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fonnesbeck/Dataset Statistics.ipynb

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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>13328</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",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13329</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>13330</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>13331</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",
"13328 1147-2010-0064 initial_assessment_arm_1 2010-2011 0 0 \n",
"13329 1147-2010-0064 year_1_complete_71_arm_1 2011-2012 0 NaN \n",
"13330 1147-2010-0064 year_2_complete_71_arm_1 2012-2013 0 NaN \n",
"13331 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",
"13328 0 0 1 3 3 ... 3 6 \n",
"13329 NaN NaN NaN NaN NaN ... 3 5 \n",
"13330 NaN NaN NaN NaN NaN ... 3 5 \n",
"13331 NaN NaN NaN NaN NaN ... 4 5 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"13328 65 0 NaN NaN NaN \n",
"13329 77 2 NaN NaN NaN \n",
"13330 89 2 NaN NaN NaN \n",
"13331 101 2 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid \n",
"13328 NaN NaN NaN \n",
"13329 NaN NaN NaN \n",
"13330 NaN NaN NaN \n",
"13331 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": [],
"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>13328</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>13329</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13330</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>13331</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",
"13328 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"13329 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"13330 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"13331 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo \\\n",
"13328 1 3 3 8 ... 6 \n",
"13329 1 3 3 8 ... 5 \n",
"13330 1 3 3 8 ... 5 \n",
"13331 1 3 3 8 ... 5 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"13328 65 0 NaN NaN NaN \n",
"13329 77 2 NaN NaN NaN \n",
"13330 89 2 NaN NaN NaN \n",
"13331 101 2 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid study_id \n",
"13328 NaN NaN NaN 1147-2010-0064 \n",
"13329 NaN NaN NaN 1147-2010-0064 \n",
"13330 NaN NaN NaN 1147-2010-0064 \n",
"13331 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>9459</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</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>4</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0735-2010-0017</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9451</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>17</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>0735-2010-0015</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9447</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>17</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>0735-2010-0014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9443</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>7</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>2</td>\n",
" <td>14</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>0735-2010-0013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5594</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2005-2006</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0521-2006-0025</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",
"9459 initial_assessment_arm_1 2009-2010 0 0 0 0 \n",
"9451 initial_assessment_arm_1 2010-2011 0 0 7 0 \n",
"9447 initial_assessment_arm_1 2010-2011 0 0 7 0 \n",
"9443 initial_assessment_arm_1 2009-2010 0 1 7 0 \n",
"5594 initial_assessment_arm_1 2005-2006 0 1 0 0 \n",
"\n",
" sib mother_ed father_ed premature_age ... a_fo fam_age \\\n",
"9459 0 5 4 9 ... NaN NaN \n",
"9451 2 6 6 7 ... 2 17 \n",
"9447 2 6 6 7 ... 2 17 \n",
"9443 1 6 6 8 ... 2 14 \n",
"5594 3 2 2 8 ... NaN NaN \n",
"\n",
" family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"9459 NaN NaN NaN NaN NaN \n",
"9451 1 NaN NaN NaN NaN \n",
"9447 1 NaN NaN NaN NaN \n",
"9443 0 NaN NaN NaN NaN \n",
"5594 NaN NaN NaN NaN NaN \n",
"\n",
" school_lunch medicaid study_id \n",
"9459 NaN NaN 0735-2010-0017 \n",
"9451 NaN NaN 0735-2010-0015 \n",
"9447 NaN NaN 0735-2010-0014 \n",
"9443 NaN NaN 0735-2010-0013 \n",
"5594 NaN NaN 0521-2006-0025 \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-4 591 523\n",
"CELF-P2 1357 1363\n",
"OWLS 1058 1066\n",
"PLS 3349 3359\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 5008\n",
"Arizonia 493\n",
"Arizonia and Goldman 73\n",
"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 5571.000000\n",
"mean 68.695028\n",
"std 30.661547\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 11068\n",
"True 2450\n",
"dtype: int64\n",
"There are 694 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 4960\n",
"4 2876\n",
"3 1922\n",
"5 1527\n",
"2 1320\n",
"1 470\n",
"0 192\n",
"dtype: int64\n",
"mother_ed:\n",
"1 3242\n",
"2 2876\n",
"3 1527\n",
"0 662\n",
"dtype: int64\n",
"\n",
"There are 5905 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": [
"(14212, 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 10371\n",
"1 2394\n",
"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": [
"0.18754406580493538"
]
},
"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 3410 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 9214\n",
"2 554\n",
"4 358\n",
"12 193\n",
"6 178\n",
"10 148\n",
"8 112\n",
"14 42\n",
"16 3\n",
"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 4813\n",
"0 4212\n",
"7 1462\n",
"5 970\n",
"2 474\n",
"6 413\n",
"8 69\n",
"9 57\n",
"3 27\n",
"4 25\n",
"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 6360\n",
"3 4272\n",
"0 1789\n",
"4 56\n",
"1 19\n",
"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 6282\n",
"3 4694\n",
"0 1462\n",
"4 57\n",
"1 27\n",
"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 4384\n",
"1 4\n",
"2 2\n",
"dtype: int64\n",
"There are 1639 null values for OAD\n",
"\n",
"hearing_aid:\n",
"2 2031\n",
"0 1593\n",
"1 737\n",
"dtype: int64\n",
"There are 1690 null values for hearing_aid\n",
"\n",
"cochlear:\n",
"0 2868\n",
"2 897\n",
"1 625\n",
"dtype: int64\n",
"There are 1639 null values for cochlear\n",
"14212\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": [
"(3445, 5169, 1385, 2076)"
]
},
"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 625\n",
"bilateral_ci 897\n",
"bilateral_ha 2031\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 5169\n",
"3 3445\n",
"4 1385\n",
"1 895\n",
"0 672\n",
"8 14\n",
"2 12\n",
"7 5\n",
"5 1\n",
"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([ 1. , 18. , 17. , 22. , nan, 3. , 2. , 5. ,\n",
" 13. , 36. , 28. , 21. , 41. , 26. , 0. , 67. ,\n",
" 20. , 24. , 4. , 40. , 60. , 10. , 6. , 25. ,\n",
" 7. , 27. , 15. , 35. , 14. , 42. , 34. , 12. ,\n",
" 9. , 32. , 50. , 8. , 33. , 23. , 11. , 31. ,\n",
" 30. , 49. , 48. , 1.5, 19. , 2.5, 39. , 52. ,\n",
" 16. , 38. , 29. , 51. , 46. , 45. , 54. , 88. ,\n",
" 65. , 44. , 81. , 116. , 72. , 57. , 62. , 43. ,\n",
" 78. , 83. , 61. , 107. , 64. , 74. , 37. , 77. ,\n",
" 96. , 97. , 79. , 47. , 53. , 59. , 84. , 95. ,\n",
" 80. , 0.5, 58. , 56. , 86. , 98. , 85. , 75. ,\n",
" 119. , 66. , 70. , 63. , 140. , 126. , 133. , 103. ,\n",
" 87. , 76. , 55. , 68. , 92. , 71. , 154. , 89. ,\n",
" 152. ])"
]
},
"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": [
"3970"
]
},
"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 7442\n",
"2 2374\n",
"1 1287\n",
"3 1006\n",
"6 691\n",
"8 519\n",
"7 240\n",
"4 64\n",
"5 28\n",
"dtype: int64\n",
"race:\n",
"0 7442\n",
"2 2374\n",
"4 1302\n",
"1 1287\n",
"3 1006\n",
"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": null,
"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(['2009-2010', '2010-2011', '2005-2006', nan, '2007-2008',\n",
" '2006-2007', '2008-2009', '2003-2004', '2012-2013', '2013-2014',\n",
" '2002-2003', '2011-2012', '2014-2015', 'June 2014', '2004-2005',\n",
" '1997-1998', '2006-2007 ', '1998-1999', '2000-2001', '2001-2002',\n",
" '2103-2014', '1999-2000', '2012', '2009-2011', '2015-2016',\n",
" '1995-1996', '2014', '2013', '2011', '2010', '2009',\n",
" ' 2010-2011',\n",
" ' 2014-2015', '2012=2013', '2015', '2015-2015', '2009 - 2010',\n",
" '2010 - 2011', '2011 - 2012', '2014-2105', '2014-2015 ', '65',\n",
" '2005-2004', '2012 - 2013', '2014-205', '2013 - 2014', '2017-2015',\n",
" '2014-1015', '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": 87,
"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": 88,
"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": 92,
"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": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x114b379b0>"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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x7JIkFWLYJUkqxLBLklSIYZckqRDDLklSIYZdkqRCDLskSYUYdkmSCjHskiQVYtglSSrE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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1120087f0>"
]
},
"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": 94,
"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": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EVT 3642\n",
"EOWPVT 2624\n",
"EOWPVT and EVT 147\n",
"dtype: int64"
]
},
"execution_count": 95,
"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": 96,
"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": 97,
"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": 98,
"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": 98,
"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": 99,
"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": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.columns"
]
},
{
"cell_type": "code",
"execution_count": 100,
"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": 101,
"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": 102,
"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": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 27 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": 104,
"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": 104,
"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": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2959,)"
]
},
"execution_count": 105,
"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": 106,
"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": 107,
"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": 108,
"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>36577</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>136</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>92</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36578</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>136</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>84</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36579</th>\n",
" <td>year_9_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>3</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
" <td>...</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</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>36580</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>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>NaN</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>185</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>125</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36581</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>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>NaN</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>186</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>101</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",
"36577 year_9_complete_71_arm_1 2012-2013 0 0 8 0 \n",
"36578 year_9_complete_71_arm_1 2012-2013 0 0 8 0 \n",
"36579 year_9_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"36580 year_9_complete_71_arm_1 2013-2014 0 1 0 0 \n",
"36581 year_9_complete_71_arm_1 2013-2014 0 1 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... sex \\\n",
"36577 2 6 4 8 ... Female \n",
"36578 2 6 4 8 ... Female \n",
"36579 3 6 6 9 ... Female \n",
"36580 2 NaN 6 8 ... Male \n",
"36581 2 NaN 6 8 ... Male \n",
"\n",
" known_synd synd_or_disab race age_test domain \\\n",
"36577 0 0 4 136 Expressive Vocabulary \n",
"36578 0 0 4 136 Receptive Vocabulary \n",
"36579 0 0 0 NaN NaN \n",
"36580 0 0 0 185 Expressive Vocabulary \n",
"36581 0 0 0 186 Receptive Vocabulary \n",
"\n",
" school score test_name test_type \n",
"36577 1147 92 NaN EVT \n",
"36578 1147 84 NaN PPVT \n",
"36579 NaN NaN NaN NaN \n",
"36580 1147 125 NaN EVT \n",
"36581 1147 101 NaN PPVT \n",
"\n",
"[5 rows x 73 columns]"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.tail()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2013 6924\n",
"2012 6628\n",
"2014 5480\n",
"2011 5205\n",
"2010 4419\n",
"nan 3177\n",
"2009 2358\n",
"2008 825\n",
"2007 531\n",
"2006 343\n",
"2005 285\n",
"2004 173\n",
"2003 90\n",
"2002 47\n",
"2001 35\n",
"1998 16\n",
"1999 15\n",
"2000 12\n",
"2015 11\n",
"1997 6\n",
"1995 1\n",
"2103 1\n",
"dtype: int64"
]
},
"execution_count": 109,
"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": 110,
"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": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x112dd3a58>"
]
},
"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": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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1Et8JLQ9vpPqp3gBwX8vy+4GFu9rv4OBAJ+X0DNu3a6Ojo12qRJoaixbtTX9//6Q/r58t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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110e9cba8>"
]
},
"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": 113,
"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": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(36582, 74)"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.shape"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5440,)"
]
},
"execution_count": 115,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5440,)"
]
},
"execution_count": 116,
"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": 117,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr.score = lsl_dr.score.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"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": 120,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['age_year'] = np.floor(lsl_dr.age/12.)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Articulation', 'Expressive Vocabulary', 'Language',\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.dropna().unique()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tech_class\n",
"Bilateral CI 0.43\n",
"Bilateral HA 0.58\n",
"Bimodal 0.51\n",
"Name: prim_lang, dtype: float64"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').prim_lang.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['non_profound'] = lsl_dr.degree_hl<6"
]
},
{
"cell_type": "code",
"execution_count": 124,
"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": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').non_profound.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BRgghhBBCCFG1JNAIIYQQQgghqpYEGiGEEEIIIUTVOtFZzoQQQgghTlg+ESe5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\n5PffsP1wsDHkz5WZyc6f4z+l5+f4e5L7jgEC8AD5ALGrWvRt8nXilcD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29nbe8pbX8sEP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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f370278>"
]
},
"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": 126,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lsl_dr.pivot_table?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plots of Demographic Data"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_color = \"#64AAE8\""
]
},
{
"cell_type": "code",
"execution_count": 128,
"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(1)\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": 129,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_students = demographic.drop_duplicates('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5440, 67)"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 4949.000000\n",
"mean 30.353708\n",
"std 28.072288\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": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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TAEBiJgCAxEwAAImZAAASMwEAJGYCAEjMBACQmAkAIDETAEBiJgCAxEwAAImZAAASMwEA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\noHg/nwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f370c50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.male, ('Female', 'Male'), label_offset=20, ylim=(0, 2600), color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4V/WxH3roBA49dMIq/NzO/g2+f+dC5i7szL//6DW7ePuma3bkd/fq1PO/3c/9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\nVciAlySpQv8fZzAawSPy7H0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f96bcc0>"
]
},
"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": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4889"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.prim_lang.count()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10f93a0b8>"
]
},
"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": 136,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4515"
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.sib.count()"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 4857 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": 138,
"metadata": {
"collapsed": false
},
"outputs": [],
"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": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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iIpJi6loXEakV5Tl63kyOnlfqCxVyEZFaMuntUhaX1k5Bb1GSoWvrklp5Lqnf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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f99c710>"
]
},
"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": 140,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3316"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_amp_counts.sum()"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"666.0"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age_amp.max()"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10f927e80>"
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ZxwLXA0ubrFWSpLGu6cP7Pwfe1/J8TmZ21I/XACcARwKdmbk1M7uA9cChwFzgzpa+xzdc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\nFcLQlySpEIa+JEmF+H91/MWNEDBdHAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f92e630>"
]
},
"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": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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B/KW37oXdaiLi0Mbd44H/Ah5h9XU87qwqV1UcwfdMR1QdQJvkg53uX76O++rB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\nZMFLklQgC16SpAJZ8JIkFciClySpQP8L3srjibRt088AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f928080>"
]
},
"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": 144,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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EvAS8AtzLMgeMLL7blXquxi1i76F+7MSYTst7ZKaXHW4RjVNUhwJ/6a1HYbea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\nC16SpAL9H8+xBSjdQTwQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112672128>"
]
},
"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": 145,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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S9GmfhwJe0qFR9CISKys3JVmxIeoqMiWeX1yk+ukcvIiISAwp4EVERGJIAS8i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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1126807f0>"
]
},
"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": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4361"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_right.count()"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4352"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_left.count()"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AFOAVoIuZNTKz5kB7YHaSWUVERLZUSV9zvxzYBrjSzK4imuA9ABgZD5h7B3jE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1126804a8>"
]
},
"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": 149,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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wq7YDMA24PoGxioiIpJREJvjH2JaUqxFMymnj7nPCY88BJwNHA3PdvcDd1wJL\ngJZAe4KJOUXnnpTAWEVERFJKwrro3T0fwMxygceBawkm7xRZB9QBcoE1xY6vB/JKHC86V0RERMog\nobPozawJ8CQwzt3/amYjiz2cC6wmGF+vU+L4qvB4bolzd2r58nWaMSgiImmjQYPcHS4zTVgXfTi2\nPhMY6u4Ph4cXmFmH8PZpBJtdvAm0N7PscEOMQ4HFwKvA6eG5p4fnioiISBkkbJmcmY0B/g/4kG3r\nbAcCYwk2yPgA6OXuhWbWg2AjjRjBLPp/mFkN4GGgEfAj0NXdv9vZNdWCFxGRdLKzFnxKrYNXghcR\nkXQSSRe9iIiIREcJXkREJAUpwYuIiKQgJXgREZEUpAQvIiKSgpTgRUREUpASvIiISApSghcREUlB\nSvAiIiIpSAleREQkBSnBi4iIVMD77y9mwIA+AKxatYqrrx7MpZf2pl+/nnz99VdbzyssLGTIkMt4\n+uknt3v+yy+/yM03Xxf3uBJaLlZERCSVPfLIVGbOfJYaNWoCMGHCPZxyymmccMJJvPPOWyxd+jmN\nG+8DwH333cv69eu3e/7dd9/Fm2++xsEHN4t7bGrBi4iIlNM++zRhxIhRW+8vXPgey5d/x+WX9+OF\nF2bSps2RALz00myqVavGMccct93zjziiJYMHD0tIbErwIiIi5dSx4wlUq1Zt6/1ly74mN7cOY8bc\nS8OGDZk+/WE+/fQTXnjheXr06EPJCq6dO5+UsNjURS8iIhIneXl5tGvXAYB27Y7nvvvuZdOmTaxY\nsYLLLruEZcu+ISsri0aNGnP00ccmNBYleBERkThp0aI1r702j1NOOY13313AAQccRN++A7Y+/uCD\n97HHHnsmPLmDuuhFRETipn//y3nuuX/Rt28P3nhjPt26dY8slljJ8YCqbPnydanzZkREJMEqS8qI\nlfuZDRrk7vDJ6qIXEZG0NWXhRlZujCbR18+JcVGLnIS9vhK8iIikrZUbC1mRH9XVE/vBQmPwIiIi\nKUgJXkREJAUpwYuIiKSghI/Bm9kxwJ/c/QQzawX8C/gofHiCuz9uZr2A3sBm4DZ3n2FmOcB0YC9g\nLfBHd/8+0fGKiIikgoQmeDO7ErgQKNpd/0jgLnf/c7FzGgIDgDZATWCumc0C+gIL3X24mXUBrgcu\nT2S8IiIiqSLRLfiPgXOBaeH9I4FmZnYOQSv+CuBoYK67FwBrzWwJ0BJoD9wRPu85ggQvIiIiZZDQ\nMXh3fwrqD2VyAAAgAElEQVQoKHbodeBKd+8IfArcCNQB1hQ7Zz2QB+QWO74uPE9ERETKINmT7P7h\n7guKbgOtCJJ48eSdC6wiGHfPLXZsdbKCFBERqeqSneBnmtlR4e0TgbeBN4H2ZpZtZnnAocBi4FXg\n9PDc04E5SY5VRESkykr2TnZ9gbFmtglYBvR29/Vmdg8wl2BD3mvcfZOZTQAeNrM5wI9A1yTHKiIi\nUmWp2IyIiKSpQka/sSGyrWr3rAmDjq5BoorNaKMbERGRFKQELyIikoKU4EVERFKQEryIiEgKUoIX\nERFJQUrwIiIiKUgJXkREJAUpwYuIiKQgJXgREZEUpAQvIiKSgpTgRUREUpASvIiISApSghcREUlB\nu0zwZnZYKceOTUw4IiIiEg87rAdvZu2AasD9ZtaDbfXsMoGJQLPEhyciIiLlscMED5wMdAQaAcOL\nHS8AJiUyKBEREamYHSZ4d78JwMwudPdpSYtIREREKmxnLfgir5jZnUB9tnXT4+7dExaViIiIVEhZ\nEvxjwJzwqzCx4YiIiEg8lCXBZ7n7kIRHIiIiInFTlnXwc83s12aWnfBoREREJC7K0oI/D7gUwMyK\njhW6e7VEBSUiIiIVs8sE7+6NK3IBMzsG+JO7n2BmBwFTgC3AYnfvH57TC+gNbAZuc/cZZpYDTAf2\nAtYCf3T37ysSi4iISLrYZYI3sxtKO+7uw0s7XuK5VwIXAuvDQ6OBa9x9jplNMLOzgdeAAUAboCbB\nkMAsoC+w0N2Hm1kX4Hrg8jK8JxERkbRXljH4WLGvbOAsoGEZX/9j4Nxi94909znh7ecINtM5Gpjr\n7gXuvhZYArQE2gPPFzv3pDJeU0REJO2VpYv+5uL3zewWYFZZXtzdnzKz/YodihW7vQ6oA+QCa4od\nXw/klThedK6IiIiUQXmqydUGmpbzeluK3c4FVhOMr9cpcXxVeDy3xLkiIiJSBmUZg/+MbRvcZAB1\ngTvLeb13zKyDu78CnAb8B3gTuC1chlcDOBRYDLwKnA68Ff47p/SXFBERkZLKskyuU7HbhcDqcKy8\nPIYAk80sC/gAeMLdC83sHmAuQRf+Ne6+ycwmAA+b2RzgR6BrOa8pIiKSdmKFhTvffdbMYsAlwIkE\nHwj+A4xz9y07fWIEli9fp610RUSkjAoZ/cYGVuRHc/U9a8Kgo2uw/fS03dOgQe4On1yWFvxI4BDg\nwTCKi4ED0ZI1ERGRSqssCf4UoHVRi93MZgCLEhqViIiIVEhZZtFnsv0HgUzgp8SEIyIiIvFQlhb8\nX4CXzOzR8P7vgUcSF5KIiIhU1E4TvJnVAyYDC4DO4dcYd5+WhNhERESknHbYRW9mrYH/Emwv+5y7\nXwnMBP5kZi2SFaCIiIjsvp2NwY8Cfu/uRfvB4+7XAN0JisaIiIhIJbWzLvp67v5SyYPuPtPM7khc\nSCJSFU2bNoV5816hoKCAc889j0MOacadd95O9erVOfjgZlx++RAApk+fwuzZs6hVqzZdu3ajbdv2\nEUcukpp2luCzzCyj5IY2ZpZBUFVORASABQve5v33FzJx4oNs2LCBRx+dxlNPPcEVVwzlsMMO5/77\nJzJr1vMcfPDBzJ49i8mTp7JlyxYuuaQ7Rx75S6pXrx71WxBJOTvron8ZuLGU49cR7A8vIgLAG2+8\nxgEHHMTVVw9m2LBBtG17PMuXf8dhhx0OwOGHt2DhwgV8/vnntG59JJmZmWRnZ9OkSRM++WRJxNGL\npKadteCvBp41sz8QFISJAW2A7whqwouIALB69Wq+/XYZI0f+ma+//ophwwbRuHFj3n33HVq1asO8\neXPYuHEjBx10MNOnT2HDhg1s2vQjixYt5Oyzfxt1+CIpaYcJ3t3XmVkH4ASgNUGp1/HurqpuIrKd\nvLw89t9/fzIzM2nadD+ys6tz2WWDuf/+SUyZcj8tW7Zm/fps9ttvf37zm98xePAAGjbcm8MOO4K8\nvLpRhy+Skna6Dt7dCwmKy/wnOeGISFXUokUrnnjir3Tp8gdWrFjOxo0bWbjwXW688Vbq1KnDmDF3\ncuyx7Vi9ejX5+fnce+/9/PDDegYNGsCBBx4UdfgiKaksO9mJiOxU27btee+9BfTq1Y3CQhg8eCib\nNm1m4MBLyMmpQZs2R3HssW0BWLr0M3r16kZWVjb9+g0kFit/JS0R2bFdloutSlQuVqQ8KsuvTdVO\n9N27X0Dt2rUBaNSoMeeddz6jRt1OZmYmTZo0Zdiw6wGYP38eU6bcD4DZoQwadFVkMYvKxYpIipuy\ncCMrN0aT6OvnxLioRU4k146XTZs2AXDPPRO3Hrvmmivp3r03xxxzHMOHX8+rr86lVas2TJhwD+PG\n3UedOnk88sg01qxZrXkIkhBK8CLCyo2FkbViKk8PQvl9/PFHbNy4gUGDLuWnn7bQu3c/mjUz1qxZ\nTWFhIfn5P5CZmcnixQs58MCDGTs2WG3w61+fkzLJ/ec9GF0YOvQKmjRpCsA555xH584n8be//YXZ\ns18gFotx3HHtuOiinlGGndKU4EVEKignJ4euXS/kzDPP4csvv2DIkMvo3r03Y8aMYurUB6lVqzat\nWx/Jiy/OZsGCt5ky5VFycnLo378nhx/egn33bRL1W6iQ0now/vWvf3D++X+gS5c/bD329ddf8e9/\nz2Ty5KkA9O3bgw4dOnHggQcnN+A0oQQvIlJBTZrsxz77NAlvN6VOnTxuu+0mpk17jP32258nn3yc\nsWNH065dB5o3/wX16tUDoGXLNixZ4lU+wZfWg/Hhhx/y5ZdfMGfOy+y7bxMGDhzCXns15K67xm59\nXkFBAdnZ2sUwUXa2k52IiJTBjBlPM27cGABWrFhOfv4P7LPPvtSoUQOAPfdswPr162nW7FA+/fQT\n1q5dQ0FBAe+/v4j99z8wytDjoqgHY/TocQwZMozhw6/j0EMPpX//yxg37j4aN96HBx+8j8zMTOrU\nyQNg/Pi7MTu0yn+4qczUghcRqaAzzzyHESNupl+/nmRkZHD11TdSWLiFG2+8hszMTLKyshg69Drq\n1atHnz6XcsUVlxKLxejc+WQOOKDqJ/jSejCOOaYtDRrsBUCHDicwZsydQNCdf/vtw6lVqzaDBw+L\nLOZ0oAQvImmu4pP8MjOrccMNw392fMKE+392rRNPPIkTTzyplOtX3WWCM2Y8zSeffMLgwVdt7cG4\n+uohDB58Fc2bH8bbb7+BWXMAhg0bxFFHHU3Xrt0ijjr1KcGLSNrTMsGKKa0Ho3r1bEaPHklWVhb1\n6+/B0KHX8sorL/HeewsoKChg/vx5xGIx+vS5dGtRIomvSBK8mb0NrAnvfgaMAKYQ7He/2N37h+f1\nAnoDm4Hb3H1G8qMVkVSX3ssEk9eD0aFDR2bPnlvh60nZJD3Bm1l1AHfvXOzY08A17j7HzCaY2dnA\na8AAggp2NYG5ZjbL3TcnO2YRkVQWVQ9GKvReVGZRtOBbArXMbCZQDbgWaFOsSt1zwCkErfm57l4A\nrDWzJUAL4O0IYhYRSVnR9WBE3XuR2qJYJpcP3OnupwJ9gb+w/eySdUAdIJdt3fgA64G8ZAUpIiJS\nlUXRgv8I+BjA3ZeY2fcE3fBFcoHVwFqCRF/yuEils2XLFu6441a++GIpGRkZDB48jIcffoBVq1ZS\nWFjIsmXfcNhhR3DTTbcxffoUZs+eRa1atenatRtt27aPOnwRSUFRJPjuwBFAfzNrTJDEZ5lZR3d/\nGTiNoP78m8BtZpYN1AAOBRZHEK/ILs2b9wqxWIwJEx5gwYK3mTz5Xm6//S4A1q1bx8CBlzBw4GA+\n/fRjZs+exeTJU9myZQuXXNKdI4/8JdWrazcvEYmvKBL8A8BDZjaHYJz9IuB74H4zywI+AJ5w90Iz\nuweYS9CFf427b4ogXpFdOv74TrRr1wGAZcu+ITd3W+fTAw9M4re/7UK9evVZsOAdWrc+kszM4Fev\nSZMmfPLJEn7xCy0TEpH4SnqCD2fBX1DKQ51KOfcBgg8EIpVeRkYGt912E3PmvMQtt9wBwKpVq3jn\nnTcZOHAwAAcddDDTp09hw4YNbNr0I4sWLeTss38bZdgikqK00U0clRyHHTLk6q3bUI4dO5qmTffn\n7LN/A8D8+fOYMiVYI2p2KIMGXRVZ3BI/1157E6tWraRXrz/yl788zksvzebkk39FLBbMI91vv/35\nzW9+x+DBA2jYcG8OO+yIlCkXKiKVi4rNxFHxcdiePS/hvvvGs3r1aoYMuYx58+ZsPS8/P58JE+7h\nzjvHMGnSQ+y9d2PWrNH8waps5sxnmTZtCgDZ2dlkZGQQi2Xw1luvc+yxbbeet3r1avLz87n33vsZ\nMmQY3333LQceeFBEUYtIKlMLPo5KG4fduHEDPXr04bXXXt163uLFCznwwIMZO/bPfP31V/z61+eo\nFVfFdezYmREjbubSS3vz008FDBw4hOzsbL788gsaN95n63l169Zl6dLP6NWrG1lZ2fTrN3Br615E\nJJ6U4OOs5Djs3ns3Yu+9GzF//ryt56xevZoFC95mypRHycnJoX//nhx+eAuVTYxMxTfbyMmpzvDh\nI372ulOn/vVn17jyyqt3cH0lehGJHyX4BCg5Dlu9+vZbMebl5dG8+S+oV68eAC1btmHJEleCj5CK\njYhIqlGCj6OZM5/lu+++48ILL9puHLakZs0O5dNPP2Ht2jXUrFmL999fxFlnnRtBxFIkvYuNiEgq\nUoLfquJ/ZDt2PCEch+3FTz/9xMCBg8nOzgIKCYZZC4FC6tWrS58+/bniikuJxaBz55M54IADwsfV\nTSsiIhWnBF9MPLpp6555A0XT5V4HXn9jQ3DnsAtYBYwuup/bnsP6BFuULguvrW5aERGJFyX4YtRN\nKyIiqULr4EVERFKQEryIiEgKUoIXERFJQUrwIiIiKUiT7CQuNm/ezIgRN/P1119Rq1ZtBg0ayoYN\n+YwZM4pq1aqRlZXNddfdvHVzHxERSSwleImLZ555ipo1azJp0kN8+eUXjB49ks2bNzFo0FUcdNDB\nPP30k0yfPoUBA66IOlQRkbSgBC9x8fnnn22tmtakSVOWLv2M+++fSr169QH46aefqF69epQhioik\nFY3BS1wcckgzXn11LgCLFy9ixYrl1K0bdMcvWvQeTz75OF26dI0yRBGRtKIWvMTFGWecxdKln9G/\nfy+OOKIlZs2JxWLMnj2LadOmMGrU3SqJKyKSRErwEhcffPBfjjzyaAYMGMSHH37AsmXfMGvWczz9\n9JOMHTuJ3NzcqEMUEUkrSvASF02aNOHGGycwdeqD5ObmctVV19OtWxf23ntvrrlmCLFYjFat2tC9\ne++oQxURSQtK8BKq2F74eXl5jBkzfrtjzz777928jirpiYjEixK8bBWPanrlUT8npkp6IiJxpgQv\nW0VXTU+V9ERE4q1SJ3gziwH3Ai2BjUBPd/802qhEREQqv8q+Dv4coLq7twWuBkZHHI+IiEiVUNkT\nfHvgeQB3fx04KtpwREREqoZK3UUP1AHWFLtfYGYZ7r4lERernxMjqvHg4NrRiur9p/N733btaOn9\n6/3rdz+qaydOrLCw8k5wMrO7gPnu/kR4/wt3bxpxWCIiIpVeZe+inwecDmBmxwKLog1HRESkaqjs\nXfRPASeb2bzw/sVRBiMiIlJVVOouehERESmfyt5FLyIiIuWgBC8iIpKClOBFRERSUGWfZCeVnJnl\nAvWB5e4eyU72IiLJZmaHA3sA37n7B1HHUxpNsisnMzsAuBToRJDgvgNmA5PcfWmEoSWFmXUD+hH+\ngAN1gVXAve7+SJSxJUv4C96Jbf8Hs939o0iDSqJ0ff9mdjxwOcFOm5uAAmA+MM7dX40ytmRJ4+99\ndeAq4P+Ab4FlQD2gMfAY8Gd33xBdhNtTgi8HM7sBOAh4HFgIfEPwTT4G6AJ87O43RRZggpnZFII9\nCh5399XFjucBXYG27n5hROElnJk1B0YB+QR7MxT//mcC17j7+9FFmFjp/P7NbCywFngU+G/Rrppm\ndgRwAZDr7v0iDDGh0vl7D1v/9v2F4APNlmLHY8CvgN+7e7eIwvsZddGXz5PuvrjEse+AfwL/DH/Z\nU9kl7r6x5EF3XwNMMLOHIogpmboAXcP3ux0zqwdcAdyQ9KiSJ53f/y3u/l3Jg+6+CLjKzBpGEFMy\npfP3Hne/aAfHC4Hnwq9KQy34ODCzU9x9VtRxJIuZHe/uc8wsA7gEaA28DUx295+ijU4kscysAdAB\nyANWE2yn/U20UUmymdmd7n5l1HHsjFrw5WBmvUscGmRmowHc/b4IQkq2m4HOwEigNvAkcCJwD9A/\nwriSwsz2AK4HTiIoiLQamAPcXFrrLtWY2W3ufq2ZNQOmA42AL4GLUn0c1sx6Ar2BucA64HDgGjO7\n390nRhpcEqT7z34JR0YdwK4owZfPOQSTyp4HYkB1gj9y6eZod+8Q3n7OzF6MNJrkeRiYRtAVuQ7I\nJaiZ8AjBH75Ud1z472jgCnefZ2YtgfHAydGFlRQXA+3cfXPRATPLJpiTkvIJHv3sA2Bm84HmZvYq\ngLu3jTikUinBl88ZwK0E/383Ap3c/eZoQ0qqpmZ2LrDGzPZ398/NrDFQM+rAkqSOu/+t2P21wF/N\nLOV7L0qo6e7zANz9PTPLijqgJMgCagCbix2rSVT1RpNPP/uB3xNMtPx91IHsjBJ8OYQTKq41s98C\nTwA5EYeUbEMIuqeqAeeEk+rmAz0ijSp5vgtXUjwPrGFbKyZdxmGbmdnTQF74O/AMwbKx9dGGlRS3\nAG+b2RKC730d4GBgUKRRJU+6/+wDEDZqNlb2JdFK8BXg7n83MwdSdklYadz9H8A/ShzeL4pYInIB\n0JdgPWwdgj90rwJ/jDKoZHH3fc3sIIIPed8S/B3Zg+D/JaW5+z/N7DmgOcH3fi3wgbsXRBtZ0qT1\nz34JZ0UdwK5oFr3sNjM7BrgX2AAMc/e54fGn3P3cSIOLQBquosh193Xh7cOBlsA7lXU3r3gKx1x7\nuvt/o44lKmaWQ/A9rwmsABaHvZopL3zvlxBMKi5aRTGHYJOjSrPBTRG14MuhlFn0W6XJLPrRBGNP\nWcA0MxsWJri60YaVHFpFwdNAZzO7mGA3w/8A/czs4TR4//WAB8xsFjCq6INOujCzM4DhwBKCyZav\nA03M7MqiD/op7iHgXeBatk0yPI1gkmGla9wowZfPocCvCWaTxoodT4tPscDmouVQZnY68IKZdSV9\n3r9WUQR6ACe4+/pwgt2LQKon+G+AU4DLgDfN7GWCzU0+dfeFkUaWHFcS7FT5Y7hk7h7gVGAGcHyk\nkSVHY3cvObFuoZnNiSSaXVCCLwd3H2RmhwLPufubUccTgbVmdhnBvvvLwuT+GEGiSwfpvooi18zq\nE+zDXTT2XABkRxdS0sTC8fbR4ba1J4VfPQg+9Ke6PKBoi9aNQFN3Xxvu0Z4ONoZ1OEpOMqyUE0yV\n4MuvG8EmL+noAoJZw9WBH919UTibekS0YSWHVlEwj6Cb/hCC4Yl7wmNTI40qOd4tuhGuha9025Mm\n2F+BN8zsJYLd/Mab2UDgnUijSp6uBHsADGTbJMN5VNJJhppkFwdm1trdF0QdR1TM7GJ3T/X950tl\nZocBF7r7sKhjSbawwEYt4AfA3P3DiENKOjM7zd3TKcEXTaxsDixy9w/NbE93XxF1XMkU1hyoCaws\nbV/+yiIj6gBSxF1RBxCxtFomWFxYOeuAqOOIgrsXuvt64K/pmNxDlXov8kRw98Xu/niY3P+WTsnd\nzI42szcJlgm/B/zDzGaHVfYqHSX4+Ijt+pSUlu7vf6+oA4hYOr9//eynlz8Bv3L344BWgBN0z4+P\nNKodUIKPj3FRBxCxnlEHELGPow4gYun8/q+NOoCIpdv3Ptfdvw9vfwEc5u7/I9i+uNLRGHw5mdnZ\nBLNni2928EQ6bPiQztXEipjZHu7+vZkdTPBJ/r/pvPlJujCzowADZhIMzR0FLAaudPcvooxNEs/M\n7iaYXDoT+BXB3/0vgbPd/bwoYyuNZtGXg5mNJ+j9eI7tNzs4lfRozaZzNTHMbBzwuZl9C1wBvAIM\nMbMn3H1UtNElXlg9rVTuvimZsURgLEG52PHAvwhmU3ckWEHQKbqwkiPNv/e4+8Bws59fAKPd/QUz\nO4RgmXClowRfPoe7e8cSx54xs3mRRBOddKwmBnCku19qZq8Ax7v7D2aWSVBwJ+UTPLAIaAisJBiD\nLiz274ERxpUMm8JloXnuPi089rSZXRVpVMmTzt97zGwAMMHdZxQdc/cl4WOZQD93vyeq+EpSgi+f\nDDM73t237l5kZh3YvoRkKkvnamIAhBu9fEqwVOYHgjWx6TLhqj1BF+WJ7r4q6mCS7HMzGwI8a2Y3\nEvzsn0H6VFNL5+89wALgeTN7H1hIUGypLnAsQau+Um14pUl25XMRQZfs/8KvL4DBQK9ow0oOd9+X\nYKObO0izamKh4cDLBDu3vRd+2HkTGBlpVEni7suBYUCbqGOJQF+Ctf/nA38g6KrPIz2G5tL9e4+7\nz3X3kwiGZw4k2L3QgKcIPvS8HGV8JWmSXTmYWU13zy/v41L1mVltoC2wJ/A9QTW15dFGJclkZg0I\n/rj/191XRh2PSElqwZfPeDPrHxZb2MrM9jSzy4EJEcWVFGaWvaOvqGNLhvAP+00EW3XOdPeZ7r48\n7LJNO0WV9NKBmc0I/z0DmAsMAF4xs3TYhx4ze8TM0m3te5WlMfhycPeLzez/CHYxakJQE7kOwTjc\nve4+JtIAEy+tJ9oQzJh+iuD35xUzO93dlxLMpk55YU30IjGguZkdC+DubaOJKmmK1jtfBbQPP9jV\nJig+8s/owkqa4wjGoMcCU9JhWXBVpgRfTu7+GPCYmeUQ1Ij+Ph2WiYTSfaJN9aK652b2LsEs6k6k\nzyS7cUB3giViPwCPAiVLaKaqopUiqwmGZgjL5VaLLqSk+pyg7vnNBGVSH2Fbudy1UQaWDOFk6lK5\n+yvJjKUslOAryN03kj4zaIFgoo2ZFU20mR11PBHINLMj3H2Ru79qZrcTzKZOi+qC7v6ImX1AMKlw\nELAh7MFIByvDGdR1gYFmNgl4nGCJZDoodPfVBO+9AXAecD3QDDgi0siSo2/470EEk2zfBFoTrCDq\nFFFMO6RJdiK7ycxaAWOALu7+bXjsAuBud99jp09OIeFSwQeAg909Hf64bxWOQ2cDy4CT3P35iENK\nCjN71N3Tpbdmh8K5GGe7e0HYezPD3X8VdVwlaZKd7DYzuy38417aYw3CFm0q+8jdOxUldwB3nw40\ngGAVRWSRJUHR+wtnjv+WoLv+Z4+nIjMbYGbV3P07d/+fuxcUJXczyzSzy6KOMcF67OzBVP7el9Co\n2O1MKmnRHXXRl4OZzSeYUFZcjKD7KtUnGQE8BDwY1gMvudnDT8DQCGNLhvFm9hZBmdTvix2vH7bk\nWxNUmEpVJd//mxCsIiHYCyGV3/8CYGZV2egkAUr92U+T731xDwDvm9li4DCCPUEqHXXRl4OZ7bej\nx9JoLJKw2ExHgrXg3wEvufsn0UaVHOEqigFAaaso/hZlbMlQyvvPJeiuTpf3fzLBmOvWn33gP+kw\nqzzdv/dFwmGag4Al7r4i6nhKowRfAWElsd8RzKyNAY3dvU+0UUkypekqiq3S/f2ns3T+3ofzcHoD\nOUXH3L37jp8RDXXRV8wjBOuh2wNfkyazqGWbdFxFUVy6v/90lubf+ykEy0W/jDiOnVKCr5j17n67\nmR3i7t3/v737j7W7ru84/iy/oYG5Nhk/BuNX2IsKYxEdMHQViIAMAg4JvweIjDB+DNiMMsQMooLZ\n1EEGE5DW8dMfQNBAphID2tGOMKWCCH0hUJjZYBGhgGCxtd0fn+9pb+/tpT33tt/P4Xxfj+TmnnNu\nSV7t5Zz398fn835L+o81/ycREfE294LtG2qHWJMU+MlZLmkbYEtJU+nIGXyzLWRD4GvAcZTbExsA\n/277oJrZon2SpnWtF3vzHjgN2BG4D3hsUO/Drk9d/N03nm16gcynWXBt+966kcZKgZ+cyyhdnW6m\njA69+a3/+NA4HbgY2AYwpcAvAzpxBUPS/YzdRQFAlw5wJL2fMk1tQ0m3A8/ZnlU5Vluuo9yWO5iy\ni+Am4M+rJmpRx3/3AJtSBg2peb4cGLgCn0V2MWGSTrc9u3aOtknqvan/AfgmMBfYBzjC9lvuEx4m\nkuYAHwLuBA4D5tp+d91U7ZB0v+0DJd1n+yBJc22/t3autnT5d/92kjP4SZD0WUrjh2W912xvVy9R\n6x6VdDWwornFIK4kXddsG0DS1s1MAoC7JJ1XMVYNy2y/JGm57cWSXqsdqEUbNXu/kbQlIz4DOqLL\nv3skPc/KIVvTKL34Z9RNNVYK/OQcDuxo+83aQSr5EmUl6Qu1g9Qi6aPAQ5TZ8J3aKgQ81XQtnN7c\nj+xMDwjgk5QrN9sCD1IG73RJl3/32F7Rya7pi3JpvTTjS4GfnPmUfZBdLfCv2r6xdoiKTqJ80B8L\n/LR53iVnAWdQ5qK/3jzuih1sqxm48mIXGtyMcjZlLU7vd/9XdePUY/s5SbvXzrE6KfCT8xjwvKQX\nWNmqdujnoUs6pHn4iqSLgR8xwCtJ17Wmg1/Pv4x4PJ1mhGhHXGn73N4TSTcBp1TM06YzgVtt/6J2\nkErusX3Imv/YcJL0VVYutN2O0rJ44KTAT85xwM6U2dBd0psm9QqwW/MFA7qSdD24bpzXlwNDv4pe\n0jnAJZTe+0c3L08BHq+XqnWbSppP2UWyDMD2iXUjteplSUcCT7Ly7/9k3UitunbE48XAD2sFeSsp\n8JPzHPB61+7B2/4IgKQ/GPWjJZI2tr2kQqzW2D6wdoaabF9DGTpyse3La+ep5BO1A1T2e8CFI553\n4uB2hPnApygDhp4EfgYMXD+AFPjJ2QF4WtIzzfOuTJPruQfYHlgA/CHwBmV18ceb8alDSdIdto8Z\nsSkJW18AAAs1SURBVJIWVt6i6dIuimslncCqsxiGfVRwz8OUIr8d5X3waN047Wq2CP4OsBPwtO1f\nVY7UttnAD4BbKQO3/g04smag1UmBn5zTgF/XDlHRQuAg2y9K+l3gBspim28DQ1vgbR/TfN92TX92\nyN0FPAHsRXkfvFE3TqtmU/4/fz9lF8ms5nEnSPow5TbNRsA3mu1yn6kcq03TbffW3/xY0jFV04wj\nBX5ybrD9vtohKtq6157T9svNvvCXJA31nmBJ4zb36UIfgBGm2D6r+fc4g450MmxMtz1b0sm250na\noHaglv0tsB/wHeAzlHvQXSrwm0vaxvYLkramtO4eOCnwk/O6pH9m1YU219eN1KqHm9Wk/wn8KeVI\n9jgGdEXpOvQeSnOfW4B5lMvTXbS0GRk6lXKrolOfJ72tUZK2B5ZWjtO239p+szlzXy7p9dqBWnYJ\nME/SK8BWDOg2wa4dda5r8ygr6LemNLzo1CVb22cDXwU2B25utkz9GBjq1cS296K06dwMuIhycPO0\n7e9WDda+a4ALKDsnfk65ZdMVfwN8BdgbuAP4u7pxWvdAc3C/vaRrKf34h17TvRRgarMl+mDbu9q+\nr2au8aQX/SRJOhzYg9LB9Fu187RB0hG275F05uifdewKBgCSZgLnUZqf7Fc7T1sknWT71ubxVrZf\nrZ2pLZKOAu62PdS3o96KpA8CfwQssH137TxtkPQz4ErK+/2LI382iJ99OYOfhKZV40coLUpPlfT5\nypHaMr35vu2or22qJapA0paSTmXlZL2hXVg4jhUHeF0q7o0PAI9I+qyknWuHaZukHwK7ANd1pbg3\nTqL0nt+UsZ9/Aydn8JMwcoKUpCnAg7b3rRxrvRvVyW0VXWh2IelY4HjKLPA7gdtsP1s1VAWSHqR8\n0HWy2YukTYCjKAf5m9j+QOVIrWkWlv0lpdnXT4Ev255bN1V7JP0J8DSwK7Cwt9h40HRqUcx6sLGk\nDZrLdFMYZ0b4EOp0Jzfga5S9/49QLlFe3psg26UCR5q97AMcSlmDc0flLK2y/X/A5yV9A/hH4G7K\nmW1X7EzZA/84sKekSwex90cK/OR8HZjbnMns2zwfeqM7uUl6B2VVbVdGRna6k90InW32IulxygHe\nDba7NGQHAEmnAKdStofNolzF6JILgb1t/6oZF3wfA3iLLgV+Emx/QdJ3gd2BWbYfq52pDZL2pryp\n9wGOoJzRvyzpY124H2f7B7UzDIguN3v5M2AJsJOkqba7tk3sj4FzbC+oHaSSZb3ufbZfk7S4dqDV\nSYGfgObodbS9Je1t+6bWA7Xvn4BTbS9pto0cRunF/G3Kpbrohi43ezmAbndyuwz4uKQVV29sP1U5\nU5uekfQFYA4wk3I/fuB06Q25Ls0Y9fVOStG7rGaoFm1o+9HmzT3V9o+aVdSd3TLUVR1u9tLr5PYi\npYPbX9SN07pZwDOUSZK9qzdd8lHK3//g5vtANrrJGfwE2P773mNJuwI3Uo5iL6gWql29aXEfBL4H\nIGljYMtqiaKG8ynNXmZQFpmdXTdOq7reya3LV28A7rF9SO0Qa5ICPwnNXOwLgAtt31M7T4u+J2ku\nZZrekc1BztV0ZJFhFLZ/Quni10UPSLqNjnVyG6nDV2+grDk6kjIqtrdFdOC2CGcf/ARI+n3KmctL\nwF/bfrlypNZJmgG8Yvt/mwK/l+27aueK9U/SQlbdErqEMjL2Tdsz6qRq34hObk907AAfSXsCX6Zc\nvVkAnG374bqp2iPp/lEvLbc9cFuEU+AnQNIi4E3K1ohV/gE7tg86OkjSppS+D9dQOpk9JOldlA/5\ngbwXuS5JOsr2t5p56J8CFgNXdHAlPc2Y6KUd2iKLpK0of+eBH4+cAj8BksbdCpQtVNEVkr5v+4AR\nz+fYnlkx0non6XOUhWXHUbYJvk65TPsu26vbXTNUxtsiC3Rii6ykcymDhZYC5w76gKncg5+AFPEI\nABZJ+jTwELA/8HzlPG2YaXt/SRtRCtz2tt+Q9EDtYC3p+hbZEwFRRsTeDAx0ge/ayseIWHdOooxL\nPpxS3If+DBboDdXZB/jJiMu0m1TK07aub5FdbPs3Te/5gf+d5ww+IiZqcfPVpVkMSyUdApxGGTTU\nGxe8qGaoFmWL7EpTagdYkxT4iJio6ymF7V5Ki9obGP6z+POByynNXb4k6VDKsJVjq6ZqT9e3yO7R\nbI+cMuIxMJgLrLPILiImZPSiOknzbO9fM1Osf13eIvt2W2CdAh8REyLpIeCAZpHZ5sD3be9bO1dE\nFLlEHxETdRXwiKTHKPMYLq0bJyJGyhl8REyYpGnALsBC27+snactkj4G3Gj7F7WzRIwnBT4i+iJp\n9ng/s316m1lqkXQWcDIrJ6l9x3Y+TGOgpMBHRF8kPQpsAdwCzGPEdqFB7+y1rknaA/gk8D5KZ7ur\nujibIgZTCnxE9K0ZNnIypeHLHOAW20/VTdUeSe8AjqdsC1xEGbyyIWWy5HtrZovoySK7iOib7ceA\ni2BFo5crJO1ge7+6yVrzX5QrGMfb/u/ei83QnYiBkDP4iJgQSVsCRwMnAFOBr9u+um6q9UtSrz3p\nhsBvR/7M9m/aTxQxvpzBR0RfJB1LuTy9I6Vd61m2n60aqj1mZUveka1Kl1N2E0QMjJzBR0RfJC0D\nFgCPNC+t+BAZxHad65Ok6cBLWUEfgyhn8BHRrwNrB6itWXfwr5RL9bdLes72rMqxIlaRM/iIiD5J\nmgN8iHKL4jBgru13100VsarMg4+I6N8y2y8By20vBl6rHShitBT4iIj+PSXpCmC6pIuA52oHihgt\nBT4ion9nUYr6A8DrwBl140SMlUV2ERH9u9L2ub0nkm6idLWLGBgp8BERa0nSOcAlwDRJRzcvTwEe\nr5cqYvWyij4iok+SLrZ9ee0cEW8lBT4iok+SpgGHAhtTzuC3s31F3VQRq8ol+oiI/t0FPAHsBfwa\neKNunIixsoo+IqJ/U2yfRWnZezAwrXKeiDFS4CMi+rdU0maUKXrLydXQGEAp8BER/bsGuAC4F/g5\nsLBunIixctQZEdG/zWx/DkDS7bZfrR0oYrScwUdE9O/M3oMU9xhUOYOPiOjfppLmAwaWAdg+sW6k\niFWlwEdE9O8TtQNErEku0UdE9O9hyva4U4HpwP/UjRMxVgp8RET/ZgPPALsBLwCz6saJGCsFPiKi\nf9NtzwaW2J5HPktjAOV/yoiICZC0e/N9e2Bp5TgRY2SRXURE/84HvgLMAO4Azq4bJ2KsTJOLiIgY\nQjmDj4hYS5IWUnrP9yyhjIx90/aMOqkiVi/34CMi1t7uwDuB+4HjbQv4MPBA1VQRq5ECHxGxlmy/\naXsxsKvth5rX5gOqmyxirFyij4jo3yJJnwYeAvYHnq+cJ2KMnMFHRPTvJGARcDiluJ9SN07EWCnw\nERH9W9x8LQOmsOrCu4iBkAIfEdG/64FdgHuBnYAbqqaJWI3cg4+I6N9utmc2j78paV7VNBGrkTP4\niIj+bSZpCwBJmwMbVs4TMUbO4CMi+ncV8Iikxyj74i+tGydirLSqjYiYAEnTKPfhF9r+Ze08EaOl\nwEdErCVJs8f7me3T28wSsSa5RB8RsfbeA2wB3ALMo2yRixhIOYOPiOiDpD2Bk4F9gDnALbafqpsq\nYqwU+IiICZI0EzgP2MH2frXzRIyUS/QREX2StCVwNHACMJVyyT5ioOQMPiJiLUk6Fjge2BG4E7jN\n9rNVQ0WMIwU+ImItSVoGLAAeaV5a8QFq+8QqoSLGkUv0ERFr78DaASLWVs7gIyIihlB60UdERAyh\nFPiIiIghlAIfERExhFLgIyIihlAKfERExBD6fxjXT+k0h7aAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f378588>"
]
},
"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": 150,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4265"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.degree_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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woOY4JXpXRKycmTPrDlKqiBgHfAZYuGhZKXMNWPDt91xEfB1IGj9QTnTTdA8B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\ng5ckqUAWvCRJBbLgJUkqkAUvSVKB/j+2uXFQBd697gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f321ba8>"
]
},
"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": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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vAWwUEZ+n6MUfCQwHJmbmAmBWRDwJDAVGAGfWvu9uioCXJEmdUNcx+My8DVjQrum3wDcy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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f375cf8>"
]
},
"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": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4201"
]
},
"execution_count": 153,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4286"
]
},
"execution_count": 154,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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LFzFmzHiWLXubJ56Y+34ikcNFF/2S9u07MnnyxG6nntr9srVri1i6dAlHH218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\nKsBmd+8YVTzZxsx+TvDv6HsEe1Ff4e4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"text/plain": [
"<matplotlib.figure.Figure at 0x10e80e908>"
]
},
"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": 156,
"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>13328</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>13329</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>13330</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>13331</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",
"13328 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"13329 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"13330 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"13331 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... bilateral_ci \\\n",
"13328 1 3 3 8 ... False \n",
"13329 1 3 3 8 ... False \n",
"13330 1 3 3 8 ... False \n",
"13331 1 3 3 8 ... False \n",
"\n",
" bilateral_ha bimodal tech implant_category age_diag sex \\\n",
"13328 True False 0 6 51 Female \n",
"13329 True False 0 6 51 Female \n",
"13330 True False 0 6 51 Female \n",
"13331 True False 0 6 51 Female \n",
"\n",
" known_synd synd_or_disab race \n",
"13328 0 0 0 \n",
"13329 0 0 0 \n",
"13330 0 0 0 \n",
"13331 0 0 0 \n",
"\n",
"[4 rows x 67 columns]"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[demographic.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
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" <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>13328</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>13329</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>13330</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",
"13328 1147-2010-0064 initial_assessment_arm_1 96 PPVT 1147 \n",
"13329 1147-2010-0064 year_1_complete_71_arm_1 91 PPVT 1147 \n",
"13330 1147-2010-0064 year_2_complete_71_arm_1 93 PPVT 1147 \n",
"\n",
" age_test domain \n",
"13328 63 Receptive Vocabulary \n",
"13329 73 Receptive Vocabulary \n",
"13330 85 Receptive Vocabulary "
]
},
"execution_count": 157,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
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" <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",
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" <th>score</th>\n",
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" <td>1147</td>\n",
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" <td>initial_assessment_arm_1</td>\n",
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" <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>19250</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",
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" <td>3</td>\n",
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" <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>19251</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",
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" <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>20546</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",
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" <td>3</td>\n",
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" <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",
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" <tr>\n",
" <th>20547</th>\n",
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" <td>3</td>\n",
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" <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>29121</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",
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" <td>3</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <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",
"8588 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"8589 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"8590 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"8591 initial_assessment_arm_1 2010-2011 0 0 0 0 \n",
"19250 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"19251 year_1_complete_71_arm_1 2011-2012 0 0 0 0 \n",
"20546 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"20547 year_2_complete_71_arm_1 2012-2013 0 0 0 0 \n",
"29121 year_3_complete_71_arm_1 2013-2014 0 0 0 0 \n",
"\n",
" sib _mother_ed father_ed premature_age ... age_test \\\n",
"8588 1 3 3 8 ... 63 \n",
"8589 1 3 3 8 ... 63 \n",
"8590 1 3 3 8 ... 59 \n",
"8591 1 3 3 8 ... 59 \n",
"19250 1 3 3 8 ... 72 \n",
"19251 1 3 3 8 ... 73 \n",
"20546 1 3 3 8 ... 88 \n",
"20547 1 3 3 8 ... 85 \n",
"29121 1 3 3 8 ... NaN \n",
"\n",
" domain school score test_name test_type \\\n",
"8588 Expressive Vocabulary 1147 91 NaN EVT \n",
"8589 Receptive Vocabulary 1147 96 NaN PPVT \n",
"8590 Language 1147 101 PLS receptive \n",
"8591 Language 1147 87 PLS expressive \n",
"19250 Expressive Vocabulary 1147 86 NaN EVT \n",
"19251 Receptive Vocabulary 1147 91 NaN PPVT \n",
"20546 Expressive Vocabulary 1147 95 NaN EVT \n",
"20547 Receptive Vocabulary 1147 93 NaN PPVT \n",
"29121 NaN NaN NaN NaN NaN \n",
"\n",
" academic_year_start tech_class age_year non_profound \n",
"8588 2010 Bilateral HA 4 True \n",
"8589 2010 Bilateral HA 4 True \n",
"8590 2010 Bilateral HA 4 True \n",
"8591 2010 Bilateral HA 4 True \n",
"19250 2011 Bilateral HA 4 True \n",
"19251 2011 Bilateral HA 4 True \n",
"20546 2012 Bilateral HA 4 True \n",
"20547 2012 Bilateral HA 4 True \n",
"29121 2013 Bilateral HA 4 True \n",
"\n",
"[9 rows x 77 columns]"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4201"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2959,)"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5440,)"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2959,)"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2959,)"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_ids = receptive.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic_ids = demographic.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 166,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 166,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[s for s in receptive_ids if s not in demographic_ids]"
]
},
{
"cell_type": "code",
"execution_count": 167,
"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": 168,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <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>395</td>\n",
" <td>93.101266</td>\n",
" <td>18.115391</td>\n",
" <td>40</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1363</td>\n",
" <td>91.903155</td>\n",
" <td>19.384986</td>\n",
" <td>0</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1497</td>\n",
" <td>90.636607</td>\n",
" <td>20.373490</td>\n",
" <td>0</td>\n",
" <td>149</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1122</td>\n",
" <td>89.802139</td>\n",
" <td>18.191470</td>\n",
" <td>0</td>\n",
" <td>142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>620</td>\n",
" <td>85.562903</td>\n",
" <td>16.478926</td>\n",
" <td>40</td>\n",
" <td>154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>411</td>\n",
" <td>82.951338</td>\n",
" <td>15.993827</td>\n",
" <td>40</td>\n",
" <td>130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>292</td>\n",
" <td>80.458904</td>\n",
" <td>17.713500</td>\n",
" <td>20</td>\n",
" <td>132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>216</td>\n",
" <td>77.805556</td>\n",
" <td>17.770086</td>\n",
" <td>25</td>\n",
" <td>160</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>185</td>\n",
" <td>75.875676</td>\n",
" <td>17.296981</td>\n",
" <td>20</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>449</td>\n",
" <td>78.632517</td>\n",
" <td>19.118076</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",
"2 395 93.101266 18.115391 40 144\n",
"3 1363 91.903155 19.384986 0 150\n",
"4 1497 90.636607 20.373490 0 149\n",
"5 1122 89.802139 18.191470 0 142\n",
"6 620 85.562903 16.478926 40 154\n",
"7 411 82.951338 15.993827 40 130\n",
"8 292 80.458904 17.713500 20 132\n",
"9 216 77.805556 17.770086 25 160\n",
"10 185 75.875676 17.296981 20 123\n",
"11 449 78.632517 19.118076 20 134"
]
},
"execution_count": 168,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary = score_summary(\"Receptive Vocabulary\")\n",
"receptive_summary"
]
},
{
"cell_type": "code",
"execution_count": 169,
"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>655.000000</td>\n",
" <td>84.673006</td>\n",
" <td>18.043673</td>\n",
" <td>20.5000</td>\n",
" <td>141.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>488.137503</td>\n",
" <td>6.392185</td>\n",
" <td>1.324983</td>\n",
" <td>16.4063</td>\n",
" <td>11.802071</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>185.000000</td>\n",
" <td>75.875676</td>\n",
" <td>15.993827</td>\n",
" <td>0.0000</td>\n",
" <td>123.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>317.750000</td>\n",
" <td>79.089114</td>\n",
" <td>17.401111</td>\n",
" <td>5.0000</td>\n",
" <td>132.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>430.000000</td>\n",
" <td>84.257121</td>\n",
" <td>17.942739</td>\n",
" <td>20.0000</td>\n",
" <td>143.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>996.500000</td>\n",
" <td>90.427990</td>\n",
" <td>18.886424</td>\n",
" <td>36.2500</td>\n",
" <td>149.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1497.000000</td>\n",
" <td>93.101266</td>\n",
" <td>20.373490</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 655.000000 84.673006 18.043673 20.5000 141.800000\n",
"std 488.137503 6.392185 1.324983 16.4063 11.802071\n",
"min 185.000000 75.875676 15.993827 0.0000 123.000000\n",
"25% 317.750000 79.089114 17.401111 5.0000 132.500000\n",
"50% 430.000000 84.257121 17.942739 20.0000 143.000000\n",
"75% 996.500000 90.427990 18.886424 36.2500 149.750000\n",
"max 1497.000000 93.101266 20.373490 40.0000 160.000000"
]
},
"execution_count": 169,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary.describe()"
]
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6550"
]
},
"execution_count": 170,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 171,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10f8e2a58>"
]
},
"execution_count": 171,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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e5vzzz+U73/ku++33dbbdtjfXXXcNy5ZNom/fnVm0qAKATz75hCuuuIQNNujI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\nhrskSRljuEuSlDGGuyRJGfP/AU6et/et8KCDAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1126958d0>"
]
},
"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": 172,
"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>375</td>\n",
" <td>92.362667</td>\n",
" <td>22.025792</td>\n",
" <td>23</td>\n",
" <td>141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1310</td>\n",
" <td>93.083206</td>\n",
" <td>21.785851</td>\n",
" <td>0</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1473</td>\n",
" <td>92.156144</td>\n",
" <td>21.912841</td>\n",
" <td>0</td>\n",
" <td>146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1097</td>\n",
" <td>91.375570</td>\n",
" <td>20.134114</td>\n",
" <td>0</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>618</td>\n",
" <td>86.344660</td>\n",
" <td>18.457169</td>\n",
" <td>20</td>\n",
" <td>146</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>414</td>\n",
" <td>83.814010</td>\n",
" <td>15.712203</td>\n",
" <td>38</td>\n",
" <td>131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>283</td>\n",
" <td>83.893993</td>\n",
" <td>16.552176</td>\n",
" <td>34</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202</td>\n",
" <td>81.321782</td>\n",
" <td>16.143647</td>\n",
" <td>36</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>179</td>\n",
" <td>81.564246</td>\n",
" <td>15.327513</td>\n",
" <td>40</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>450</td>\n",
" <td>84.935556</td>\n",
" <td>17.521297</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",
"2 375 92.362667 22.025792 23 141\n",
"3 1310 93.083206 21.785851 0 145\n",
"4 1473 92.156144 21.912841 0 146\n",
"5 1097 91.375570 20.134114 0 145\n",
"6 618 86.344660 18.457169 20 146\n",
"7 414 83.814010 15.712203 38 131\n",
"8 283 83.893993 16.552176 34 122\n",
"9 202 81.321782 16.143647 36 145\n",
"10 179 81.564246 15.327513 40 122\n",
"11 450 84.935556 17.521297 18 146"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary = score_summary(\"Expressive Vocabulary\")\n",
"expressive_summary"
]
},
{
"cell_type": "code",
"execution_count": 173,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6401"
]
},
"execution_count": 173,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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VogMAqBAdAECF6AAAKkQHAFAhOgCACtEBAFSIDgCgQnQAABWiAwCoEB0AQIXoAAAqRAcA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\nUOkQERGRUKh0iIiISChUOkRERCQU/w/5Nrm3xzoY6wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e80efd0>"
]
},
"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, 1400)"
]
},
{
"cell_type": "code",
"execution_count": 175,
"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>282</td>\n",
" <td>85.124113</td>\n",
" <td>15.168643</td>\n",
" <td>50</td>\n",
" <td>122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1131</td>\n",
" <td>83.458002</td>\n",
" <td>18.272973</td>\n",
" <td>40</td>\n",
" <td>126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1309</td>\n",
" <td>83.409473</td>\n",
" <td>20.701802</td>\n",
" <td>0</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1026</td>\n",
" <td>83.880117</td>\n",
" <td>35.350375</td>\n",
" <td>39</td>\n",
" <td>999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>583</td>\n",
" <td>78.895369</td>\n",
" <td>21.792075</td>\n",
" <td>39</td>\n",
" <td>112</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>389</td>\n",
" <td>80.095116</td>\n",
" <td>51.718446</td>\n",
" <td>3</td>\n",
" <td>999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>244</td>\n",
" <td>78.959016</td>\n",
" <td>21.168498</td>\n",
" <td>40</td>\n",
" <td>107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>169</td>\n",
" <td>81.118343</td>\n",
" <td>20.524903</td>\n",
" <td>40</td>\n",
" <td>108</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>131</td>\n",
" <td>80.954198</td>\n",
" <td>20.111177</td>\n",
" <td>40</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>305</td>\n",
" <td>81.947541</td>\n",
" <td>19.283989</td>\n",
" <td>39</td>\n",
" <td>105</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 282 85.124113 15.168643 50 122\n",
"3 1131 83.458002 18.272973 40 126\n",
"4 1309 83.409473 20.701802 0 121\n",
"5 1026 83.880117 35.350375 39 999\n",
"6 583 78.895369 21.792075 39 112\n",
"7 389 80.095116 51.718446 3 999\n",
"8 244 78.959016 21.168498 40 107\n",
"9 169 81.118343 20.524903 40 108\n",
"10 131 80.954198 20.111177 40 105\n",
"11 305 81.947541 19.283989 39 105"
]
},
"execution_count": 175,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary = score_summary(\"Articulation\")\n",
"articulation_summary"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5569"
]
},
"execution_count": 176,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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F2ZC/jE8u0bQC6AhUAMsatK8EKgtWqCQlxI47dmXs2PHrt+fPf4H33nuXCy8c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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111ed3b70>"
]
},
"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": 179,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, 'Articulation', 'Expressive Vocabulary', 'Language',\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 179,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.unique()"
]
},
{
"cell_type": "code",
"execution_count": 180,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, 'Goldman', 'EVT', 'receptive', 'expressive', 'EOWPVT',\n",
" 'ROWPVT', 'PPVT', 'EOWPVT and EVT', 'Arizonia',\n",
" 'Arizonia and Goldman', 'PPVT and ROWPVT'], dtype=object)"
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.test_type.unique()"
]
},
{
"cell_type": "code",
"execution_count": 181,
"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>932</td>\n",
" <td>85.894850</td>\n",
" <td>21.996331</td>\n",
" <td>50</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1315</td>\n",
" <td>84.855513</td>\n",
" <td>19.730693</td>\n",
" <td>50</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1301</td>\n",
" <td>85.104535</td>\n",
" <td>19.598740</td>\n",
" <td>43</td>\n",
" <td>145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>930</td>\n",
" <td>83.733333</td>\n",
" <td>18.762635</td>\n",
" <td>47</td>\n",
" <td>140</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>479</td>\n",
" <td>77.805846</td>\n",
" <td>17.642483</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>195</td>\n",
" <td>74.641026</td>\n",
" <td>19.685003</td>\n",
" <td>40</td>\n",
" <td>123</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>52</td>\n",
" <td>69.846154</td>\n",
" <td>20.649631</td>\n",
" <td>40</td>\n",
" <td>109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>42</td>\n",
" <td>77.000000</td>\n",
" <td>20.167591</td>\n",
" <td>40</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>68</td>\n",
" <td>75.602941</td>\n",
" <td>21.490810</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",
"2 932 85.894850 21.996331 50 150\n",
"3 1315 84.855513 19.730693 50 144\n",
"4 1301 85.104535 19.598740 43 145\n",
"5 930 83.733333 18.762635 47 140\n",
"6 479 77.805846 17.642483 11 127\n",
"7 318 75.877358 18.713363 40 123\n",
"8 195 74.641026 19.685003 40 123\n",
"9 52 69.846154 20.649631 40 109\n",
"10 42 77.000000 20.167591 40 119\n",
"11 68 75.602941 21.490810 40 139"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary = score_summary(\"Language\", \"receptive\")\n",
"receptive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5632"
]
},
"execution_count": 182,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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RJElNozHH3N/ms4vXlAHrAVcWMpQkSfryGvNRuN4NHtcBc1JKcwsTR5IkravGTMtPBw4G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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f9a8a20>"
]
},
"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": 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>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>926</td>\n",
" <td>88.035637</td>\n",
" <td>18.268384</td>\n",
" <td>50</td>\n",
" <td>150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1316</td>\n",
" <td>82.285714</td>\n",
" <td>17.541316</td>\n",
" <td>20</td>\n",
" <td>147</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1293</td>\n",
" <td>80.304718</td>\n",
" <td>19.572599</td>\n",
" <td>45</td>\n",
" <td>141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>948</td>\n",
" <td>78.530591</td>\n",
" <td>20.053469</td>\n",
" <td>45</td>\n",
" <td>144</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>496</td>\n",
" <td>71.512097</td>\n",
" <td>19.214889</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>200</td>\n",
" <td>67.520000</td>\n",
" <td>21.275168</td>\n",
" <td>40</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>51</td>\n",
" <td>64.725490</td>\n",
" <td>20.570929</td>\n",
" <td>40</td>\n",
" <td>106</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>42</td>\n",
" <td>74.595238</td>\n",
" <td>23.463578</td>\n",
" <td>40</td>\n",
" <td>119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>67</td>\n",
" <td>73.417910</td>\n",
" <td>22.766369</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",
"2 926 88.035637 18.268384 50 150\n",
"3 1316 82.285714 17.541316 20 147\n",
"4 1293 80.304718 19.572599 45 141\n",
"5 948 78.530591 20.053469 45 144\n",
"6 496 71.512097 19.214889 6 140\n",
"7 338 66.789941 20.660322 40 124\n",
"8 200 67.520000 21.275168 40 118\n",
"9 51 64.725490 20.570929 40 106\n",
"10 42 74.595238 23.463578 40 119\n",
"11 67 73.417910 22.766369 40 132"
]
},
"execution_count": 184,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary = score_summary(\"Language\", \"expressive\")\n",
"expressive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5677"
]
},
"execution_count": 185,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Lpm0rVqzguuuu4IMP3qd1630LlCw5ysrK2HfffSkuLqZ166/TuHFjVqxYwV/+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\nSVLCWO6SJCXM/wdDjoLaL9osFwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10e82d470>"
]
},
"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": 187,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10f933d30>"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dc9SccapIkjRVGPANa9cd7rCe2eNUjSRpqvAcvCRJBTLgJUkqkAEvSVKBDHhJkgpkwEuS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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111ece390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"(unique_students.age/12.).hist(grid=False, bins=np.sqrt(unique_students.shape[0]))\n",
"plt.ylabel('Count')\n",
"plt.xlabel('Age at enrollment')"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 4949.000000\n",
"mean 2.529476\n",
"std 2.339357\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": 188,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(unique_students.age/12.).describe()"
]
},
{
"cell_type": "code",
"execution_count": 189,
"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": 190,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"audition = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference).dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10f8dd5c0>"
]
},
"execution_count": 191,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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mTtUsWxZw5ozpsGtqovzpn6ZpbY2ydm3IxYsKy1KsWRPw9ttRIhHFV76S4tZb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110e92898>"
]
},
"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": 192,
"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": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x110ea1da0>"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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P09Rkw1zn9i8pUSxblmHUKIPjKFatSrF9u0ckYrjxxgy//GUUUKxYYc9NSQlc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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f339978>"
]
},
"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": 194,
"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": 195,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x10f94ab00>"
]
},
"execution_count": 195,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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W2LzZoaHBQSk4eRK2bdNEIoqlSwPq6vq74euCdh3KsStsG59du8z6tbU+nZeM\noQTRQiGNjRbJZIZEIqTnzvqBbKu3oHy+BnN+DP070v++BxLuJAwKcSkoVg/ZXwPfB/6wSPsX5+j5\nptI9FG3dalNVpXOhqP+Lv1KakhINaHbutNi/P8qxYxb33ptm9mzNxo0R0mnIZDTvvmtj2xZHjthM\nnBh2lCuVMvs4cQIef9wmlVKUltps2uRQWalpb4eZM01Z6usLy9fbjTSkoUGxe7dCKU15uUVzs7mR\nV1RASwtorViwIMR14eBBi3/+5yiZjFn/hz+MoZQmCEwQ3L/fRinNO++Y0HL6tKKszGbduhhBoPj8\n59NMnJihtNSmvFx3ac+1ax2uucbHtkNKS22amhxsO+TFFx2OHbMYN06zYUOEmpqQkyctTp9WuG6W\nROLc49cZwjS7d1tEo7Brl82cObrj2LluQEuL2bcJxVYP68P27TaWFXLqVJQHHywB4CtfSfHlL2cA\nG8/rGoJNqATPK+zZ6x4e8/sI2bDBYf36KJGIzWc+Y/PRjwaAVXCcBhImuwflDF//uinfwENK37+E\nKKWZOTOkqcnUadasgEOHLLRWvX5H6uudXo5R4f46A3hv++693l2319BgsXGjWX7lyvMNg0KIYhn2\nQOa67n8Gjnmet9F13W8P9/5Fz3q7qRRSSpNOw4MP5nuy+r74JxIhM2eGrF8fJZWCK68MaGpSBIHi\nhRccwlBx9KhNKgXt7ZqampAzZ0zQ+b3fi1NaGnL99QG7dil8X7FkiU8qpVAKfvKTCIsXB0yYEPLc\ncw5hCJmMwrIUtm3R2mozb57PoUPmRlp4U12yJMtLL9k8/niU6uqAREIX3BgtTpxQfPCBxapVWV5+\nWTNunAkbZ88qLAvCUJFKQWurxe7dFnPmhMTjAaWl8NOfxtEarrsuS3V1wL59EX72swhTpwY0Njos\nWZIlPySbSsGBAxapVATHURw+HOGmm0JKSkJOnIBx4zTxuObAAcXcuYr2dsWOHTYtLX7uZn9uT1gq\npQkCeOYZhxkzQhKJkF27LBwHLrvM5+mnbX72sxgAq1dnWLPGB1RHCEilIAw1b7xhY1mas2cVkYhp\n2xdeiPDhhzYTJ4JlaaJRE1zXro0wb16AUpoDB2zefde08+nTqqBHM6Shwaa+3mHiRJ+HH45SVqaI\nRBSbNkXYvdthwoTOwFNSotm3z2bGDLqcj4UhZ+9eix//2KGmxhyfH//Y4fbbfWpqBj7U3VNvZUuL\nYutWG60uB0XTAAAgAElEQVQVJSWa9euj1NaafaxfH+W223za21WP35G+de5v3DhFIuF0nJNDC3dm\njuHatREOHjTbOX5c4brpXpcXQoxcxZjU/+vAStd1nwdqgX/LzScTI1AioVm+3EcpTTwecuyYRSym\n0NrcOPK9Cz1JJi2amiyuuiqkpiZk2zabSZPMv02cqGlqstAabBtaWxWJRMicOT5btzoopQCLn/88\nwooVWW65xWf3bptYDCwr5PLLQzZtivD00w433xxQX++wfbvN+PHw2msWr7zisHZtlJISTSxmbqTt\n7abcGzZE2LnTIQgsJk/W7NzpsG+fzb59Njt2OJSXw4QJ8MorDk8/HeXZZyPcdluWaDTEtkNuvjnL\ne+9Z/PKXFh/7WJa33rKIxWDnTodUygTOTZsiuK4JV0uWBNx/f5yf/zzK1q0RFi7Mkk5rWls1lZWm\nTQFsW7F5s8NTT0W56SaftjZNczN89KNZXn3VYv9+i0gEysvNPLuGBov774/w0EMOa9dG0FrR0mKx\ndm2Uytw36vRpm+3bHXbscGhttfjFLxyCwJRx/fpobtiwMwSkUmYItrJSo5TirbcsFi/2ue46nz17\nTEjRWvHaazZlZQFBEPLBByaYRSLwwgsOmYwiDBXPP++wYYMp43PP2R0hR2vF6dMK34dJk0J27TKf\nt7ebMsViZntNTRapVO/nZioVcs01JpA/95zDNdeEpFJdQ01f52n35dauNW354IOm11apgT1MUfgd\nUcr8bHq8et9fJKK7nJP9fZd609KiOr5HWkNTk9XRAyqEGF2GvYfM87xb8j/nQtnXPM871tc6FRXl\nF71cI9Fw1nvKFM1ddwW8+KL5TfvmmwPmzYuhlGLVKp377T3g3//dIZ/jldIkEhEqKnqet6O1z7Fj\nigMHzPJz5viUlEBJiUVdHUyaZIbGtNasXOlz9dUWth2yfbtNPG7hOOYG5zgO48fbzJ0LpaUWkYjP\nsWM2WlssWOCzfr0JIJYFTz4Z4XOf8zlzxuboUYvycshmbSIRRUmJTVmZRSrlU14eYtumRy2VUkSj\nFmGoacvNapw+PeSVVxxiMUUmY7N/f8DXvpbm1Ckzb2zatAjXXBPy9NMmeFVUwJkzinjchMvp00PS\naUVdnc+OHQ5tbXYuDEb5xCcCqqocHCdg61aLSMQBfFpbLWIxU++XXrK47rqQVMoEvUWLQo4eVblA\nGwccdu60KC1VxGIBR44oZsyAsjJNPG7C3ZQpiuees5k7VxGLKd55J8L06SGHD5uvvWWFlJfHSCRM\nu5hgEhCPw9GjNkePKlasyLJjh0MioVm0KOCyyxxsWzFjRpYnnojS1qa4/fYskUgU29bMmgVhaI73\n+PEhllVCaanDjh0BkyZpUimHIIjw6U+b7VpWyMKFiunTo7S1aSIRiMctMhmLG24wc9bKyqwu52Ne\nSclZdu0yPa0Au3Y5lJQ4JBLxjvr0fZ4GHcu1toYcOQILFjgkEhYHD4bU1GgyGYsvfMHn8GEzPPuF\nL/gcORJj3DjV43fEfJe6lrOn/UFAJGJ3nJP5Mk6ZYvX6PeyZzw036C4PgFRXx6moKNrzWv2S6/nY\nMlbrPRTF/tYO6FfQ5uaWi12OEaeionzY633NNZoZMzrn0xw/3vUmkEholizpOsQDIc3NPd8sTp3S\nTJwYwfc7J4Xfc0+W0lJFdXVAQ4NPPN4538h1NYlEHMtKsX59lGxW85nP+Dz7rEMYalavznDnnT5H\nj1o89pjF/PmaqVNDtDY9Ba2tMHu26bWxLJ/lywPOng1obw+4+24zZNnWZnqcXFdx6pQiDDW3357l\n4EGbIIDp0wNOn1ZUVIRUVoacPWvR2mrmqIWhCX1LlgSMHx8wblzIxo02vq/Yu9fizjuzvPmmGdL7\nxCcyrFrlk8nAN79pc/asmUc3eXLIgQOQTJohvssu82lvD5g2zcJxAiZPVqTTcOqUuUlns9DWZlFd\nHVBeDs3N0NKSATK0tppesbY2zcKFmnRaUVqq+epXNc8+66B1yOLFkE4rQDF9us+NN2ZZt84cr3vu\nyZBImCHLxYvNcc1k4I47NDt32tTUaI4ft7jlloBIRJNIhJw+HVJWFvLeew7Hj5venZdesvnc59pJ\nJm1Wrw7Yt88hnYZ4XHPypAl5SmlcN8u2bSbY3HFHhi98IU15eQzPy1BfH0EpuPvuwjla2S5Pl3Y/\nH0HjOJqqKhOEbFsDIdDaUZ++z1PdsVw6DQsXmiFgrRWVlZqVK7P9zEXsqUxw/HiPX4cu+7OsKHff\n3d5xTubLePy46vd72H2by5erLt8j0L1+J4utGNe1kUDqPbYMNYQqrUf8O470WD2gI7PeA3+iK5mE\nBx5wiJkpS6TT8OUv+wXzW87dlqn3aRobLdra4IknbNrazM0mHtfcd1+WRCJk3TozKdxxQhYtCnjy\nyQhawz33pCkr0wSB3RHyzA29+wRqTWOjmTx++DBs325u3hUVAS0tEI1q3nwzwoEDZt+1tT6/9ms+\n5eV0TGi3rJB4HDZtiuD7cP31GaZO1YShoqVF8Ru/EeTKavNv/2YaYc2aNM3NFkFgtmtZIatXm16N\nhoZUR4iYNStg/36LMITJk3Vunpzqcd4X0C28dH0CdeNGE4LMhG8/9289T+pvaYFHHrGJRhWxWMgz\nz0SordWUlHSW9eRJ+OpX4/h+fqg15Ec/amf27MLAos+Z+F9XF5wzid0c7zO9PNHY30MjAd/7XoQH\nHzRt+6Uvpfn617MMbVJ/T+W9GJPjzf4SiVLg7CDq2v82z387F9/Iva5dXFLvsaWionxIX0IJZCPU\npXEiD/51AIX1Tibh/vsjXYaeTCCD/GsTAKqrffbuNfuoqfFJJs3PA7859fSKA93HE4OFyxeGH6iv\nj/ZQ18KyBjQ0OOe0SUXF+D6CSdBniOq7roN/hUJvTxh2TjzX/MVfxHj+eRNebrsty7e/nSaR6D4l\ntf99n/95fiFfezF8webS+H4PntR7bBnD9ZZAdim5dE7kwd3kutb7Qr/fabAu1vugeusZHCnHu7d3\ncBW+mkHx8ssm/CxbNpB3o/VsZNV7+Ei9xxap99gy1EBW7Dlk4pKnCoYoB3uOKurqwo5XCwz/cMxg\nyz7Q5c+nTYZDYfmsHsqqqKvTuG4AjPxhMiGEGA0kkIkRbqSHl7FKjosQQlxI8sfFhRBCCCGKTAKZ\nEEIIIUSRSSATQgghhCgyCWRCCCGEEEUmgUwIIYQQosgkkAkhhBBCFJkEMiGEEEKIIpNAJoQQQghR\nZBLIhBBCCCGKTAKZEEIIIUSRyZ9OEqKo8n/IOwDkb0IKIcRYJYFMFMiHg2L/wejeylHM8l3Ifee3\npfE8i/p6h7Iyi8WLLerqwvPc9vmWqdjHvpikDYQQxSOBbMzpPew0NJhwALB8eZaqKvPv1dUByaTd\n4zr938AGd5PT+txyuK4G6Agv+c8nTjTbq6nx2bvXfF5T45NMOrn9hSSTVu5nn127zOe1tT5dT/3e\nyhjS2GgBmsOH4YknIgCsWpWlri6/XMDevXZu3wFg91k/086K7dttYrGQN990mDFDobWivt7BdbMF\nf7S75/U7yxrQ2Gj2V10d0vMMhIEdo4YG1Uv9ettf4TlR2M4XMkDn27/7/i5GWOp+3vlFDMcjkYRV\nIS42CWSjxmAviD0FkN5vOsmkYutWm5ISDWh++EOHadM0Z84o5s+H9nYLUCxb5hcEJBMsAJYu9QsC\nnE9jo9nf4cOK+vpIx/5c1+zP1IFzbvaNjWm2bYOysgClNFu2wJ49ipIS+OUvbWbMMCHhu9+NUFam\naW5WLFumOXVKAYpp0+zcjVtx9dUB+/Y5QIhl2Tz+eIQgUKxZk+H3fz9dUEbNCy+YNrn11ixVVaa9\nX37ZZv36GI4TMmmS5rXXHHxfceRIiGVliUZDGhosduww9bvuupDaWhPKPvKRriHR7AvKy0M2b46x\nfbtDZWXIkSOKWEyTSgXE47rg+BWGkcL2JNeemrIyi5dftglDxac+lWXNmgATyvIhUXPqFF3av64u\nOCc8JZOa738/xptvmjI2NSlcN0UiQcf5opSmtNRiyxazzC23+CSTFmGomDcv4NAhC61VQZCHw4c1\nr71m9nXttZq6usLgZo53MpkhkSgMk/nzPOTZZ20efjgGaFas8EkmFWGoWLnywoelZNIEYq3NNnsP\nx2MxmEhYHZyxeI6IC0EC2agw2Auiz1/9VYz7748B8JWvpPjylzO0tDhs3Wp3u+lkAGhp0aTTsGWL\nQyYDFRUBhw6BZZkg9O67EZSCAwcUixb5aK3Yt89i9+4IlqX54APFe+9Z+L6irs7mlVccpkzRtLfD\n/PnmorR2bYR58wLa2y2WL88CKlcnzbhxigMHLLTWjBsHzzwTYfz4ANcN2bo1QmVlyPHjJoz5vuad\nd2zuustn5kyf996LsGFDFK3hYx/Lcvo0+L7Fs886XHaZRmvFnj0WS5YEHD1q8eijDtOnBzzySIxo\nNOSyywKOHLFQSnP8eIQDB2ymTNG8/75FJKLIZCx279b85m+209YGx44pvvOdOFVVPrGYYtMmE3hK\nSjQ7dmgOH3b4yEcUnqdIp20WL7bYsiUCKO69N80bbygcR9PSornsspAXXohgWYovfjGkpSWgpUWx\nbZvF889HsayQuXMVb77pEIaKK67w8X1NS4vixRejTJwIBw/atLZCbW3A1KkB69bZPP54jGhUM3t2\nSGWlJgwt6uttfF+zbVthQAs5etTirbc6z4u337Y5eNCipQVeesmipETjOAEPPxyjtdUiHtc88IBN\nbW3A6dMWL75oc/fdGVIpxaOPOhw4YFNZGRCJwFNPmeNyzz0ZgiDDa6/ZWFaIUja7d0eIRGDZMot7\n7zXnc0ODxcaNDmfPwsmToDXYtuIHP4hy440BH35oc/IkVFVlKC8fzA3vQtwkx2YwGXhYFb2fI0L0\nz/7Od75T7DL05zttbZlil2HYlZXFyNc7mVQ89lgkd0FUNDZaXH11SDze87q7dll8+9txfN9CKWhp\nUbz1loPn2YSh6aVxHAgCKCkJefZZh4MHYds2h4MHbbTWRKOKt992cjdEi9dfdzh+3Ka9HWpqsuzd\na9PYaOF5DqWl0NBgM2uWxrYVTz4ZYd48c9Pbvdtm5kzTY/LOOwrXDVBKceSIufGXlUE6HfLqqw7b\nt0fYu9emokLz7rsWkydrTpyw8TybpiaLq64KOHvWlHv+/JCHH44ya5Zmy5YI118fUF0dsmWLzbJl\nAR9+aHH0qIXWcOKERXk5nDxp2mHhwoDmZs0rr8TIZjXjxyteeilCEEBTk000qvF9i927bRIJTRAE\nLF0a8tBDJezZ4zB3ruboUdMD97OflZDJmP00NVlUVWnq6yOUl2tmzw4oLVX84hcxjh2zOXnS4u23\nLZYsybJ3r0Nlpaa+3uGGGwKmT1e88IIp84YNDvv3m3pns9DSYlFfH+HAAYtMRmFZmu3bI/i+orQU\nTp+2qKjQ7N9vs3Wrzd69Njt2OJw4YXPqlCIeD9m71xzLMNRkMnaX80hrzTvvmH1blubGG33ee89i\nzx6LVEqxdavD2bOKpiazXjQKH36ouPLKkPZ2RTIJx45ZNDZavP22TTyumDMn4Kc/jRGJmF6td96x\nuPLKgA0bYsTjIa+/HuHFFyPs32/Ot3nzAtraFD/8YYS333Y4eVLheTZXXKE5e1bR3KxYvNgnEtFk\nMpq9ex3eesvGtnXu/OorFJmb5GOPRdi5s+d14nGNbWsaG813Zvlyv+Mczhvs93Cg3+++mSCZSpHr\nQR3+8JdKwc6ddse+lYLFiy92vUen3s6RyZMv7Xr35lI/3r0pK4v9z6GsJz1kl7iZM0PefNPmyis1\nsZhmzx6LeNzcQO++O82WLQ6vvhph0iTTUzJvnulleeKJKCUlMH48NDQ4zJ4d8v775gb//vs2zzwT\nY9GigMsv9wlDRSKh2LfPJh6HSESjFBw/rqitNUOPSsGiRSEbNkQIQ8Utt6TxfXjuOYfy8pC9e23S\naYXvKx57LModd2SZNEnz3HM2p06Znrc9ezS33prm6FELz7NJpy1OnFDU1QU8+6zpifn4xzMcPWqG\n3W66yaehwQSRqVM1ySRorfjgA4tx4xRnzihuuCHkqacilJaanrBDhxRTp4Y0NipuuMHnrbdsPvIR\nzebNptzRqObJJyOsWJHF902vWHs7xGLQ3q4IAlDK9GhFIuYGmkopfN/8eySiqKzUHDhgEY1qJk2C\n996zcRyLI0cUQRAyYYLZ39mzFrEYbNxoccUVmoMHTUi86iofgDlzQlpaNJdfrmlpgbfesikt1eza\nZTNjhunhO37corQUjhxRXHmlRil9zjlSXa1ZsSKLbZv5YEeOKKqqTJB7+GHTs3f6tM1ll4UcO6Zo\nb1d84hNZTpxQaK2ZPVvz3ns206eHnDqlUAoOH7YoL9e5NlFMnAj795shzkmTNP/yLw6WZWHbmkOH\nbH70oxgzZoQcOGACkdaKyZM1jmN6oD772ZBHHokybhwsWOBTUqLR2hpQb83AengUdXUhrpsFRspQ\n08jokUskNMuX+13KMTLaR4hLi7yHbBTIXxBNsNEFF8Se1db63HdfGscJcZyQxYsDpk0LSaUULS0W\n8+aF1NaGeJ5DY6OZg3TsmMWUKWb76bTiqquC3E0PKis18XhIIhFSXR1y5IhNEJj5Y4sXB0yerJk7\nN0BraGszw4atrSaErViR5ZvfTPOlL2U4dUoRBGau0alT+R4sc1FvbTX/n8lAJGKGqlIpSKfNzTkW\n05w4YfH++w6HDjns2OEwd665Ob3yitNxE3/1VYfJk0MWLAh56SWbK68MmTcv5M03LaqqYNasEKVM\nELIsTRBAebnG96Gx0WLx4oBsFkpLNUGgcd0A1/XJZiGVUmQyCqUUx49b7NrlsHy5z/jxIWVlIbff\nnmX3bpuJE03P0dmzJkBdf32WWMzMzbvjjiybNztMmmR6tqZPDykpMb0zd92VZf9+C9s2dQcTJhIJ\n3RFqZ882wx8LFwb4vuaaa0JuvjlDW5spU3OzOY6xWIhta666KmDKlIBrrw1yv63rHs4ji3vvDfj2\ntzN86Us+V19tbraZjKKlxfTCaa1oadHcfXeae+/NcMstWZYv91mxIkN7O8ycqYnHNbW1AZalOXDA\nhLby8pAJE0JWrswSj5velaNHLaqrzXGYPTvk4EFT57Y2i8pKTTRq/u3mm32+8Y0Mf/zHaV55xWbK\nFEUiATt2OEPqnemf2b4JaueGjcF+D89XYZDMP/SRH3YdXias3ndflvvuy46JYdqhGu5zRFxapIds\nVBjsb+8O3/pWmpUrzbutUikzsV4pzZIlpkerpMTcHM3EchvbVpSXB9x4o086rbj11ixvvhkBQhYv\n9tm2zeGaa3zKy82TgeXlmkQiYNy4kNmzFUePkptUbiZk33efT2lp16f/qqrMZG6ASZPAtjXTpgVk\nsyFKZTuGSD/+8QwffGDKd+ONPq+8YgJSIqFparJJJhU1NSYAKgWOo5k4UROGmpMnFfG4xQcfWLiu\n6fVra1OsWpWlsdGisjLkc5/L8PLLivnzfVIpEyA3bzZDlnPm+Cxc6BONKn70oxhnzjhEoyF33OHz\n5JNmntmNN5r2KC0F2/a5/XYTSiZPDikrs5g4McgFV4vx46GxUbFiRYajR21aWjTl5RZnz5ohjSuu\n8PmTP2mnoiLO5s1Z1q0r4cwZxe23+7z3no1Smquv9nn3XYuSkpDly3127nQIQ4s1a9IsWxZw8qTi\nwIGA116zSKXMAxa+H3YE5e3bTS/X0qU+ixaFLFrU03lkUV0N5kGAgPp6xenTcNttPi+9ZEJBTY3m\nzjtDpk7VuScrAUISCVi/PobvK+bM8bn11iyRCDzzjM1996WJRKC9Ha6/PkQpnyAwbf7CCzBjhmb8\neBg3DqJRxbhxmo98JEs6bbF0aUBNjWm/sjIz704puOyysIcbXu/fhwvXwzMSe9GGiyroURwrdR6K\nsXyOiPMlgWzUGOwF0aG2Nr+szl0gNJ7XGc5WrvQBTSRitrdyZf4VEyp3w81fVHx27QqBEM9TnD1r\nAwGf+UyGZcsCIOz2NKWmpiZ/Iep8oq/wprh0qVmvvt4hHld8/ONZ6up8HMdh4sQsx46ZpzenTMkw\nZ06I74ecPm3xyCMxtIaVK33icfNk4pQpEZ56KopS8NnPZkinQ2bPDqiuDjh+3ExWnzYt5Ld+K0tp\nqXkKtLTU4fnnzY29ujrLpz4VorWittZn5UrTm2XbGf793xWxmKK1NeSLX0wxblzAc89FcBxFW5tp\n6SuuCEinLa680mf2bJ0bIg15550o6bTZ1o4dTu6JRNNrOGuWWffuu7PU1FhUVMSYPDlFXV2K/NOU\n27fn2yrLxIkmwJknNk0vWT7slpdrLr88xLLMUOakSQE1NSHZrOmdmjkTIGDp0oBEovDc6ek86ryh\ntLRoHnrIZvFis9zs2SFTp+Z7kKyO/1+zJmDZsny5O8+vT386pKnJJpPpHG6bPz9/HmqqqkJKSiJE\nIlmamszDHffem38SV3fczKqrQ1avzrB+fRSA1asz3HlnkCvDQG54F/ImOXzBRIYKRysJr2JolNYj\nvjtVNze3FLsMw66iopyLU+/uT5sxyKfPCl/H0NPrCgbzTrLCfZtXIiQSpcDZgtcj5F+VYG7iGzaY\nVz5UVfkcOGCGKmfN8glDE+CuvTagqspss2uoCQrerdW9Hp2vpehap8L3kCk2bjRPlCYSAS+/bJZf\ntSrLnXeadbq+j6uw3HQsv2xZ0O31D6ZM5x7vwTwV2Plus3xduwbrnt4RNhDnbrdrG/a8Tvdj2vO+\ndW44tvvx7m37vZ13o9PAv9+X1isULt51bWSTeo8tFRXlQ/qiSiAbocbwidxHvQfyktKL9Wb/wb6Q\ntbd1ey7H+R/vi3XjvriBQM7zsUXqPbaM4XoP6UIpQ5ZiFCkcCrBzc57Mzz0PEVzIoYPe9j2Qnprh\nGMK4WPuQ4RchhBgOo7vfXwghhBDiEiCBTAghhBCiyPocsnRddwrwO8CngCuAEHgX+A/g+57nHb/o\nJRRCCCGEuMT12kPmuu5vAw8DzcCXgJnAdOCLwEng/7iu+43hKKQQQgghxKWsrx6yDzzPu6OHz9/K\n/e+fXNe95+IUSwghhBBi7Oi1h8zzvMfyP7uuG839/xWu637CdV0rt8zPL34RhRBCCCEubf1O6ndd\n9/8F7nddtxp4EfivwD9f7IIJIYQQQowVA3nK8lPAV4BfBX7qed4K4NqLWiohhBBCiDFkIIHM9jwv\nDawCnsoNV5Zd3GIJIYQQQowdAwlkm13XfROIYoYstwC/uKilEkIIIYQYQ/oNZJ7n/T7wcaDO87wQ\n+F3P8/7bRS+ZEGKINMkkJJPmZyGEECNfr6+9cF33X+h2NXddt+P/Pc/7jYtbNDEy9fbHpofjj1sP\n9o96jyX5dtJ4nqK+PgLA8uU+dXUhl+7foby4f/xcCCGGS1/vIXthuAohRqKQxkYTeKqrA5JJm/zN\nfuNGc7NfuTLDxIkms586xQBCQF83z/y/BZjfA0y4aGiwqK93UEpTWmqzaZMDKFavzrBmjY8JZYVl\nHWhQKyxLSDJp9VKuwRhKOS6EznZKpcCyNFpDGCrq6x1cN1vwB8IHtr0L0zYXOyx11hvGQvgUQlzK\neg1knuc9mP/Zdd3LgPnABmCW53nvD3WHrus6wI+ByzDz0v7c8zyZk3ZBDKT3ymfvXnPYa2oCQBWE\nCJ/GRgcIaWhQvPKKWW7ePNizxyabtZg4MeTVV004O3w4QlOThdYwZ05IdbVGa6tbCOjac9MZ5nzq\n6oLczV7jefDyyzbRaMDSpSETJ1qkUoqXXlK0tysiEc3DD1t8/OMZwhAef9xizhyL8eND3nhD8dxz\nUQBuvz2duylbBUGye+9aQEOD03Ejr67O0tKi0BoWLNDU1eXbrqdQ2j2k5NszZPdueOopU46PfSzD\nRz9qytF7+CzcVgAMLfwkkyZ4tbcr0mn45S9tqqs1R49azJ3r09JitmW2ST8hqWsInjkzpKnJ1Htw\ngaensBRcoODbtd5am+0MLXx2LbP0tgkhiqXPv2UJ4LruvcD/AOLAMmCb67q/73neT4e4z18Djnue\n90XXdRPALuQhgfNQGHh66i2AhgbFyy/b2HZINhvloYdiAHzlK2kqKgJ+8pMSHCfk5ptDTpxQTJ0K\ne/Y4PP20CRe33aaors7y7rs2Bw8qZszw8X2LV1+1WLBAk04r9uzR1NaagHf2bMC+fRbRqCaVgg0b\nbMrKQvbssclkzE3u+HGbIPB5912IRGDnTovDh20ikZAjRyLs22cDissv9zl+PGTSpIDrr6ejTDfd\nlOa11wJ836KpCQ4fBqU0L7/s8NBDJjyuWpUmFgvRWpHJOGzZEkFrxac/neHIEcWECQFBoHnxRUUm\nY8p27FiA6/okEpp162weecTs7447MnzwgYXvWyxenOHgQQvb1liWzeHDNtGoIp027R8EirY2SCbT\nnDhhc8cdWaqqAFRHGNy4MR92fd56y8ZxNFdeaXPokI3WiuXLs7huYZDqKxxoDh+GM2cUvq8pKQmI\nREy7jh8f8POf20QiiqVLfUCxfbsJWEuXBgXh0ygMOSUlmvXro9TWhpSUmMBTVZWlvLx7YAnYu9ds\n04R8u8ew5Puabdu696L2dj4Pdyg63942CXMjlxwbMTr0G8iAP8AEsRc9zzvmuu61wCZgqIHs/7J3\n7+F1VPf9799rzeyLJMv2ti1fZEu+gBkb22Bs/7AwyBgMBAKUhBACpUmalKT9pe2v7elznvP0d5pz\nep4+v9PznPZp054mv7aQ0CQQwAQTwh3jC5YxwuAbvo7vlq+yZG/buu3LzKzzx5Jk2ZbsbWFZGH1f\nz8PD1tbMrLVmb+35eK01ay8CXmp/rIF8L48zwHT3oXLmIlJUFLFzp8PYsXbrVascyssNELF4cYxl\ny+jC9yEAACAASURBVOLtQcoeIwwVP/lJgkceyTByZMS0aa3s2pXk179OMGNGQCwGEyfagLVypcvD\nD0d89JHD/feHtLUZmpth3jzDm28mcJyIhx4K+c1v4uRyinnz8nz/+zHGjDGMHm1YvjzGxIk2JOza\n5ZDPK266Kc/q1Zrdu10SiQilYP16h8mTQ/J5lzVrXIIAXNdQVhZiDHz6aYzWVo3WhrVr42zYAHV1\nDnPm5MlkYOhQGwLmzAk5dUrxi1/EWLAgT0ODy8GDimHDoLUVXn9dM3UqPP98HMeJmDcv5PXX4wSB\nwpgcW7fmicUUzzwTZ/duF63hyBG4554cBw4o3n47xrZtLmVlAZ7n8MIL8c72XXddQEODw6ZNmspK\nl08+cTl+HA4e1GQyDr/3exk++MBl2zaXKDIsX64ZPBj279eMGxfn3nvztLY6LF3qsmSJIpmE6up8\n+2upehgKjUgmDW+8YUPRAw/k2LtXYYzh5EnIZhWOA1EE6bStE8Dp01BeHnQTsM7Q2lBSEhGPw759\n8M47NnieCXMRP/lJnGeescH1O9/J8YMf5AAHpQzJpOks69NPbQgG+OADjecZysq6vrcvbf5bKmWo\nrg7OClG9veB+tt62i4W5Sw0EfTGcPlDJsLa4ehQSyELf95s6JvT7vn/E87zu/mlbEN/3WwE8zyvF\nBrP/vbfHGji6/1DpOlSlteLAAc3w4RFFRYZczvDrXzs4jmLNGofiYkMiAVu3OpSVGXbv1owfHzBs\nWMSKFZoFC1z+/u/jKGWHCNNpxeDBEfv2aWbMCDl4UDF6tGHHDpdTp+yHWUuLIZEwzJgRsm2by8aN\nLmEIjgMLF+ZpbHR4++0YQ4Yo2to0rhuRShmOHbM9bvv3x/jNbxK4ruHLX85TVZWjvNzw3HNJ8nlN\nLGb4+OMYt9wCxhjyedvGZNKelTlz8gSB4vBhTUuL5uhRxezZeerrFcePKyZPjigujti1SzNihOHQ\nIU1Li+Leew1Ll8YIAkVFBbzwQpyKCjhwQPH++zEmTw44etTh4EF7IXRdw+DBsHJlnGPHNFOmBEyf\nHjBqVMSzzyYYPRqKihRbtmjmzAk4elQzdWrA2rUu+/a5uK7tCdu0KcaSJS67djns2uVQVGTI5w23\n3RbQ0qLYutXha1/LEUWGDz90mTkzwhjD//yfcYqK4NgxzTe+keWee0K6DoXW1zssXRpj1ChIJAyL\nF8eZOzekpSUik3F4550YYQjf/rZh5UqHdNrBcQzbtjn8+MeaQYM6hpAjUik6Q04uZ7jzzoD33osR\nRbYH9K234hijOHVK4Xl56usdnnkmThjac/XMM3HuvDNkypSIceMiFi2yQe3hhzPs2ePw2mv25wce\nyAO2fefOf4vHwZhC5r8pqqoiPM/+m+7KBJbzw9WFw1xPgaDn41/6kLH0APXk8g9rC9F3CglkWzzP\n+xMg5nneTOAH2GHGXvM8rwJYDPyr7/svXmz7srLSz1LcVauj3Q0NIevWaYqL7YfKunWGqqqIoUNt\nuNm3z/YaeV5AUZHL8OERO3dqNmxwSaUCokjR1gYnTijKyiIcx4aMadMCtm512bnTYcsWxfDhhvp6\nyGQUuRzkcop8XtHUBKAYM8awerXtedNasWYN3HJLyJQpIf/+7zEcRxGGtqfrtttybN6sGTnSkMtB\nGMKuXZo77wzYvdtlzBjDv/1bnChShKFi2bIYd9+dY/t2h3HjIvbts0NmiYShsVEzalSI40AyCY5j\nuO66kA0bYhw/rtt7pwxRpDhxwgap+nrNmDERI0catIb6ek19vUM2q8jlFJmMIp3WDBsWtQc8g1Iw\nbJjh1CnN3r0OkybZepSX27lPLS0KpQyZjGbdOhsY58yJWL/e4dAhh1tvzaOU4dprQ5Sy588Y2LHD\nYcqUkPp6TUWFoqIiZM8e2wM4c2ZINmuHW2+6KSQIHIqLY1xzjWLYMJcTJyJ8X3HTTYZYTLN8OezY\noRg+3GH+/IBp0xTFxQGVlYa6OgelIoqKIAhg2rSQl19O0tqqCUNYvTpGWZmhqUkxdmzE5s22ly6X\nczh9OsbcuYaRI10eeMC0B/6QX/3KpaoKHCfgpZcSTJgAYaj59FMHcCgujnAcTUevneNAcXEMiHH8\nuKKqyr6XwzBOTY2D1jZcrF6t+NM/dWhsjFi3roTiYoUxEWvXwt13G7JZ28OWSsUoK3Mu8nfy2f/W\nRoww3HdfyMqVtqz580MmT06g1JlwY4xhxYqzt1mwwAEiSkp050W/a717+tu19T7/c63r9olEyCuv\nKGbPhpIS3blv1/PRU5261vvz5sp+noc9vjZX2kC/jomLKySQ/TF2DlkbdjL+MuAve1ug53mjsDcH\n/LHv+8sL2aehoam3xV21yspKO9udTkNLS+ysD5V0Og8Yhg6NEQT2ZSwtDfjGNzK0tipefz1JGEYc\nPmznILntr3RFRYgxirIyQzwesWFDnHgcNm1yqaoKWL5cMWSIvagfOaIZNSriyBHNxIkRe/bA1Kkh\nx45pWlrA8yJOnIAdOzSeF7Fzp63f1Kkh27drdu3SfOMbOd56K0Y+D489FvDyyy7NzYrHHoPSUkM2\na3t1wtAGmIMHNePHRxgDsZihsjLi/fdjFBUZ9u/XeF7A4MGGtWtdiorAGDh0yPZIDRsGGzc6pFI2\nhK1b53D//YoZM0LeeivGkCF2PtmxY3Y+1WuvOdTV2Tll69Y5jB4Nt96a5/33XY4dc5k1K89dd2Up\nLY1YuTLOiRMuU6eG+L5DImGHAI8csRP+9+51GDky4tgxxYgREQcOaBIJ0xke9+yxd4NOmRKwebPL\n5MkBYCgqChk50hCGmhtvDLjnnjylpfnO+YC5HMyYoTh6VDNiRMjatZrhw0NaWgJefdXwyis2uN55\nZ46VKyOyWaiqili1ysXzbB0BtIa9ezV/+IcZokgxbFjEiRMO2Szk8yG7dxsOHMhw7nW8tdX2yMTj\nEYMGKYIAgiBi/PgQyFJZGfGtb0WdQ5bf+laOysoc6XSe1tYYHb01uVyeKNJks3YIMwwjmpszDBtW\nQktLtvO9PXo0ZDIhbW2a6uoAiGhouDLhYvp0w9ixZ3qaGhvPLjedhrfeimGMHXZ96y3D2LFtpFKG\nWbPO7gXrqHdPf7td/77PLaNj+yiKyOddMpnorH0Lq9PlOy+XU0/t7js9vzZX0pVv9+fDQG53bxQS\nyL4H/Mj3/b/qVQnn+ytgKPDD9i8uN8B97V/PJLrR01yZdFqRSMCddwaAnStUWqooLTVUVETs32/n\na+VyhtmzA2KxiN27XdavdzEGvvrVgPLyjguzZsuWiCeeyDBkSMjatTE+/dRFa0N1dZ6hQwMqKhyG\nDbNhSGvFggXZ9t45qK7OopS9KE+ZEjB+fMCUKRE7dxoeesheULZtUyxYEBKGIc8/7/DEE1mefTZB\nPG7bt2GDQzJpaGiAm27KE48bGho0Q4ca9u2zS128+moMpez8Nq0NYWhDQi6nSCYjxo5V1NXZnrDy\ncsPOnYr9+zULFtihRFBcf33EsmUud9+dJ5EwvP++5o//OEs+Dx9+6JDL2Ts/J0wI+fa3cwwbprj+\n+hz/+Z+KUaNCmpqgtVWTTNoexfvvD7jppoAVK2JkMppTpxQLF+aIxyMmTLA3JORymsrKgJYWqK4O\nWbbM9ijNnh2SSkXMmuVy3XUBlZWKs4fiIt591+HFFxM4juH660OGDLFzs84MaypaWuBv/iYHGH7z\nG82MGfamgwcfzHUOEz74YJ65cwPicU0+bygvj9iwIYbW9v1RWnr2cFfX9102C08+mWu/2cIOcdqL\nvsMPfpDjzjvD9tfeTuo/9z07eXLE/PkBr7+uOt/DpaWGESP0Wds9/niAnRFRyM0Ml5vqEmQupdye\nh057nufWvXPP+aOP5s4aspQhyUvVH8PaQvSOMubCK3l7nvf3wCOAj53Iv7hjHtgVYgZqwj673Ree\n1A/n3lmpWbLEfqjH4xEbN7qMGhXh+5prr7U9SMeO2flLa9bYi/KUKRHNzZpEImLo0LDz4jtuXMDO\nnTGiSDFlSpaSEjvMmMsFZLMxtIbRowOam12UgmTS8O67CYJA8V/+S55hw+zQ42uvxdi3z06Sv+GG\ngB/9qJWtW10gYM0ah5deKiIWM9x6q/15/HgbqIYNs6FLqRxf+YohkzG0tChqa+3k71mz8qxdG2fU\nqJAwVGzdagOn5wVMnhxx+rTD4MF2aDIet3PP1q1zeeaZBErB17+eQyloa9O0tNghXWMU2azhr/86\nIJXqugRGxOrVDr/4RQKlDAsW5Nm4UTN8uGH3bgdj7L5FRSE//GGeYcPsHaBLlthQZOdq5bssPRKQ\nTrukUsVAC91fLDrKNhw+bCe9n3sTh1KGJ5/Mk0oZamvtnZRKGcrKAo4csa/j3LkhVVWctdRIxwT/\n7u64PP99d6kTzM/sC4af/cxtD33Q1KT47ndDrruulIaG01fJHKjeThA//2/3wj0Hl3LOr65J6wO5\nx0TaPXCUlZX26g/wooGsg+d51cA3gHuAj3zf/2ZvCuwFCWQXdLG1x85ceLW2H9TvvWfDwaOP5pgw\nIeCTT2ywmTMnwN67obqsr2U4eTJk3z77gR+PB+zbZ5fNmDcvT3m57dHpfs0vuxxDTU28fYJyyObN\n9jgPPJDvDADpNPz0p7Y3Lx532L49YOLEiLY2GDKE9voqfv/3s+1La9iJ9XbNNLt+2oYNtq6ZDKxY\nYW8uUApiMTt/ROuIRx/tekdh1GWpBhuKul8rrbuLW9Tt2m2rVzssXtwxiT3HY4/ZuhYy6frSX++e\nljlRFH5Bv5KTwbsPDmVlg6+yD+zLc84u74Xq6pnUP4Av0NLuAaRPA5nneQq4A3gMmA+s8n3/yd4U\n2AsSyD6zC339kCrgw/zy9JL0tG86DU8/befNlJQkaG1t48EHA4qLuwaeQle//6xfIfRZLm69X6m/\nd6/31XMhti61p+iLS9o9sEi7B5beBrJCFob9/4CvAOuxQ5b/zff9TG8KE/2l69wYh8rKjscd4ejM\ndhffX1/iPJuL79t13oxShurqkClTOgKGe159CytPUVVlejF3pLfziGz9Lq2un9VnqWt/uNrqK4QQ\nV04hk/p3ALN832/o68qIgerMxNtUKgZcrjkwEgCEEEJcHQoJZP8B/KVnV4b9U+DPgf/H9/1cn9ZM\nDDA2PNm1myQ8CSGEGFgKGVf5V2AQMBsIgGuBn/ZlpYQQQgghBpJCAtls3/f/O5BvX+7i28BNfVst\nIYQQQoiBo5BAZjzPi2MXcAUY0eWxEEIIIYT4jAoJZD8C3gNGe573I+AT4J/6tFZCCCGEEAPIRSf1\n+77/S8/z1mLXIXOAB33f/7TPayaEEEIIMUD0GMg8z/vWOU91rO420/O8mb7v/6LvqiWEEEIIMXBc\nqIfsjgv8zgASyIQQQgghLoMLBbL/erEV+T3PS8qq/UIIIYQQn82FAtlznue9Dbzg+/5ZX0bleV4p\n8C3gLuCrfVg/IcRV952VQgghLtWFAtnXgf8KfOx53kngIHZh2AnAcOCf27cRXxg9fRH4pX6heCHH\ntyunSNC4GENtraamxv6pFv4l6UIIIa4mPQYy3/cj4MfAjz3PuxGYjP2Swd2+72+8QvUTl0XXIBSw\nfbt92adMCQHd/juD78OmTQqlDIMGaXbujAEweXKeU6cMxihmzgxJJh1AMXNmQDrtth83pK7OAaCy\nMuL8FVXODxZgqKmJtf+cx/MMEALn1tFpP0bI9u1O+/Ndy75cgbE3IurqbNndt/uzSacVNTUuxtg2\n1dS47d/5eVmLuUKkp08IIXpSyHdZ0h7AJIR9LnUNBCHptA0sqVTAhg0uYMhkQnzfRWtDfX2MRYuS\nAPz+72eZNSuH77vEYrB3r+21KimBd9/VjBplUMqwbp1LZaW9mNbXK15+OUEUKX73d7OMG5cjihxa\nWx3ee88e99FHs8ybZ8NeZWVAXZ1La6ti1SpFENgyli7VDBsWMmpUHoD334fVq11SqYB0OsZbb8UB\nePjhLHfcEQKwbFmMp55KAPC7v5ulsjJPGDokkxE7dthgt2BBQFVVdxf7rmGua8g79xx2DVU99Rh2\nhM+INWtg7Vr7ZzRnToZrrgHQ54TVcwNjIT2DtuympvN/0/Hc2fv2VNfLGXwuNVCdXafaWqebnr6+\nKvtKHUsIIS6PggKZ+DzoehE5Ewg+/FDz3HM2CN17b462NoXWEa2tcZ57LoHjGL72tTzLlztMmBCx\ne7dDNgvGKH7+c5emJsMrrySIxyPmzw95880YgwdHzJkT8frrMYyB+fPzjB4dMGyY5vXXY5SUwIkT\nml/9Ks43vxmxe3eM3bs1xcUGrTU//nGCPXuyHD2qqaxUvPdenLFjQxxHsW6dfctVVeVJJOB//I9i\n4nHD449nOX5cMWpUQG1tjJ07HYxRvPqqYc+ePCNHGp56KkEm46CU4T//M8H8+YoPP4zxO7+TZ8cO\nRVubw7FjhjDME4s53HxzRygKef55l5desmHu61/P8oMf5Nt/F/Huuw7PPdcR9HI89lgAKGprFa+/\nboPejBl59u+3IWfwYMPGjS7GGEaONHzwgYPjGFw3wQ9/GCeK4JvfzJHNQhRppk8POHjQtqe62gbQ\njuM+8EC+PUCe/VrX1io++cS2taIi4MABe94qKkJ++UuXKFLcffeZUNOxPUBZmeLAgY7ycpSX29Bx\nfg9eIcGkI6waDh9WXXo0uw6ddvfePHv72bMD1q1zzuvpKyu74Ju+y/noadj20kOiDAFfiIRVIfqL\nBLKrwpmLiFKG4mLFypUxXNcAhqYmQxhqnnkmzve/n6WkJOLv/q6I66+PGD065K23XEaOBNe1PS7l\n5RGnTtmg8PLLCQ4edLnhhjzPPpugrMygteLNN2NorThxQrF9u0sUGY4ccZg+PaS5OWLwYENLiyEI\nQGtoaYGhQw2trYYoijh1ShEEUFvr4vsOI0eG7Njh0NCgMQa2b3fI5UIefDBPWVnAli0uW7bEGDUq\nZNAgw+2358lmFU1NBt+PEY8HBAEMGRIRj0NDA5SVGaZOjViyxGXMGMOWLQ5Dh4a88EKMfftizJ2r\nKSkxDB/u8KtfJdi/3waWF15IMHFixCefxGhuhvp6w6hRIVGk+OUvHaZMCUkmDU89FaOlRVNUFLJj\nR5w9exxc1zB9esiqVS4jRkSUlxu0hhEjFK+8EqOyMqK1VfPcc3HmzAk5ckSzaZPiwQfztLU5rF+v\n+OCDGNu22T+9AwcUnpc5K5ik07BsmdNZ3wkTAp54IgfAP/1TnL177b6NjYry8iwAS5e6fPKJSxDA\n2LEB1dX2fK1YAZs2xVHKhr/HHuvoRTX4/sWCScQLL7gsWhTHdQ3jx0eMHGnDfE2NS3l5ntJSg+/b\n4KV1RFGR5u2348RihkmTQoYMgShSfPCBSyoV0dp66e/+nodtLz1cfbGGgC83CatC9KeLBrL277Gc\n4vv+p57n/S72i8X/0ff9I31eOwHYi8iqVQ7JpMF1Q156Kc6+fXYIcty4CK0NO3c6TJoUkEhEFBcb\nZs8O2LHDpbQUxo83rF3rsn275t5787z3Xox0WvPVrxqWLlVEkQ1V2SwoBcmkHRZbsCAgk1H4vmHY\nMGhrgxMnFJ4Xsn+/YtasgPXrXY4e1cyZE3D6NLS2wq23hvzqVwkSCcWMGQFz5+YZN86wYoVi9OiI\nIFAcOqR44AHDj34U49ZbYedOl0xG0dSkGD3aDpO2tCgWLsyzfbvinXdcvv71PM8/b8PBE0/kWL3a\n9riUlRlKSgxDhhgaGhz273c5eFCTSBgeeihDLKbZt09jjEIpqK/XrF+vWbvWRamQhgYbfPbtc7jr\nrhw//rFLFOn2QOkwdqyipMRgDJSXG954I8bo0VBcDL7vMGiQYfNmTUVFSFFRRCajiMVg3z7N3r0O\nkyZBYyN89JHLPffYodOOQLBtm8P+/ZpUKsQu72dD8/79mvr6jguhg51bp9i/38G0d6ht2aJ55x3b\n47V8uUtJiZ3/d/q05l//NcnJk4r778+zc6emrc2hqUkxZEiOzZtjFBVF7NzpMHasPVZ3waSuTrNo\nUZwwVGgNK1a4PPZYnjBUHDyo+O1vHdJpjdaGeBwGD474939PEgSa0lLDtm2a228POHLEYfz4gDvv\nDFi/3h57zhzb3oaGEOjdEOvVH64+X71RV//5FOLqVsgM5GeBRzzPmwv8X8Bp4Od9WitxDkM2C8uW\nuTQ0qM6ekyhSHDiguf76AGMMY8dGDBoUoZTtAduzx/ayHD6sicehosLw9tsxJkyIGDfODrV95Ss5\nwLBrl+bxx3PU1yuOHoXHH8+zcqXLhg2a++8PeffdGBs2OEycCBs3uqxf79LU5HDggMPevQ7NzZrT\npzUtLZqtW11SKcjlFEGgOHVKsXq15tFH8xw6pDh8WPGlLwW8+26M1lbN/v0OjgODBhkqKgyffOLi\nOIp4HJYujTF6tKGyEt591+HWWwNuuy1g1SqXtjYH33eJxxVtbVBaaqir0zQ02Lps2uSSSNjeu/nz\nAxzHoLVh3rw8R47YnrriYhvQOkLH+vUupaWKfF7x6acuRUWQzysOHrQho6UFhg0z5POQzar2YUnb\nxlxOEYaKhgZFdXXA0aP2wpZMGpJJe1PE1q0O1dUBWhscJ2LevIDFi11+8hNNba0dGgRDa6tq7zW0\n8+/AUFpqg7bWhigyTJwYkc1qslnbE2gDesiaNS7ZrCaKNC+8EGf27IjGRhtI33knhjEKY+x7J1Pg\nKoJBoCgvj1DKkM0aRo6MaGuzx1y71iWZNMRi0NRk29zcDIMGgevaeYijRkVks4p0WpNOa/btU/z0\npzF+/GPFCy+4/PSnLk8/HetyDs5IpQzV1QFK2WNVVwedc/Eu1eU81mdhjO2NevrpWI/tFkIMLIUE\nsom+7/8fwNeAp33f/1tA/s10hR07Zi9+uRzMnBmSzUImAzfeGDJpUsC99+ZoaYFly5Js2BDjwAFN\nURGA4sgRTXl5RFGRoaQEDh3SHDigSSYVra32Dsc5cwLa2kLuvDPHww9nWbrUZcgQW9bLLycwxoat\n99+PMWaMIZlULF4cp7ISJkwwLFtmh/dOntSsX+9w7bUREyYE1Ncrjh1zKC3VLFniMmtWyNSptoet\nrU3jurB/v8OoURGelyceN4wZE5HJQFGRIRaz/w0dGhFFmvffj7Nnj8uuXQ6lpZBIwMaNmlGjIgYN\nMhQVnbmoFRfbXq2TJx2qq3N8+cv2v4UL84wZA1obXNcwcWJIU5Ni5EhDY6MNK66rOH0axo2zc7Qq\nKiJSKUNbG/zO7+QoKoooLg6ZOzcgk6F9qBJGjoy44YaIN9+MUVUVMnlySDqtSCQUs2aFVFbCvHn2\nfN91V55Tp2DQINU5DGh7TOxrVlJib7A4ckQDilQKFi4MWLgwzx135Jk4MaStTZPJ2GNnsxAEdkja\nGHBdcBwAGz6GD49wHHt+MhnF7NkXDiaVlRGPPprrDLKPP57jO9/J8+1v2/l/xiiSSXtulDK0tsLD\nD+eIxaLO4HvjjbZnbMgQwwcfxEgkbFsXLUp0huVFi+KdxztzDrpSVFVFPPlkniefzHcOo/UuXHV/\nrCutsTHq7I3qud1X1uclrAoxUBUyh8z1PG8E8BXgYc/zRgPFfVstcTbFuHGG4cNDkkmIxyM8L8AY\nKCkJWbEizokT9gI+cmREKmWDTiplOHZMcfPNAfv3a44ft5PK9+5VDB8ecfPNeWpqXNraFLGYIpdz\nGD3aMGQInDxpe3uiSNHcbHuSHAcaGhTFxRCGNizk84Zk0pZ59KgtY+rUAMeJmDzZ9jhlMvZCk05r\nRoyIaGjQJJMht9+eZ+dOOzn++usDRo2ywWj8eM2yZXEcx/DQQ3m2btVUVARMnqw5dkxz8qSd3/Lp\npw65HFxzTYTr2l6oigrD0aOglOG66wImToy4556wfbK5PZuVlSG1tTB4MBQXB+ze7fLssw7NzfDd\n7+bYssX2lv3RH+V4/32XYcPgttvyVFbaGxM++kgxf35ARUXAxo0OI0bYYeJk0rBmTYxsVjFlSohS\nhmQSHnkkR2urpqjIUF0dUlUVMndujtZWeO01F2PO/ndRaSlMmxaxe7c9b9dcE1Faat8HVVUGz7PD\nfXbuliaZNBw6pLnvvgCtI0aMMCxdGkNrwwMP5Nm0STF1akguB/ffH7B2rZ1ov3Bh0L7UiOphyEzz\n2GMB8+ZF7efN3hSQShluuy2kpsZu//jjAZ5nt/H9iEmT7F2xQ4YY6ups6LjllogPPzwz3Nqbv4Ez\nQ2eq8/9VVTbIw6UM+3V3LNH78ymEuByUucgnZPu8sb8Ffuv7/l94nrcD+KHv+y9eiQoCpqGhm3v/\nv+DKyko50+4zk22LiiLWrbNDkFrD1q2KKVPsv/J37dLMn297iXI5w4kT9sM0Fgs5ccIhiuD06ZB5\n8yJaWhRDh+ZpaIjxwQfx9h6NgJUrXW68MUdzs8uSJXFGjIi4+eaAd96JEUU20OVyEXV1LvPn56mv\n10QRTJ8esnKlHQ574oksFRURuZzihRdibNwYw3Ei5swJ2bjRobVV861vZWhujjh50v6bYPDgkC9/\nOSKZjPHGG4bSUhuqfB8yGQfXtTcSbNtmg8b11+dZvdre0WiHMB3CUHHHHXk2bLBzwG67Lcdf/mWe\n7v/d0TF/x+D7EVu22GHgadMCysvtOmsdS3bYOwahpibeftdjSEODRmvbM7Rxo53Qfs01ER9+aM/B\n17+ebQ8y+pzlSM5eqqLjdS0pSTBrVkuXuyZtjyLQ5W7Kcy+OZ5bGePHFWOfcH8cJWbjQ3phw8mTE\nK6/Yu3C/+lXbI9Q3i/xebPmNs5e9qKgIOXhQU1SUYPjwNg4e1O13hQ6MieQjRgzi9ddbBtwE+rM/\n1wYOaffAUlZW2qs/5IsGsq48zxsMVPi+v6U3hfWSBDLg7AChWLIkRjZrh/U6LsRKGaLIDiOdJjYu\nyQAAIABJREFUWWiVzu0B7r47TzJpL6AzZwbU1ip27LBh4brrArR2yOcNK1cq6ups+BkzJuhc58vz\n8hQXA2huuSWgvNwOp50JL13X8op48UWH5cvtvnfemeWGGwygGTUq5Gc/i1NaauvY1KT47ndDJk8+\n90KVY+hQewZOnozYtMk+P2NGHq1tcLr55q4LyXasvwYzZwYU1glcyOTqi61JRg/noLCyU6lioIXu\ng83FgtPlXBaiL51/Dm27m/txYd/+Yf++T3+OXpsrYwBfoKXdA0ifBTLP8/4AuBX434D1QBPwsu/7\nf92bAntBAtl5zg5n5692f+4Q1IUuyt39zlBbC5s2aZSyi8yePNnRgxTiebqbMnrS86Kr3YWIsrLB\nF7hQfZ7CxeX12T+4rs5zM4A/sKXdA4i0e2DpbSArpPvgB8DdwO8BrwJ/BtQCVyqQifN0zIHpmFPU\n3ZwP1c325z7f0+86jmvaj6tIp2l/rHsooyeaysozj7uW2/N8lZ7qK3N/eibnRgghrmYFffGe7/sn\ngC8Db/i+HwBFfVorcQnshbgjoPXNcfUVKENChBBCiIGrkEC2xfO814FJwHue5y0CPu7bagkhhBBC\nDByFBLLvAv8vMNf3/RzwS+DJPq2VEEIIIcQAUkgg00A18KP2uyxvKnA/IYQQQghRgEKC1Y+BEmA2\nEADXAj/ty0oJIYQQQgwkhQSy2b7v/3cg7/t+K/BtbC+ZEEIIIYS4DApaudLzvDhnvvl2RJfHQggh\nhBDiMyokkP0IeA8Y7Xnej4BPgH/q01oJIYQQQgwghSwM+xawFrgDcIAHfd//tE9rJYQQQggxgBQS\nyGp8358KbO3rygghhBBCDESFBLKNnud9E1gDtHU86ft+XZ/VSgghhBBiACkkkM1t/68rg125X1x2\nHV8SHWJP80D8SqGr84uyhRBCiN66aCDzfX/ilaiIADDU1kZs2OAQi2WZNi2iqqrjy7wLCSkRdXX2\nPo3KyoDt2+3LO2VKQDrttu8bkU7rLscxZ+1TV+d2Pt6wwT6eOfPsx2eO1VM9eqrrhdrQ8buA2lrF\nkiUxAO6+O6CqKurmHHRtx7ltKiTASegTQgjx+XHRQOZ53s/Oecpghy63AU+1f51SwTzPU8BPgBuB\nDPCk7/t7LuUYX1TpdMiyZQqlAELq68HzQlIph9pa2LTJhoYZMyI8D+yXc3eEkYglSxQrVsTQOqKi\nAj7+2IaaW2+F4cMNxihKSyPyeQUoxo8P2b/f5c0342htmDEDNmyw+0yZYtrrAW+/rdi61QUU06ZF\nTJoUEIYuEyaEJJMOoJg5syPMGQ4fVtTU2ONUV3cEKqit1dTUuF2eD6irc9r3MWzapBk0KM/bb8c4\nftxu19gI5eU5SksNvh+xZYsDwNChEc3NNoQVF8Pu3fb5BQvy3Zyb84Nhba3B9+3PnmeoqoLCQ1nX\n4BuSTjvdlnHx8HklwuC5ZdEHZfemPdITLIQQXRUyZBkCw4Cft//8GFDa/vy/Yb/r8lJ8BUj4vj/P\n87y5wD+2Pzfgbd0KJ0/GeffdOAD33JPj00+zFBUZli51WbLEPr9wYY716yNiMUVpqWbHjhjNzYZM\nBlascJk0KWD3bpfNm120NrS1aRKJiAMHHO6/P8fu3YpMxuGmm2D7dof16x0mTQp4++0EDQ2aoqII\nx4lTV6cJQ7j55oCNG12MgVQqxkcfaTIZxbx5cPiwJgw148fDoUMOgwdDQwNcc43t1Vq1SuN5Ngis\nWuWgtX28erVi927N9u0ax7Hhb+PGOJ4XsnmzSxTZYHrypMOKFYqmJkNDQ4w33kigteFLX8py7Jgm\nihTDhkW88UaMIFA0NiocB2IxxYwZeRobNcYo5swJ2uthQ8Dbbyd5/nl7Ph9/PNse+pwCev0i3nnH\n4Re/SADw4IMZ4nGIIsWMGSHl5TaodR9KbS/f+cE0ukB5nyU4mfOCPDhnle15tl6frYzu2nOhY53Z\np6REM2uWLmAfIYT4YiskkN3k+/6cjh88z3sN+Mj3/Uc9z9vYizJvA94G8H3/I8/z5lxk+wGjqQmW\nLYtz+LDtcVm2LM7UqVlefTVJSYlh8OAIpWDPHkinbdBobjaUlNiL2bvvOnz5yzkmTAj5h38oorIy\nYujQiG3bNNdfDw0Nms2bYxw+rNi71yGZNBQV5Zg2zWHGjIBXX40xcmTE5MkBq1bFGDoUHAfefjvG\niBEGY2wP2v79DpmMDSFg2LbN5b77IJczHDniMHp0xM9/bgPSvffm2b8/ByhOnQrZtMmGoNtvz/HR\nR3EWL04wfHhIVVXI3r2KtjbNhAkRBw/aYHPttQHr12uKigy1tTEOHtQMGWL4+GOXa66JaGiwvYN/\n9EdtnD6teemlGFOngjGwZ0+MINAEgeL4ccXSpRCPKzwPXnrJZexYg1KGV16JkUpF5HJuD4HiTIAY\nNizPU08lOHXKwXEM770XJwjg9GnF7bdrNmxwicdh/PiIkSPBGEVNjYvn5UmlbO9UTY2LMfb4Hb8r\nK6Pb8qDQkNOV7cFrbTUsW+bw8cfx9vdXjpYWh0TCHuf552NMnhzS1qYvUMaFg2FP7Umleq5d1306\nzk95eZ7S0oE2fCzD5kKIMwpZGLbE87zRXX4eCRS1Py4k0J1rMHCqy8+B53nyZeVAPg9Hjii0Bq3t\n49JSzbFjmt27HYqKAAxDhiiWLUuwdGmckhLNhx+6vPVWjIULA3bscNi7V3H33QF792p27XK47baA\no0cVEydGfPCBQzyuCEPFqlUu48c7rFgR4623YsyfH3LggKahQTNypMH3HY4etYGmuBimTw/ZudP2\nShUVwcaNDpMmGbRWvPdenCjSbNvmsn+/w9ixES0tmo8/dnjzTYe/+ZskUaTZsUOzdauDMQ6vvhon\nCBTJpOK3v40xapRh48YYuZyhqirHbbdlKS2FF19Msm+fy9GjmqIiO1xZVgbvvx9nzRqXeFzzzjsx\nnn02SVWVYcMGzbFjiu3bXdraFMbAG2/Y0JVMGrZvV0yfHrFrl2bPHoexYw3x+Jnw1HGR7NA1QCST\ncPKkIpeD0aMj1q93Sac1jY0Ov/1tnFTKEEWKFSvczt7AS9VdYDm3TpYhnYZ0GiBqfxzx4osOf/7n\nSf78z4s5dsxhxw7N5s0uO3e6NDfb42QycOCAvkgZNhg+/XSMp5+OUVurudxf0mGM4eBBxW9/6/RZ\nGZ9HxvT9uRVCXF0KCVT/J7DW87zV2IVh5wB/5nne3wBLelHmaeyQZwft+353YzadyspKL/TrLwzX\nTbNwYb5zyHLhwjxKRQwfHmGM4tNP7bytTz5xOH1atV/4Y0ydGrJ9O+zb53LwoOb0aUU8DiNHRjgO\nHDqkSSbth/2ECRGNjfbCO3duyJIlLoMGwbhxpj1UKdJpjdYwdKjh9GnFHXfkqa9XNDXBnDkh69Yp\nYjHDjBkhx4/bABkE0Npqg97GjQ733ZensTEik1Fs3RrnwAGXTEZx220BjY0u27Y5jBlj2L/f7jd4\nsO3VAkVzs+Kaa0KiCP75n5MEgebQIYdx4yKOHoVp0wJWrYqTydiw9dprce67L8eOHfZmgKlTQ06f\n1gSBDTexmGLwYMPmzXGOH3eprs7iOIpEgvZ5cop4PE5JSRylDKlUjLIyp8srE1JSYsOL6wbcfXee\nl19OYIyhoiKisdGG1JMnoawMmptdKipCEgkHrW3QnTw5gVKKESMM990XsnKlPX7H76Dr+/xMeUC3\ndTLGsGKFPY5ShtGjAw4fdjlxIuLQIUMsZufzLV6suf32kNpahzVrXP72b/Ps25dAqYhbbolwnCQl\nJarbMhoaQtat0xQX23qsW2eoqorO2qan9ijVc29P131aWiLGjdNoHae4WHVbxheRPbclFzy3X1QD\n5fP8XNJucTGF3GW5yPO8ZUA1EADf932/0fO8933fP9GLMj8AHgB+7XleFbDpYjs0NDT1opirTxRB\nJmOYNy8A7OPWVigpMezda+dt2Z4pzYgRcOoUNDYqRo4McRxYutRl9GgIAs3u3XD77SHptA1L06dH\nNDXBhAmGN9+MMXp0yMyZeX796zitrQrHUZw8af9/+rSmvh7mzQvYt89hyxbNQw/l2LLF4frr82Sz\ntjevqirgzTcdiooi5s0LWLPGIYoMU6eG7N+vUcowZUrI5s0OrmuIIjtn7ehRxb33wmOPtfHUU0U4\njuGhh/KsXetw7bUhJSWGlhZFJgPJpCGfhz17HObOzTN+vB2Sra2Nkc3aoBmGkM/buWNNTVBeHnL6\ntGLy5AjXtcOuxcWGgwcdtA45dswwdWrAuHE2pGazhra2iLa2iOrqAIhoaDh7yHLWLDuEmM1GrFkT\nY/78gJEjA/bvdzh1ytb1rrvynDwJYRjw6KNZvvSlEMiSSpnOEAwwfbph7NgzQ1WNjYqystIu7/Mz\n5QHd1imdhrfeimFMSFFRxC9/6TJzpn1t1q51mDkzT3OzZtAgiMcjBg2KmD495JZbstxySwDYmxq6\nznPrroyWlthZwTCdzp/3vu2uPRdzZp8EP/1pSHNzdMEyvniKaWnJXvTcftGc/T4fOKTdA0tvQ2gh\nd1kWA/8rcBe2h2yZ53k/7GUYA3gFuNvzvA/af/5OL4/zhZNM2rsFR42yP9fXO50BY+9eB62hrk4x\nd27I3r0apWwv2s6d9nczZ4Zs2mRf0jvuyBMEhnhcMWlSRBRBaSm4bsAdd0AYQmlpnptucjlyxHDo\nENx6a56NG11OnzZ87WsBy5Y55HJw550h27c71NXF8P2Av/iLLIcPKzZvNixcaGhsjAhDQ1mZHdK7\n9dYcQWDvfvzoI5eWFsWECSHptGbMmIjiYoNSim9/O2DBgjYgZM0azc6dLlEUMXVqyLp1MYyBe+7J\n8+GHNszNmZOnrMxQXBxRXZ1nyRLbs/PII3mWL7fn6g/+IMvWrYrRoyPGjw+pqIB43PDuuy4zZkQk\nErZes2ZFrF1rR8ovPrldUVUV4Xl56usNL78Me/dqDhyIMWlSwG23RUSRorIy5CtfCSkuVlRWRpyZ\nEXD+8c7MseouvJwpDy5tftGQIYbrr7cBPZEw/Mmf5Dh2TDFmTJ4HHsiTSp2pU1WVuWAZqZShujo4\nKxj2dH4u3J7u2H1GjIhx2205amrURcr4YhkxQhd4boUQA4Uy5sLzFtqXvWgF/gP7afE9YIjv+9/s\n++oBYAZKwk6nA/7lX2K8+qodwnrggSz79yvC0KGyMuLTT12UMsyenaeuziGKFIMHh4wZE5HJQFER\nfPhhHGPg5pvzJJMQhpooishkNFpHlJYGDBli1zYrKgr4+78v4YYbDI4T0dRkmDABwtAwYkSe5mYX\npaCoyPDLXyYJQ8UTT2TZtQuyWYdZswLWrnUJQ82XvpSlpMT2nB0/bjov/MePQ0ODy7BhEceOaYYP\ntxecoiLDk092nfxtQ19RkcuaNXmef95OU3zssTauu84AunMNtKYmw3PPaY4e7VgCI8f06Xa4dMKE\ngPJy+3zHnY5KGcaNs3eZQsck+bAXa5cBRLzwgsPixXZ4c8GCPFGk2u/kDKmq6t1F9dL/JXlm4v/5\n7ctTXm7r0fOyHIWX05cTz227Tw+4ye0Du90D4/O8K2n3wFJWVtqrP+ZCAtlG3/dvPOe5rb7vX9+b\nAnthwAQye5ENOXTITggPw5Bt22wwufvuHOXldqvDhw2rV9uhpnnzgvbnVZfFYA0nTxpqamywq67O\ndy75kEqdvcjrT34S56mn7Hbf/36G++8PAM3hwyFr19rtZs8OaG62643dddfZC86eWUi248J/7lBY\nvnPZC9+/+J2D9g/4VJd1vrr2NHU9T4pPPrFBY86csEv7eloLrDeLx/akkHXILk3vPrj6qn1XzgD+\nwJZ2DyDS7oGlLwPZJqDa9/2T7T8PBVb6vn9DbwrshQEUyKDjIptKFQPNPS5sevF/WRfas2F7pgCm\nTAmxo9KXsn/PbehNvQv/A/5iLRkwgD+4pN0DiLR7YBnA7e7VBamQuyz/EVjTvv4YwO8Af9ebwkQh\n7NyasjKHhgbdw9ycQubsFDqvx2HKlDOPL33/Syn7sxyz0DKEEEKIq08hgew14GPgduzY0cO+71/0\nzkghhBBCCFGYQgJZje/7U4HNfV0ZIYQQQoiBqJBAttHzvG8Ca7BfKg6A7/t1fVYrIYQQQogBpJBA\nNrf9v64MMOnyV0cIIYQQYuApZKX+iVeiIkIIIYQQA1WPgczzvHLgX4HJwCrgrzqWvhBCCCGEEJfP\nuStudvUMsB37tUlJ4J+uSI2EEEIIIQaYCw1ZjvV9/0sAnuctBTZcmSoJIYQQQgwsF+ohy3U88H0/\n3/VnIYQQQghx+VwokJ3rwt+xJIQQQggheuVCQ5bTPM/b0+Xnse0/K8D4vi/LXgghhBBCXAYXCmTX\nXbFaCCGEEEIMYD0GMt/391/JiogrxZBO2y/jTqUMPX8xd6HbCSGEEOKzKmSlfvGFYaitVXzyiQPA\nnDkhVVXdhS273euvxwB44IF8j9v1X2jrqeyuz0ek07qf6ieEEEIUTgLZVamQIBRRV2fDSGVlQF2d\nS2srLFvmsGGDfdlPnVJ4Xp5U6uzjNjUZ/u3fEmzaZLc7fNigdY54XDFzZoB92xhqazVLltht7r47\noKoq6qYuXesRkk47XepNN+EpBC4WpGzZNTW27OrqjrI56/mKipCDBzXGqC7bXI5QdiWC6OUqQ3o6\nhRDiaiCB7KrRcWE1+L6ipsb2XnUfNCJeeMFh8eI4jhMxe7bm449dRo8O2bHDpbHRhqJsFr75zY5A\ndibkhGFEXZ1i3LgQrQ2ZjOLP/qyYEyc03/tehkcfzdHa6vDii5pjx2y5jY0Onhd1CXcd9XBZtCiO\n1oY77wxoaVGAoro6D6jzwlNRkWL4cPeCQSqdtvsZY5+rqXHxvHznY2MUmQwsWhTnjjsC2tpU5zZn\n1+/cc1tIaLG9h6tX23M4b15PvYyfRU+B81LLuFzHEUII0dckkF0VzlxYMxnQ2hCPgzHnBg0bLOrr\nFc8/H+fgQQfPMyxalCCKFKmUoa5Ok83aC3IiobC9UQ5NTYqaGoe2NkUmo5g8OWLjRpdx40IOHHBo\nbtZEkWLx4ji7d2vSaUVREaxe7RKGihtuCGhqypJKOZ21rqvTLFoUJwwVw4cbnnkmzj33hAwZAp98\n4pBOaxKJs8NTSwssXRrn1lsDWlt7DlJKGZJJ28uWzV6ecwsXDy3ptOHll+MsX24D8ZEjeTwvSyp1\n+UJOT4Gz+zDZ98cRQgjR9y5lHTLRT7peWI1RrF3rdoaRM2ywePrpGMuXaw4dsj1MqZTh8GFFGEIU\nQWVlRHGxYfBgw9SpIXv32n1ee02zebNm3TqHEycUO3ZocjnI5RSNjYp4nPZjaRobHQ4fdtiyxWXC\nhKizBidOXJ630+nT4PsO69Y5HDxoewW7SqUixo2LWL7cZflyl3HjIlKpiFTKUF0doJShqMjw6KM5\nslkb3qqrg85h0gud25oat7O3rDv19ZoVK1yiSBFFihUrXOrr5c9ICCHEZyM9ZFeZZBIqKiKUMmcF\nja7BIgw1U6eG+D4cPapYuDDP5s0ujY2K0aMjyssNrgvjxkVs3ux0hpF4HMLQkEpFnD7tks9rDh6E\nadNCGhs1QQA33xyxb59DMglHjijuvz9AKcWhQ4pk8uy6VlZGPPpojkWL4qTT8J3v5DqHLOfMsXPF\nampciorg0UdzHDyoO9v06acOShlGjozOOwfptObAAYeZM+3vDhxwSKftcGlVVdQ5fNkXk/qLiw3l\n5VF7UITychtwL+cwYEew7Npr15v6X67jCCGE6HsSyK4C515YH388wPPssFp3F9i2NhvIpk0LSSRs\ngLjxRhviyssjGhrssOK0aSFr19pjGqNIpxULFgSMGBGyc2fEnj0KrRXDh0f8xV9kicUMixfH2LPH\npa3N8MgjOTZudDh1Cr73vTxTpkSA06UmmsceC5g3zwan8yf1m27Ck8PPfmZYsCAEOoYjuw8Q5wZA\nS3UZktNdHnd/jEsNLZWVhscfz7F4cRyAhx/OUVl5uUOOOidY9vb4l+s4Qggh+poEsqvChS6sZyaj\ndw0Wd94Z4nl2Oxt2aN/u7MdFRSE1NYpsFh55JN85X+yaa0KGD7dDfOPHG66/HlIpRRSFjBxpJ23N\nm5fnD//Q9nhNmRJydhjroKms7HjsnBeQzg1PI0bEuPXW3AUD0uXt+bnU0KJ57LGQefPsOaisjOib\nkX910TB5ZY8jhBCiL0kgu2pc7MJ6oWChe+g10t0M8UXYOzmjs9Yrs/soqqoMnhe2b6+6lNFdGLt0\nShUSkC53z8+lhpauIVPmjwkhhPjsJJB9ofSmN6S7Ib5zg9fZPXJ93+NSSBnS8yOEEOKLQwKZ6IEE\nHiGEEOJKkfEWIYQQQoh+JoFMCCGEEKKfSSATQgghhOhnEsiEEEIIIfqZBDIhhBBCiH4mgUwIIYQQ\nop9JIBNCCCGE6GcSyIQQQggh+pkEMiGEEEKIfiaBTAghhBCin0kgE0IIIYToZxLIhBBCCCH6mQQy\nIYQQQoh+5l7JwjzPGww8CwwGYsBf+r5feyXr8MVmSKcVAKmUAdQlblPI/kIIIYS43K5oIAP+F+A9\n3/f/xfO864DngdlXuA5fUIbaWk1NjX1Jq6sDqqoizg1cPW9TyP72GJ/v0PZ5r58QQghxvisdyP4R\nyLY/jgFtV7j8L6x0WlFT42KMDSA1NS6elyeVKmybQvYvPLT1l897/YQQQoju9Vkg8zzvu8BfAB3d\nFAb4ju/7az3PGw38EvhvfVW+uPwKC2395/NePyGEEKInfRbIfN//GfCzc5/3PG8G8Cvs/LFVhRyr\nrKz0Mtfu6nAp7R4xwnDffSErVzoAzJ8fMnlyAqVUQdsUsj+ElJTozsCjlCGVilFW5nzGlp6t96/3\nlalfX5H3+cAi7R5YpN3iYpQx5ooV5nne9cDLwKO+728qcDfT0NDUh7X6fCorK+XS293Xk/r7fkiw\nd+2+cvXrK5+t3VcvaffAIu0eWAZwu3t10bnSc8j+byAB/LPneQo46fv+V69wHb7AVJfhuZ7eDxfa\n5mL7K6qqIjwvD3weJ81/3usnhBBCdO+KBjLf979yJcsTfaGQ0NefPu/1E0IIIc4nC8MKIYQQQvQz\nCWRCCCGEEP1MApkQQgghRD+TQCaEEEII0c8kkAkhhBBC9DMJZEIIIYQQ/UwCmRBCCCFEP5NAJoQQ\nQgjRzySQCSGEEEL0MwlkQgghhBD9TAKZEEIIIUQ/k0AmhBBCCNHPJJAJIYQQQvQzCWRCCCGEEP1M\nApkQQgghRD+TQCaEEEII0c8kkAkhhBBC9DMJZEIIIYQQ/UwCmRBCCCFEP5NAJoQQQgjRzySQCSGE\nEEL0MwlkQgghhBD9TAKZEEIIIUQ/k0AmhBBCCNHPJJAJIYQQQvQzCWRCCCGEEP1MApkQQgghRD+T\nQCaEEEII0c8kkAkhhBBC9DMJZEIIIYQQ/UwCmRBCCCFEP5NAJoQQQgjRzySQCSGEEEL0MwlkQggh\nhBD9TAKZEEIIIUQ/k0AmhBBCCNHPJJAJIYQQQvQzCWRCCCGEEP1MApkQQgghRD+TQCaEEEII0c/c\n/ijU87wpQC0w0vf9XH/UQQghhBDi8+KK95B5nlcK/AOQudJli0thSKchnbaPhRBCCNF3+qOH7D+A\nvwJe7YeyRUEMtbWamhr79qiuDqiqigDVv9USQgghvqD6LJB5nvdd4P9v704D5KrKNI7/OwsMIMGA\nQVEGQZSHZRAISMISAkgGYTLI4oiGZSQQR8jICMNoCLIIBoRRZDMsQQaRYR0FIawJRAnIhF2CwMsW\n0RkXWhYJS4CQ8sM5RW43lW7SpOs01c/vS9e9Vffe8557u+qtc07dcxgdm1d+C1waEXMl+dO9j3r+\n+TZmzx5ErZZO0ezZg5DeYOjQwgUzMzNrUW21WvO6oyQ9BvwfqallJDAnIrbvZjP3lzVZe/ubTJ06\n4K2ErK2txiGHLGLYsIGFS2ZmZtbn9ajBqaldlhGxXv2xpHnAmHeyXXv7/F4rU181bNjKBeOuMXx4\nxy5LWER7e+83apaNuxzH3b847v7Fcfcvw4at3KPtivzKMqvhQUl9VBsjRy5CegOAoUN9qszMzHpT\nsYQsIj5W6tj2TrRVxow5GTMzM+tNvjGsmZmZWWFOyMzMzMwKc0JmZmZmVpgTMjMzM7PCnJCZmZmZ\nFeaEzMzMzKwwJ2RmZmZmhTkhMzMzMyvMCZmZmZlZYU7IzMzMzApzQmZmZmZWmBMyMzMzs8KckJmZ\nmZkV5oTMzMzMrDAnZGZmZmaFOSEzMzMzK8wJmZmZmVlhTsjMzMzMCnNCZmZmZlaYEzIzMzOzwpyQ\nmZmZmRXmhMzMzMysMCdkZmZmZoU5ITMzMzMrzAmZmZmZWWFOyMzMzMwKc0JmZmZmVpgTMjMzM7PC\nnJCZmZmZFeaEzMzMzKwwJ2RmZmZmhTkhMzMzMyvMCZmZmZlZYU7IzMzMzApzQmZmZmZWmBMyMzMz\ns8KckJmZmZkV5oTMzMzMrDAnZGZmZmaFOSEzMzMzK2xQMw8maQBwKrA5sDxwXERc38wymJmZmfU1\nzW4h2w8YFBGjgN2Bjzf5+GZmZmZ9TlNbyICdgYckTc/LX23y8c3MzMz6nF5LyCSNBw4DapXV7cCr\nETFW0nbAhcDo3iqDmZmZ2XtBW61W6/5Vy4ikS4ErIuKqvPyHiFijaQUwMzMz64OaPYbsdmBXAEmb\nAE83+fhmZmZmfU6zx5BNA86WdGde/kqTj29mZmbW5zS1y9LMzMzM3s43hjUzMzMrzAmZmZmZWWFO\nyMzMzMwKa/ag/ndE0hDgYmAIMBg4PCLmSBoJnAa8AcyIiOMLFnOZk9QGTAU2ARYAB0XEU2VL1Tsk\nDQIuANYGlgOmAA+T7k23CHgoIiaWKl9vk7Q6cA+wE/Am/SfuScBupP/rqcBttHjs+VqBU5E+AAAM\nDElEQVT/EelaXwhMoIXPuaQRwHciYgdJ69IgTkkTgC+T3sunRMR1pcq7rHSKe1PgDNL5fg3YPyLa\nWz3uyrpxwL9GxNZ5uaXjljSM9KPF9wMDSed73tLG3VdbyA4HZkbE9sABpDdugLOBL+Spl0bkW2e0\nkt2B5fNFfCRp3s9WtS/w54jYDvgMcBYp3skRMRoYIOmzJQvYW/IH9DnAK3lVf4l7NLBVvr63B9ai\nf8S+KzAwIrYBTgBOpEXjlvQfpA+m5fOqt8Up6YOkWVq2Iv3vnyRpcJECLyMN4j4NmBgROwJXAd/o\nJ3EjaTNgfGW5P8R9CnBxzlmOBtbvSdx9NSE7FTg3Px4MvCppZWC5iPhNXn8TqXWhlWwL3AgQEXOA\nLcoWp1ddQbpwIX2jWAgMj4jZed0NtN75rfsu6cvF74E2+k/c9anTrgauAabTP2J/DBiUW8BXIX1b\nbtW4nwD2qCxv3inOMcCWwO0RsTAiXgQeBz7Z3GIuc53j3jsi5ubHg0g9Hi0ft6TVgG8D/1Z5TcvH\nDWwDrClpBjAO+Dk9iLt4QiZpvKS5kh6s/wU+ERGvSfoQ8GNgEqn78sXKpvNJb26tZAjwl8ryQknF\nz1FviIhXIuLlnGhfCRxFSk7qWvH8IulLwDMRMYPF8VbPcUvGnX0A2Bz4HHAw8N/0j9hfAtYBHiV9\n0TyDFr3W8ywsCyurOsc5BFiZju9zL/Eej79z3BHxJwBJWwMTge/z9vf3loo7f1adT+rhernyspaO\nO1sbeC4ixgC/Y3HOslRxFx9DFhEXkMYSdSBpY+AS4N8j4vb8wT2k8pKVgReaU8qmeZEUV92AiFhU\nqjC9TdLfAj8FzoqIyySdUnm6Fc8vpC74RZLGkMYKXgQMqzzfqnEDPAs8EhELgcckLQDWrDzfqrEf\nBtwYEUdJ+gjp2/NyledbNW5IY8fq6nG+SOu/lyNpb9LQk10j4llJrR73cODjpNb/FYANJJ0KzKK1\n44b03nZtfnwtaUz03Sxl3H2y9UXShqQurXERcTNARMwHXpO0Tm763xmY3cVu3ovuYPHUUiOBuV2/\n/L0r96/fBHw9In6UV9+fJ50H2IXWO79ExOiI2CEPgH0A2A+4odXjzm4njaVA0oeBlYBb8tgyaN3Y\nn2PxN+UXSF+E7+8HcQPc1+DavhvYVtJyklYB1gceKlXA3iBpX1LL2PYRUZ8i8C5aN+62iLgnIjbO\n4+a+ADwcEYfT2nHXzSZ/dgPbkeJb6uu8eAvZEpxIGix3ek6+XoiIPUjdHJeQEsmbI+LugmXsDVcB\nYyTdkZcPKFmYXnYk6RcpR0s6BqiRxh2cmQc+PgL8T8HyNdMRwLRWjzsirpM0StJdpK6sg4HfAOe3\neOynARdIuo00JnYScC+tHzc0uLYjoibpDFKC3kYa9P96yUIuS7nr7nTSXM1XSaoBv4iIb7Vw3Euc\n8ici/tTCcdcdQfp/Ppj05WtcRPxlaeP21ElmZmZmhfXJLkszMzOz/sQJmZmZmVlhTsjMzMzMCnNC\nZmZmZlaYEzIzMzOzwpyQmZmZmRXmhMysCSR9VNIiSWd3Wr9pXr9/D/c7Id8RHEn/1ZP95LLN6+lx\n34skjZY0Kz+eJml4D/bRK3UgaVblZqr1dW+Vt4vtxkr6Wg+PeUGeOaNHqttLmp6nvTOzpeCEzKx5\nngU+k292XLc38My72OfWpJsov1tLe0PCZXXckmoAETEhIu7rwfbNroPuztHmdJyqZWnsQMd5J3u8\nfUSMjYg/vot9mfVLffVO/Wat6CXgftLUGr/I68YAM+svkDQWOIH04fYU8C8R0Z5bsH5MmjJsRWB/\nYFVgN2AHSX/IuxgraSKwOjAlIs6X9GngZNK8gs8DX4yI5zqVbQVJlwMCngAOzHea3oI0MfIKwJ+B\nrwDrVo77IWDPiBgpacW8/20j4u7cGngLcBtpUu01cxkmR8QtklYCfgBsBAwETo6IyyX9M2mKpVWB\nj5Fm5ZjYuTIlfQP4POmL5U0RMSmvnwLsCAzNZd4zIp6R1A7cA3wQ+HplP7OAYyPitkb7zPPoXpq3\nAzgeeKVa93my+Pr+NgLOJE0NtTrwvYg4S9KxwEeATwBrAT+MiBMlLUealHlz0t3dV+sca6e4RwPf\nzudkaI7l4XxuapKeJt35v1Hdbgycl9ctAMYDewEfBq6XNCoinq8cax4whzTv6ijSvJzVut0L+FJl\n++1IsxCMJiVpDc+jpJPytu3AH4GfRcRFXcVt1urcQmbWXFcA/wSQk51fAa/n5WHAOcBuEbEp8Evg\nrMq27RExgpTcTI6IW4BrgGMqCcHy+TVjSVOQARxFSuy2JE1826h7bnXgtHzcJ4Fj8nQ355MSuC2A\nU4FpleMeHRGnAGvkpGUUad7G+hyNO5HmKz2dlHx8CvgscG5Oxr4J3JPXjwa+KWntvO1WwB7AJ4F/\nzEnOWyTtTEpgtsjxrClpnKR1gfUiYquIWD/Hsk/ebDXgxIgYDrzRuQKWsM99cjnm5XLuR0o4G9V9\n3YHACfk87Fg5DwAb53oZCUySNAT4KlCLiI2AQ0kTNHdlIilh3gI4KJfhEdK1c06eG7ZR3a5DSqi+\nm6+FM4EREXEy8Htgl2oyVnFdRGwArNKgbsd12v45Orbkve085i8dWwMbAP8AbNZNvGb9glvIzJqn\nRkqIpuTlvYHLgS/m5S2BORHxu7x8Hmnew7qb8t+HSB9yjfwMICJ+Lane0nINcLWkq0ktETMbbPdo\nRNyZH18MXAisR2oNu6bSzfq+yjb1dTeTWkO2Ic3bOFrSdcDTETFf0k6AJJ2QXz8w73cnUsvcgXn9\nCqQWHYBfRsQrpA2fIrWyVO1Eqq97czn+Jh/vEklHSJpAau0bSWrxq7urQexd7hO4AJgiaU3gOlIL\nZleOIHVNTyIlIitVnpsVEW8C7ZKeJSU525OSKSLiicpctkuyH6kl9PM5vvc1eE2jut0QmA5MlbRL\nflydQ3NJXZZ35bI92U3dtnX6Cx3P45Ok8zgGuCLXwwv5ujTr99xCZtZEEfEy8ICkUaQkppocDaDj\nh9kAOn5pWpD/1ljyh+fCBsc8jdRK8jhwiqQju9mujdSCNAB4MiKGR8RmpFajUQ22vYGUAGxL6ib7\nO1IL3fRKHDtGxGZ5P1uRksqBwL6V9VuzOOlcUNl/o3gHklr06mUbQUqahpMSxDbgSuDq6rYR8VqD\n8ne5z4h4EliflKiOAu7uYh/k4+4O/BqY3Om5RnHV6Phe/GY3+78d+BSp+3UKja+FRnV7Y0T8lNQi\nNQf4GjkR7MarAN3V7RIs6LTcRorPnz1mnfifwqz5rgS+Q+pSWlRZPwcYIWmtvPxl4NZu9rWQblq6\nJf0vMCQiziCNB2vUZbmhpE3y4/HADCCAVSVtm9cfBFxSOe7g/HgGaWzbmxFRHyd3KIsTsltJ3WxI\n2hCYS2qxuRU4JK9fA3gQeKe/9LsV2E/SSpIGkVoGP0dKPGdFxHnAo8Dfk5KTHu8zj8k7PiJ+kuMY\nlrsaq3VQ9WlSN+K1pNYvOv2Qo66+biYwTlKbpI+SkqeGJA0ldWkeExE3kuq9Hl/1WmhUt2tJuozU\nTTkNOJrF10K31xFd1+072b5uBrCXpMG5Hsey9D8qMWs5TsjMmu9a0iDpy/Jy/dd+z5CSsKslzSUN\n/j+4+poGZgKTJe3ZxWsmAxdKugeYABzb4DWPk8aNPQh8ADgpIl4njXf7nqQHSF1l4yvHPVLSnhEx\nH/gtMDs/dyvwckTUu7MOBUZK+hVpcPw+uaXwW6Rutbl5f0dERKPbb7wtroiYDvyElMQ+CNyXB4Vf\nDmyayzuTNEZvnSXtp7q+i31eROpyfRD4OekHAC9W66DT/o4D7sj1PQaYVylDo7imAvNJA/PPJSWs\nDeUxXj8EHpZ0L+lcrShpBdKPJ/bJCeRxNK7bE0nXy73Af5LGlEFKnq/PCWGjMkLXdVvffm26r+cb\nSNfKfaT/hf8nt8KZ9WdttZq/mJiZWXNIGkn6ccBFuSXyTuCAiHiocNHMinJCZmZmTZO7XS8B1iB1\n214YEd8vWyqz8pyQmZmZmRXmMWRmZmZmhTkhMzMzMyvMCZmZmZlZYU7IzMzMzApzQmZmZmZWmBMy\nMzMzs8L+CmDdIhIuSuohAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110e93a58>"
]
},
"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": 196,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 16633\n",
"4 4334\n",
"3 4159\n",
"5 4058\n",
"2 1653\n",
"0 1270\n",
"1 281\n",
"dtype: int64"
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.degree_hl.dropna().value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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UF6m9BDyemRMRsQUYAgZoXeR2KCK2Ag9FxCBwELhxNjZEkqR+Mu0Az8yfAR+c4qnLpqjd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f8f56d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = lsl_dr.degree_hl.hist(bins=7)"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10e5722b0>"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10e5c9828>"
]
},
"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": 199,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.19320977529932754"
]
},
"execution_count": 199,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age_int<6).mean()"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.12798644141927723"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age<6).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Counts by year"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>redcap_event_name</th>\n",
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" <th>sib</th>\n",
" <th>_mother_ed</th>\n",
" <th>father_ed</th>\n",
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" <th>...</th>\n",
" <th>age_test</th>\n",
" <th>domain</th>\n",
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" <th>score</th>\n",
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" <th>test_type</th>\n",
" <th>academic_year_start</th>\n",
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" <tr>\n",
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" <td>2013-2014</td>\n",
" <td>0</td>\n",
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" <td>2</td>\n",
" <td>0</td>\n",
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" <td>6</td>\n",
" <td>6</td>\n",
" <td>9</td>\n",
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" <td>24</td>\n",
" <td>Language</td>\n",
" <td>0062</td>\n",
" <td>50</td>\n",
" <td>PLS</td>\n",
" <td>receptive</td>\n",
" <td>2013</td>\n",
" <td>Bilateral CI</td>\n",
" <td>2</td>\n",
" <td>False</td>\n",
" </tr>\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",
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" <td>44</td>\n",
" <td>Articulation</td>\n",
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" <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>...</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>5440 rows × 76 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year hl male _race \\\n",
"study_id \n",
"00624-2010-0049 initial_assessment_arm_1 2013-2014 0 1 2 \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",
"... ... ... .. ... ... \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",
"00624-2010-0049 0 NaN 6 6 9 \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",
"... ... ... ... ... ... \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",
"00624-2010-0049 ... 24 Language 0062 50 \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",
"... ... ... ... ... ... \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",
"00624-2010-0049 PLS receptive 2013 Bilateral CI \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",
"... ... ... ... ... \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",
"00624-2010-0049 2 False \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",
"... ... ... \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",
"[5440 rows x 76 columns]"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').first()"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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my+FniyLiSTtvRMTTaE7TKkmS9rAuS+SnAu+PiF+l2VZ+HfDCoqOSJEmddNlr/WrgoRFx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\nVDGLXJKkilnkkiRV7P8DOhdeO2iy4QQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f3220f0>"
]
},
"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": 203,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10f9f9198>"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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MPAaYFxEnRcSBwNnA4cDxwAURsaCiMUuS1FfKbKZ+H3ApcDfFcf+HZuaaxm3XAMcBzwTW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\nmv1/OidjXXLXpXYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f9eb978>"
]
},
"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": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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