfH39zf+/v6mV69e2cbj+nPUt29f4+fnZ0JCQszHH39svL29c+zr+PHjTVBQkPHz8zPh4eFm4cKFxtvb26SkpBhjjNm8ebNp1KiRqVKlimnevLnp3bu3adeunTHGmIULF5pnnnkm27Fbt25txo4da38dFxdnQkJCTNWqVU3Tpk3Nxo0b7eu2bdtmmjdvbnx9fU1gYKAZOHCgSU9PN8YYs3XrVhMcHGxq1KhhbDab8fb2NmvWrDHGGJOammr69OljAgICTI0aNUz//v1NWlqaMcaY+Ph4U6tWLfPkk0+aV155xSxZssRUqVLFfP/998YYY+bOnWt8fX1N165djTHG7Ny507Ru3dpUq1bNBAcHm5EjR5rMzEx7jZ9//rmpXbu28fX1NUOHDjUNGzY0CxcuzPG8A8g9Dsb8yV+PAgAA99SRI0d04sSJbDfeDxw4UJcuXbI/ahYA7gdMowIAII9JTU21T+9KSUnR119/rcWLF+ull17K7dIAIBuubAAAkAfNnz9fU6dO1fHjx1WiRAm1b98+2+NgAeB+QNgAAAAAYAmmUQEAAACwBGEDAAAAgCUIGwAAAAAsQdgAAAAAYAnCBgAAAABLEDYAAAAAWOL/AVMe27qVFbq1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing the required library\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# a4_dims = (11.7, 8.27)\n",
    "# fig, ax = pyplot.subplots(figsize=a4_dims)    \n",
    "plt.figure(figsize=(12,11))\n",
    "\n",
    "sns.set_style('ticks')\n",
    "\n",
    "f = sns.barplot(x = \"Count\", y = \"Average_age_onset\", data = Age_onset, palette = 'coolwarm')\n",
    "\n",
    "for p in f.patches:\n",
    "    width = p.get_width()\n",
    "    plt.text(100+p.get_width(), p.get_y()+0.55*p.get_height(),\n",
    "             '{:1.2f}'.format(width),\n",
    "             ha='center', va='center')\n",
    "\n",
    "plt.xlabel(\"Count of Rare Diseases diagnosticated\", size=14)\n",
    "plt.ylabel(\"Age of Onset\", size=14) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Average_age_death</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No data available</td>\n",
       "      <td>1207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>normal life expectancy</td>\n",
       "      <td>1026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>any age</td>\n",
       "      <td>447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adult</td>\n",
       "      <td>270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>early childhood</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>infantile</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>adolescent</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>elderly</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>embryofetal</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>late childhood</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>young Adult</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>stillbirth</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Not yet documented</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Average_age_death  Count\n",
       "0        No data available   1207\n",
       "1   normal life expectancy   1026\n",
       "2                  any age    447\n",
       "3                    adult    270\n",
       "4          early childhood    235\n",
       "5                infantile    209\n",
       "6               adolescent     97\n",
       "7                  elderly     64\n",
       "8              embryofetal     21\n",
       "9           late childhood     20\n",
       "10             young Adult     18\n",
       "11              stillbirth     13\n",
       "12      Not yet documented      4"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Age_death = pd.DataFrame(Disorder_Age.Average_age_death.value_counts())\n",
    "Age_death = Age_death.reset_index()\n",
    "Age_death = Age_death.rename(columns = {'index':'Average_age_death','Average_age_death':'Count' })\n",
    "Age_death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# importing the required library\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# a4_dims = (11.7, 8.27)\n",
    "# fig, ax = pyplot.subplots(figsize=a4_dims)    \n",
    "plt.figure(figsize=(12,11))\n",
    "\n",
    "sns.set_style('darkgrid')\n",
    "\n",
    "k = sns.barplot(x = \"Count\", y = \"Average_age_death\", data = Age_death, palette = 'hls')\n",
    "\n",
    "for p in k.patches:\n",
    "    width = p.get_width()\n",
    "    plt.text(40+p.get_width(), p.get_y()+0.55*p.get_height(),\n",
    "             '{:1.2f}'.format(width),\n",
    "             ha='center', va='center')\n",
    "\n",
    "plt.xlabel(\"Count of Rare Diseases diagnosticated\", size=14)\n",
    "plt.ylabel(\"Age of Death\", size=14) \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}