Skip to content

Instantly share code, notes, and snippets.

@fonnesbeck

fonnesbeck/Dataset Statistics.ipynb

Created December 30, 2015 20:51
Show Gist options
  • Star 0 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save fonnesbeck/294a89309231816eeb6e to your computer and use it in GitHub Desktop.
Save fonnesbeck/294a89309231816eeb6e to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
Dataset Statistics.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import modules and set options\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Connect to database to import data for the three test domains and demographic information:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from redcap import Project\n",
"api_url = 'https://redcap.vanderbilt.edu/api/'\n",
"api_key = open(\"/Users/fonnescj/Dropbox/Collaborations/LSL-DR/api_token.txt\").read()\n",
"\n",
"lsl_dr_project = Project(api_url, api_key)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metadata = lsl_dr_project.export_metadata()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# for i,j in zip(lsl_dr_project.field_names, \n",
"# lsl_dr_project.field_labels):\n",
"# print('{0}: \\t{1}'.format(i,j))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import each database from REDCap:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation_fields = ['study_id','redcap_event_name', 'age_test_aaps','aaps_ss','age_test_gf2','gf2_ss']\n",
"articulation = lsl_dr_project.export_records(fields=articulation_fields, format='df', df_kwargs={'index_col':None})"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"records = lsl_dr_project.export_records(fields=articulation_fields)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0101-2003-0101\n"
]
}
],
"source": [
"print(records[0]['study_id'])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive_fields = ['study_id','redcap_event_name','age_test_eowpvt','eowpvt_ss','age_test_evt','evt_ss']\n",
"expressive = lsl_dr_project.export_records(fields=expressive_fields, format='df', \n",
" df_kwargs={'index_col':None,\n",
" 'na_values':[999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_fields = ['study_id','redcap_event_name','age_test_ppvt','ppvt_ss','age_test_rowpvt','rowpvt_ss']\n",
"receptive = lsl_dr_project.export_records(fields=receptive_fields, format='df', \n",
" df_kwargs={'index_col':None,\n",
" 'na_values':[999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"language_fields = ['study_id','redcap_event_name','pls_ac_ss','pls_ec_ss','pls_choice','age_test_pls',\n",
" 'owls_lc_ss','owls_oe_ss','age_test_owls',\n",
" 'celfp_rl_ss','celfp_el_ss','age_test_celp',\n",
" 'celf_elss','celf_rlss','age_test_celf']\n",
"language_raw = lsl_dr_project.export_records(fields=language_fields, format='df', \n",
" df_kwargs={'index_col':None, \n",
" 'na_values':[999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic_fields = ['study_id','redcap_event_name','redcap_data_access_group', 'academic_year',\n",
"'hl','prim_lang','mother_ed','father_ed','premature_age', 'synd_cause', 'age_disenrolled', 'race',\n",
"'onset_1','age_int','age','age_amp', 'age_ci', 'age_ci_2', 'degree_hl_ad','type_hl_ad','tech_ad','degree_hl_as',\n",
"'type_hl_as','tech_as','etiology','etiology_2', 'sib', 'gender', 'time', 'ad_250', 'as_250', 'ae',\n",
"'ad_500', 'as_500', 'fam_age', 'family_inv', 'demo_ses', 'school_lunch', 'medicaid', 'hearing_changes',\n",
"'slc_fo', 'sle_fo', 'a_fo', 'funct_out_age',\n",
"'att_days_hr', 'att_days_sch', 'att_days_st2_417']\n",
"demographic_raw = lsl_dr_project.export_records(fields=demographic_fields, format='df', \n",
" df_kwargs={'index_col':None, \n",
" 'na_values':[888, 999, 9999]})"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>...</th>\n",
" <th>sle_fo</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13565</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13566</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>77</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13567</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>89</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13568</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>101</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name academic_year hl gender \\\n",
"13565 1147-2010-0064 initial_assessment_arm_1 2010-2011 0 0 \n",
"13566 1147-2010-0064 year_1_complete_71_arm_1 2011-2012 0 NaN \n",
"13567 1147-2010-0064 year_2_complete_71_arm_1 2012-2013 0 NaN \n",
"13568 1147-2010-0064 year_3_complete_71_arm_1 2013-2014 0 NaN \n",
"\n",
" race prim_lang sib mother_ed father_ed ... sle_fo a_fo \\\n",
"13565 0 0 1 3 3 ... 3 6 \n",
"13566 NaN NaN NaN NaN NaN ... 3 5 \n",
"13567 NaN NaN NaN NaN NaN ... 3 5 \n",
"13568 NaN NaN NaN NaN NaN ... 4 5 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"13565 65 0 NaN NaN NaN \n",
"13566 77 2 NaN NaN NaN \n",
"13567 89 2 NaN NaN NaN \n",
"13568 101 2 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid \n",
"13565 NaN NaN NaN \n",
"13566 NaN NaN NaN \n",
"13567 NaN NaN NaN \n",
"13568 NaN NaN NaN \n",
"\n",
"[4 rows x 46 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic_raw[demographic_raw.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attendance information"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Several fields in the demographic data have missing values."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>...</th>\n",
" <th>sle_fo</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2002-2003</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>54</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2003-2004</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>80</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2004-2005</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>80</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2005-2006</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>96</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_4_complete_71_arm_1</td>\n",
" <td>2006-2007</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>109</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name academic_year hl gender race \\\n",
"0 0101-2003-0101 initial_assessment_arm_1 2002-2003 0 0 0 \n",
"1 0101-2003-0101 year_1_complete_71_arm_1 2003-2004 0 NaN NaN \n",
"2 0101-2003-0101 year_2_complete_71_arm_1 2004-2005 0 NaN NaN \n",
"3 0101-2003-0101 year_3_complete_71_arm_1 2005-2006 0 NaN NaN \n",
"4 0101-2003-0101 year_4_complete_71_arm_1 2006-2007 0 NaN NaN \n",
"\n",
" prim_lang sib mother_ed father_ed ... sle_fo a_fo fam_age \\\n",
"0 0 1 6 6 ... 2 2 54 \n",
"1 NaN NaN NaN NaN ... 4 4 80 \n",
"2 NaN NaN NaN NaN ... 4 4 80 \n",
"3 NaN NaN NaN NaN ... 5 5 96 \n",
"4 NaN NaN NaN NaN ... 5 5 109 \n",
"\n",
" family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"0 2 NaN NaN NaN NaN \n",
"1 1 NaN NaN NaN NaN \n",
"2 2 NaN NaN NaN NaN \n",
"3 3 NaN NaN NaN NaN \n",
"4 2 NaN NaN NaN NaN \n",
"\n",
" school_lunch medicaid \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 NaN NaN \n",
"4 NaN NaN \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic_raw.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can fill missing values forward from previous observation (by `study_id`)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n",
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"demographic = demographic_raw.sort(columns='redcap_event_name').groupby('study_id').transform(\n",
" lambda recs: recs.fillna(method='ffill'))#.reset_index()\n",
"demographic[\"study_id\"] = demographic_raw.sort(columns='redcap_event_name').study_id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Random check to make sure this worked"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" <th>study_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13565</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13566</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>77</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13567</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>89</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13568</th>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>101</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl gender race prim_lang \\\n",
"13565 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"13566 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"13567 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"13568 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo \\\n",
"13565 1 3 3 8 ... 6 \n",
"13566 1 3 3 8 ... 5 \n",
"13567 1 3 3 8 ... 5 \n",
"13568 1 3 3 8 ... 5 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"13565 65 0 NaN NaN NaN \n",
"13566 77 2 NaN NaN NaN \n",
"13567 89 2 NaN NaN NaN \n",
"13568 101 2 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid study_id \n",
"13565 NaN NaN NaN 1147-2010-0064 \n",
"13566 NaN NaN NaN 1147-2010-0064 \n",
"13567 NaN NaN NaN 1147-2010-0064 \n",
"13568 NaN NaN NaN 1147-2010-0064 \n",
"\n",
"[4 rows x 46 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[demographic.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Demographic data without missing values:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>gender</th>\n",
" <th>race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" <th>study_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2002-2003</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>54</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0101-2003-0101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7486</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0626-2014-0035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7484</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2014-2015</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>56</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0626-2014-0034</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7483</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2014-2015</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0626-2014-0033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7482</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2014-2015</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0626-2014-0032</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 46 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl gender race prim_lang \\\n",
"0 initial_assessment_arm_1 2002-2003 0 0 0 0 \n",
"7486 initial_assessment_arm_1 2013-2014 0 0 1 0 \n",
"7484 initial_assessment_arm_1 2014-2015 0 1 6 0 \n",
"7483 initial_assessment_arm_1 2014-2015 0 1 3 0 \n",
"7482 initial_assessment_arm_1 2014-2015 0 1 0 0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo fam_age \\\n",
"0 1 6 6 9 ... 2 54 \n",
"7486 0 2 2 8 ... 1 7 \n",
"7484 1 4 4 8 ... 3 56 \n",
"7483 1 4 5 8 ... 0 29 \n",
"7482 1 3 5 8 ... 1 11 \n",
"\n",
" family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"0 2 NaN NaN NaN NaN \n",
"7486 3 NaN NaN NaN NaN \n",
"7484 1 NaN NaN NaN NaN \n",
"7483 2 NaN NaN NaN NaN \n",
"7482 1 NaN NaN NaN NaN \n",
"\n",
" school_lunch medicaid study_id \n",
"0 NaN NaN 0101-2003-0101 \n",
"7486 NaN NaN 0626-2014-0035 \n",
"7484 NaN NaN 0626-2014-0034 \n",
"7483 NaN NaN 0626-2014-0033 \n",
"7482 NaN NaN 0626-2014-0032 \n",
"\n",
"[5 rows x 46 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning languge dataset\n",
"\n",
"5 language measures:\n",
"\n",
"- 3 versions of CELF\n",
"- PLS\n",
" - pls_ac_rs: \tPLS: Auditory Comprehension Raw Score\n",
" - pls_ac_ss: \tPLS: Auditory Comprehension Standard Score\n",
" - pls_ec_rs: \tPLS: Expressive Communication Raw Score\n",
" - pls_ec_ss: \tPLS: Expressive Communication Standard Score\n",
" - pls_tl_rs: \tPLS: Total Language Score Standard Score Total\n",
" - pls_tl_ss: \tPLS: Total Language Score Standard Score\n",
"- OWLS\n",
" - age_test_owls: \tAge at time of testing (OWLS)\n",
" - owls_lc_rs: \tOWLS: Listening Comprehension Raw Score\n",
" - owls_lc_ss: \tOWLS: Listening Comprehension Standard Score\n",
" - owls_oe_rs: \tOWLS: Oral Expression Raw Score\n",
" - owls_oe_ss: \tOWLS: Oral Expression Standard Score\n",
" - owls_oc_sss: \tOWLS: Oral Composite Sum of Listening Comprehension and Oral Expression Standard Scores\n",
" - owls_oc_ss: \tOWLS: Oral Composite Standard Score\n",
" - owls_wes_trs: \tOWLS: Written Expression Scale Total Raw Score\n",
" - owls_wes_as: \tOWLS: Written Expression Scale Ability Score\n",
" - owls_wes_ss: \tOWLS: Written Expression Scale Standard Score\n",
" - owsl_lc: \tOWLS: Written Expression Scale Language Composite (Sum of written expression age-based standard score, listening comprehension standard score and oral expression standard score)\n",
" - owls_lcss: \tOWLS: Language Composite Standard Score"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test_type expressive receptive\n",
"test_name \n",
"CELF-4 593 525\n",
"CELF-P2 1374 1379\n",
"OWLS 1065 1072\n",
"PLS 3387 3397\n",
"There are 0 null values for score\n"
]
}
],
"source": [
"# Test type\n",
"language_raw[\"test_name\"] = None\n",
"language_raw[\"test_type\"] = None\n",
"language_raw[\"score\"] = None\n",
"CELP = language_raw.age_test_celp.notnull()\n",
"CELF = language_raw.age_test_celf.notnull()\n",
"PLS = language_raw.age_test_pls.notnull()\n",
"OWLS = language_raw.age_test_owls.notnull()\n",
"\n",
"language_raw['age_test'] = None\n",
"language_raw.loc[CELP, 'age_test'] = language_raw.age_test_celp\n",
"language_raw.loc[CELF, 'age_test'] = language_raw.age_test_celf\n",
"language_raw.loc[PLS, 'age_test'] = language_raw.age_test_pls\n",
"language_raw.loc[OWLS, 'age_test'] = language_raw.age_test_owls\n",
"\n",
"language1 = language_raw[CELP | CELF | PLS | OWLS].copy()\n",
"language2 = language1.copy()\n",
"\n",
"language1[\"test_type\"] = \"receptive\"\n",
"\n",
"language1.loc[CELP, \"test_name\"] = \"CELF-P2\"\n",
"language1.loc[CELF, \"test_name\"] = \"CELF-4\"\n",
"language1.loc[PLS, \"test_name\"] = \"PLS\"\n",
"language1.loc[OWLS, \"test_name\"] = \"OWLS\"\n",
"\n",
"language1.loc[CELP, \"score\"] = language1.celfp_rl_ss\n",
"language1.loc[CELF, \"score\"] = language1.celf_rlss\n",
"language1.loc[PLS, \"score\"] = language1.pls_ac_ss\n",
"language1.loc[OWLS, \"score\"] = language1.owls_lc_ss\n",
"\n",
"\n",
"language2[\"test_type\"] = \"expressive\"\n",
"\n",
"language2.loc[CELP, \"test_name\"] = \"CELF-P2\"\n",
"language2.loc[CELF, \"test_name\"] = \"CELF-4\"\n",
"language2.loc[PLS, \"test_name\"] = \"PLS\"\n",
"language2.loc[OWLS, \"test_name\"] = \"OWLS\"\n",
"\n",
"language2.loc[CELP, \"score\"] = language1.celfp_el_ss\n",
"language2.loc[CELF, \"score\"] = language1.celf_elss\n",
"language2.loc[PLS, \"score\"] = language1.pls_ec_ss\n",
"language2.loc[OWLS, \"score\"] = language1.owls_oe_ss\n",
"\n",
"language = pd.concat([language1, language2])\n",
"language = language[language.score.notnull()]\n",
"print(pd.crosstab(language.test_name, language.test_type))\n",
"print(\"There are {0} null values for score\".format(sum(language[\"score\"].isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"language[\"school\"] = language.study_id.str.slice(0,4)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>score</th>\n",
" <th>test_type</th>\n",
" <th>test_name</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>51</td>\n",
" <td>receptive</td>\n",
" <td>PLS</td>\n",
" <td>0101</td>\n",
" <td>54</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>61</td>\n",
" <td>receptive</td>\n",
" <td>OWLS</td>\n",
" <td>0101</td>\n",
" <td>113</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>55</td>\n",
" <td>receptive</td>\n",
" <td>PLS</td>\n",
" <td>0101</td>\n",
" <td>44</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>77</td>\n",
" <td>receptive</td>\n",
" <td>PLS</td>\n",
" <td>0101</td>\n",
" <td>54</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>93</td>\n",
" <td>receptive</td>\n",
" <td>CELF-P2</td>\n",
" <td>0101</td>\n",
" <td>68</td>\n",
" <td>Language</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name score test_type test_name \\\n",
"0 0101-2003-0101 initial_assessment_arm_1 51 receptive PLS \n",
"5 0101-2003-0101 year_5_complete_71_arm_1 61 receptive OWLS \n",
"9 0101-2003-0102 initial_assessment_arm_1 55 receptive PLS \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 77 receptive PLS \n",
"11 0101-2003-0102 year_2_complete_71_arm_1 93 receptive CELF-P2 \n",
"\n",
" school age_test domain \n",
"0 0101 54 Language \n",
"5 0101 113 Language \n",
"9 0101 44 Language \n",
"10 0101 54 Language \n",
"11 0101 68 Language "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"language = language[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"test_name\", \"school\", \"age_test\"]]\n",
"language[\"domain\"] = \"Language\"\n",
"language.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning articulation dataset\n",
"\n",
"We converted the articulation dataset into a \"long\" format:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Goldman 5098\n",
"Arizonia 498\n",
"Arizonia and Goldman 73\n",
"Name: test_type, dtype: int64\n",
"There are 0 null values for test_type\n"
]
}
],
"source": [
"# Test type\n",
"articulation[\"test_type\"] = None\n",
"ARIZ = articulation.aaps_ss.notnull()\n",
"GF = articulation.gf2_ss.notnull()\n",
"articulation = articulation[ARIZ | GF]\n",
"articulation.loc[(ARIZ & GF), \"test_type\"] = \"Arizonia and Goldman\"\n",
"articulation.loc[(ARIZ & ~GF), \"test_type\"] = \"Arizonia\"\n",
"articulation.loc[(~ARIZ & GF), \"test_type\"] = \"Goldman\"\n",
"\n",
"print(articulation.test_type.value_counts())\n",
"print(\"There are {0} null values for test_type\".format(sum(articulation[\"test_type\"].isnull())))\n",
"\n",
"# Test score (Arizonia if both)\n",
"articulation[\"score\"] = articulation.aaps_ss\n",
"articulation.loc[(~ARIZ & GF), \"score\"] = articulation.gf2_ss[~ARIZ & GF]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation[\"school\"] = articulation.study_id.str.slice(0,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The age was taken to be the Arizonia age if there are both test types:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 5666.000000\n",
"mean 68.598835\n",
"std 30.694788\n",
"min 23.000000\n",
"25% 47.000000\n",
"50% 60.000000\n",
"75% 80.000000\n",
"max 243.000000\n",
"Name: age_test, dtype: float64\n"
]
}
],
"source": [
"articulation[\"age_test\"] = articulation.age_test_aaps\n",
"articulation.loc[articulation.age_test.isnull(), 'age_test'] = articulation.age_test_gf2[articulation.age_test.isnull()]\n",
"print(articulation.age_test.describe())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we dropped unwanted columns and added a domain identification column for merging:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>test_type</th>\n",
" <th>score</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>78</td>\n",
" <td>0101</td>\n",
" <td>80</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>72</td>\n",
" <td>0101</td>\n",
" <td>44</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>97</td>\n",
" <td>0101</td>\n",
" <td>54</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>75</td>\n",
" <td>0101</td>\n",
" <td>53</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>80</td>\n",
" <td>0101</td>\n",
" <td>66</td>\n",
" <td>Articulation</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name test_type score school \\\n",
"1 0101-2003-0101 year_1_complete_71_arm_1 Goldman 78 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 Goldman 72 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 Goldman 97 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 Goldman 75 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 Goldman 80 0101 \n",
"\n",
" age_test domain \n",
"1 80 Articulation \n",
"9 44 Articulation \n",
"10 54 Articulation \n",
"14 53 Articulation \n",
"15 66 Articulation "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation = articulation.drop([\"age_test_aaps\", \"age_test_gf2\", \"aaps_ss\", \"gf2_ss\"], axis=1)\n",
"articulation[\"domain\"] = \"Articulation\"\n",
"articulation.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning demographic dataset\n",
"\n",
"We excluded unwanted columns and rows for which age, gender or race were missing:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Retain only subset of columns\n",
"#demographic = demographic[demographic.gender.notnull()]\n",
"demographic = demographic.rename(columns={'gender':'male'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Due to sample size considerations, we reduced the non-English primary language variable to English (0) and non-English (1):"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False 11198\n",
"True 2478\n",
"Name: non_english, dtype: int64\n",
"There are 691 null values for non_english\n"
]
}
],
"source": [
"demographic[\"non_english\"] = None\n",
"demographic.loc[demographic.prim_lang.notnull(), 'non_english'] = demographic.prim_lang[demographic.prim_lang.notnull()]>0\n",
"print(demographic.non_english.value_counts())\n",
"print(\"There are {0} null values for non_english\".format(sum(demographic.non_english.isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mother's education (`mother_ed`) and father's education (`father_ed`) were both recoded to: \n",
"\n",
"* 0=no high school diploma\n",
"* 1=high school\n",
"* 2=undergraduate\n",
"* 3=graduate\n",
"\n",
"Category 6 (unknown) was recoded as missing."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_mother_ed:\n",
"6 5001\n",
"4 2921\n",
"3 1950\n",
"5 1545\n",
"2 1342\n",
"1 474\n",
"0 194\n",
"Name: _mother_ed, dtype: int64\n",
"mother_ed:\n",
"1 3292\n",
"2 2921\n",
"3 1545\n",
"0 668\n",
"Name: mother_ed, dtype: int64\n",
"\n",
"There are 5941 null values for mother_ed\n"
]
}
],
"source": [
"demographic = demographic.rename(columns={\"mother_ed\":\"_mother_ed\"})\n",
"demographic[\"mother_ed\"] = demographic._mother_ed.copy()\n",
"demographic.loc[demographic._mother_ed==1, 'mother_ed'] = 0\n",
"demographic.loc[(demographic._mother_ed==2) | (demographic.mother_ed==3), 'mother_ed'] = 1\n",
"demographic.loc[demographic._mother_ed==4, 'mother_ed'] = 2\n",
"demographic.loc[demographic._mother_ed==5, 'mother_ed'] = 3\n",
"demographic.loc[demographic._mother_ed==6, 'mother_ed'] = None\n",
"print(\"_mother_ed:\")\n",
"print(demographic._mother_ed.value_counts())\n",
"print(\"mother_ed:\")\n",
"print(demographic.mother_ed.value_counts())\n",
"print(\"\\nThere are {0} null values for mother_ed\".format(sum(demographic.mother_ed.isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Secondary diagnosis"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(14367, 48)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.shape"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic['secondary_diagnosis'] = demographic.etiology==0\n",
"# Suspected or unknown treated as missing\n",
"demographic.loc[demographic.etiology > 1, 'secondary_diagnosis'] = None"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 10492\n",
"1 2416\n",
"Name: secondary_diagnosis, dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.secondary_diagnosis.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.18717074682367524"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.secondary_diagnosis.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Premature status was recoded to True (premature) and False (full-term). Here, premature indicates <36 weeks."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 3437 null values for premature_weeks\n"
]
}
],
"source": [
"demographic['premature_weeks'] = demographic.premature_age.copy()\n",
"demographic.loc[demographic.premature_age==9, 'premature_weeks'] = None\n",
"demographic.premature_weeks = abs(demographic.premature_weeks-8)*2\n",
"print(\"There are {0} null values for premature_weeks\".format(sum(demographic.premature_weeks.isnull())))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 9331\n",
"2 560\n",
"4 356\n",
"12 195\n",
"6 181\n",
"10 149\n",
"8 113\n",
"14 42\n",
"16 3\n",
"Name: premature_weeks, dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.premature_weeks.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode impant technology variables for each ear to one of four categories (None, Baha, Hearing aid, Cochlear implant):"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 4853\n",
"0 4246\n",
"7 1475\n",
"5 988\n",
"2 481\n",
"6 414\n",
"8 71\n",
"9 58\n",
"3 27\n",
"4 25\n",
"10 2\n",
"Name: tech_ad, dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_ad.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tech_cats = [\"None\", \"OAD\", \"Hearing aid\", \"Cochlear\", \"Other\"]\n",
"\n",
"demographic[\"tech_right\"] = 4\n",
"demographic.loc[demographic.tech_ad==7, 'tech_right'] = 0\n",
"demographic.loc[demographic.tech_ad==3, 'tech_right'] = 1\n",
"demographic.loc[demographic.tech_ad.isin([1,2,4,5,10]), 'tech_right'] = 2\n",
"demographic.loc[demographic.tech_ad.isin([0,8,6]), 'tech_right'] = 3\n",
"demographic.loc[demographic.tech_ad.isnull(), 'tech_right'] = None\n",
"\n",
"demographic[\"tech_left\"] = 4\n",
"demographic.loc[demographic.tech_as==7, 'tech_left'] = 0\n",
"demographic.loc[demographic.tech_as==3, 'tech_left'] = 1\n",
"demographic.loc[demographic.tech_as.isin([1,2,4,5,10]), 'tech_left'] = 2\n",
"demographic.loc[demographic.tech_as.isin([0,8,6]), 'tech_left'] = 3\n",
"demographic.loc[demographic.tech_as.isnull(), 'tech_left'] = None"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2 6423\n",
"3 4309\n",
"0 1802\n",
"4 57\n",
"1 19\n",
"Name: tech_left, dtype: int64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_left.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2 6349\n",
"3 4731\n",
"0 1475\n",
"4 58\n",
"1 27\n",
"Name: tech_right, dtype: int64"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_right.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Substitute valid missing values for hearing loss:"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[demographic.type_hl_ad==5, 'type_hl_ad'] = None\n",
"demographic.loc[demographic.type_hl_as==5, 'type_hl_ad'] = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create `degree_hl`, which is the maximum level of hearing loss in either ear:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic[\"degree_hl\"] = np.maximum(demographic.degree_hl_ad, demographic.degree_hl_as)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create compound indicator variable for each technology (Baha, Hearing aid, Chochlear implant): \n",
"\n",
"* 0=none\n",
"* 1=one ear\n",
"* 2=both ears."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['redcap_event_name', 'academic_year', 'hl', 'male', 'race', 'prim_lang',\n",
" 'sib', '_mother_ed', 'father_ed', 'premature_age', 'onset_1', 'age_amp',\n",
" 'age_int', 'age', 'synd_cause', 'etiology', 'etiology_2',\n",
" 'hearing_changes', 'ae', 'ad_250', 'ad_500', 'degree_hl_ad',\n",
" 'type_hl_ad', 'tech_ad', 'age_ci', 'as_250', 'as_500', 'degree_hl_as',\n",
" 'type_hl_as', 'tech_as', 'age_ci_2', 'time', 'age_disenrolled',\n",
" 'funct_out_age', 'slc_fo', 'sle_fo', 'a_fo', 'fam_age', 'family_inv',\n",
" 'att_days_sch', 'att_days_st2_417', 'att_days_hr', 'demo_ses',\n",
" 'school_lunch', 'medicaid', 'study_id', 'non_english', 'mother_ed',\n",
" 'secondary_diagnosis', 'premature_weeks', 'tech_right', 'tech_left',\n",
" 'degree_hl'],\n",
" dtype='object')"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.columns"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"oad:\n",
"0 4417\n",
"1 4\n",
"2 2\n",
"Name: oad, dtype: int64\n",
"There are 1674 null values for OAD\n",
"\n",
"hearing_aid:\n",
"2 2048\n",
"0 1604\n",
"1 741\n",
"Name: hearing_aid, dtype: int64\n",
"There are 1727 null values for hearing_aid\n",
"\n",
"cochlear:\n",
"0 2894\n",
"2 903\n",
"1 626\n",
"Name: cochlear, dtype: int64\n",
"There are 1674 null values for cochlear\n",
"14367\n"
]
}
],
"source": [
"demographic[\"oad\"] = 0\n",
"demographic.oad = demographic.oad.astype(object)\n",
"demographic.loc[(demographic.tech_right==1) | (demographic.tech_left==1), 'oad'] = 1\n",
"demographic.loc[(demographic.tech_right==1) & (demographic.tech_left==1), 'oad'] = 2\n",
"demographic.loc[(demographic.tech_right.isnull()) & (demographic.tech_left.isnull()), 'oad'] = None\n",
"print(\"oad:\")\n",
"print(demographic.drop_duplicates(subset='study_id').oad.value_counts())\n",
"print(\"There are {0} null values for OAD\".format(sum(demographic.oad.isnull())))\n",
"\n",
"demographic[\"hearing_aid\"] = 0\n",
"demographic.hearing_aid = demographic.hearing_aid.astype(object)\n",
"demographic.loc[(demographic.tech_right==2) | (demographic.tech_left==2), 'hearing_aid'] = 1\n",
"demographic.loc[(demographic.tech_right==2) & (demographic.tech_left==2), 'hearing_aid'] = 2\n",
"demographic.loc[(demographic.tech_right.isnull()) & (demographic.tech_right.isnull()), 'hearing_aid'] = None\n",
"print(\"\\nhearing_aid:\")\n",
"print(demographic.drop_duplicates(subset='study_id').hearing_aid.value_counts())\n",
"print(\"There are {0} null values for hearing_aid\".format(sum(demographic.hearing_aid.isnull())))\n",
"\n",
"demographic[\"cochlear\"] = 0\n",
"demographic.cochlear = demographic.cochlear.astype(object)\n",
"demographic.loc[(demographic.tech_right==3) | (demographic.tech_left==3), 'cochlear'] = 1\n",
"demographic.loc[(demographic.tech_right==3) & (demographic.tech_left==3), 'cochlear'] = 2\n",
"demographic.loc[(demographic.tech_right.isnull()) & (demographic.tech_left.isnull()), 'cochlear'] = None\n",
"print(\"\\ncochlear:\")\n",
"print(demographic.drop_duplicates(subset='study_id').cochlear.value_counts())\n",
"print(\"There are {0} null values for cochlear\".format(sum(demographic.cochlear.isnull())))\n",
"print(len(demographic))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Identify bilateral and bimodal individuals:"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic[\"unilateral_ci\"] = demographic.cochlear==1\n",
"demographic[\"bilateral_ci\"] = demographic.cochlear==2\n",
"demographic[\"bilateral_ha\"] = demographic.hearing_aid==2\n",
"demographic[\"bimodal\"] = (demographic.cochlear==1) & (demographic.hearing_aid==1)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3479, 5224, 1387, 2082)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.bilateral_ci.sum(), demographic.bilateral_ha.sum(), demographic.bimodal.sum(), demographic.unilateral_ci.sum()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"unilateral_ci 626\n",
"bilateral_ci 903\n",
"bilateral_ha 2048\n",
"bimodal 375\n",
"dtype: int64"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.drop_duplicates(subset='study_id')[['unilateral_ci','bilateral_ci', \n",
" 'bilateral_ha',\n",
" 'bimodal']].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create variable that identifies bilateral (0), bilateral HA left (1), bilateral HA right (2)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 0 null values for tech\n"
]
}
],
"source": [
"demographic['tech'] = 0\n",
"demographic.loc[(demographic.bimodal) & (demographic.tech_left==2), 'tech'] = 1\n",
"demographic.loc[(demographic.bimodal) & (demographic.tech_right==2), 'tech'] = 2\n",
"print(\"There are {0} null values for tech\".format(sum(demographic.tech.isnull())))"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 5224\n",
"3 3479\n",
"4 1387\n",
"1 911\n",
"0 676\n",
"8 14\n",
"2 12\n",
"7 5\n",
"5 1\n",
"Name: implant_category, dtype: int64"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[\"implant_category\"] = None\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==0) & (demographic.oad==0), \n",
" 'implant_category'] = 0\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==1) & (demographic.oad==0), \n",
" 'implant_category'] = 1\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==0) & (demographic.oad==1), \n",
" 'implant_category'] = 2\n",
"demographic.loc[(demographic.cochlear==2) & (demographic.hearing_aid==0) & (demographic.oad==0), \n",
" 'implant_category'] = 3\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==1) & (demographic.oad==0), \n",
" 'implant_category'] = 4\n",
"demographic.loc[(demographic.cochlear==1) & (demographic.hearing_aid==0) & (demographic.oad==1), \n",
" 'implant_category'] = 5\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==2) & (demographic.oad==0), \n",
" 'implant_category'] = 6\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==1) & (demographic.oad==1), \n",
" 'implant_category'] = 7\n",
"demographic.loc[(demographic.cochlear==0) & (demographic.hearing_aid==0) & (demographic.oad==2), \n",
" 'implant_category'] = 8\n",
"demographic.implant_category.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Age when hearing loss diagnosed** Data are entered inconsistently here, so we have to go in and replace non-numeric values."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 15. , 1. , 37. , 10. , 0. , 19. , 6. , 33. ,\n",
" 26. , 2. , 60. , 16. , 50. , 39. , 28. , 17. ,\n",
" 4. , 18. , nan, 3. , 35. , 38. , 95. , 42. ,\n",
" 7. , 13. , 12. , 31. , 14. , 27. , 11. , 36. ,\n",
" 41. , 22. , 24. , 51. , 84. , 61. , 5. , 30. ,\n",
" 88. , 46. , 23. , 80. , 9. , 8. , 83. , 74. ,\n",
" 25. , 64. , 107. , 21. , 72. , 116. , 40. , 57. ,\n",
" 78. , 65. , 43. , 47. , 79. , 34. , 62. , 77. ,\n",
" 48. , 96. , 52. , 97. , 67. , 20. , 45. , 29. ,\n",
" 59. , 53. , 1.5, 81. , 55. , 54. , 49. , 70. ,\n",
" 58. , 44. , 32. , 71. , 63. , 140. , 66. , 87. ,\n",
" 76. , 68. , 92. , 86. , 126. , 85. , 133. , 103. ,\n",
" 56. , 119. , 2.5, 98. , 75. , 0.5, 152. , 89. ,\n",
" 154. ])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.onset_1.unique()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Don't need this anymore\n",
"# demographic['age_diag'] = demographic.onset_1.replace({'birth': 0, 'R- Birth L-16mo': 0, 'birth - 3': 0, 'at birth': 0, 'NBHS': 0, \n",
"# 'at Birth': 0, '1-2': 1.5, '2-3': 2.5, '0-3': 1.5}).astype(float)\n",
"demographic['age_diag'] = demographic.onset_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Number of null values for `age_diag`"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3994"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.age_diag.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"demographic['sex'] = demographic.male.replace({0:'Female', 1:'Male'})"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sb\n",
"\n",
"unique_students = demographic.dropna(subset=['sex']).groupby('study_id').first()\n",
"\n",
"# ag = sb.factorplot(\"sex\", data=unique_students, \n",
"# palette=\"PuBuGn_d\", kind='count')\n",
"# ag.set_xticklabels(['Female ({})'.format((unique_students.male==0).sum()), \n",
"# 'Male ({})'.format((unique_students.male==1).sum())])\n",
"# ag.set_xlabels('')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Child has another diagnosed disability"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic['known_synd'] = (demographic.synd_cause == 0)\n",
"# Unknown or suspected\n",
"demographic.loc[demographic.synd_cause > 1, 'known_synd'] = None"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# If either known syndrome or secondary diagnosis\n",
"demographic['synd_or_disab'] = demographic.apply(lambda x: x['secondary_diagnosis'] or x['known_synd'], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Missing sibling counts were properly encoded as `None` (missing)."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[demographic.sib==4, 'sib'] = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We reduced the number of race categories, pooling those that were neither caucasian, black, hispanic or asian to \"other\", due to small sample sizes for these categories. Category 7 (unknown) was recoded as missing."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_race:\n",
"0 7531\n",
"2 2412\n",
"1 1300\n",
"3 1011\n",
"6 698\n",
"8 521\n",
"7 242\n",
"4 65\n",
"5 28\n",
"Name: _race, dtype: int64\n",
"race:\n",
"0 7531\n",
"2 2412\n",
"4 1312\n",
"1 1300\n",
"3 1011\n",
"Name: race, dtype: int64\n",
"There are 801 null values for race\n"
]
}
],
"source": [
"races = [\"Caucasian\", \"Black or African American\", \"Hispanic or Latino\", \"Asian\", \"Other\"]\n",
"demographic = demographic.rename(columns={\"race\":\"_race\"})\n",
"demographic[\"race\"] = demographic._race.copy()\n",
"demographic.loc[demographic.race==7, 'race'] = None\n",
"demographic.loc[demographic.race>3, 'race'] = 4\n",
"print(\"_race:\")\n",
"print(demographic._race.value_counts())\n",
"print(\"race:\")\n",
"print(demographic.race.value_counts())\n",
"print(\"There are {0} null values for race\".format(sum(demographic.race.isnull())))\n",
"# Replace with recoded column"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recode implant technology variables"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tech_cats = [\"None\", \"Baha\", \"Hearing aid\", \"Cochlear\", \"Other\"]\n",
"\n",
"demographic[\"tech_right\"] = demographic.tech_ad.copy()\n",
"demographic.loc[demographic.tech_right==6, 'tech_right'] = 0\n",
"demographic.loc[demographic.tech_right==4, 'tech_right'] = 1\n",
"demographic.loc[demographic.tech_right==5, 'tech_right'] = 1\n",
"demographic.loc[demographic.tech_right==3, 'tech_right'] = 2\n",
"demographic.loc[demographic.tech_right==7, 'tech_right'] = 3\n",
"demographic.loc[demographic.tech_right==8, 'tech_right'] = 3\n",
"demographic.loc[demographic.tech_right==9, 'tech_right'] = 4\n",
"demographic.tech_right = np.abs(demographic.tech_right - 3)\n",
"\n",
"demographic[\"tech_left\"] = demographic.tech_as.copy()\n",
"demographic.loc[demographic.tech_left==6, 'tech_left'] = 0\n",
"demographic.loc[demographic.tech_left==4, 'tech_left'] = 1\n",
"demographic.loc[demographic.tech_left==5, 'tech_left'] = 1\n",
"demographic.loc[demographic.tech_left==3, 'tech_left'] = 2\n",
"demographic.loc[demographic.tech_left==7, 'tech_left'] = 3\n",
"demographic.loc[demographic.tech_left==8, 'tech_left'] = 3\n",
"demographic.loc[demographic.tech_left==9, 'tech_left'] = 4\n",
"demographic.tech_left = np.abs(demographic.tech_left - 3)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Don't need this anymore\n",
"# demographic['age_amp'] = demographic.age_amp.replace({'22 mo': 22, 'unknown': np.nan, 'none': np.nan, \n",
"# 'n/a unilateral loss': np.nan, 'not amplified yet': np.nan,\n",
"# 'not amplified': np.nan, 'n/a': np.nan, '4 months-6 months': 5,\n",
"# '6 weeks': 0.5, '13-18': 15.5, '0-3': 1.5, '24-36': 30}).astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['2002-2003', '2013-2014', '2014-2015', '2012-2013', '2011-2012',\n",
" '2009-2010', '2010-2011', '2007-2008', '2008-2009', nan,\n",
" '2009-2011', '2006-2007', '2005-2006', '2012', '2006-2007 ',\n",
" '2004-2005', '2003-2004', '2015-2016', '2015', '2014', '2001-2002',\n",
" '2000-2001', '1995-1996', '1998-1999', '1999-2000', '1997-1998',\n",
" '2013', '2010', '2009', '2011',\n",
" ' 2010-2011',\n",
" '2015-2015', '2014-2015 ', '2012-2013 '], dtype=object)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.academic_year.replace(\n",
" {'12013-2014': '2013-2014', '2010 - 20111': '2010 - 2011', \n",
" '2020-2011': '2010-2011', '2012-20013': '2012-2013', \n",
" '642014-2015': '2014-2015', '20114-2015': '2014-2015',\n",
" '2011-012': '2011-2012',\n",
" '0000-0000': np.nan}).str.replace('*', '-').unique()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic['academic_year'] = demographic.academic_year.replace(\n",
" {'12013-2014': '2013-2014', '2010 - 20111': '2010 - 2011', \n",
" '2020-2011': '2010-2011', '2012-20013': '2012-2013', \n",
" '642014-2015': '2014-2015', '20114-2015': '2014-2015',\n",
" '2011-012': '2011-2012', '2014-2105': '2014-2015', '2005-2004': '2004-2005',\n",
" '2014-205': '2014-2015', '2017-2015': '2014-2015', '2014-1015': '2014-2015',\n",
" '2015-2015': '2014-2015', '2009-2011': '2009-2010',\n",
" '0000-0000': np.nan}).str.replace('*', '-')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removed entries that don't contain dashes"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[~(demographic.academic_year.notnull() & demographic.academic_year.str.contains('-')), \n",
" 'academic_year'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic.loc[demographic.academic_year.notnull(), 'academic_year'] = demographic.academic_year[demographic.academic_year.notnull()].apply(lambda x: ''.join(x.split()))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10fd84cc0>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfYAAAFVCAYAAAAdY838AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAEMIOwAAhhB2AAAMIewAABjyP+AY6IZo1x0mAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10defb438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"demographic.age_amp.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning expressive vocabulary dataset\n",
"\n",
"We converted the expressive vocabulary dataset to \"long\" format:"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 0 null values for test_type\n"
]
}
],
"source": [
"# Test type\n",
"expressive[\"test_type\"] = None\n",
"EOWPVT = expressive.eowpvt_ss.notnull()\n",
"EVT = expressive.evt_ss.notnull()\n",
"expressive = expressive[EOWPVT | EVT]\n",
"expressive.loc[EOWPVT & EVT, \"test_type\"] = \"EOWPVT and EVT\"\n",
"expressive.loc[EOWPVT & ~EVT, \"test_type\"] = \"EOWPVT\"\n",
"expressive.loc[~EOWPVT & EVT, \"test_type\"] = \"EVT\"\n",
"print(\"There are {0} null values for test_type\".format(sum(expressive[\"test_type\"].isnull())))\n",
"\n",
"expressive[\"score\"] = expressive.eowpvt_ss\n",
"expressive.loc[~EOWPVT & EVT, \"score\"] = expressive.evt_ss[~EOWPVT & EVT]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EVT 3691\n",
"EOWPVT 2657\n",
"EOWPVT and EVT 147\n",
"Name: test_type, dtype: int64"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive.test_type.value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive[\"school\"] = expressive.study_id.str.slice(0,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The age was taken to be the EOWPVT age if there are both test types:"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive[\"age_test\"] = expressive.age_test_eowpvt\n",
"expressive.loc[expressive.age_test.isnull(), 'age_test'] = expressive.age_test_evt[expressive.age_test.isnull()]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we dropped unwanted columns and added a domain identification column for merging:"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>score</th>\n",
" <th>test_type</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>58</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>54</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>84</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>80</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>90</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>113</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>90</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>53</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0101-2004-0101</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>87</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>66</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name score test_type school \\\n",
"0 0101-2003-0101 initial_assessment_arm_1 58 EOWPVT 0101 \n",
"2 0101-2003-0101 year_2_complete_71_arm_1 84 EOWPVT 0101 \n",
"5 0101-2003-0101 year_5_complete_71_arm_1 90 EOWPVT 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 90 EOWPVT 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 87 EOWPVT 0101 \n",
"\n",
" age_test domain \n",
"0 54 Expressive Vocabulary \n",
"2 80 Expressive Vocabulary \n",
"5 113 Expressive Vocabulary \n",
"14 53 Expressive Vocabulary \n",
"15 66 Expressive Vocabulary "
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive = expressive[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"school\", \"age_test\"]]\n",
"expressive[\"domain\"] = \"Expressive Vocabulary\"\n",
"expressive.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cleaning receptive vocabulary dataset"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We converted the receptive vocabulary data table to \"long\" format:"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['study_id', 'redcap_event_name', 'age_test_ppvt', 'ppvt_ss',\n",
" 'age_test_rowpvt', 'rowpvt_ss'],\n",
" dtype='object')"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.columns"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 0 null values for test_type\n"
]
}
],
"source": [
"# Test type\n",
"receptive[\"test_type\"] = None\n",
"PPVT = receptive.ppvt_ss.notnull()\n",
"ROWPVT = receptive.rowpvt_ss.notnull()\n",
"receptive = receptive[PPVT | ROWPVT]\n",
"receptive.loc[PPVT & ROWPVT, \"test_type\"] = \"PPVT and ROWPVT\"\n",
"receptive.loc[PPVT & ~ROWPVT, \"test_type\"] = \"PPVT\"\n",
"receptive.loc[~PPVT & ROWPVT, \"test_type\"] = \"ROWPVT\"\n",
"print(\"There are {0} null values for test_type\".format(sum(receptive[\"test_type\"].isnull())))\n",
"\n",
"receptive[\"score\"] = receptive.ppvt_ss\n",
"receptive.loc[~PPVT & ROWPVT, \"score\"] = receptive.rowpvt_ss[~PPVT & ROWPVT]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive[\"school\"] = receptive.study_id.str.slice(0,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The age was taken to be the PPVT age if there are both test types:"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive[\"age_test\"] = receptive.age_test_ppvt\n",
"receptive.loc[receptive.age_test.isnull(), 'age_test'] = receptive.age_test_rowpvt[receptive.age_test.isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 28 null values for age_test\n"
]
}
],
"source": [
"print(\"There are {0} null values for age_test\".format(sum(receptive.age_test.isnull())))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we dropped unwanted columns and added a domain identification column for merging:"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>score</th>\n",
" <th>test_type</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>90</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>80</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2003-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>101</td>\n",
" <td>ROWPVT</td>\n",
" <td>0101</td>\n",
" <td>113</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>55</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>44</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>80</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>54</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0101-2003-0102</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>101</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>68</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name score test_type school \\\n",
"2 0101-2003-0101 year_2_complete_71_arm_1 90 PPVT 0101 \n",
"5 0101-2003-0101 year_5_complete_71_arm_1 101 ROWPVT 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 55 PPVT 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 80 PPVT 0101 \n",
"11 0101-2003-0102 year_2_complete_71_arm_1 101 PPVT 0101 \n",
"\n",
" age_test domain \n",
"2 80 Receptive Vocabulary \n",
"5 113 Receptive Vocabulary \n",
"9 44 Receptive Vocabulary \n",
"10 54 Receptive Vocabulary \n",
"11 68 Receptive Vocabulary "
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive = receptive[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"school\", \"age_test\"]]\n",
"receptive[\"domain\"] = \"Receptive Vocabulary\"\n",
"receptive.head()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3021,)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.study_id.unique().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Merge datasets\n",
"\n",
"The four datasets were mereged into a single table. First, we concatenate the test scores data:"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"test_scores = pd.concat([articulation, expressive, receptive, language])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we perform a merge between the demographic data and the test scores data:"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr = pd.merge(demographic, test_scores, on=[\"study_id\", \"redcap_event_name\"], how='left')"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>male</th>\n",
" <th>_race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>_mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>sex</th>\n",
" <th>known_synd</th>\n",
" <th>synd_or_disab</th>\n",
" <th>race</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" <th>school</th>\n",
" <th>score</th>\n",
" <th>test_name</th>\n",
" <th>test_type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>36993</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>162</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>0102</td>\n",
" <td>84</td>\n",
" <td>NaN</td>\n",
" <td>ROWPVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36994</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>Female</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36995</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>123</td>\n",
" <td>Articulation</td>\n",
" <td>1147</td>\n",
" <td>102</td>\n",
" <td>NaN</td>\n",
" <td>Goldman</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36996</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>125</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>102</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36997</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>123</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>95</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 73 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang \\\n",
"36993 year_9_complete_71_arm_1 2011-2012 0 1 0 0 \n",
"36994 year_9_complete_71_arm_1 NaN 0 0 0 0 \n",
"36995 year_9_complete_71_arm_1 2013-2014 0 1 3 0 \n",
"36996 year_9_complete_71_arm_1 2013-2014 0 1 3 0 \n",
"36997 year_9_complete_71_arm_1 2013-2014 0 1 3 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... sex \\\n",
"36993 3 6 6 8 ... Male \n",
"36994 NaN 6 6 9 ... Female \n",
"36995 1 5 5 8 ... Male \n",
"36996 1 5 5 8 ... Male \n",
"36997 1 5 5 8 ... Male \n",
"\n",
" known_synd synd_or_disab race age_test domain \\\n",
"36993 0 0 0 162 Receptive Vocabulary \n",
"36994 NaN NaN 0 NaN NaN \n",
"36995 0 1 3 123 Articulation \n",
"36996 0 1 3 125 Expressive Vocabulary \n",
"36997 0 1 3 123 Receptive Vocabulary \n",
"\n",
" school score test_name test_type \n",
"36993 0102 84 NaN ROWPVT \n",
"36994 NaN NaN NaN NaN \n",
"36995 1147 102 NaN Goldman \n",
"36996 1147 102 NaN EVT \n",
"36997 1147 95 NaN PPVT \n",
"\n",
"[5 rows x 73 columns]"
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.tail()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2013 6928\n",
"2012 6630\n",
"2014 5560\n",
"2011 5216\n",
"2010 4425\n",
"nan 3157\n",
"2009 2362\n",
"2008 830\n",
"2007 531\n",
"2006 343\n",
"2015 336\n",
"2005 286\n",
"2004 172\n",
"2003 90\n",
"2002 47\n",
"2001 35\n",
"1998 16\n",
"1999 15\n",
"2000 12\n",
"1997 6\n",
"1995 1\n",
"Name: academic_year_start, dtype: int64"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr['academic_year_start'] = lsl_dr.academic_year.apply(lambda x: str(x).strip()[:4])\n",
"lsl_dr.academic_year_start.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"current_year_only = False\n",
"\n",
"if current_year_only:\n",
" lsl_dr = lsl_dr[lsl_dr.academic_year_start=='2013']"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAFkCAYAAADIefl6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt0lPWdx/HP5AaSmeGypJ6tYIppBGwJGkkEhBiRarai\nVSBggkAL7UIoXgiyhmugXUCgEGtrV/Sw6zqikNWgS6tUG0UKWQ0cFbkIKgql1EAkhJ2ZJcmQefYP\nDlMQdGQyk0l+836dwzmZZ/I88/2SST7z3H4/m2VZlgAAgJHiol0AAACIHIIeAACDEfQAABiMoAcA\nwGAEPQAABiPoAQAwWEIkN75hwwZVVFRIkhobG7Vv3z4999xzWrx4seLi4pSenq7S0lLZbDaVl5dr\n/fr1SkhIUFFRkXJzcyNZGgAAMcHWWvfR/+IXv1Dfvn31xhtvaNKkScrKylJpaamGDh2q/v37a9Kk\nSaqoqFBjY6MKCgr04osvKikpqTVKAwDAWK1y6H7Xrl365JNPlJ+frz179igrK0uSlJOTo6qqKu3a\ntUuZmZlKTEyU3W5Xamqq9u/f3xqlAQBgtFYJ+tWrV2v69OmSpHMPICQnJ8vtdsvj8cjhcJy33OPx\ntEZpAAAYLeJB/7//+786ePCgsrOzz7xg3N9f0uPxyOl0ym63y+v1BpZ7vV45nc6v3Caj9gIA8M1E\n9GI8Sdq+fbsGDhwYeNy3b19VV1crOztbW7Zs0aBBg5SRkaGysjI1NTWpsbFRBw4cUHp6+ldu02az\nqbbWHenS26yUFAf903+0y4iKWO5don/6dwT/pouIeNAfPHhQV155ZeBxSUmJ5s+fL5/Pp7S0NOXl\n5clms2nChAkqLCyU3+9XcXExF+IBABAGrXbVfbjF+qc6+qf/WBTLvUv0T/+h7dEzYA4AAAYj6AEA\nMBhBDwCAwQh6AAAMRtADAGAwgh4AAIMR9AAAGIygBwDAYAQ9AAAGI+gBADAYQQ8AgMEIegAADEbQ\nAwBgMIIeAACDEfQAABiMoAcAwGAEPQAABkuIdgEAYo0V8prNzc3nrG8LSzWA6Qh6AK2urPx91dY3\nhLRuSpeOmjHm2jBXBJiLoAfQ6mrrG1RTdyraZQAxgXP0AAAYjKAHAMBgBD0AAAYj6AEAMBhBDwCA\nwQh6AAAMxu11AC5R6APehGd9AJeCoAdwyVoy4M3VPZ1hrgbA1yHoAVyylgx4071zhzBXA+DrcI4e\nAACDEfQAABiMoAcAwGCcowdiDlfNA7GEoAdiEFfNA7GDoAdiEFfNA7GDc/QAABgsonv0q1ev1ptv\nvqmmpiYVFhYqKytLJSUliouLU3p6ukpLS2Wz2VReXq7169crISFBRUVFys3NjWRZAADEjIjt0b/z\nzjt67733tG7dOj377LOqqanRI488ouLiYq1du1aWZamyslK1tbVyuVxat26d1qxZo5UrV6qpqSlS\nZQEAEFMiFvTbtm1T7969NW3aNE2dOlW5ubnas2ePsrKyJEk5OTmqqqrSrl27lJmZqcTERNntdqWm\npmr//v2RKgsAgJgSsUP3dXV1+vzzz7V69WodPnxYU6dOlWX9/bac5ORkud1ueTweORyO85Z7PJ5I\nlQUAQEyJWNB37dpVaWlpSkhIUK9evdShQwcdO3Ys8LzH45HT6ZTdbpfX6w0s93q9cjqD376TkuII\n+j0mo3/6D1Vzc3MYK4mObt2SFR8fH+0yooL3fmz3H4qIBf3111+vZ555Rj/5yU909OhRNTQ0aODA\ngaqurlZ2dra2bNmiQYMGKSMjQ2VlZWpqalJjY6MOHDig9PT0oNuvrXVHqvQ2LyXFQf/034IttP8B\nb+rqvJJs0S6j1fHep/9QRCzoc3NztX37do0ePVp+v1+lpaW64oorNH/+fPl8PqWlpSkvL082m00T\nJkxQYWGh/H6/iouLlZSUFKmyAACIKRG9vW7WrFkXLHO5XBcsy8/PV35+fiRLAQAgJjFgDgAABiPo\nAQAwGEEPAIDBCHoAAAxG0AMAYDCCHgAAgxH0AAAYjKAHAMBgBD0AAAaL6Mh4AC6mZWPNn5mUxlIs\njvUO4NIR9EAUlJW/r9r6hpDWTenSUTPGXBvmigCYiqAHoqC2vkE1daeiXQaAGMA5egAADEbQAwBg\nMIIeAACDEfQAABiMi/GAdiY+zqaW3aLXstv7ALQvBD3QznR1JKmsfGfIt+dd3dMZ5ooAtGUEPdAO\nteT2vO6dO4S5GgBtGefoAQAwGEEPAIDBCHoAAAxG0AMAYDCCHgAAgxH0AAAYjKAHAMBgBD0AAAYj\n6AEAMBhBDwCAwQh6AAAMRtADAGAwgh4AAIMR9AAAGIygBwDAYAQ9AAAGI+gBADBYQqRf4O6775bd\nbpck9ezZU1OmTFFJSYni4uKUnp6u0tJS2Ww2lZeXa/369UpISFBRUZFyc3MjXRoAAMaLaNA3NjZK\nklwuV2DZ1KlTVVxcrKysLJWWlqqyslL9+/eXy+VSRUWFGhsbVVBQoMGDByspKSmS5QEAYLyIBv2+\nfft06tQpTZ48WadPn9aMGTO0d+9eZWVlSZJycnK0bds2xcXFKTMzU4mJiUpMTFRqaqr279+vfv36\nRbI8AACMF9Ggv+yyyzR58mTl5+fr4MGD+ulPf3re88nJyXK73fJ4PHI4HOct93g8kSwNAICYENGg\n/853vqPU1NTA1126dNGHH34YeN7j8cjpdMput8vr9QaWe71eOZ3Or912Sorja583Hf233/6bm5uj\nXUK7161bsuLj46NdRlS05/d+OMR6/6GIaNC/+OKL+uijj1RaWqqjR4/K6/XqxhtvVHV1tbKzs7Vl\nyxYNGjRIGRkZKisrU1NTkxobG3XgwAGlp6d/7bZra92RLL1NS0lx0H+77t+KdgHtXl2dV5It2mW0\nuvb/3m8Z+g/tQ05Eg3706NEqKSlRYWGhbDabli5dqi5dumj+/Pny+XxKS0tTXl6ebDabJkyYoMLC\nQvn9fhUXF3MhHgAAYRDRoE9MTNTKlSsvWH7uVfhn5efnKz8/P5LlAAAQcxgwBwAAgxH0AAAYLOIj\n4wFAOMXH2RS+Cxpj74I+xB6CHkC70tWRpLLynaqtbwh5GyldOmrGmGvDWBXQdhH0ANqd2voG1dSd\ninYZQLvAOXoAAAxG0AMAYDCCHgAAgxH0AAAYjKAHAMBgBD0AAAYj6AEAMBhBDwCAwQh6AAAMRtAD\nAGAwgh4AAIMR9AAAGIygBwDAYAQ9AAAGI+gBADAYQQ8AgMEIegAADEbQAwBgMIIeAACDJUS7AABo\nbfFxNklWGLZkC8M2gMgi6IFL1tKACEfAoCW6OpJUVr5TtfUNIa2f0qWjZoy5NsxVAZFB0AMhKCt/\nP+SQuLqnM8zVIBS19Q2qqTsV7TKAiCPogRC0JCS6d+4Q5moA4KtxMR4AAAYj6AEAMBhBDwCAwQh6\nAAAMRtADAGAwgh4AAIMR9AAAGIygBwDAYBEP+uPHj+umm27SZ599pkOHDqmgoEDjxo3TwoULZVln\nhgItLy/XqFGjNHbsWG3evDnSJQEAEDMiGvQ+n08LFizQZZddJsuytHTpUhUXF2vt2rWyLEuVlZWq\nra2Vy+XSunXrtGbNGq1cuVJNTU2RLAsAgJgRNOh/9rOf6dVXX5XP57vkjS9fvlwFBQVKSUmRJO3d\nu1dZWVmSpJycHFVVVWnXrl3KzMxUYmKi7Ha7UlNTtX///kt+LQAAcKGgY93/7Gc/04YNG7RixQrd\ndNNNuvvuu5WRkRF0wxUVFerWrZuGDBmi1atXy7KswKF6SUpOTpbb7ZbH45HD4ThvucfjCbr9lBRH\n0O8xGf1Hr//m5uaovTbajm7dkhUfH9/qr8vvfmz3H4qgQZ+dna3s7Gw1NDRo06ZNuu++++RwODR6\n9GgVFhYqKSnpoutVVFTIZrOpqqpK+/btU0lJiU6cOBF43uPxyOl0ym63y+v1BpZ7vV45ncFn96qt\ndX+T/oyUkuKg/6j2zzSzkOrqvGrt+eij/96PLvoP7UPONzpH//bbb2vRokUqKytTTk6O5syZo9ra\nWhUVFX3lOs8++6xcLpdcLpf69OmjZcuWaciQIaqurpYkbdmyRQMGDFBGRoZ27NihpqYmud1uHThw\nQOnp6SE1AwAAzhd0j/7mm29Wjx49NGrUKJWWlqpjx46SpBtuuEGjRo36xi9ks9lUUlKi+fPny+fz\nKS0tTXl5ebLZbJowYYIKCwvl9/tVXFz8lUcJAADApQka9E8//bSSk5PVvXt3nTp1SocOHVJqaqri\n4+P10ksvfaMXcblcF/36rPz8fOXn519C2QAA4JsIeuj+rbfe0k9/+lNJZ+6Jnzp1qtatWxfxwgAA\nQMsFDfr169frueeekyT16NFDFRUVevbZZyNeGAAAaLmgQX/69GklJiYGHicmJspma90rTQEAQGiC\nnqMfPny4Jk6cqB/+8IeyLEuvvfaahg0b1hq1AQCAFgoa9A899JA2bdqkHTt2KCEhQRMnTtTw4cNb\nozYAANBCQYPeZrMpLS1N3bt3D4xst3379sBQtgAAoO0KGvSLFi3Sm2++qZ49e563/GK3yQEAgLYl\naNBv27ZNmzZtCgyUAwAA2o+gV9337NlTfr+/NWoBAABhFnSP3ul06vbbb9d1112nDh06BJYvXbo0\nooUBAICWCxr0Q4cO1dChQwP3zluWxX30AAC0E0GDfuTIkTp8+LA++eQTDR06VJ9//vkFF+YBAIC2\nKeg5+j/84Q+aNm2aFi9erJMnT+qee+75xpPZAACA6Aoa9E899ZSef/552e12/cM//IMqKir05JNP\ntkZtAACghYIGfVxcnOx2e+Dx5Zdfrvj4+IgWBQAAwiPoOfr09HS5XC75fD59+OGHeu6559SnT5/W\nqA0AALRQ0D36BQsW6OjRo+rQoYPmzJkju92u0tLS1qgNAAC0UNA9+uTkZD300EOtUQsAAAizoEF/\nscP03/rWt7Rly5aIFAQAAMInaNDv27cv8LXP59Of/vQnvffeexEtCgAAhEfQc/TnSkxM1D/90z/p\n7bffjlQ9AAAgjILu0W/YsCHwtWVZ+vjjj5WUlBTRogAAQHgEDfp33nnnvLHtu3btqrKysogWBQAA\nwiNo0D/yyCOtUQcAAIiAoEE/bNgw2Ww2WZZ1wXM2m02VlZURKQwAALRc0KAfMWKEkpKSNGbMGCUk\nJGjjxo364IMPVFxcfNHwBwAAbUfQoN+6dasqKioCjydOnKi7775bV1xxRUQLAwAALRf09jrLsrRt\n27bA4zfeeOO8SW4AAEDbFXSP/pe//KX+5V/+RcePH5ck9erVS8uXL494YUDktPSUE6esALQfQYP+\n+9//vl555RXV1dUpKSmJvXkYoaz8fdXWN4S07tU9nWGuBgAiJ2jQ//Wvf9X8+fP117/+VWvXrlVR\nUZGWLFminj17tkZ9QETU1jeopu5USOt279whzNUAQOQEPUdfWlqqSZMmKTk5Wd27d9cdd9yhkpKS\n1qgNAAC0UNCgP3HihIYOHXrmm+PiNGbMGLnd7ogXBgBtVXycTWeu1WjpPyDygh6679ixo2pqagKP\nd+zYoQ4dOHQJIHZ1dSSprHxnyNd5pHTpqBljrg1zVcDFBQ36kpIS/fM//7MOHz6sO++8UydPntSv\nf/3r1qgNANqsllznAbSmoEFfV1enF154QQcPHpTf79dVV13F7HUAALQTQYN++fLleuWVV3T11Vdf\n8sabm5s1b948HTx4UDabTYsWLVJSUpJKSkoUFxen9PR0lZaWymazqby8XOvXr1dCQoKKioqUm5sb\nSj8AAOAcQYP+yiuv1OzZs9W/f//AuXmbzaa77ror6MbffPNNxcXF6fnnn1d1dbVWrVolSSouLlZW\nVpZKS0tVWVmp/v37y+VyqaKiQo2NjSooKNDgwYM5cgAAQAt9ZdAfPXpUl19+ubp06SJJ2rlz53nP\nf5OgHz58uG6++WZJ0pEjR9S5c2dVVVUpKytLkpSTk6Nt27YpLi5OmZmZSkxMVGJiolJTU7V//371\n69cv5MYAAMDXBP2UKVP00ksv6ZFHHtGaNWs0efLkkF4gPj5eDz/8sCorK/XrX//6vHHzk5OT5Xa7\n5fF45HA4zlvu8Xi+drspKY6vfd509B96/83NzWGsBAhNt27Jio+Pv+T1+N2P7f5DEfTQvSRt3Lgx\n5KCXpGXLlumLL75Qfn6+mpqaAss9Ho+cTqfsdru8Xm9gudfrldP59cOM1tbG7r38KSkO+m9R/9y/\njOirq/NKsl3SOvzu038ogg6Y0xIvv/yynnzySUln7sePi4vT97//fVVXV0uStmzZogEDBigjI0M7\nduxQU1OT3G63Dhw4oPT09EiWBgBATPhGe/ShuvXWWzV79mzde++9On36tObOnaurrrpK8+fPl8/n\nU1pamvLy8mSz2TRhwgQVFhbK7/eruLiYC/EAAAiDrwz6Tz75RMOGDZMkHTt2LPC1dOaq+8rKyqAb\nv+yyy/Too49esNzlcl2wLD8/X/n5+d+oaAAA8M18ZdBv2rSpNesAgJjx97HyL82ZC0nPXe/SzvEj\nNn1l0Pfo0aM16wCAmMFY+WhNET1HDwC4OMbKR2sh6BGCcN2exmFHAIg0gh4hKSt/n8OOANAOEPQI\nCYcdAaB9iOiAOQAAILoIegAADEbQAwBgMIIeAACDEfQAABiMoAcAwGAEPQAABiPoAQAwGEEPAIDB\nCHoAAAxG0AMAYDCCHgAAgxH0AAAYjKAHAMBgBD0AAAYj6AEAMBhBDwCAwQh6AAAMRtADAGAwgh4A\nAIMR9AAAGIygBwDAYAQ9AAAGI+gBADAYQQ8AgMEIegAADJYQ7QKAS2VZliSrJVsIVykA0OYR9Gh3\n/H6/ysrfV219Q0jrX93TGeaKAKDtIujRLtXWN6im7lRI63bv3CHM1QBA2xWxoPf5fJozZ47+9re/\nqampSUVFRUpLS1NJSYni4uKUnp6u0tJS2Ww2lZeXa/369UpISFBRUZFyc3MjVRYAADElYkG/ceNG\ndevWTStWrNDJkyf1ox/9SH379lVxcbGysrJUWlqqyspK9e/fXy6XSxUVFWpsbFRBQYEGDx6spKSk\nSJUGAEDMiFjQ5+Xl6bbbbpN05pxqQkKC9u7dq6ysLElSTk6Otm3bpri4OGVmZioxMVGJiYlKTU3V\n/v371a9fv0iVBgBAzIjY7XWdOnVScnKyPB6PHnjgAT344IPy+/2B55OTk+V2u+XxeORwOM5b7vF4\nIlUWAAAxJaIX433++eeaPn26xo0bpxEjRmjFihWB5zwej5xOp+x2u7xeb2C51+uV0xn8quiUFEfQ\n7zFZNPtvbm5u0frxcTZ17txR8fHxUXl9wATduiWH/DvUnsX63/5QRCzov/jiC02aNEmlpaUaOHCg\nJKlv376qrq5Wdna2tmzZokGDBikjI0NlZWVqampSY2OjDhw4oPT09KDbr611R6r0Ni8lxRHl/lt2\nH3pXR5IWPvU2t8cBLVBX55Vki3YZrSr6f/uiK9QPOREL+ieeeEJut1uPP/64Hn/8cUnS3LlztXjx\nYvl8PqWlpSkvL082m00TJkxQYWGh/H6/iouLuRAvBnB7HAC0jogF/bx58zRv3rwLlrtcrguW5efn\nKz8/P1KlAAAQsxjrHgAAgxH0AAAYjKAHAMBgBD0AAAYj6AEAMBhBDwCAwQh6AAAMRtADAGCwiI51\nDwAIv/g4m1o6FPUZsTWEbqwi6AGgnenqSFJZ+c6Q54tI6dJRM8ZcG+aq0FYR9ADQDrVkvgjEFs7R\nAwBgMIIeAACDEfQAABiMoAcAwGAEPQAABiPoAQAwGEEPAIDBCHoAAAxG0AMAYDCCHgAAgxH0AAAY\njKAHAMBgBD0AAAYj6AEAMBhBDwCAwQh6AAAMRtADAGAwgh4AAIMR9AAAGCwh2gUAAFpXfJxNkhWG\nLdnCsA1EGkEPADGmqyNJZeU7VVvfENL6KV06asaYa8NcFSKFoAeAGFRb36CaulPRLgOtgHP0AAAY\njKAHAMBgBD0AAAaLeNDv3LlT48ePlyQdOnRIBQUFGjdunBYuXCjLOnPVZ3l5uUaNGqWxY8dq8+bN\nkS4JAICYEdGgf+qppzRv3jz5fD5J0tKlS1VcXKy1a9fKsixVVlaqtrZWLpdL69at05o1a7Ry5Uo1\nNTVFsiwAAGJGRIM+NTVVv/3tbwN77nv37lVWVpYkKScnR1VVVdq1a5cyMzOVmJgou92u1NRU7d+/\nP5JlAQAQMyIa9Lfeeqvi4+MDj88GviQlJyfL7XbL4/HI4XCct9zj8USyLAAAYkar3kcfF/f3zxUe\nj0dOp1N2u11erzew3Ov1yul0Bt1WSooj6PeYLJr9Nzc3R+21AbQN3boln7cj11pi/W9/KFo16Pv2\n7avq6mplZ2dry5YtGjRokDIyMlRWVqampiY1NjbqwIEDSk9PD7qt2lp3K1TcNqWkOKLcfziGzgTQ\nntXVedXaQ+BG/29fdIX6IadVgt5mO/NmKCkp0fz58+Xz+ZSWlqa8vDzZbDZNmDBBhYWF8vv9Ki4u\nVlJSUmuUBQCA8SIe9D169NC6deskSd/5znfkcrku+J78/Hzl5+dHuhQAAGIOA+YAAGAwgh4AAIMR\n9AAAGIygBwDAYAQ9AAAGI+gBADAYQQ8AgMEIegAADEbQAwBgMIIeAACDteqkNgCA9i8+zqbwTG7V\nupPixCqCHgBwSbo6klRWvlO19Q0hrZ/SpaNmjLk2zFXhqxD0AIBLVlvfoJq6U9EuA98A5+gBADAY\nQQ8AgMEIegAADEbQAwBgMIIeAACDEfQAABiMoAcAwGAEPQAABiPoAQAwGEEPAIDBCHoAAAxG0AMA\nYDCCHgAAgzF7XUxq6TzS4ZiHGkCsCnU+++bm5i+tx3z23wRBH6PKyt8PeS7pq3s6w1wNgFjCfPat\ni6BvhyzLUsv2qq0WzSXdvXOHFrw2ALRsPvtQjwhcKDaOCBD0UdGyN6jP52OPHEDM4ojApSHoo6Sl\nQc0eOYBY1pK/gbGGoI8SghoA0Bq4vQ4AAIMR9AAAGIygBwDAYO3yHP27H/5NBw7Xh7z+1T0769vd\nufIcAGC+NhP0fr9fCxcu1EcffaTExEQtXrxYV1555UW/9+PDJ/XsHz8O6XXi42yaObafvt091Fvc\nzq7XkvsvGVkOANA62kzQ/+lPf5LP59O6deu0c+dOPfLII/rd734X9tdJ6dJRr75zWM+E+EHh6p5O\nnXA3hXxr3NltAADQGtpM0L/77rsaOnSoJKl///7avXt3xF6rpbe2fXGysUX3b3J7HACgtbSZoPd4\nPLLb7YHH8fHx8vv9iou78HrBK1I66dasK0J6HUenRH10+GTIdXZzdpDN1rJhE1u6DdZnfdbnd5D1\nQ18/pUvHkNdtj9pM0Nvtdnm93sDjrwp5SRpyXaqGXJfaWqUBANButZnb6zIzM7VlyxZJ0vvvv6/e\nvXtHuSIAANo/m3VmKrSosyxLCxcu1P79+yVJS5cuVa9evaJcFQAA7VubCXoAABB+bebQPQAACD+C\nHgAAgxH0AAAYjKAHAMBgbT7ofT6fZs2apXHjxik/P19vvPGGDh06pIKCAo0bN04LFy5ULFxPePz4\ncd1000367LPPYq7/1atX65577tHIkSP1wgsvxFT/Pp9PM2fO1D333KNx48bp008/jYn+d+7cqfHj\nx0vSV/ZbXl6uUaNGaezYsdq8eXMUqw2/c/v/8MMPNW7cOI0fP16TJ0/W8ePHJZnb/7m9n7Vx40bd\nc889gcem9i6d3//x48dVVFSke++9VwUFBTp8+LCkEPq32rgXX3zRWrJkiWVZllVfX2/ddNNN1tSp\nU63q6mrLsixrwYIF1uuvvx7NEiOuqanJmjZtmnXbbbdZBw4csKZMmRIz/b/99tvWlClTLMuyLK/X\na/3mN7+JqZ//66+/bj3wwAOWZVnWtm3brOnTpxvf/5NPPmmNGDHCGjt2rGVZ1kXf78eOHbNGjBhh\nNTU1WW632xoxYoTV2NgYzbLD5sv933vvvdaHH35oWZZlrVu3zlq6dKlVW1trZP9f7t2yLGvPnj3W\nxIkTA8ti6Wf/8MMPW6+++qplWWf+Fm7evDmk/tv8Hn1eXp7uv/9+SWdGy0tISNDevXuVlZUlScrJ\nyVFVVVU0S4y45cuXq6CgQCkpKZIUU/1v27ZNvXv31rRp0zR16lTl5uZqz549MdN/r1691NzcLMuy\n5Ha7lZiYaHz/qamp+u1vfxvYc7/Y+33Xrl3KzMxUYmKi7Ha7UlNTA2NwtHdf7n/VqlXq06ePJOn0\n6dPq0KGDPvjgAyP7/3LvJ06cUFlZmebMmRNYZmrv0oX9v/fee6qpqdFPfvITbdy4UdnZ2SH13+aD\nvlOnTkpOTpbH49EDDzygBx98UH6//7zn3W53FCuMrIqKCnXr1k1DhgyRdGZgIeucQ7Wm919XV6fd\nu3frscce06JFizRz5syY6r9Tp046cuSI8vLytGDBAo0fP974/m+99VbFx8cHHp/bb3Jystxutzwe\njxwOx3nLPR5Pq9YZKV/u/+wH/HfffVdr167Vj3/8Y2P7P7d3v9+vuXPnqqSkRJ06dQp8j6m9Sxf+\n7I8cOaLOnTvrP/7jP/SP//iPeuqpp+T1ei+5/zYf9JL0+eefa+LEibrrrrs0YsSI88bA93q9cjrN\nnfa1oqJCVVVVGj9+vPbt26eSkhKdOHEi8Lzp/Xft2lVDhgxRQkKCevXqpQ4dOpz3pja9/6efflpD\nhw7VH//4R7388st6+OGHdfr06cDzpvcv6bzfd4/HI6fTecHcGKb/P7zyyitauHChnnzySXXt2jUm\n+t+9e7f+8pe/aOHChZo5c6Y++eQTLV26VA6Hw/jez+rSpYuGDRsmSRo2bJh2794d0s++zQf9F198\noUmTJmnWrFkaOXKkJKlv376qrq6WJG3ZskUDBgyIZokR9eyzz8rlcsnlcqlPnz5atmyZhgwZEjP9\nX3/99frzn/8sSTp69KgaGho0cODAmOm/c+fOSk5OliQ5nU6dPn1a11xzTcz0L1389z0jI0M7duxQ\nU1OT3G6RXBiPAAAHXklEQVS3Dhw4oPT09ChXGhkvv/yy1q5dK5fLpR49ekhSTPSfkZGh3//+93K5\nXFq1apW++93vavbs2erXr5/xvZ+VmZkZuNiuurpa6enpIf3s28zsdV/liSeekNvt1uOPP67HH39c\nkjR37lwtXrxYPp9PaWlpysvLi3KVrcdms6mkpETz58+Pif5zc3O1fft2jR49Wn6/X6Wlpbriiiti\npv8f//jHmjNnjsaNGxe4Av973/teTPR/dhrSi73fbTabJkyYoMLCQvn9fhUXFyspKSnKFYeXzWaT\n3+/XkiVL9O1vf1vTp0+XJN1www2aPn260f1/eQpay7ICy1JSUozuXTr/vT9v3jw9//zzcjqdWrly\npRwOxyX3z1j3AAAYrM0fugcAAKEj6AEAMBhBDwCAwQh6AAAMRtADAGAwgh4AAIMR9EAbtGnTJo0c\nOVI/+tGPdMcdd2jNmjWB5x577DHt2LEjLK8zbNgw/e1vf4va+gAir80PmAPEmqNHj2r58uXasGGD\nOnfurP/7v//Tvffeq6uuuko333yztm/froEDB0a7TADtBEEPtDEnTpyQz+fTqVOn1LlzZ3Xq1EnL\nly9XUlKSXnrpJe3evVvz58/Xb37zG9XX1+vRRx9VQ0ODTp48qVmzZikvL08lJSVyOBzas2ePampq\nNH36dI0cOVL19fWaNWuWampq9N3vfldNTU2SzowhP2fOHB07dkzHjh3TgAEDtHz5cr3zzjtasWKF\n/H6/evfurZKSEj300EMXrH+uffv2qbS0NDDT2tKlS5WamqqNGzfqiSeekM1mU79+/fTLX/5SPp9P\n8+bN00cffSSbzaZJkybprrvuUkVFhTZs2KD6+noNGzZM48eP14IFC1RTU6O4uDjNnDlTgwYN0v/8\nz/9oxYoVstls6ty5s1auXKmuXbu29o8MaNsiMKUugBYqLS21vve971mjR4+2VqxYEZiP3LLOzE9+\ndn72++67z/r0008ty7Ksqqoqa8SIEZZlnZnH+r777rMsy7L2799vZWdnW5ZlWYsWLbIeffRRy7Is\na/v27Vbv3r2tI0eOWL///e+tJ554wrIsy2psbLR+8IMfWLt377befvtta8CAAZbb7f7a9c9VUlIS\nmEP7D3/4g/Xyyy9bNTU11uDBg62amhrLsixr1qxZ1uuvv24tW7bM+td//VfLsiyrrq7OuuWWW6x9\n+/ZZL774onXrrbdazc3NlmVZ1oMPPmhVVlZalmVZR48etYYPH255PB5r/Pjx1q5duyzLsqxnnnnG\n2rp1axj+9wGzsEcPtEELFy7UtGnTtHXrVm3dulVjx47Vr371K/3gBz+Q9PepW3/1q1/pjTfe0Kuv\nvqqdO3fq1KlTks6MlX3jjTdKktLT03Xy5ElJ0vbt27Vq1SpJ0oABA9SzZ09J0u23364PPvhATz/9\ntD799FPV19cHttWrVy/Z7favXf9cubm5+sUvfqE///nPuvnmm5WXl6fXXntN119/vS6//HJJ0vLl\nyyVJ//Zv/6YlS5ZIOjNT4S233KLq6mrZ7XZdc801gZnrqqqq9Nlnn+mxxx6TJDU3N+vw4cMaNmyY\nfv7zn2v48OG65ZZbNHjw4PD8AACDEPRAG/PWW2/J6/Xqhz/8oUaOHKmRI0fqv/7rv/TCCy8Egv7s\npBcFBQUaNGiQsrOzNWjQIM2cOTOwnbMTXXx5gpDm5ubA1/Hx8bIsSy6XS6+99prGjh2rG2+8UR9/\n/HHgw0SHDh2+dv0vu+2223Tttddq8+bN+s///E+99dZbys3NPW9e+bq6OklnPrCcu9zv9we237Fj\nx8Byy7L0zDPPBKbjPHbsmLp3764+ffpo2LBhevPNN7VixQrddtttmjp16tf/BwMxhqvugTamY8eO\nWrVqlY4cOSLpTMh9/PHHuuaaayRJCQkJOn36tOrr63Xo0CHdf//9ysnJ0datW+X3+wPrXMzgwYP1\n3//935KkDz74QH/5y18kndljHjt2rEaMGCHpzHn2c+e9D7b+uWbMmKFdu3Zp7Nixuv/++7V37171\n69dPO3fu1BdffCFJWrJkiSorK3XDDTfohRdekHQm/M8u+3L9AwcO1Nq1ayVJH3/8se68806dOnVK\nY8aMkdfr1cSJEzVx4kTt3bv3m/43AzGDPXqgjTk7DenUqVPl8/kkSUOHDtXPf/7zwNelpaVatmyZ\n8vPzdfvtt8tut+vaa69VQ0ODTp06JZvNdt6e/Nmv77vvPs2ePVsjRozQVVddpZ49e8pms2nixIla\nuHCh/v3f/13JycnKzMzUkSNHdOWVV563nYut/2VTpkzRvHnz9Lvf/U7x8fGaPXu2vvWtb2nu3Lma\nPHmy/H6/rrvuOo0ePVper1eLFi3SHXfcIb/fr6KiIvXt21f79u07b5vz5s3TggULdOedd8qyLK1Y\nsULJycmaMWOGSkpKFB8fr8suu0yLFi0K+88DaO+YphYAAINx6B4AAIMR9AAAGIygBwDAYAQ9AAAG\nI+gBADAYQQ8AgMEIegAADPb/nqROiFXO3fgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cd7e3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_scores = lsl_dr[(lsl_dr.domain=='Expressive Vocabulary') & (lsl_dr.score>=20)].score\n",
"expressive_scores.hist(bins=25)\n",
"plt.xlabel('Standard scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAFkCAYAAADIefl6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9x4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e4e8390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_lang_scores = lsl_dr[(lsl_dr.domain=='Language') \n",
" & (lsl_dr.test_type=='expressive')].score\n",
"expressive_lang_scores.hist(bins=25)\n",
"plt.xlabel('Standard scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Export dataset"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if current_year_only:\n",
"\n",
" lsl_dr.to_csv('lsl_dr_current_year.csv')\n",
"\n",
"else:\n",
" lsl_dr.to_csv('lsl_dr.csv')"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(36998, 74)"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.shape"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5511,)"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5511,)"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.study_id.unique().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert score to floating-point number"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr.score = lsl_dr.score.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['tech_class'] = 'Bimodal'\n",
"lsl_dr.loc[lsl_dr.bilateral_ci==True, 'tech_class'] = 'Bilateral CI'\n",
"lsl_dr.loc[lsl_dr.bilateral_ha==True, 'tech_class'] = 'Bilateral HA'"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['age_year'] = np.floor(lsl_dr.age/12.)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Expressive Vocabulary', 'Language', 'Articulation',\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.dropna().unique()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tech_class\n",
"Bilateral CI 0.43\n",
"Bilateral HA 0.59\n",
"Bimodal 0.50\n",
"Name: prim_lang, dtype: float64"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').prim_lang.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['non_profound'] = lsl_dr.degree_hl<6"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tech_class\n",
"Bilateral CI 0.08\n",
"Bilateral HA 0.86\n",
"Bimodal 0.30\n",
"Name: non_profound, dtype: float64"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').non_profound.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAzQAAAJoCAYAAACwSaRPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8VNX9//HX7DOZyZ6B7Av7TotCxa1+3W0RBayiFNd+\nxQVbBUu1LqhF/Cq12mrrwtfq16WKCwhSf13cW6xbKci+ZgdCErLOZPb7+2MmNzMhCQnkZv08H488\nkpl7586dk3vnzHvOuefoFEVREEIIIYQQQoh+SN/bOyCEEEIIIYQQx0sCjRBCCCGEEKLfkkAjhBBC\nCCGE6Lck0AghhBBCCCH6LQk0QgghhBBCiH5LAo0QQgghhBCi35JAI7rNmDFjuPjii7n00ktjfg4c\nONDbu3aUG2+8kX379p3wdu6++27uv//+o+7/61//yiWXXHLC24/21FNP8atf/apLjykrK+O73/1u\nt+6HEEKIEyPvzUJ0L2Nv74AYWF555RWSkpJ6ezeO6fnnn++W7cybN49rr72We+65B4vFot7/5ptv\n8uMf/7hbnqOZTqfr1u0JIYQQQgwE0kIjulV787SuWbOGc889F7fbjdvt5qKLLmLt2rV8+eWXzJkz\nh1tvvZWZM2dy+eWXqy0nd911FzfddBMzZszg8ccfx+/3s3z5cmbPns0ll1zC3XffTWNjIwB/+tOf\nuOSSS7jsssuYN2+euo327j/77LPZunUrixcv5o9//KO6n6+//jp33HEHAB999BGXX345s2bN4sor\nr2TTpk1Hva4JEyZQUFDAX/7yF/W+srIytm7dysUXX8yhQ4e46aabuPjii7n44ot54YUX1PU+/vhj\nLr30UmbOnMncuXPZuXMnAM8++yw/+tGPmDlzJueddx4ffPCB+ph9+/bx4x//mIsvvpglS5bgcrli\nXk+zs88+m23btsXsa1VVFbfccgtz587lnHPOYf78+Rw5ckRd/4477uAHP/gBzz77LGeddZb6v2xq\nauLUU09V1xVCCKGNwsJCrrvuOubOncvZZ5/NLbfcgs/nA2DixIk8/fTTXHnllZxzzjn83//9HwDB\nYJBHHnmE888/n9mzZ/PAAw8wf/58AObPn89f//pXdfvRt99++221jjv77LN5/fXXj7m9hoYG7rrr\nLmbPns3MmTN55JFHCAaDPVY+QrRHAo3oVldffXVMd7PbbrsNgFmzZvGd73yHFStWsGzZMqZOnap2\nydqxYwfXX38969atY/bs2SxZskTdns/nY/369SxevJjnnnsOo9HI6tWrWbt2LU6nk8cff5xQKMQj\njzzCCy+8oL5Bb9y4UX1Tbn1/M51Ox+WXX86aNWvU+9asWcPll19OUVERTzzxBCtXrmTNmjU89NBD\nLFy4kKampqNe87x583j77bfV22+99RazZs3CarVy5513csopp/Dee+/x+uuvs27dOt5//32qqqpY\nsmQJ//M//8O6deu44YYbePzxxzlw4AD/+te/eO2111i3bh233347v/3tb4FwWCwpKeGpp57ivffe\nQ1EUnnnmmZjX05H333+fKVOm8MYbb/Dhhx9is9lYu3atunzUqFG8//773HTTTSQlJfHZZ58B8Oc/\n/5np06eTkpJy7ANACCHEcXvrrbeYPXs2b7zxBn/7298oKyvj008/BcDv95OcnMzrr7/O7373Ox5/\n/HF8Ph9vvfUW27dvZ/369axatYrS0tKY+qB13aDT6XC73bz99ttqHfeb3/yGFStWqPvQ3vaWL1/O\nhAkTWL16NWvWrOHIkSO8+OKLPVQ6QrRPupyJbtVRl7MHH3yQmTNnYrPZWL16tXr/mDFjOOmkkwCY\nPXs2Dz30ELW1teh0OqZMmaKu98knn9DQ0MDnn38OhN/cU1NT0ev1XHjhhVxxxRWcddZZnHbaaZx1\n1lnt3h9t2rRp+Hw+tm7ditVq5ciRI0yfPp3XXnuNyspKrrnmGnVdg8FASUkJo0ePjtnGRRddxKOP\nPkppaSkZGRmsWbOGV199FbfbzX/+8x/1zd7hcDBr1iw+++wzTCYTI0eOZMyYMQCcd955nHfeeQA8\n+uijrF27lpKSEjZt2hQTos4//3ySk5PVsmqugDrj6quv5ptvvuHFF1+kqKiIPXv2MHnyZHX5ySef\nrP49b9483nrrLb7//e+zatUqfvGLX3T6eYQQQhyfn//85/zzn//kf//3fyksLOTw4cNqSzzAueee\nC8C4cePw+Xy43W4+/fRTLr30UsxmMwBz587l5Zdf7vB54uLiePbZZ/n4448pLi5mx44dal3T0fY+\n+eQTtm7dqn6J5/V60evlu3HR+yTQiB5TVVWFz+cjEAhQUVFBTk4OEA4K0RRFUe+Li4tT7w+FQtx7\n772cccYZALjdbrxeLwArVqxg7969bNiwgZUrV/L222/zhz/8od37m+l0OubMmcO7776L2WzmRz/6\nkboP06dP54knnlDXPXToEEOGDDnqdVksFmbPns3bb7/NxIkTGT16NLm5uTQ2NqIoSkw3vFAohN/v\nx2AwHPWt2c6dOwkGg9xyyy1cd911nH766UydOpUHHnggZn+jy8lkMqn3Rz+P3+8/aj9XrFjBli1b\nuOyyyzjllFMIBoMxj4ku6xkzZvCb3/yGL774gqamppiwI4QQQht33HEHoVCIiy66iLPOOotDhw7F\nLG++VrO5LmiuB0KhkLpO69aZ6GXNdcOhQ4e44oormDt3LieffDIXXHABn3zyCUCH2wuFQvz2t79l\n2LBhQLgLmlzfKfoCidWiW7V3DY3f72fRokX87Gc/49Zbb2Xx4sUEAgEg3OVs165dAKxatYqTTjqJ\n+Pj4o7Z1xhln8Oqrr+L3+9Vw88QTT1BTU8NZZ51FYmIi11xzDT/72c/YtWtXu/e3Nnv2bD766CP+\n8pe/MHv2bABOOeUUNmzYwP79+4Hwt1IzZ85U+zK3NnfuXN5//33WrFnDvHnzgHCLzOTJk/nTn/4E\nhN/4165dy+mnn87kyZPZt28fe/fuBeCDDz5gyZIlfPPNN0ycOJFrr72Wk08+mQ8++CCmYvnoo4+o\nr68nGAyyatUqNdylpKSwZcsWADZt2kRlZeVR+7hhwwauueYaZs6cSUpKCp9//nnMtqPZbDZmzpzJ\nPffcw5VXXtnmOkIIIbrXhg0buOWWW7jooosA2Lx5c4fXqOh0Or7//e+zbt069QvDNWvWqK0mKSkp\n6vWVJSUlah24detWUlNTufnmmznttNP4+OOPgXBg6Wh7p59+Oi+99BIQ7hJ+00038dprr2lSFkJ0\nhbTQiG519dVXH9Xicscdd/Dll18yZMgQLrvsMiD8Af7JJ5/kzDPPxOl08sQTT1BeXk5aWhqPPfYY\nEH6jjv7m55ZbbuHRRx9l1qxZBINBxo0bxy9+8Qvsdjs333wz1157LRaLBaPRyLJly0hOTm7z/tbS\n0tIYP348wWAQp9MJwIgRI3jooYdYtGgRiqJgNBp55plnsFqtbb7unJwchg0bxp49e2K6tf3617/m\noYce4p133sHv9zNz5kxmzZqlLvvFL35BMBgkPj6eJ554gqSkJP72t7/xwx/+EJPJxPTp06mrq8Pl\ncqHT6RgxYgQLFiygvr6ek046iRtvvBGAO++8kwceeIBVq1Yxfvx4JkyYoO5DcxneeuutPPbYY/z+\n97/HaDRy0kknUVxc3O7/ctasWbz55pvdPvy0EEKI8IArrYduXrRoEQsXLiQxMRGbzca0adMoKSkB\n2r4WBsJfyhUWFjJr1izi4uLIzs5W66qbb76Zu+66i08//ZSCggKmTZsGhIPJO++8wwUXXEBcXBwT\nJ04kNTWVkpKSDrd377338vDDD3PxxRfj9/s57bTT+MlPfqJpOQnRGTqlva/UhegBX375JcuWLeO9\n997r7V0RURRFYeXKlRw8eJClS5f29u4IIYRox4YNG6iurmbmzJkALFu2DJvNxuLFi/vE9oToCcfs\ncrZ582Z1uL4dO3Ywb9485s+fzw033EB1dTUQnnNjzpw5XHHFFWofTCFE/3XOOefwwQcfsHDhwt7e\nFSGOSeopMZiNGDGCd999l0suuYQZM2ZQW1vLggUL+sz2hOgJHbbQrFy5knXr1mG323njjTeYP38+\n99xzD2PGjGHVqlUUFhbyk5/8hOuuu47Vq1fj9Xq58soreeedd9TRMYQQQgitSD0lhBCiwxaavLw8\nnn76afXi7N/85jfqMLOBQACLxcK3337LlClTMJlMOBwO8vLy2rzwWgghhOhuUk8JIYToMNCcf/75\nMRd4N18wvXHjRl577TWuvfZaGhsbiY+PV9ex2+3q7O1CCCGElqSeEkII0eVRzt5//32effZZnn/+\neZKTk3E4HDGTPrlcLhISEjrchqIoMm65EEIITUg9JYQQg0uXAs3atWt58803eeWVV0hMTARg0qRJ\nPPHEE/h8PrxeL/v27WPkyJEdbken01FZ2XD8ey2OyemMlzLWmJSx9qSMteV0xh97pX5G6qn+Q85v\n7UkZa0/KWHudqas6FWiaZ5pdvnw5mZmZ6shH3/ve91i4cCFXX301V111FaFQiEWLFsmFlkIIIXqU\n1FNCCDF49do8NJJmtSXfGGhPylh7UsbaGogtNN1Jjj1tyfmtPSlj7UkZa68zddUx56ERQgghhBBC\niL5KAo0QQgghhBCi35JAI4QQQgghhOi3JNAIIYQQQggh+i0JNEIIIYQQQoh+SwKNEGLA8vl8rF//\nbpceM3PmBSf0nJdddjF+v/+EtiGEEGJwkHqqe0igEUIMWNXVVbz33touPeZEJ4eX2eWFEEJ0ltRT\n3aNTE2sKIUR/9PLLf6SoaD8vvriSffv2Ul9fB8Dtt9/JsGEjWL/+Xd59dzWhUJDTTjuTG25YgM/n\n58EH76Wi4hCJiYn86lePYjS2/Va5YcM/eOmllSgKjBo1mp///Jfqsv379/L0008SDIaoq6vlzjvv\nYsKESSxf/iDl5WV4vV5+9KO5XHDBD3juud+zadO/CQSCnHXW2cybd02PlI8QQojeJfVU95BAI4QY\nsK655gb279+Hx+Ph5JOncumll1FaWsIjjzzEww+v4NVXX+bll9/AbDbz3HO/p6mpiaYmNwsWLCQ9\nPZ3bblvAnj27GDt2/FHbDgQCPPnkClaufJmkpCT+9KdXOHy4AgBFUSgsLGThwtsZNmwEf//7X/jz\nn99j2LARbN78H55//iUAvvrqCwA++OCvPPXU86SmpvL+++/1WPkIIYToXVJPdQ8JNEKIAUtRFCD8\nLdTGjV/z4Yd/B6ChoZ4DB8oZNmw4ZrMZgAULbgUgISGR9PR0AFJSUvF4PG1uu66ulvj4eJKSkgC4\n6qr56jKdTkdampOXXnoBi8WC2+3CbncQFxfHT3+6mEcffRiXy8UFF1wEwP33/4pnnvkdR45Uc8op\np2pQEkIIIfoiqae6hwQaIcSApdfrCYVC5Obmc/75F3HeeRdSU3OE9evXkpWVTUlJEX6/H5PJxL33\n/oLbb7+z032Tk5NTaGhopL6+noSEBJ588tfqG7+iKPz2t79m6dJl5OXl88ILz3Ho0EGqq6vYtWsH\ny5evwOv1MmfODM4770I+/vgDHnxwOYqiMH/+5Zx77gUMHZquYckIIYToC6Se6h4SaIQQA1ZycgqB\ngJ+mJjcfffQB69atweVyccMNC0hKSmLevGtYuPBGdDodp512JmlpTiC2pmjv4km9Xs/ixb9gyZLb\n0ev1jBo1JtLkH17/ggsu4r77fkF8fAJO5xDq6+tITU3jyJFqbr75evR6A1ddNR+TyURCQiI33ngt\nFouFadNO6VOVhBBCCO1IPdU9dEpzW1cPq6xs6I2nHTSczngpY41JGWtPylhbTmd8b+9CnybHnrbk\n/NaelLH2pIy115m6SlpohBCiAzt2bOMPf/jdUfefc855XHrpZb2wR0IIIUQLqaekhWbAkm8MtCdl\nrD0pY21JC03H5NjTlpzf2pMy1p6UsfY6U1fJxJpCCCGEEEKIfksCjRBCCCGEEKLfkkAjhBBCCCGE\n6Lck0AghhBBCCCH6LRnlTAghgI0bv+H++++moGAYiqLg9/u58867GDlyNL/73eNcccU81q9fS2pq\nGpdeOqfNbVRUHGLv3j2cdtoZJ7Qfa9eu5sEHlx+17aeffpLa2hq8Xi+jR4/lZz9bjNFo5LLLLub1\n11djMpmO+3mFEEL0bVJPta9TLTSbN29m/vz56u2///3vLF68WL29adMmLr/8cq688kqefvrp7t9L\nIYTQmE6n4+STp/HUU8/x9NPP85OfLGDlymcB+OlPFzN0aHq7k5c1+/e/v2bLls0nvB+tBYNB7r57\nMVddNZ+nnnqO559/CaPRyAsvPNfuYwYbqaeEEAOd1FPtO2YLzcqVK1m3bh12ux2AZcuWsWHDBsaN\nG6eu88ADD/DUU0+Rk5PDjTfeyI4dOxg7dqx2ey2EGNDe/GgvX+88jMGgIxjsnpHlp44ZwuVnj2h3\nuaIoRI9iX19fT0pKCgALF97IkiW/VJeFQiEee+xhDh8+THV1FaeffiY33LCAV199Ca/Xy8SJk0lP\nz+C3v/01iqKQmJjI3Xffz65dO3nmmacwm83MnDkLs9nMmjVvEwgE0Ol0LF++grZG0v/2200MHZoe\nmeE57Oabb2tz3cFI6ikhRE+TeipWb9dTxww0eXl5PP300yxZsgSAKVOmcN5557Fq1SoAGhsb8fl8\n5OTkAHD66afz+eefS0UhhOh3Nm78httuW4Df72fv3t088sjjwNHfLB0+XMGECROZMeNSvF4vc+b8\nkP/+75uZP/86SkqKOe20M7jxxmu5554HyMvLZ/36tbz22stMnfo9/H4/K1f+HwCvvPIiK1Y8icVi\nZcWK5Xz55Rc4nc6j9qu6uorMzKyY+8xms0al0P9IPSWEGCyknmrbMQPN+eefT1lZmXr7Bz/4AV9+\n+aV6u7GxEYfDod622+2UlpZ2824KIQaTy88eweVnj+jxCcumTDlZ7RNcUlLMTTddz5o17x+1XkJC\nAjt2bGfjxn8TF2fH5/MDsd+eFRcX8utfPwJAIBAgJycXgNzcPHU7SUnJLFv2ADabjZKSYiZMmNTm\nfqWnZ/DJJx/F3FdXV8vWrVtOqB/0QCH1lBCip0k9Fau366kTHhTA4XDgcrnU242NjSQkJBzzcTJD\ntfakjLUnZay9nirjpKQ4rFaT+nwWSy56vQ6nMx6TyUBysh273UJ8vJXPPvs7Q4akcuedd1JcXMx7\n763B6YwnMTEOq9WI0xnP8OHDefLJ35Cens7GjRuprKwkKSkOm82M0xlPQ0MDL720kk8//ZRQKMT1\n11+Pw2E5aj8AzjrrVJ566nEOHixk0qRJKIrCs88+ic1m49JLf4BeryMtzSGtNu2QeqrvkjLWnpSx\n9qSe6v16qlsCjclkorS0lOzsbDZs2MDChQuP+bieTLODUU9/YzAYSRlrryfLuK6uic8//xdz516F\nXm/A7XZx6623U1/vw+8PUlPjwuXyYrV6mTTpO7zxxr18/fW/MZlM5OTksmNHIUOGZPP73/+BnJzh\n/OxnS7j99kUEg0F0Oh13330/lZWH8XoD6msaP34Ss2bNwWg0EB+fSFFRGQ5HSsw6zZYuXc5vfvMY\nTU1NeDweJkyYyE9+spDKygZCIaiqauzy6DGD5YOO1FN9k7yHak/KWHtST7XQop6CztVVOqUTV+uU\nlZVx55138sYbbwDw1VdfsWrVKh5/PNxvb/PmzSxfvpxgMMjpp5/O7bfffswnlhNMW/Impj0pY+1J\nGWtrIAUaqaf6Hzm/tSdlrD0pY+11W6DRgvzztSUnmPakjLUnZaytgRRotCDHnrbk/NaelLH2pIy1\n15m6qlPz0AghhBBCCCFEXySBRgghhBBCCNFvSaARQgghhBBC9FsSaIQQQgghhBD9lgQaIYQQQggh\nRL8lgUYIIYCNG79hxozzuO22BSxceCMLFlzHnj27APjd7x6nouIQL7zwHO+++06726ioOMSGDf84\n4f1YuvSXMfcdPHiABQuui7nv3Xff5o9/fF69vX37Vv7rv6azc+f2E3p+IYQQfZPUU+2TQCOEEIBO\np+Pkk6fx1FPP8fTTz/OTnyxg5cpnAfjpTxczdGg6Op2uw238+99fs2XL5hPej06uGXPrvffeZe7c\nH7N69Vsn9PxCCCH6Jqmn2mfs9i0KIcQJWr13Pf85vAWDXkcw1D1TZX13yERmj5jR7nJFUYielqu+\nvp6UlBQAFi68kSVLWr6NCoVCPPbYwxw+fJjq6ipOP/1MbrhhAa+++hJer5eJEyeTnp7Bb3/7axRF\nITExkbvvvp9du3byzDNPYTabmTlzFmazmTVr3iYQCKDT6Vi+fAWdnxqsZT23283Gjd/wyitvcvXV\nc6mrqyUxMalrBSSEEKLTpJ7qjJ6rpyTQCCFExMaN33DbbQvw+/3s3bubRx4JzzLf+tuow4crmDBh\nIjNmXIrX62XOnB/y3/99M/PnX0dJSTGnnXYGN954Lffc8wB5efmsX7+W1157malTv4ff72flyv8D\n4JVXXmTFiiexWKysWLGcL7/8AqfT2ea+FRXt57bbFqi3q6oqOf/8iwD48MO/8f3v/xdms5lzzjmP\n9evXMm/eNVoUkRBCiF4k9VTbJNAIIfqc2SNmMHvEjB6fgXnKlJN58MHlAJSUFHPTTdezZs37R62X\nkJDAjh3b2bjx38TF2fH5/EDst2fFxYX8+tePABAIBMjJyQUgNzdP3U5SUjLLlj2AzWajpKSYCRMm\ntbtv+fnDeOqp59Tb7777DkeOVAPhZnyj0cjixT/F6/Vw+HAFV111dRe6BQghhOgKqaeO1pv1lAQa\nIYRoQ3JyCu29z77//ns4HPH8/Oe/pKyslPfeWwOAXq8nFAoBkJubz333PcSQIUPZsmUz1dVVQMu3\naI2Njfzxj8+zevWfCYVCLFq0sAvN+NDclL9v314UJcQf/vC/6pI77riVDRv+wemnn9nFVy2EEKK/\nkHqqhQQaIYQg/Abe3JSv1xtwu10sXHgHFovlqPVOOmkaDz54L9u2bcFkMpGTk0tVVRXDh4/g5Zf/\nyOjRY7nzzrv51a/uJxgMotPpuPvu+6msPKxWFA6Hg4kTJ3PjjddiNBqIj0+kurqKjIzMNr+xOvq+\n8O333nuXCy/8YcySiy+exerVb0mgEUKIAUTqqQ7KRula1Oo2Pdk8Nxj1dBPoYCRlrD0pY205nfG9\nvQt9mhx72pLzW3tSxtqTMtZeZ+oqGbZZCCGEEEII0W9JoBFCCCGEEEL0WxJohBBCCCGEEP2WBBoh\nhBBCCCFEvyWBRgghhBBCCNFvSaARQgghhBBC9FvHDDSbN29m/vz5ABQXF3PllVcyb948HnjgAXVy\nnTfffJM5c+ZwxRVX8Mknn2i6w0IIoYWNG79hxozzuO22BSxceCMLFlzHnj27+N3vHqei4lC3PEdx\ncRG33bagw31YuvSX3fJcg4nUU0KIwUDqqfZ1OLHmypUrWbduHXa7HYBHHnmERYsWMXXqVJYuXcqH\nH37I5MmTeeWVV1i9ejVer5crr7ySU089FbPZ3CMvQAghuoNOp+Pkk6fxwAMPA/D111+wcuWzPPbY\nEz26D6JrpJ4SQgwWUk+1r8NAk5eXx9NPP82SJUsA2L59O1OnTgXgzDPPZMOGDej1eqZMmYLJZMJk\nMpGXl8euXbuYOHGi9nsvhBiQKt96g4ZvvqbYoCcYDHXLNuNPnorzR3PbXa4oCtHzDNfX15OcnMxt\nty3g5z+/m7///a8cOFBGbW0d9fW1zJ59OZ988iGlpSXcc8+DjB8/gddff5WPPvobBoORyZO/y803\n30ZVVRUPPXQvACkpqer2P/74A9aseZtAIIBOp2P58hX00jzH/ZrUU0KI3iD1VN/SYaA5//zzKSsr\nU29Hvwi73U5DQwONjY3Ex8fH3N/Y2KjBrgohhLY2bvyG225bgN/vZ9++PSxfvoKXX34RCH8rZbFY\nefzxX/Hqqy/xr39t4NFHn+D999/jww//is1m5eOPP+DZZ1/EYDBwzz0/5/PP/8kXX2zg/PMvZMaM\nS/nww7/z7rtvA1BWVsqKFU9isVhZsWI5X375BU6nszdffr8k9ZQQYjCReqptHQaa1vT6lktuGhsb\nSUhIwOFw4HK51PtdLhcJCQndt4dCiEHH+aO54R9nPJWVDT32vFOmnMyDDy4HoKSkmAULriM3N09d\nPmrUGAAcjngKCoYBEB8fj8/no7i4iPHjJ2IwGACYPPm7FBbuo6yslJkzZwMwceIktaJISkpm2bIH\nsNlslJQUM2HCpB56lQOb1FNCiJ4g9VTf0qVAM3bsWL766iumTZvGZ599xvTp05k0aRJPPPEEPp8P\nr9fLvn37GDly5DG35XTGH3MdcWKkjLUnZay9nirjpKQ4rFaT+nwWSy4Ggx6TyUBysh273UJ8vBWn\nMx6Hw4LXa8bpjCchwYbVauI73xnPO++8QUpKHHq9nh07tnDppZfidtdTXLyb6dOnsGnTF5hMBmw2\nHS+9tJJPP/2UUCjE9ddfj8NhOWofRNdJPdW/SBlrT8pYe1JP9b5OBZrmC4Duuusu7rvvPvx+P8OH\nD+fCCy9Ep9Nx9dVXc9VVVxEKhVi0aFGnLrTsyTQ7GPX0NwaDkZSx9nqyjOvqmvj8838xd+5V6PUG\n3G4Xt956O//v/62npsaFy+XFavVSWdlAY6MXt9tHZWUD9fVNeDwBkpMzOOOM/+Kyyy5HUUJMmvRd\nJk/+Hvn5o3nwwft49911ZGRkEgiEaGpSGD9+ErNmzcFoNBAfn0hRURkORwpeb6DHXnNfq5BOhNRT\n/Y+8h2pPylh7Uk9przN1lU7ppat75ATTlryJaU/KWHtSxtoaSIFGC3LsaUvOb+1JGWtPylh7namr\nZGJNIYQQQgghRL8lgUYIIYQQQgjRb0mgEUIIIYQQQvRbXRrlTPRtQbebpt27cO/cQU3QB0MysOTl\nY83NQ2+19vbuCSGEEEII0e0k0PRjIZ+Ppr17aNq5A/fO7XgKC6GtMR50OszpGVjy8rDmF2DNy8eS\nkyshRwgOiEsyAAAgAElEQVQhhBBC9HsSaPoRJRDAU1SIe8d23Dt34Nm3FyUQCC80GLAOG07c2HHE\njRmLsyCTg5u24y0qwlNchKe4GN/BAzR88a/w+jod5oxIC05ePta8Aiy5uegtlt57gUIIIYQQQnSR\nBJo+TAmF8JaV4t6xPdwKs3s3itcTXqjTYcnJJW7MWOLGjsM2ciR6q019bJwzngRLInxvurotf8Uh\nNdx4m0POgQM0/OtzdZvmjIxwuIkEHQk5QgghhBCiL5NA04coioK/4pDaAuPeuYOQy6UuN6Wnqy0w\ncaPHYnA4Or1tnV6POSMTc0YmCaecGn6+5pATacXxFhfhKQmHHP61IfJAHeaMzHC4yc9v6a4mIUcI\nIYQQQvQBEmh6mb+6GvfO7WqICdbWqsuMKSk4Jn833AIzZiym5ORufe6YkDO9JeT4Dh2KtOAU4i0u\njoSc8tiQk5mFNS8PS/M1Odk5EnKEEEIIIUSPk0DTwwL19ZGL+MM//sMV6jJDfDzxU6dhGzOOuLHj\nMDmd6HS6Ht0/nV6PJTMTS2brkHMwEnKK8BQV4S0pxldeBp9HQk4kHB3VkmM29+j+CyGEEEKIwUUC\njcZahlLejnvHjnAIiNDbbNgnf0ftRmbOyu7xANMZ4ZCThSUzi4TppwFRIUcddCA65Pwz/MDmkJNf\nEG7NkZAjhBBCCCG6mQSabtbRUMo6k4m4seOJGzsW25hxWPPy0BkMvbzHxycm5JwaFXIORlpyigrD\nIae0BF95GfUb/hF+oF4f6a6WjzU/PxxysnMk5AghhBBCiOMigeYEdWUoZevw4ehNA/eDu06vx5KV\nhSUrKuQEg/gOHQx3U2tuySktwVdWGhNyLFlZLSOr5RVgycke0GUlhBBCCCG6hwSaLjqRoZQHI53B\ngCUrG0tWNpx2OhAJOQcPtIysVhQOOd7SUur/GQk5BkP4Wp68yKADefkScoQQQgghxFEk0ByDlkMp\nD1Y6gwFLdg6W7Bw47QwgKuREDSHdEnI+Cz/QYAh3c4t0V7Pm5WPOzkFvMvXiqxFCCCGEEL1JAk0b\nenMo5WMJKSGaAh4a/S4afS4a/Y2R3+GfBp8Ll9+F1WLCaR5Cdnwm2Y5M0mwp6HX6Ht3XrogOOYmn\nR4WcAwcigw4URkJOKd7SktiQk5WNJS8vMvhAPuasbAk5QgghhBCDhAQaenco5UAoQKPfhcvvpsHX\nqAaT6JDSGHW/y+8mpIS6/DxWg4UsRwbZ8VlkOzLJic8kwz4Uo77vHgI6gwFLTg6WnKiQEwio3dU8\nxUV4m7urlRRT/4/YkNM86IA1rwBzVpaEHCGEEEKIAajvfprVkFZDKSuKgjfoi4SPxlahxNXm/U0B\nT6e2bTPaiDfZcdpScZgcOExxOMwOHCZ7+Mdsj/rbQUKyhc1FuylrPEBpQzlljQfZX1fMvroidZsG\nnYF0+xByHFlqS052fAY2Y9+97kdnNGLJycWSk0vi6WcCUSGnqBBPcXFLd7WSYuDT8APVkFPQMvhA\ndjY646A8BYQQQgghBoxB8WnueIdSDikhXH43jf5GtStXQySchP9ujPo7HFACocAx90ev0+Mw2Um2\nJJHjaA4jjphQEm+2YzfZ1fBi0HdteOcEi4MxKSMZkzJSvc8X9FHeeIiyxgOURUJOeeSHQy2PTbOm\nxLTkZMdnkmhO6JNz5ECrkBNuyEEJBPAeKFcHHfAUF+ErK42EnJbHmWNacvKxZEnIEUIIIYToT3SK\nEvlk38MqKxs023aHQynr9ehzswkMz8Gdl05dRjyNijemW1dzC4rL70bh2MVjNpiJN0UCiNlOvKmN\nlhM1rDiwGa2ahwOnM75TZRwMBTncVEVZwwFKG8spbzhIaWM5Lr87Zj2HyU5OJOQ0t+YMiUvr09fl\ntKaGnKjJQH1lpS3HBpGQk52DNS8Pa14Blvx8LJlZbYaczpaxOH5SxtpyOuN7exf6NDn2tCXnt/ak\njLUnZay9ztRVXQ40Pp+Pu+++m7KyMhwOB/fffz8Ad911F3q9npEjR7J06dJjfmA/0X++oih4gh4a\nfC4avQ24S4rw796Dbl8JlpJDGHzhD6kKUJNqoXSomcIheg44TfhN7X8I16EjzmSL6b7lMMUd1YLS\nHFzsJjtmQ9+7NuNETjBFUaj11sV0VytrKKfaUxOznllvUq/LyYkEnUx7OqY+WB7taQ45nqLCyDw5\nxR2EnEgrTiTkDMlIljcxjUlFoa2BGmj6Sj0lOibnt/akjLUnZay9ztRVXe5b8+abb2K321m1ahWF\nhYU89NBDmM1mFi1axNSpU1m6dCkffvgh5557bpe2GwwFcQXcbVx30nj0RfLeRkxH6sk82ER2hZ/s\nCh82n0LzDCVHEgyU5tkoG2riYLoNoyNeDSCTIteexLao2NXrUeKMti537xpodDodydYkkq1JTEwb\np97v9jeFu6s1Hgi36DSUU9xQRmF9ibqOXqdnaJyTbEdWuLtaJOjYTXG98VKOSWc0Ys3Nw5qbp96n\nBAJ4y8uOmgzUW1RIXdTjKsaMxjx6PPZJkzBnZvXZLnlCDDZa1VNCCCH6pi4Hmn379nHmmeGLsQsK\nCti3bx+KojB16lQAzjzzTDZs2NBhRfG//36ditpqGvzh608afS7cgaYOu3fFu4JkV/iYVBH+Hedu\n+Qbdl2CjcWwWyoh8zKNGMjQtneGRFhWrwSIfNLtJnMnGqOThjEoert7nD/o56KqItOaEw0554wEO\nuir4umKjul6yJSnSZS3SohOfSbIlqU/+b3RGo9oa0yzk9+MrL4/MkVOIp6iI+m3bYes2qt55E2NK\nCvaJk7BPnEzcmLHordbeewFCDHLdUU8JIYToP7ocaMaOHcvHH3/Mueeey6ZNmzh8+DCpqanq8ri4\nOBoaOm56+9ve8PC6OnTYTXHEW+LJcAyN6daV4NOTWHoEa9FBDPtKUaqq1ccb4uOJmzpWs6GUReeZ\nDCZyE7LJTchW7wspISqbqilriGrNaSzn26ptfFu1TV3PbowjKz6TbEeGen3O0Dhnn2wh05tM4ck8\n8/OBswBIMoco+fQLXFu+xbV1C3WffkLdp5+gMxqxjRqtBhxzenpv7roQg0531FNCCCH6jy4Hmjlz\n5rBv3z6uuuoqpkyZwvjx46msrFSXu1wuEhISOtzG0zOW4ar1E2eyqReVq0Mp/2c77h0bY4ZS1tls\nxB3nUMqi5zV3Oxsa5+SkoZPV++u8DZQ1lqstOWUN5eyu2cvumr3qOka9kUx7eqS7Wng46SxHBhaD\nua2n6lWmxEQSpp9KwvRTUYJBPPv349qyGdeWb3Fv34Z7+zYqV72OacjQSLiZhG30aPSmvvdahBhI\nuqOeEkII0X90eVCATZs2UVtby1lnncWWLVt48cUXaWpq4rrrrmPatGncf//9TJ8+nYsuuqjD7QS9\nXhp27KRuy1Zqv91C4959EApPGKk3m4kfO4akSRNJnDQRx/Bh6lDKYmBp8nsori2jsKaUotoyimpK\nKak/QDAUVNfRoSMjfgj5yTkUJOWQn5xNQVIOCda+e0Gzt7qa2o3/oebfG6nd9C3BpiYgfGwnTp5I\n8klTSD5pCtYhQ3p5T4UYeLqrnhJCCNE/dDnQ1NTUsGjRIpqamkhISODhhx/G5XJx33334ff7GT58\nOMuWLeuwBWXrvUup37GzZSQpgwFrfoHaAmMdPly+xT5B/XnUjUAowCHX4ZjuamUNB/EEYychTTQn\nRA08EL4uJ9Wa0mOtd50tYyUQoGnvHrX1xnfggLrMnJmpdk2zjRgpc+C00p+P4/5goI5y1h31FMgo\nZ1qT81t7UsbakzLWnibDNneHDbN+hCU7h7gxY4kbOw7byJHorX13dvr+aKCdYIqiUO05Egk4B9Tr\nc2q9dTHrWQ1WsuMzWkKOI5N0+xCM+u4PCsdbxv6qyvB1N1u+xb1zB4rPB4DeaiVu3Hi1e5oxKbm7\nd7nfGWjHcV8zUANNd5FjT1tyfmtPylh7Usba67OBRgkGqTriPvaK4rgNlhOswdeotuQ0j7R22F0Z\nM2KeUWcgwz6UrPhMcqKuy7EZT2wksu4o45DPR9Punbi+/RbXls34o/r5W3Lz1HBjHTYcnb7/TGLa\nXQbLcdxbJNB0TI49bcn5rT0pY+1JGWuvzwYakIpCa4P5BPMGfRxoPBg1+MABDrgO4g8FYtZz2lLV\nlpzmkdYSLZ2/ULi7y1hRFPwVFWrXtKbdu9RumXq7Hfv4idgnTiJuwgSM8YPjgubBfBz3BAk0HZNj\nT1tyfmtPylh7Usba02RiTSH6OovBTEFiHgWJLZNlBkNBKtyVUdflhEdZ+0/lFv5TuUVdL97sINuR\nGTNnjtOWqo7GpyWdToc5PR1zejrJ511AyOPBvXMHrm/DAafhqy9o+OoL0OmwFhRgnzgZ+8RJWHLz\nBmXrjRBCCCEESAvNgCXfGByboijUeGuPui7niKcmZj2zwRwON44ssuMzyHFkkWEfSmZ6So+VsaIo\n+MrL1GtvmvbuUUcFNCQkYJ8wCfukScSNG48hzt4j+9QT5DjWlrTQdEyOPW3J+a09KWPtSRlrT7qc\nDWJygh0/l99NeeR6nOYWnUPuw4SUkLqOXqdniD2VRFMiydYkUixJJFuTSbEmkWxNItmShNlg0mwf\ng24X7u3bwtfebP2WYH19ZMf02EaMVK+96e9zNslxrC0JNB2TY09bcn5rT8pYe1LG2pNAMwiEQgoe\nXxCPL4DbG8DjDdLkC5CaYsdu0pMQZ+rXH2j7Cl/Qz0HXoZjBB454j1Dnbf84jjc5wmEnEnJah554\nk6Nb/jdKKIS3pES99sZTuB8ip7UxOUUNN3Fjx6G3nthACD1NKgptSaDpmBx72pLzW3tSxtqTMtae\nBJo+LKQoeH1BmrwBmnxBPN6A+ndT89/eAB5fMBJUWtZzR+5vXt4Rh81EttNOVpqDLKedbKeDzDQ7\ncVa5fOpEOZ3xlB86Qq23liOeWmo8tRzxRn57atTbgVaDETQz6o2RkNN24EmxJGE6jlaeQEM97q1b\nw93Ttm4h5HYBoDMasY0cjX1SeN4b09ChfT7sSkWhLQk0HZNjT1tyfmtPylh7Usbak0CjAUVR8PqD\nNHlbtYpEBZKW0BGgKdJi0hTVetL89/EUvEGvw2YxYrMYsJmNkb+NWGNuGzCYjOwpPkJ5pYvK2qaj\nnislwRIVcsKBJyM1DrPJ0B3FNCh05k1MURQa/a6YgNMceJpDUIO/sd3Hd9TKk2JNxmGydxhKlGAQ\nT+F+9dobb0mxuszkHBJuvZk0CduoMejNfW8yW6koTlwgFKDR76LR5wr/jvy4fC6u/d6c3t69Pk2O\nPW3J+a09KWPtSRlrTwJNFEVR8AVCR7VwhH/CQcPjjQ0gsfe33D6eEtPrdOEQEgkgNrMBq8VInMWI\ntfVtc9R6rYKK0aDv1Lfq0SeY1xfkQLWL8koXZZWNlFe5KK9spLbRF/MYnQ6GJMeRnWZXW3OynHaG\nJNswyChaR+muNzFf0K9JK09K5Fqe6FaeQG0Nrq1bwpN6bttKyOMBQGc2EzdmrNo9zZTmPOHX1R2k\nooilKApNAU9LKPG7aPBFfvsbcfncNPobafS7afSFf3uCnna39+YVz/Tg3vc/vVFPhZQQISK/Y34U\nQkow8jtEiNbLo9cLtbNOq23ErNfWc0avoxBUgi372Gq7QSWEQij8W2n9O/yY1uvE2+wMcwxjQuoY\nshwZfb7FuD+S91DtSRlrb0AEGkVRCARDuL2RblnNrR7tdMlS7/cGwo/xtawXDHX9pep0xASKNkNH\nTBgxthlczMbOBZHu0pkTrLHJz4GqSMipDIecskoXbm/sh2ejQUdGaqQlx+kgKxJ4UhOsg7oC6qk3\nMS1aeVKsySQZHTjKawjt2I1767f4DhxQH2POyIy03kzGNmIkOmPvdFEc6BVFc+uJy++mwdcYCSbh\n1hO1NaVVy0r04BTtMegMOExxOMwO7CY78SY7dpMdh9mOw9Tyc/ro7/bAq+yfXtz4JjUNDcf8oB6M\nBISj11FibkeHh9bL1N/H1W7fv+h1+vAPOvyhgPqaE80JjE8dw4S0MYxOHoH1BCc+FmED/T20L5Ay\n1l6fDTT/+E855RX1LV2yIkHF440OJy3LjiuIQLjlI6qFo3W3rKPutxoj9xmwmsMBxWzq2SDSXY73\nBFMUhdpGnxpuyqvCYedAlQtfIPaDlNVsICv6+pw0O1lDHCTE9b2uS1roS29ibbXyNAegzrbyZPqs\n5B7w4iyuwV58GJ0/vL7OasU+brzaemNMSu6x19WXyvhYurv1JJrNaG0JIuZIOIn+UYOKA4c5Dquh\nc182yDU07bt81c1dWl/9oK7To0ePQReuO2Lu1+nR63TodQb06CLr6NXf0cv0+ujttLE99K3W0WHQ\nGSK/I9uLWRb7XNG/9ejaX9a8T83PTetlrV9fZN/aWKZDF3NcWhN0/HP3RrZW72T7kV24/G4gHMhH\nJBUwIXUM41PHMCTO2S/r4b6gP72H9ldSxtrrs4Hm4sVrO1zeVutH899td9lqdT2J2YDFHH4DHqy6\n+wQLhRQq65rUlpzyKhdllS4OVbsJtTqEEuJMMS05zQMR2CwDayCC/vQm1lYrj/p3G608hqBCVoWP\n/AM+Cg74SGpsGXyiaUgi/lH5mMePJWHEGFLtace8lud49WYZ93brSfNtuykOo16bc0cCTfuONNVS\nUVl39Adz2viQ3wMT7w5E0ed3SAlRXF/K1uqdbKveSWlDubpemjWF8WljGZ86hpFJwzQdEn+g6U/1\nVH8lZay9PhtoPvqmlCa3NyacNP892INId+mpE8wfCFFxxE1ZVXO3tXAXtqq6o795Tk2whlt0mq/P\nSbOTkWrHZOyfHwYG2pvYUa08nhq1e5u/ooKkokpyy5rIOuzHGPnc7jHrKM4wU5ploz5/CHHJaZHu\nbcnqtT1tXcvTWd1Vxm21njR2EEwafa4Taj2JNzmwR0KLwxR3XK0nPUECTccG0vndF3V0ftd569lW\nvYtt1TvZeWQ3nqAXAJPexOjkEYyPtN6k2nquxbg/Gmj1VF8kZay9PhtoQCoKrfX2CebxBThQ5W65\nPicSeOpcsQMR6HU6hqbYyHI61MEIspwOhiTZ0Ov7xoe+9vR2Gfe05lae6rpDNGzfgn/7Tsy7SzA3\nNIWXA4dSjRRlWijKNHM4xRi+CC0i+lqeFGty1CAG7Y/Y1l4ZD4bWk54ggaZjg+n87g2dfQ8NhALs\nryuKtN7s4pCrQl2Wbh+qdk0bnpiPQS8jdUYbbPVUb5Ay1p4EmkGsr55gDW5fJOBEBiGoCrfqNLUa\niMBk1JOZaldbdLLSHGQ77STHW/rUt9t9sYx7kqIo+A6U4/r2W1xbNtO0dw+EwsEhZLfROCydirwk\nitNNVCqNHV7LY9IbSY4KOEmWBExWPYdrazRuPXGoy62GvnN89QQJNB0b7Oe31o73PbSq6QjbI13T\ndtXswx/yA2A1WBmbMpLxqWMYlzqGRIsc31JPaU/KWHsSaAax/nSCKYpCTYM3ZhCC8koXB6pd+FsN\nRGCzGFsGIHA61JHXHLae71Pdn8q4pwTdLtzbt0fmvdlMsL4+vECvxzZ8BHETJ6EbM5L6VBs13jpq\norq1HWvEtnDrSavWkg4ukO/vrSc9QQJNx+T81lZ3vIf6gn721O5jW/VOtlbtpNpzRF2WG5+ldk3L\nS8gZlNc6ST2lPSlj7UmgGcQGwgkWCikcrm0KD0JQ6Yq05jRScaTpqIEIEu3mmJacLKeDzLQ4rGbt\nPtAOhDLWkhIK4S0pwbVlM64t3+Ip3E/zJE7G5OTIqGmTiRs7Dr21ZYjW5mt5ar11DElNwt+oG5St\nJ1pTQiGGDE3s7d3o0+T81lZ3v4cqikKFuzIcbqp3sq+2kKASHtDEYbIzNmU0E1JHMzZ1NHZTXLc9\nb18m9ZT2pIy1J4FmEBvIJ5g/EORgtTsy0lpLi051/dHdkNISreoEoeGWHQfpqXEYDSf+Td1ALmMt\nBBsacG0LT+rp2rqFkMsVXmAwEDdqTGTem0mYhqarwaWvlrGiKBAMogSDKMEASjAYvh0IRO47+rYS\nuR3zuECw7e20fmy7j2v1fJFlzeuF96HtfURROG3tO71dlH1aXzz2BhKtz++mgIddNXvZVrWDbdU7\nqfOFn0uHjoLEvPC8NwN8Us+++h46kEgZa08CzSA2GE+wJm8gdqLQSItOvdsfs55Br2NoSly4JSfS\ndS3LaceZ2LWBCAZjGXcXJRTCU7g/3Hrz7bd4S4rVZSanE/vEScRNmEha9lCOVNUf9SG/9Yd5JRiE\njsJDoFVYaH5sm8EgNhS0Fx76HJ0OncEABiM6gyH8YzRG7mv79pTHHu7tve7T5PzWVk++hyqKQlnj\nQbZVh8NNYV3JoJjUU+op7UkZa08CzSAmJ1iLepcvZgCC5ut0PL7YD6Vmo57MNPtRXdeSHOY2v72T\nMu4+gdoaXFvDrTfubVsJeTp30X+3av7QbzCgMxjB2PJ3OAy0CgsGY/gxrdYjKji0dVtnMIAxdjsd\nhY7YbRljn8/YvJ4Rnb7rrY5yDU3H5PzWVm++hzb6Xeyo3s22AT6pp9RT2pMy1p4mgcbv93PXXXdR\nXl6OwWDgV7/6FQaDgbvuugu9Xs/IkSNZunTpMd8A5J+vLTnBOqYoCtX1ntgR1ypdHKx2EQjGnhJ2\nqzGmJad5otCC3BQpYw0ogQBNe/fg3rkDm1lPky/Y6kN/bAtETBBp43b7YaFV6OjHH1qO10ANNFJP\n9Q99pZ4KKSGK6kvZNgAn9ewrZTyQSRlrT5NA88EHH7B+/XqefPJJPv/8c15//XUCgQDXX389U6dO\nZenSpZxxxhmce+65HW5H/vnakhPs+ARDIQ7XNKkThDYPRnC4xk3rMyU9NY4RmYmMzk1idE4SaUm2\n3tnpAUyOY20N1EAj9VT/0FfP71pvHdsjrTf9fVLPvlrGA4mUsfY6U1d1eQiogoICgsEgiqLQ0NCA\nyWRi8+bNTJ06FYAzzzyTDRs2HLOiEKIvMuj1ZKTayUi1c/KYIer9Pn/zQAThlpyyykYKDzbwzy0H\n+eeWgwCkJljVcDM6LxlnYt+ZEV6IwUTqKXEikiyJnJo5lVMzp8ZO6lm1k63VO9havQOADPtQNdzI\npJ5C9K4uB5q4uDjKy8u58MILqa2t5dlnn+Xrr7+OWd7QIElVDCxmk4G89Hjy0lu+JUhJdbBp+0F2\nltSyq6SG3aW1fL71EJ9vPQRAcrxFDThjcpMZkmyTgCNED5B6SnQXo97IqOQRjEoewewRM1pN6rmX\nD0o+5YOST2VSTyF6WZcDzUsvvcQZZ5zBHXfcwaFDh7j66qsJBFpm/na5XCQkJBxzOwO1q0NfImWs\nvZMmZHLShEwgPG9OSUUDW/ZWsXV/FVv3VfPFtgq+2FYBQEqChQnD0pgwPJUJw9PIHuKQgNMJchyL\nruqOemrbg8sgFEJvMaO3WDBYrFF/W9BbLejNLX8bLBb0kZ/mvw3WyH1m83EN2jAY9Lfz20k8Y3Pz\nmMMF+AI+tlXu5j8HtrHx4Bb+Uxn+ARiWnMt3Mybw3YzxjEjJR9+L///+Vsb9kZRx7+tyoElMTMRo\nDD8sISGBQCDAuHHj+Oqrr5g2bRqfffYZ06dPP+Z2pL+htqRPp/baKmO7UccpY5ycMsaJoigcqHaz\nq6SGXSW17Cqt5bNN5Xy2KXzBaYLdzKicSBe13CQy0+zoJeDEkONYWwO1Eu6Oeqp++45uHW1PZzKh\naw43ZjN6czjsNP+tM5vRW8zozLHrxDwmav2YdS2W8Pb72fvHQDi/s415ZOfmMSPnophJPffW7md/\nTQnvbH+/ZVLPtDGMTRnVo5N6DoQy7uukjLWnyaAAbrebX/7yl1RWVuL3+7nmmmsYP3489913H36/\nn+HDh7Ns2TIZPaaXyQmmva6WsaIoHDriVsPNrpIaaht96nKHzcTonCRGRbqpZQ9xDPqAI8extgZq\noOmOekpRFA4frEHx+Qj5fCheLyGfN3zb62253+cl5I38VtfzRZa3Wqau41O3ddRoIyegJQRFh6Tm\nEGSOCkfhQNS8TvN66jJ1G9GhyYzO2L2haSCf300BD7uO7FFHTms9qWfzsNBaT+o5kMu4r5Ay1p7M\nQzOIyQmmvRMtY0VROFzbFA44JTXsKq3lSL1XXW63GqNacJLJGeLo0sSfA4Ecx9oaqIGmu2h97CmK\nghLwxwSco8NSVECKXhYdsFoHKXW9SGjqLjpdTMBRQ1Or1qO2W5Wag1TLbWduOg1G+4DvjtfRpJ5J\nlkTGp45mfKo2k3rKe6j2pIy1J4FmEJMTTHvdXcaKolBV52FnSQ27I604VXUtXV5sFiOjshMZnZvM\n6Nwkcoc6MAzwDwJyHGtLAk3HBsKxp4RCKH5/qxaj5rDT6nZ0sFIDVsvfzWGpdfhS/P7j3j+dxYo1\nLw9rfgGW/HyseQWYhgzpd93nuqInJ/WU91DtSRlrTwLNICYnmPZ6ooyr6prULmq7S2o5XNukLrOa\nDYzMDl9/Mzo3ibyh8RgNAyvgyHGsLQk0HZNjr3OaQ1M44LTR9S6qxSi6lcnY1Ejd7j34Dh6M6Xqn\nj4vDmhcJOPn5WPMLMKakDsiQ09lJPUclDcN0HJN6ynuo9qSMtSeBZhCTE0x7vVHGR+o9ketvwiGn\n4ohbXWYxGRiRnagOE52f0f8DjhzH2pJA0zE59rTVfH6HPE14SkrwFhXiKSrCU1yIv6IiZl1DfDyW\nvAI14FjzCzAmJfXSnmunuyf1lPdQ7UkZa08CzSAmJ5j2+kIZ1zZ6YwYZOFjdEnDMRj3DsxIZnRsO\nOAUZCZiM/Svg9IUyHki8viCVdU1U1XqorGviqovG9fYu9Wly7HUfRVEIBEM0+YJ4fUE8viBpqXYs\nOtq8NjDocuEtKcZTVKj+BKqrY9YxJCWFw01ePtaCAix5+Rjjjz1tRH/RelLPQ+7D6rLOTuop76Ha\nk1S4gBAAACAASURBVDLWngSaQUxOMO31xTKuc/nYXdoyyEB5pUtdZjLqGZ6ZwKhIC86wzATMpr49\ns3VfLOO+LBAMcaTeQ2Wdh6raJqrqPFTWNlFZ66GqrokGd+y1Du89fkkv7Wn/MJiPvZCi4POHg4dH\nDSEB9bbHF1CDicffxn3Nt/1BPN7w7VAbHzdsFgPDMhMZkRX+GZaZgM3S9owSgYZ6vMVF4VacSMgJ\n1tbGrGNMTVVbcKz5BVjy8jDE2TUpo57WPKnn1uqd7K7Ziz8Unlupo0k95T1Ue1LG2pNAM4jJCaa9\n/lDGDe7mgBNuxSk73EjzCW806BiWkcCo3GTG5CYxPCsRSx8LOP2hjHtSSFGoa/RRFdXKUlUbDi1V\ndU0cafC2OQqwQa8jNdGKM9FKWpKNtEQrziQbPzxzRM+/iH6kPx17wVBIDR5NrQKI+nckXHj9rcNJ\nq8DiD+LzBTmRDwdGgx6r2RD1Y8TS/LcpfFvR69i2vzqm66wOyHI6GJkdDjjDsxNxJlrbvX4mUFsT\n6aZWhKewEG9RIcHG2P+bacjQSMDJx5JfgDU3F73VdgKvrvf5gn721O5ja9VOtlXvoNpToy7Ljc+K\ntN6M5aRhYzgS1XIvup/UU9qTQDOIyQmmvf5Yxo1NfvaU1qrX4ZQcblA/ABv0OgoyEtRBBkZkJWI1\nd3nu3W7VH8v4RLk8/qiQ0hJaqurCt/2B0FGP0QFJ8RbSEq2kJdpwJrX8dibZSHJY2uzWI9fQdEyr\nY6+t7ldttoA0t3x4g3j80eEkdl2vP9jmcdEVFlM4bFiiAkhzGLGYYm+3rGeM/dtkwGoJr9+Z6/ea\nz+8Gt4995fXsLa9jb3kdhQfrY15Pgt2stuCMyEokLz2+3e6ziqIQOHJEbcHxRq7JCbmjPtTrdJgz\nMqIGHijAkpOL3mw+oTLsLYqiUOGuZGv1DrZV72Jv7X5CSkv5mfUmLEYLVkP4R/3baMXS+r52lluN\nFiwGCya9cUAOznAiBmM91dMk0AxicoJpbyCUsdvjZ3dZXWSY6BqKDsUGnLz0eHUenJHZie12BdHK\nQCjj1nz+IFV14YDS3BUsurXF7Q20+Ti71Uhakk1tZYlubUlLtGIydr11TQJNx5qPvejuV+0FCo+3\npXUjvF5sC0h09yuvP0gwdPxVr07HUQHDajaGg4elpQUkOqA0hw5b1N/RIaY3JvFt7/wOBEOUHm5k\nT1k44Owtq42ZhNho0JGfnhBuwclKZER2Ion29sOIoij4KyvxFO0PB5yiQjzFxSjelmHx0euxZGVF\nDTwwDEt2Njpj736pczyiJ/WsDdTS4HHjDXjxBMM/vuDxz02k1+ljQo7VEA46zYEnfJ81JhhZotaz\nGWPX1+v613WdbRmI9VRfI4FmEJMTTHsDsYybvAH2lNWxqzQ8F07hwQa137tOB3lD4yMtOMmMyk4k\nztr1YUS7oj+WcSikcKTBEw4rtU3h61miQktdY9sfJsxGfUt3sEQbaUmxrS1x1u7/YCWBpn0/ffxj\njtR78Pi6t/tVTAuIqb3WjjZaSCwt65uM+gHxLXlnz29FUThS71VbcPaW11Fa0RhzTY4zyRpuwckO\nty5npdk7nIhYCYXwHTqEt7hQvSbHW1oSMxGpzmjEnJ2jDjpgzSvAnJmJztC3uuZ2pK0yDikhvEEv\nnoA3/Dv670jwiQ5AzX+Hl3ti1w96Y1qDuiqm9choPTogRQKRrVUwat16ZDVYMPZS61F/rKf6Gwk0\ng5icYNobDGXs8QXYW16nXoNTeKBe/WZZB+QMdTA6J3wNzsicJBy27g04fbGMFUWhwe0/6vqV5taW\nI/XeNr991+t0pCRYcDa3qrRqbUmwm3u8MpZA074bH/mAYDCkhgirxdiqW1Z0a4cBi8kYbh2Jukak\neVl/Hz5dKydyfnt8AQoPNrC3vI595XXsLauLad20mg0Mz0xQW3CGZx67hVkJBvEdOICnVcghGFTX\n0ZnNWHJyW67JySvAnJ6Oro9Ocqz1e6iiKARCATXkRAcfb6tAFLvc28ZyD77Q8U/SqtfpYwJRdPhp\nv1tdW+t3rfWoL9ZTA40EmkFMTjDtDcYy9vqD7IsKOPsP1BEItgScLKcjMkx0EqNykoiPO7E+6b1V\nxk3eQLhbWKSFpbK2SR01rKrOg9cfbPNxCXYzzsgF92oLSyS0pCRYMPSxDz0SaDo22M7vntad53dI\nUThU7Y50UQu34hxqY7CBEdmJjMgKd1dzJtmO+SVCyO/HV14eDjmFhXiLC/GWl0OopVVCZ7FizcsL\nX4uTn481rwDTkCF9ohWtv9VT7bUexbQUtdF6FB2guq31yGA+KgAd3b3OQp4zg7hgPEPjhhBn6t+D\nTfRVEmgGsf72JtYfSRmHrwfZf6BenQdn34HYi3mz0uyMisyDMyonqcN+7m3RqowDwRDVdbGjhEUP\ndfz/2bvz8CiqfI3jby/Z90BDWCMiAsqiDqACMojKgCKyiSwXUVFQB0bFDWdERBFHcQdHgRnHK6Li\nyKKoV0XE0QEBFVxZRoQEELKRfe3udN0/kjRplpBAKqGS7+d5eJLurq46fejuX946Vafyi469lzAs\nxKGmMYdnCas82tI0JvS0myXuRAg0VWvsn2+zmf0dmlfo1q8Hcv0jOHsO5spdebKB8CC1bxWjDuWH\nqSUmRFbrXDSf262SfXsDJh1wHzyoylMM2sPDK006UHZOjjM+vl5GYRvr+7i6o0dFRx1WV3LE48Uq\n8ZZUa/QoOjhKCeHN1DyiWflPlxLCmyk2JOa0CLhWRaBpxBrzl1hdoY+P5vH6tOdgrv86OLt+y5Hb\nc/gPiBZNwv2TDHRsG6vYyJAq13eyfewzDGXnlfivw+IfbSkPLtl5Jcc8J8LpsKlJTOChYJVHWyJC\nG9YMPwSaqvH5Nlddf4dWTDZQMYKz67ccZeWV+B93OsomQqk8o1rMCb6jKviKi1W8N1klSeWHqyXv\nkSc1NWAZR1RUpUkHyq6T44yNrdXXeCTqVO051uhRkbdYbmehfkndq9SCdKUUpimz0hTaFUIcwWoe\n3kzNw5spIaKZEsJdSohopqZhTeS0W2/iibpGoGnE+BIzH318Yt5Sn5IO5mnnvizt3JutX/bnBByu\n1TwuzD/JQMc2sYqPDg14/vH62DAMFRR7A8NKpdByKLfYfyhcZTZJcdEhAYeCVR5tiYkMrpfZnuoL\ngaZqfL7NdTp8h2bmFmvXbzn+GdWOO9lA+YxqrV2RVU42UFlpQYFK9ib7p5AuTk6SNyMjYBlHbGyl\nC4GeoZDEM+SMiq6113c69HFDd2Qfl5S6lVaYrtSCNKUUpiml/Pe0wnR5jcDDle02u1xhTfxBp3l5\n0Gke3kxhztAjN9VoEWgaMb7EzEcf15y31Kfk1LzyaaKz9d992Sp2H/6Cd8WGqmObstGbDq1jFBkd\npv/uORRw1fuKn5WfV1lkWJB/ZrCmsYdnDHPFhqlJdCgnaFdCoKkan29znY7foSXuUu05ePiaOL/+\nlqOC4sDJBs5sGV0+o1qMzmwRU6MZCL15uSpJTvJPOlCctEel2dkByzibNKkUctopJDFRjvCIk3o9\np2MfNzTV7WOf4VNGUaZSC9OUUpCm1MJ0pZSHniJv0VHLxwRHH3XoWkJEM8UERzeoIwWqg0DTiPEl\nZj76+NSV+nzam5qvnXvLws3OfdkqOs51WCqEBDkOB5VjXJOlrq+VY2UEmqrx+TaXFb5DAyYbKA84\nBw8dOdlARMA1cZpVY7KByrzZWeWHqSX5Jx4ozQvsl6DmzRVafrhayBntFNo2UfbQE+/Bt0IfW92p\n9rFhGMrz5PtHdCoOXUspSFNWSfZRy4c6Qo45ouMKayKH3VrncVYXgaYR40vMfPRx7fP5DO1Ly/fP\noBYXE6bIihPxy0NMVHhQo9s7ZRYCTdX4fJvLqt+h+UWesokGTjDZQNmMajE6IyGqRhe+NQxD3sxM\n/whOxcQDvsLDQUo2m4JbtKg08UA7hbRpK3tw4MQrVu1jKzGzj4u9JUorSg8Y0UktTFNaYYZKj3n4\nWtPDQadS6Am1+OFrBJpGjC8x89HH5qOPzUWgqRrvPXM1lM+3f7KBimvi/JajzNzDkw047DadkRBV\nPqNa2UjOiSZEOZJhGPKkp6s4aXdZwEnao+LkZBklxYcXstsV0qpV2cQD5RcCbdntbGXmHvtivqgd\n9fE+LvWV6lBx5uGgU5jmH+Ep8hYftXxsSEzAoWsVIzzRwVGW2EFIoGnEGkqhOJ3Rx+ajj81FoKka\n7z1zNeTPd8VkAxUzqu09YrKBpjGh/hGcs2o42UAFw+eTJzWlfCTn8IVADXdggHFERskZF1f+L97/\nMyg+3n/bHlKzgIXDTqf3sWEYynXnVzpP5/D5Osc6fC3MGVo++9rhQ9cSIpqpaWj8aXP4mlFaqmYJ\nJ54NkEDTQJ1OH7CGij42H31sLgJN1Xjvmasxfb5L3KVKSskNCDmVJxsICXaofcVkA61idGbLmk02\nUMEoLZX7wAH/hUCVlaHCtHR5MzOPCjqV2cMjjgg6R4ef6pyz0xhZ5X1c7C1R2hGjOSmF6Uo/xuFr\nDptDrvCmZdNLV7quTrNwl0Kdpx5+DcOQr7BQ3pxslebkyJudXfYvJ1ulOdny5uTIm1N2n+H1qs+K\nt0+4zhp/WlauXKkVK1ZIkkpKSrRjxw698cYbeuyxx2S329WhQwfNmjXLEkNYAICGhzqF001IsKP8\n+ltxksomG0jNLAy4Js62pCxtSyq7holNUsvyyQbOqsFkAzaHQyFt2iikTRvF9O3n/2Pb/wdkVqa8\nWVnylP/0ZmXKm1l+X0aG3L/tP+667WFhASHHGRdXaZSnfKQnrGYTIqDuhDpD1Da6tdpGtw64v9RX\nqgz/4WtHzMBWkHrUeuJCYo8a0Wke3kzRwZGSYai0IF+l2eWBpDyU+ENKdnmAycmW4aniQqU2mxyR\nUQpu1kzOJk2r9fpOaYTmkUceUefOnfXZZ5/ppptuUs+ePTVr1ixdcskluvzyy6t8rhXSrJVZZY+B\nldHH5qOPzdUYRmioU6cvPt+BKk828OtvOdp9MDfgwsRR4UEB18Rp1+LEkw3UtI9Li4r8ocebeeiI\n8FMWgAImJziCLSRUQXFxclY6nM0ffuLi5YyPlz08vEGFnob6Pi47fC2vLODkpygjY79yMw6qMDNd\nys1TRJGv7F9xqSKKfIosMhRe7JPdV0WssNvliI6WMyZWzthYOWNi5YiJ8f/ujI2VIyZWzqgo2ZyH\nx1yqU6tOen7TH3/8Ubt27dJDDz2k+fPnq2fPnpKkfv36af369ScsFAAAmIk6BSuJDAtS97OaqvtZ\nZXukvaU+7U/PDxjF2fpLhrb+UnZxzsqTDVSM4tR0soEjOcLC5AhrpZCWrY67jK+4qDzoZB0OPxUj\nP5mZ8mZlyp1y8LjPtwUHB4acIwJQUFy87JGRDSr0nK4Mr1fe3Bx5s3PKR1EqRlKy/IeCOXNylJCb\no4Qqxj98dpuKwp1Ki7MrP8yugvJ/hWF2FYc5FRzfRFHxCYpv2koJkQlqHuFS0/BmCnEEH3edNXXS\ngWbhwoWaOnWqpLIUVyE8PFx5eQ0vqQIArIU6BStzOuw6IyFaZyRE6/IebSRVmmygfBQnKSVPvx7I\n1Sdf75NUPtlAxTVxWsUoLv7kLshZFXtomIJbhCm4RcvjLuMrKZE3+/CojiczMPh4szJVlJqioy8n\nWcbmdJYFnErn8wT5R3vK7ndERspm50LJx+LzuAMP+8rJUWml81S8OWUB5sjrHR3JFhwsZ0yMgtqf\ndXgUJSambBQl9vAoiz0iQjabrezwtaJDAdfTyS9M028FaSou/K+0978B648LiVWC/+KhzcrO2Ylo\nrsigiBoH2pMKNLm5uUpKSlKvXr0kSfZKb6iCggJFR0efcB2N4VCH+kYfm48+Nh99jJNBnbIG+rhm\nXK4odWzv8t8udnu1a1+2tidlantSpnYkZWrjtlRt3FZ27oPNJsVEhCg+OlRx0RU/QxUfHar46BD/\n73FRoQpy1mY4iJJaV33ug8/tVsmhTLkPHVJJxiG5Dx3y/15xu2jnjuM+3+Z0KrhJvEKaNFFw0ybl\nP5sG3A6KjamT0FNX7+PS4mK5MzPlzsqSOzNbnqys8t8z5c4s+92TlS1vfn6V63GEhSkoLk4RiW0V\nHB+n4Liyf0FxcWW3y+9znMThgQmKVRe1D7jPMAxlFefot9wU/78DeSnan5ui7Zn/1fbMwKATERyu\n1lEJahmdoMTYVrrSNeCE2z2pQPP111/roosu8t/u3LmzNm/erF69eumLL77QxRdffMJ1NMTjDU8n\nDfWYztMJfWw++thcDfmPSerU6Y/Pd+1oHh2i5t1aqH+3FjIMQymZhf7Z1DLz3UrPKtRv6fnafSCn\nyvVEhgUpNjJYMZEhio0o/xkZrNjIEMWU/4yNDK7RRUJPyBkhNY+QrXlbhUgKkVT5W8nwev0jPZ5K\nExhUntygZPsO6XiHQzkcZaMK8ZVHeCr9jG8iZ8yphZ5TfR8bhiFfUVHZIV/ZR4yo+O8rG1HxFR99\njZnK7OERcsbGKLxNWzkqjag4Y2IP346NrXKabk/5v4JCn1RYdTCqGYcS7K2UENtKv6s0C3ORt1ip\nlUZ0KmZg+yUzSTsP7ZYkXXm2SYEmKSlJbdu29d+eMWOGZs6cKY/Ho/bt22vQoEEns1oAAGoFdQqN\nkc1mU4smEWrRJEKXdGsZ8Md2UYlX2fklysl3K7ugRNl5buUUlN/OL1F2vluHcou1P72gym2EhzgV\nGxWimIjgwwGoPOzERAQrNipEsREhCgk+9eBjczoV1NSloKYuhR1nGcPrLTvvo9LhbAHn92RmqvjX\nXSo+Xuix28v+0K+4Vk98k6PDT0xMwEnq1WEYhnwFBWWBzD/DV+CUxBW3q5pSW5IcUVFyNnWVhZPY\nuLLDvmIPh5Wyk+ljZA+qvXNS6kqYM1RnRLfVGdFtA+73+rzKKDqkXHf1wiLXoWmg2PNlPvrYfPSx\nuRryCE1t4L1nLj7f5juZPi5xlyr7iKCTU/Gz4PDtytfROZbQYMfhoBNZEYBCKoWgstuhwQ7TJwAw\nSkvlzc0tDzmHg05A+MnOlkpLj70Cm02O6JhjXKMnTpEhdmXtTy27hkp2jrw5WWUn2efmyPBW0Ufl\n63RWzPJVMcNXxYhKxXkq0dE1DlMNjamznAEAAKBhCQl2qHlwuJrHhVe5nMdbWh5uKoJPiXIK3IdH\ngcpDUErm8ad5lqTgIHtZ0Ck/zC0mMlhx5T8rj/6EhzhPOvjYHA4FlZ8joiPO76hg+HwqzcuVNzPz\nmDO4eTMzVbJvr4r37K56Yw6HnDExCmnTtnxK4rhKh31VGlGJimZSg1pEoAEAAECNBDkdcsWGyRV7\nvIPBynhLff7D3I4c9ckpcCs7r0TZBW79kpWjqg4ZCnLa/aM8MZHBio0IUWxUsGIiAs/1iQwLOqng\nY6s49CwmVqHtjr2M4fOpND+/UtjJUnSTKBXZQw+fnxIRQVCpBwQaAAAAmMLpsKtJTKiaxIRWuVyp\nz6fcAs8RIzxloScn362s/BLl5Jdo94Fc+ao4W8Jht/knMTjeYW4xkSGKCg+SvYbBx2a3yxkdLWd0\ntJR4hqTGd+ikzzDk9frkLfXJ4/XJU+qTt7TsPk/5fd7KP0t98nqN8p9HPq/isVJ5vEbA8yqe67DZ\n9PRd/U/YLgINAAAA6pXDbldcVIjioqq+OKjPZyiv0B1wTs+Rh7nlFJQoOSVPpVVctd5uKws+AaM+\nxxj9iY4IkqOeR1x8PsMfADxe3xHhwfD/8V/xWGCgCAwK/jBR+fFK9wX8rLSNivur6tPa5rDbFBZS\nvahCoAEAAIAl2O228nNtjpzkOZDPMJRf5FFO+eFtWeWhxz/LW/nt/ekFSko5/giLzSZFhwcfd9Qn\nLq1AhzILAkYnjhk4jgwJlQJFwKhEQOAoe6yqEana5rDb5HTaFeSwK8hZ9i881CGnw6Ygh13O8vud\nDnul5WwB9wdVeszptJc9t+J2wPMqr8921PNqMoJGoAEAAECDYrfZFB0erOjwYLVpFnnc5QzDUEGx\nt2wmt/Jzeo6c3CAn362UQ4Xam1qb12U5zOkIDAQhTociQ4PK//g/4g/9o4KD7dhBwv9cxwkDR1B5\n4HA4ahYiTicEGgAAADRKNptNkWFBigwLUivX8ZczDENFJaVHHeYWERGs4mJPwAjE4eBQPtpRKUj4\nRzcqjUyYPW11Y0CgAQAAAKpgs9kUHupUeKhTLZpE+O9vbJMCnK6YVw4AAACAZRFoAAAAAFgWgQYA\nAACAZRFoAAAAAFgWgQYAAACAZRFoAAAAAFgWgQYAAACAZRFoAAAAAFgWgQYAAACAZRFoAAAAAFgW\ngQYAAACAZRFoAAAAAFiW82SetHDhQq1bt05ut1vjxo1Tz549NWPGDNntdnXo0EGzZs2SzWar7bYC\nAFAt1CkAaDxqPEKzadMmbd26VW+99ZZef/11paSk6K9//aumT5+upUuXyjAMrV271oy2AgBwQtQp\nAGhcahxo1q9fr44dO+r222/Xrbfeqv79++vnn39Wz549JUn9+vXThg0bar2hAABUB3UKABqXGh9y\nlpmZqYMHD2rhwoXat2+fbr31VhmG4X88PDxceXl5tdpIAACqizoFAI1LjQNNXFyc2rdvL6fTqXbt\n2ikkJERpaWn+xwsKChQdHX3C9bhcUTXdNGqIPjYffWw++hg1RZ2yDvrYfPSx+ejj+lfjQPO73/1O\nr732mm688UalpqaquLhYF110kTZv3qxevXrpiy++0MUXX3zC9aSns3fMTC5XFH1sMvrYfPSxuRpq\nEaZOWQOfb/PRx+ajj81XnVpV40DTv39/ff311xo1apR8Pp9mzZqlVq1aaebMmfJ4PGrfvr0GDRp0\nUg0GAOBUUacAoHGxGZUPLK5DpFlzscfAfPSx+ehjczXUEZrawnvPXHy+zUcfm48+Nl91ahUX1gQA\nAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZF\noAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAA\nAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJblPJknDR8+XJGRkZKkNm3aaMqUKZoxY4bs\ndrs6dOigWbNmyWaz1WpDAQCoLuoUADQeNQ40JSUlkqQlS5b477v11ls1ffp09ezZU7NmzdLatWt1\n+eWX114rAQCoJuoUADQuNT7kbMeOHSoqKtKkSZM0ceJEfffdd9q2bZt69uwpSerXr582bNhQ6w0F\nAKA6qFMA0LjUeIQmLCxMkyZN0rXXXqukpCTdfPPNAY+Hh4crLy+v1hoIAEBNUKcAoHGpcaA544wz\nlJiY6P89NjZW27dv9z9eUFCg6OjoE67H5Yqq6aZRQ/Sx+ehj89HHqCnqlHXQx+ajj81HH9e/Ggea\n5cuX67///a9mzZql1NRUFRQUqE+fPtq8ebN69eqlL774QhdffPEJ15Oezt4xM7lcUfSxyehj89HH\n5mqoRZg6ZQ18vs1HH5uPPjZfdWpVjQPNqFGjNGPGDI0bN042m02PP/64YmNjNXPmTHk8HrVv316D\nBg06qQYDAHCqqFMA0LjYDMMw6mPDpFlzscfAfPSx+ehjczXUEZrawnvPXHy+zUcfm48+Nl91ahUX\n1gQAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAA\nAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQa\nAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZ10oHm0KFD+v3vf689e/YoOTlZY8eO\n1fjx4/Xwww/LMIzabCMAADVGnQKAxuGkAo3H49FDDz2ksLAwGYahxx9/XNOnT9fSpUtlGIbWrl1b\n2+0EAKDaqFMA0HicVKB58sknNXbsWLlcLknStm3b1LNnT0lSv379tGHDhtprIQAANUSdAoDGo8aB\nZsWKFYqPj1ffvn0lSYZhBAzdh4eHKy8vr/ZaCABADVCnAKBxcdb0CStWrJDNZtOGDRu0Y8cOzZgx\nQ1lZWf7HCwoKFB0dfcL1uFxRNd00aog+Nh99bD76GDVFnbIO+th89LH56OP6V+NA8/rrr/t/nzBh\ngmbPnq0nn3xSmzdvVq9evfTFF1/o4osvrtVGAgBQXdQpAGhcahxojmSz2TRjxgzNnDlTHo9H7du3\n16BBg2qjbQAAnDLqFAA0bDaDuSsBAAAAWBQX1gQAAABgWQQaAAAAAJZFoAEAAABgWac8KUBNeDwe\n/fnPf9aBAwfkdrt12223acCAAXXZhAavtLRUDz74oJKSkmSz2TR79mx16NChvpvV4Bw6dEgjRozQ\nq6++qnbt2tV3cxqc4cOHKzIyUpLUpk0bzZ07t55b1PAsXLhQ69atk9vt1rhx4zRq1Kj6btJpgTpl\nPupU3aFWmYc6Zb6a1Kk6DTSrV69WfHy85s2bp5ycHA0bNoxCUcvWrVsnu92uN998U5s3b9azzz6r\nv/3tb/XdrAbF4/HooYceUlhYWH03pUEqKSmRJC1ZsqSeW9Jwbdq0SVu3btVbb72lwsJCvfLKK/Xd\npNMGdcp81Km6Qa0yD3XKfDWtU3UaaAYNGqQ//OEPkiSfzyeHw1GXm28ULr/8cl166aWSpN9++00x\nMTH13KKG58knn9TYsWO1cOHC+m5Kg7Rjxw4VFRVp0qRJ8nq9mj59urp3717fzWpQ1q9fr44dO+r2\n229Xfn6+7rvvvvpu0mmDOmU+6lTdoFaZhzplvprWqToNNOHh4ZKk/Px83XHHHbrrrrvqcvONPk88\nvQAAIABJREFUhsPh0P33369PP/1UL7zwQn03p0FZsWKF4uPj1bdvXy1cuFDMel77wsLCNGnSJF17\n7bVKSkrSLbfcoo8//lh2O6f81ZbMzEwdPHhQCxcu1L59+3Tbbbfpo48+qu9mnRaoU3WDOmUuapW5\nqFPmq2mdqtNAI0kHDx7U1KlTNX78eF111VV1vflG44knnlBGRoZGjx6tDz/8UKGhofXdpAZhxYoV\nstls2rBhg3bs2KEZM2bob3/7m5o2bVrfTWswzjjjDCUmJvp/j42NVXp6upo3b17PLWs44uLi1L59\nezmdTrVr104hISHKzMxUfHx8fTfttECdqhvUKfNQq8xFnTJfTetUnUbJjIwM3XTTTbr33ns1YsSI\nutx0o/Huu+9q0aJFkqTQ0FDZbDb2GNSi119/XUuWLNGSJUvUqVMnPfHEExSIWrZ8+XL99a9/lSSl\npqYqPz9fLpernlvVsPzud7/Tl19+Kamsj4uKihQXF1fPrTo9UKfMR50yH7XKXNQp89W0TtmMOhyH\nnDNnjj766KOAmTb+/ve/KyQkpK6a0OAVFRXpgQceUEZGhrxeryZPnswJrSaZMGGCHnnkEWaOqWUe\nj0czZszQwYMHZbPZdO+99+q8886r72Y1OPPmzdOmTZvk8/l09913q0+fPvXdpNMCdcp81Km6Ra2q\nfdSpulGTOlWngQYAAAAAahNjvAAAAAAsi0ADAAAAwLIINAAAAAAsi0ADAAAAwLIINAAAAAAsi0AD\nAAAAwLIINAAAAAAsi0ADAAAAwLKc9d0AwExer1cPP/ywdu3apYyMDLVr104LFizQsmXLtHTpUkVF\nRenMM89U27ZtNXXqVH3xxReaP3++vF6vWrdurUcffVSxsbHHXHdycrJuuOEGrVu3TpK0efNmLV68\nWIsXL9aiRYv00UcfqbS0VH379tW9994rSXr22We1ceNGZWdnKy4uTgsWLFDTpk110UUXqUuXLsrI\nyNDy5cvlcDjqrI8AAPWHOgWcOkZo0KB99913CgkJ0VtvvaU1a9aouLhYixcv1htvvKEVK1bojTfe\nUHJysiQpMzNTzzzzjF555RWtXLlSffr00VNPPXXcdScmJqp169bauHGjJGnlypUaMWKEvvjiC/38\n88965513tHLlSqWkpOi9997T3r17tWfPHi1btkwff/yxEhMTtXr1aklSdna2pkyZolWrVlEkAKAR\noU4Bp44RGjRoPXr0UGxsrJYuXardu3crOTlZF154oS699FJFRERIkq666irl5ubqhx9+0MGDBzVh\nwgRJUmlp6XH3elUYOXKk3n33XZ133nnatGmTHnnkET3zzDP64YcfNGLECElSSUmJWrduraFDh+r+\n++/XsmXLtGfPHn333Xdq27atf13du3c3qRcAAKcr6hRw6gg0aNDWrl2r+fPna+LEiRo5cqSys7MV\nHR2tvLw8/zKGYUgqKwwXXHCBXnrpJUmS2+1Wfn5+lesfNGiQnn32WX300Uf6/e9/r6CgIPl8Pk2c\nOFE33HCDJCkvL08Oh0M//fST7r77bt10000aNGiQHA6Hf9uSFBwcXMuvHgBwuqNOAaeOQ87QoH31\n1VcaPHiwhg8friZNmujrr7+WJP373/9Wfn6+3G63PvnkE9lsNnXv3l3fffedkpKSJEkvvvii5s2b\nV+X6Q0ND1a9fPz377LMaPny4JOmiiy7Su+++q8LCQnm9Xt1+++36+OOP9c033+jCCy/Uddddp/bt\n2+s///mPfD6fqa8fAHB6o04Bp44RGjRoo0eP1t13362PPvpIwcHBOu+885SZmakJEyZozJgxCg8P\nV1xcnEJDQ9W0aVPNnTtXd955p0pLS9WiRYsTFgpJGjx4sLZu3apu3bpJki699FLt2LFDo0ePVmlp\nqfr166fhw4crNTVV06ZN09ChQ+V0OtW5c2ft379fkmSz2UztBwDA6Yk6BZw6m1F5LBFoBJKSkvT5\n55/7h9pvv/12jR49Wv3796/xukpLS/XMM8/I5XL51wcAwKmgTgE1wwgNGp2WLVvqxx9/1NVXXy1J\nuuSSS6osEvfcc4927dp11P0DBgzQunXrFB8frzvuuMOs5gIAGhnqFFAzjNAAAAAAsCwmBQAAAABg\nWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEA\nAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZF\noAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAA\nAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQa\nAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABg\nWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEA\nAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZF\noAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAA\nAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaAAAAAJZFoAEAAABgWQQaNDgej0d9+/bVzTffXOVy\nN910k7KzsyVJkydP1q+//npS25s/f74effTREy5XW9sDAJy6Tp066eqrr9awYcM0fPhwDRo0SKNG\njdJPP/1Up+3Iy8vT9ddf7789bNgw5efnn/J6J0yYoEWLFh11/yuvvKLbbrvtlNdf2YwZM/TKK6/U\n6DmbNm3S1VdfXavtQONFoEGDs2bNGnXq1Enbtm2rMjRs2LBBhmFIkhYtWqT27duf1PZsNlu1lqut\n7QEAaseSJUu0atUqrVy5Uh999JGuvPJKzZkzp07bkJOTox9//NF/e9WqVYqMjDzl9f7P//yPli9f\nftT9//rXvzRhwoRTXn9l1a2DgFkINGhw3nzzTV1xxRUaPHiw/vd//1dS2Z6goUOHasyYMbrmmmv0\nwAMPSJImTpyolJQUDRgwQD///LMk6Z133tGQIUM0dOhQ/+NH7kmqfLsipEjSunXrNGbMGI0cOVKX\nXnqpnn/+eUmqcnvLli3T1VdfrWuuuUaTJk1SUlKSpLI9XnPmzNH111+vgQMH6tZbb1VhYaGJPQcA\njUvl72+v16sDBw4oNjbWf99LL72kESNGaNiwYfrjH/+otLQ0SVJ6erpuv/12DR48WFdddZWWLFki\nqWy0ZcaMGRoxYoSGDh2qxx9/XKWlpZKkc845R0888YRGjBihwYMHa82aNZLK6kNJSYmGDx8un8+n\nTp06KSsrS2PGjNHHH3/sb8tTTz2lp556SlJZKBkxYoSGDx+uG2+8Ubt37z7qtV122WUqKirSN998\n479v8+bNMgxDvXv31i+//KIJEyZo6NChuuaaa7Rq1Sr/cseqgz6fT3PmzNHo0aN11VVX6corr9SW\nLVv8z/n222913XXX6aqrrtLcuXP9r7tTp07+oxOOdVuS9uzZoxtvvFFjxozRgAEDdPvtt8vtdkuS\nunTpojvvvFODBg3SggULNGbMGP/zDhw4oEsuuURer7eK/2U0CgbQgPzyyy9G165djZycHOOHH34w\nunfvbmRlZRkbN240OnfubBw4cMC/bMeOHY2srCzDMAzj0ksvNX766Sdj+/btxkUXXWSkpKQYhmEY\nr776qvHQQw8ZmzZtMoYMGeJ/7saNG/23X3jhBePRRx81DMMwJkyYYCQnJxuGYRgpKSnGOeec49/G\nsba3YcMG44orrjAyMzMNwzCMFStWGFdeeaVhGIZx//33G2PHjjXcbrfh8XiM4cOHG8uXLzet7wCg\nMenYsaMxZMgQY+jQoUbfvn2Nyy67zJgzZ45x6NAhwzAMY+XKlcZdd91leL1ewzAM46233jJuueUW\nwzAM449//KMxb948wzAMIy8vzxgyZIiRnJxszJgxw1iyZIlhGIbh9XqNe+65x1i8eLF/ewsXLjQM\nwzB27Nhh9OjRw8jMzDT2799vnHfeeQHtysrKMpYvX25MmTLFv65+/foZycnJxqZNm4zx48cbRUVF\nhmEYxpdffumvG0eaP3++MWPGDP/t6dOnG6+99prh8XiMyy67zFizZo1hGIaRmppq9OvXz9i6detx\n6+DWrVuNO+64w7+uhQsX+tt3//33GyNHjjSKiooMt9ttTJgwwXjjjTcCXs+Rr69yHX3iiSeM9957\nzzAMw/B4PMbVV19tfPLJJ/7l3333XcMwDMPtdhu9e/c2du3aZRiGYTz33HPGM888U+X/MxoHZ30H\nKqA2vfnmm+rfv7+io6PVtWtXtW7dWsuWLdP555+vhIQEtWjR4rjPNQxDX331lS655BI1b95cUtmI\nilQ2IlMVo3wv38svv6x169bpvffe0+7du2UYhoqKigL2+FV+zpdffqkrr7xScXFxkqThw4frscce\n0/79+2Wz2XTJJZcoKChIknT22WcrJyen5p0CADimJUuWKDY2Vtu3b9ctt9yi888/X/Hx8ZLKRtx/\n/PFHjRw5UpJUWlqqkpISSdJXX32l+++/X5IUGRmp1atXS5I+//xz/fTTT3rnnXckScXFxbLbDx8M\nM378eElSx44ddfbZZ+vrr7/WOeecc8y2DRo0SE888YQyMjL0888/KzExUW3bttVbb72l5OTkgJGK\nnJwc5ebmKjo6OmAdFSMmhYWFcrvdWr9+vWbPnq2kpCS53W5dfvnlkqRmzZpp4MCB+vLLLxUVFXXM\nOihJd9xxh9544w3t27dPmzdv9h8aZ7PZdM011yg0NFSSNHToUP373//W2LFjq/X/cO+99+o///mP\n/v73v2vPnj1KS0tTQUGB//EePXpIkoKCgjRq1Ci9/fbbuv/++7Vq1SotXbq0WttAw0agQYNRWFio\nVatWKSwsTAMGDJAkFRQUaOnSperatasiIiJOuA6nM/AjUVJS4g8XRqVDEzweT8ByNptNRUVFGjZs\nmAYOHKgePXpo1KhR+vTTTwOedyTDMI563DAM//B5SEhIwDaqWhcA4OR07txZDzzwgP7yl7+oe/fu\natWqlQzD0OTJk/3Bwe12Kzc3V9LRtWLfvn2Ki4uTz+fT888/rzPPPFNS2SFolc8vcTgc/t99Pl/A\n7SOFh4dr0KBBev/997V161aNHj1aUlmNuOaaa3TPPff4b6elpR0VZiTJ5XKpd+/e+uCDD1RQUKBB\ngwYpMjJSPp/vqGV9Pp+8Xu9Rbaqog/v27dPcuXN100036fLLL9eZZ56p9957z79c5eBmGIZ/Z1zF\n7Yo+PJa77rpLPp9PgwcPVv/+/ZWSknJUX1QYM2aMrr32WvXs2VNnn322WrZseewORKPCOTRoMFav\nXq0mTZroyy+/1GeffabPPvtMn376qQoLC3Xo0KGjlnc4HAHBxGaz6cILL9SGDRuUnp4uqWzE56mn\nnlKTJk104MABZWZmyjAMffrppwHrMgxDycnJKigo0B133KH+/ftr06ZNcrvd/uOIj7W9Sy65RP/3\nf/+nzMxMSdLy5csVFxenxMREwgsA1KGrrrpK559/vubOnStJ6tu3r95++23/jGPPP/+87rvvPknS\nxRdf7D/hPi8vTzfccIOSk5PVt29fvfrqq5LK/ni/9dZbA0YQKs5T+fnnn7V792716tVLTqfzmAFD\nkkaPHq3ly5fru+++08CBAyVJffr00QcffOCvU2+88UbAKMqRxo0bp/fee0/vvvuuf4SoXbt2CgoK\n8p/Hk5qaqk8++UR9+vQ5bh3csGGDLr30Uo0ZM0ZdunTRp59+6m+3YRj64IMP5Ha7VVJSopUrV6pf\nv36SpPj4eP+kBxXbO9L69ev95yRJ0vfff++vnUdq0aKFzjvvPD3++OPVHgFCw8cIDRqMt956Szfc\ncEPA3rCoqChNmDDBPzlAZVdccYXGjx+vF1980X/f2Wefrfvuu88/5XOzZs00d+5cuVwuXXfddRo5\ncqRcLpf69+/vf47NZpPNZlPHjh3Vv39/DR48WNHR0Wrbtq06dOigvXv3qk2bNsfcXu/evTVx4kRN\nnDhRhmEoPj5eCxcu9K/zyJljmEkGAGrHsb5PZ86cqaFDh2r9+vW69tprlZqaquuuu042m00tW7bU\nX//6V0nSQw89pIcfflhDhw6VYRi69dZbde655+rBBx/UY489pquvvloej0d9+vQJuITA1q1b9fbb\nb8vn8+m5555TVFSUIiIidM455+jKK6/UG2+8EdCuc889V0FBQRo4cKCCg4MlyX9Zgptuukk2m01R\nUVEBdeVIvXr1UnZ2tmJjY9WhQwdJZYduvfjii3rsscc0f/58lZaWaurUqerVq5ckHbMO5uXl6Z57\n7tHQoUPlcDjUo0cPrVmzRoZhyGazqU2bNho3bpwKCwt1xRVXaNiwYZKkBx98UI888oiio6PVu3dv\nNWvW7Kg23nXXXZo6dapiYmIUFhamXr16ae/evcf9fxo+fLi2bdum3//+91X8D6MxsRkn2A38/fff\n66mnntKSJUu0fft2zZkzR3a7XcHBwXryySfVpEkTvf3221q2bJmcTqduu+22gD/2AAAwE3UKVtCp\nUydt3LjxmOdUovp8Pp9mz56tNm3anPB6c2g8qhyhWbx4sd577z3/uQdz587VzJkz1alTJy1btkyL\nFy/WzTffrCVLlmjFihUqKSnR2LFj1bt3b/+eBAAAzEKdglUwwn7q8vPzNWDAAHXr1s1/OQRAOsE5\nNImJiVqwYIH/WP5nnnlGnTp1klQ2X3tISIh++OEHXXDBBQoKClJkZKQSExO1c+dO81sOAGj0qFOw\niu3btzM6c4oiIyO1efNm/f3vf/fPqAZIJwg0AwcODJjtwuVySZK2bNmipUuX6oYbblB+fr6ioqL8\ny0RERPhPoAMAwEzUKQBAjScF+PDDD/Xyyy9r0aJFiouLU2RkZMBc4QUFBcecOhAAgLpAnQKAxqVG\n0za/++67Wrp0qZYsWaLWrVtLkrp166ZvvvlGbrdbeXl5+vXXX/2zaBwP09ECAMxAnQKAxqdaIzQ2\nm00+n09z585Vy5YtNXXqVEnShRdeqKlTp+r666/XuHHj5PP5NH369BOeaGmz2ZSennfqrcdxuVxR\n9LHJ6GPz0cfmcrmiTryQRVCnrIfPt/noY/PRx+arTq064bTNZuE/31x8wMxHH5uPPjZXQwo0ZuC9\nZy4+3+ajj81HH5uvOrWqRoecAQAAAMDphEADAAAAwLIINAAAAAAsi0ADAAAAwLIINAAAAAAsi0AD\nAAAAwLIINAAaLLfbrfffX1Wj5wwd+odT2uaoUVfL4/Gc0joAAI0Ddap2EGgANFiHDmVo9ep3a/Qc\nm+3Utmk71RUAABoN6lTtcNZ3AwDALK+99oqSknbrn/9crF9/3aXc3BxJ0p133qMzzzxL77+/SqtW\nrZDPV6o+ffpp0qQpcrs9mj37QaWmpigmJkaPPvqEnM5jf1WuX/+lXn11sQxDOvvsjrr33j/7H9u9\ne5cWLHhOpaU+5eRk6557ZqhLl26aO3e2fvttv0pKSnTttWP0hz9cqYULX9R3330rr7dU/fsP0Pjx\nE+ukfwAA9Ys6VTsINAAarIkTJ2n37l9VXFysHj16atiwUdq3b68ef/wRPfbYPL3++mt67bW3FBwc\nrIULX1RRUZGKigo1ZcpUJSQkaNq0Kfrll53q3Pnco9bt9Xr13HPztHjxa4qNjdUbbyxRWlqqJMkw\nDO3Zs0dTp96pM888S2vWfKQPPlitM888S99/v1WLFr0qSdq8eaMk6dNPP9b8+YvUpEkTffjh6jrr\nHwBA/aJO1Q4CDYAGyzAMSWV7obZs+Vpr166RJOXl5erAgd905pntFRwcLEmaMuWPkqTo6BglJCRI\nkuLjm6i4uPiY687JyVZUVJRiY2MlSePGTfA/ZrPZ1LSpS6+++g+FhISosLBAERGRCg8P15/+dLee\neOIxFRQU6A9/GCxJeuihR/XSSy8oM/OQLrqotwk9AQA4HVGnageBBkCDZbfb5fP51LbtGRo4cLCu\nuGKQsrIy9f7776pVq9bauzdJHo9HQUFBevDB+3XnnfdU+9jkuLh45eXlKzc3V9HR0Xruuaf8X/yG\nYej555/SrFlzlJh4hv7xj4VKSTmoQ4cytHPnds2dO08lJSUaOXKIrrhikNat+1SzZ8+VYRiaMGG0\nLr/8D2rePMHEngEAnA6oU7WDQAOgwYqLi5fX61FRUaE+++xTvffeShUUFGjSpCmKjY3V+PETNXXq\nZNlsNvXp009Nm7okBVaK4508abfbdffd9+u+++6U3W7X2Wd3Kh/yL1v+D38YrJkz71dUVLRcrmbK\nzc1RkyZNlZl5SLfddpPsdofGjZugoKAgRUfHaPLkGxQSEqJevS46rYoEAMA81KnaYTMqxrrqWHp6\nXn1sttFwuaLoY5PRx+ajj83lckXVdxNOa7z3zMXn23z0sfnoY/NVp1YxQgMAVdi+/Wf97W8vHHX/\nZZddoWHDRtVDiwAAOIw6xQhNg8UeA/PRx+ajj83FCE3VeO+Zi8+3+ehj89HH5qtOreLCmgAAAAAs\ni0ADAAAAwLIINAAAAAAsi0ADAAAAwLKqFWi+//57TZhw+Oqia9as0d133+2//d1332n06NEaO3as\nFixYUPutBACTbdnyjYYMuULTpk3R1KmTNWXKjfrll52SpBdeeFqpqSn6xz8WatWq5cddR2pqitav\n//KU2zFr1p+Pue6ZM2do2rQpmjz5Bj399BPyer2SpFGjrpbH4zml7VoddQpAQ0edOr4TBprFixfr\nwQcf9Ddizpw5euaZZwKWefjhh/X000/rzTff1A8//KDt27eb01oAMInNZlOPHr00f/5CLViwSDff\nPEWLF78sSfrTn+5W8+YJx714WYVvv/1aP/74/Sm340ilpaV64IG7NW7cBM2fv1CLFr0qp9Opf/xj\n4XGf05hQpwA0BtSp4zvhdWgSExO1YMEC3XfffZKkCy64QFdccYWWLVsmScrPz5fb7VabNm0kSX37\n9tWGDRvUuXNnE5sNoCF7+7Nd+npHmhwOm0pLa2dm+Z6dmmn0gLOO+7hhGKo8i31ubq7i4+MlSVOn\nTtZ99x3eG+Xz+fTkk48pLS1Nhw5lqG/ffpo0aYpef/1VlZSUqGvX7kpIaKHnn39KhmEoJiZGDzzw\nkHbu3KGXXpqv4OBgDR06XMHBwVq58h15vV7ZbDbNnTtPx5pJ/4cfvlPz5gnlV3guc9tt0465bGNE\nnQJQ16hTgeq7Tp0w0AwcOFD79+/3377yyiu1adMm/+38/HxFRkb6b0dERGjfvn213EwAMN+WLd9o\n2rQp8ng82rXrv3r88aclHb1nKS0tVV26dNWQIcNUUlKikSOv0i233KYJE27U3r3J6tPnEk2efIP+\n8peHlZh4ht5//10tXfqaeva8UB6PR4sX/68kacmSf2revOcUEhKqefPmatOmjXK5XEe169ChDLVs\n2SrgvuDgYJN6wXqoUwAaC+rUsZ0w0JxIZGSkCgoK/Lfz8/MVHR19qqsF0IiNHnCWRg84q84vWHbB\nBT00e/ZcSdLevcm69dabtHLlh0ctFx0dre3bt2nLlm8VHh4ht7vsUKfKe8+Sk/foqacelyR5vV61\nadNWktS2baJ/PbGxcZoz52GFhYVp795kdenS7ZjtSkhooc8//yzgvpycbP3004/q0+eSU3rNjQF1\nCkBto04Fqu86VSuBJigoSPv27VPr1q21fv16TZ069YTP4wrV5qOPzUcfm6+u+jg2NlyhoUH+7YWE\ntJXdbpPLFaWgIIfi4iIUERGiqKhQffHFGjVr1kT33HOPkpOTtXr1SrlcUYqJCVdoqFMuV5Tat2+v\n5557RgkJCdqyZYvS09MVGxuusLBguVxRysvL06uvLta///1v+Xw+3XTTTYqMDDmqHZLUv39vzZ//\ntA4e3KNu3brJMAy9/PJzCgsL07BhV8put6lp00hGbY6DOnX6oo/NRx+bjzpV/3Wq2oGm8lCWzWYL\nuD179mzdc889Ki0tVd++fdWt27HTW2V1mWYbo7reY9AY0cfmq8s+zskp0oYNX2nMmHGy2x0qLCzQ\nH/94p3Jz3fJ4SpWVVaCCghKFhpaoW7fz9NZbD+rrr79VUFCQ2rRpq+3b96hZs9Z68cW/qU2b9rrj\njvt0553TVVpaKpvNpgceeEjp6WkqKfH6X9O553bT8OEj5XQ6FBUVo6Sk/YqMjA9YpsKsWXP1zDNP\nqqioSMXFxerSpatuvnmq0tPz5PNJGRn5CgoKqtFrbmh/6FCnrIXvUPPRx+ajTh1mRp2SqlerbEY9\nnVXKB8xcfImZjz42H31sroYWaGob7z1z8fk2H31sPvrYfNWpVVxYEwAAAIBlEWgAAAAAWBaBBgAA\nAIBlEWgAAAAAWBaBBgAAAIBlEWgAAAAAWBaBBgAkbdnyjYYMuULTpk3R1KmTNWXKjfrll52SpBde\neFqpqSn6xz8WatWq5cddR2pqitav//KU2zFr1p8D7jt48ICmTLkx4L5Vq97RK68s8t/etu0nXXrp\nxdqxY9spbR8AcHqiTh0fgQYAVHYhxh49emn+/IVasGCRbr55ihYvflmS9Kc/3a3mzRMCLtR4LN9+\n+7V+/PH7U25HNZcMuLV69SqNGfM/WrHiX6e0fQDA6Yk6dXzOWl8jAJyiFbve19a0H+Ww21Tqq51r\n/57frKtGnDXkuI8bhqHK1xnOzc1VfHy8JGnq1Mm6777De6N8Pp+efPIxpaWl6dChDPWrYGCEAAAg\nAElEQVTt20+TJk3R66+/qpKSEnXt2l0JCS30/PNPyTAMxcTE6IEHHtLOnTv00kvzFRwcrKFDhys4\nOFgrV74jr9crm82muXPnqfrXOj68XGFhobZs+UZLlryt668fo5ycbMXExNasgwAA1Uadqo66q1ME\nGgAot2XLN5o2bYo8Ho927fqvHn/8aUlH741KS0tVly5dNWTIMJWUlGjkyKt0yy23acKEG7V3b7L6\n9LlEkyffoL/85WElJp6h999/V0uXvqaePS+Ux+PR4sX/K0lasuSfmjfvOYWEhGrevLnatGmjXC7X\nMduWlLRb06ZN8d/OyEjXwIGDJUlr136i3//+UgUHB+uyy67Q+++/q/HjJ5rRRQCAekSdOjYCDYDT\nzoizhmjEWUPkckUpPT2vzrZ7wQU9NHv2XEnS3r3JuvXWm7Ry5YdHLRcdHa3t27dpy5ZvFR4eIbfb\nIylw71ly8h499dTjkiSv16s2bdpKktq2TfSvJzY2TnPmPKywsDDt3ZusLl26HbdtZ5xxpubPX+i/\nvWrVcmVmHpJUNozvdDp1991/UklJsdLSUjVu3PU1OCwAAFAT1Kmj1WedItAAwDHExcXreN+zH364\nWpGRUbr33j9r//59Wr16pSTJbrfL5/NJktq2PUMzZz6iZs2a68cfv9ehQxmSDu9Fy8/P1yuvLNKK\nFR/I5/Np+vSpNRjGlyqG8n/9dZcMw6e//e3v/kfuuuuPWr/+S/Xt26+GrxoAYBXUqcMINACgsi/w\niqF8u92hwsICTZ16l0JCQo5a7ne/66XZsx/Uzz//qKCgILVp01YZGRlq3/4svfbaK+rYsbPuuecB\nPfroQyotLZXNZtMDDzyk9PQ0f6GIjIxU167dNXnyDXI6HYqKitGhQxlq0aLlMfdYHX1f2e3Vq1dp\n0KCrAh65+urhWrHiXwQaAGhAqFNV9I1Rs6hVa+pyeK4xqush0MaIPjYffWwulyuqvptwWuO9Zy4+\n3+ajj81HH5uvOrWKaZsBAAAAWBaBBgAAAIBlEWgAAAAAWBaBBgAAAIBlEWgAAAAAWBaBBgAAAIBl\nnTDQfP/995owYYIkKTk5WWPHjtX48eP18MMP+y+u8/bbb2vkyJG67rrr9Pnnn5vaYAAww5Yt32jI\nkCs0bdoUTZ06WVOm3KhfftmpF154WqmpKbWyjeTkJE2bNqXKNsya9eda2VZjQp0C0BhQp46vygtr\nLl68WO+9954iIiIkSY8//rimT5+unj17atasWVq7dq26d++uJUuWaMWKFSopKdHYsWPVu3dvBQcH\n18kLAIDaYLPZ1KNHLz388GOSpK+/3qjFi1/Wk08+W6dtQM1QpwA0FtSp46sy0CQmJmrBggW67777\nJEnbtm1Tz549JUn9+vXT+vXrZbfbdcEFFygoKEhBQUFKTEzUzp071bVrV/NbD6BBSv/XW8r75msl\nO+wqLfXVyjqjevSU69oxx33cMAxVvs5wbm6u4uLiNG3aFN177wNas+ZjHTiwX9nZOcrNzdaIEaP1\n+edrtW/fXv3lL7N17rld9Oabr+uzzz6Rw+FU9+7n67bbpikjI0OPPPKgJCk+vol//evWfaqVK9+R\n1+uVzWbT3LnzVE/XObY06hSA+kCdOr1UGWgGDhyo/fv3+29XfhERERHKy8tTfn6+oqKiAu7Pz883\noakAYK4tW77RtGlT5PF49Ouvv2ju3Hl67bV/SirbKxUSEqqnn35Ur7/+qr76ar2eeOJZffjhaq1d\n+7HCwkK1bt2nevnlf8rhcOgvf7lXGzb8Rxs3rtfAgYM0ZMgwrV27RqtWvSNJ2r9/n+bNe04hIaGa\nN2+uNm3aKJfLVZ8v35KoUwAaE+rUsVUZaI5ktx8+5SY/P1/R0dGKjIxUQUGB//6CggJFR0efcF0u\nV9QJl8GpoY/NRx+bw3X7LZJuqdNtxsaGq3fvi/XMM89Ikvbs2aPrrrtO7dq1U1xchCIiQpSYeJ5c\nrii1aOFSUJBNLleUWrVqpt27dyorK1U9elyghIRYSVLv3hcpLW2/0tIOauLE/5HLFaX+/Xvrgw9W\nyuWKUps2LTRv3hyFh4dr7949uvjiXoqNDVdoaBDvq1NAnbIW+th89LE5qFOn1/uqRoGmc+fO2rx5\ns3r16qUvvvhCF198sbp166Znn31WbrdbJSUl+vXXX9WhQ4cTris9Pe+kG40Tc7mi6GOT0cfmq8s+\nzs4uVHGxx789wwiRYUgeT6mysgpUUFCikJBipafnKS+vSAUFJUpPz1NOTpGKiz2Ki2uub7/dqpSU\nbNntdq1fv1GDBl2lli3b6MsvN6pJk1b6z382y+MpVVLSQT3//AtaseID+Xw+TZ8+Vbm5RYqICGyD\n2U63glQbqFPWwXeo+ehj81GnzFedWlWtQFNxAtCMGTM0c+ZMeTwetW/fXoMGDZLNZtP111+vcePG\nlb/g6ZxoCcBybDabfyjfbneosLBA06bdpf/7v/cDlin/zf972Q+bzjzzLA0YcLluu22SDMOnbt3O\nV79+/dW9+3maPXum1q79RC1atJTNZlNERKS6du2uyZNvkNPpUFRUjA4dyvA/jpqjTgFo6KhTx2cz\n6unsHvYYmIu9Muajj81HH5urIY7Q1Cbee+bi820++th89LH5qlOruLAmAAAAAMsi0AAAAACwLAIN\nAAAAAMsi0AAAAACwLAINAAAAAMsi0AAAAACwLAINAAAAAMuq1oU1AQAATpVhGPKkpapw+zYVugvl\nS2ijsLM6yBEeXt9NA2BhBBoAAGAab3a2CndsU+G2bSrcsU3ezMzABWw2hbRpq7CzOyrs7I4K73C2\nHFFc9BVA9RFoAABArSktLFTRf3eWjcJs/1nuAwf8j9kjIhT5ux4K73yOmrRrrdQtP6rovztVvGe3\nSvYmK/vTTyRJwS1blQecsxV+dkc5Y+Pq6+UAsAACDQAAOGk+j1vFv/7qDzDFSUmSzydJsgUHK/zc\nLvr/9u48So6zMBv9U713T+8zPavWGY1Wb9hItmxJlo2TiI8bvtiAE8znmNhJAF9yCSYEwY0tMDvk\n4pxASAz5+DgxBpwT7BBycuyALVmbbcnG8iJZ24xGGs2+9b5VddX9o3qqu2ef0dR0V8/zO0en9+6a\nV1X19tPv5tq0Ga5Nm2FfuQqCSR2+Gwx5kFu9vvAenZ1InT2D1NmzSHWcQ7a3B5EDLwAArPUNcK5f\nD2f7Brg2bICltg6CIJTl7yWiysNAQ0RERHOmyDIyly6qAebUKaTOn4UiiuqDJhMca1u1AONobYPJ\nap31PU1WG1wbNsK1YaP6GZKE9MUuNdycO4PUubOIHj6E6OFDAABLMAhne76L2oYNsDY0MuAQLWMM\nNERERDQtRVEgDvRrASZ55jTkZEJ73NayQgswzvUbYHY6r/gzBYsFzrZ1cLatA977P9QQdbk734Kj\ntuLEXnkJsVdeAgCYPV61BWfDRrjaN8DW0qK1BBFR9WOgISIiohJSeKwQYE6fgjQ2pj1mqa2F+/ob\n1BCzcRMsPp/u2yOYTHCsWg3HqtUI3PG7UBQF2b5eLdwkz55G/LVXEX/tVQCAyVUDZ3u72oKzfgPs\nq1ZDMJt1304iKg8GGiIiomUul0wgdeY0EqdOIfXOKWT7+7THzG4P3O/eprXCWEOhsnfvEgQB9uYW\n2Jtb4N99u9qKNDRU1IJzBok3TiDxxgn1+XYHnOvWFQLOmrVz6gpHRMbAQENERLTMyNks0h3nkTh1\nEsl3TiFzsQtQFAD5gfxXXQPXpk3qQP4VKyu++5YgCLDV18NWXw/fjp0AAHF0BKlzZ9WAc+YMkiff\nRvLk2xgBIFitcLS2aQHH0doGk91e3j+CiBaMgYaIiKjKKbKMdFcXUqdPIXHqJNLnz0GRJPVBsxnO\nde1wblQDjLO1DYLF+F8PrMFaWG/cDu+N2wEAUjSqTjBw9myhJefMaYwCgNkMx5q1cLavV6eL5mKf\nRIZi/DMWERERlVDHmPTlF7Q8idSZ05BTKe1x24qVqNm0Gc5Nm+Favx4mx5UP5K90Fq8Xnhu2wnPD\nVgBALpFA6vw5LdykL3Qi3XEeY8/+Fxf7JDIYBhoiIqIqII6OamvBJE+/g1w4rD1mDYXg2boNro2b\n4dy4CRavt4xbWhnMNTVwX3sd3NdeBwCQ02mkOs6rrThnuNgnkZEw0BARERlQLh5H8szpfIg5BXGg\nX3vM7PHAs+1GuDYWBvLPRFEUyIqMnJJT/8nTXC+6LU96TEZOnup58pTv4+uvgQ9+NLub0OAKwWwq\n7yxkJocDNVuuQs2WqwDkF/u8cEEbg8PFPokq17wDTTabxec//3lcvnwZbrcbjzzyCABg7969MJlM\naG9vx759+3hQExFRWSxVPaV+UVe/xMvjX+iVXNGXenna6/JMz9MeyweC/HU5m4Hj8jDcXQPwXByC\nayCC8b9AspoxtiaIkRYvhlo8CAcdyEGCLL+OXOdryHWUvufEgCEr8hWW+pUxC2Y0uEJodjeixd2E\n5hr10m/3le37hMlqgys/aQD+r/xin5cuFsbfcLFPooox70Dzr//6r6ipqcFTTz2FCxcu4NFHH4XN\nZsNDDz2ErVu3Yt++fXj++edxxx136LG9REREM1qMeurzv/4GYqnE1C0P+dsKFF3/DkFW0DAqYWV/\nFiv7s2gaFmHJ546cCeipt6K7wYbuRisGaq2QTQKAJIAkhKgAs8kMs2CCWTCr//K3rSY7TCWPmWAq\netw88bpghtlUuG4quj7xsZLrgin/Ofn3MpW+xuE2452eC+iN96En0Y++eD96E/14deCEVgZOiwPN\nNY1ozoccNfA0wmlZ+jE/gsUCZ2sbnK1twJ7ixT7PInX29PSLfa7fANf6jVzsk0hH8w40HR0d2LVr\nFwBg7dq16OjogKIo2LpVHWS3a9cuHDlyhIGGiIjKYjHqqWg6hkxOhFkwwyKYYbfapv6Srn35n/il\nfbrn5cPDhOeOX1qHwrB2Xoa54xJMnZchZDLaNgktTTCvXwfrhnbY2tpQ73Di+uLPK/mMyv/iHAp5\n0Ghq0W7LioyR1Bh6E33ojfejJ9GP3ngfOiMX0RHpKnltwO4vac1pdjeiwRWCxbR0PelLF/v8HW0i\nhvFwM3mxT5c2ixoX+yRaXPM+8jdt2oT9+/fjjjvuwIkTJzA4OIja2lrtcZfLhVgstqgbSURENFeL\nUU/9w+9/FUND+tdl4shIfgzMCXUgfySiPWYN1cO1WR0D49qwqepn2TIJJoRctQi5anFt6Crt/mxO\nRH9yAL3xfvVfPuicHDmNkyOnS17f6KpHs7tRCznNNU0IOvxL0vVLXeyzGfbm5sJin8ND6vibs2eQ\nOsfFPon0Mu9A84EPfAAdHR245557cP3112PLli0YGhrSHk8kEvDOYfaUUKi6T8yVgGWsP5ax/ljG\nNF+VXE+J0Rgib72N8BtvIvLmm0j3FQbyW/1+BHbthP/aq+G75mo46usX/fMrzVzLuAVB3IBNJffF\nMnFcivTiUrhHvYyol72J/pLnOa0OrPI2Y6W/Bat9LVjlb8YqXwtqbEuwzky9F9jcBuB/AAAywyOI\nnjyFyMlTiJ48NWmxT8+G9fBt2Qzvls3wbFgPs8NxxZvAc6j+WMblN+9A89Zbb2H79u34/Oc/j7fe\negu9vb2oq6vDsWPHsG3bNhw8eBDbt2+f9X2W4pev5SwU8rCMdcYy1h/LWF/VWglXUj0lZzJInTur\nTqX8zjvIdF8CFHXsjcnhQM2116ktMJs2w9bcorUkxADEqnzfX4zju15oQn2gCe/Oz6AsKzJG02Po\n0Vpz1O5r50a7cGaks+S1frtP7bZW06S16jTU1MOqa7c1G7D5Ovg2XwffhyYv9hk9eQrRt0+qTzWb\n4Vi9RlsLZyGLffIcqj+Wsf7mUlcJiqLMa1Tj2NgYHnroIaRSKXi9Xnz1q19FIpHAww8/DFEU0dbW\nhq985SuzNu/yP19fPMD0xzLWH8tYX9UaaMpZTymShHTXBW0q5VTHeSCXA6AOKne0rdMCjGPN2mU9\nhmIpj28xJ6I/OYTeeF++y5radS2ciZQ8zySYUO8KoUXrsqaO0wk6AkvSbS2XTCB17pzWRS3d1QXI\n+ZkgFrDYJ8+h+mMZ60+XQLNY+J+vLx5g+mMZ649lrK9qDTSLZS77nqIoyPZc1gJM8swZKJm0+mD+\nC6hr8xa4Nm2Gc107THa7zlttHJVwfCfEZH4Cgj5tjE5foh/pXKbkeQ6zHU3jIcfdmA88Taix6ttt\nrWSxz7Nnke7sgCJJ2uOzLfZZCWVc7VjG+ptLXcWFNYmIiOZBHB4qBJh33kEuFtUeszY0wLXpZrg2\nbVIH8rvdZdxSmk2N1YX2QCvaA63afYqiYDQ9ht5Ef77rmtqqczHWjQvRiyWv99m8RSFH7brW6KqH\n1bw4g/tnXOzz7Bmkzs+w2Of6DVDquP/R8sAWmiqgyDKksVFk+/qQ7e9Htr8PNkFGrsYLa10drLV1\nsNTVwRoIQrAwwy4W/iqjP5axvthCM7PxfU+KRZE6fTo/DuYUxKIJBsw+n9aFzLVpM6zB2unejiYw\n2vEtyhIGk0PoifeVtOpM2W3NWafNsja+dk7QEVj06bSnWuxTTqW0x221QdjWtMLR2gZn2zrYV6+G\nyWpb1G1Y7oy2HxsRu5xVGTmdRnagXwstYn8fsv19yA4MQMlmZ38DQYAlECgEnLoQrLV1hdATCDDw\nzANPYvpjGeuLgWZ64RNvoPfIMSTfOYlMd7d2v8nphHPDxsJA/qZmrga/QNVyfCfFJHoTA9oCoeNd\n19K5dMnz7Gab2m2taIHQ5pomuG01i7YtExf7zFzogDgWLjzBbFbXzmltg6NNXSTUUlvHffgKVMt+\nXMkYaAxIURRIY2OlgaWvH9mBPkijo5OeL9hssDU2qf+ammBraIStqQl1zbUYOHcJ4vAwxJFhSPlL\ncXgY0tioNstO6ZsJsASCpa06DDzT4klMfyxjfTHQTO/I//wAgPxA/nXthYH8q9cs64H8i6maj29F\nUTCWCWvhZrw1pz85CFmRS57rs3nQXLRAaLO7EU2uhkXptlZX50bfmS6kOs4j3dGBdGcH0pcuahNV\nAGoro7N1nRZyHKvXcKzXPFTzflwpGGgqmJzNQixqbVGDSx+yA/1QMplJz7cEgrA1NsHaqAYWNcQ0\nwuIPQDBNbsKe6QBTJAni2GhJyCkOPdLY2OyBp64OlqKwY62rgyUQXFYVPU9i+mMZ64uBZnq9//Gf\nyPrq1IH8NnbR0cNyPL4lWcJAcqhkgdCeeD/GMuGS5wkQUO+qKwo5auCpcwbn1W1tqjKWs1lkLl1U\nQ06nGnKksbHCE8xm2FeszHdTa4OjbR2sdSG24kxjOe7HS42BpswURUEuEikElqLQIo2MTAoNgtUK\na0NjobWlMX+9oRGmeS6udSUH2IyBZ3gYUniawGMyaV3alkPg4UlMfyxjfTHQzIz7nr54fBekpBR6\n4wPaujk9+TV0UlJptzWbyYqmolnWxgOPxzb14P+5lrE4OoJ0ZwdS+VaczMWuktnUzB6v1kXN0doG\nx5q18/5eUq24H+snJ+eQzmWwprlh1ucy0CwCWRQhDg6UBJZsXx/Egf6SwXnjzD5/Sfew8eBiCdZO\n2dqyEHoeYIokQRwdhTQyDHF4qNCVbWRk9sATDKoBZ2LoqatTW5sMFHh4EtMfy1hfDDQz476nLx7f\nM1MUBeFMRJ2EoGjtnP7EIHJKruS5Hpu7ZIHQZncjmmoa0NJYu6AylkURmUsXS0KONDpSeIIgqK04\nbevUkNPWBmt9w7JsxeF+vHCKoiAuJjCcGsVIelS9TI1iOD2KkdQIxjIRyIqMf/3Df5z1vRho5khR\nFORiMa2lRewbb3Xphzg8NLm1xWKBtb6hpHuYrbEJ1obGea/0uxDlPMCmCzxq6BmGFA4vIPCEYPH7\nKyrw8CSmP5axvhhoZsZ9T188vhcmJ+fUbmtayFFbdUbSYyXPEyCgriYIn9WLoCOAoN2PgMOPgCOA\noMOPgN0Ph2XuY2Wk8Fg+3JxHurMT6a4LUERRe9zkdmstOM62dXCsXQuTw7lof3el4n48s2wui5H0\nmBpUUqMYTo9gJDWG4dQIRtKjyOSmntTKZ/Og1lmLBlcIn951/6yfw0AzgSJJEIcGC60tRWNc5GRy\n0vPNHq8aVvLBxdrYCFtjM6x1dYvW2rIQlXyAyaIIaXR00mQF4sgsgcdshjUQnDxZQfGkBUtY5pVc\nxtWCZawvBpqZcd/TF4/vxZWS0ujT1s5Rg85oZgxjqQgUTP1Vz2VxIuDwI+jwI+gIIGBXr4+HHq/N\nM+2YHUWSkOm+hFRnhzbhgDhcmNIcgqAu/Nm2ThuPY21oLOt3Iz0s9/1YVmREMlEMp0YwnB7DSGqk\npMUlmp26bOxmG+qctahzBFHrVP/VOYKocwYRdARhK5oUg2NoZpCLx/Pdw8aDS761ZWgQkEtnIIHZ\nDFuoHtaSbmLqP3PN4k23uJiMfIBNCjzFrTwjw8iFw1O/cKrAU9TKM90ECgtl5DI2CpaxvhhoZsZ9\nT188vvUXCnnQNzCGSCaK0fQYRtNhjGXC6mU6jNFMGKPpMWSn+ZXcJJgQsPvUlh17vmUnH36mauWR\nIuGSbmrprgsly0qYXDVwtLZqIcextnVJeq3oaTnsx0kxieFJXcLy/9Jjk7pAAuq+E7T71aDiDKI2\nH1bU4FKLGqtrzl0U51JXlWUO3qOXXkUiLsJpdsBhscNpccBhccBpdsBisixaH0wll4M4PFQSWNTp\nkPuRi0/e+Uw1NXCsbS0JLLbGJrW1hdMVLxmT1QpbQwNsDVMPAlMDz8iUU1KLI8NInX4Hk0cuQQ08\nweDkyQp0CjxERETlZjFZtF/Ap6IoClJSCqNpNdyMZtSwM5YOawGoI9wFBRemfH1xK0/AHkAw6Eeg\neT2Cv3Mj6qwe2AcjyF7o1KaOTr79FpJvv6W+WBBga2qCo3V8LM462JqaWBcvMUmWMJoeU7uEFY9n\nyV+mpCm/VcFtrcEKT3O+ZaUWtc4A6hy1qHUGEbD7YDYt3TCBsnxL/7uX/ve0j5kFsxpwzIWg47DY\n4TA74bTYteAzfr/T4oA9o8A+GoNlJAJhaATKwBDEgQFkBwdK5loHAJhMsIZCcLS1FWYRG29t8fDX\nSiNQA08jbA2NUz4ui1m1hWfC7GzjoWdugSc0KfRY/H6eZIloWZEVBZIkQ8opkHJy0T+l5FLMycjl\nZIiSgpwsQ5Rk5GRFvdQeVyAWvd7rcaDWbcWqBg+aal0w8/xaFoIgwGV1wWV1YYWnecrnSLI0YyvP\nUGoEPfG+KV+rtfJc70dg+/UIyQ7UDaXh7YvAdnkA4qUeZHt7ET18UH2+0wnH2tbChANrW2F2Tz2L\nG82NoiiIZmOlA++LxrOEM1N3S7SaLKh11qLNtxq1zlrUOQLqpTOIWkcADkvlzHRXli5nL154Gf2j\nI0jlMkhLaaSlNFJSGulcRr3Ubqe1wUKCrMCTlBGMSAhEcwjE1MtgVIIrPflPyFgFRHw2JAJOJAMu\nZGu9kOp8UGoDcNhcJYHIYXYUWomKwpTNbJvXfO+VZDk0gS6ULGYhjYwWzc5WGnhykRm6tAVrtYDT\ncMM1UDZcwzUqdMT9WF/scjazpdr3ZLkQFsScogUAKZcPE7Kcv5wtXEy4LimTXqsFj6LPGQ8aE++X\nJAXyEn1FsFpMWBGqwaoGT/6fGytDbtislTMRjBEt1Tl0Lq08kUx0yi/NgqygJW7B2rAJTSMSAv0J\nOMbiJc+xNjaqi3/mQ46tpaVifmCslHoqLaUxkm9lGZk0nmUMoixOeo0AAX67b3KXsPxtr81TETPX\nGXIMTS6VgljUPSzT14tMfx+kwUGgaE50AFAEQPJ7kAm4kQy6EPc7EPFaMeo1I2rL5QNTBqlcGpIs\nTfl5MxEg5FuHxgPPhOslrUWOSS1L42HJuojd6OaqUg4wI9ICT8mU1EVjeCIR7bkmVw18t+yAb/dt\n07YY0cJxP9YXA830fnt6ED0DkaIv+/nWhnzrw7QhYqrrxWFkPGAUtXAsVWiYyGwSYLGYYNEuTeql\nWYDFrF5azab89eL7J1y3CIXXjr/XpOdPfr3DZcdbZwdxcSCGSwMx9AwlkJMLZSEIQHNtDVY1uEuC\nTo3DOsNfRcUq6Ryak3MIZyJa6JlpLI8jI6NxWETjiIimYRGNwxJsUmHfyNkskFoaYFm7GjVt7ajd\ncDVq/HVl+buWqoxzcg5jmUh+DMvIpPEscTEx5eucFmdJYCm+HnAEYDVV/pCKig00/8/fvoDVDhHt\njiyaEYcjNqIFmKkGfAt2R+lCk/mFJ6319TBZ5/bruChL+dagDFK5lHo53hqUU+8vXC9tNSq+Livy\n7B82gVkwq61B5qKgM0s3uqlajebTF7GSTmLVRs5mIQ4NIvf26+h77tfIRaMAANfmLfDtvh3ua6+r\nqOmljYz7sb4YaKb3+5/55aK8z7QhYNpwMPPzrGYTzGYTrGYhfzkhSJQEipnfq9y/vE48vqWcjJ6h\nBC4NxHBpII6LgzF0D8aRyZZ2Ha/zObRws6rBg9UNHvjdtrL/PZXISOfQ4laesUwYI+mxQitPcgzK\n4CDcvWNq0BkWURst3S8iHgvCjR6kW+qgrGqBc+UqBFxBbfICn92rS6+bxSpjRVGQEJP5bmCjk8az\njGXCU34HNQtm1DoCJTOFjbey1DmCcFmNPekCUMGBZv+dfwTbFE1fktsPW2MjPKtWwN5UCC5mn78i\nTlSKoiAriyVd4lLjIank9nhQmqpLXWraObdnYzNZS1p+Zmo1qg8GICUBh8UBl8UBh8UJl8UBq5m/\nbC2WUMiDwb4xxH/7GsIHXkDq7BkAgCUQgG/Xbvh23gqL31/mrTQ2I1XGRpFLpRA/fgyRI4dww3e+\nWe7NqVj//cpFhCOpSS0OhUBhglm7XXS/xQSzSdAuK6HuqlRzOb5lRcHgWAqXBpQhEcoAACAASURB\nVGL5lpw4Lg3EEEuWfofwuKxayFmdDzmhgBOmZV7+1XYOLW7lCYf7kezsgNLVDVvPELz9EdiyhS/8\nohkYqLWir86K/jorBkI22H3BoqmpS2drCzr8CxoTMp8yzuZEjKbHx68UZgpTZxAbmfb7odfmybes\n1KLOGdDGs9Q5a3ULapWkYgPNm3v/XyStNRiz+3Ap58I7cSsuSk5I+WYvp92C9hU+rF/px/qVfqxp\n9MBirp7/LFmRkdHGC00OQxPHExXCUark+eICutFZBDOcFiecFkfRpWPC7envd1jsVX/gzNXEk1im\npwfhAy8g9tIRyOk0YDbDfd274L/tPXBu2MgvNgtQbZVxuSiKgtS5s4gePoTYq8fUaVQFAbf8+7+V\ne9MqGvc9fS30+FYUBeF4Vuuqdmkgjov9MYxE0yXPc9jMWFnvLgk6zXU1VfV9YjbL6RyqyDKy/X2I\nnnsH0XOnIV7ogjAwjOKaN+axoidoRl+dBf11VgwHLJBNhWc4Lc6SgBNw+BG0+xF0BqZt5Sku48Ka\nLIXAMt7KMpIaQWSGNVlqJ8wUNj6epdYRgM28vMfqVmygAUorCkVRMBRJ4+ylMM52h3H2chiDY4V5\nqGwWE9pafGhf4cOGlX60tvhg50BBSLKkdpWbIgyZHQqGwmGkpDRSUip/mZ5wO7WgUDTeIjSfIFR8\nWY4xRXqYrqKQ02lEX3kJ4f0vIHu5GwBga2yCb/ft8N58M8yuyly7qBItp8pYD1J4DNGjRxA5cgji\nwAAAwFoXgnfHTnhvvgXNG9aUdwMrHPc9fS328R1PiegeiOHiQByXBtWg0zeSKFmn2WIW0FxXo3VV\nW9Xgxsp6Nxy2yh9HsBDL/RyaSyaR7rqAdMd5bW0cOVkYa6JYLUg3BhFp9KC/zoaLQQW9pviM6/L4\n7T4t8PjtPghWGd1j/RhJj2I0NQZpmjVZAnZfvmUlOGltFre1piq+F+nFMIFmKmOxDM5dDuNMdxjn\nusO4PFTYAc0mAWuaPGoLzgo/2lf44OIgwRJzPYmNjy0qDj1JKYV0yWV6wu3S+6dbgXg6FsGc7wrn\nnHDpmOF+tctcJbUSzVbGiqIg3XEe4f0vIP7acSiSBMFmg/em7fDtvh2OVauXcGuNablXxguhSBLi\nb76B6OGDSLz1JqAoEKxWuG94N3w7dsG5foM2OxDH0MyM+56+luL4zog5XB6Ka13VLg3E0D2YgJQr\ndE0SADQEXVorzniLjsdl/F/FeQ4tpSgKxIH+fLg5j3RnBzKXL6M49Vpqa2FdswbSykYkmgMYCdox\nKkVnnbHNba2ZsHhkIbgE7P4lXZOl2hg60EwUT4k4dznfgtMdxsX+uDYzjABgZb1b66LWvtIPX43x\nT0RXYqlOYmr3uawWdCa3CE11X3W0Es2njKVYFNHDhxB+cT+k4WH1b2htg3/37XBv3TrnyS2WG1bG\nc5fp7UH08CFEXzqCXEwtM/uatfDt2AnPthunbBlkoJkZ9z19lev4zsky+kaSWne1S/lWnVSmtC4K\neOxaK854yKn1Ogz1SzrPobOT0ymku7ryC3+eR7qzs2TxdcFigX31Gm3hT0drG0x+H8KZCMYyETSH\ngjCl7BW1Jku10SXQiKKIvXv3oqenB2azGV/+8pdhNpuxd+9emEwmtLe3Y9++fbMe8Fd6gKWzEjp6\nojiTDzidvdGSX1wag658wFHH4tT5nFf0eUZjpJOY8VqJ1Oc01PvmXcaKLCPx9luIHHhB+/Xc5Har\nUz/fejts9fXzer9qZ6T9uBxyqRRix19B9PAhpDs7AAAmtxvem26G75adsK9cOePrqzXQVEo9RTOr\npON7vOv7pf6Y1l3t4kAMkXhp16Mah2XSDGuNQRdMpsoMOZVUxkahKArEwUGkOzuQ6jyPdEcHMpe7\nAbnwHdMSCMLR2gpn2zoEVjUjns5BsFphstkgWK0QrDYINitM1vxtmxWCxWqoMFxJdAk0v/nNb/Cf\n//mf+Lu/+zscPXoUP/vZzyBJEu6//35s3boV+/btw86dO3HHHXfM+D6LfYCJkowLfVGtBedcT6Rk\nqsdar11rwVm/0o/GoKuqd6zldBKbvZVo9paiqRacmo3f4UWtPYiQsw4hVy1CzlqEXHUIOWvhtMwe\noMXhIYRfPIDo4YPaL+quLVfBf9t7UHPNtRWzaFg5Laf9eK4KA/wPIvbqcW2Av2vLVfDt2IWaa6+D\nyTq3LrjVGmgqtZ6iUkY4viOJrNZV7WK+Nad4jC8A2KwmrAy5S4LOilANrJbydzEyQhkbgZzJqGNx\nOjvyLTkdyMWi836fqcOODab8pWC1qtdnesxm0x432QrPNeXfV7CWvqYavkvMpa6a9yi4tWvXIpfL\nQVEUxGIxWK1WvPHGG9i6dSsAYNeuXThy5MisFcVis1pMWlgB1Cbl7sE4zl7Kj8O5HMFLJwfw0kl1\nYKzHZS0EnBV+rKx3V+wvLDQzk2DSupgFsLBpkiVZmlcQSkpJRMUoOiMX0RHpmvR+bmtNadBx1qE+\nH3bG54S31oUQ+sCHUPv+P0D8t68ivP8FJE++jeTJt2EJBvNTP++Cxcepn6logP/hQxAHJw/wtwZr\ny7yFlaNS6ykyHl+NDVe31uLq1sLxlcpI6B6Ml8yy1tUfQ0dv4Quu2SSgqdalLQi6usGNlfUeuBzV\nOflAtTPZ7XBt2AjXho0A1B+WpOFhpC90wilIiI5GoYgilGwWiihCHr8uiZDz9ynZrHq/dj0LJZnQ\nbutFsFjyQSofkCxFAUlrURoPT4XbM7U2qeFpQsiyFAUwy9Lv5/P+RJfLhZ6eHuzZswfhcBj/9E//\nhOPHj5c8HouV/9cAs8mENY1erGn04ne3rYKsKOgbSWotOGe7w3jtzBBeOzMEAHDazWhf4a/aqaJp\nZhaTBR6bGx6be86vCYU86BsYw0h6DEPJYQylRvL/hjGcHMHFWDcuRC9Oel2NxYW6oqATctaifmMz\nQu/6S1gGRhA5sB/Rl1/CyL8/jZFf/RKe62+Ab/ft6mDuKm5VpMmmG+DvuWn7pAH+VGCUeoqMyWm3\nlPyACqi9RHqG41pXNXXygTguDyVw9O1+7Xkhv6NohjU16Pjc9nL8GXQFBEGANRSCNRRCKOSB5Qpb\nwRRFgSKNhx1RDTv564qYLYQicTwgTbw9/jxRe6xwvfR2Lh7PB6tsSTe6RWUyTWo1mqlFaabHzDU1\nCP3Orlk/ct6B5sc//jF27tyJT3/60+jv78cf//EfQ5IKA+kSiQS8Xu+s71OOrg4N9V5ct6kRgLrz\nDIwmcbJzBCc7R/B25wje7FD/AYDNasbG1QFsaa3FltZabFgdMNy0jtXanaSSNDUE0IQAgNZJj0ly\nDsPJUfTHhtAfH0R/fAj98SEMxIbQE+/DxWj3pNe4rE40XhVC87t2oLUjCv/xDsSOH0Ps+DE4V65A\n03v3IHTbrbC4jL/y71wtx/04eakbA795HkMHXoQYUX/1dbevQ/17bkdo5w5Y3Jz6eyZGrqeWm2oq\n4+YmH7ZeXbidkxX0DsXR2RPR/nX0REp+TAXUyQdaW3xobfGhrUVdmqKxdvG6xVdTGVcqo5axLEmQ\ns2ookrOZ/OX47an+FT2WyUDOByo5k79PLL4uFq5n0sjFoxAzWSjS/CeCWqtHoPH5fLDkm5K8Xi8k\nScLmzZtx7NgxbNu2DQcPHsT27dtnfZ9K6NNpBnDNmgCuWRMAbl+HcDxT0oLz5vlhvHlenZHKbBKw\nptFTmEmtwqeKZr9Z/c2ljM1woMWyEi3+lSjuDScrMsbSYa1FZyhZaN3pjvSiU76Ewx4At5nQPOTH\nNedSWNd9GZ0/+Gec+z//B9Gr1gDbb0Bw7QaEXHXw2bxV2XqznPbjwgD/g0h3dgJQB/j77/hd+Hbs\nhH2FOsB/LCUDqcUpE6NWwrOppnqqmi2H49thAjav9GHzSh8A9cfUsVgm34pTmEr6tdODeO30oPY6\np92MlfWekqmkm2pd8+45shzKuNyqo4xNgOAE7E5gigZDAep35sUYFabI8uTWpqyotRpNbFHCHJfp\nmPekAMlkEl/4whcwNDQEURRx3333YcuWLXj44YchiiLa2trwla98pSpmj4mnRJy/HMHZbnUczsX+\nWMlU0SvyU0VvqMCpoqvjAKtsepXx+ErDE4NOdKQf9W9fxqazcXiTajNxb50Vb7Y70bXGjVp3nTYp\nQfG4nalWNjaKat+PtQH+hw4i9tqVDfBfiGoNNMupnjKyaj++5yOeEkvG5FwaiKF/JFkyf6fFbMKK\nUI3WVW1Vgwcr6t0zLjTOMtYfy1h/VbUOTSVIZyV09EZx9lJ+qui+KESp0P+wIejChvw00eWeKpoH\nmP7KUcaKoiCcDmPoty8je/glWM9dggAg4zDjVJsLJ9psiLpLKzeLyYI6LeTUapMV1DvrEHD4Kzrs\nVOt+LI6NIfbShAH+oRC8tyztAP9qDTSLpRr3vUpSrcf3Yslkc+geipfMstYzFIeUK3xtEwR1mYri\nBUFXNXjgdqo/hLCM9ccy1h8Djc5ESUZXf1RrwTl/OYL0hKmi2/PhZsMSTxXNA0x/lVDG2cFBRF7c\nj8iRQ5DjcbV227AOsXevR+8KN4bSo1oLT0pKT3q9WTCjzhnUgo46WYHaylPrCJR9ZeNKKOPFokgS\n4m+cQPTIoZIB/u4b3r0oA/xzcq4wE18uhZSYRiqXRkpMqZdTzNb3ld/9zCL+hdWnWva9SlVNx/dS\nkXKFRUHHu611D8aQyuRKnlfrtWNVgwdb2urQ6HdgbZMXTruxxgEbBfdj/THQLLGcLOPyYEJb7PNs\ndxjxVGF9E4/LivVFM6npOVU0DzD9VVIZy2IW8VePI3xgP9Id5wEAltpa+G+9Dd4du2D2eJAQk2o3\nttRI6axsyWEkpOSk9zQJJtQ6AkXTTxe6s9U6g7CY9K8cK6mMFyrT04Po4YOIvnxUW2/IvmYtfDt3\nwbP1RphdLiiKgkwuM+3CspOnEZ88rXh2AWsp/esf/uNi/7lVxej7XqWrhuO7EsiKguFwSpthbTzo\nRBOFqYAFAC2hGrS1+NDa7MW6Fh8agi6YqnDs5VLjfqw/BpoyUyZMFX2mO4yxWEZ73Gk3Y12LH+tX\n+rBhZQBrmhZvqmgeYPqr1DJOX7qYn/r5qDomw2yG591b4d99Oxzr2qdsJUyISQxPDDr5MTwxMT7p\n+QIEBB2BksVEx6/XOYKwmhdn3EellvFEoiyVrlkUj0D87Rswv/omrJfVgb6S04bBTU3o3hjCiN9S\nFFrSSEtpKJjfqdgkmOCyOLU1mBwWJ1wWBxwWB1wWZ8ml0+KY9JjT7EBjA9c4mokR9j0jM8rxbVTh\neAYjCRGvvzOAjp4ILvRHkRUL3eRrHBasbfZiXbMPbS0+rG3ycp2cBeB+rD8GmgqjKApGIumSFpyB\nohWHrRYT2pq9WgtOW7MPdtvCuvzwANNfpZdxLplE9KUjiBzYj2xfLwDA1rIC/t23w7t9O0yOuY3x\nSkmpfEtOadAZTg0jkp389wsQ4Lf7JgWd8es289wnz1iKMpYVWWsFUcPF3BZXLe7WJckSoChoGRSx\nuTON9ktpWHOALACXGm042eZAZ4sdsrkQJh1mhxZG1H/Oaa4XwkrxY1aT9Yq7sHIMzcwq+fiuBpV+\nDq0GxWU83ouko1edPrqjJ4rBcOE7iACgua4GbS1etOZDTlMtW3Fmw/1Yfww0BhCJZ3D2cgRnL6kt\nOD1Dce132vGpotuLpoqumeNU0TzA9GeUMlYUBakzpxE+sB/x118DcjkIdge822+Gf/dt2nTAC5GW\nMmrLTsn002orTzgTmfI1fruvZIKCwridIBwWR8lzZytjRVGQlcUZu2Kl8i0gE1tExm+nc5lp3386\nFpNFCxuBtBmt5yJYeWYIzvyXA9HvRupdGyHfcDUctXVFAcWZDyj2ipiMgYFmZkY4vo3MKOdQI5ut\njKPJLDp7olrIudAXQ0YsjMdx2i1obfaiLd9NbW2zd87fQ5YL7sf6Y6AxoERaxLn8VNFn81NF5+QJ\nU0Wv8GP9Kj/Wr/BNu8IwDzD9GbGMpXAYkUMvInLwRUhjowAAZ/t6+HbfDvf1NyzqFMHZXBbDqdEp\nx+2MpcNTdrHy2Nyod9apQccZhMNlxUg0MuMAd1mZ30rHAoSilo/J3bPGu2YVB5CJz7HIUAf4Hz6I\nxNtvqQP8bTZ1gP8tO694gL+esmIO4UQW0XgW29+1otybU9GMdnwbjRHPoUYz3zLOyTJ6hhLo6I3m\nW3EiJT1JAKCp1oW2Fh/amr1oa/Ghua5mWbficD/WHwNNFchkc+joLQScjt6pp4puX6HOpFbrc0AQ\nBB5gS8DIZazkcki8eQLhA/uRPPk2AMDs8cK3cxd8t+6GtbZO188XcyJGxmdgmzBBwUh6bMbxJDaT\ndVKLx0zdtsaDyPj9drN9wV215jLAvxwURUEiLSEczyASzyKSGL/MFt2n3l88G9Kv/r//WZbtNQqj\nHt9GYeRzqFEsRhnHkll09o634kTR2RdFJlvcimPG2iYv2vLd1Fqbvdq00csB92P9MdBUIVGScbE/\nhjPdYzjbHcG5y+GSqaKDXjvWr/Rjc2sdFCkHl8MCp90Cl8MCl90Cl8MKp90Mc4X+emwk1XISyw70\nI/LiAUQOH4KcTACCgJqrr4H/tvfAteWqJW9pkGQJI+kxjKRGURtwI5NQ4DQXAslSTyWdSyYRO34M\n0cMHkb7QCQAwuz3wbL8Zvlt2XFGXvdlIObkQRuIZLaBEE1mEx4NLIotIPKu15E7H47LCV2ODz23P\nX9rw4Ifepdu2V4NqOL4rWbWcQyuZHmUsywp6hkvH4vSPls6U2Rh0oa2lEHJa6mp0m9W13Lgf64+B\nZhmQZQXdg/GSmdSKp4qejt1q1kJOceBxjgef6a47rHDZzbBayrs+SSWotpOYnM0idvwYIgde0L64\nW+tC8N16G3w7dsLsWfrxFuUqY0VRkDp7BtHDhxB77bg6W5wgwLXlavh27oT72ndBsCxsNiBFUZDK\n5LRWlHAig2g8i3BRaBkPMbMdyxazAF+NHT63TQsr/nxYKb7fW2ObcgZFjqGZWTUd35Wo2s6hlWip\nyjieEtVWnJ4IOnoj6OyNlvzYareZ0drk1UJOa7MXHtfcJ4ipZNyP9cdAswyNTxWdlBQMDMWQTEtI\nZiSkMpJ2PZkWkcrkkMyI2n3z3QssZtPUgaikNSh/e4rH7Vbzki0yqpdqPomlu7oQPvACYsdehpLN\nQrBY4H73Vvhvew8crW1Vu0CsODaG6NHDiB4+BHFInW7ZGgrBe8tOeG/eAWswOO1rc7KMWFLUunyF\ni1tWirp/RRNZZKWZx/247BYtjPjd9kkBxee2w++2wWW3XNH/BQPNzKr1+K4U1XwOrRTlKmNZVtA7\nksgHHDXo9I2UtuI0BJwlY3FaQjWG7D3C/Vh/DDTL2HwOMEVRkBFzRYEnH4CKQlBxIEqlRSQzuaLr\nEqTcfNfQEPKBxwyX3Tpra9HEcOSwW8o+CHE5nMRyiQSiLx1BeP8LEAf6AQD2lSvh2/0eeG+8CSaH\nY5Z3uDJLUcaKJCH+xuuIHj405QB/05p1iKZEhOPZfFevQitKcetKLJmd8YcBkyDAW2PVunz5S0KK\nejneurJULaAMNDOr9uO73JbDObTcKqmME2kRF3qjOJ8POZ29UaQykva43WrG2iZPPuT40NrihdcA\nrTiVVMbVioFmGVvyX7alokCUkZCaYzgaby0qniZyLgQADrsFLrsZzukC0VStRUWPXekipsvpJKYo\nClKn30H4wAuIv/5bQJZhcjrh3X4zfLtvh725RZfP1auMZUXBWEcXIocOQnz9GIRkAgAQDzbjcssW\nnPW3YiQjIBLPlHSbmIrdZlYDyoTxKb4atRVl/D63y1r2ED4RA83MlsvxXS7L6RxaLpVcxnK+R0lH\nTwSd+QkHeocTJVPC1Pud2ro46/KtOIu1APliqeQyrhYMNMuY0Q4wKSdrwUcLPFMGoNKAlMqorUXF\nv/LMlc1qKhoXNPfucuPXm5t8GB6O61AalU0cG0Pk4AFEDr2IXDgMAHCu3wD/be+B+13XL3hsyVTm\nux+LklwyUL4wkF5tXUmEowj1nMa6odNoTg8DAJImO972tOJN7zoM2wMA1MDscVnh1UJJaZevQjcw\nGxw2466szUAzMyOdQ43IaPWUERmtjJNpCRf61C5q53sj6OyJIllUv9usJqxp9JZMOOCrKW8rjtHK\n2IgYaJax5XaAybKCdHaaEFTcYjRD65G8gEPBJAgwmQCTSYBJEGA2CRDyl+P3qY+bYBJQcr/ZJEAw\nCTAL+ftK3qPoudr7FB7Xbk/4bJMw+TWFz5zw3Cmeby5+3qT7hZL7BTmH3Km3kD76IrJnT6vl4fHC\ns2MXfLt2w1ZXe8UtEqGQB4ODareE4jEpWvevoumJI/EMEukpgq2iYGV6ANdGz2ND/CKsSg4KBAyH\n1mCs/TrI67bA73OVtKx4XNaK+xVQDww0M1tO59ByWG71VDkYvYxlRcHAaFLtptYTRWdvBD1Dpa04\ndT5HyViclfXuJT1/G72MjYCBZhnjATY/4+OIUpkckvlxQbN1l4MgIJOVIMsKcrICRVaQUxTIcv7f\n+HUFyMlT3Z9/XVmOwMUVzEbwrshZXB07D4csQoaA8zUr8Lp3PS65WyCYTPkQhWlC1MQgBgiCgFQ2\nh7FoumTtpanUOCwl3b38NXYEkEao60043/kthDG1NcYaqod3x054t98y4wD/5YKBZmY8h+qL9ZT+\nqrGMUxkJnX1RdBZNOFD8Y5bVYsKaRk9JyPFPswj5YqjGMtaToijIijKSGQmJtDo5VeFSHYpQfD2n\nKPj6/71z1vc1bl8JokUkCAIcNgscNgsCnrmd+BbrJKYo4yEHJUFHLg5HJfdjUjhSg1Hp69QQhdL3\nmfa91Vm6pnvv4nAmyzJkGRM+M4RRuQ0vilk09p3B6ktvYH2sG+sT3Yg7fOho2oLz9ZuQNttLPnv8\nPbK50s/OKer9HpcNLXU18Lvt8GoD6fPjUsav19hhtai/xo0P8I8ceg7Jk0UD/LffDN+OXXC2r1/y\ndXWIiGjxOO0WbFkTxJY16o9SiqJgYCyVXxNHDTnneyI4dzmivabW6yjppraqYWlbcaqNoihIZ3Na\nECkOIIm0hGRGvUxNvD9/Odu6acVs1rn9P7GFpkrxFwP9sYynpygK0hcuIHLgBcSOvwJFFCFYLPBs\nuxG+3bfDsbZ1TtMNz7WMMz2XETl0ELGXX0Iurj7fsbYV3h274Nm6DWaX64r/pmrEFpqZ8fjWF8+h\n+luuZZzKSOjqi2otOB290ZJ1vSzm8VacQsiZ64+ZExm1jGVFQSpTGjhSU7SWaK0m2nPn303fbFJn\ntnU5rKhxqOORa/Ljl7XrDkv+sfxz8mOcHXYzGuq9s34GA02VMuoBZiQs47nJxeOIHj2M8IH9EAcH\nAAD2Vavh3307PDfeBJN9+kpkpjLOJZOIHX8FkUMHkem6AAAwuz3wbL8Zvh07YW9Zsfh/TJVhoJkZ\nj2998RyqP5axSlEUDIbHW3Gi6OiN4PJgouRLedBrV2dTy3dTW9Xg0Vr/Z1LOMs7J8jQtJJNbRYq7\ndo13qZ9PALCYTaVhZDyA2KcII0XPWYy1BzmGZhnjSUx/LOP5UWQZyXdOIXzgBSROvA4oijr18807\n4N99G2xNzZNeM7GMFVlG6uwZRA4fRPy3r0HJZgFBQM1VV8O7Yxfc1163qLOsVTsGmpnx+NYXz6H6\nYxlPL52V0NUXQ0dvIeTEksWtOAJWN6hjcVqbvVjX4kPQO3nttSstY1GS5xRASlpO8l26MrMsKzCR\nzWoqhBF7cQAphI/SYFJoLbFZl2Z9tKnMpa5izU9ES0IwmVCz5SrUbLkK4ugIIgdfROTQiwg//2uE\nn/81nBs3wb/7drive9ekUCKOjiJ69DCiRw5BHBoCUDTA/+YdsAYC5fiTiIjIoBw2CzauDmDjarX+\nUBQFQ5F0yVicC30xdPRGtdcEPHa0Nqvd1Na1+LC60V26OPkUYUQLJZmp78/OMunN5O02o8ZhQb3f\nWRJGirtpTddaUs3jhubdQvPMM8/g6aefBgBkMhmcPn0aP/3pT/HVr34VJpMJ7e3t2Ldv36xNS/zF\nQF/8VUZ/LOMrp0gS4id+i/D+F5A6o079bPb54dt1K3w374BtbADd//XfJQP8PTdshXfHTjjXb7ii\nJmyq3hYa1lPGwHOo/ljGVyYj5iaNxYkmstrj40s1SLm5hxIB0Na1m3LsyDRhpMZhhdNuhnkZTmyj\ne5ezRx99FJs2bcILL7yA+++/H1u3bsW+ffuwc+dO3HHHHTO+lgeYvngS0x/LeHFlensQObAf0ZeO\nQE6lSh5ztLbCewsH+C+2ag00xVhPVS6eQ/XHMl5ciqJgOJLWuqld6IvCbDbBZjFNDiX20jAyHlwc\ndssVr9G23Oja5eytt97C+fPn8cgjj+C73/0utm7dCgDYtWsXjhw5MmtFQURUzN7cgvp7/hfq7vog\nosdeRvy1V+FftxbW62/kAH9aENZTRLSYBEFAyO9EyO/ETZsbATA0VooFB5rHH38cn/zkJwGoiXWc\ny+VCLMb/WCJaGJPDAf+u3fDv2s2Kgq4I6ykiouVhQYEmGo2iq6sL27ZtAwCYivrzJRIJeL2zzxe9\nHLo6lBvLWH8sY/2xjGkhWE8ZA8tYfyxj/bGMy29Bgeb48eO46aabtNubNm3CsWPHsG3bNhw8eBDb\nt2+f9T34q6u++Mu2/ljG+mMZ66uaK2HWU5WPx7f+WMb6YxnrT7cxNF1dXVi1apV2e+/evXj44Ych\niiLa2tqwZ8+ehbwtERHRomA9RUS0fHBhzSrFXwz0xzLWH8tYX9XcQrMYuO/pi8e3/ljG+mMZ628u\nddXym8yaiIiIiIiqBgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNogoRQQAADx5JREFUERER\nEREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZ\nFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMN\nEREREREZFgMNEREREREZFgMNEREREREZlmUhL3r88cexf/9+ZLNZ3HPPPdi6dSv27t0Lk8mE9vZ2\n7Nu3D4IgLPa2EhERzQnrKSKi5WPeLTSvvPIKXn/9dfz85z/HT37yE/T39+Mb3/gGHnroITz55JNQ\nFAXPP/+8HttKREQ0K9ZTRETLy7wDzZEjR7BhwwY8+OCD+PjHP47du3fj5MmT2Lp1KwBg165dOHr0\n6KJvKBER0VywniIiWl7m3eVsdHQUfX19ePzxx9Hd3Y2Pf/zjUBRFe9zlciEWiy3qRhIREc0V6yki\nouVl3oEmEAigra0NFosFa9euhd1ux+DgoPZ4IpGA1+ud9X1CIc98P5rmiWWsP5ax/ljGNF+sp4yD\nZaw/lrH+WMblN+9Ac8MNN+Bf/uVf8Cd/8icYGBhAOp3GTTfdhGPHjmHbtm04ePAgtm/fPuv7DA3x\n1zE9hUIelrHOWMb6Yxnrq1orYdZTxsDjW38sY/2xjPU3l7pq3oFm9+7dOH78OD74wQ9ClmXs27cP\nLS0tePjhhyGKItra2rBnz54FbTAREdGVYj1FRLS8CEpxx+IlxDSrL/5ioD+Wsf5Yxvqq1haaxcJ9\nT188vvXHMtYfy1h/c6mruLAmEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNERER\nEREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZ\nFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMNEREREREZFgMN\nEREREREZFgMNEREREREZFgMNEREREREZlmUhL7rzzjvhdrsBACtXrsTHPvYx7N27FyaTCe3t7di3\nbx8EQVjUDSUiIpor1lNERMvHvANNJpMBADzxxBPafR//+Mfx0EMPYevWrdi3bx+ef/553HHHHYu3\nlURERHPEeoqIaHmZd5ez06dPI5VK4YEHHsB9992HEydO4NSpU9i6dSsAYNeuXTh69OiibygREdFc\nsJ4iIlpe5t1C43Q68cADD+BDH/oQurq68Kd/+qclj7tcLsRisUXbQCIiovlgPUVEtLzMO9CsWbMG\nq1ev1q77/X6888472uOJRAJer3fW9wmFPPP9aJonlrH+WMb6YxnTfLGeMg6Wsf5YxvpjGZffvAPN\nL37xC5w9exb79u3DwMAAEokEbrnlFhw7dgzbtm3DwYMHsX379lnfZ2iIv47pKRTysIx1xjLWH8tY\nX9VaCbOeMgYe3/pjGeuPZay/udRV8w40H/zgB7F3717cc889EAQBX//61+H3+/Hwww9DFEW0tbVh\nz549C9pgIiKiK8V6iohoeREURVHK8cFMs/riLwb6Yxnrj2Wsr2ptoVks3Pf0xeNbfyxj/bGM9TeX\nuooLaxIRERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx\n0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BAR\nERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERERkWEx0BARERER\nkWEx0BARERERkWEtONCMjIzg1ltvxYULF3Dx4kV8+MMfxkc+8hF88YtfhKIoi7mNRERE88Z6ioho\neVhQoBFFEY888gicTicURcHXv/51PPTQQ3jyySehKAqef/75xd5OIiKiOWM9RUS0fCwo0HzrW9/C\nhz/8YYRCIQDAqVOnsHXrVgDArl27cPTo0cXbQiIionliPUVEtHzMO9A8/fTTCAaD2LFjBwBAUZSS\npnuXy4VYLLZ4W0hERDQPrKeIiJYXy3xf8PTTT0MQBBw9ehSnT5/G3r17MTY2pj2eSCTg9XpnfZ9Q\nyDPfj6Z5Yhnrj2WsP5YxzRfrKeNgGeuPZaw/lnH5zTvQ/OQnP9Gu33vvvfjSl76Eb33rWzh27Bi2\nbduGgwcPYvv27Yu6kURERHPFeoqIaHmZd6CZSBAE7N27Fw8//DBEUURbWxv27NmzGNtGRER0xVhP\nERFVN0Hh3JVERERERGRQXFiTiIiIiIgMi4GGiIiIiIgMi4GGiIiIiIgM64onBZgPURTxhS98Ab29\nvchms/jEJz6B22+/fSk3oerlcjn8zd/8Dbq6uiAIAr70pS+hvb293JtVdUZGRnDXXXfhxz/+Mdau\nXVvuzak6d955J9xuNwBg5cqV+NrXvlbmLao+jz/+OPbv349sNot77rkHH/zgB8u9SRWB9ZT+WE8t\nHdZV+mE9pb/51FNLGmh+9atfIRgM4tvf/jYikQj+4A/+gBXFItu/fz9MJhN+9rOf4dixY3jsscfw\n/e9/v9ybVVVEUcQjjzwCp9NZ7k2pSplMBgDwxBNPlHlLqtcrr7yC119/HT//+c+RTCbxox/9qNyb\nVDFYT+mP9dTSYF2lH9ZT+ptvPbWkgWbPnj34vd/7PQCALMswm81L+fHLwh133IHbbrsNANDT0wOf\nz1fmLao+3/rWt/DhD38Yjz/+eLk3pSqdPn0aqVQKDzzwACRJwkMPPYRrr7223JtVVY4cOYINGzbg\nwQcfRDwex1//9V+Xe5MqBusp/bGeWhqsq/TDekp/862nljTQuFwuAEA8HsenPvUpfPrTn17Kj182\nzGYzPve5z+E3v/kN/v7v/77cm1NVnn76aQSDQezYsQOPP/44OOv54nM6nXjggQfwoQ99CF1dXfiz\nP/szPPfcczCZOORvsYyOjqKvrw+PP/44uru78YlPfALPPvtsuTerIrCeWhqsp/TFukpfrKf0N996\nakkDDQD09fXhk5/8JD7ykY/gfe9731J//LLxzW9+E8PDw7j77rvxX//1X3A4HOXepKrw9NNPQxAE\nHD16FKdPn8bevXvx/e9/H3V1deXetKqxZs0arF69Wrvu9/sxNDSEhoaGMm9Z9QgEAmhra4PFYsHa\ntWtht9sxOjqKYDBY7k2rCKynlgbrKf2wrtIX6yn9zbeeWtIoOTw8jPvvvx+f/exncddddy3lRy8b\nv/zlL/GDH/wAAOBwOCAIAn8xWEQ/+clP8MQTT+CJJ57Axo0b8c1vfpMVxCL7xS9+gW984xsAgIGB\nAcTjcYRCoTJvVXW54YYbcOjQIQBqGadSKQQCgTJvVWVgPaU/1lP6Y12lL9ZT+ptvPSUoS9gO+ZWv\nfAXPPvtsyUwb//zP/wy73b5Um1D1UqkUPv/5z2N4eBiSJOHP//zPOaBVJ/feey8effRRzhyzyERR\nxN69e9HX1wdBEPDZz34W1113Xbk3q+p8+9vfxiuvvAJZlvGZz3wGt9xyS7k3qSKwntIf66mlxbpq\n8bGeWhrzqaeWNNAQEREREREtJrbxEhERERGRYTHQEBERERGRYTHQEBERERGRYTHQEBERERGRYTHQ\nEBERERGRYTHQEBERERGRYTHQEBERERGRYTHQEBERERGRYVnKvQFEepIkCV/84hdx/vx5DA8PY+3a\ntfje976Hp556Ck8++SQ8Hg9aW1uxatUqfPKTn8TBgwfx3e9+F5IkYcWKFfjyl78Mv98/5XtfvHgR\nH/3oR7F//34AwLFjx/DDH/4QP/zhD/GDH/wAzz77LHK5HHbs2IHPfvazAIDHHnsML7/8MsLhMAKB\nAL73ve+hrq4ON910E6666ioMDw/jF7/4Bcxm85KVERERlQ/rKaIrxxYaqmonTpyA3W7Hz3/+c/z6\n179GOp3GD3/4Q/z0pz/F008/jZ/+9Ke4ePEiAGB0dBTf+c538KMf/QjPPPMMbrnlFvzt3/7ttO+9\nevVqrFixAi+//DIA4JlnnsFdd92FgwcP4uTJk/i3f/s3PPPMM+jv78d//Md/4NKlS7hw4QKeeuop\nPPfcc1i9ejV+9atfAQDC4TA+9rGP4d///d9ZSRARLSOsp4iuHFtoqKq9+93vht/vx5NPPonOzk5c\nvHgRN954I2677TbU1NQAAN73vvchGo3izTffRF9fH+69914AQC6Xm/ZXr3Ef+MAH8Mtf/hLXXXcd\nXnnlFTz66KP4zne+gzfffBN33XUXACCTyWDFihV4//vfj8997nN46qmncOHCBZw4cQKrVq3S3uva\na6/VqRSIiKhSsZ4iunIMNFTVnn/+eXz3u9/Ffffdhw984AMIh8Pwer2IxWLacxRFAaBWDNdffz3+\n8R//EQCQzWYRj8dnfP89e/bgsccew7PPPotbb70VVqsVsizjvvvuw0c/+lEAQCwWg9lsxttvv43P\nfOYzuP/++7Fnzx6YzWbtswHAZrMt8l9PRESVjvUU0ZVjlzOqai+99BLe+9734s4770RtbS2OHz8O\nAHjxxRcRj8eRzWbx3//93xAEAddeey1OnDiBrq4uAMA//MM/4Nvf/vaM7+9wOLBr1y489thjuPPO\nOwEAN910E375y18imUxCkiQ8+OCDeO655/Dqq6/ixhtvxB/+4R+ira0Nhw8fhizLuv79RERU2VhP\nEV05ttBQVbv77rvxmc98Bs8++yxsNhuuu+46jI6O4t5778Uf/dEfweVyIRAIwOFwoK6uDl/72tfw\nl3/5l8jlcmhqapq1ogCA9773vXj99ddxzTXXAABuu+02nD59GnfffTdyuRx27dqFO++8EwMDA/iL\nv/gLvP/974fFYsGmTZtw+fJlAIAgCLqWAxERVSbWU0RXTlCK2xKJloGuri4cOHBAa2p/8MEHcffd\nd2P37t3zfq9cLofvfOc7CIVC2vsRERFdCdZTRPPDFhpadpqbm/HWW2/h93//9wEAO3funLGS+Ku/\n+iucP39+0v2333479u/fj2AwiE996lN6bS4RES0zrKeI5octNEREREREZFicFICIiIiIiAyLgYaI\niIiIiAyLgYaIiIiIiAyLgYaIiIiIiAyLgYaIiIiIiAyLgYaIiIiIiAzr/weyRMcMu22NXAAAAABJ\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1096d55f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(2, 2, figsize=(14,10))\n",
"for ax, dom in zip(np.ravel(axes), lsl_dr.domain.dropna().unique()):\n",
" plot_data = lsl_dr[lsl_dr.domain==dom].pivot_table(index='age_year', columns='tech_class', values='score', aggfunc='mean')\n",
" plot_data[(plot_data.index>1) & (plot_data.index<7)].plot(ax=ax)\n",
" ax.set_ylim(40, 120)\n",
" ax.set_xticks(range(2,7))\n",
" ax.set_title(dom)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lsl_dr.pivot_table?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plots of Demographic Data"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_color = \"#64AAE8\""
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_demo_data(series, labels=None, color=plot_color, rot=0, label_offset=20, xlim=None, \n",
" ylim=None, title=None, **kwargs):\n",
" ax = kwargs.get('ax')\n",
" if ax is None:\n",
" f, ax = plt.subplots()\n",
" counts = series.value_counts().sort_index()\n",
" counts.plot(kind='bar', grid=False, rot=rot, color=color, **kwargs)\n",
" if xlim is None:\n",
" ax.set_xlim(-0.5, len(counts)-0.5)\n",
" if ylim is not None:\n",
" ax.set_ylim(*ylim)\n",
" ax.set_ylabel('Count')\n",
" if labels is not None:\n",
" ax.set_xticklabels(labels)\n",
" if title:\n",
" ax.set_title(title)\n",
" for i,x in enumerate(counts):\n",
" ax.annotate('%i' % x, (i, x + label_offset))\n",
" \n",
"# plt.gca().tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_students = demographic.drop_duplicates('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5511, 67)"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 5025.000000\n",
"mean 30.382886\n",
"std 27.944080\n",
"min 0.000000\n",
"25% 9.000000\n",
"50% 25.000000\n",
"75% 41.000000\n",
"max 298.000000\n",
"Name: age, dtype: float64"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAFUCAYAAADI9i7qAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b3407b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.male, ('Female', 'Male'), label_offset=20, ylim=(0, 2800), color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 167,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAF1CAYAAAAEBvh5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b3cde10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.prim_lang, \n",
" ('English', 'Spanish', 'Chinese', 'French', 'German', 'Tagalong', 'Other'), \n",
" rot=70, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4964"
]
},
"execution_count": 168,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.prim_lang.count()"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAFVCAYAAAADqv1PAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAG6hJREFUeJzt3X10VPW97/HPzkxCyEwCDaK9lRAphgcPJN6UYKAmUI6y\nuC2tKKImEDSgIBJBg5jQlIZiUpAHOUcioPSoFRBDF6mmV8rRYm2KgMjlSQwEDteitNQmZBUyE/LE\nzP2Dw1iuxfA0s5Nf3q+/MntvZr6zhrXes/fs2WP5/X6/AACAUcLsHgAAAFx7BB4AAAMReAAADETg\nAQAwEIEHAMBABB4AAAM5g3XHzc3N+vGPf6y//OUvampq0rRp09S7d2/l5+crLCxMCQkJKiwslGVZ\n2rBhg0pLS+V0OjVt2jQNHz5cDQ0Nmj17tmpra+VyubRw4ULFxsYGa1wAAIxiBet78GVlZaqqqtKc\nOXN06tQp3XXXXerfv78mTZqklJQUFRYWKi0tTUlJSZo0aZLKysrU2NiojIwMbdy4UevWrZPX61VO\nTo42bdqkPXv2qKCgIBijAgBgnKAdoh81apRmzJghSfL5fHI6naqsrFRKSookKT09Xdu2bdPHH3+s\n5ORkhYeHy+12Kz4+XlVVVdq9e7fS09MlSWlpadq+fXuwRgUAwDhBC3xUVJRcLpc8Ho9mzpypJ554\nQj6fL7De5XKprq5OHo9H0dHRFyz3eDzyeDxyuVwXbAsAAC5N0D6Dl6QTJ04oJydH48eP1+jRo7V4\n8eLAOo/Ho5iYGLndbnm93sByr9er6OjoC5Z7vV7FxMS0+njV1bwJAAB0HN27R190XdD24GtqajRp\n0iTNnj1b99xzjySpf//+2rlzpySpoqJCgwYNUmJionbt2qWmpibV1dXp6NGj6tOnj5KTk1VRUXHB\ntgAA4NIE7SS7oqIibd68Wb169QosKygoUHFxsZqbm9W7d28VFRXJsiz96le/UmlpqXw+n6ZNm6Y7\n77xTDQ0NysvLU3V1tSIiIrR06VJ169btax+TPXgAQEfydXvwQQu8HQg8AKAjseUQPQAAsE9QT7ID\n8KVPPjmgVauWa/nyF1VVdUhLlvxcERGdlJDQRzNnPiXLslRe/muVl/9aDodDDz44WUOH3q7Tp09p\n/vy5qq+vV5cuXfT00z/RN77xDbufDoA2jj14IATWrfulFi0qUnNzsyRp0aJizZw5Wy+8sFoul1vv\nvrtZJ0/WaOPGUq1a9bKee265XnyxRM3NzXrttVeUlJSsFSt+obFj79dLL71g87MB0B4QeCAEevSI\nU3HxYp0/5aWm5m8aMGCgJGnAgETt379XBw9WauDAJDmdTrlcbt14Y5yOHj2iY8c+VWrqEEnSwIHn\ntgWA1hB4IASGDRshh8MRuP2tb92ovXt3S5I++OCPOnPmjOrrvXK53IFtoqKi5PF4dPPNfbR167mv\njG7dWqGGhobQDg+gXSLwgA3mzCnUmjWvaubMxxQbG6uuXbsqKsql+vr6wDb19fWKjo5RVla2Tpz4\ni3Jypuivfz2h66+/wcbJAbQXBB6wwfbtW1VY+Iz+/d9X6PTpU0pJSdUtt/yL9u/fo6amJnk8Hh07\n9qm+/e3e2rdvt370o7tVUvKSbryxhxITb7V7fADtAGfRAyFkWZYkqUePnpo5c5oiIyOVnJyi1NSh\nkqR7731A06c/LJ/PrylTpis8PFw9e96koqKfSpK6d79B+flzbZsfQPvBhW4AAGinvu5CN+zBowMy\n5j1tKyy7BwBgIwKPDunV/Q2qbTAz9LGRlh5KjLR7DAA2I/DokGob/Kqpb3279snMNy4ALg9n0QMA\nYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAA\nABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8\nAAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCAC\nDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiI\nwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAG\nIvAAABiIwAMAYKCgB37fvn3KysqSJFVWVio9PV1ZWVnKysrSb3/7W0nShg0bNHbsWN1///16//33\nJUkNDQ16/PHHNX78eE2ZMkW1tbXBHhUAAGM4g3nnq1evVnl5uVwulyTpk08+UXZ2trKzswPbVFdX\na82aNSorK1NjY6MyMjI0dOhQrV+/Xn379lVOTo42bdqklStXqqCgIJjjAgBgjKDuwcfHx6ukpER+\nv1+SdODAAb3//vuaMGGCCgoK5PV6tX//fiUnJys8PFxut1vx8fGqqqrS7t27lZ6eLklKS0vT9u3b\ngzkqAABGCWrgR44cKYfDEbidlJSkvLw8rV27VnFxcSopKZHX61V0dHRgG5fLJY/HI4/HE9jzd7lc\nqqurC+aoAAAYJaQn2d1555265ZZbAn8fPHhQbrdbXq83sM354P/jcq/Xq5iYmFCOCgBAuxbSwE+e\nPFn79++XJG3btk0DBgxQYmKidu3apaamJtXV1eno0aPq06ePkpOTVVFRIUmqqKjQoEGDQjkqAADt\nWlBPsjvPsixJ0rx58/TMM8/I6XTq+uuv1/z58+VyuTRx4kRlZmbK5/MpNzdXERERysjIUF5enjIz\nMxUREaGlS5eGYlQAAIxg+c+fAWeA6mo+p8el8Ou5nWdUU2/3HMFxXZSUO7izJMvuUQAEWffu0Rdd\nx4VuAAAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4A\nAAMReAAADETgAQAwUEh+LhbXziefHNCqVcu1fPmL+vTT/6tFi4olSXFxPZWX9xM5HA6Vlq7Tli3v\nSpKGDPmusrMfkd/v1913f19xcT0lSQMGJGrq1Om2PQ8AQHAR+HZk3bpf6p13fqvOnaMkSS+9tEKP\nPvq4kpJu1c9//jN98MEfdfPNCXr33f/U6tW/lGVZmjZtstLTv6dOnTqpb99+evbZZTY/CwBAKHCI\nvh3p0SNOxcWL5ff7JUnFxYuUlHSrmpubdfLkSbndbl1//Q1auvR5Wda53wJvaWlRp06dVFV1UNXV\n1Zox41HNnj1Tn312zM6nAgAIMgLfjgwbNkIOhyNwOywsTH/96wllZd2v06f/rptvTpDT6VSXLl3l\n9/tVUvJv6tu3n3r0iNN113XXxInZev75VcrKytYzz8y18ZkAAIKNwLdz3/zm/9Abb5Tprrvu0fLl\n5w6/NzY26mc/+4kaGs5o1qx8SVK/fv11++3DJEmJibeqpqbGtpkBAMFH4Nux/PxcHT/+uSSpc+co\nhYWdeznnzJmlhIQ+euqpOYFD9S+/vFobNrwuSTpy5LBuuOGb9gwNAAgJTrJrh85He8KEh1RcPE/h\n4eGKjOys/Pyf6A9/+L327t2jlpYW7dixTZL06KOPa8KEh/TMM3O1bdtWOZ1O/fjHhXY+BQBAkFn+\n82dsGaC6us7uEdAu+PXczjOqqbd7juC4LkrKHdxZkmX3KACCrHv36IuuYw/+ihnzvqgVRAIA2iMC\nfxVe3d+g2gYzQx8baemhxEi7xwAAXCECfxVqG/zGHubtOEcoAMBMnEUPAICBCDwAAAYi8AAAGIjA\nAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi\n8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICB\nCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBg\noFYDf+TIka8s27t3b1CGAQAA14bzYit27doln8+nuXPnqqioSH6/X5ZlqaWlRYWFhXrnnXdCOScA\nALgMFw38tm3b9NFHH+lvf/ubnn/++S//gdOpBx54ICTDAQCAK3PRwM+YMUOS9Oabb2rMmDEhGwgA\nAFy9iwb+vEGDBunZZ5/V3//+9wuWL1iwIGhDAQCAq9Nq4J944gmlpKQoJSUlsMyyrKAOBQAArk6r\ngT979qzy8vJCMQsAALhGWv2a3He+8x1t2bJFTU1NoZgHAABcA63uwW/evFlr1669YJllWTp48GDQ\nhgIAAFen1cBv3br1qh5g3759WrJkidasWaNjx44pPz9fYWFhSkhIUGFhoSzL0oYNG1RaWiqn06lp\n06Zp+PDhamho0OzZs1VbWyuXy6WFCxcqNjb2qmYBAKCjaDXwJSUl/3R5Tk5Oq3e+evVqlZeXy+Vy\nSTp35n1ubq5SUlJUWFioLVu2KCkpSWvWrFFZWZkaGxuVkZGhoUOHav369erbt69ycnK0adMmrVy5\nUgUFBZf59AAA6Jha/Qze7/cH/m5ubtZ7772nkydPXtKdx8fHq6SkJHAflZWVgbPx09PTtW3bNn38\n8cdKTk5WeHi43G634uPjVVVVpd27dys9PV2SlJaWpu3bt1/2kwMAoKNqdQ/+8ccfv+D29OnTlZ2d\nfUl3PnLkSB0/fjxw+x/fLLhcLtXV1cnj8Sg6OvqC5R6PRx6PJ7Dnf35bAABwaS771+Q8Ho9OnDhx\nZQ8W9uXDeTwexcTEyO12y+v1BpZ7vV5FR0dfsNzr9SomJuaKHhMAgI6o1T34ESNGXHD71KlTmjx5\n8hU9WP/+/bVz504NHjxYFRUVGjJkiBITE7Vs2TI1NTWpsbFRR48eVZ8+fZScnKyKigolJiaqoqJC\ngwYNuqLHBACgI2o18K+99lrgynWWZQX2ui/H+X+fn5+vuXPnqrm5Wb1799aoUaNkWZYmTpyozMxM\n+Xw+5ebmKiIiQhkZGcrLy1NmZqYiIiK0dOnSK3h6AAB0TJb/Hz8Y/yd8Pp/Wr1+vHTt2qKWlRamp\nqcrKyrrgcHtbUV0dys/p/Xpu5xnV1IfwIUPouigpd3BnSSZelpjXDoAZunePvui6VvfgFy9erGPH\njmns2LHy+/3auHGjjh8/zlfWAABowy7pQjdvvvmmHA6HJGn48OEaPXp00AcDAABXrtXj7D6fT2fP\nng3cPnv2rJzOVt8XAAAAG7Va6h/+8IfKysrS6NGj5ff79fbbb+sHP/hBKGYDAABX6GsDf+rUKd13\n333q37+/duzYoR07dujBBx/UmDFjQjUfAAC4Ahc9RF9ZWanvf//7OnDggIYNG6a8vDylpaVpyZIl\nOnToUChnBAAAl+migV+4cKGee+65wPXgJSk3N1cLFizQwoULQzIcAAC4MhcN/OnTp3Xbbbd9ZXla\nWppqa2uDOhQAALg6Fw382bNn5fP5vrLc5/OppaUlqEMBAICrc9GT7AYNGqSSkhLNmDHjguUrVqzQ\ngAEDgj4YALQVLS0tKioq1BdfnFBYmEN5eQVqaGjQ008/qbi4npKku+8epxEj7lB5+a9VXv5rORwO\nPfjgZA0dervN06OjumjgZ82apUceeUTl5eVKTEyUz+dTZWWlYmNjtXLlylDOCAC22r59q3w+n1au\nfFkfffShXnpphVJTh+qBB8brgQcmBLY7ebJGGzeW6j/+Y60aGxv02GMPKyXlNoWHh9s4PTqqiwbe\n7XZr3bp1+vDDD1VZWSmHw6EJEybwq24AOpyePW/S2bMt8vv98no9cjicOnTokD7//Ji2bq1Qjx5x\nmjFjlg4e/EQDBybJ6XTK6XTrxhvjdPToEfXrd4vdTwEd0Nd+Dz4sLExDhgzRkCFDQjUPALQ5kZGR\nOnHihDIzx+r06VN69tll+uyzY/rRj8aoT59+eu21l/XKK6uVkNBHLteXv7YZFRUlj8dj4+ToyNre\nT8IBQBuzYcPruu22IVq/vkyvvPK6iormKTV1qPr06SdJSk//ng4frlJUlEv19V/+TGF9fb2io2Ps\nGRodHoEHgFZER8fI5XIF/j57tkV5eU/q4MFPJEm7dn2ofv3665Zb/kX79+9RU1OTPB6Pjh37VN/+\ndm87R0cHxq/GAEAr7r9/vBYsmK/p0x9Rc3Ozpk6drp4947Vs2WI5nU5163adnn66QFFRUbr33gc0\nffrD8vn8mjJlOifYwTaW3+/32z3EtVJdXRfCR/PruZ1nVFPf+pbt0XVRUu7gzpIsu0cJAl47AGbo\n3j36ouvYgwfQzhizT9IK3qDh6hB4AO3Oq/sbVNtgZuhjIy09lBhp9xgwAIEH0O7UNviN/Yil4xyh\nQLBxFj0AAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjA\nAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi\n8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICB\nCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGMhp9wAA\nAATTpEnj5XK5JUnf+taNysrKVnHxPIWFhalXr96aNStPlmVp48YN2rz5f0uylJGRpREj7rB38KtE\n4AEAxmpsbJQkLV/+YmBZXt6Tmjp1um69NVlLlizQH//4ByUm3qq33tqoV155XY2NjZowYVy7DzyH\n6AEAxvqv/zqihoYG5ebmaObMaTpw4GMdPlylW29NliSlpg7Vrl0fqmvXrnrlldflcDh08mSNIiI6\n2Tz51WMPHgBgrM6dI5WZmaXRo8fo888/06xZj/9/66Pk9XokSQ6HQxs3lurll1/SuHEZdox7TbEH\nDwAwVlxcvO6883/999891aVLF9XWngysr6/3yu2ODtweO/Z+vfXWf2rPnt3avXtXyOe9lgg8AMBY\nb7/9lkpK/k2SVFNTrfr6eg0enKo9e/6PJGnHjm1KSkrWZ58dU0HBbEnn9uQjIsLlcDhsm/tasOUQ\n/d133y23+9wZjXFxcZo6dary8/MVFhamhIQEFRYWyrIsbdiwQaWlpXI6nZo2bZqGDx9ux7gAgHZq\n9OgxKi6ep8cee1iWZWnOnEJ16dJFzz5bpJaWFt10Uy9973v/KsuydPPNfTR1arYsy1Jq6lAlJf1P\nu8e/KiEP/PkzGtesWRNY9uijjyo3N1cpKSkqLCzUli1blJSUpDVr1qisrEyNjY3KyMjQ0KFDFRER\nEeqRAQDtlNPpVGFh0VeWl5S89JVl2dmPKDv7kVCMFRIhD/yhQ4d05swZTZ48WS0tLXryySdVWVmp\nlJQUSVJ6ero++OADhYWFKTk5WeHh4QoPD1d8fLyqqqo0cODAUI8MAEC7E/LAd+7cWZMnT9a4ceP0\npz/9SQ8//PAF610ul+rq6uTxeBQdHX3Bco/HE+pxAQDXlN/uAULAsnsASTYE/qabblJ8fHzg765d\nu+rgwYOB9R6PRzExMXK73fJ6vYHlXq9XMTExoR4XAHCNvbq/QbUN5oU+NtLSQ4mRdo8REPLAb9y4\nUYcPH1ZhYaG++OILeb1effe739XOnTs1ePBgVVRUaMiQIUpMTNSyZcvU1NSkxsZGHT16VAkJCaEe\nFwBwjdU2+FVTb/cUwdC23rSEPPD33nuv8vPzlZmZKcuytGDBAnXt2lVz585Vc3OzevfurVGjRsmy\nLE2cOFGZmZny+XzKzc3lBDsAAC5RyAMfHh6upUuXfmX5P55Vf964ceM0bty4UIwFAIBRuNANAAAG\nIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCA\ngQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMA\nYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAA\nABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8\nAAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCAC\nDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiI\nwAMAYCACDwCAgQg8AAAGcto9wNfx+XyaN2+eDh8+rPDwcBUXF6tnz552jwUAQJvXpvfgf/e736m5\nuVlvvPGGnnrqKS1cuNDukQAAaBfadOB3796ttLQ0SVJSUpIOHDhg80QAALQPbfoQvcfjkdvtDtx2\nOBzy+XwKC2sb70tiIy1JfrvHCIpzz81cvHbtG69f+2bq69fWXrs2HXi32y2v1xu43Vrcu3ePDsVY\nAbP/NSakj4drh9eufeP1a994/UKjbewKX0RycrIqKiokSXv37lXfvn1tnggAgPbB8vv9bfY4id/v\n17x581RVVSVJWrBggXr16mXzVAAAtH1tOvAAAODKtOlD9AAA4MoQeAAADETgAQAwEIEHAMBABL6d\n2Ldvn7KysuweA5epublZs2fP1vjx4zVu3Di99957do+ES3T27FnNmTNHGRkZyszM1JEjR+weCZfo\n5MmTGjZsmD799FO7R7FVm77QDc5ZvXq1ysvL5XK57B4Fl+k3v/mNYmNjtXjxYp06dUpjxozRiBEj\n7B4Ll+D3v/+9wsLCtH79eu3cuVPLli3TihUr7B4LrWhubtZPf/pTde7c2e5RbMcefDsQHx+vkpIS\n8Y3G9mfUqFGaMWOGpHNXYnQ4HDZPhEt1xx13aP78+ZKkP//5z+rSpYvNE+FSLFq0SBkZGerevfsF\ny0eMGKGmpiabprIHgW8HRo4cSRjaqaioKLlcLnk8Hs2cOVNPPvmk3SPhMjgcDuXl5amoqEijR4+2\nexy0oqysTLGxsbr99tslnbtY2pw5c5SVlaWamhpNmjRJDz30kL1DhhAXumknjh8/rlmzZqm0tNTu\nUXCZTpw4oZycHI0fP1733HOP3ePgCtTU1Oi+++7Tpk2bFBkZafc4uIgJEybIss794MuhQ4fUq1cv\nrVy5Ut26ddOIESO0efNmRURE2Dxl6PAZPBBE5/caCgsLlZqaavc4uAxvvfWWvvjiC02ZMkWRkZGy\nLKvN/JIl/rm1a9cG/s7KytL8+fPVrVs3SQqEvyMh8O1IR/wP2t6tWrVKdXV1euGFF/TCCy9Ikn7x\ni1+oU6dONk+G1owcOVJz5szRhAkT1NLSooKCgg6192eaLVu22D1CyHGIHgAAA3G8CQAAAxF4AAAM\nROABADAQgQcAwEAEHgAAAxF4AAAMROABADDQ/wMcBDmDsTqEhAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d05e668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.sib, ('1', '2', '3', '4+'), \n",
" color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4587"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.sib.count()"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 4883 null values for age_amp\n"
]
}
],
"source": [
"amp_ages = [\"Birth-3 months\", \"4 months - 6 months\", \"7 months - 9 months\", \"10 months- 12 months\", \n",
"\"13 months - 18 months\", \"19 months - 24 months\", \"2 years 1 day - 3 years\", \n",
"\"3 years 1 day - 4 years\", \"4 years 1 day - 5 years\", \"5 years 1 day - 6 years\", \"6 years\"]\n",
"\n",
"demographic.loc[demographic.age_amp==11, 'age_amp'] = None\n",
"print(\"There are {0} null values for age_amp\".format(sum(demographic.age_amp.isnull())))"
]
},
{
"cell_type": "code",
"execution_count": 172,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"age_classes = pd.Series(pd.cut(unique_students.sort('age_amp').age_amp.dropna(), [-1,3,6,9,12,18,24,36,48,60,72,1000],\n",
" labels=amp_ages))"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAGzCAYAAADUlZUZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naTb2P81lHkJwochsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10c93a5c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"age_amp_counts = age_classes.value_counts()[amp_ages]\n",
"age_amp_counts.plot(kind='bar', grid=False, rot=90, color=plot_color)\n",
"plt.xlim(-0.5, len(age_amp_counts)-0.5)\n",
"plt.ylabel('Count')\n",
"for i,x in enumerate(age_amp_counts):\n",
" plt.annotate('%i' % x, (i, x + 10))"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3355"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_amp_counts.sum()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"666.0"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age_amp.max()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109c03518>"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAf0AAAFkCAYAAAAqpeIDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b2ed4a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(unique_students.age_amp/12.).hist(bins=16, grid=False, color=plot_color)\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age at amplification')"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAF/CAYAAAClndsfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtU1HXi//HXBwdUGPCWmrfM9bZWaiFa2krmdnHN0jI1\nMEyjMvJS4RommXYxSMO+Jq621uYlQ+0onmrdLtoqS97yZ6apkdHaTdtQ0phRrjO/P1pnwzJAnfnA\nm+fjnD0HPjPqizN7es5nZpixvF6vVwAAwChBdg8AAADnH4EHAMBABB4AAAMReAAADETgAQAwEIEH\nAMBADn/9xSUlJZo6daoOHTqk4uJiJSQk6MILL9TYsWN18cUXS5JiY2P1pz/9SatWrdLKlSvlcDiU\nkJCgfv36qbCwUJMnT1Z+fr7CwsKUmpqqxo0b+2suAABGsfz1e/Br1qxRTk6OHn30UR0/flyDBw/W\nuHHj5HK5NGbMGN/18vLydPfdd2vNmjUqKipSTEyMVq9ereXLl8vtdmv8+PFat26dPvroIyUnJ/tj\nKgAAxvHbQ/QDBgzQxIkTJUkej0cOh0N79+7Vxo0bdeeddyo5OVlut1u7d+9WZGSkgoOD5XQ61bZt\nW+Xk5Gjnzp2Kjo6WJPXt21dbtmzx11QAAIzjt4foQ0NDJUkul0sPPvigHn74YRUVFWn48OG65JJL\ntHDhQqWnp6tLly4KDw/3/bmwsDC5XC65XC6FhYX5jhUUFPhrKgAAxvFb4CXp8OHDGj9+vEaOHKmb\nbrpJBQUFvphff/31euqpp9SzZ0+53W7fn3G73QoPD5fT6fQdd7vdioiIqPDfy8vjTgAAoPZo2jT8\njJf57SH6I0eO6O6779bkyZN12223SZLi4+O1e/duSdLmzZt12WWXqVu3btqxY4eKi4tVUFCg3Nxc\nderUSZGRkcrKypIkZWVlKSoqyl9TAQAwjt9eZPf000/r7bffVrt27XzHEhMTNWvWLDkcDjVr1kxP\nPvmkwsLC9Prrr2vlypXyeDxKSEjQ9ddfr8LCQiUlJSkvL08hISFKS0tTkyZNfvPf5AweAFCb/NYZ\nvN8CbwcCDwCoTWx5iB4AANiHwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAA\nABjIrx82AwAmKC0tVUrKE/ruu+9UXFysu+6KV6tWrTVr1kxJUps2Fykp6THVqVNHK1cu14YN70mS\neve+WmPG3Ov7e7788qDGjh2tN998T8HBwbb8LKg9CDwAVODdd/+hhg0badq0p/Tjjz9q9OgY/f73\nl+j++yeoe/fL9cwzT+iDD/6lDh066r333tGiRUtkWZYSEuIVHX2t2rfvILfbpfT05xUSUtfuHwe1\nBA/RA0AFrr32OsXH3y9J8no9cjgcmjlzlrp3v1wlJSU6evSonE6nmjVrrrS0F2RZlqSfzvzr1q0r\nr9erWbOe0dix41W3LoFHYBB4AKhA/fr1FRoaqhMn3Jo2bYruu+8BWZal7747rLi4Efrxx2Pq0KGj\nHA6HGjRoKK/Xq/T0/1Pnzr9X69Zt9Le//VV9+vxBHTp0lCQZ9BlfqMb4NDkAqIT//Oc7JSc/ottu\nG6aBA28ud9lbb63Vxx/vUnLyDBUVFSkl5Uk5nU5NmjRFlmXpjjtuVdOmzSRJe/d+oksuuVTp6X+1\n48eAYX7r0+R4Dh4AKpCff1SJieM1adIURUZGSZKmTEnU+PEPq3XrNqpfP1RBQT89IProo5PUo0dP\njRx5l+/Pr1iR6ft62LBb9Pzz8wP7A6BWIvAAUIGlS1+Ry+XSK68s0iuvLJIk3XffA5o5c4aCg4NV\nr159TZnymDZt+qd27fpIpaWl2rp1syRp7Njxuuyyrj/72ywbfgLURjxEDwBADcVD9AAMYsw5SQU4\n08e5IfAAapzFuwuVX2hm6BvXszS6Wz27Z8AABB5AjZNf6NWRE3av8Bcz77gg8Pg9eAAADETgAQAw\nEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAA\nDETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4A\nAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwkMNf\nf3FJSYmmTp2qQ4cOqbi4WAkJCWrfvr2mTJmioKAgdezYUdOnT5dlWVq1apVWrlwph8OhhIQE9evX\nT4WFhZo8ebLy8/MVFham1NRUNW7c2F9zAQAwit8C/+abb6px48aaPXu2jh8/rsGDB6tLly5KTExU\nz549NX36dG3YsEHdu3fXsmXLtGbNGhUVFSkmJkZ9+vRRRkaGOnfurPHjx2vdunVasGCBkpOT/TUX\nAACj+O0h+gEDBmjixImSJI/HI4fDoX379qlnz56SpOjoaG3evFl79uxRZGSkgoOD5XQ61bZtW+Xk\n5Gjnzp2Kjo6WJPXt21dbtmzx11QAAIzjt8CHhoYqLCxMLpdLDz74oB566CF5PB7f5WFhYSooKJDL\n5VJ4eHi54y6XSy6XS2FhYeWuCwAAKsevL7I7fPiw7rrrLg0ZMkSDBg1SUND//jmXy6WIiAg5nU65\n3W7fcbfbrfDw8HLH3W63IiIi/DkVAACj+C3wR44c0d13363JkyfrtttukyR16dJF27dvlyRlZWUp\nKipK3bp1044dO1RcXKyCggLl5uaqU6dOioyMVFZWVrnrAgCAyvHbi+wWLlyogoICzZ8/X/Pnz5ck\nJScna+bMmSopKVH79u01YMAAWZalUaNGKTY2Vh6PR4mJiQoJCVFMTIySkpIUGxurkJAQpaWl+Wsq\nAADGsbxer9fuEedLXh7P0wPm82rO9pM6csLuHf5xQaiU2Ku+JMvuKagBmjYNP+NlvNENAAAGIvAA\nABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8\nAAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCAC\nDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiI\nwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAG\nIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAG8nvgP/74Y8XFxUmS9u3b\np+joaMXFxSkuLk7/+Mc/JEmrVq3S0KFDNWLECG3cuFGSVFhYqAkTJmjkyJG67777lJ+f7++pAAAY\nw+HPv3zRokV64403FBYWJknau3evxowZozFjxviuk5eXp2XLlmnNmjUqKipSTEyM+vTpo4yMDHXu\n3Fnjx4/XunXrtGDBAiUnJ/tzLgAAxvDrGXzbtm2Vnp4ur9crSfrkk0+0ceNG3XnnnUpOTpbb7dbu\n3bsVGRmp4OBgOZ1OtW3bVjk5Odq5c6eio6MlSX379tWWLVv8ORUAAKP4NfA33HCD6tSp4/u+e/fu\nSkpK0quvvqo2bdooPT1dbrdb4eHhvuuEhYXJ5XLJ5XL5zvzDwsJUUFDgz6kAABgloC+yu/7663XJ\nJZf4vt6/f7+cTqfcbrfvOqeC//PjbrdbERERgZwKAECNFtDAx8fHa/fu3ZKkzZs367LLLlO3bt20\nY8cOFRcXq6CgQLm5uerUqZMiIyOVlZUlScrKylJUVFQgpwIAUKP59UV2p1iWJUmaMWOGnnrqKTkc\nDjVr1kxPPvmkwsLCNGrUKMXGxsrj8SgxMVEhISGKiYlRUlKSYmNjFRISorS0tEBMBQDACJb31Cvg\nDJCXx/P0gPm8mrP9pI6csHuHf1wQKiX2qi/JsnsKaoCmTcPPeBlvdAMAgIEIPAAABiLwAAAYiMAD\nAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABqow\n8AcOHPjFsV27dvllDAAAOD8cZ7pgx44d8ng8mjZtmp5++ml5vV5ZlqXS0lJNnz5d7777biB3AgCA\nKjhj4Ddv3qwPP/xQ33//vV544YX//QGHQ3fccUdAxgEAgLNzxsBPnDhRkrR27VoNGTIkYIMAAMC5\nO2PgT4mKitKzzz6rY8eOlTuekpLit1EAAODcVBj4hx56SD179lTPnj19xyzL8usoAABwbioMfFlZ\nmZKSkgKxBQAAnCcV/ppcjx49tGHDBhUXFwdiDwAAOA8qPIN/++239eqrr5Y7ZlmW9u/f77dRAADg\n3FQY+Ozs7EDsAAAA51GFgU9PT//V4+PHjz/vYwAAwPlR4XPwXq/X93VJSYnef/99HT161K+jAADA\nuanwDH7ChAnlvh83bpzGjBnjt0EAAODcVfnT5Fwulw4fPuyPLQAA4Dyp8Ay+f//+5b4/fvy44uPj\n/TYIAACcuwoDv3TpUt8711mWpYiICDmdTr8PAwAAZ6/CwLds2VIZGRnaunWrSktLddVVVykuLk5B\nQVV+dB8AAARIhYGfPXu2vvzySw0dOlRer1erV6/WN998o+Tk5EDsAwAAZ6FSb3Szdu1a1alTR5LU\nr18/DRo0yO/DAADA2asw8B6PR2VlZb7Al5WVyeGo8I8BAFAt7N37iRYunKd5816UJG3a9E9t3LhB\n06c/LUnasWO7XnppoRwOhxo2bKRp055Q3br1NGVKoo4fPy6Hw6F69epp9uy5dv4YVVZhqW+++WbF\nxcVp0KBB8nq9+vvf/66bbropENsAADgny5cv0bvv/kP164dKkv7v/57Thx9uVceOnX3XmTPnWc2f\n/5IaNWqkF1+crzffXKvbb79D33zzjV59dZVd08/Zb75S7vjx4xo+fLgeeOABHTp0SJmZmYqJiVFC\nQkKg9gEAcNZat26jmTNn+96VtWvX7po0aUq5d2lNT/+rGjVqJEkqLS1V3br19MMP+XK5CvTIIw/r\ngQfu0ebNNe9zWc4Y+H379mngwIH65JNPdM011ygpKUl9+/bVc889p08//TSQGwEAOCvXXNPf9xSz\nJP3xj9f/4jqNGzeRJG3a9L527fp/GjDgJpWUlCgm5k6lpqZp5szZeuGFOfrhhx8Ctvt8OGPgU1NT\nNWfOHEVHR/uOJSYmKiUlRampqQEZBwBAIKxcuVwrV76mtLR5Cg4OVuPGTTR48FAFBQWpUaNG6tSp\ns77++ku7Z1bJGQP/448/6sorr/zF8b59+yo/P9+vowAACJQlS17W7t279Pzz8xUR0UCS9OGH2zRt\nWpIk6cSJE/rii1xdfHE7O2dW2RlfZFdWViaPx/OLN7TxeDwqLS31+zAAAM6XU+/IeurrU9/n5x/V\n4sUvqXPnLvrznydKkv74xxs0ZMhQffjhVo0dO0ZBQZbuv3+cL/41heX9+SsNfuaJJ55Qo0aNNHHi\nxHLH09PT9dVXX2nWrFkBGVgVeXkFdk8A4Hdezdl+UkdO2L3DPy4IlRJ71ZdkVXhdoGnT8DNedsYz\n+EmTJunee+/VG2+8oW7dusnj8Wjfvn1q3LixFixY4JehAADT/eo5pWGqx52zMwbe6XRq+fLl2rZt\nm/bt26c6derozjvvVFRUVCD3AQAMs3h3ofILzQt943qWRnerZ/cMn998o5ugoCD17t1bvXv3DtQe\nAIDh8gu9hj7FUr3utPCRcAAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi\n8AAAGMjvgf/4448VFxcnSfryyy8VExOjkSNHasaMGTr1OTerVq3S0KFDNWLECG3cuFGSVFhYqAkT\nJmjkyJG67777+IhaAACqwK+BX7RokR577DGVlJRIklJSUpSYmKjly5fL6/Vqw4YNysvL07Jly7Ri\nxQq9/PLLSktLU3FxsTIyMtS5c2ctX75cQ4YM4QNuAACoAr8Gvm3btkpPT/edqe/bt089e/aUJEVH\nR2vz5s3as2ePIiMjFRwcLKfTqbZt2yonJ0c7d+5UdHS0JKlv377asmWLP6cCAGAUvwb+hhtuUJ06\ndXzf//yj58PCwlRQUCCXy6Xw8PByx10ul1wul8LCwspdFwAAVE5AX2QXFPS/f87lcikiIkJOp1Nu\nt9t33O12Kzw8vNxxt9utiIiIQE4FAKBGC2jgu3Tpou3bt0uSsrKyFBUVpW7dumnHjh0qLi5WQUGB\ncnNz1alTJ0VGRiorK6vcdQEAQOX85ufBny+WZUmSpkyZomnTpqmkpETt27fXgAEDZFmWRo0apdjY\nWHk8HiUmJiokJEQxMTFKSkpSbGysQkJClJaWFoipAAAYwfL+/InxGi4vj+fpAfN5NWf7SR05YfcO\n/7ggVErsVV+SZfcUPzH39rPjtmvaNPyMl/FGNwAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBg\nIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCHwNUlpaqhkzkpWQcLfGjbtXX3110HfZCy+kae3a\n1b7vy8rK9Nhjj2jbti02LAUA2I3A1yBbtmTL4/FowYK/afToe/TXv/5Fx44d06RJE/XBB//yfWrf\nt99+o/Hj79Wnn+73HQMA1C4Evga56KKLVVZWKq/XK7fbJYcjWCdPnlB8/H268caBOvXBgCdPntSU\nKY8rMjJKBn1YIACgCgLyefA4P+rVq6fDhw8rNnaojh8/rlmznleLFi3VokVLbd262Xe9Dh062rgS\nAFAdcAZfg6xa9ZquvLK3MjLWaPHi1/T00zNUUlJi9ywAQDXEGXwNEh4eIYfD4fu6rKxUHk+ZpGB7\nhwEAqh0CX4OMGDFSKSlPaty4e1VSUqKxY8epbt16vst/7QV1vMgOAGony2vQq7Dy8grsngDA77ya\ns/2kjpywe4d/XBAqJfaqL8nUO+fm3n523HZNm4af8TLO4M+aMfeLKmDqf2QAwGwE/hws3l2o/EIz\nQ9+4nqXR3epVfEUAQLVE4M9BfqHXyIeZfmLmHRcAqC34NTkAAAxE4AEAMBCBBwDAQAQeAAADEXgA\nAAxE4AEAMBCBBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQe\nAAADEXgAAAxE4AEAMBCBBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQeAAADEXgAAAxE4AEAMBCB\nBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQeAAADOez4R2+99VY5nU5JUps2bTR27FhNmTJFQUFB\n6tixo6ZPny7LsrRq1SqtXLlSDodDCQkJ6tevnx1zAQCocQIe+KKiIknSsmXLfMfuv/9+JSYmqmfP\nnpo+fbo2bNig7t27a9myZVqzZo2KiooUExOjPn36KCQkJNCTAQCocQIe+E8//VQnT55UfHy8SktL\n9fDDD2vfvn3q2bOnJCk6OloffPCBgoKCFBkZqeDgYAUHB6tt27bKyclR165dAz0ZAIAaJ+CBr1+/\nvuLj4zVs2DAdPHhQ99xzT7nLw8LCVFBQIJfLpfDw8HLHXS5XoOcCAFAjBTzwF198sdq2bev7umHD\nhtq/f7/vcpfLpYiICDmdTrndbt9xt9utiIiIQM8FAKBGCvir6FevXq3U1FRJ0n/+8x+53W5dffXV\n2r59uyQpKytLUVFR6tatm3bs2KHi4mIVFBQoNzdXHTt2DPRcAABqpICfwd9+++2aMmWKYmNjZVmW\nUlJS1LBhQ02bNk0lJSVq3769BgwYIMuyNGrUKMXGxsrj8SgxMZEX2AEAUEkBD3xwcLDS0tJ+cfzn\nr6o/ZdiwYRo2bFggZgEAYBTe6AYAAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEH\nAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETg\nAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMR\neAAADETgAQAwEIEHAMBABB4AAAMReCBA9u79RBMmjJUkHTiQo3Hj7tWECWOVmDhBP/yQ77uex+PR\npEkTtXbtarumAjAAgQcCYPnyJZo162mVlJRIkubOTdPDDz+iefNe1DXXXKtXX13iu+6iRQvkchXI\nsiy75gIwAIEHAqB16zaaOXO2vF6vJOmJJ55Rhw4dJUmlpaWqW7euJOmf/1yvoKAgXXllb991AeBs\nEHggAK65pr/q1Knj+75JkwskSXv2fKzMzNc1YkSsvvjic61f/47uued+4g7gnDnsHgDUVhs2vKul\nS1/R7Nlz1aBBQy1fvlR5eXmaOPF+fffdYTkcDrVs2Uq9el1l91QANRCBB2zwzjvr9MYbmZo370VF\nRERIkh54YKLv8r/97a9q0uQC4g7grBF4IIAsy5LH49HcuWm68MILlZw8WZJ0+eWRio8fa/M6ACYh\n8ECAtGjRUgsX/k2StG7dht+87t133xeISQAMRuBRC9WWF7Dxa3ZAbUbgUSst3l2o/EIzQ9+4nqXR\n3erZPQOAzQg8aqX8Qq+OnLB7hb+YeccFQNXwe/AAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMA\nYCACDwCAgQg8AAAGIvAAABioWr+Tncfj0YwZM/TZZ58pODhYM2fO1EUXXWT3LAAAqr1qfQa/fv16\nlZSUaMWKFfrzn/+s1NRUuycBAFAjVOvA79y5U3379pUkde/eXZ988onNiwAAqBmq9UP0LpdLTqfT\n932dOnXk8XgUFFQ97pc0rmfJ1A/2+OlnMxe3Xc3G7VezmXr7VbfbrloH3ul0yu12+76vKO5Nm4YH\nYpbP5D9GBPTfw/nDbVezcfvVbNx+gVE9ToXPIDIyUllZWZKkXbt2qXPnzjYvAgCgZrC8Xm+1fZzE\n6/VqxowZysnJkSSlpKSoXbt2Nq8CAKD6q9aBBwAAZ6daP0QPAADODoEHAMBABB4AAAMReAAADFSt\nfw++tistLVVmZqYOHTqkK6+8Up06dVLjxo3tngUY69FHHz3jZSkpKQFcgnNRnd4QzU4Evhp7/PHH\n1bx5c33wwQfq2rWrkpKStGjRIrtnoZI++ugjrVmzRqWlpfJ6vcrLy9PLL79s9yz8hj/96U+SpBUr\nVuiKK65QZGSk9uzZo927d9u8DFURHx+vV155xe4ZtuMuTjX29ddf68EHH1TdunXVv39/FRQU2D0J\nVTBjxgxdeeWVcrlcatmypRo2bGj3JFQgOjpa0dHROnnypO6991716NFDo0ePVn5+vt3TUAUNGjTQ\n+vXrlZubq3//+9/697//bfckW3AGX42VlZX5/sPicrl4yKmGadSokQYNGqTs7GxNnDhRI0eOtHsS\nKunEiRPasmWLunbtqo8++kjFxcV2T0IVHD16VEuWLCl3bNmyZTatsQ+Br8YeeughxcTEKC8vT8OH\nD1dycrLdk1AFderU0WeffabCwkLl5ubqxx9/tHsSKumZZ57RrFmzdPDgQXXo0EHPPvus3ZNQBafH\nvLbeQeOd7GqA/Px8NWrUSJZVvT6pCL/ts88+0+eff65mzZrpmWee0S233KLRo0fbPQu/oaSkRMHB\nwb8ahJCQEBsW4WxkZGRo8eLFvte/OBwOvfvuu3bPCjgCX41lZ2dr8eLFKioqkiRZlqWlS5favApV\nkZ+fr5MnT/q+b9WqlY1rUJHExETNmTNH/fv3/8Vl77//vg2LcDZuvvlmvfTSS1q4cKFuvPFGLV26\nVH/5y1/snhVwPERfjaWkpCg5OVnNmze3ewrOwrRp07RlyxY1adLEd2zlypU2LkJF5syZI4mY13TN\nmjVT8+bN5XK5dNVVV2n+/Pl2T7IFga/GWrZsqT59+tg9A2cpJydH7733Hk+t1EDr16/Xa6+95nuI\n99ixY3rzzTftnoVKcjqdeu+99xQUFKSMjAwdO3bM7km24GXZ1ViTJk30+OOPKyMjQytWrODsr4Zp\n2rSpXC6X3TNwFubOnasJEyaoRYsWGjJkiDp16mT3JFTBzJkz1apVKyUmJurLL7/UY489ZvckW3AG\nX421atVKlmXp6NGjdk9BFYwYMULST8+/33DDDWrTpo0sy5JlWVqxYoXN61AZTZs21RVXXKGMjAwN\nHTpUmZmZdk9CFdSrV0979+7VoUOH1K9fP3Xs2NHuSbYg8NXYhAkTtHHjRh04cEDt2rXTddddZ/ck\nVEJaWprvax6er5lCQkK0fft2lZWVKSsrq9Y+xFtT8S6gP+Eh+mrsueee0+rVqxUcHKy1a9cqNTXV\n7kmohNatW6t169YqLS3Vm2++qczMTK1Zs0Yvvvii3dNQSTNmzFBZWZnuv/9+vf7660pISLB7EqqA\ndwH9CWfw1diOHTt8D+neddddGjZsmM2LUBWTJk3SDTfcoJ07d6pZs2Zyu912T0IlXXjhhbrwwgsl\nSfPmzbN5DaqKdwH9Se38qWuI0tJSlZWVSeLTkWqi0NBQjR07Vs2bN1dqaqqOHDli9ySgVjj1LqB7\n9+7V8OHDNW7cOLsn2YIz+Gps4MCBiomJUffu3bV7924NHDjQ7kmogqCgIH3//fdyu91yu93l3vAG\ngP/06tVL77zzTq1/F1ACXw2desVuo0aNdMstt6ioqEht27aV0+m0eRmqYvz48Vq/fr0GDx6s6667\nToMHD7Z7EiopPT293PcOh0MtWrTQwIEDFRwcbNMqVOTUb7Ccrrb+BguBr4Zyc3PL3eP0eDzKzMxU\nvXr1NGTIEBuXoTL279+vuXPnqkmTJho4cKAefvhhWZal3//+93ZPQyXl5OSobt26ioqK0q5du3T4\n8GE1a9ZM2dnZmj17tt3zcAb8Bkt5vBd9NffVV18pKSlJ7dq109SpUzmLrwFGjBihiRMn6vjx45o6\ndaoyMzM6fANxAAAFrklEQVTVpEkTxcfH6/XXX7d7Hiph1KhR5T73YcyYMXrllVcUExOjjIwMG5eh\nMnbv3q3MzEwVFhb6jqWkpNi4yB6cwVdjy5cv1+LFizV16lRde+21ds9BJYWEhOjqq6+WJC1dulTt\n2rWTJIWFhdk5C1XgcrmUn5+vxo0bKz8/XwUFBSouLi4XDFRfM2bMUFxcnO9zIGrr2TyBr4a+++47\nPfroo2rYsKFef/11NWzY0O5JOEs/f7721G9EoPqbMGGChg8fLqfTKbfbrWnTpmnx4sW6/fbb7Z6G\nSggPD9ett95q9wzb8RB9NRQVFaWQkBBdddVV5Y5bllXuOSZUT71791afPn3k9Xq1detW3+24detW\nbd682eZ1qCyPx6P8/Hw1adKk1p4B1jT/+te/JP30qY2XXXaZLr30Ukk//bfzD3/4g53TbEHgq6Ft\n27ZJ+un/lD+/eSzLUq9eveyahUratm3bL247iduvJsnOztbixYtVVFQk6afb7ufPyaN6mjJlyhnv\njNXG5+AJPACc5qabblJycrKaN2/uO9a+fXsbF6Eq8vPztX//fl199dV69dVXdfPNN6tBgwZ2zwo4\n3hoNAE7TsmVL9enTR+3bt/f9DzVHYmKi79GXiIgIPfLIIzYvsgcvsgOA0zRp0kSPP/64unTp4vuo\n3zO9iQqqn8LCQvXv31+SdMstt9TaX08l8ABwmlatWsmyLB09etTuKTgLDodD2dnZuvzyy7Vnzx7V\nqVPH7km24Dl4APivw4cPq0WLFvriiy9+cdnvfvc7GxbhbBw8eFDPPvusDh48qPbt2+uRRx7RRRdd\nZPesgCPwAPBfzzzzjKZOnaq4uLhfXLZs2TIbFuFsffbZZ/r888918cUX65JLLrF7ji0IPACc5qWX\nXtI999xj9wycpaVLl+qtt95S9+7d9dFHH2nAgAG18vbkVfQAcJpNmzaptLTU7hk4S2+99ZZee+01\nJScnKyMjQ+vWrbN7ki14kR0AnObYsWPq27evWrduraCgoFr7caM1mcPxU96Cg4MVEhJi8xp7EHgA\nOM2CBQt4e9oaLDIyUhMmTFCPHj20c+dOXXHFFXZPsgXPwQPAaQ4ePKi3335bpaWl8nq9ysvL05NP\nPmn3LFTCypUrddtttyk7O1t79+5VgwYNfvVFk7UBz8EDwGkmTZoky7K0c+dOffvtt/rhhx/snoRK\nmDdvnrKzs1VaWqprr71WgwcP1rZt25Senm73NFsQeAA4TWhoqMaOHavmzZsrNTVVR44csXsSKmHT\npk2aO3eu6tevL0lq06aNnn/+eb3//vs2L7MHgQeA0wQFBen777+X2+3WiRMndPLkSbsnoRJCQ0MV\nFFQ+a8HBwQoLC7Npkb0IPACcZty4cVq/fr1uueUWXXfddbrqqqvsnoRKqF+/vr766qtyx77++utf\nRL+24EV2APArCgoK9O2336pNmza19gywpjlw4IASExPVu3dvtW7dWocPH1Z2drZSU1N16aWX2j0v\n4Ag8AJzm7bff1sKFC1VWVqYbb7xRQUFBeuCBB+yehUr48ccftWHDBuXl5ally5bq16+fnE6n3bNs\nQeAB4DR33HGHlixZonvuuUdLlizR0KFDlZmZafcsoEpq5xMTAPAbgoKCVLduXd/XoaGhNi8Cqo7A\nA8BpevToocTERH3//fd6/PHH1bVrV7snAVXGQ/QA8Cs2bdqkAwcO6He/+5369+9v9xygygg8APzX\nmZ5ntyxLQ4YMCfAa4NzwYTMA8F+5ubmyLEter1d///vfNWjQILsnAWeNM3gA+BVxcXFatmyZ3TOA\ns8aL7AAAMBCBBwDAQDxEDwD/lZiY6Pt669atvvegtyxLaWlpds0CzgqBB4D/2rZtm+9Fdj9nWZZ6\n9epl0yrg7BB4AAAMxHPwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAY6P8DjWvfpVxhO8MAAAAASUVO\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d8db048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.tech_left, tech_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAF/CAYAAAClndsfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl0k3WixvEnJS3QpAWLgGxWZHFQKFpbNqcVuS6ICCiC\ntlgUARGhoGGQpbK4IFXEGa6gzHUcgYJlGanbMC6g2KlsclGLgBWroHfEsVDBJnQluX+gGUGxiyZv\n++v3c86cQ9+k8PRkjt+8SZrYfD6fTwAAwCghVg8AAAC/PQIPAICBCDwAAAYi8AAAGIjAAwBgIAIP\nAICB7IH6i8vLyzVz5kx99dVXKisr0/jx43XOOedo3LhxOu+88yRJycnJuvbaa7V27VqtWbNGdrtd\n48ePV9++fVVSUqKpU6eqsLBQDodD6enpioqKCtRcAACMYgvU78GvX79eeXl5mjFjho4dO6bBgwdr\nwoQJcrvdGjVqlP96BQUFuuOOO7R+/XqVlpYqKSlJL7zwglatWiWPx6OJEydqw4YNev/995WWlhaI\nqQAAGCdgD9H3799fkyZNkiR5vV7Z7Xbt2bNHmzdv1q233qq0tDR5PB7l5uYqNjZWoaGhcjqdio6O\nVl5ennbt2qXExERJUkJCgrZu3RqoqQAAGCdgD9GHh4dLktxutyZPnqx7771XpaWlGj58uC688EIt\nXbpUixcvVpcuXRQREeH/PofDIbfbLbfbLYfD4T9WVFQUqKkAABgnYIGXpEOHDmnixIkaMWKErrvu\nOhUVFfljftVVV+mhhx5SfHy8PB6P/3s8Ho8iIiLkdDr9xz0ejyIjIyv99woKuBMAAKg/mjePOONl\nAXuI/vDhw7rjjjs0depU3XjjjZKk0aNHKzc3V5K0ZcsWde3aVTExMdq5c6fKyspUVFSk/Px8de7c\nWbGxscrOzpYkZWdnKy4uLlBTAQAwTsBeZPfwww/rtddeU/v27f3HXC6XHnvsMdntdrVo0UIPPvig\nHA6H1q1bpzVr1sjr9Wr8+PG66qqrVFJSomnTpqmgoEBhYWFauHChmjVr9ov/JmfwAID65JfO4AMW\neCsQeABAfWLJQ/QAAMA6BB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBA\nBB4AAAMReAAADETgAQAwEIEHAMBABB4AAAMReAAADETgAQAwEIEHAMBABB4AAAPZrR4AALVdRUWF\n5s9/QF9//bXKysp0222j1bJlS/3pT48rJCREoaFhmjXrAZ11VpQkyev1aurUe5SQcLmGDBkqt9ut\nOXNmqqSkWKGhoZo9+yFFRTWz+KeC6Qg8AFTijTf+oaZNz9KsWQ/pu+++0+23J6lNm7a699771LFj\nJ7300nqtXLlcqan3SpKeeeZpud1FstlskqQNG15Rx46dNH58ql555UU9/3yGJk68x8ofCfUAgQeA\nSlxxxZXq2/e/JEk+n1d2u10PPPCI/yy8oqJCDRs2lCS9/fZGhYSEqGfP3vL5fJKkjh076YsvDkqS\n3G63QkNDLfgpUN/wHDwAVKJx48YKDw/X8eMezZo1XXfeebc/7rt3f6isrHW6+eZkffbZp9q48XWN\nGXOXP+6SFBERqffe26Zbbx2u1atX6rrrBln1o6Ae4QweAKrg3//+Wmlp9+nGG4fpyiuvkSRt2vSG\nVqx4TgsWLFKTJk21atUKFRQUaNKku/T114dkt9vVqlVrvfTSeo0YcZsGDbpB+fmfKi3tPi1fnmnx\nTwTTEXgAqERh4RG5XBM1Zcp0xcbGSZJef32DXn45S08++WdFRkZKku6+e5L/e/761/9Rs2Znq2fP\n3nr77Y1yOBySpKZNm+r4cU/wfwjUOzbfjx9HquMKCoqsngDAQH/60+N6++2NOvfcaEknXyX/2Wf5\natWqlRwOpyTp4otjNXr0OP/3/BD4wYNv1OHDh/Xoow+puLhYFRUVGjPmLsXF9bDkZ4FZmjePOONl\nBB4AgDrqlwLPQ/QA6hhjzkkqYbN6AOo4Ag+gzlmWW6LCEjNDH9XIpttjGlk9AwYg8ADqnMISnw4f\nt3pFoJh5xwXBx+/BAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBg\nIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIAIPAICBCDwAAAYi8AAA\nGIjAAwBgIAIPAICBCDwAAAYi8AAAGIjAAwBgIHug/uLy8nLNnDlTX331lcrKyjR+/Hh16NBB06dP\nV0hIiDp16qQ5c+bIZrNp7dq1WrNmjex2u8aPH6++ffuqpKREU6dOVWFhoRwOh9LT0xUVFRWouQAA\nGCVggX/llVcUFRWlBQsW6NixYxo8eLC6dOkil8ul+Ph4zZkzR5s2bVL37t2VkZGh9evXq7S0VElJ\nSerTp48yMzN1wQUXaOLEidqwYYOefvpppaWlBWouAABGCdhD9P3799ekSZMkSV6vV3a7XXv37lV8\nfLwkKTExUVu2bNHu3bsVGxur0NBQOZ1ORUdHKy8vT7t27VJiYqIkKSEhQVu3bg3UVAAAjBOwwIeH\nh8vhcMjtdmvy5Mm655575PV6/Zc7HA4VFRXJ7XYrIiLilONut1tut1sOh+OU6wIAgKoJ6IvsDh06\npNtuu01DhgzRwIEDFRLyn3/O7XYrMjJSTqdTHo/Hf9zj8SgiIuKU4x6PR5GRkYGcCgCAUQIW+MOH\nD+uOO+7Q1KlTdeONN0qSunTpoh07dkiSsrOzFRcXp5iYGO3cuVNlZWUqKipSfn6+OnfurNjYWGVn\nZ59yXQAAUDUBe5Hd0qVLVVRUpCVLlmjJkiWSpLS0NM2bN0/l5eXq0KGD+vfvL5vNppEjRyo5OVle\nr1cul0thYWFKSkrStGnTlJycrLCwMC1cuDBQUwEAMI7N5/P5rB7xWyko4Hl6wHw+PbGjWIePW70j\nMM4Ol1w9GkuyWT0FdUDz5hFnvIw3ugEAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAM\nROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAA\nAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcA\nwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROAB\nADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4\nAAAMROABADAQgQcAwEABD/yHH36olJQUSdLevXuVmJiolJQUpaSk6B//+Ickae3atRo6dKhuvvlm\nbd68WZJUUlKi1NRUjRgxQnfeeacKCwsDPRUAAGPYA/mXP/PMM3r55ZflcDgkSXv27NGoUaM0atQo\n/3UKCgqUkZGh9evXq7S0VElJSerTp48yMzN1wQUXaOLEidqwYYOefvpppaWlBXIuAADGCOgZfHR0\ntBYvXiyfzydJ+uijj7R582bdeuutSktLk8fjUW5urmJjYxUaGiqn06no6Gjl5eVp165dSkxMlCQl\nJCRo69atgZwKAIBRAhr4q6++Wg0aNPB/3b17d02bNk0rV65Uu3bttHjxYnk8HkVERPiv43A45Ha7\n5Xa7/Wf+DodDRUVFgZwKAIBRgvoiu6uuukoXXnih/8/79u2T0+mUx+PxX+eH4P/4uMfjUWRkZDCn\nAgBQpwU18KNHj1Zubq4kacuWLeratatiYmK0c+dOlZWVqaioSPn5+ercubNiY2OVnZ0tScrOzlZc\nXFwwpwIAUKcF9EV2P7DZbJKkuXPn6qGHHpLdbleLFi304IMPyuFwaOTIkUpOTpbX65XL5VJYWJiS\nkpI0bdo0JScnKywsTAsXLgzGVAAAjGDz/fAKOAMUFPA8PWA+n57YUazDx63eERhnh0uuHo0l2aye\ngjqgefOIM17GG90AAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAg\nAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEqDfz+/ft/cuyDDz4IyBgAAH5re/Z8pNTUcZKk/fvz\nNGHCWKWmjpPLlapvvy2UJGVmrtTo0SkaO3aksrM3n/L9Bw8eUP/+fVVeXh7s6b+K/UwX7Ny5U16v\nV7NmzdLDDz8sn88nm82miooKzZkzR2+88UYwdwIAUG2rVi3XG2/8Q40bh0uSFi1aqHvvvU8dO3bS\nSy+t18qVyzVq1Fj97W+rtWbNiyouLtaoUclKTOwrSfJ43Fq8+I8KC2to4U9RM2cM/JYtW/Tee+/p\nm2++0X//93//5xvsdt1yyy1BGQcAwK/Rtm07zZu3QA89NFuS9MADj6hZs7MlSRUVFWrYsKEaNWqk\nc85ppeLiYh0/7pHNdvLBbZ/Pp8cee0Tjxk3UjBlTLPsZauqMgZ80aZIk6cUXX9SQIUOCNggAgN/K\n5Zf306FDX/m//iHuu3d/qKysdVqy5BlJUvPmLXTrrcPk9XqVkjJKkvTXv/6P+vT5vTp27CTpZPDr\nkjMG/gdxcXF69NFHdfTo0VOOz58/P2CjAAAIlE2b3tCKFc9pwYJFatKkqXJy3lFh4RH97W+vyOfz\nyeWaqG7dYvTmm6+pefMWevXVl3TkyBG5XBO1ePH/WD2/yioN/D333KP4+HjFx8f7j9lstoCOAgAg\nEF5/fYNefjlLTz75Z0VGRkqSIiKaqGHDhgoNDf3+6wi53W6tXp3l/75hwwbpj39cYsnmmqo08CdO\nnNC0adOCsQUAgICw2Wzyer1atGihzjnnHKWlTZUkXXLJpbrjjju1c+eFuvPO2xUSEqKYmIsVH9/z\n9L8h+KN/JZuvkicVHn74YfXu3VsJCQkKCwsL1q4aKSgosnoCgIDz6YkdxTp83OodgXF2uOTq0Vh1\nMSgIvubNI854WaVn8K+99ppWrlx5yjGbzaZ9+/b9+mUAgHqmbr1QrWZqx52zSgOfk5MTjB0AgHpi\nWW6JCkvMC31UI5tuj2lk9Qy/SgO/ePHinz0+ceLE33wMAMB8hSU+Q59iqV13Wip9q9ofP0VfXl6u\nt956S0eOHAnoKAAA8OtUegafmpp6ytcTJkzQqFGjAjYIAAD8etX+NDm3261Dhw4FYgsAAPiNVHoG\n369fv1O+PnbsmEaPHh2wQQAA4NerNPArVqzwv3OdzWZTZGSknE5nwIcBAICaqzTwrVu3VmZmprZt\n26aKigr16tVLKSkpCgmp9qP7AAAgSCoN/IIFC3Tw4EENHTpUPp9PL7zwgv7v//5PaWlpwdgHAABq\noEpvdPPiiy+qQYMGkqS+fftq4MCBAR8GAABqrtLH2b1er06cOOH/+sSJE7LbK71fAAAALFRpqa+/\n/nqlpKRo4MCB8vl8+vvf/67rrrsuGNsAAEAN/WLgjx07puHDh6tLly7atm2btm3bpttuu01DhgwJ\n1j4AAFADZ3yIfu/evRowYIA++ugjXX755Zo2bZoSEhL0+OOP6+OPPw7mRgAAUE1nDHx6erqeeOIJ\nJSYm+o+5XC7Nnz9f6enpQRkHAABq5oyB/+6779SzZ8+fHE9ISFBhYWFARwEAgF/njIE/ceKEvF7v\nT457vV5VVFQEdBQAAPh1zhj4uLi4n/0s+Keeekpdu3YN6CgAAPDrnPFV9FOmTNHYsWP18ssvKyYm\nRl6vV3v37lVUVJSefvrpYG4EAADVdMbAO51OrVq1Stu3b9fevXvVoEED3XrrrYqLiwvmPgAAUAO/\n+HvwISEh6t27t3r37h2sPQAA4DfAR8IBAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAA\nBiLwAAAYiMADAGCggAf+ww8/VEpKiiTp4MGDSkpK0ogRIzR37lz5fD5J0tq1azV06FDdfPPN2rx5\nsySppKREqampGjFihO68804+ohYAgGoIaOCfeeYZ3X///SovL5ckzZ8/Xy6XS6tWrZLP59OmTZtU\nUFCgjIwMrV69Ws8++6wWLlyosrIyZWZm6oILLtCqVas0ZMgQPuAGAIBqCGjgo6OjtXjxYv+Z+t69\nexUfHy9JSkxM1JYtW7R7927FxsYqNDRUTqdT0dHRysvL065du5SYmChJSkhI0NatWwM5FQAAowQ0\n8FdffbUaNGjg//qH0EuSw+FQUVGR3G63IiIiTjnudrvldrvlcDhOuS4AAKiaoL7ILiTkP/+c2+1W\nZGSknE6nPB6P/7jH41FERMQpxz0ejyIjI4M5FQCAOi2oge/SpYt27NghScrOzlZcXJxiYmK0c+dO\nlZWVqaioSPn5+ercubNiY2OVnZ19ynUBAEDV/OLnwf9WbDabJGn69OmaNWuWysvL1aFDB/Xv3182\nm00jR45UcnKyvF6vXC6XwsLClJSUpGnTpik5OVlhYWFauHBhMKYCAGAEm+/HT4zXcQUFPE8PmM+n\nJ3YU6/Bxq3cExtnhkqtHY0k2q6cEiLm3nxW3XfPmEWe8jDe6AQDAQAQeAAADEXgAAAxE4AEAMBCB\nBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQeAAADEXgAAAxE\n4AEAMBCBBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQeAAADEXgAAAxE4AEAMBCBBwDAQAQeAAAD\nEXgAAAxE4AEAMJDd6gGonoyM5/Tuu/9UeXm5brjhJr333nYVFh6RJB069JW6do3R3Lnz9PLLWXr5\n5Sw1aNBAt902Wn36/N7i5QCAYCLwdciuXTv10Ue5Wrr0ryouLlZmZoYeeOARSVJRUZEmTRqnSZNc\nOnLksF54YY2efXalSktLdPfdYxQf31OhoaEW/wQAgGAh8HXIe+9t1/nnd9SMGVPk8Xh0992T/Zc9\n++xS3XTTLYqKaqacnHfUrVt32e122e1OtWnTTvn5+/W7311o4XoAQDDxHHwdcvTot8rL+1gPPfSo\n/vCHGXrwwfslSd9+W6j//d/3NGDA9ZKk48ePy+Fw+r8vPDxcbrfbks0AAGtwBl+HNGnSVNHR58lu\nt+vcc6MVFtZQ3377rd5+e5Ouvvpa2Ww2SVJ4uEPHjx/3f9/x48cVERFp1WwAgAU4g69DYmIu1vbt\nWyVJhw8XqKSkRE2aNNHOnTvUq1cf//UuvPAi5ea+r7KyMrndbh08+LnOP7+DVbMBABbgDL4O6dPn\n9/rgg10aO3akvF6fpky5TyEhIfryy4Nq3bqN/3pRUc100023aMKEMfJ6fbrzzgm8wA4A6hmbz+fz\nWT3it1JQUGT1BAAB59MTO4p1+Hjl16yLzg6XXD0aS7JZPSVAzL39rLjtmjePOONlnMHXmDH3iyph\n6n9kAMBsBP5XWJZbosISM0Mf1cim22MaWT0DAFBDBP5XKCzxGfkw00lm3nEBgPqCV9EDAGAgAg8A\ngIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMAD\nAGAgAg8AgIEIPAAABrLk8+BvuOEGOZ1OSVK7du00btw4TZ8+XSEhIerUqZPmzJkjm82mtWvXas2a\nNbLb7Ro/frz69u1rxVwAAOqcoAe+tLRUkpSRkeE/dtddd8nlcik+Pl5z5szRpk2b1L17d2VkZGj9\n+vUqLS1VUlKS+vTpo7CwsGBPBgCgzgl64D/++GMVFxdr9OjRqqio0L333qu9e/cqPj5ekpSYmKh3\n331XISEhio2NVWhoqEJDQxUdHa28vDx169Yt2JMBAKhzgh74xo0ba/To0Ro2bJgOHDigMWPGnHK5\nw+FQUVGR3G63IiIiTjnudruDPRcAgDop6IE/77zzFB0d7f9z06ZNtW/fPv/lbrdbkZGRcjqd8ng8\n/uMej0eRkZHBngsAQJ0U9FfRv/DCC0pPT5ck/fvf/5bH49Fll12mHTt2SJKys7MVFxenmJgY7dy5\nU2VlZSoqKlJ+fr46deoU7LkAANRJQT+Dv+mmmzR9+nQlJyfLZrNp/vz5atq0qWbNmqXy8nJ16NBB\n/fv3l81m08iRI5WcnCyv1yuXy8UL7AAAqKKgBz40NFQLFy78yfEfv6r+B8OGDdOwYcOCMQsAAKPw\nRjcAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCA\ngQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMAYCACDwCAgQg8AAAGIvAAABiIwAMA\nYCACDwCAgQg8AAAGIvAAABiIwANBsmfPR0pNHSdJysv7WGPHjtSECWP1pz8tkM/n81/v22+/1S23\n3Kjy8nKrpgIwAIEHgmDVquV67LGH/dF+7LF5mjx5qpYseUYOh1NvvvmaJGn79q1yuSbo6NFCK+cC\nMACBB4Kgbdt2mjfvP2fqhw9/o65du0mSunaNUW7uB5KkkJAQLVr0tCIiIi3bCsAMBB4Igssv76cG\nDRr4v27duo0++GCXJOndd/+p4uJiSVJ8fE9FRjaxZCMAsxB4wAIzZsxRRsYyTZ58t6KiotS0aVOr\nJwEwDIEHLLB1a47mzHlIixY9pe++O6b4+F5WTwJgGLvVA4D6xGazSZLatj1XkyePV6NGjRQbG69e\nvfqcfs3gjwNgFAIPBEmrVq21dOlfJUmXXZagyy5LOON11617KVizABiKwKMe8lV+FSPwKABQnxF4\n1EvLcktUWGJm6KMa2XR7TCOrZwCwGIFHvVRY4tPh41avCBQz77gAqB5eRQ8AgIEIPAAABiLwAAAY\niMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAA\nBiLwAAAYiMADAGAgAg8AgIHsVg/4JV6vV3PnztUnn3yi0NBQzZs3T+eee67VswAAqPVq9Rn8xo0b\nVV5ertWrV+sPf/iD0tPTrZ4EAECdUKsDv2vXLiUkJEiSunfvro8++sjiRQAA1A21+iF6t9stp9Pp\n/7pBgwbyer0KCakd90uiGtkk+ayeERAnfzZzcdvVbdx+dZupt19tu+1qdeCdTqc8Ho//68ri3rx5\nRDBm+U39r8ig/nv47XDb1W3cfnUbt19w1I5T4TOIjY1Vdna2JOmDDz7QBRdcYPEiAADqBpvP56u1\nj5P4fD7NnTtXeXl5kqT58+erffv2Fq8CAKD2q9WBBwAANVOrH6IHAAA1Q+ABADAQgQcAwEAEHgAA\nA9Xq34Ov7yoqKpSVlaWvvvpKPXv2VOfOnRUVFWX1LMBYM2bMOONl8+fPD+IS/Bq16Q3RrETga7HZ\ns2erZcuWevfdd9WtWzdNmzZNzzzzjNWzUEXvv/++1q9fr4qKCvl8PhUUFOjZZ5+1ehZ+wbXXXitJ\nWr16tS655BLFxsZq9+7dys3NtXgZqmP06NF67rnnrJ5hOe7i1GJffvmlJk+erIYNG6pfv34qKiqy\nehKqYe7cuerZs6fcbrdat26tpk2bWj0JlUhMTFRiYqKKi4s1duxYXXrppbr99ttVWFho9TRUQ5Mm\nTbRx40bl5+fr888/1+eff271JEtwBl+LnThxwv8fFrfbzUNOdcxZZ52lgQMHKicnR5MmTdKIESOs\nnoQqOn78uLZu3apu3brp/fffV1lZmdWTUA1HjhzR8uXLTzmWkZFh0RrrEPha7J577lFSUpIKCgo0\nfPhwpaWlWT0J1dCgQQN98sknKikpUX5+vr777jurJ6GKHnnkET322GM6cOCAOnbsqEcffdTqSaiG\n02NeX++g8U52dUBhYaHOOuss2Wy165OK8Ms++eQTffrpp2rRooUeeeQRDRo0SLfffrvVs/ALysvL\nFRoa+rNBCAsLs2ARaiIzM1PLli3zv/7FbrfrjTfesHpW0BH4WiwnJ0fLli1TaWmpJMlms2nFihUW\nr0J1FBYWqri42P91mzZtLFyDyrhcLj3xxBPq16/fTy576623LFiEmrj++uv1l7/8RUuXLtU111yj\nFStW6KmnnrJ6VtDxEH0tNn/+fKWlpally5ZWT0ENzJo1S1u3blWzZs38x9asWWPhIlTmiSeekETM\n67oWLVqoZcuWcrvd6tWrl5YsWWL1JEsQ+FqsdevW6tOnj9UzUEN5eXl68803eWqlDtq4caOef/55\n/0O8R48e1SuvvGL1LFSR0+nUm2++qZCQEGVmZuro0aNWT7IEL8uuxZo1a6bZs2crMzNTq1ev5uyv\njmnevLncbrfVM1ADixYtUmpqqlq1aqUhQ4aoc+fOVk9CNcybN09t2rSRy+XSwYMHdf/991s9yRKc\nwddibdq0kc1m05EjR6yegmq4+eabJZ18/v3qq69Wu3btZLPZZLPZtHr1aovXoSqaN2+uSy65RJmZ\nmRo6dKiysrKsnoRqaNSokfbs2aOvvvpKffv2VadOnayeZAkCX4ulpqZq8+bN2r9/v9q3b68rr7zS\n6kmogoULF/r/zMPzdVNYWJh27NihEydOKDs7u94+xFtX8S6gJ/EQfS32+OOP64UXXlBoaKhefPFF\npaenWz0JVdC2bVu1bdtWFRUVeuWVV5SVlaX169frz3/+s9XTUEVz587ViRMndNddd2ndunUaP368\n1ZNQDbwL6EmcwddiO3fu9D+ke9ttt2nYsGEWL0J1TJkyRVdffbV27dqlFi1ayOPxWD0JVXTOOefo\nnHPOkSQ9+eSTFq9BdfEuoCfVz5+6jqioqNCJEyck8elIdVF4eLjGjRunli1bKj09XYcPH7Z6ElAv\n/PAuoHv27NHw4cM1YcIEqydZgjP4WmzAgAFKSkpS9+7dlZubqwEDBlg9CdUQEhKib775Rh6PRx6P\n55Q3vAEQOD169NDrr79e798FlMDXQj+8Yvess87SoEGDVFpaqujoaDmdTouXoTomTpyojRs3avDg\nwbryyistBt3jAAAGXElEQVQ1ePBgqyehihYvXnzK13a7Xa1atdKAAQMUGhpq0SpU5offYDldff0N\nFgJfC+Xn559yj9Pr9SorK0uNGjXSkCFDLFyGqti3b58WLVqkZs2aacCAAbr33ntls9n0u9/9zupp\nqKK8vDw1bNhQcXFx+uCDD3To0CG1aNFCOTk5WrBggdXzcAb8BsupeC/6Wu6LL77QtGnT1L59e82c\nOZOz+Drg5ptv1qRJk3Ts2DHNnDlTWVlZatasmUaPHq1169ZZPQ9VMHLkyFM+92HUqFF67rnnlJSU\npMzMTAuXoSpyc3OVlZWlkpIS/7H58+dbuMganMHXYqtWrdKyZcs0c+ZMXXHFFVbPQRWFhYXpsssu\nkyStWLFC7du3lyQ5HA4rZ6Ea3G63CgsLFRUVpcLCQhUVFamsrOyUYKD2mjt3rlJSUvyfA1Ffz+YJ\nfC309ddfa8aMGWratKnWrVunpk2bWj0JNfTj52t/+I0I1H6pqakaPny4nE6nPB6PZs2apWXLlumm\nm26yehqqICIiQjfccIPVMyzHQ/S1UFxcnMLCwtSrV69TjttstlOeY0Lt1Lt3b/Xp00c+n0/btm3z\n347btm3Tli1bLF6HqvJ6vSosLFSzZs3q7RlgXfPPf/5T0slPbezatasuuugiSSf/2/n73//eymmW\nIPC10Pbt2yWd/D/lj28em82mHj16WDULVbR9+/af3HYSt19dkpOTo2XLlqm0tFTSydvux8/Jo3aa\nPn36Ge+M1cfn4Ak8AJzmuuuuU1pamlq2bOk/1qFDBwsXoToKCwu1b98+XXbZZVq5cqWuv/56NWnS\nxOpZQcdbowHAaVq3bq0+ffqoQ4cO/v+h7nC5XP5HXyIjI3XfffdZvMgavMgOAE7TrFkzzZ49W126\ndPF/1O+Z3kQFtU9JSYn69esnSRo0aFC9/fVUAg8Ap2nTpo1sNpuOHDli9RTUgN1uV05Oji6++GLt\n3r1bDRo0sHqSJXgOHgC+d+jQIbVq1UqfffbZTy47//zzLViEmjhw4IAeffRRHThwQB06dNB9992n\nc8891+pZQUfgAeB7jzzyiGbOnKmUlJSfXJaRkWHBItTUJ598ok8//VTnnXeeLrzwQqvnWILAA8Bp\n/vKXv2jMmDFWz0ANrVixQq+++qq6d++u999/X/3796+XtyevogeA07zzzjuqqKiwegZq6NVXX9Xz\nzz+vtLQ0ZWZmasOGDVZPsgQvsgOA0xw9elQJCQlq27atQkJC6u3HjdZldvvJvIWGhiosLMziNdYg\n8ABwmqeffpq3p63DYmNjlZqaqksvvVS7du3SJZdcYvUkS/AcPACc5sCBA3rttddUUVEhn8+ngoIC\nPfjgg1bPQhWsWbNGN954o3JycrRnzx41adLkZ180WR/wHDwAnGbKlCmy2WzatWuX/vWvf+nbb7+1\nehKq4Mknn1ROTo4qKip0xRVXaPDgwdq+fbsWL15s9TRLEHgAOE14eLjGjRunli1bKj09XYcPH7Z6\nEqrgnXfe0aJFi9S4cWNJUrt27fTHP/5Rb731lsXLrEHgAeA0ISEh+uabb+TxeHT8+HEVFxdbPQlV\nEB4erpCQU7MWGhoqh8Nh0SJrEXgAOM2ECRO0ceNGDRo0SFdeeaV69epl9SRUQePGjfXFF1+ccuzL\nL7/8SfTrC15kBwA/o6ioSP/617/Url27ensGWNfs379fLpdLvXv3Vtu2bXXo0CHl5OQoPT1dF110\nkdXzgo7AA8BpXnvtNS1dulQnTpzQNddco5CQEN19991Wz0IVfPfdd9q0aZMKCgrUunVr9e3bV06n\n0+pZliDwAHCaW265RcuXL9eYMWO0fPlyDR06VFlZWVbPAqqlfj4xAQC/ICQkRA0bNvT/OTw83OJF\nQPUReAA4zaWXXiqXy6VvvvlGs2fPVrdu3ayeBFQbD9EDwM945513tH//fp1//vnq16+f1XOAaiPw\nAPC9Mz3PbrPZNGTIkCCvAX4dPmwGAL6Xn58vm80mn8+nv//97xo4cKDVk4Aa4wweAH5GSkqKMjIy\nrJ4B1BgvsgMAwEAEHgAAA/EQPQB8z+Vy+f+8bds2/3vQ22w2LVy40KpZQI0QeAD43vbt2/0vsvsx\nm82mHj16WLQKqBkCDwCAgXgOHgAAAxF4AAAMROABADAQgQcAwEAEHgAAA/0/xDMT8g0RPNgAAAAA\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110889780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.tech_right, tech_cats, rot=90, color=plot_color, ylim=(0, 2500))"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAGHCAYAAABcXEBrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ni36qsGoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110866278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axes = plt.subplots(2, 1)\n",
"plot_demo_data(unique_students.tech_right, [\"\"]*len(tech_cats), rot=90, \n",
" ax=axes[0], title='Right ear', color=plot_color, ylim=(0, 2500))\n",
"plot_demo_data(unique_students.tech_left, tech_cats, rot=90, \n",
" ax=axes[1], title='Left ear', color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4393"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_right.count()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4384"
]
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_left.count()"
]
},
{
"cell_type": "code",
"execution_count": 177,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAHDCAYAAAA5nlOcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOSBtAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110b464a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"degree_hl_cats = 'Normal (0-14)', 'Slight (15-25)', 'Mild (26-40)', \\\n",
"'Moderate (41-55)', 'Moderately Severe (56-70)', 'Severe (71-90)', 'Profound (90+)'\n",
"plot_demo_data(unique_students.degree_hl_ad, degree_hl_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAHMCAYAAAA5w7rEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQnohKvCEEEJIL/T/3pHZXB7r5z8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10b332748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"_, axes = plt.subplots(2, 1)\n",
"plot_demo_data(unique_students.degree_hl_ad, [\"\"]*7, rot=90, \n",
" color=plot_color, ax=axes[0], title='Right ear')\n",
"plot_demo_data(unique_students.degree_hl_as, degree_hl_cats, rot=90, \n",
" color=plot_color, ylim=(0,2000), ax=axes[1], title='Left ear');"
]
},
{
"cell_type": "code",
"execution_count": 179,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4298"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.degree_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAGJCAYAAABmViEbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nADAQBQ8AgIEoeAAADETBAwBgoP8HKv6kkCqnztQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109bf3b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"type_hl_cats = 'Sensorineural', 'Conductive', 'Mixed', 'Neural', 'Normal', 'Unknown'\n",
"plot_demo_data(unique_students.type_hl_ad, type_hl_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAGJCAYAAABmViEbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10aa95208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.type_hl_as, type_hl_cats, rot=90, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4236"
]
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4320"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAGSCAYAAAAPR9NDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIYQQ0gNRgSeEEEJ6ICrwhBBCSA/0H1+hwgeovnuBAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110b70390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"type_hl_cats = 'Sensorineural', 'Conductive', 'Mixed', 'Neural', 'Normal', 'Unknown'\n",
"f, axes = plt.subplots(2,1)\n",
"plot_demo_data(unique_students.type_hl_ad, [\"\"]*len(type_hl_cats), rot=90, \n",
" title='Right ear', ax=axes[0], color=plot_color)\n",
"plot_demo_data(unique_students.type_hl_as, type_hl_cats, rot=90, \n",
" title='Left ear', ax=axes[1], color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>male</th>\n",
" <th>_race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>_mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>bilateral_ci</th>\n",
" <th>bilateral_ha</th>\n",
" <th>bimodal</th>\n",
" <th>tech</th>\n",
" <th>implant_category</th>\n",
" <th>age_diag</th>\n",
" <th>sex</th>\n",
" <th>known_synd</th>\n",
" <th>synd_or_disab</th>\n",
" <th>race</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13565</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13566</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13567</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13568</th>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>False</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>51</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 67 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang \\\n",
"13565 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"13566 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"13567 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"13568 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... bilateral_ci \\\n",
"13565 1 3 3 8 ... False \n",
"13566 1 3 3 8 ... False \n",
"13567 1 3 3 8 ... False \n",
"13568 1 3 3 8 ... False \n",
"\n",
" bilateral_ha bimodal tech implant_category age_diag sex \\\n",
"13565 True False 0 6 51 Female \n",
"13566 True False 0 6 51 Female \n",
"13567 True False 0 6 51 Female \n",
"13568 True False 0 6 51 Female \n",
"\n",
" known_synd synd_or_disab race \n",
"13565 0 0 0 \n",
"13566 0 0 0 \n",
"13567 0 0 0 \n",
"13568 0 0 0 \n",
"\n",
"[4 rows x 67 columns]"
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[demographic.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>study_id</th>\n",
" <th>redcap_event_name</th>\n",
" <th>score</th>\n",
" <th>test_type</th>\n",
" <th>school</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13565</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>96</td>\n",
" <td>PPVT</td>\n",
" <td>1147</td>\n",
" <td>63</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13566</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>91</td>\n",
" <td>PPVT</td>\n",
" <td>1147</td>\n",
" <td>73</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13567</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>93</td>\n",
" <td>PPVT</td>\n",
" <td>1147</td>\n",
" <td>85</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name score test_type school \\\n",
"13565 1147-2010-0064 initial_assessment_arm_1 96 PPVT 1147 \n",
"13566 1147-2010-0064 year_1_complete_71_arm_1 91 PPVT 1147 \n",
"13567 1147-2010-0064 year_2_complete_71_arm_1 93 PPVT 1147 \n",
"\n",
" age_test domain \n",
"13565 63 Receptive Vocabulary \n",
"13566 73 Receptive Vocabulary \n",
"13567 85 Receptive Vocabulary "
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>male</th>\n",
" <th>_race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>_mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" <th>school</th>\n",
" <th>score</th>\n",
" <th>test_name</th>\n",
" <th>test_type</th>\n",
" <th>academic_year_start</th>\n",
" <th>tech_class</th>\n",
" <th>age_year</th>\n",
" <th>non_profound</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5777</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>63</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>91</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" <td>2010</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5778</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>63</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>96</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" <td>2010</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5779</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>Language</td>\n",
" <td>1147</td>\n",
" <td>101</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2010</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5780</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>59</td>\n",
" <td>Language</td>\n",
" <td>1147</td>\n",
" <td>87</td>\n",
" <td>PLS</td>\n",
" <td>expressive</td>\n",
" <td>2010</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13901</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>72</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>86</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" <td>2011</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13902</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>73</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>91</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" <td>2011</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21515</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>88</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>95</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" <td>2012</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21516</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>85</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>93</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" <td>2012</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27748</th>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9 rows × 77 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang \\\n",
"5777 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"5778 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"5779 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"5780 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"13901 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"13902 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"21515 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"21516 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"27748 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... age_test \\\n",
"5777 1 3 3 8 ... 63 \n",
"5778 1 3 3 8 ... 63 \n",
"5779 1 3 3 8 ... 59 \n",
"5780 1 3 3 8 ... 59 \n",
"13901 1 3 3 8 ... 72 \n",
"13902 1 3 3 8 ... 73 \n",
"21515 1 3 3 8 ... 88 \n",
"21516 1 3 3 8 ... 85 \n",
"27748 1 3 3 8 ... NaN \n",
"\n",
" domain school score test_name test_type \\\n",
"5777 Expressive Vocabulary 1147 91 NaN EVT \n",
"5778 Receptive Vocabulary 1147 96 NaN PPVT \n",
"5779 Language 1147 101 PLS receptive \n",
"5780 Language 1147 87 PLS expressive \n",
"13901 Expressive Vocabulary 1147 86 NaN EVT \n",
"13902 Receptive Vocabulary 1147 91 NaN PPVT \n",
"21515 Expressive Vocabulary 1147 95 NaN EVT \n",
"21516 Receptive Vocabulary 1147 93 NaN PPVT \n",
"27748 NaN NaN NaN NaN NaN \n",
"\n",
" academic_year_start tech_class age_year non_profound \n",
"5777 2010 Bilateral HA 4 True \n",
"5778 2010 Bilateral HA 4 True \n",
"5779 2010 Bilateral HA 4 True \n",
"5780 2010 Bilateral HA 4 True \n",
"13901 2011 Bilateral HA 4 True \n",
"13902 2011 Bilateral HA 4 True \n",
"21515 2012 Bilateral HA 4 True \n",
"21516 2012 Bilateral HA 4 True \n",
"27748 2013 Bilateral HA 4 True \n",
"\n",
"[9 rows x 77 columns]"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4236"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3021,)"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5511,)"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3021,)"
]
},
"execution_count": 120,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3021,)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_ids = receptive.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic_ids = demographic.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[s for s in receptive_ids if s not in demographic_ids]"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def score_summary(domain, test_type=None):\n",
" subset = lsl_dr[lsl_dr.domain==domain].copy()\n",
" if test_type is not None:\n",
" subset = subset[subset.test_type==test_type]\n",
" subset['age_test'] = (subset.age_test/12).dropna().astype(int)\n",
" subset.loc[subset.age_test > 11, 'age_test'] = 11\n",
" subset = subset[subset.age_test>1]\n",
" byage = subset.groupby('age_test')\n",
" n = byage.study_id.count()\n",
" mean = byage.score.mean()\n",
" sd = byage.score.std()\n",
" min = byage.score.min()\n",
" max = byage.score.max()\n",
" summary = pd.DataFrame({'Sample Size':n, 'Mean':mean, \n",
" 'SD':sd, 'Min':min, 'Max':max})\n",
" summary.index = summary.index.values.astype(int)\n",
" return summary[['Sample Size','Mean','SD','Min','Max']]"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>403</td>\n",
" <td>93.265509</td>\n",
" <td>18.062536</td>\n",
" <td>40</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1397</td>\n",
" <td>92.099499</td>\n",
" <td>19.389263</td>\n",
" <td>0</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1515</td>\n",
" <td>90.675248</td>\n",
" <td>20.314116</td>\n",
" <td>0</td>\n",
" <td>149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1128</td>\n",
" <td>89.859043</td>\n",
" <td>18.185493</td>\n",
" <td>0</td>\n",
" <td>142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>624</td>\n",
" <td>85.669872</td>\n",
" <td>16.504194</td>\n",
" <td>40</td>\n",
" <td>154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>413</td>\n",
" <td>83.053269</td>\n",
" <td>16.021892</td>\n",
" <td>40</td>\n",
" <td>130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>295</td>\n",
" <td>80.610169</td>\n",
" <td>17.686631</td>\n",
" <td>20</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>218</td>\n",
" <td>77.885321</td>\n",
" <td>17.816542</td>\n",
" <td>25</td>\n",
" <td>160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>188</td>\n",
" <td>76.303191</td>\n",
" <td>17.550191</td>\n",
" <td>20</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>450</td>\n",
" <td>78.668889</td>\n",
" <td>19.112355</td>\n",
" <td>20</td>\n",
" <td>134</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 403 93.265509 18.062536 40 144\n",
"3 1397 92.099499 19.389263 0 150\n",
"4 1515 90.675248 20.314116 0 149\n",
"5 1128 89.859043 18.185493 0 142\n",
"6 624 85.669872 16.504194 40 154\n",
"7 413 83.053269 16.021892 40 130\n",
"8 295 80.610169 17.686631 20 132\n",
"9 218 77.885321 17.816542 25 160\n",
"10 188 76.303191 17.550191 20 123\n",
"11 450 78.668889 19.112355 20 134"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary = score_summary(\"Receptive Vocabulary\")\n",
"receptive_summary"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.0000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>663.100000</td>\n",
" <td>84.809001</td>\n",
" <td>18.064321</td>\n",
" <td>20.5000</td>\n",
" <td>141.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>496.314069</td>\n",
" <td>6.359775</td>\n",
" <td>1.291166</td>\n",
" <td>16.4063</td>\n",
" <td>11.802071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>188.000000</td>\n",
" <td>76.303191</td>\n",
" <td>16.021892</td>\n",
" <td>0.0000</td>\n",
" <td>123.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>322.000000</td>\n",
" <td>79.154209</td>\n",
" <td>17.584301</td>\n",
" <td>5.0000</td>\n",
" <td>132.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>431.500000</td>\n",
" <td>84.361570</td>\n",
" <td>17.939539</td>\n",
" <td>20.0000</td>\n",
" <td>143.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1002.000000</td>\n",
" <td>90.471196</td>\n",
" <td>18.880639</td>\n",
" <td>36.2500</td>\n",
" <td>149.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1515.000000</td>\n",
" <td>93.265509</td>\n",
" <td>20.314116</td>\n",
" <td>40.0000</td>\n",
" <td>160.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"count 10.000000 10.000000 10.000000 10.0000 10.000000\n",
"mean 663.100000 84.809001 18.064321 20.5000 141.800000\n",
"std 496.314069 6.359775 1.291166 16.4063 11.802071\n",
"min 188.000000 76.303191 16.021892 0.0000 123.000000\n",
"25% 322.000000 79.154209 17.584301 5.0000 132.500000\n",
"50% 431.500000 84.361570 17.939539 20.0000 143.000000\n",
"75% 1002.000000 90.471196 18.880639 36.2500 149.750000\n",
"max 1515.000000 93.265509 20.314116 40.0000 160.000000"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary.describe()"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6631"
]
},
"execution_count": 128,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109bac2b0>"
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAFlCAYAAAD226FvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109ba9898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_summary[\"Sample Size\"].plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(receptive_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>383</td>\n",
" <td>92.488251</td>\n",
" <td>21.829641</td>\n",
" <td>23</td>\n",
" <td>141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1343</td>\n",
" <td>93.293373</td>\n",
" <td>21.700653</td>\n",
" <td>0</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1492</td>\n",
" <td>92.269437</td>\n",
" <td>21.873205</td>\n",
" <td>0</td>\n",
" <td>146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1103</td>\n",
" <td>91.451496</td>\n",
" <td>20.127388</td>\n",
" <td>0</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>623</td>\n",
" <td>86.491172</td>\n",
" <td>18.464257</td>\n",
" <td>20</td>\n",
" <td>146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>416</td>\n",
" <td>83.899038</td>\n",
" <td>15.723956</td>\n",
" <td>38</td>\n",
" <td>131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>286</td>\n",
" <td>84.006993</td>\n",
" <td>16.518993</td>\n",
" <td>34</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>204</td>\n",
" <td>81.431373</td>\n",
" <td>16.195243</td>\n",
" <td>36</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>182</td>\n",
" <td>81.758242</td>\n",
" <td>15.388049</td>\n",
" <td>40</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>451</td>\n",
" <td>84.944568</td>\n",
" <td>17.502864</td>\n",
" <td>18</td>\n",
" <td>146</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 383 92.488251 21.829641 23 141\n",
"3 1343 93.293373 21.700653 0 145\n",
"4 1492 92.269437 21.873205 0 146\n",
"5 1103 91.451496 20.127388 0 145\n",
"6 623 86.491172 18.464257 20 146\n",
"7 416 83.899038 15.723956 38 131\n",
"8 286 84.006993 16.518993 34 122\n",
"9 204 81.431373 16.195243 36 145\n",
"10 182 81.758242 15.388049 40 122\n",
"11 451 84.944568 17.502864 18 146"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary = score_summary(\"Expressive Vocabulary\")\n",
"expressive_summary"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6483"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAFlCAYAAAD226FvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11409a940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_summary[\"Sample Size\"].plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(expressive_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.xlim(-0.5, 9.5)\n",
"if current_year_only:\n",
" plt.ylim(0, 400)\n",
"else:\n",
" plt.ylim(0, 1600)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>288</td>\n",
" <td>85.180556</td>\n",
" <td>15.086812</td>\n",
" <td>50</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1167</td>\n",
" <td>83.655527</td>\n",
" <td>18.397543</td>\n",
" <td>40</td>\n",
" <td>126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1333</td>\n",
" <td>83.588147</td>\n",
" <td>20.725702</td>\n",
" <td>0</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1032</td>\n",
" <td>83.908915</td>\n",
" <td>35.255688</td>\n",
" <td>39</td>\n",
" <td>999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>589</td>\n",
" <td>79.049236</td>\n",
" <td>21.785893</td>\n",
" <td>39</td>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>391</td>\n",
" <td>80.191816</td>\n",
" <td>51.611731</td>\n",
" <td>3</td>\n",
" <td>999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>248</td>\n",
" <td>79.084677</td>\n",
" <td>21.061047</td>\n",
" <td>40</td>\n",
" <td>107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>172</td>\n",
" <td>81.412791</td>\n",
" <td>20.488435</td>\n",
" <td>40</td>\n",
" <td>108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>134</td>\n",
" <td>81.052239</td>\n",
" <td>19.973786</td>\n",
" <td>40</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>310</td>\n",
" <td>84.835484</td>\n",
" <td>55.537870</td>\n",
" <td>39</td>\n",
" <td>999</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 288 85.180556 15.086812 50 122\n",
"3 1167 83.655527 18.397543 40 126\n",
"4 1333 83.588147 20.725702 0 123\n",
"5 1032 83.908915 35.255688 39 999\n",
"6 589 79.049236 21.785893 39 112\n",
"7 391 80.191816 51.611731 3 999\n",
"8 248 79.084677 21.061047 40 107\n",
"9 172 81.412791 20.488435 40 108\n",
"10 134 81.052239 19.973786 40 105\n",
"11 310 84.835484 55.537870 39 999"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary = score_summary(\"Articulation\")\n",
"articulation_summary"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5664"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAFgCAYAAACmFjDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtcVHXi//H3GRjuYFn9Ki+ha95K0SVIYxUv6yprtetm\nbKFhXspbeAk0SUHUJLxlF0m8bGUhm9pKaltbbppR8lVzTfNS1mpr1qoppDIgMDDz+6NtNlbNS8wM\nHF/Px4PHw/OZ4fieUt98PnPmcwyn0+kUAAAwDYu3AwAAgNpFuQMAYDKUOwAAJkO5AwBgMpQ7AAAm\n4+vtALXl+PESb0cAAMBjrrsu9LyPmabcUTft3btHixYt0IIFi/Xllwc1Z06mJKlp05s0aVKafHx8\ntHr1Kr399l8lGUpISFTPnr105swZTZ8+RTabTb6+vkpLm65rr73Ouy8GAOoJluXhNnl5L2vOnJmy\n2+2SpCVLFmrkyDHKyXlBkrR58wc6efKk1q5drUWLXtKzz+YoO/tpSdIbb6xRmza3KDt7ifr06au8\nvFe89joAoL5h5g63adKkqTIz5+qJJ6ZKkjIz58hischut6uoqEghISG66qqrtGzZq7JYLCoqOiE/\nP39J0h//mCCHwyFJOnr0iEJDz7/8BACoiZk73KZbt57y8fFxHVssFh09ekSJiffp9OmTuvnmlq7x\n1atXauTIIYqL61vj+WPHjlR+/mvq2rW7p+MDQL1FucOjbrjhRq1Yka/f//4eLVjwtGu8f//7tHbt\nO/r44x3asWO7a/y55xbp+eeXKC3tMW/EBYB6iXKHx6SmJuvrrw9LkgIDg2SxWPTVV4c0ZcpESZKP\nj4/8/KyyWCzKzV2md955S5IUEBBYYwUAAPDTeM8dbmcYhiTpgQcGKzNzmqxWqwICApWamqaGDa/R\nzTe30ogRQ2QYhjp3jlHHjpEKD2+mmTOn6a9/XSuHw6HJkzO8+hoAoD4xzHJXOD7nDgC4kvzU59xZ\nlgcAwGRYlocbeGIxyPDA7wEA9RPlDrdY9km5istrv+QbBhgaHBFQ6+cFADOh3OEWxeVOnShzx5lN\ncYkIALgV77kDAGAylDsAACZDuQMAYDKUOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKU\nOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKUOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsA\nACZDuQMAYDKUOwAAJkO5AwBgMpQ7AAAm49Zy37VrlxITEyVJn376qQYOHKjExEQNGzZMRUVFkqRV\nq1apf//+uu+++7Rp0yZJUnl5ucaMGaOBAwdq+PDhKi4udmdMAABMxdddJ166dKnWrVun4OBgSdKT\nTz6p9PR0tWnTRitXrtTSpUv10EMPKTc3V/n5+aqoqFBCQoJiYmL06quvqnXr1kpKStJbb72lnJwc\nTZkyxV1R65W9e/do0aIFWrBgsSTp/fff06ZNG5SRMVOS9PXXhzVvXpaqqqpktVo1ffqT+vTTfVq+\nfJkkyel0avfuXcrNXambbmrmpVcBAHAnt5V7eHi4srOz9dhjj0mS5s+fr+uuu06SVFVVJX9/f33y\nySeKjIyU1WqV1WpVeHi49u/frx07dujhhx+WJHXt2lULFy50V8x6JS/vZa1f/zcFBgZJkp55Zp4+\n+miLWrZs7XrOnDmZGjkySbfc0k7vv79RX331lTp1ukOdOt0hSfrzn3MVEdGRYgcAE3Pbsnzv3r3l\n4+PjOv6h2Hfs2KG8vDwNHjxYNptNoaGhrucEBwfLZrPJZrO5ZvzBwcEqKSlxV8x6pUmTpsrMnCun\n0ylJat++g1JSUl3HFRXlOnnyO334YYHGjBmhPXt265ZbbnV9/7ffHtM777yloUOHeyU/AMAzPHpB\n3VtvvaVp06ZpyZIluvrqqxUSEqLS0lLX46WlpQoNDa0xXlpaqrCwME/GrLO6detZ4wemX//6NzUe\nP336tL788qCioztpwYLFKik5rb/97a+ux1euzNP99w+Ur6/bFmwAAHWAx8p97dq1ysvLU25urpo0\naSJJioiI0Pbt21VZWamSkhIdOHBArVq1UmRkpAoKCiRJBQUFioqK8lTMei0sLExBQUH65S9vkyTF\nxHTVZ599KklyOBwqLPxQv/51b29GBAB4gNuncIZhyOFw6Mknn1SjRo2UlJQkSerUqZOSkpI0aNAg\nDRgwQA6HQ8nJyfLz81NCQoImTZqkAQMGyM/PT0899ZS7Y5qCv3+AmjYN165dO9WhQ0ft3PkP/eIX\nLSRJBw8eUHh4M/n5+Xk5JQDA3dxa7k2aNNGKFSskSVu3bj3nc+Lj4xUfH19jLCAgQM8++6w7o9Vr\nhmHU+PWPj1NT0zV//mxVV1erUaPGGj16nCTp8OFDaty4icezAgA8z3D+cDVWPXf8OBfd1R1Ozd92\nRifKav/M1wZJybcHSjIu+FwAMLPrrgs972PsUAcAgMlw2XS94O7FFWbBAGAmlHs9seyTchWX127J\nNwwwNDgioFbPCQDwPsq9nigud7rhPWxTXG4BAPgfvOcOAIDJUO4AAJgM5Q4AgMlQ7gAAmAzlDgCA\nyVDuAACYDOUOAIDJUO4AAJgM5Q4AgMlQ7gAAmAzlDgCAyVDuAACYDOUOAIDJUO4AAJgM5Q4AgMlQ\n7gAAmAzlDgCAyVDuAACYjK+3AwB1zd69e7Ro0QItWLBYX399WJmZ02SxWNS8eQulpEySYRhavXqV\n3n77r5IMJSQkqmfPXrLZbJoxI11lZaWqqqpSUtKjateuvbdfDoArEOUO/Ehe3stav/5vCgwMkiQt\nWDBfI0Y8oo4dIzVvXpY++OB9RUR01Nq1q/XSS39WRUWFHnggXj179tLKlXmKju6k+Pj79dVXhzRt\n2hS9+OJyL78iAFciluWBH2nSpKkyM+fK6XRKkj7/fL86doyUJHXuHKPt27fqqquu0ksv/Vk+Pj4q\nKjohf39/SdJ99w3U7373B0lSVVWVaxwAPI1yB36kW7ee8vHxcR3/UPKSFBgYpNJSmyTJx8dHq1ev\n1MiRQ9SnT19JUkhIiPz9/VVUdEIzZ07VyJFJng0PAP9BuQM/wWL571+RsrJShYSEuo77979Pa9e+\no48/3qEdO7ZLkg4c+KfGjx+tESOS1KHDLz2eFwAkyh34SS1bttLHH/9DkrRlS6E6dIjUV18d0pQp\nEyV9P4P387PKx8dHX355UOnpkzRt2pPq1OkOb8YGcIXjgjrgHAzDkCQlJT2q2bNnqqqqSs2aNVeP\nHr+WYRi6+eZWGjFiiAzDUOfOMerQ4Zd6/PEU2e1VeuaZuZKkkJBQZWXN8+bLAHCFMpw/flOxHjt+\nvMTbEdzIqfnbzuhEWe2e9dogKfn2QElG7Z7YTXkld2YGgPrluutCz/sYy/IAAJgMy/KAJMndC1is\nNADwHMod+I9ln5SruLx2S75hgKHBEQG1ek4AuBDKHfiP4nKnG64TMMUlLQDqGd5zBwDAZCh3AABM\nxq3lvmvXLiUmJkqSDh06pISEBA0cOFDTpk1zbeu5atUq9e/fX/fdd582bdokSSovL9eYMWM0cOBA\nDR8+XMXFxe6MCQCAqbit3JcuXaq0tDTZ7XZJUlZWlpKTk5WXlyen06kNGzbo+PHjys3N1YoVK/TC\nCy/oqaeeUmVlpV599VW1bt1aeXl56tevn3JyctwVEwAA03FbuYeHhys7O9s1Q9+3b5+io6MlSbGx\nsSosLNTu3bsVGRkpq9WqkJAQhYeHa//+/dqxY4diY2MlSV27dtX//d//uSsmAACm47Zy792793nv\nrhUcHKySkhLZbDaFhobWGLfZbLLZbAoODq7xXAAAcHE8dkHdj++uZbPZFBYWppCQEJWWlrrGS0tL\nFRoaWmO8tLRUYWFhnooJAEC957Fyb9u2rbZt2yZJKigoUFRUlCIiIrR9+3ZVVlaqpKREBw4cUKtW\nrRQZGamCgoIazwUAABfH7ZvY/HB3rdTUVKWnp8tut6tFixaKi4uTYRgaNGiQBgwYIIfDoeTkZPn5\n+SkhIUGTJk3SgAED5Ofnp6eeesrdMQEAMA3uClcvcFe4H9S3zNzFDoC7cFc4AACuIJQ7AAAmQ7kD\nAGAylDsAACZDuQMAYDKUOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKUOwAAJkO5AwBg\nMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKUOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKU\nOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKUOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsA\nACZDuQMAYDKUOwAAJuPryd/MbrcrNTVV33zzjXx8fPTEE0/Ix8dHqampslgsatmypTIyMmQYhlat\nWqWVK1fK19dXo0aNUvfu3T0ZFQCAesuj5f7++++rurpaK1asUGFhoZ5++mlVVVUpOTlZ0dHRysjI\n0IYNG9ShQwfl5uYqPz9fFRUVSkhIUExMjPz8/DwZFwCAesmjy/LNmzdXdXW1nE6nSkpKZLVatXfv\nXkVHR0uSYmNjVVhYqN27dysyMlJWq1UhISEKDw/X/v37PRkVAIB6y6Mz96CgIH3zzTeKi4vTyZMn\ntWjRIn300Ueux4ODg1VSUiKbzabQ0NAa4zabzZNRAQCotzxa7suWLVPXrl316KOP6ujRoxo0aJCq\nqqpcj9tsNoWFhSkkJESlpaWu8dLSUoWFhXkyKgAA9ZZHl+UbNGig4OBgSVJYWJiqqqp0yy23aNu2\nbZKkgoICRUVFKSIiQtu3b1dlZaVKSkp04MABtWzZ0pNRAQCotzw6cx88eLAmT56sgQMHym63KyUl\nRbfeeqvS09Nlt9vVokULxcXFyTAMDRo0SAMGDJDD4VBycjIX0wEAcJE8/p77M888c9Z4bm7uWWPx\n8fGKj4/3RCwAAEyFTWwAADAZyh0AAJOh3AEAMBnKHQAAk6HcAQAwGcodAACTodwBADCZC5b7F198\ncdbYzp073RIGAAD8fOfdxGb79u1yOBxKT0/XzJkz5XQ6ZRiGqqqqlJGRofXr13syJwAAuEjnLffC\nwkJ99NFH+vbbb/Xcc8/99xt8fXX//fd7JBwAALh05y33sWPHSpLWrFmjfv36eSwQAAD4eS64t3xU\nVJRmz56tkydP1hjPyspyWygAAHD5Llju48ePV3R0tKKjo11jhmG4NRQAALh8Fyz36upqTZo0yRNZ\nAABALbjgR+Fuu+02bdiwQZWVlZ7IAwAAfqYLztzffvttLV++vMaYYRj69NNP3RYKAABcvguW+4cf\nfuiJHAAAoJZcsNyzs7PPOZ6UlFTrYQAAwM93wffcnU6n69d2u10bN25UUVGRW0MBAIDLd8GZ+5gx\nY2ocP/LIIxoyZIjbAgEAgJ/nku8KZ7PZdOTIEXdkAQAAteCCM/eePXvWOD516pSGDRvmtkAAAODn\nuWC5v/LKK64d6QzDUFhYmEJCQtweDAAAXJ4LlnujRo306quvasuWLaqqqlLnzp2VmJgoi+WSV/QB\nAIAHXLDc586dq0OHDql///5yOp1avXq1vv76a02ZMsUT+QAAwCW6qE1s1qxZIx8fH0lS9+7ddddd\nd7k9GAAAuDwXXFt3OByqrq52HVdXV8vX94I/EwAAAC+5YEvffffdSkxM1F133SWn06k333xTd955\npyeyAQCAy/CT5X7q1Cn98Y9/VNu2bbVlyxZt2bJFDz74oPr16+epfAAA4BKdd1l+37596tu3r/bs\n2aNu3bpp0qRJ6tq1q+bNm6fPPvvMkxkBAMAlOG+5z5o1S/Pnz1dsbKxrLDk5WVlZWZo1a5ZHwgEA\ngEt33nI/ffq0OnXqdNZ4165dVVxc7NZQAADg8p33Pffq6mo5HI6zNqtxOByqqqpyezAAl27o0IEK\nDv5+B8kbb2yk+Pj7NXdulnx9fdW06U1KTU2XYRhavnyZNmxYr+DgEA0YMEgxMV28nBxAbTpvuUdF\nRSk7O1tjx46tMb5w4UK1a9fO7cEAXJqKigpJ0oIFi11jkydP1NChw9W5c4xmzEhXYeGHuuGGG/Xu\nu+u1dOnLcjqdGjlyqG67LUr+/gHeig6glp233FNSUvTwww9r3bp1ioiIkMPh0L59+9SwYUPl5OR4\nMiOAi/DPf36h8vJyJScnqbq6Wg8/PFqtWrXW6dOn5HQ6VVZWKqvVqkOH/qVf/vI2Wa1WSVLTpk31\nz3/+U7feyg/tgFmct9xDQkKUl5enrVu3at++ffLx8dEDDzygqKgoT+YDcJECAwM0YECi7rqrnw4f\n/koTJozV0KHD9cwz8/Tyyy8oJCRUHTtG6siRf2v58pdUVlYmu71Su3d/ot//vr+34wOoRT/5OXeL\nxaI77rhDd9xxh6fyALhMTZuGq3Hjpv/59U0KCwvTzJkZys1dpWbNmis//zVlZz+t5ORJuueePyol\nZYyuv/4G3XJLOzVocJWX0wOoTR7fR3bx4sV67733VFlZqQEDBig6OlqpqamyWCxq2bKlMjIyZBiG\nVq1apZUrV8rX11ejRo1S9+7dPR0VqFfefHOtDhw4oJSUSTpx4rjKysrUuHFTBQUFSZKuueZa7dnz\niU6ePKmysjLl5Lwgm82m5OQk/eIXLbycHkBt8mi5b926VR9//LFWrFihsrIyvfjii5o1a5aSk5MV\nHR2tjIwMbdiwQR06dFBubq7y8/NVUVGhhIQExcTEyM/Pz5NxgXrlrrv6KTNzmkaPfkiGYejxxzPk\ncFQrI2OyfHx85Ofnp8ceS9NVV12lQ4e+1MMPD5Kvr1WPPDJehmF4Oz6AWuTRct+8ebNat26t0aNH\ny2az6bHHHtOqVasUHR0tSYqNjdXmzZtlsVgUGRkpq9Uqq9Wq8PBw7d+/X+3bt/dkXKBe8fX1VUbG\nzLPGc3JeOGts4sTJnogEwEs8Wu7FxcU6cuSIFi9erMOHD2vkyJFyOp2ux4ODg1VSUiKbzabQ0NAa\n4zabzZNRAQCotzxa7ldffbVatGghX19fNW/eXP7+/vr2229dj9tsNoWFhSkkJESlpaWu8dLSUoWF\nhXkyKgAA9dYF7+dem2677TZ98MEHkqRjx46pvLxcnTt31rZt2yRJBQUFioqKUkREhLZv367KykqV\nlJTowIEDatmypSejAvWA081fAOorj87cu3fvro8++kj33nuvHA6HMjIy1LhxY6Wnp8tut6tFixaK\ni4uTYRgaNGiQBgwYIIfDoeTkZC6mA85h2SflKi6v3SJuGGBocAS71QH1mcc/Cjdx4sSzxnJzc88a\ni4+PV3x8vCciAfVWcblTJ8pq+6zM2oH6zqPL8gAAwP0odwAATIZyBwDAZCh3AABMhnIHAMBkKHcA\nAEyGcgcAwGQodwAATIZyBwDAZCh3AABMhnIHAMBkKHcAAEyGcgcAwGQodwAATIZyBwDAZCh3AABM\nhnIHAMBkKHcAAEyGcgcAwGQodwAATIZyBwDAZCh3AABMhnIHAMBkKHcAAEyGcgcAwGQodwAATIZy\nBwDAZCh3AABMhnIHAMBkKHcAAEyGcgcAwGR8vR0AwJWrurpas2fP1OHDX8kwDE2Y8LjsdrvmzXtS\nfn7+atmylcaNmyDDMCRJ3333nUaNGqbc3JWyWq1eTg/UXZQ7AK8pLPxAFotFOTkv6OOP/6ElS57X\niRMnNH78RLVr115Ll+bo739/W717/1Zbt/6fFi1aoJMni70dG6jzWJYH4DVdu3bXxImTJUlHjx5R\naGiYjh8/pnbt2kuS2rWL0Cef7JQkWSwWPftsjkJDw7yWF6gvKHcAXuXj46OZMzP07LPz1Lt3nBo1\naqydO3dIkjZv/kBnzpyRJEVHd1JYWANvRgXqDZblAXhdWtp0FRcXafjwwZo1a75ychbopZf+pA4d\nOqq01M/b8YB6xysz96KiInXr1k1ffvmlDh06pISEBA0cOFDTpk2T0+mUJK1atUr9+/fXfffdp02b\nNnkjJgA3e+edt5Sbu0yS5O/vL8OwqLDwA2VkPKFnn12o06dPKTq6s3dDAvWQx2fudrtdU6dOVWBg\noJxOp7KyspScnKzo6GhlZGRow4YN6tChg3Jzc5Wfn6+KigolJCQoJiZGfn78BA+YSbduPfXkk9OV\nlDRcVVVVGjcuRYZhaNy4UQoICFBkZLQ6d475n+8yvJIVqE88Xu5z5sxRQkKCFi9eLEnat2+foqOj\nJUmxsbHavHmzLBaLIiMjZbVaZbVaFR4erv3796t9+/aejgvAjQICAjRjRtZZ47/6Vdfzfs9rr611\nZyTAFDy6LJ+fn6+GDRuqS5cukiSn0+lahpek4OBglZSUyGazKTQ0tMa4zWbzZFQAAOotj87c8/Pz\nZRiGCgsL9dlnnyk1NVXfffed63GbzaawsDCFhISotLTUNV5aWqqwMD7+AgDAxfDozH358uXKzc1V\nbm6u2rRpo9mzZ6tLly7atm2bJKmgoEBRUVGKiIjQ9u3bVVlZqZKSEh04cEAtW7b0ZFQAtc7pgS8A\nkpc/CmcYhlJTU5Weni673a4WLVooLi5OhmFo0KBBGjBggBwOh5KTk7mYDjCBZZ+Uq7i89ku4YYCh\nwREBtX5eoL7yWrnn5uae89c/iI+PV3x8vCcjAXCz4nKnTpS548zM2oEfY4c6AABMhnIHAMBkKHcA\nAEyGcgcAwGQodwAATIa7wgEA8DNUV1dr9uyZOnz4KxmGoQkTHtcvftFCkvTcc0/pppuaqV+//pKk\ndete17p1r8vHx0cPPjhMMTFd3JKJmTsAAD9DYeEHslgsysl5QQ8/PEpLly7UyZMnlZIyVps3fyDD\n+P5mR0VFJ7R69UotWvSi5s9foMWLs2W3292SiZk7AAA/Q9eu3RUT8/3Njo4ePaLQ0DCdOVOmYcOG\na8uWQtc9VD79dK/at+8gX19f+fqGqHHjpjpw4Au1aXNLrWdi5g4AwM/k4+OjmTMz9Mwzc/Wb3/TR\njTc20i23tKvxnLKyMgUHh7iOg4KC3HZTNModAIBakJY2Xa++mq/ZszNVUVF+1uNBQcEqK/vvFo1l\nZWUKDXXPTdGu+GX5qqoqZWVN19GjR1VZWakHHxym66+/XnPnZsnX11dNm96k1NR0GYahV19drnff\nfUcWi6HExKGKje3u7fgAAC9755239O233yoxcbD8/f1lGBYZxtlz57Ztb9HSpQtVWVmpyspKHTr0\npevCu9p2xZf7+vV/01VXXa309Cd0+vRpDR6coDZtbtHQocPVuXOMZsxIV2Hhh4qI6Ki//GWFVq5c\nozNnzmjIkAGUOwBA3br11JNPTldS0nBVVVVp3LiUGjc7++GCumuuuVb33nu/HnnkITkcTg0f/ois\nVqtbMl3x5d6jRy917/5rSZLT6ZCvr69atWqt06dPyel0qqysVFarVYGBgbrhhht15swZlZWVymLh\nHQ0AgBQQEKAZM7LO+djQocNrHN99dz/dfXc/t2e64ss9MDBQklRWVqr09FQNHz5aTqdTTz89Vy+/\n/IJCQkLVsWOkJOm66/6fHnggXg6HQ4mJQ7wZGwCA87riy12Sjh07qilTHtM998SrV68+uuuu32jh\nwj+pWbPmys9/TdnZT+v22+9QcXGR/vKXN+R0OpWcnKT27SPUtu2t3o4PAPAod99i2PjZZ7jiy724\nuEjJyUlKSUlVZGSUJKlBgwYKCgqS9P17JHv2fKLQ0DD5+/u73h8JDQ1120cYAAB127JPylVcXrsl\n3zDA0OCIgFo51xVf7q+88pJsNpteemmpXnppqSTpscfSlJExWT4+PvLz89Njj6Xphhtu0Pbtt2j4\n8MGyWCyKiOio6OhOXk4PAPCG4nKnTpRd+HmXpvZ+WLjiy338+AkaP37CWeM5OS+cNTZs2AgNGzbC\nE7EAALhsXPINAIDJXIEz97p/IQSAuulcm1516RIrSVq//m3l56/SokUvShKbXsGrrsByr/sXQgCo\nm/5306shQwaoS5dYff75Z3rzzXWu55WUlLDpFbzqiiz3un4hBIC66VybXp0+fUpLlizUuHHJmj07\nU5LY9Aped0WWOwBcjv/d9GrYsJHKypqhMWOSa2w3KrHpFbyLcgeAS/DjTa+aNm2qr78+rHnzslRZ\nWal//eugFiyYr1/+MopNr+BVlDsAXKRzbXqVm7tKknT06BFlZEzWmDHJ2rVrJ5tewasodwC4SOfa\n9GrevOfk7+8vp9PpuvtXhw4d2fQKXkW5A8BFOt+mV5J0442NXB+Dk9j0Ct7FJZwAAJgMM3cAOC82\nvUL9RLkDwE9g0yvUR5Q7APwENr1CfcR77gAAmAzlDgCAyVDuAACYDOUOAIDJUO4AAJiMR6+Wt9vt\nmjx5sv7973+rsrJSo0aNUosWLZSamiqLxaKWLVsqIyNDhmFo1apVWrlypXx9fTVq1Ch1797dk1EB\nwDT27t2jRYsWaMGCxcrIeFzFxcWSpCNH/q127SI0bVqmVq7M04YNf5ck3XHHrzRkyMPejIyfyaPl\n/sYbb6hhw4aaO3euTp06pd///vdq27atkpOTFR0drYyMDG3YsEEdOnRQbm6u8vPzVVFRoYSEBMXE\nxJx1S0UAwE/Ly3tZ69f/TYGBQZKk6dOzJEklJSUaO3aExo5N1jfffK2///0dLV36sgzD0KhRwxQb\n20MtWtzszej4GTy6LB8XF6exY8dKkhwOh3x9fbVv3z5FR0dLkmJjY1VYWKjdu3crMjJSVqtVISEh\nCg8P1/79+z0ZFQBMoUmTpsrMnCuns+Zn6194YZHuvfd+NWx4ja6//gbNn7/AdeObqqoq+fv7eyMu\naolHyz0oKEjBwcGy2WwaN26cxo8fL4fD4Xo8ODhYJSUlstlsCg0NrTHO7RIB4NJ169ZTPj4+Nca+\n+65Y//jHR+rb925Jkq+vr8LCGsjpdCo7+xm1bt1GTZo09UZc1BKPX1B35MgRPfjgg+rXr5/uuusu\nWSz/jWCz2RQWFqaQkBCVlpa6xktLSxUWFubpqABgSu+9t0G9e//WNVOXpIqKCk2fnqby8jNKSUn1\nYjrUBo9iLV88AAAL+ElEQVSW+4kTJzR06FBNnDhR99xzjySpbdu22rZtmySpoKBAUVFRioiI0Pbt\n21VZWamSkhIdOHBALVu29GRUADCtf/xjmzp3jnEdO51OPf54ilq2bKUJEx6vUfqonzx6Qd2iRYtU\nUlKi559/Xs8//7wkacqUKcrMzJTdbleLFi0UFxcnwzA0aNAgDRgwQA6HQ8nJyVxMBwA/w48L+6uv\nDqlRo8au44KCTdq582NVVVVpy5ZCSdKIEUlq1669x3Oidni03NPS0pSWlnbWeG5u7llj8fHxio+P\n90QsADC1G29spEWLXnQd5+auqvF4t249tHHjZk/HghuxiQ0AoM7Zu3ePxowZIUn68suDGjVqmEaN\nGqYnn5yu6upq1/McDodSUsZqzZrV3opaJ1HuAGAqTjd/uV9e3suaM2em7Ha7JGnJkoUaOXKMcnJe\nkCRt3vyB67lLl+bIZivhOoH/wf3cAcBkln1SruLy2i3ihgGGBkcE1Oo5z+eHz+Y/8cRUSVJm5hxZ\nLBbZ7XYVFRUpJCREkvTee+/KYrGoU6c7zvoc/5WOcgcAkykud+pEWW2f1XPl2a1bTx058m/XscVi\n0dGjRzR+/GiFhobq5ptb6uDBf+rdd9/RzJlz9OKLSzyWrb6g3AEAdd4NN9yoFSte11//ukYLFjyt\nq69uqOPHj2vs2JE6evSIfH191ahRY91+e2dvR60TKHcAQJ2WmpqspKRH1aRJUwUGBslisWj06LGu\nx198cYmuueZaiv1HKHcAQJ30w0VyDzwwWJmZ02S1WhUQEKjU1LM/Uo2aKHcAQJ3z48/mt2sX4bpS\n/lyGDh3uqVj1Bh+FAwDAZCh3AABMhmV5AIAXeeIjdlfeBjeUOwDAq9yx6Y7k2Y136hrKHQDgVe7Z\ndEfy5MY7dQ3vuQMAYDKUOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKUOwAAJkO5AwBg\nMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKUOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACZDuQMAYDKU\nOwAAJkO5AwBgMpQ7AAAmQ7kDAGAylDsAACbj6+0A5+NwODRt2jR9/vnnslqtyszM1E033eTtWAAA\n1Hl1dub+7rvvym63a8WKFZowYYJmzZrl7UgAANQLdXbmvmPHDnXt2lWS1KFDB+3Zs6fWzt0wwJDk\nrLXz/fec7lPfMrsj73/P6x717b/xf89ffzLz5+LH53Sf+paZPxc/PmftMJxOZ+3/F60FaWlp6t27\nt2JjYyVJPXr00IYNG2Sx1NnFBgAA6oQ625QhISEqLS11HTscDoodAICLUGfbMjIyUgUFBZKknTt3\nqnXr1l5OBABA/VBnl+WdTqemTZum/fv3S5KysrLUvHlzL6cCAKDuq7PlDgAALk+dXZYHAACXh3IH\nAMBkKHcAAEyGcr8IFRUV3o5wUcrLy1VZWentGJfkxIkT3o5wSRwOh44dOyaHw+HtKBetuLhYdf3S\nGpvN5u0IP1tlZaXKy8u9HeOi1PU/D/j5KPcf2bhxo3r06KFevXrpzTffdI0/9NBDXkx1fl988YVG\njx6txx9/XJs3b1bfvn3129/+Vhs3bvR2tPP68ssvXV8HDx7U6NGjXcd11eTJkyVJu3btUp8+fZSU\nlKQ777xTO3fu9HKyc/vLX/6i7Oxs7dmzR3FxcRoyZIj69OmjzZs3ezvaecXExOi1117zdoxLcvDg\nQY0dO1YpKSn6+OOPdffdd6tv3741/u2oSw4dOqRhw4apR48euvXWWxUfH6+UlBQdP37c29HgBnV2\n+1lvyMnJ0Zo1a+RwODRu3DhVVFTonnvu8Xas88rIyND48eP1zTffaOzYsXrnnXcUEBCghx56SD17\n9vR2vHMaPHiwAgMDdd1110n6vuynTp0qScrNzfVmtPM6fPiwJGn+/PlaunSpmjVrpmPHjik5OVl5\neXleTne2P//5z1q+fLlGjhypnJwcNW/eXMeOHdOoUaP0q1/9ytvxzqlNmzb69NNPlZiYqDFjxuj2\n22/3dqQLSk9P1yOPPKKSkhKNHDlSa9euVVhYmAYPHqw777zT2/HOMmPGDKWlpal58+bauXOn3n33\nXfXp00dTpkzRkiVLvB3vnJxOpzZs2KDCwkKVlJQoLCxMUVFRiouLk2G4dwvfy5WSkiLp7NURwzD0\n1FNPeSwH5f4jfn5+atCggSRp4cKFevDBB9WoUSMvpzo/p9Pp+kdwy5YtuvbaayVJvr51939rfn6+\npk6dqoSEBHXp0kWJiYl1ttT/l6+vr5o1ayZJuv766+vs0qafn5+CgoIUEhKipk2bSvo+b13e4dHf\n319Tp07V7t27tXjxYs2YMUOdO3fWTTfdpEGDBnk73jlVV1crJiZGTqdT8+fP1w033CBJslqtXk52\nbjabzbVXSMeOHTVnzhxNmDBBp0+f9nKy85s+fbqcTqdiY2MVFBSk0tJSFRQU6MMPP1RmZqa3451T\nXFyc5s+fr2nTptUY9/QPI3W3BbygUaNGysrK0tixYxUSEqLs7GwNHTpUJSUl3o52Ts2aNdOUKVM0\nY8YMzZ49W5K0ZMkSV8nXRddcc42eeeYZzZkzR7t37/Z2nItis9n0hz/8QWfOnNFrr72m3/3ud5o1\na5ZuvPFGb0c7px49emjkyJFq3bq1RowYoS5duuiDDz5Qp06dvB3tgtq3b6/s7GydPn1aH330kf71\nr395O9J5NW7cWOPHj1d1dbWCg4P19NNPKzg42LUqVdc0adJEU6dOVWxsrN577z21b99emzZtUmBg\noLejndcXX3xx1upYr169dP/993sp0YX95je/0datW1VUVKS+fft6LQeb2PyI3W7XG2+8obi4OAUF\nBUn6/oKvRYsWKS0tzcvpzlZdXa333ntPvXr1co2tXbtWvXv3rtN/YX+Qn5+v/Px8LV++3NtRLqii\nokKfffaZAgMD1axZM61evVr33ntvnZ2lbd26VZs3b1ZxcbGuvvpq3Xbbberevbu3Y53X66+/rj/8\n4Q/ejnFJ7Ha73n//fTVv3lxBQUFatmyZGjRooMGDB7v+/ahLKisrtWrVKh04cEBt27ZV//79tXv3\nboWHh+vqq6/2drxzSkhIUHJysqKjo11j27Zt04IFC+rNip+3UO4AgDrp0KFDysrK0r59++R0OmWx\nWNS2bVulpqa63iKraxITE2W328/5nvuKFSs8loNyBwCgluzatUtpaWnKzs6Wj49PjceaNGnisRyU\nOwCgTqors+BLtXTpUoWHh6t3795ey0C5AwDqpLoyC66PKHcAQJ1VF2bB9RHlDgCAydTdXS0AAMBl\nodwBADAZyh0AAJOh3AFclM8//1xt2rTR+vXrvR0FwAVQ7gAuSn5+vvr06VOnP18M4HvcOAbABVVV\nVemNN95QXl6e7r//fh0+fFhNmzbV1q1bNXPmTPn6+qpDhw46cOCAcnNzdejQIU2fPl0nT55UQECA\n0tPT1bZtW2+/DOCKwcwdwAVt2rRJjRs3VrNmzdSrVy+tWLFCVVVVmjRpkp566im9/vrrslqtrtta\nTpo0SRMnTlR+fr5mzJihRx991MuvALiyUO4ALig/P991+8rf/va3ev3117Vv3z41bNhQrVq1kiT1\n799fTqdTZWVl2rNnjx5//HH169dPEyZM0JkzZ3Tq1ClvvgTgisKyPICfVFRUpIKCAu3du1evvPKK\nJOn06dMqKCg4a89vSXI4HPL399eaNWtcY8eOHVODBg08lhm40jFzB/CT1q1bp5iYGL3//vvauHGj\nNm7cqJEjR+rDDz/U6dOn9fnnn0uS3njjDVksFoWEhCg8PFzr1q2TJG3evFkDBw705ksArjhsPwvg\nJ919991KSUlR9+7dXWNFRUXq1auX/vSnP2nmzJkyDEPNmzdXSUmJlixZooMHDyojI0OnTp2Sn5+f\npk2bpnbt2nnvRQBXGModwGVxOp2aN2+ekpKSFBgYqJdeeknffvutJk2a5O1owBWP99wBXBbDMNSg\nQQPde++9slqtatKkiTIzM70dC4CYuQMAYDpcUAcAgMlQ7gAAmAzlDgCAyVDuAACYDOUOAIDJ/H9Q\nSMULltHRegAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1096f1a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size = articulation_summary[\"Sample Size\"]\n",
"sample_size.plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(articulation_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.ylim(0, sample_size.max()+50);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Language scores"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Expressive Vocabulary', 'Language', nan, 'Articulation',\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.unique()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['EOWPVT', 'receptive', 'expressive', nan, 'Goldman', 'EVT', 'PPVT',\n",
" 'Arizonia', 'ROWPVT', 'Arizonia and Goldman', 'EOWPVT and EVT',\n",
" 'PPVT and ROWPVT'], dtype=object)"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.test_type.unique()"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>942</td>\n",
" <td>86.061571</td>\n",
" <td>22.053419</td>\n",
" <td>50</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1336</td>\n",
" <td>84.869760</td>\n",
" <td>19.694166</td>\n",
" <td>50</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1310</td>\n",
" <td>85.103817</td>\n",
" <td>19.572003</td>\n",
" <td>43</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>934</td>\n",
" <td>83.780514</td>\n",
" <td>18.783587</td>\n",
" <td>47</td>\n",
" <td>140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>481</td>\n",
" <td>77.860707</td>\n",
" <td>17.628083</td>\n",
" <td>11</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>318</td>\n",
" <td>75.877358</td>\n",
" <td>18.713363</td>\n",
" <td>40</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>197</td>\n",
" <td>74.817259</td>\n",
" <td>19.682871</td>\n",
" <td>40</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>53</td>\n",
" <td>70.792453</td>\n",
" <td>21.579333</td>\n",
" <td>40</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>44</td>\n",
" <td>77.954545</td>\n",
" <td>20.185137</td>\n",
" <td>40</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>69</td>\n",
" <td>76.014493</td>\n",
" <td>21.604393</td>\n",
" <td>40</td>\n",
" <td>139</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 942 86.061571 22.053419 50 150\n",
"3 1336 84.869760 19.694166 50 144\n",
"4 1310 85.103817 19.572003 43 145\n",
"5 934 83.780514 18.783587 47 140\n",
"6 481 77.860707 17.628083 11 127\n",
"7 318 75.877358 18.713363 40 123\n",
"8 197 74.817259 19.682871 40 123\n",
"9 53 70.792453 21.579333 40 120\n",
"10 44 77.954545 20.185137 40 119\n",
"11 69 76.014493 21.604393 40 139"
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary = score_summary(\"Language\", \"receptive\")\n",
"receptive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5684"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAFgCAYAAACmFjDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d4a70b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size = receptive_language_summary[\"Sample Size\"]\n",
"sample_size.plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(receptive_language_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.ylim(0, sample_size.max()+50);plt.xlim(-0.5, 9.5);"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>936</td>\n",
" <td>88.157051</td>\n",
" <td>18.278298</td>\n",
" <td>50</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1337</td>\n",
" <td>82.311892</td>\n",
" <td>17.566191</td>\n",
" <td>20</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1303</td>\n",
" <td>80.346124</td>\n",
" <td>19.558155</td>\n",
" <td>45</td>\n",
" <td>141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>952</td>\n",
" <td>78.564076</td>\n",
" <td>20.089026</td>\n",
" <td>45</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>499</td>\n",
" <td>71.647295</td>\n",
" <td>19.240286</td>\n",
" <td>6</td>\n",
" <td>140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>338</td>\n",
" <td>66.789941</td>\n",
" <td>20.660322</td>\n",
" <td>40</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202</td>\n",
" <td>67.787129</td>\n",
" <td>21.338290</td>\n",
" <td>40</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>52</td>\n",
" <td>65.557692</td>\n",
" <td>21.233911</td>\n",
" <td>40</td>\n",
" <td>108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>44</td>\n",
" <td>75.750000</td>\n",
" <td>23.544243</td>\n",
" <td>40</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>68</td>\n",
" <td>73.794118</td>\n",
" <td>22.807801</td>\n",
" <td>40</td>\n",
" <td>132</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 936 88.157051 18.278298 50 150\n",
"3 1337 82.311892 17.566191 20 147\n",
"4 1303 80.346124 19.558155 45 141\n",
"5 952 78.564076 20.089026 45 144\n",
"6 499 71.647295 19.240286 6 140\n",
"7 338 66.789941 20.660322 40 124\n",
"8 202 67.787129 21.338290 40 118\n",
"9 52 65.557692 21.233911 40 108\n",
"10 44 75.750000 23.544243 40 119\n",
"11 68 73.794118 22.807801 40 132"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary = score_summary(\"Language\", \"expressive\")\n",
"expressive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5731"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAFgCAYAAACmFjDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109bcf048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sample_size = expressive_language_summary[\"Sample Size\"]\n",
"sample_size.plot(kind='bar', grid=False, color=plot_color)\n",
"for i,x in enumerate(expressive_language_summary[\"Sample Size\"]):\n",
" plt.annotate('%i' % x, (i, x+10), va=\"bottom\", ha=\"center\")\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age')\n",
"plt.ylim(0, sample_size.max()+50);plt.xlim(-0.5, 9.5);"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 5025.000000\n",
"mean 2.531907\n",
"std 2.328673\n",
"min 0.000000\n",
"25% 0.750000\n",
"50% 2.083333\n",
"75% 3.416667\n",
"max 24.833333\n",
"Name: age, dtype: float64"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(unique_students.age/12.).describe()"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def calc_difference(x, col='a_fo', jitter=True):\n",
" if (len(x)<2 or x[col].isnull().sum() or x['funct_out_age'].isnull().sum()):\n",
" return None\n",
" diff = x[col][x.funct_out_age.argmax()] - x[col][x.funct_out_age.argmin()]\n",
" if jitter:\n",
" diff += np.random.normal(scale=0.05)\n",
" if (x.funct_out_age.max() - x.funct_out_age.min()) > 1000:\n",
" print(x['funct_out_age'])\n",
" return({'difference':diff, 'months': x.funct_out_age.max() - x.funct_out_age.min()})\n"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"audition = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference).dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x109bee080>"
]
},
"execution_count": 148,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAGJCAYAAAAkIy99AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYyAjIiIiUhgDGREREZHC/gcy8wAls+jFhgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109bf39b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.scatter(audition.months, audition.difference, alpha=0.5)\n",
"plt.xlabel('Months between earliest and latest rating'); plt.ylabel('Progress (levels)')\n",
"plt.title('Audition')"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"slc = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference, col='slc_fo').dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10aaa6da0>"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAGJCAYAAAAkIy99AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICMiIiIy2P8DCoXXyMZSyIIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ceb9da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.scatter(slc.months, slc.difference, alpha=0.5)\n",
"plt.xlabel('Months between earliest and latest rating'); plt.ylabel('Progress (levels)')\n",
"plt.title('Spoken language (comprehension)')"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sle = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference, col='sle_fo').dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10aaa2908>"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
" if self._edgecolors == str('face'):\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAGJCAYAAAAkIy99AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10aa9d898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,6))\n",
"plt.scatter(sle.months, sle.difference, alpha=0.5)\n",
"plt.xlabel('Months between earliest and latest rating'); plt.ylabel('Progress (levels)')\n",
"plt.title('Spoken language (expression)')"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 16788\n",
"4 4377\n",
"3 4228\n",
"5 4090\n",
"2 1667\n",
"0 1267\n",
"1 285\n",
"Name: degree_hl, dtype: int64"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.degree_hl.dropna().value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfAAAAFVCAYAAAAQfb27AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109c27208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = lsl_dr.degree_hl.hist(bins=7)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10d4d0780>"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfEAAAFVCAYAAAD/v9aFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1096ed860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"diff = (lsl_dr['age'] - lsl_dr['age_int'])\n",
"diff[diff>0].hist(bins=50)"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.19517271203848857"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age_int<6).mean()"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.1296826855505703"
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age<6).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Counts by year"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
" <th>academic_year</th>\n",
" <th>hl</th>\n",
" <th>male</th>\n",
" <th>_race</th>\n",
" <th>prim_lang</th>\n",
" <th>sib</th>\n",
" <th>_mother_ed</th>\n",
" <th>father_ed</th>\n",
" <th>premature_age</th>\n",
" <th>...</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
" <th>school</th>\n",
" <th>score</th>\n",
" <th>test_name</th>\n",
" <th>test_type</th>\n",
" <th>academic_year_start</th>\n",
" <th>tech_class</th>\n",
" <th>age_year</th>\n",
" <th>non_profound</th>\n",
" </tr>\n",
" <tr>\n",
" <th>study_id</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0101-2003-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2002-2003</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>54</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>0101</td>\n",
" <td>58</td>\n",
" <td>PLS</td>\n",
" <td>EOWPVT</td>\n",
" <td>2002</td>\n",
" <td>Bimodal</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2003-0102</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2003-2004</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>44</td>\n",
" <td>Articulation</td>\n",
" <td>0101</td>\n",
" <td>72</td>\n",
" <td>PLS</td>\n",
" <td>Goldman</td>\n",
" <td>2003</td>\n",
" <td>Bilateral HA</td>\n",
" <td>3</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2004-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2006-2007</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>37</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>0101</td>\n",
" <td>62</td>\n",
" <td>PLS</td>\n",
" <td>PPVT</td>\n",
" <td>2006</td>\n",
" <td>Bimodal</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2004-0102</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2004-2005</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2004</td>\n",
" <td>Bimodal</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2004-0103</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>96</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>0101</td>\n",
" <td>104</td>\n",
" <td>CELF-4</td>\n",
" <td>EVT</td>\n",
" <td>2012</td>\n",
" <td>Bilateral CI</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2004-0104</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2004-2005</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>32</td>\n",
" <td>Articulation</td>\n",
" <td>0101</td>\n",
" <td>84</td>\n",
" <td>PLS</td>\n",
" <td>Goldman</td>\n",
" <td>2004</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2004-0105</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2004-2005</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>47</td>\n",
" <td>Articulation</td>\n",
" <td>0101</td>\n",
" <td>78</td>\n",
" <td>CELF-P2</td>\n",
" <td>Goldman</td>\n",
" <td>2004</td>\n",
" <td>Bimodal</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2005-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2006-2007</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>28</td>\n",
" <td>Articulation</td>\n",
" <td>0101</td>\n",
" <td>61</td>\n",
" <td>PLS</td>\n",
" <td>Goldman</td>\n",
" <td>2006</td>\n",
" <td>Bilateral HA</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2005-0102</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2004-2005</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>63</td>\n",
" <td>Articulation</td>\n",
" <td>0101</td>\n",
" <td>87</td>\n",
" <td>CELF-P2</td>\n",
" <td>Goldman</td>\n",
" <td>2004</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2006-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2005-2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2005</td>\n",
" <td>Bimodal</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2006-0104</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2006-2007</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2006</td>\n",
" <td>Bilateral CI</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2007-0104</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2007-2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>41</td>\n",
" <td>Articulation</td>\n",
" <td>0101</td>\n",
" <td>122</td>\n",
" <td>NaN</td>\n",
" <td>Goldman</td>\n",
" <td>2007</td>\n",
" <td>Bimodal</td>\n",
" <td>4</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2007-0105</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2007-2008</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2007</td>\n",
" <td>Bimodal</td>\n",
" <td>11</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2007-0107</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2005-2006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2005</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2008-0102</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2008-2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2008</td>\n",
" <td>Bimodal</td>\n",
" <td>14</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2008-0106</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2007-2008</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2007</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2009-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2008-2009</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2008</td>\n",
" <td>Bimodal</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2010-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2008-2009</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>104</td>\n",
" <td>Articulation</td>\n",
" <td>0101</td>\n",
" <td>90</td>\n",
" <td>CELF-4</td>\n",
" <td>Arizonia</td>\n",
" <td>2008</td>\n",
" <td>Bilateral HA</td>\n",
" <td>8</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2010-0103</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>25</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>63</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2010</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2010-0104</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>30</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>0101</td>\n",
" <td>90</td>\n",
" <td>PLS</td>\n",
" <td>EOWPVT</td>\n",
" <td>2010</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2010-0105</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>...</td>\n",
" <td>30</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>66</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2011</td>\n",
" <td>Bilateral CI</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2012-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" <td>Bimodal</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0101</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>58</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2012</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0103</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012</td>\n",
" <td>Bilateral CI</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0104</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>12</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>83</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2012</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0112</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>90</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2012</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0113</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>96</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2013</td>\n",
" <td>Bimodal</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0114</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>6</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>50</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2013</td>\n",
" <td>Bimodal</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0115</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>Language</td>\n",
" <td>0101</td>\n",
" <td>79</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2013</td>\n",
" <td>Bilateral HA</td>\n",
" <td>0</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0101-2013-0116</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2013</td>\n",
" <td>Bimodal</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2012-0008</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2012-0009</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>1</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2012-0010</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>2</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2012-0011</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2012-0012</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2012-0013</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2012-0014</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0001</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0002</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral CI</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0003</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral CI</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0004</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0005</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0006</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0007</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0008</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral CI</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0009</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0010</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral CI</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0011</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>1</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2013-0012</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0001</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral CI</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0002</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0003</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0004</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0005</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0006</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral CI</td>\n",
" <td>3</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0007</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0008</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>5</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0009</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral HA</td>\n",
" <td>6</td>\n",
" <td>True</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1151-2014-0010</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bilateral CI</td>\n",
" <td>6</td>\n",
" <td>False</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9308-2015-0002</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>nan</td>\n",
" <td>Bimodal</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5511 rows × 76 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race \\\n",
"study_id \n",
"0101-2003-0101 initial_assessment_arm_1 2002-2003 0 0 0 \n",
"0101-2003-0102 initial_assessment_arm_1 2003-2004 0 0 0 \n",
"0101-2004-0101 initial_assessment_arm_1 2006-2007 0 1 0 \n",
"0101-2004-0102 initial_assessment_arm_1 2004-2005 0 0 0 \n",
"0101-2004-0103 initial_assessment_arm_1 2012-2013 0 1 0 \n",
"0101-2004-0104 initial_assessment_arm_1 2004-2005 0 1 0 \n",
"0101-2004-0105 initial_assessment_arm_1 2004-2005 0 0 0 \n",
"0101-2005-0101 initial_assessment_arm_1 2006-2007 0 1 0 \n",
"0101-2005-0102 initial_assessment_arm_1 2004-2005 0 1 0 \n",
"0101-2006-0101 initial_assessment_arm_1 2005-2006 0 0 0 \n",
"0101-2006-0104 initial_assessment_arm_1 2006-2007 0 0 0 \n",
"0101-2007-0104 initial_assessment_arm_1 2007-2008 0 0 0 \n",
"0101-2007-0105 initial_assessment_arm_1 2007-2008 0 1 0 \n",
"0101-2007-0107 initial_assessment_arm_1 2005-2006 0 0 0 \n",
"0101-2008-0102 initial_assessment_arm_1 2008-2009 0 0 0 \n",
"0101-2008-0106 initial_assessment_arm_1 2007-2008 0 0 0 \n",
"0101-2009-0101 initial_assessment_arm_1 2008-2009 0 0 0 \n",
"0101-2010-0101 initial_assessment_arm_1 2008-2009 0 1 0 \n",
"0101-2010-0103 initial_assessment_arm_1 2010-2011 0 0 0 \n",
"0101-2010-0104 initial_assessment_arm_1 2010-2011 0 1 3 \n",
"0101-2010-0105 initial_assessment_arm_1 2011-2012 0 1 0 \n",
"0101-2012-0101 initial_assessment_arm_1 2013-2014 0 1 0 \n",
"0101-2013-0101 initial_assessment_arm_1 2012-2013 0 1 0 \n",
"0101-2013-0103 initial_assessment_arm_1 2012-2013 0 1 0 \n",
"0101-2013-0104 initial_assessment_arm_1 2012-2013 0 1 0 \n",
"0101-2013-0112 initial_assessment_arm_1 2012-2013 0 1 0 \n",
"0101-2013-0113 initial_assessment_arm_1 2013-2014 0 0 0 \n",
"0101-2013-0114 initial_assessment_arm_1 2013-2014 0 1 0 \n",
"0101-2013-0115 initial_assessment_arm_1 2013-2014 0 1 0 \n",
"0101-2013-0116 initial_assessment_arm_1 2013-2014 2 0 0 \n",
"... ... ... .. ... ... \n",
"1151-2012-0008 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2012-0009 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2012-0010 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2012-0011 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2012-0012 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2012-0013 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2012-0014 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2013-0001 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2013-0002 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2013-0003 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2013-0004 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2013-0005 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2013-0006 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2013-0007 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2013-0008 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2013-0009 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2013-0010 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2013-0011 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2013-0012 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2014-0001 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2014-0002 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2014-0003 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2014-0004 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2014-0005 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2014-0006 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2014-0007 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2014-0008 initial_assessment_arm_1 NaN 0 0 2 \n",
"1151-2014-0009 initial_assessment_arm_1 NaN 0 1 2 \n",
"1151-2014-0010 initial_assessment_arm_1 NaN 0 1 2 \n",
"9308-2015-0002 initial_assessment_arm_1 NaN 0 NaN NaN \n",
"\n",
" prim_lang sib _mother_ed father_ed premature_age \\\n",
"study_id \n",
"0101-2003-0101 0 1 6 6 9 \n",
"0101-2003-0102 0 1 2 2 8 \n",
"0101-2004-0101 0 0 6 6 8 \n",
"0101-2004-0102 0 1 5 6 9 \n",
"0101-2004-0103 0 1 4 4 8 \n",
"0101-2004-0104 0 1 6 6 8 \n",
"0101-2004-0105 0 2 6 6 9 \n",
"0101-2005-0101 0 2 5 4 8 \n",
"0101-2005-0102 0 2 3 2 9 \n",
"0101-2006-0101 0 1 6 6 8 \n",
"0101-2006-0104 0 0 5 5 9 \n",
"0101-2007-0104 0 1 4 6 9 \n",
"0101-2007-0105 0 0 6 6 9 \n",
"0101-2007-0107 0 1 4 4 8 \n",
"0101-2008-0102 0 1 6 6 9 \n",
"0101-2008-0106 0 1 4 4 8 \n",
"0101-2009-0101 0 1 6 6 9 \n",
"0101-2010-0101 0 1 6 6 9 \n",
"0101-2010-0103 0 2 4 3 8 \n",
"0101-2010-0104 0 1 2 2 8 \n",
"0101-2010-0105 0 0 5 6 6 \n",
"0101-2012-0101 0 0 6 6 9 \n",
"0101-2013-0101 0 0 3 2 8 \n",
"0101-2013-0103 0 2 6 6 9 \n",
"0101-2013-0104 0 1 4 4 8 \n",
"0101-2013-0112 0 1 3 6 9 \n",
"0101-2013-0113 0 1 2 2 8 \n",
"0101-2013-0114 0 0 3 3 8 \n",
"0101-2013-0115 0 2 2 2 8 \n",
"0101-2013-0116 0 3 4 4 8 \n",
"... ... ... ... ... ... \n",
"1151-2012-0008 1 2 4 2 8 \n",
"1151-2012-0009 1 1 4 6 8 \n",
"1151-2012-0010 1 1 6 6 8 \n",
"1151-2012-0011 1 0 6 6 8 \n",
"1151-2012-0012 1 0 6 6 8 \n",
"1151-2012-0013 1 0 2 2 8 \n",
"1151-2012-0014 1 2 1 1 NaN \n",
"1151-2013-0001 1 0 2 2 8 \n",
"1151-2013-0002 1 2 2 3 8 \n",
"1151-2013-0003 1 0 1 1 8 \n",
"1151-2013-0004 1 2 0 1 8 \n",
"1151-2013-0005 1 0 1 6 8 \n",
"1151-2013-0006 1 0 1 1 4 \n",
"1151-2013-0007 1 0 4 2 NaN \n",
"1151-2013-0008 1 0 3 2 8 \n",
"1151-2013-0009 1 0 2 6 NaN \n",
"1151-2013-0010 1 0 4 4 8 \n",
"1151-2013-0011 1 0 6 6 8 \n",
"1151-2013-0012 1 2 3 3 5 \n",
"1151-2014-0001 1 3 6 4 8 \n",
"1151-2014-0002 1 1 2 4 7 \n",
"1151-2014-0003 1 0 6 6 8 \n",
"1151-2014-0004 1 1 2 1 8 \n",
"1151-2014-0005 1 3 6 6 8 \n",
"1151-2014-0006 1 0 4 0 8 \n",
"1151-2014-0007 1 2 6 6 8 \n",
"1151-2014-0008 1 1 1 1 8 \n",
"1151-2014-0009 1 2 4 2 8 \n",
"1151-2014-0010 1 1 2 2 8 \n",
"9308-2015-0002 NaN NaN NaN NaN NaN \n",
"\n",
" ... age_test domain school score \\\n",
"study_id ... \n",
"0101-2003-0101 ... 54 Expressive Vocabulary 0101 58 \n",
"0101-2003-0102 ... 44 Articulation 0101 72 \n",
"0101-2004-0101 ... 37 Receptive Vocabulary 0101 62 \n",
"0101-2004-0102 ... NaN NaN NaN NaN \n",
"0101-2004-0103 ... 96 Expressive Vocabulary 0101 104 \n",
"0101-2004-0104 ... 32 Articulation 0101 84 \n",
"0101-2004-0105 ... 47 Articulation 0101 78 \n",
"0101-2005-0101 ... 28 Articulation 0101 61 \n",
"0101-2005-0102 ... 63 Articulation 0101 87 \n",
"0101-2006-0101 ... NaN NaN NaN NaN \n",
"0101-2006-0104 ... NaN NaN NaN NaN \n",
"0101-2007-0104 ... 41 Articulation 0101 122 \n",
"0101-2007-0105 ... NaN NaN NaN NaN \n",
"0101-2007-0107 ... NaN NaN NaN NaN \n",
"0101-2008-0102 ... NaN NaN NaN NaN \n",
"0101-2008-0106 ... NaN NaN NaN NaN \n",
"0101-2009-0101 ... NaN NaN NaN NaN \n",
"0101-2010-0101 ... 104 Articulation 0101 90 \n",
"0101-2010-0103 ... 25 Language 0101 63 \n",
"0101-2010-0104 ... 30 Expressive Vocabulary 0101 90 \n",
"0101-2010-0105 ... 30 Language 0101 66 \n",
"0101-2012-0101 ... NaN NaN NaN NaN \n",
"0101-2013-0101 ... 12 Language 0101 58 \n",
"0101-2013-0103 ... NaN NaN NaN NaN \n",
"0101-2013-0104 ... 12 Language 0101 83 \n",
"0101-2013-0112 ... 11 Language 0101 90 \n",
"0101-2013-0113 ... 4 Language 0101 96 \n",
"0101-2013-0114 ... 6 Language 0101 50 \n",
"0101-2013-0115 ... 11 Language 0101 79 \n",
"0101-2013-0116 ... NaN NaN NaN NaN \n",
"... ... ... ... ... ... \n",
"1151-2012-0008 ... NaN NaN NaN NaN \n",
"1151-2012-0009 ... NaN NaN NaN NaN \n",
"1151-2012-0010 ... NaN NaN NaN NaN \n",
"1151-2012-0011 ... NaN NaN NaN NaN \n",
"1151-2012-0012 ... NaN NaN NaN NaN \n",
"1151-2012-0013 ... NaN NaN NaN NaN \n",
"1151-2012-0014 ... NaN NaN NaN NaN \n",
"1151-2013-0001 ... NaN NaN NaN NaN \n",
"1151-2013-0002 ... NaN NaN NaN NaN \n",
"1151-2013-0003 ... NaN NaN NaN NaN \n",
"1151-2013-0004 ... NaN NaN NaN NaN \n",
"1151-2013-0005 ... NaN NaN NaN NaN \n",
"1151-2013-0006 ... NaN NaN NaN NaN \n",
"1151-2013-0007 ... NaN NaN NaN NaN \n",
"1151-2013-0008 ... NaN NaN NaN NaN \n",
"1151-2013-0009 ... NaN NaN NaN NaN \n",
"1151-2013-0010 ... NaN NaN NaN NaN \n",
"1151-2013-0011 ... NaN NaN NaN NaN \n",
"1151-2013-0012 ... NaN NaN NaN NaN \n",
"1151-2014-0001 ... NaN NaN NaN NaN \n",
"1151-2014-0002 ... NaN NaN NaN NaN \n",
"1151-2014-0003 ... NaN NaN NaN NaN \n",
"1151-2014-0004 ... NaN NaN NaN NaN \n",
"1151-2014-0005 ... NaN NaN NaN NaN \n",
"1151-2014-0006 ... NaN NaN NaN NaN \n",
"1151-2014-0007 ... NaN NaN NaN NaN \n",
"1151-2014-0008 ... NaN NaN NaN NaN \n",
"1151-2014-0009 ... NaN NaN NaN NaN \n",
"1151-2014-0010 ... NaN NaN NaN NaN \n",
"9308-2015-0002 ... NaN NaN NaN NaN \n",
"\n",
" test_name test_type academic_year_start tech_class \\\n",
"study_id \n",
"0101-2003-0101 PLS EOWPVT 2002 Bimodal \n",
"0101-2003-0102 PLS Goldman 2003 Bilateral HA \n",
"0101-2004-0101 PLS PPVT 2006 Bimodal \n",
"0101-2004-0102 NaN NaN 2004 Bimodal \n",
"0101-2004-0103 CELF-4 EVT 2012 Bilateral CI \n",
"0101-2004-0104 PLS Goldman 2004 Bilateral HA \n",
"0101-2004-0105 CELF-P2 Goldman 2004 Bimodal \n",
"0101-2005-0101 PLS Goldman 2006 Bilateral HA \n",
"0101-2005-0102 CELF-P2 Goldman 2004 Bilateral HA \n",
"0101-2006-0101 NaN NaN 2005 Bimodal \n",
"0101-2006-0104 NaN NaN 2006 Bilateral CI \n",
"0101-2007-0104 NaN Goldman 2007 Bimodal \n",
"0101-2007-0105 NaN NaN 2007 Bimodal \n",
"0101-2007-0107 NaN NaN 2005 Bilateral HA \n",
"0101-2008-0102 NaN NaN 2008 Bimodal \n",
"0101-2008-0106 NaN NaN 2007 Bilateral HA \n",
"0101-2009-0101 NaN NaN 2008 Bimodal \n",
"0101-2010-0101 CELF-4 Arizonia 2008 Bilateral HA \n",
"0101-2010-0103 PLS receptive 2010 Bilateral HA \n",
"0101-2010-0104 PLS EOWPVT 2010 Bilateral HA \n",
"0101-2010-0105 PLS receptive 2011 Bilateral CI \n",
"0101-2012-0101 NaN NaN 2013 Bimodal \n",
"0101-2013-0101 PLS receptive 2012 Bilateral HA \n",
"0101-2013-0103 NaN NaN 2012 Bilateral CI \n",
"0101-2013-0104 PLS receptive 2012 Bilateral HA \n",
"0101-2013-0112 PLS receptive 2012 Bilateral HA \n",
"0101-2013-0113 PLS receptive 2013 Bimodal \n",
"0101-2013-0114 PLS receptive 2013 Bimodal \n",
"0101-2013-0115 PLS receptive 2013 Bilateral HA \n",
"0101-2013-0116 NaN NaN 2013 Bimodal \n",
"... ... ... ... ... \n",
"1151-2012-0008 NaN NaN nan Bimodal \n",
"1151-2012-0009 NaN NaN nan Bilateral HA \n",
"1151-2012-0010 NaN NaN nan Bimodal \n",
"1151-2012-0011 NaN NaN nan Bimodal \n",
"1151-2012-0012 NaN NaN nan Bilateral HA \n",
"1151-2012-0013 NaN NaN nan Bimodal \n",
"1151-2012-0014 NaN NaN nan Bimodal \n",
"1151-2013-0001 NaN NaN nan Bilateral HA \n",
"1151-2013-0002 NaN NaN nan Bilateral CI \n",
"1151-2013-0003 NaN NaN nan Bilateral CI \n",
"1151-2013-0004 NaN NaN nan Bimodal \n",
"1151-2013-0005 NaN NaN nan Bimodal \n",
"1151-2013-0006 NaN NaN nan Bilateral HA \n",
"1151-2013-0007 NaN NaN nan Bimodal \n",
"1151-2013-0008 NaN NaN nan Bilateral CI \n",
"1151-2013-0009 NaN NaN nan Bimodal \n",
"1151-2013-0010 NaN NaN nan Bilateral CI \n",
"1151-2013-0011 NaN NaN nan Bimodal \n",
"1151-2013-0012 NaN NaN nan Bimodal \n",
"1151-2014-0001 NaN NaN nan Bilateral CI \n",
"1151-2014-0002 NaN NaN nan Bilateral HA \n",
"1151-2014-0003 NaN NaN nan Bilateral HA \n",
"1151-2014-0004 NaN NaN nan Bilateral HA \n",
"1151-2014-0005 NaN NaN nan Bimodal \n",
"1151-2014-0006 NaN NaN nan Bilateral CI \n",
"1151-2014-0007 NaN NaN nan Bilateral HA \n",
"1151-2014-0008 NaN NaN nan Bimodal \n",
"1151-2014-0009 NaN NaN nan Bilateral HA \n",
"1151-2014-0010 NaN NaN nan Bilateral CI \n",
"9308-2015-0002 NaN NaN nan Bimodal \n",
"\n",
" age_year non_profound \n",
"study_id \n",
"0101-2003-0101 4 False \n",
"0101-2003-0102 3 True \n",
"0101-2004-0101 2 True \n",
"0101-2004-0102 0 True \n",
"0101-2004-0103 0 False \n",
"0101-2004-0104 0 True \n",
"0101-2004-0105 2 False \n",
"0101-2005-0101 2 True \n",
"0101-2005-0102 4 True \n",
"0101-2006-0101 0 False \n",
"0101-2006-0104 2 False \n",
"0101-2007-0104 4 True \n",
"0101-2007-0105 11 False \n",
"0101-2007-0107 0 True \n",
"0101-2008-0102 14 False \n",
"0101-2008-0106 0 True \n",
"0101-2009-0101 6 False \n",
"0101-2010-0101 8 True \n",
"0101-2010-0103 0 False \n",
"0101-2010-0104 0 False \n",
"0101-2010-0105 2 False \n",
"0101-2012-0101 2 False \n",
"0101-2013-0101 0 True \n",
"0101-2013-0103 4 False \n",
"0101-2013-0104 0 True \n",
"0101-2013-0112 0 True \n",
"0101-2013-0113 0 True \n",
"0101-2013-0114 0 True \n",
"0101-2013-0115 0 True \n",
"0101-2013-0116 1 False \n",
"... ... ... \n",
"1151-2012-0008 3 False \n",
"1151-2012-0009 1 True \n",
"1151-2012-0010 2 True \n",
"1151-2012-0011 4 False \n",
"1151-2012-0012 5 False \n",
"1151-2012-0013 5 False \n",
"1151-2012-0014 NaN False \n",
"1151-2013-0001 4 False \n",
"1151-2013-0002 2 False \n",
"1151-2013-0003 2 False \n",
"1151-2013-0004 3 False \n",
"1151-2013-0005 3 False \n",
"1151-2013-0006 3 False \n",
"1151-2013-0007 NaN False \n",
"1151-2013-0008 2 False \n",
"1151-2013-0009 NaN False \n",
"1151-2013-0010 6 False \n",
"1151-2013-0011 1 False \n",
"1151-2013-0012 3 False \n",
"1151-2014-0001 2 False \n",
"1151-2014-0002 3 False \n",
"1151-2014-0003 3 False \n",
"1151-2014-0004 3 False \n",
"1151-2014-0005 4 False \n",
"1151-2014-0006 3 False \n",
"1151-2014-0007 4 False \n",
"1151-2014-0008 5 False \n",
"1151-2014-0009 6 True \n",
"1151-2014-0010 6 False \n",
"9308-2015-0002 NaN False \n",
"\n",
"[5511 rows x 76 columns]"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').first()"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFxCAYAAACItSqmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x109b9f588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"unique_students = lsl_dr.groupby('study_id').first()\n",
"unique_students.academic_year_start.value_counts().sort_index()[:-1].plot(kind='bar')\n",
"plt.ylabel('Frequency'); plt.xlabel('Academic year');"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1096d47b8>"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAFxCAYAAABA2cBwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt0VPW9/vFnckNIMkAk1mPViDmRUivRYFgkSrjU0mg9\nVcAREiTiDeFYFIOXqGDAS1EUsAUqUvAWLZfzEw/HYy8u4yVLYg1YoSgSahCxKhIMKZlJmMnl+/sj\nh1GEJJPM7OyZ8H6txVows/PMZzaZPNl79uztMMYYAQAA20TZPQAAACc6yhgAAJtRxgAA2IwyBgDA\nZpQxAAA2o4wBALBZTHt3NjY2qqioSF988YWio6P14IMPKjo6WkVFRYqKilJaWpqKi4vlcDi0fv16\nrVu3TjExMZoxY4ZGjRrVTU8BAIDI1m4Zv/3222pubtbatWtVXl6uJUuWqKmpSYWFhcrMzFRxcbFK\nS0uVnp6ukpISbdiwQV6vV3l5ecrOzlZcXFx3PQ8AACJWu7upBw4cqObmZhljVFdXp9jYWH300UfK\nzMyUJOXk5Ki8vFzbt29XRkaGYmNjlZCQoJSUFFVWVnbLEwAAINK1u2Xcp08fffHFF8rNzVVtba1W\nrFihzZs3+++Pj49XXV2d3G63EhMTj7rd7XZbNzUAAD1Iu2X87LPPasSIEbr99tu1b98+FRQUqKmp\nyX+/2+2W0+lUQkKCPB6P/3aPxyOn09nuAzc1NSsmJjrI8QEAiHztlnHfvn0VE9O6iNPpVFNTk378\n4x+roqJCw4YNU1lZmbKysjRkyBAtWbJEPp9PXq9XVVVVSktLa/eBDx6s79SgycmJqq6u69TXkBse\n2ZGWa2V2pOVamR1puVZmR1quldmRltvZ7OTkxOPe3m4ZT506Vffee68mT56sxsZGzZ49W+eee67m\nzp2rxsZGpaamKjc3Vw6HQwUFBcrPz1dLS4sKCws5eAsAgAB1+J7xE088ccztJSUlx9zmcrnkcrlC\nNxkAACcITvoBAIDNKGMAAGxGGQMAYDPKGAAAm7V7ABcAAPYwIU1rbm7+v0xHSHNDhTIGAISlJeu3\nqrr2cEiykvudpNuvPj8kWVagjAEAYam69rD21TR02+P97W9bdO+9d+j559fplFN+IEl68smlOuus\ngbr00sstfWzeMwYA4P/Exsbp17+e7/+3w9E9u7XZMgYAQK3Fm5FxoSSjl15arwkTrvbft2bNC3rj\njdcUHR2j9PQLNGPGzJA+NlvGAABIMqb1oLHZs4u0fv0f9MUX/5Qk1dd79Oabr2vFime0YsXT+uc/\n96q8/J2QPjZlDADAdzidfXXrrbP10EPFamlpkdfr07nnnqfo6NYrDaanX6BPP60K6WOymxoAEJaS\n+51kW9ZFF41QWdmb+tOf/ldTp96gHTs+VHNzs6KiorR16we69NJfhGw2iTIGAISpUH4UKSkpXjU1\nnnaXcTgcRx2wddtts/X++5vVp0+8xoy5RDNm3CBjWjRkyAUaMWJUyGaTKGMAQFgK7VHMrbuY28+8\n4IKhuuCCof5/9+kTr//3/17x/3vixMkhnem7eM8YAACbUcYAANiMMgYAwGaUMQAANuMALgBAGOKq\nTQAA2G75tlU60FATkqwBvZN0S/qNIcmyAmUMAAhLBxpqtL/+m259zDlz7tagQYM1ZcpUSa2nwrzx\nxgI9+OCjSk39d8sel/eMAQBtMG3++Xa37/f/RLY777xHGze+pD17PpUkLV/+G11xxXhLi1hiyxgA\n0I5AdxWH+27gQPXt20+3336XHn30Qd1003/qq6++0rXX3qDZs2+Vz+dVr169dNdd96lv3366//4i\neTweNTc36vrrb1Zm5vAuPy5lDABokx27iu125LzUCxY8qCefXK3f/naRXK5JGj48W1u2VGjFimWa\nMuU6HTr0Ly1atFQOh09bt+4I6jEpYwAAvic39xfyer0aMGCAdu/+RCUlz+jFF5+TMUaxsbEaOPBs\n/fKX4zVv3n2KipJ++curgno8yhgAEJYG9E6yLcsY47++cUrKWcrLm6Kf/GSI9u7dow8++Jt27/5E\n9fX1WrjwCRnToKuvnqTs7Iu7PB9lDAAIS6F8DzqQqzZ913ev4HTLLbP0+OOPyOfzyuv1atasO3X6\n6Wfq6ad/rzfffF3R0Q7ddNP0oOajjAEAYaj7r9r0Xd+9gtNpp/1QixcvPWaZhx56VJKUnJyo6uq6\noObjo00AANiMMgYAwGaUMQAANuvwPeOXX35ZGzZskCR5vV7t3LlTf/jDH/Twww8rKipKaWlpKi4u\nlsPh0Pr167Vu3TrFxMRoxowZGjVqlNXzAwAQ8Tos43HjxmncuHGSpAceeEAul0vLly9XYWGhMjMz\nVVxcrNLSUqWnp6ukpEQbNmyQ1+tVXl6esrOzFRcXZ/mTAAAgkgW8m3r79u365JNP5HK59NFHHykz\nM1OSlJOTo/Lycm3fvl0ZGRmKjY1VQkKCUlJSVFlZadngAAD0FAF/tOmpp57Sr371K0nyfxBakuLj\n41VXVye3263ExMSjbne73W3m9e/fRzEx0Z0aNjk5seOFuoBc67MjLdfK7EjLtTI70nKtzA7H3NaL\nQQQuKSn+/z5CFJxwXBdWZwdUxocOHdKePXs0bNgwSVJU1Lcb1G63W06nUwkJCfJ4vv1AtcfjkdPp\nbDPz4MH6Tg0ais9xkWtPdqTlWpkdablWZkdarpXZ4ZvbuaswtZ5UI7jPB4fvughNdlulHdBu6s2b\nN2v48G+vRjF48GBVVFRIksrKynThhRdqyJAh2rJli3w+n+rq6lRVVaW0tLSAhgMA4EQW0Jbxnj17\ndOaZZ/r/XVRUpLlz56qxsVGpqanKzc2Vw+FQQUGB8vPz1dLSosLCQg7eAgAgAAGV8Q033HDUv886\n6yyVlJQcs5zL5ZLL5QrNZAAAnCA46QcAADajjAEAsBllDACAzShjAABsRhkDAGAzyhgAAJtRxgAA\n2IwyBgDAZpQxAAA2o4wBALAZZQwAgM0oYwAAbEYZAwBgM8oYAACbUcYAANiMMgYAwGaUMQAANqOM\nAQCwGWUMAIDNKGMAAGxGGQMAYLMYuwcAAJxoTJv3NDc3t3O/w5JpwgFlDADodsu3rdKBhpqAlh3Q\nO0m3pN9o8UT2oowBAN3uQEON9td/Y/cYYYP3jAEAsBllDACAzShjAABsRhkDAGAzyhgAAJtRxgAA\n2KzDjzY99dRTevPNN+Xz+ZSfn6/MzEwVFRUpKipKaWlpKi4ulsPh0Pr167Vu3TrFxMRoxowZGjVq\nVDeMDwBA5Gt3y/i9997TBx98oLVr1+qFF17Qvn379Mgjj6iwsFAvvviijDEqLS1VdXW1SkpKtHbt\nWq1evVqLFi2Sz+frrucAAEBEa7eMN23apEGDBuk///M/NX36dI0aNUofffSRMjMzJUk5OTkqLy/X\n9u3blZGRodjYWCUkJCglJUWVlZXd8gQAAIh07e6mrqmp0VdffaWnnnpKn3/+uaZPny5jvj1naHx8\nvOrq6uR2u5WYmHjU7W6327qpAQDoQdot4/79+ys1NVUxMTEaOHCgevXqpf379/vvd7vdcjqdSkhI\nkMfj8d/u8XjkdDrbfeD+/fsoJia6U8MmJyd2vFAXkGt9dqTlWpkdablWZkdarpXZ4ZjbetGGwCUl\nxSs6uuOf653N7Ux2e8L5+6LdMh46dKief/55XXfddfr66691+PBhDR8+XBUVFRo2bJjKysqUlZWl\nIUOGaMmSJfL5fPJ6vaqqqlJaWlq7D3zwYH2nBk1OTlR1dV2nvobc8MiOtFwrsyMt18rsSMu1Mjt8\nc9u+utLx1NR4FNiVlTqX27ns4wuX74u2SrvdMh41apQ2b96sq666Si0tLSouLtYPf/hDzZ07V42N\njUpNTVVubq4cDocKCgqUn5+vlpYWFRYWKi4urvPPCACAE1CHH2268847j7mtpKTkmNtcLpdcLldo\npgIA4ATCST8AALAZ1zMGgIh3/PdgWw+Uauv92a6//4rQo4wBoAdYvm2VDjTUdLjcgN5JuiX9xm6Y\nCJ1BGQNAD3CgoUb767+xewx0Ee8ZAwBgM8oYAACbUcYAANiM94wBAG0wGtA7KaAlW5cz4ijtrqGM\nAQBtiv8yW95D3o6Xc/aS0rthoB6KMgYAtMGhXZ8f0r6ahg6XPDWpt9gq7jreMwYAwGaUMQAANmM3\nNQB0i7YvG8hpK0EZA0A3CfSUlRKnrey6yDxPN2UMAN2EU1Z2j0g8TzdlDADoUSLxlx4O4AIAwGaU\nMQAANqOMAQCwGWUMAIDNKGMAAGxGGQMAYDPKGAAAm1HGAADYjDIGAMBmlDEAADajjAEAsBllDACA\nzShjAABsRhkDAGCzgC6hOG7cOCUkJEiSzjjjDN18880qKipSVFSU0tLSVFxcLIfDofXr12vdunWK\niYnRjBkzNGrUKCtnBwCgR+iwjL1erySppKTEf9v06dNVWFiozMxMFRcXq7S0VOnp6SopKdGGDRvk\n9XqVl5en7OxsxcXFWTc9AAA9QIdlvHPnTjU0NOiGG25QU1OTbr/9du3YsUOZmZmSpJycHG3atElR\nUVHKyMhQbGysYmNjlZKSosrKSp133nmWPwkAQCQxGtA7KeClW5c1khyWTWS3Dsu4d+/euuGGG+Ry\nubRnzx7deOONR90fHx+vuro6ud1uJSYmHnW72+0O/cQAgIgX/2W2vIe8gS3r7CWlWzyQzTos47PO\nOkspKSn+v/fr108ff/yx/3632y2n06mEhAR5PB7/7R6PR06ns83c/v37KCYmulPDJicndrxQF5Br\nfXak5VqZHWm5VmZHWm4w2c3NzZ3+mqSkeEVHd/xzsrPZ4ZC76/ND2lfTEFDuqUm9lZSUYOvMHQn2\ne67DMn7ppZe0a9cuFRcX6+uvv5bH49FFF12kiooKDRs2TGVlZcrKytKQIUO0ZMkS+Xw+eb1eVVVV\nKS0trc3cgwfrOzVocnKiqqvrOvU15IZHdqTlWpkdablWZkdabvDZptNfUVPjUWC7ZjuXHWm5VmYH\nntu2znxftFXaHZbxVVddpaKiIuXn58vhcGjBggXq16+f5s6dq8bGRqWmpio3N1cOh0MFBQXKz89X\nS0uLCgsLOXgLAIAAdFjGsbGxWrRo0TG3f/fo6iNcLpdcLldoJgMA4ATBST8AALAZZQwAgM0oYwAA\nbEYZAwBgM8oYAACbUcYAANiMMgYAwGaUMQAANqOMAQCwWYdn4AIAhLvAL0l4IlyOMBJRxgDQAwR6\nScIT4XKEkYgyBoCI5wj4koSnJvUWW8Xhh/eMAQCwGWUMAIDNKGMAAGxGGQMAYDPKGAAAm1HGAADY\njI82AQB6kMg8AQplDADoUSLxBCiUMQB0i8C32KTw2mqLLJF5AhTKGAC6SaBbbFJ4bbXBepQxAHSL\nwLfYpPDaaoP1OJoaAACbUcYAANiM3dQAcBTT5j3Nzc1t3M/uZASHMgaA71m+bZUONNR0uNyA3km6\nJf3GbpgIPR1lDADfc6ChRvvrv7F7DJxAeM8YAACbUcYAANiMMgYAwGYBlfE333yjkSNH6tNPP9Vn\nn32mvLw8TZ48WfPmzZMxrUcWrl+/XhMmTNDEiRP11ltvWTkzAAA9Sodl3NjYqPvvv1+9e/eWMUYL\nFixQYWGhXnzxRRljVFpaqurqapWUlGjt2rVavXq1Fi1aJJ/P1x3zAwAQ8Tos44ULFyovL0/JycmS\npB07digzM1OSlJOTo/Lycm3fvl0ZGRmKjY1VQkKCUlJSVFlZae3kAAD0EO2W8YYNG5SUlKSLL75Y\nkmSM8e+WlqT4+HjV1dXJ7XYrMTHxqNvdbrdFIwMA0LO0+znjDRs2yOFwqLy8XDt37lRRUZEOHjzo\nv9/tdsvpdCohIUEej8d/u8fjkdPpbPeB+/fvo5iY6E4Nm5yc2PFCXUCu9dmRlmtldqTlWpkdjrmt\nZ9kKXFJSvKKjO/5Z1tlcK7MjLdfK7EBzOxLs93K7ZfzCCy/4/z5lyhTNnz9fCxcuVEVFhYYNG6ay\nsjJlZWVpyJAhWrJkiXw+n7xer6qqqpSWltbuAx88WN+pQZOTE1VdXdepryE3PLIjLdfK7EjLtTI7\nfHPbPh3m8dTUeBTY6TA7l2tldqTlWpkdeG7bOvM911Zpd+oMXA6HQ0VFRZo7d64aGxuVmpqq3Nxc\nORwOFRQUKD8/Xy0tLSosLFRcXFxnogEAOGEFXMYlJSXH/fsRLpdLLpcrNFMBAHAC4aQfAADYjDIG\nAMBmlDEAADajjAEAsBllDACAzShjAABsRhkDAGAzyhgAAJtRxgAA2IwyBgDAZpQxAAA2o4wBALAZ\nZQwAgM0oYwAAbEYZAwBgM8oYAACbUcYAANiMMgYAwGaUMQAANqOMAQCwGWUMAIDNKGMAAGxGGQMA\nYDPKGAAAm1HGAADYjDIGAMBmlDEAADajjAEAsBllDACAzShjAABsFtPRAs3NzZozZ4727Nkjh8Oh\n+fPnKy4uTkVFRYqKilJaWpqKi4vlcDi0fv16rVu3TjExMZoxY4ZGjRrVDU8BAIDI1mEZv/nmm4qK\nitKaNWtUUVGhxYsXS5IKCwuVmZmp4uJilZaWKj09XSUlJdqwYYO8Xq/y8vKUnZ2tuLg4y58EAISO\n0YDeSQEt2bqckeSwdCL0fB2W8SWXXKLRo0dLkr744gv17dtX5eXlyszMlCTl5ORo06ZNioqKUkZG\nhmJjYxUbG6uUlBRVVlbqvPPOs/YZAECIxX+ZLe8hb8fLOXtJ6d0wEHq8DstYkqKjo3X33XertLRU\nv/nNb7Rp0yb/ffHx8aqrq5Pb7VZiYuJRt7vd7tBPDACWcmjX54e0r6ahwyVPTeottooRCgGVsSQ9\n+uijOnDggFwul3w+n/92t9stp9OphIQEeTwe/+0ej0dOp7PNvP79+ygmJrpTwyYnJ3a8UBeQa312\npOVamR1puVZmh2Nuc3Nzp5ZPSopXdHTHP8s6m2tldqTlWpkdaG5Hgv1e7rCMN27cqK+//lrTpk3T\nSSedpKioKP3kJz9RRUWFhg0bprKyMmVlZWnIkCFasmSJfD6fvF6vqqqqlJaW1mbuwYP1nRo0OTlR\n1dV1nfoacsMjO9JyrcyOtFwrs8M313Rq6ZoajwLbOu5crpXZkZZrZXbguW3rzPdcW6XdYRmPHTtW\n99xzj6655ho1NTXpvvvu09lnn625c+eqsbFRqampys3NlcPhUEFBgfLz89XS0qLCwkIO3gIAIAAd\nlnHv3r31xBNPHHN7SUnJMbe5XC65XK7QTAYAwAmCk34AAGAzyhgAAJtRxgAA2IwyBgDAZpQxAAA2\no4wBALAZZQwAgM0oYwAAbEYZAwBgM8oYAACbUcYAANiMMgYAwGaUMQAANqOMAQCwGWUMAIDNKGMA\nAGxGGQMAYDPKGAAAm1HGAADYjDIGAMBmlDEAADajjAEAsBllDACAzShjAABsRhkDAGAzyhgAAJtR\nxgAA2IwyBgDAZpQxAAA2o4wBALBZTHt3NjY26t5779WXX34pn8+nGTNmKDU1VUVFRYqKilJaWpqK\ni4vlcDi0fv16rVu3TjExMZoxY4ZGjRrVTU8BAIDI1m4Zv/LKK0pKStJjjz2mf/3rX7riiis0ePBg\nFRYWKjMzU8XFxSotLVV6erpKSkq0YcMGeb1e5eXlKTs7W3Fxcd31PAAAiFjtlnFubq5+/vOfS5Ja\nWloUExOjHTt2KDMzU5KUk5OjTZs2KSoqShkZGYqNjVVsbKxSUlJUWVmp8847z/pnAABAhGv3PeM+\nffooPj5ebrdbt912m2bNmqWWlhb//fHx8aqrq5Pb7VZiYuJRt7vdbuumBgCgB2l3y1iSvvrqK/3q\nV7/S5MmTdfnll+uxxx7z3+d2u+V0OpWQkCCPx+O/3ePxyOl0tpvbv38fxcREd2rY5OTEjhfqAnKt\nz460XCuzIy3XyuxwzG1ubu7U8klJ8YqO7vhnWWdzrcyOtFwrswPN7Uiw38vtlvGBAwd0/fXXq7i4\nWMOHD5ckDR48WBUVFRo2bJjKysqUlZWlIUOGaMmSJfL5fPJ6vaqqqlJaWlq7D3zwYH2nBk1OTlR1\ndV2nvobc8MiOtFwrsyMt18rs8M01nVq6psYjyRHyXCuzIy3XyuzAc9vWme+5tkq73TJesWKF6urq\ntHz5ci1fvlySdN999+nhhx9WY2OjUlNTlZubK4fDoYKCAuXn56ulpUWFhYUcvAUAQIDaLeM5c+Zo\nzpw5x9xeUlJyzG0ul0sulyt0kwEAcILgpB8AANiswwO4ACA8Hf+9wdYDeNp63zC49wYBq1DGACLW\n8m2rdKChpsPlBvRO0i3pN3bDREDXUMYAItaBhhrtr//G7jGAoPGeMQAANqOMAQCwGWUMAIDNKGMA\nAGxGGQMAYDPKGAAAm1HGAADYjDIGAMBmlDEAADajjAEAsBllDACAzShjAABsRhkDAGAzyhgAAJtR\nxgAA2IwyBgDAZpQxAAA2i7F7AADoGqMBvZMCWrJ1OSPJYelEQFdRxgAiVvyX2fIe8na8nLOXlN4N\nAwFdRBkDiFAO7fr8kPbVNHS45KlJvcVWMcIZ7xkDAGAzyhgAAJtRxgAA2IwyBgDAZpQxAAA2o4wB\nALAZZQwAgM0CKuNt27ZpypQpkqTPPvtMeXl5mjx5subNmydjjCRp/fr1mjBhgiZOnKi33nrLsoEB\nAOhpOizj3//+95ozZ44aGxslSQsWLFBhYaFefPFFGWNUWlqq6upqlZSUaO3atVq9erUWLVokn89n\n+fAAAPQEHZZxSkqKli1b5t8C3rFjhzIzMyVJOTk5Ki8v1/bt25WRkaHY2FglJCQoJSVFlZWV1k4O\nAEAP0WEZjx07VtHR0f5/HyllSYqPj1ddXZ3cbrcSExOPut3tdod4VACRx7T5p7m5uZ37gRNLp89N\nHRX1bX+73W45nU4lJCTI4/H4b/d4PHI6ne3m9O/fRzEx0e0u833JyYkdL9QF5FqfHWm5VmZHWm4w\n2c3NzXrwzd/oQENNQMsP6J2kuaNvO2oDoL3szkhKio+oXCuzIy3XyuxAczsS7Ouv02U8ePBgVVRU\naNiwYSorK1NWVpaGDBmiJUuWyOfzyev1qqqqSmlpae3mHDxY36nHTU5OVHV1XWfHJTcMsiMt18rs\nSMsNPtvoQEON9td/E/BX1NR4FNhFHTq3BR1puVZmR1quldmB57atM6+Rtko74DJ2OFqHLSoq0ty5\nc9XY2KjU1FTl5ubK4XCooKBA+fn5amlpUWFhoeLi4gKNBgDghBZQGZ9++ulau3atJOmss85SSUnJ\nMcu4XC65XK7QTgcAwAmAk34AAGAzyhgAAJtRxgAA2IwyBgDAZpQxAAA2o4wBALBZp0/6AQCBMxrQ\nOyngpVuXNQr2JAxApKGMAai9sxZ9ew7p7wusMOO/zJb3kDewZZ29pPSAFgV6FMoYgCRp+bZVAZ1D\nekDvJN2SfmOAqQ7t+vyQ9tU0BLT0qUm9xVYxwpN1v7BKlDGA/9PZc0gDJxprfmFtRRkDABAAK39h\npYyBiHL8XWVt7yaT2O0LhELgByN25UBEyhiIMFbuKgPQtkAPRuzKgYiUMRBheG8XsEPgByN25UBE\nyhiArN4FB6B9lDEASdbuggPQPsoYgKzeBQegfZQxEFHYnQz0RJQxEGHYnQz0PJQxTmDWnt7OGuxO\nBnoiyhgnND6zCyAcUMY4oVnzmd2ubHFLbMUCJy7KGLBAoFvcElvdAChjwBKcJQtAZ1DGOIHxMSEA\n4YEyxgnNmo8JBV7yEkUPgDJGRLDqsoHWfUwo0JKX+DwwAMoYESKyPoIUeMlLfB4YAGWMkLJqC7bt\njwq1vTzlBiByUMYIIaO1uzao9vC/Olyy30l9Nemc8Qq0NDkFJICeLKRl3NLSonnz5mnXrl2KjY3V\nww8/rDPPPDOUDxGGundrMNxPGnG4arDqAyjNk5y9pHMCTeUUkAB6tpCW8euvv67GxkatXbtW27Zt\n0yOPPKLf/e53oXyIsLRk/VZV1x7ucLnkfifp9qvPDzDVqq1Mo7bK3OfzSWr53q2OAHNbl6U0AaDz\nQlrGf/vb3zRixAhJUnp6uj788MNQxgfB2gsCVNceDvhgnc6wZitTevZPH6smgNwkZy9NvfTHgQcD\nALokpGXsdruVkJDg/3d0dLRaWloUFRXViZTvb5m1Ov5WW+C5a3e91ImtzAkB50rSOWc4NaBvrw6X\nS3J2vMy3HDpY59OBf3Vcmg5HZ7Ze1cnczknud1JIl+vpuVZmR1quldmRlmtldqTlWpkdLrmS5DDG\ndPZQ1TY98sgjSk9P16WXXipJGjlypN5+++1QxQMA0CN1ZpO1QxkZGSorK5Mkbd26VYMGDQplPAAA\nPVJIt4yNMZo3b54qKyslSQsWLNDAgQNDFQ8AQI8U0jIGAACdF9Ld1AAAoPMoYwAAbEYZAwBgM8oY\nAACbUcYAANjshLlq08UXX6yFCxcqOzs75NkHDhzQ6tWrFRsbqwkTJmjmzJlyu916+OGHlZWV1eXc\nmpoaLV68WO+//74OHz6sU089VUOHDtWMGTMUHx8fVO6TTz6pd999V3V1dXI6nbrwwgv1q1/9Sief\nfHKXcyVp9uzZklo/5vZdDodDixYt6nLu1q1b9cADD6hXr16aPXu2LrzwQknSLbfcouXLl3c59+uv\nv9aqVavUt29fXXLJJZo5c6aio6O1YMECXXDBBV3OlY6cNe5bN9xwg1avXi1JiouL63Lu4sWLVVhY\nqE8//VR33nmn9u/fr9NOOy3ojxK+9dZb+uyzzzRmzBjdc8892rNnj0477TTNnz9fgwcP7nKuZN3r\nj9fet/7jP/5DBw8ePO5977zzTpdzee19y6rXniTJhJlFixYZY4zZvXu3mTBhghkxYoSZOHGi2b17\nd1C5v/zlL83NN99s7rrrLrN3795QjOo3depUs379erN69Wpz0UUXmZ07d5r9+/ebiRMnBpU7Y8YM\nU15ebhoaGsyrr75qVq5caf785z+b2267Lajcm266ybz66qvm0KFDprm52Rw6dMj87//+r7n22muD\nyjXGmNfqkbqxAAASLUlEQVRee83k5uaav/71r0f9ee+994LKPfI9sGvXLnPllVeasrIyY4wx11xz\nTVC5U6dONRs2bDBLly41WVlZpqqqynz11VcmPz8/qFxjjBk6dKjJzs42o0ePNqNHjzbnnXeeGT16\ntBkzZkxQuUee80033WS2bNlijDHm448/NlOnTg0qd/z48Wbfvn3mpptuMhUVFf7cq6++OqhcY6x7\n/fHa+9aePXvM+PHjTX19fdBZ38Vr71tWvfaMMSbstow/+OADSa0nDLnnnns0dOhQ7dy5Uw888ICe\neeaZLuc6nU6tWLFCr732mm6//XY5nU7l5OTojDPO0E9/+tOgZvb5fHK5XJKkl156yX/msZiY4FZv\nbW2t/7f7yy67TNdcc41eeOGFoNaDJHk8Hl122WX+fycmJuoXv/iFXnzxxaByJelnP/uZ3nvvPX3z\nzTdHPUawYmNj/b95rly5Utddd51OOeWUoHMbGxs1btw4SVJFRYXOPvtsSerk+dSPb926dXr00UdV\nWFioH/3oR5oyZYpKSkqCzj3i8OHDGjp0qCTpRz/6kZqamoLKi4uL0w9+8AM5HA5lZmb6c0PBqtcf\nr71vpaSkaMqUKXrvvfc0atSooPOO4LV3rFC/9qQw3k1txZOVpLFjx2rs2LH65JNPVF5erk2bNgVd\nxr1799bjjz8ut9stn8+n9evXKyEhQX369AkqNz4+XitXrtSIESNUWlqqM844w//LSjCSkpK0bNky\n5eTkKD4+Xh6PR2VlZUpOTg46W5LmzJkTkpzvio+P1/PPP6+JEycqOTlZixYt0m233abGxsagchMT\nE/W73/1O06dP1/PPPy9J2rhxo3r16sxFPY4vNTVVixcv1v3336+RI0cGnXfEnj17NH36dLndbv3l\nL3/RmDFj9NxzzwX9/Xbuuedq/vz5uuCCC3Tvvfdq1KhRevvtt5WamhqiyUP/+ovk115CQoLcbndI\nX3tXXnllSHK+i9fet6x67UlheAauESNG6Nxzz9X+/ft18803+5/s5s2b9dRTT3U5d+XKlZo2bVoI\nJ/1WXV2dNmzYoEGDBqlfv35atmyZ+vbtq9tuuy2o3yBra2v11FNPqaqqSoMHD9a0adO0ZcsWDRw4\nUGeeeWaXcw8fPqw1a9bob3/7m/9KWxkZGcrLy9NJJ3X+aiPf19jYqJ07d8rtdsvpdCotLS2o92mk\n1nX87LPPaurUqUpMTJQkffLJJ1q8eHFQ18yur6/Xf/3Xf+naa6/137Zy5UpNmDAh6PfwjjDGaNmy\nZXrllVf02muvhSRv7969+uijj3TKKafoJz/5iZYtW6Zp06bJ6XR2Obe5uVkbN27Upk2bdPDgQfXr\n109Dhw6Vy+UK+v/Pqtcfr71vff990u8K5v+P197ReVa89qQwLGMrn+zhw4dVWVmphoYG9e/fX+ec\nc06XLhPYUXa/fv10zjnnhGR3y5Hc+vp6JSUlKS0tLSS5Pp9PlZWVcrvdSkxM1DnnnBP0D1yp9SCg\nRYsWKSUlxb/VXVVVpcLCQv3sZz8L2cyhKnkrc7+fHcr1bMUvPFbO+/3sSJjZyu8LK/z85z/XN998\nc8zPSYfDodLSUpumQqDCroylY0szFAX01ltv6be//a1SUlL0wQcfaMiQIdq3b5/uuusu/9GB4ZZt\nZa5VhTlx4kStXr36qOta19XV6dprr9WGDRvCbmYr10Wkzcy6sD7Xqq1XqfVI7euvv17PPvus+vXr\nF1TWd1k1s5XrIhJnDrujqd98800zbtw4M2vWLDNy5Egzc+ZM43K5zObNm4PKnTx5svF6vcYYY2pq\nakxhYaE5dOiQmTRpUtAzW5VtVe7VV19t6urqjrrt0KFDZty4cUHlGtN6RK7P5zvqNq/XayZMmBBU\nrlUzW7kuIm1m1oX1uWPHjjVDhw71H+V75E+wR/keUVZWZjZt2hSSrCOsmtnKdRGJM4fdAVyrVq3S\n2rVrFRcXp4MHD+qhhx7S6tWrNW3aNK1Zs6bLuW632//3uLg4ffnll0pMTAz6IAQrs63KbWpqOuYg\niV69eoVk9/fEiRM1fvx4ZWRkKDExUR6PR1u2bFFBQUFQuVbNbOW6iLSZWRfW565Zs8aSrdcjRowY\nEfJMq2a2cl1E4sxhV8ZWFdBll10ml8ulYcOGacuWLZo8ebKee+45/fjHPw52ZMuyrcq1qjAl6eqr\nr9bo0aO1fft2/wEqt9xyiwYMGBCWM1u5LiJtZtaF9blJSUmaPXu2duzYEfIToBhjVFpaqvLy8qNO\nKJKbmxvUsTFWzWzluojEmcPuPeOVK1fq1VdfPaqAjrxf88ADDwSVXVlZqd27d+ucc85Ramqqampq\nlJSUFJK5rcq2Kre6uvqowhwyZEjQhSlZ9wPBypmtyrUyO9JyrcyOtFyrzJs3T8YY5eTkqE+fPv6P\nLDY3N+vhhx+2ezx0IOy2jKdNm6aRI0dq9+7dmjRpUsgKyBijyspKvf/++3rzzTeVlJSk7Oxs5eTk\nBD2zVdlW5m7btu2owvR6vSEpzPnz5/t/IMTHx/s/R/nOO+8E9QPBqpmtXBeRNjProntyrfpl9R//\n+McxJw+55JJLNGnSpKByrZrZynURiTOH3ZaxMUavvPKK3n//fTU0NISsgB566CH/Z/reeOMNDRgw\nQLW1tUpISNCsWbPCMtuq3O/+Bv3dwgzFb9CTJ08+7tmEJk2apLVr13Y516qZrVwXkTYz66J7c0O9\n9ZqXl6fCwkL/2dOk1jNbLV26NKizT1k1s5XrIhJnDrujqR988EGzZMkS8/bbb5vi4mKzdOlS/23B\n+P75To+cCzbYc9hamd1duUeEYl1MmjTJf17jI957772gz2Nr1cxWrotIm5l1Ebm5xrSem3r69Okm\nJyfHXHzxxSYnJ8fcfPPN5uOPPw4qNxLXRSTOHHaXUPz44481a9Ys5eTkaN68edqyZYvmzJmjv/71\nr0Hler1ebd26VZK0efNmxcTEqLa2VocPHw56ZquyrcptaWnR5s2bj7qtoqJCsbGxQeVK0iOPPKKn\nn35aI0eO1IgRIzRy5Eg9/fTTuu+++4LKtWpmK9dFpM3MuojcXEmqqqrSxx9/rNjYWN199916++23\ntWLFiqC32CJxXUTizGG3m/qqq67SnDlzdP755/tPgfn4449r6tSp+u///u8u53700UeaO3eu9u/f\nr9NPP10LFizQ22+/rZSUFI0ePTqoma3Ktir3s88+0yOPPKIdO3aopaVFUVFRGjx4sGbNmhX0hQHe\neOMNPfDAA4qJidGsWbN0+eWXS1LQJ2q3amYr10Wkzcy6iNxcSXK5XFq1apVaWlp022236corr9T4\n8eN57UXIzGG3m/rDDz8048aNMxdddJH/0l3PPPOMeeONN+werccoLS01I0eOND/96U/NK6+84r89\n2F3Jxhhz1VVXmdraWlNTU2OmTJliXnrppZBkWzWzlesi0mZmXURurjFH70Ktq6sz48ePN++++27Y\nzsz329HC7mjqc88995jTJgZ90Wa1bpk1NjYe96L3wRxYZGW2VblPPvmkNm7c6P8N2ufzafz48V3O\n+664uDj17dtXkvS73/1O1157rU477bSgc62a2cp1EWkzsy4iN1eS/yL3t956qxISErRs2TJdf/31\nqqurC8uZ+X47WtiVsVUFdMcdd2jOnDlatmyZoqOjgx2zW7KtyrWqMCXrfiBYNbOV6yLSZmZdRG6u\nJP3617/WK6+84v+Izb/927+ppKREK1asCCo3EtdFJM4cdrupt27dai6//HKzZ88e8/nnnx/1J1gr\nV640f/nLX0IwZfdlW5F7xx13mF//+tfG7XYbY4z58ssvTW5urrnooouCzvb5fOall14yHo/Hf1t1\ndbV58MEHg8q1amYr10Wkzcy6iNxcK0XiuojEmcOujI2xtjRhXWFayaqZrVwXkTYz6yJyc60Uiesi\nEmcOu6OpAQA40YTd54wBADjRUMYAANiMMgYAwGaUMWCjMWPG6Msvvww658orrwzBNPb47W9/qy1b\ntlj+NUA4o4yBHiCYU8XabfPmzWppabH8a4BwFnYn/QDCVVNTk+bNm6dPPvlEBw4c0MCBA7Vs2TKt\nWbNGa9euVXR0tEaPHq077rhDu3bt0kMPPaT6+nrV1NTouuuu05QpU1RbW6s777xT+/bt07//+7/L\n5/NJkpqbm7Vw4UJt3rxZzc3NGjdunKZOnar33nvPf9KGvXv36uc//7kSExP1+uuvyxij3//+9zr5\n5JP1ox/9SDt37lRtba3uu+8+ffrpp4qLi1NRUZGGDx9+3Ofz7rvv6je/+Y3/ZDovv/yytm3bpvvv\nv1+PPvroMbO09fyrq6t14403KikpSSeddJKefvrp4z7evn37dMcdd6ihoUFRUVGaM2eOPv30U334\n4YeaO3euli5dqtraWj3xxBM6fPiw/vWvf+nOO+9Ubm6uioqKVFtbq71792ratGn+r1m2bJnS0tIs\n+N8GulnQH44CThCbN282DzzwgDHGmJaWFnPNNdeYlStXmrFjx5q6ujrT1NRkpk6daj788EPz8MMP\nm3fffdcYY8zevXvNBRdcYIwxZv78+eaJJ57w5w0aNMj885//NH/4wx/MggULjDHGeL1ec80115jN\nmzebv/71ryYjI8Ps27fPNDQ0mPPPP9+sW7fOGGNMUVGRee6554wxxgwaNMgYY8y8efPMwoULjTHG\nVFZWdnhpt5/+9Kdm7969xhhjCgoKzLZt29qc5XjP/y9/+Yv5/PPPzaBBg8wXX3zR7mMtXbrUrFq1\nyhjTelnNp59+2hjTel7fI5fdnDlzptm9e7cxxpjy8nJz+eWXG2OMufvuu01RUZE/67tfA/QEbBkD\nAbrwwgvVr18/vfjii9q9e7c+++wzeb1ejRkzRgkJCZKkZ555RpI0ePBglZWVaeXKldq5c6caGhok\nte5eXbx4sT/vjDPOkNS6lbpz507/pUIbGhr0j3/8Q6mpqUpLS9MPfvADSVL//v2VlZUlSfrhD3+o\nQ4cOHTXjli1btGjRIknSOeec0+EpZK+88kpt3LhR48eP1zfffKMhQ4Zo1apVx50lLy9Pffv2Per5\n19fXS5JOPvnkDk8LmJ2drZkzZ2rHjh0aNWqUJk+e7L/P/N/pDh5//HG98cYb+tOf/qRt27b515vD\n4VB6evpReYZTJKAHoYyBAJWWlmrp0qW69tprNWHCBNXW1srpdMrtdvuX+frrr9W7d2/dd9996tev\nn0aPHq3LLrtMf/zjH/3LNDc3+/9+5JzjLS0tuuuuu3TJJZdIkg4ePKg+ffpo69atx1wrtb3zlMfE\nxBxVUlVVVTr77LP95yv+vvHjx+vGG29Ur169/AeBtTXL8Z7/Eb169Wp/5UnKyMjQq6++qrfeekt/\n/OMf9fLLL/t3aR+ZLy8vT1lZWRo2bJiysrI0e/bsNh+jrecERCIO4AIC9O677+rSSy/VuHHjdPLJ\nJ/vfUy0rK1N9fb2ampp0xx136KOPPlJ5eblmzpypMWPGqKKiQlJryWVnZ+t//ud/JEl///vftXfv\nXknS8OHDtW7dOjU1NcntdisvL09///vf253neFuGF154ob/4q6qqdNNNN7VbWqeddppOPfVUrVmz\nRldcccVxZ8nPz9e2bdvafP6Beuyxx7Rx40ZdeeWVmjt3rnbs2CGp9ReIpqYm1dbW6rPPPtOtt96q\nnJwcvfPOO/6DtL7/XI98DdBTsGUMBOjqq6/W7Nmz9ec//1lxcXE6//zzdejQIU2ePFkTJ06UMUZj\nx45VVlaWZs6cqfz8fDmdTg0cOFCnn366vvjiC82cOVP33HOPLr/8cp199tk644wz5HA4NGnSJO3Z\ns0fjxo1TU1OTrrrqKmVmZqqioqLNMv3u7Uf+fuutt2rOnDm64oorFB0drccee6zD53XppZfq9ddf\nV3JysiQdM8uECRM0bNgw9evX75jn/89//lPDhw8PaCt1ypQpmj17tl5++WVFRUVp3rx5kqQRI0ao\nuLhYjz76qFwul37xi18oISFB559/vg4fPqyGhgY5HI6jHuPI1yxcuFDnn39+h48NhDvOTQ2cwJqa\nmnTXXXfpsssu8++WBtD92DIGeriCgoJjDvSSpIkTJ2rp0qW66KKLQlbEW7Zs0UMPPXTc+1auXKlT\nTjklJI8D9DRsGQMAYDMO4AIAwGaUMQAANqOMAQCwGWUMAIDNKGMAAGxGGQMAYLP/DwTRrY65amrl\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1096d9ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"disab_by_year = unique_students.groupby('academic_year_start')['synd_or_disab'].value_counts().unstack().fillna(0)\n",
"disab_by_year.columns = ['No', 'Yes']\n",
"disab_by_year[disab_by_year.index!='nan'].plot(kind='bar', stacked=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment