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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,\n",
" 'na_values':[999, 9999]})"
]
},
{
"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": [
"0101-2002-0101\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",
" 'celfp_ss_ss', 'celfp_ws_ss', 'celfp_ev_ss', 'celfp_fd_ss',\n",
" 'celfp_rs_ss', 'celfp_bc_ss', 'celfp_wcr_ss', 'celfp_wce_ss',\n",
" 'celfp_wct_ss']\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', 'academic_year_rv',\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/plain": [
"2013.0 2501\n",
"2012.0 2429\n",
"2014.0 2170\n",
"2011.0 1901\n",
"2010.0 1609\n",
"2009.0 1021\n",
"2015.0 931\n",
"2008.0 436\n",
"2007.0 277\n",
"2006.0 189\n",
"2005.0 138\n",
"2004.0 89\n",
"2003.0 65\n",
"2002.0 36\n",
"2001.0 24\n",
"2000.0 12\n",
"1999.0 12\n",
"1998.0 9\n",
"15.0 3\n",
"1997.0 2\n",
"1995.0 1\n",
"Name: academic_year_rv, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic_raw.academic_year_rv.value_counts()"
]
},
{
"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>academic_year_rv</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>...</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>14665</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>2010.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>65.0</td>\n",
" <td>0.0</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>14666</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>2011.0</td>\n",
" <td>0.0</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>3.0</td>\n",
" <td>5.0</td>\n",
" <td>77.0</td>\n",
" <td>2.0</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>14667</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>2012.0</td>\n",
" <td>0.0</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>3.0</td>\n",
" <td>5.0</td>\n",
" <td>89.0</td>\n",
" <td>2.0</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>14668</th>\n",
" <td>1147-2010-0064</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>2013.0</td>\n",
" <td>0.0</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>4.0</td>\n",
" <td>5.0</td>\n",
" <td>101.0</td>\n",
" <td>2.0</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",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 47 columns</p>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name academic_year \\\n",
"14665 1147-2010-0064 initial_assessment_arm_1 2010-2011 \n",
"14666 1147-2010-0064 year_1_complete_71_arm_1 2011-2012 \n",
"14667 1147-2010-0064 year_2_complete_71_arm_1 2012-2013 \n",
"14668 1147-2010-0064 year_3_complete_71_arm_1 2013-2014 \n",
"\n",
" academic_year_rv hl gender race prim_lang sib mother_ed \\\n",
"14665 2010.0 0.0 0.0 0.0 0.0 1.0 3.0 \n",
"14666 2011.0 0.0 NaN NaN NaN NaN NaN \n",
"14667 2012.0 0.0 NaN NaN NaN NaN NaN \n",
"14668 2013.0 0.0 NaN NaN NaN NaN NaN \n",
"\n",
" ... sle_fo a_fo fam_age family_inv att_days_sch \\\n",
"14665 ... 3.0 6.0 65.0 0.0 NaN \n",
"14666 ... 3.0 5.0 77.0 2.0 NaN \n",
"14667 ... 3.0 5.0 89.0 2.0 NaN \n",
"14668 ... 4.0 5.0 101.0 2.0 NaN \n",
"\n",
" att_days_st2_417 att_days_hr demo_ses school_lunch medicaid \n",
"14665 NaN NaN NaN NaN NaN \n",
"14666 NaN NaN NaN NaN NaN \n",
"14667 NaN NaN NaN NaN NaN \n",
"14668 NaN NaN NaN NaN NaN \n",
"\n",
"[4 rows x 47 columns]"
]
},
"execution_count": 13,
"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": 14,
"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>academic_year_rv</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>...</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>0101-2002-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2002-2003</td>\n",
" <td>2002.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>54.0</td>\n",
" <td>2.0</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>1</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2003-2004</td>\n",
" <td>2003.0</td>\n",
" <td>0.0</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>4.0</td>\n",
" <td>4.0</td>\n",
" <td>80.0</td>\n",
" <td>1.0</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>2</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2004-2005</td>\n",
" <td>2004.0</td>\n",
" <td>0.0</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>4.0</td>\n",
" <td>4.0</td>\n",
" <td>80.0</td>\n",
" <td>2.0</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>3</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2005-2006</td>\n",
" <td>2005.0</td>\n",
" <td>0.0</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>5.0</td>\n",
" <td>5.0</td>\n",
" <td>96.0</td>\n",
" <td>3.0</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>4</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_4_complete_71_arm_1</td>\n",
" <td>2006-2007</td>\n",
" <td>2006.0</td>\n",
" <td>0.0</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>5.0</td>\n",
" <td>5.0</td>\n",
" <td>109.0</td>\n",
" <td>2.0</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",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 47 columns</p>\n",
"</div>"
],
"text/plain": [
" study_id redcap_event_name academic_year academic_year_rv \\\n",
"0 0101-2002-0101 initial_assessment_arm_1 2002-2003 2002.0 \n",
"1 0101-2002-0101 year_1_complete_71_arm_1 2003-2004 2003.0 \n",
"2 0101-2002-0101 year_2_complete_71_arm_1 2004-2005 2004.0 \n",
"3 0101-2002-0101 year_3_complete_71_arm_1 2005-2006 2005.0 \n",
"4 0101-2002-0101 year_4_complete_71_arm_1 2006-2007 2006.0 \n",
"\n",
" hl gender race prim_lang sib mother_ed ... sle_fo a_fo \\\n",
"0 0.0 0.0 0.0 0.0 1.0 6.0 ... 2.0 2.0 \n",
"1 0.0 NaN NaN NaN NaN NaN ... 4.0 4.0 \n",
"2 0.0 NaN NaN NaN NaN NaN ... 4.0 4.0 \n",
"3 0.0 NaN NaN NaN NaN NaN ... 5.0 5.0 \n",
"4 0.0 NaN NaN NaN NaN NaN ... 5.0 5.0 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr demo_ses \\\n",
"0 54.0 2.0 NaN NaN NaN NaN \n",
"1 80.0 1.0 NaN NaN NaN NaN \n",
"2 80.0 2.0 NaN NaN NaN NaN \n",
"3 96.0 3.0 NaN NaN NaN NaN \n",
"4 109.0 2.0 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 47 columns]"
]
},
"execution_count": 14,
"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": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n",
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:3: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"demographic = demographic_raw.sort(columns='redcap_event_name').groupby('study_id').transform(\n",
" lambda recs: recs.fillna(method='ffill'))#.reset_index()\n",
"demographic[\"study_id\"] = demographic_raw.sort(columns='redcap_event_name').study_id"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Random check to make sure this worked"
]
},
{
"cell_type": "code",
"execution_count": 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>academic_year_rv</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>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>14665</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>2010.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>6.0</td>\n",
" <td>65.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14666</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>2011.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>77.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14667</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>2012.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>89.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14668</th>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>2013.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>101.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1147-2010-0064</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 47 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year academic_year_rv hl gender \\\n",
"14665 initial_assessment_arm_1 2010-2011 2010.0 0.0 0.0 \n",
"14666 year_1_complete_71_arm_1 2011-2012 2011.0 0.0 0.0 \n",
"14667 year_2_complete_71_arm_1 2012-2013 2012.0 0.0 0.0 \n",
"14668 year_3_complete_71_arm_1 2013-2014 2013.0 0.0 0.0 \n",
"\n",
" race prim_lang sib mother_ed father_ed ... a_fo \\\n",
"14665 0.0 0.0 1.0 3.0 3.0 ... 6.0 \n",
"14666 0.0 0.0 1.0 3.0 3.0 ... 5.0 \n",
"14667 0.0 0.0 1.0 3.0 3.0 ... 5.0 \n",
"14668 0.0 0.0 1.0 3.0 3.0 ... 5.0 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"14665 65.0 0.0 NaN NaN NaN \n",
"14666 77.0 2.0 NaN NaN NaN \n",
"14667 89.0 2.0 NaN NaN NaN \n",
"14668 101.0 2.0 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid study_id \n",
"14665 NaN NaN NaN 1147-2010-0064 \n",
"14666 NaN NaN NaN 1147-2010-0064 \n",
"14667 NaN NaN NaN 1147-2010-0064 \n",
"14668 NaN NaN NaN 1147-2010-0064 \n",
"\n",
"[4 rows x 47 columns]"
]
},
"execution_count": 16,
"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": 17,
"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>academic_year_rv</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>a_fo</th>\n",
" <th>fam_age</th>\n",
" <th>family_inv</th>\n",
" <th>att_days_sch</th>\n",
" <th>att_days_st2_417</th>\n",
" <th>att_days_hr</th>\n",
" <th>demo_ses</th>\n",
" <th>school_lunch</th>\n",
" <th>medicaid</th>\n",
" <th>study_id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2002-2003</td>\n",
" <td>2002.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>54.0</td>\n",
" <td>2.0</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>0101-2002-0101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8001</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>2009.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>5.0</td>\n",
" <td>138.0</td>\n",
" <td>0.0</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>0628-2005-1814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7995</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>2009.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>3.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>78.0</td>\n",
" <td>0.0</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>0628-2005-1756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7990</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>2009.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>77.0</td>\n",
" <td>0.0</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>0628-2005-1744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7987</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2009-2010</td>\n",
" <td>2009.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>4.0</td>\n",
" <td>118.0</td>\n",
" <td>4.0</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>0628-2005-1741</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 47 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year academic_year_rv hl gender \\\n",
"0 initial_assessment_arm_1 2002-2003 2002.0 0.0 0.0 \n",
"8001 initial_assessment_arm_1 2009-2010 2009.0 0.0 0.0 \n",
"7995 initial_assessment_arm_1 2009-2010 2009.0 0.0 0.0 \n",
"7990 initial_assessment_arm_1 2009-2010 2009.0 0.0 0.0 \n",
"7987 initial_assessment_arm_1 2009-2010 2009.0 0.0 1.0 \n",
"\n",
" race prim_lang sib mother_ed father_ed ... a_fo \\\n",
"0 0.0 0.0 1.0 6.0 6.0 ... 2.0 \n",
"8001 0.0 0.0 1.0 5.0 3.0 ... 5.0 \n",
"7995 6.0 0.0 0.0 4.0 3.0 ... 4.0 \n",
"7990 1.0 0.0 1.0 3.0 4.0 ... 4.0 \n",
"7987 1.0 0.0 2.0 6.0 6.0 ... 4.0 \n",
"\n",
" fam_age family_inv att_days_sch att_days_st2_417 att_days_hr \\\n",
"0 54.0 2.0 NaN NaN NaN \n",
"8001 138.0 0.0 NaN NaN NaN \n",
"7995 78.0 0.0 NaN NaN NaN \n",
"7990 77.0 0.0 NaN NaN NaN \n",
"7987 118.0 4.0 NaN NaN NaN \n",
"\n",
" demo_ses school_lunch medicaid study_id \n",
"0 NaN NaN NaN 0101-2002-0101 \n",
"8001 NaN NaN NaN 0628-2005-1814 \n",
"7995 NaN NaN NaN 0628-2005-1756 \n",
"7990 NaN NaN NaN 0628-2005-1744 \n",
"7987 NaN NaN NaN 0628-2005-1741 \n",
"\n",
"[5 rows x 47 columns]"
]
},
"execution_count": 17,
"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": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test_type expressive receptive\n",
"test_name \n",
"CELF-4 627 545\n",
"CELF-P2 1511 1516\n",
"OWLS 1093 1099\n",
"PLS 3572 3584\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": 19,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"language[\"school\"] = language.study_id.str.slice(0,4)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"language_subtest = language[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \n",
" \"test_name\", \"school\", \"age_test\", \n",
" 'celfp_ss_ss', 'celfp_ws_ss', \n",
" 'celfp_ev_ss', 'celfp_fd_ss',\n",
" 'celfp_rs_ss', 'celfp_bc_ss', \n",
" 'celfp_wcr_ss', 'celfp_wce_ss',\n",
" 'celfp_wct_ss']]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>0101-2002-0101</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>0101-2002-0101</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 0101-2002-0101 initial_assessment_arm_1 51 receptive PLS \n",
"5 0101-2002-0101 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": 21,
"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": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Goldman 5437\n",
"Arizonia 503\n",
"Arizonia and Goldman 73\n",
"Name: test_type, dtype: int64\n",
"There are 0 null values for test_type\n"
]
}
],
"source": [
"# Test type\n",
"articulation[\"test_type\"] = None\n",
"ARIZ = articulation.aaps_ss.notnull()\n",
"GF = articulation.gf2_ss.notnull()\n",
"articulation = articulation[ARIZ | GF]\n",
"articulation.loc[(ARIZ & GF), \"test_type\"] = \"Arizonia and Goldman\"\n",
"articulation.loc[(ARIZ & ~GF), \"test_type\"] = \"Arizonia\"\n",
"articulation.loc[(~ARIZ & GF), \"test_type\"] = \"Goldman\"\n",
"\n",
"print(articulation.test_type.value_counts())\n",
"print(\"There are {0} null values for test_type\".format(sum(articulation[\"test_type\"].isnull())))\n",
"\n",
"# Test score (Arizonia if both)\n",
"articulation[\"score\"] = articulation.aaps_ss\n",
"articulation.loc[(~ARIZ & GF), \"score\"] = articulation.gf2_ss[~ARIZ & GF]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `school` variable was added, which is the first four columns of the `study_id`:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 6011.000000\n",
"mean 68.857095\n",
"std 30.613506\n",
"min 23.000000\n",
"25% 47.000000\n",
"50% 60.000000\n",
"75% 81.000000\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": 25,
"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>0101-2002-0101</td>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>Goldman</td>\n",
" <td>78.0</td>\n",
" <td>0101</td>\n",
" <td>80.0</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>72.0</td>\n",
" <td>0101</td>\n",
" <td>44.0</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>97.0</td>\n",
" <td>0101</td>\n",
" <td>54.0</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>75.0</td>\n",
" <td>0101</td>\n",
" <td>53.0</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>80.0</td>\n",
" <td>0101</td>\n",
" <td>66.0</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 0101-2002-0101 year_1_complete_71_arm_1 Goldman 78.0 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 Goldman 72.0 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 Goldman 97.0 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 Goldman 75.0 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 Goldman 80.0 0101 \n",
"\n",
" age_test domain \n",
"1 80.0 Articulation \n",
"9 44.0 Articulation \n",
"10 54.0 Articulation \n",
"14 53.0 Articulation \n",
"15 66.0 Articulation "
]
},
"execution_count": 25,
"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": 26,
"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": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False 11986\n",
"True 2688\n",
"Name: non_english, dtype: int64\n",
"There are 622 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": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_mother_ed:\n",
"6.0 5340\n",
"4.0 3140\n",
"3.0 2127\n",
"5.0 1696\n",
"2.0 1489\n",
"1.0 498\n",
"0.0 222\n",
"Name: _mother_ed, dtype: int64\n",
"mother_ed:\n",
"1.0 3616\n",
"2.0 3140\n",
"3.0 1696\n",
"0.0 720\n",
"Name: mother_ed, dtype: int64\n",
"\n",
"There are 6124 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": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(15296, 49)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.shape"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0 11224\n",
"1.0 2526\n",
"Name: secondary_diagnosis, dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.secondary_diagnosis.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.18370909090909091"
]
},
"execution_count": 32,
"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": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 3394 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": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0 10190\n",
"2.0 609\n",
"4.0 386\n",
"12.0 202\n",
"6.0 186\n",
"10.0 160\n",
"8.0 124\n",
"14.0 42\n",
"16.0 3\n",
"Name: premature_weeks, dtype: int64"
]
},
"execution_count": 34,
"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": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0 5221\n",
"0.0 4497\n",
"7.0 1588\n",
"5.0 1056\n",
"2.0 529\n",
"6.0 433\n",
"8.0 76\n",
"9.0 70\n",
"4.0 31\n",
"3.0 26\n",
"10.0 4\n",
"Name: tech_ad, dtype: int64"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_ad.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"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": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.0 6919\n",
"3.0 4579\n",
"0.0 1925\n",
"4.0 61\n",
"1.0 18\n",
"Name: tech_left, dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.tech_left.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.0 6841\n",
"3.0 5006\n",
"0.0 1588\n",
"4.0 70\n",
"1.0 26\n",
"Name: tech_right, dtype: int64"
]
},
"execution_count": 38,
"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": 39,
"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": 40,
"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": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['redcap_event_name', 'academic_year', 'academic_year_rv', 'hl', 'male',\n",
" 'race', 'prim_lang', 'sib', '_mother_ed', 'father_ed', 'premature_age',\n",
" 'onset_1', 'age_amp', 'age_int', 'age', 'synd_cause', 'etiology',\n",
" 'etiology_2', 'hearing_changes', 'ae', 'ad_250', 'ad_500',\n",
" 'degree_hl_ad', 'type_hl_ad', 'tech_ad', 'age_ci', 'as_250', 'as_500',\n",
" 'degree_hl_as', 'type_hl_as', 'tech_as', 'age_ci_2', 'time',\n",
" 'age_disenrolled', 'funct_out_age', 'slc_fo', 'sle_fo', 'a_fo',\n",
" 'fam_age', 'family_inv', 'att_days_sch', 'att_days_st2_417',\n",
" 'att_days_hr', 'demo_ses', 'school_lunch', 'medicaid', 'study_id',\n",
" 'non_english', 'mother_ed', 'secondary_diagnosis', 'premature_weeks',\n",
" 'tech_right', 'tech_left', 'degree_hl'],\n",
" dtype='object')"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.columns"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"oad:\n",
"0 4770\n",
"1 4\n",
"Name: oad, dtype: int64\n",
"There are 1711 null values for OAD\n",
"\n",
"hearing_aid:\n",
"2 2249\n",
"0 1669\n",
"1 824\n",
"Name: hearing_aid, dtype: int64\n",
"There are 1765 null values for hearing_aid\n",
"\n",
"cochlear:\n",
"0 3203\n",
"2 935\n",
"1 636\n",
"Name: cochlear, dtype: int64\n",
"There are 1711 null values for cochlear\n",
"15296\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": 43,
"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": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3718, 5632, 1437, 2149)"
]
},
"execution_count": 44,
"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": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"unilateral_ci 636\n",
"bilateral_ci 935\n",
"bilateral_ha 2249\n",
"bimodal 384\n",
"dtype: int64"
]
},
"execution_count": 45,
"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": 46,
"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": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6 5632\n",
"3 3718\n",
"4 1437\n",
"1 1034\n",
"0 692\n",
"8 13\n",
"2 12\n",
"7 5\n",
"5 1\n",
"Name: implant_category, dtype: int64"
]
},
"execution_count": 47,
"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": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 15. , 80. , 14. , 62. , 2. , 49. , 19. , 9. ,\n",
" 18. , 4. , 0. , 10. , 12. , 1. , 31. , 16. ,\n",
" 26. , 61. , 46. , 24. , 36. , 21. , 52. , 30. ,\n",
" 7. , 51. , 8. , 3. , 6. , 17. , 50. , 23. ,\n",
" 42. , 37. , 33. , 60. , 13. , nan, 22. , 28. ,\n",
" 82. , 34. , 35. , 38. , 95. , 5. , 59. , 25. ,\n",
" 48. , 1.5, 41. , 53. , 88. , 29. , 27. , 39. ,\n",
" 65. , 64. , 47. , 79. , 97. , 96. , 107. , 77. ,\n",
" 74. , 11. , 84. , 20. , 45. , 32. , 81. , 55. ,\n",
" 58. , 70. , 154. , 54. , 57. , 72. , 43. , 83. ,\n",
" 78. , 116. , 40. , 44. , 119. , 63. , 66. , 140. ,\n",
" 56. , 87. , 76. , 68. , 92. , 86. , 126. , 85. ,\n",
" 133. , 103. , 67. , 71. , 2.5, 98. , 75. , 0.5,\n",
" 89. , 152. ])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.onset_1.unique()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3864"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.age_diag.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"demographic['sex'] = demographic.male.replace({0:'Female', 1:'Male'})"
]
},
{
"cell_type": "code",
"execution_count": 52,
"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": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5522, 64)"
]
},
"execution_count": 162,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Child has another diagnosed disability"
]
},
{
"cell_type": "code",
"execution_count": 53,
"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": 54,
"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": 55,
"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": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"_race:\n",
"0.0 7955\n",
"2.0 2649\n",
"1.0 1407\n",
"3.0 1074\n",
"6.0 751\n",
"8.0 542\n",
"7.0 241\n",
"4.0 66\n",
"5.0 37\n",
"Name: _race, dtype: int64\n",
"race:\n",
"0.0 7955\n",
"2.0 2649\n",
"1.0 1407\n",
"4.0 1396\n",
"3.0 1074\n",
"Name: race, dtype: int64\n",
"There are 815 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": 57,
"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": 58,
"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": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['2002-2003', '2009-2010', '2011-2012', '2009-2011', '2006-2007',\n",
" '2007-2008', '2008-2009', '2014-2015', '2013-2014', '2012-2013',\n",
" nan, '2015-2016', '2010-2011', '2014', '2005-2006', '2004-2005',\n",
" '2003-2004',\n",
" '2010-2011 2010-2011',\n",
" '2011', '2010', '2009', '2012', '2013', '1995-1996', '1999-2000',\n",
" '2000-2001', '1998-1999', '1997-1998', '2001-2002', '2014-15',\n",
" '2015-2015', '2015', '2041-2015', '2015-2106', '22014-2015',\n",
" '2014-1015'], dtype=object)"
]
},
"execution_count": 59,
"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": 60,
"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": 61,
"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": 62,
"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": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11a63eeb8>"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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nrq4ueb1e5ebmKhQKSZJCoVD0kjwAALg4zvqI/HTKy8u1evVqRSIRZWVlqaioSA6HQ6Wl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\nAGAwQg4AgMEIOQAABiPkAAAY7P8DnPaNWKGb4aYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a63ecc0>"
]
},
"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": 64,
"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": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"EVT 3881\n",
"EOWPVT 2784\n",
"EOWPVT and EVT 149\n",
"Name: test_type, dtype: int64"
]
},
"execution_count": 65,
"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": 66,
"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": 67,
"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": 68,
"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>0101-2002-0101</td>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>58.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>54.0</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>84.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>80.0</td>\n",
" <td>Expressive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>90.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>113.0</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>90.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>53.0</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>87.0</td>\n",
" <td>EOWPVT</td>\n",
" <td>0101</td>\n",
" <td>66.0</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 0101-2002-0101 initial_assessment_arm_1 58.0 EOWPVT 0101 \n",
"2 0101-2002-0101 year_2_complete_71_arm_1 84.0 EOWPVT 0101 \n",
"5 0101-2002-0101 year_5_complete_71_arm_1 90.0 EOWPVT 0101 \n",
"14 0101-2004-0101 year_2_complete_71_arm_1 90.0 EOWPVT 0101 \n",
"15 0101-2004-0101 year_3_complete_71_arm_1 87.0 EOWPVT 0101 \n",
"\n",
" age_test domain \n",
"0 54.0 Expressive Vocabulary \n",
"2 80.0 Expressive Vocabulary \n",
"5 113.0 Expressive Vocabulary \n",
"14 53.0 Expressive Vocabulary \n",
"15 66.0 Expressive Vocabulary "
]
},
"execution_count": 68,
"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": 69,
"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": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.columns"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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": 71,
"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": 72,
"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": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 23 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": 74,
"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>0101-2002-0101</td>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>90.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>80.0</td>\n",
" <td>Receptive Vocabulary</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0101-2002-0101</td>\n",
" <td>year_5_complete_71_arm_1</td>\n",
" <td>101.0</td>\n",
" <td>ROWPVT</td>\n",
" <td>0101</td>\n",
" <td>113.0</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>55.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>44.0</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>80.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>54.0</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>101.0</td>\n",
" <td>PPVT</td>\n",
" <td>0101</td>\n",
" <td>68.0</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 0101-2002-0101 year_2_complete_71_arm_1 90.0 PPVT 0101 \n",
"5 0101-2002-0101 year_5_complete_71_arm_1 101.0 ROWPVT 0101 \n",
"9 0101-2003-0102 initial_assessment_arm_1 55.0 PPVT 0101 \n",
"10 0101-2003-0102 year_1_complete_71_arm_1 80.0 PPVT 0101 \n",
"11 0101-2003-0102 year_2_complete_71_arm_1 101.0 PPVT 0101 \n",
"\n",
" age_test domain \n",
"2 80.0 Receptive Vocabulary \n",
"5 113.0 Receptive Vocabulary \n",
"9 44.0 Receptive Vocabulary \n",
"10 54.0 Receptive Vocabulary \n",
"11 68.0 Receptive Vocabulary "
]
},
"execution_count": 74,
"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": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3108,)"
]
},
"execution_count": 75,
"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": 76,
"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": 77,
"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": 78,
"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>academic_year_rv</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>...</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>39154</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>2011.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>Male</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>162</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>0102</td>\n",
" <td>84</td>\n",
" <td>NaN</td>\n",
" <td>ROWPVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39155</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</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>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NaN</td>\n",
" <td>203</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>1147</td>\n",
" <td>95</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39156</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>119</td>\n",
" <td>Articulation</td>\n",
" <td>0624</td>\n",
" <td>102</td>\n",
" <td>NaN</td>\n",
" <td>Goldman</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39157</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>119</td>\n",
" <td>Expressive Vocabulary</td>\n",
" <td>0624</td>\n",
" <td>96</td>\n",
" <td>NaN</td>\n",
" <td>EVT</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39158</th>\n",
" <td>year_9_complete_71_arm_1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>6.0</td>\n",
" <td>...</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>119</td>\n",
" <td>Receptive Vocabulary</td>\n",
" <td>0624</td>\n",
" <td>82</td>\n",
" <td>NaN</td>\n",
" <td>PPVT</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 74 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year academic_year_rv hl male \\\n",
"39154 year_9_complete_71_arm_1 2011-2012 2011.0 0.0 1.0 \n",
"39155 year_9_complete_71_arm_1 NaN NaN 0.0 NaN \n",
"39156 year_9_complete_71_arm_1 NaN NaN 0.0 0.0 \n",
"39157 year_9_complete_71_arm_1 NaN NaN 0.0 0.0 \n",
"39158 year_9_complete_71_arm_1 NaN NaN 0.0 0.0 \n",
"\n",
" _race prim_lang sib _mother_ed father_ed ... sex \\\n",
"39154 0.0 0.0 3.0 6.0 6.0 ... Male \n",
"39155 NaN NaN NaN NaN NaN ... NaN \n",
"39156 2.0 0.0 1.0 6.0 6.0 ... Female \n",
"39157 2.0 0.0 1.0 6.0 6.0 ... Female \n",
"39158 2.0 0.0 1.0 6.0 6.0 ... Female \n",
"\n",
" known_synd synd_or_disab race age_test domain \\\n",
"39154 0.0 0.0 0.0 162 Receptive Vocabulary \n",
"39155 0.0 0.0 NaN 203 Expressive Vocabulary \n",
"39156 0.0 0.0 2.0 119 Articulation \n",
"39157 0.0 0.0 2.0 119 Expressive Vocabulary \n",
"39158 0.0 0.0 2.0 119 Receptive Vocabulary \n",
"\n",
" school score test_name test_type \n",
"39154 0102 84 NaN ROWPVT \n",
"39155 1147 95 NaN EVT \n",
"39156 0624 102 NaN Goldman \n",
"39157 0624 96 NaN EVT \n",
"39158 0624 82 NaN PPVT \n",
"\n",
"[5 rows x 74 columns]"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.tail()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2013 6952\n",
"2012 6641\n",
"2014 6144\n",
"2011 5256\n",
"2010 4457\n",
"nan 3164\n",
"2009 2502\n",
"2015 1646\n",
"2008 827\n",
"2007 536\n",
"2006 345\n",
"2005 286\n",
"2004 172\n",
"2003 90\n",
"2002 47\n",
"2001 37\n",
"1998 16\n",
"1999 16\n",
"2000 12\n",
"1997 6\n",
"2201 5\n",
"2041 1\n",
"1995 1\n",
"Name: academic_year_start, dtype: int64"
]
},
"execution_count": 79,
"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": 80,
"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": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Pk5w8VlFRUWGoCOidkALf7XbL6/UGb48aNUpud0gHB7Rv3z7NmjVLv/71rxUVFaXi4mK5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\nCHwAACxA4AMAYAECHwAAC/w/zFSWZTgPfj4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118341438>"
]
},
"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": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AgN4TVOBPnDhREydOtLoWAABgkaACf+rUqdqxY4c++eQTnX/++dq1a9cxB/ABfa2jo0Pb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118a615f8>"
]
},
"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": 83,
"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": 163,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(39159, 79)"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.shape"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5898,)"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5898,)"
]
},
"execution_count": 86,
"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": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr.score = lsl_dr.score.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"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": 89,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['age_year'] = np.floor(lsl_dr.age/12.)"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Expressive Vocabulary', 'Language', 'Articulation',\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.dropna().unique()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tech_class\n",
"Bilateral CI 0.45\n",
"Bilateral HA 0.58\n",
"Bimodal 0.50\n",
"Name: prim_lang, dtype: float64"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').prim_lang.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['non_profound'] = lsl_dr.degree_hl<6"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"tech_class\n",
"Bilateral CI 0.08\n",
"Bilateral HA 0.87\n",
"Bimodal 0.31\n",
"Name: non_profound, dtype: float64"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('tech_class').non_profound.mean().round(2)"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['age_test_year'] = -999\n",
"lsl_dr.loc[lsl_dr.age_test.notnull(), 'age_test_year'] = (lsl_dr.age_test/12).dropna().astype(int)\n",
"lsl_dr.loc[lsl_dr.age_test_year==-999, 'age_test_year'] = np.nan"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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vvPEqS5cu69TXaOsiZh1dL4QQou+Seqp1EmyEED1SyXvv4tr6NQU6LYFAsFO2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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ad7b080>"
]
},
"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": "markdown",
"metadata": {},
"source": [
"## PPVT"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11ad41be0>"
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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eAFAY8QaAwog3ABRGvAGgMOINAIURbwAojHgDQGHEGwAKI94AUBjxBoDCiDcAFEa8AaAw\n4g0AhRFvACiMeANAYZrrPQCnl6HDh7Nt21/qPcZxdXZ+N01NTfUeA+CYxJtv1F/37sjSVTvTetZH\n9R7lqPZ99kl++eAN6erqrvcoAMck3nzjWs86N5VJ59V7DIBiec8bAAoj3gBQGPEGgMKINwAURrwB\noDDiDQCFEW8AKIx4A0BhxBsACiPeAFAY8QaAwog3ABRGvAGgMOINAIURbwAojHgDQGHEGwAKI94A\nUBjxBoDCiDcAFEa8AaAw4g0AhRFvACiMeANAYcQbAAoj3gBQGPEGgMKINwAURrwBoDDiDQCFaa73\nAMDoHDp0KFu3bhmTv3v37kp27ap+7b+ns/O7aWpqOgkTAUcj3lCYrVu3ZN6/vJHWs86t9yhHte+z\nT/LLB29IV1d3vUeBU5Z4Q4Fazzo3lUnn1XsMoE685w0AhfHKG44wdPhwtm37S73HOK7xPh8w9sQb\njvDXvTuydNXOtJ71Ub1HOaZP//cH+fb5l9R7DKCOxBu+ZLy/n7zvs4/rPQJQZ+INnFQlvPXgV9ko\nnXgDJ9V4f+vBr7JxKhjzeA8NDWXx4sX58MMP09LSkp///Oe54IILxvqwQB2N97ceoHRjHu81a9bk\n4MGD+e1vf5uNGzdmyZIlWb58+VgfFuCoXNbnVDDm8d6wYUOuueaaJMnll1+e9957b6wPCXBM4/2y\nfu3//p/8ZNaV+ad/mjzqnz1Zt7c9nkOHDiVpSFPT+L1NyOnw5GfM412tVtPe3v73AzY35/Dhw2ls\nPPbCN3z2fg59fnisRzshh6rbs695Yr3HOKa/7t2VpKHeYxyT+b6+8T5jCfOd2f7teo9xTPuru/PU\nv/2PnFE5p96jHNVnH2/Jt9rOHrfz7a/uyr8++V9P+c80jHm8K5VKarXa8PcjhTtJ3ljx6FiPBQDF\nGvPrHt///vczMDCQJPnjH/+Yiy66aKwPCQCntIahoaGhsTzAkZ82T5IlS5bkO9/5zlgeEgBOaWMe\nbwDg5Bq/HxcEAI5KvAGgMOINAIURbwAozLj5j0ncA718N998cyqVSpLk/PPPz9NPP13niRjJxo0b\n8+yzz2blypXZtm1bHn744TQ2Nqa7uzuLFi2q93iM4Mj1++CDD3Lvvfems7MzSTJ79uxcd9119R2Q\no/r888/z6KOPZvv27RkcHMzcuXNz4YUXjurxN27i7R7oZTt48GCS5IUXXqjzJHxVK1asyOuvv562\ntrYkf/s1zvnz56enpyeLFi3KmjVrMn369DpPybF8ef3ee++93HPPPbn77rvrOxgjeuONNzJp0qT8\n4he/yJ49e3LjjTfm4osvHtXjb9xcNncP9LJt3rw5+/btS39/f+6+++5s3Lix3iMxgsmTJ2fZsmXD\n37///vvp6elJkvT29mb9+vX1Go2v4Gjrt3bt2txxxx1ZuHBh9u3bV8fpOJ7rrrsu8+bNS/K3e8U3\nNTVl06ZNo3r8jZt4H+se6JThjDPOSH9/f37zm99k8eLF+clPfmL9xrkZM2Z84T9vOPKWD21tbdm7\nd289xuIr+vL6XX755XnooYfy4osv5oILLsivfvWrOk7H8Zx55plpbW1NtVrNvHnz8sADD4z68Tdu\n4n0i90Bn/Ojs7MwNN9ww/PXZZ5+dHTt21HkqRuPIx1utVsvEieP3P+DhH02fPj2XXnppkr+FffPm\nzXWeiOP56KOPctddd+Wmm27K9ddfP+rH37ipo3ugl+2VV17JM888kyT5+OOPU6vV0tHRUeepGI1L\nL70077zzTpJk3bp1mTp1ap0nYjT6+/vzpz/9KUmyfv36XHbZZXWeiGPZuXNn+vv78+CDD+amm25K\nklxyySWjevyNmw+szZgxI2+99VZmzZqV5G8fnqEct956ax555JH09fWlsbExTz/9tCsnhVmwYEEe\nf/zxDA4OpqurKzNnzqz3SIzC4sWL8+STT2bChAnp6OjIE088Ue+ROIbnn38+e/bsyfLly7Ns2bI0\nNDRk4cKFeeqpp77y48+9zQGgMF4aAUBhxBsACiPeAFAY8QaAwog3ABRGvAGgMOINAIX5f4qCP7p3\nU9tyAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ad786d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ppvt_only = lsl_dr[lsl_dr.test_type=='PPVT']\n",
"ppvt_only.age_year.hist()"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ppvt_345 = ppvt_only[ppvt_only.age_test_year.isin([3,4,5])]"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 2576.000000\n",
"mean 92.463509\n",
"std 20.127618\n",
"min 20.000000\n",
"25% 79.000000\n",
"50% 94.000000\n",
"75% 107.000000\n",
"max 153.000000\n",
"Name: score, dtype: float64"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ppvt_345.score.describe()"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">score</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>median</th>\n",
" <th>count_nonzero</th>\n",
" </tr>\n",
" <tr>\n",
" <th>age_test_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>36.0</td>\n",
" <td>153.0</td>\n",
" <td>95.0</td>\n",
" <td>873.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>20.0</td>\n",
" <td>149.0</td>\n",
" <td>94.0</td>\n",
" <td>936.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>20.0</td>\n",
" <td>142.0</td>\n",
" <td>91.0</td>\n",
" <td>767.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score \n",
" min max median count_nonzero\n",
"age_test_year \n",
"3.0 36.0 153.0 95.0 873.0\n",
"4.0 20.0 149.0 94.0 936.0\n",
"5.0 20.0 142.0 91.0 767.0"
]
},
"execution_count": 145,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ppvt_345.groupby('age_test_year').agg({'score':[min, max, np.median, np.count_nonzero]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EVT"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"expressive 6803\n",
"receptive 6744\n",
"Goldman 5437\n",
"PPVT 4445\n",
"EVT 3881\n",
"EOWPVT 2784\n",
"ROWPVT 2346\n",
"Arizonia 503\n",
"PPVT and ROWPVT 199\n",
"EOWPVT and EVT 149\n",
"Arizonia and Goldman 73\n",
"Name: test_type, dtype: int64"
]
},
"execution_count": 146,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.test_type.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11ad78470>"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NTTbDAACA8+AiLQAAGIZ4AwBgGOINAIBhiDcAAIYh3gAAGIZ4AwBgGOINAIBhiDcAAIYh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\nzP8CvB6gKbwupZQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b01bc18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"evt_only = lsl_dr[lsl_dr.test_type=='EVT']\n",
"evt_only.age_test_year.hist()"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"evt_345 = evt_only[evt_only.age_test_year.isin([3,4,5])]"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">score</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>median</th>\n",
" <th>count_nonzero</th>\n",
" </tr>\n",
" <tr>\n",
" <th>age_test_year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>19.0</td>\n",
" <td>147.0</td>\n",
" <td>100.0</td>\n",
" <td>767.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>20.0</td>\n",
" <td>146.0</td>\n",
" <td>99.0</td>\n",
" <td>813.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>20.0</td>\n",
" <td>150.0</td>\n",
" <td>97.0</td>\n",
" <td>644.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score \n",
" min max median count_nonzero\n",
"age_test_year \n",
"3.0 19.0 147.0 100.0 767.0\n",
"4.0 20.0 146.0 99.0 813.0\n",
"5.0 20.0 150.0 97.0 644.0"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"evt_345.groupby('age_test_year').agg({'score':[min, max, np.median, np.count_nonzero]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PLS"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"pls_only = (language[(language.test_name=='PLS')]\n",
" .convert_objects(convert_numeric=True))\n",
"pls_only['age_year'] = np.floor(pls_only.age_test/12).astype(int)\n",
"pls_345 = pls_only[pls_only.age_year.isin([3,4,5])]"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th colspan=\"4\" halign=\"left\">score</th>\n",
" <th>normal_limits</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th></th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" <th>median</th>\n",
" <th>len</th>\n",
" <th>mean</th>\n",
" </tr>\n",
" <tr>\n",
" <th>age_year</th>\n",
" <th>test_type</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">3</th>\n",
" <th>expressive</th>\n",
" <td>50.0</td>\n",
" <td>145.0</td>\n",
" <td>78.0</td>\n",
" <td>813.0</td>\n",
" <td>0.355474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>receptive</th>\n",
" <td>50.0</td>\n",
" <td>140.0</td>\n",
" <td>80.0</td>\n",
" <td>813.0</td>\n",
" <td>0.404674</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">4</th>\n",
" <th>expressive</th>\n",
" <td>50.0</td>\n",
" <td>141.0</td>\n",
" <td>73.0</td>\n",
" <td>602.0</td>\n",
" <td>0.284053</td>\n",
" </tr>\n",
" <tr>\n",
" <th>receptive</th>\n",
" <td>50.0</td>\n",
" <td>136.0</td>\n",
" <td>77.0</td>\n",
" <td>606.0</td>\n",
" <td>0.381188</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">5</th>\n",
" <th>expressive</th>\n",
" <td>50.0</td>\n",
" <td>138.0</td>\n",
" <td>68.0</td>\n",
" <td>304.0</td>\n",
" <td>0.259868</td>\n",
" </tr>\n",
" <tr>\n",
" <th>receptive</th>\n",
" <td>50.0</td>\n",
" <td>129.0</td>\n",
" <td>73.0</td>\n",
" <td>306.0</td>\n",
" <td>0.290850</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score normal_limits\n",
" min max median len mean\n",
"age_year test_type \n",
"3 expressive 50.0 145.0 78.0 813.0 0.355474\n",
" receptive 50.0 140.0 80.0 813.0 0.404674\n",
"4 expressive 50.0 141.0 73.0 602.0 0.284053\n",
" receptive 50.0 136.0 77.0 606.0 0.381188\n",
"5 expressive 50.0 138.0 68.0 304.0 0.259868\n",
" receptive 50.0 129.0 73.0 306.0 0.290850"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(pls_345.assign(normal_limits=pls_345.score>=85).groupby(['age_year', 'test_type'])\n",
" .agg({'score':[min, max, np.median, len], \n",
" 'normal_limits': np.mean}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## CELF"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:2: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"celf_only = (language_subtest[(language_subtest.test_name=='CELF-P2')]\n",
" .convert_objects(convert_numeric=True))\n",
"celf_only['age_year'] = np.floor(celf_only.age_test/12).astype(int)\n",
"celf_46 = celf_only[celf_only.age_year.isin([4,6])]"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"subtests = ['celfp_ss_ss', 'celfp_ws_ss', \n",
" 'celfp_ev_ss', 'celfp_fd_ss',\n",
" 'celfp_rs_ss', 'celfp_bc_ss', \n",
" 'celfp_wcr_ss', 'celfp_wce_ss',\n",
" 'celfp_wct_ss']"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>age_year</th>\n",
" <th>4</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>celfp_wct_ss</th>\n",
" <td>10.0</td>\n",
" <td>8.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_ev_ss</th>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_wcr_ss</th>\n",
" <td>10.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_wce_ss</th>\n",
" <td>9.0</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_ss_ss</th>\n",
" <td>8.0</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_ws_ss</th>\n",
" <td>6.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_fd_ss</th>\n",
" <td>8.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_rs_ss</th>\n",
" <td>7.0</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>celfp_bc_ss</th>\n",
" <td>9.0</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"age_year 4 6\n",
"celfp_wct_ss 10.0 8.0\n",
"celfp_ev_ss 8.0 5.0\n",
"celfp_wcr_ss 10.0 10.0\n",
"celfp_wce_ss 9.0 7.0\n",
"celfp_ss_ss 8.0 5.0\n",
"celfp_ws_ss 6.0 4.0\n",
"celfp_fd_ss 8.0 4.0\n",
"celfp_rs_ss 7.0 4.0\n",
"celfp_bc_ss 9.0 4.5"
]
},
"execution_count": 155,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(celf_46.groupby('age_year')\n",
" .agg({st:np.median for st in subtests})).T"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Proportions in normal range"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def calc_norm_range(dataset):\n",
" return (dataset.groupby('study_id').score.mean() >= 85).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Mean score of each domain"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.40083217753120665"
]
},
"execution_count": 156,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calc_norm_range(lsl_dr[(lsl_dr.domain=='Language') \n",
" & (lsl_dr.test_type=='expressive')\n",
" & (lsl_dr.age_test_year.isin([3,4,5]))])"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2010: 0.53\n",
"2011: 0.48\n",
"2012: 0.5\n",
"2013: 0.55\n"
]
}
],
"source": [
"for year in range(2010, 2014):\n",
" value = calc_norm_range(lsl_dr[(lsl_dr.domain=='Language') \n",
" & (lsl_dr.test_type=='receptive') & (lsl_dr.academic_year_rv==year)\n",
" & (lsl_dr.age_test_year.isin([3,4,5]))]).round(2)\n",
" print('{}: {}'.format(year, value))"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.63506493506493511"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calc_norm_range(lsl_dr[(lsl_dr.domain=='Receptive Vocabulary')\n",
" & (lsl_dr.age_test_year.isin([3,4,5]))])"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.64257555847568992"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calc_norm_range(lsl_dr[(lsl_dr.domain=='Expressive Vocabulary')\n",
" & (lsl_dr.age_test_year.isin([3,4,5]))])"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.49158249158249157"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"calc_norm_range(lsl_dr[(lsl_dr.domain=='Articulation')\n",
" & (lsl_dr.age_test_year.isin([3,4,5]))])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Summary statistics"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.46830858384643242"
]
},
"execution_count": 189,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.groupby('study_id').male.first().dropna()==0).mean()"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.54349040789718761"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.groupby('study_id').race.first().dropna()==0).mean()"
]
},
{
"cell_type": "code",
"execution_count": 204,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4404"
]
},
"execution_count": 204,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.groupby('study_id').non_english.first().dropna()==False).sum()"
]
},
{
"cell_type": "code",
"execution_count": 208,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5040"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').sib.first().dropna().count()"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4161"
]
},
"execution_count": 213,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').onset_1.first().dropna().count()"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8.0"
]
},
"execution_count": 215,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').age_amp.first().dropna().median()"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"9.0"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').age_int.first().dropna().median()"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5404"
]
},
"execution_count": 220,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').age.first().dropna().count()"
]
},
{
"cell_type": "code",
"execution_count": 247,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1868"
]
},
"execution_count": 247,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_unique = lsl_dr.dropna(subset=['age_disenrolled', 'age']).groupby('study_id').first()\n",
"(_unique.age_disenrolled - _unique.age).count()"
]
},
{
"cell_type": "code",
"execution_count": 254,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"synd_cause = lsl_dr.groupby('study_id').synd_cause.first().dropna()\n",
"synd_cause = synd_cause[synd_cause<3]"
]
},
{
"cell_type": "code",
"execution_count": 257,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0 0.885766\n",
"0.0 0.091387\n",
"2.0 0.022847\n",
"Name: synd_cause, dtype: float64"
]
},
"execution_count": 257,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"synd_cause.value_counts()/synd_cause.value_counts().sum()"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"etiology = lsl_dr.groupby('study_id').etiology.first().dropna()\n",
"etiology = etiology[etiology<3]"
]
},
{
"cell_type": "code",
"execution_count": 264,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1.0 0.791393\n",
"0.0 0.163977\n",
"2.0 0.044630\n",
"Name: etiology, dtype: float64"
]
},
"execution_count": 264,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"etiology.value_counts()/etiology.value_counts().sum()"
]
},
{
"cell_type": "code",
"execution_count": 267,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['concerns'] = lsl_dr.etiology_2.replace({0:'none', 4:'none', 1:'mild', 2:'moderate', 3:'severe'})"
]
},
{
"cell_type": "code",
"execution_count": 270,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"none 3328\n",
"moderate 546\n",
"mild 436\n",
"severe 344\n",
"Name: concerns, dtype: int64"
]
},
"execution_count": 270,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.groupby('study_id').concerns.last().dropna().value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plots of Demographic Data"
]
},
{
"cell_type": "code",
"execution_count": 271,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_color = \"#64AAE8\""
]
},
{
"cell_type": "code",
"execution_count": 272,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def plot_demo_data(series, labels=None, color=plot_color, rot=0, label_offset=20, xlim=None, \n",
" ylim=None, title=None, **kwargs):\n",
" ax = kwargs.get('ax')\n",
" if ax is None:\n",
" f, ax = plt.subplots()\n",
" counts = series.value_counts().sort_index()\n",
" counts.plot(kind='bar', grid=False, rot=rot, color=color, **kwargs)\n",
" if xlim is None:\n",
" ax.set_xlim(-0.5, len(counts)-0.5)\n",
" if ylim is not None:\n",
" ax.set_ylim(*ylim)\n",
" ax.set_ylabel('Count')\n",
" if labels is not None:\n",
" ax.set_xticklabels(labels)\n",
" if title:\n",
" ax.set_title(title)\n",
" for i,x in enumerate(counts):\n",
" ax.annotate('%i' % x, (i, x + label_offset))\n",
" \n",
"# plt.gca().tight_layout()"
]
},
{
"cell_type": "code",
"execution_count": 273,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_students = demographic.drop_duplicates('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 274,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5898, 68)"
]
},
"execution_count": 274,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "code",
"execution_count": 275,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 5381.000000\n",
"mean 29.302360\n",
"std 27.507899\n",
"min 0.000000\n",
"25% 8.000000\n",
"50% 24.000000\n",
"75% 40.000000\n",
"max 298.000000\n",
"Name: age, dtype: float64"
]
},
"execution_count": 275,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 276,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AAC0TsC/erVixQnV1dSosLNTy5ctlsViUnZ2thQsXyuv1KiYmRsOHD5fFYlFqaqpSUlLk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\nKCIPAICh/h8PORWaUeYjjgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bbb6748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.male, \n",
" ('Female', 'Male'), label_offset=20, color=plot_color)"
]
},
{
"cell_type": "code",
"execution_count": 277,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YpKQkPD09GTlyJL169aK0tJSxY8dSWFiI2Wxm2rRp1K5d21lxRUREDMdpJf/VV19Ru3Zt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\nIiISFEOeiIhIUL8AaBwGCfq5NEcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c886a90>"
]
},
"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": 278,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5419"
]
},
"execution_count": 278,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.prim_lang.count()"
]
},
{
"cell_type": "code",
"execution_count": 279,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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8AACGCvL1AOficrk0d+5cHTlyRCEhIVq0aJE6dOjg67EAAPALl/WR/Pbt21VX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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120096ef0>"
]
},
"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": 280,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5013"
]
},
"execution_count": 280,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.sib.count()"
]
},
{
"cell_type": "code",
"execution_count": 281,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"There are 4806 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": 282,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/fonnescj/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" if __name__ == '__main__':\n"
]
}
],
"source": [
"age_classes = pd.Series(pd.cut(unique_students.sort('age_amp').age_amp.dropna(), [-1,3,6,9,12,18,24,36,48,60,72,1000],\n",
" labels=amp_ages))"
]
},
{
"cell_type": "code",
"execution_count": 283,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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TRURESiSXD/mHH36Y0NBQAHJzc3F3d2fv3r2EhIQA0KpVK7Zt24abmxvBwcF4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\nzLgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb0e5c0>"
]
},
"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",
"plt.ylim(0,1000)\n",
"for i,x in enumerate(age_amp_counts):\n",
" plt.annotate('%i' % x, (i, x + 10))"
]
},
{
"cell_type": "code",
"execution_count": 284,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3806"
]
},
"execution_count": 284,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"age_amp_counts.sum()"
]
},
{
"cell_type": "code",
"execution_count": 285,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"173.0"
]
},
"execution_count": 285,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age_amp.max()"
]
},
{
"cell_type": "code",
"execution_count": 286,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11d496198>"
]
},
"execution_count": 286,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Nf4cOHbR582ZJ0tq1axUXF6eOHTuqqKhIlZWVKi8v1+7duxUbG6suXbqooKBAklRQUOB7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\nA08y9uBtAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bbb6d68>"
]
},
"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": 287,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qU6GSX7Ro0U+Ojxw58rJ/t2zZMv3973+Xn5+fJGnfvn0aNmyYhg4d6t4nNzdXqampWrt2\nrQoKChQZGamuXbtq5cqVatu2rUaOHKkNGzYoOTlZkyZNquDbAgAAV3zv+uLiYm3evFmnT5/+xX1v\nuOEGLV682P18//792rJliwYPHqzJkyfL6XRqz549Cg0NlY+PjxwOhwIDA5WZmamMjAyFh4dLksLD\nw7Vjx44rjQoAwFWtQjP5H87Yn3nmGQ0bNuwX/+6BBx7QsWPH3M87duyoRx55RB06dNDSpUu1aNEi\ntW/fXv7+/u596tWrp/z8fDmdTjkcDkmSn5+f8vPzK/SGAADAdyq1Cp3T6dTx48ev+O969uypDh06\nuB9nZmbK39+/XIE7nU4FBATI4XDI6XS6xy79IgAAAH5ZhWby9913n/smOC6XS+fPn1dMTMwVv1hM\nTIymTJmikJAQ7dixQ7fccotCQkI0f/58FRUVqbCwUNnZ2QoODlanTp2Unp6ukJAQpaenKyws7Ipf\nDwCAq1mFSj41NdX92GazuWfaV2r69On685//LLvdriZNmmjGjBny8/NTdHS0oqKi5HK5FBcXJ19f\nX0VGRio+Pl5RUVHy9fVVUlLSFb8eAABXM5vr0pvR/wyXy6WVK1dq586dKikp0V133aXBgwfLy6tS\nR/s96tSpPKsjADVWVtZBzfuwQPVbBlkdpULOHc9SXJc6CgoKtjoKUGM1afLzp7MrNJOfO3eucnJy\n9NBDD8nlcmnNmjU6cuQIP2kDAKAGq1DJb9u2TevWrXPP3Lt3766+fft6NBgAAPh1KnS8vbS0VCUl\nJeWee3t7eywUAAD49So0k+/bt6+GDBmi3r17S5LWr1+vPn36eDQYAAD4dX6x5M+dO6dHHnlE7du3\n186dO/Xhhx9qyJAh6t+/f3XkAwAAlXTZw/UHDhxQ7969tW/fPt17772Kj4/XPffco6SkJGVmZlZX\nRgAAUAmXLfk5c+YoKSnJfQ95SYqLi9Ps2bOVmJjo8XAAAKDyLlvy58+fV5cuXX403q1bN509e9Zj\noQAAwK932ZIvKSlRWVnZj8bLyspUXFzssVAAAODXu2zJ33HHHT+5lnxycrJuvfVWj4UCAAC/3mWv\nro+Li9Mf/vAHvfPOOwoJCZHL5dKBAwfUqFEjLVmypLoyAgCASrhsyTscDq1YsUI7d+7U559/Li8v\nLz322GOsCAcAQC3wi7+Tt9lsuvvuu3X33XdXRx4AAFBFat4ycgAAoEpQ8gAAGIqSBwDAUJQ8AACG\nouQBADAUJQ8AgKEoeQAADEXJAwBgKEoeAGqh2bOf16pVr5cbO3HiPxow4Hc6f/6ce+z8+fOaMWOK\nhg17TIMHD9TGjRvK/U1RUZHGjHlG6embqyU3qhclDwC1SE7OIY0e/ZTef39TufF//vMfGjnyDzp9\nOrfc+OzZ09WsWXO98soKzZ+/WAsWJCk395Qkad++PYqNfUJ7935WbflRvSh5AKhF1qxZrd69f68e\nPXq6x3Jzc7Vt21a9+OL/ltv3/Pnz+vjjXRo69ElJUpMmTfWXv/xN/v4BkqS3316t4cOfVocOrCpq\nql+8dz0AoOYYM+ZPkqSPP97lHmvcuLFmzpwrSXK5XO7xY8eOqFGja7Vq1evauXO7SkqKNWjQYF1/\nfStJ0rRpMyVJb7yRUl3xUc0oeQAwVElJib755rgcDn8tWbJcx44d1dNPP6lWrVqrbdt2VsdDNeBw\nPQAYqnHjJrLZbOrVq48k6brrrtdtt92uAwf2W5wM1YWSBwBDtWjRUm3bttM///kPSdKZM6e1f/9e\ntWvXweJkqC4crgcAg9hstnLPZ89+QUlJiVq37i25XNITTwxXu3btL/s3MIfNdelVGgY4dSrP6ghA\njZWVdVDzPixQ/ZZBVkepkHPHsxTXpY6CgoKtjgLUWE2a+P/sNmbyAFCFSktLdehQttUxrlhgYBt5\ne3tbHQNVjJIHgCp06FC2nn/nS/k3bW11lArLO3lY0/qKIyYGouQBoIr5N21da06JwGxcXQ8AgKEo\neQAADEXJAwBgKEoeAABDUfIAABiKkgcAwFCUPAAAhqLkAQAwFCUPAIChKHkAAAzl8ZL/7LPPFB0d\nLUk6fPiwoqKiNHjwYD3//PPufVavXq2HHnpIgwYN0pYtWyRJhYWFevbZZ/XYY49pxIgROnv2rKej\nAgBgFI+W/LJlyzR58mQVFxdLkhISEhQXF6fXX39dZWVl2rRpk3Jzc5Wamqq0tDQtW7ZMSUlJKi4u\n1sqVK9W2bVutWLFC/fr1U3JysiejAgBgHI+W/A033KDFixe7n+/fv19hYWGSpPDwcG3fvl179uxR\naGiofHx85HA4FBgYqMzMTGVkZCg8PNy9744dOzwZFQAA43i05B944IFy6xO7XC73Yz8/P+Xn58vp\ndMrf/78L3terV8897nA4yu0LAAAqrlovvPPy+u/LOZ1OBQQEyOFwlCvwS8edTqd77NIvAgAA4JdV\na8l36NBBH330kSRp69atCg0NVUhIiDIyMlRUVKS8vDxlZ2crODhYnTp1Unp6uiQpPT3dfZgfAABU\njE91vlh8fLymTJmi4uJiBQUFKSIiQjabTdHR0YqKipLL5VJcXJx8fX0VGRmp+Ph4RUVFydfXV0lJ\nSdUZFQCAWs/muvREuQFOncqzOkKlpKe/r1de+Yu8vGwKCKivP/1pkpYsWajjx49K+u56hm++Oa5O\nnUKVkJCkTz75WIsWvaSysjLVr19fo0bF6aabgi1+F6jpsrIOat6HBarfMsjqKBVy7niW4rrUUVBQ\n7flvu7Z9xlLt/JzxX02a/Pzp7GqdyeOnFRYWaubMqXrttVVq2fI6rV79hhYseFFz577k3icz84Cm\nTBmvsWPHy+nM16RJf9KsWXPVuXOYDh8+pPHjxyolJU0+PvwrBQB8hzve1QBlZWWSpPz8745CXLhw\nQb6+17i3l5SUaObM6Ro9eqwaN26iI0eOyOHwV+fO312n0Lp1oPz8/LRv357qjg4AqMGY9tUAdevW\n1dix4xUbO0z16zdQWVmpkpOXu7e/8846NWnSRPfcc68kqXXr1rp48YI++uhD3XFHF33++X59/XW2\nTp/OteotAABqIGbyNUB29lf629+WacWKt7R27QZFRz+hSZP+5N6+evUbGjr0SffzevX8lJiYpJSU\nV/TEE1HauPGfCg29Qz4+diviAwBqKGbyNcCHH+7UbbfdrhYtWkqSHnzwES1cOF/nz5/TiRP/UVlZ\nmTp27OTe3+VyqU6dulq4cKl7bPDggbr++lbVnh0AUHMxk68Bbr65nXbv/kRnz56RJG3d+r5atLhO\nAQH1tXv3J+rc+Y5y+9tsNo0bN1qZmZ9LkjZv3iQfH7uCgm6q9uwAgJqLmXwN0LlzmKKiojVq1AjZ\n7XYFBNTXnDnzJElHjx5WixYtfvQ306fP0ty5M1VSUqJrr22shIQXqzs2AKCGo+RriAEDHtaAAQ//\naDwuLv4n9+/YsZNeeWWFp2MBAGoxSr4CSktLdehQttUxrlhgYJtyCwQBAK4ulHwFHDqUreff+VL+\nTVtbHaXC8k4e1rS+4g5WAHAVo+QryL9p61p1m0oAALi6HgAAQ1HyAAAYipIHAMBQlDwAAIai5AEA\nMBQlDwCAoSh5AAAMRckDAGAoSh4AAENR8gAAGIqSBwDAUJQ8AACGouQBADAUJQ8AgKEoeQAADEXJ\nAwBgKEoeAABDUfIAABiKkgcAwFCUPAAAhqLkAQAwFCUPAIChKHkAAAxFyQMAYChKHgAAQ1HyAAAY\nipIHAMBQlDwAAIai5AEAMBQlDwCAoSh5AAAMRckDAGAoHyte9MEHH5TD4ZAkXX/99YqNjdX48ePl\n5eWl4OBgTZs2TZK0evVqpaWlyW63KzY2Vt27d7ciLgAAtVK1l3xRUZEkKSUlxT321FNPKS4uTmFh\nYZo2bZo2bdqk22+/XampqVq7dq0KCgoUGRmprl27ym63V3dkAABqpWov+czMTF24cEExMTEqLS3V\nmDFjdODAAYWFhUmSwsPDtW3bNnl5eSk0NFQ+Pj5yOBwKDAzUF198oVtvvbW6IwMAUCtVe8nXqVNH\nMTExGjhwoA4dOqThw4fL5XK5t/v5+Sk/P19Op1P+/v7u8Xr16ikvL6+64wIAUGtVe8kHBgbqhhtu\ncD9u0KCBDhw44N7udDoVEBAgh8Oh/Pz8H40DAICKqfar699++20lJiZKkk6cOKH8/Hx17dpVu3bt\nkiRt3bpVoaGhCgkJUUZGhoqKipSXl6fs7GwFBwdXd1wAAGqtap/JP/zww5owYYKioqLk5eWlxMRE\nNWjQQJMnT1ZxcbGCgoIUEREhm82m6OhoRUVFyeVyKS4uTr6+vtUdFwCAWqvaS95ut+vFF1/80Xhq\nauqPxgYOHKiBAwdWRywAAIzDzXAAADAUJQ8AgKEoeQAADEXJAwBgKEoeAABDUfIAABiKkgcAwFCU\nPAAAhqLkAQAwFCUPAIChKHkAAAxFyQMAYChKHgAAQ1HyAAAYipIHAMBQlDwAAIai5AEAMBQlDwCA\noSh5AAAMRckDAGAoSh4AAENR8gAAGMrH6gBAdZs9+3m1aROkQYMGS5L69Omppk2bubdHRkbrgQci\ntG3bvzVr1nQ1b97cvW3x4mWqW7dutWcGgMqg5HHVyMk5pHnz5ujAgX1q0yZIknT4cI4CAurrlVdW\n/Gj/ffv2KDIyWtHRQ6s5KQBUDUoeV401a1ard+/fq1mz/87M9+3bIy8vLz37bKzOnTunHj3u1+OP\nx8hms2nv3s9kt9u1Zct7qlu3roYPf0odO3ay8B0AwJWh5HHVGDPmT5Kkjz/e5R4rLS3VHXfcpWee\nGa3CwgL98Y+j5efn0MCBg9SgQQNFRPTWPffcqz17PtWECWP12mur1LhxE6veAgBcES68w1Wtb9/+\nGj16rHx8fOTn59CgQY9p69b3JUkzZ87VPffcK0m67bbbdeutt+mjjz60Mi4AXBFKHle1jRs3KCvr\nK/dzl8slHx8fOZ35Sk19tdy+Lpfk7c3BLwC1ByWPq1p2dpaWL1+qsrIyFRYW6O23V+v++3+junXr\nac2aN5We/t2s/ssvM5WZeUB33XW3xYkBoOKYluCqNmzYcM2f/4KGDBmk0tIS3XffA+rTp58kKTFx\nnubPn6vly1+Wj4+PZsxIUEBAfYsTA0DFUfK46kycOM39+Jpr6mj8+Ck/ud/NN7fTyy+/Ul2xAKDK\nUfKoEUpLS3XoULbVMa5YYGAbeXt7Wx0DAH4SJY8a4dChbD3/zpfyb9ra6igVlnfysKb1lYKCgq2O\nAgA/iZJHjeHftLXqtwyyOgYAGIOr6wEAMBQlDwCAoSh5AAAMRckDAPAzZs9+XqtWvW51jEqj5AEA\n+IGcnEMaPfopvf/+Jquj/CpcXQ8AwA/81NLUtRElDwDAD/zU0tS1EYfrAQAwFCUPAIChavThepfL\npenTp+uLL76Qr6+vZs2apVatWlkdCwCAWqFGz+Q3bdqkoqIirVq1SmPHjlVCQoLVkQAAqDVq9Ew+\nIyND3bp1kyR17NhR+/btszgRAOBqcunS1LVRjS75/Px8+fv7u5/7+PiorKxMXl7VfwAi7+Than/N\nX+O7vG2tjnFF+IyrR236nPmMq0dt/Jyzsg5aHeGKWbFipc3lcrmq/VUrKDExUbfffrsiIiIkSd27\nd9eWLVusDQUAQC1Ro8/Jd+7cWenp6ZKkTz/9VG3b1q5vmgAAWKlGz+QvvbpekhISEnTjjTdanAoA\ngNqhRpc8AACovBp9uB4AAFQeJQ8AgKEoeQAADEXJAwBgKEoeAABD1eg73gGoeSZMmPCz21hfouq5\nXC7ZbDarYxjN5M+YkrdIaWmp1qxZo+PHj+uuu+5ScHCwGjVqZHUso3z66adas2aNiouLJUknT57U\n8uXLLU5V+/3ud7+TJK1cuVKdOnVS586dtXfvXu3du9fiZGaKiYnRK6+8YnUMo5n8GXO43iJTp07V\n8ePHtX37djmdTsXHx1sdyTjTp0/XnXfeqfz8fLVs2VINGjSwOpIRunXrpm7duqmgoEDDhw9XaGio\nhg4dqjNnzlgdzUgBAQHatGmTsrKy9PXXX+vrr7+2OpJxTP6Mmclb5PDhw5o1a5YyMjJ033336S9/\n+YvVkYzRa31SAAAL3ElEQVTTsGFD9enTR9u2bdOoUaM0ePBgqyMZ5cKFC9qxY4dCQkK0e/duFRYW\nWh3JSKdPn9Zrr73mfm6z2ZSSkmJhIvOY/BlT8hYpLS11z3zy8/MtWVnPdF5eXjp48KAuXryo7Oxs\nnTt3zupIRpk1a5ZeeOEFff311woODtacOXOsjmSk1NTUcs+LioosSmKu1NRU5eXl6dixY2rVqpX8\n/PysjlRluK2tRXbt2qUpU6bo1KlTatGihSZOnKiuXbtaHcsoBw8e1MGDB9WsWTPNmjVLv//97zV0\n6FCrY9V6JSUl8vHx+cmy8fX1tSCR2VatWqVXX31VJSUlcrlcstvt2rhxo9WxjLJx40YtWbJEpaWl\nioiIkM1m09NPP211rCpByVvszJkzatiwobFXdlrtzJkzKigocF8927JlS6sj1Xpjx45VUlKS7rvv\nPvd/t99/vu+9957F6czTt29fLV++XEuWLFFERIRee+01JScnWx3LKIMGDVJKSopiYmKUkpKihx56\nSGvWrLE6VpXgcL1Ftm3bpr/97W/lzmOacg6oppgyZYp27Nihxo0bu0to1apVVseq9ZKSkiRJmzdv\ntjjJ1aFp06Zq2rSpnE6nunTpokWLFlkdyTje3t7y9fWVzWaTzWZT3bp1rY5UZSh5iyQkJGjixIlq\n3ry51VGM9cUXX+hf//oXR0k85L333tMbb7yh4uJiuVwuffvtt3rnnXesjmUcf39/bdq0yf0l9dtv\nv7U6knFCQ0MVFxenEydOaOrUqQoJCbE6UpWh5C3SokUL/c///I/VMYz2/ezH4XBYHcVIL730kmbM\nmKFVq1apS5cu2rZtm9WRjDRz5kwdOXJEcXFxevXVVzV58mSrIxknLi5OW7duVYcOHRQUFKQePXpY\nHanKUPIWufbaazV16lR16NDBPdN89NFHLU5lhkcffVQ2m02nT5/Wb37zG7Vq1UqSOFxfxZo2bapO\nnTpp1apVevDBB7V27VqrIxmpbt262rdvn44fP64ePXooODjY6kjGOXr0qA4ePKiCggLt379f+/fv\n18iRI62OVSUoeYtcf/31kqTc3FyLk5hn3rx5Vke4Ktjtdn300UcqKSnRv//9b509e9bqSEaaOnWq\nmjZtqu3btyskJETx8fH661//anUso4wdO1bdunVT48aNrY5S5bi63kJbtmzRwYMHdeONN6pnz55W\nxzFOTk6O3n333XK3tZ0xY4bFqcxx4sQJZWdnq0mTJlqwYIEiIiLUu3dvq2MZJzo6WqmpqRoyZIhS\nUlI0aNAgjkhVsccff7zczXBMwkzeIklJScrJyVHnzp21bt06ZWRkcGvbKjZ27Fg98MAD+uSTT9S0\naVNduHDB6khGadasmZo1ayZJWrhwocVpzMWNszzn+9vXNm7cWP/4xz/KnT698cYbrYxWZSh5i3z0\n0Ufub+OPP/64HnnkEYsTmadevXoaMWKEDh06pISEBEVFRVkdCbhizz33nCIjI3Xq1Ck9+uijmjhx\notWRjDF16lT347S0NPdjbmuLX62kpERlZWXy8vIyeplDK9lsNp06dUpOp1MXLlxgJo9a6c4779TG\njRu5cZYHfH/L4Pfff7/cFfUbNmywKlKVo+Qt0rt3b0VGRqpjx47as2ePe/lOVJ2RI0dq06ZN6tev\nn3r27Kl+/fpZHckoP7wpi91uV/PmzfW73/1OdrvdolTm+P5XIj+Fc/JV4/3339cnn3yi9evXa/fu\n3ZK+Oz2yefNmY/6fTMlXs3Xr1kn6boW0vn37qrCwUH369OG33FUoMzNTL730kq699lr17t1bY8aM\nkSTdfPPNFiczyxdffKFrrrlGYWFh+uyzz/TNN9+oSZMm+uCDD/TCCy9YHa/W41cinteuXTt9++23\nOnXqlNq0aaOysjJ5e3urT58+VkerMpR8NcvKyir33OVyac2aNapTp4769+9vUSqzTJ8+XaNGjdK5\nc+f0zDPPaO3atWrUqJGefPJJPuMqdP78efcVyYMGDdKwYcP0wgsvKDIy0uJkZrjuuuskSXv37tXa\ntWt18eJF97aEhASrYhklICBAGzdu1M0336x///vfysnJUaNGjXT//fdbHa3KUPLVbOzYse7Hhw8f\nVnx8vLp3787FNFXIbre7V/RLSUlRYGCgpO8uxEPVycvL05kzZ9SoUSOdPXtWeXl5Ki4uVkFBgdXR\njDJ9+nQNHjzYyN9wWy0pKUkRERHlvvy/+eabmjt3rjE/t6XkLbJixQq99tprmjBhglG3UKwJLj2P\neenSp2VlZVbEMdaoUaP0yCOPyOFw6MKFC5o8ebJeffVVPfzww1ZHM4rD4dCAAQOsjmGkzMzMclfY\nS9LAgQP11ltvWZSo6lHy1ezEiROaMGGC6tevrzfffFP169e3OpJxvvrqK40dO1Yul6vc4x+eKsGv\n06NHD9177706c+aMrr32WtlsNoWHh1sdyxgffPCBpO8WqHn55Zd1yy23uL/A3nPPPVZGM4aPz09X\noLe3dzUn8RxKvpr17t1bvr6+uuuuu350OOj7JTzx67z00kvux4MGDfrJx/j1WC7Zs9avXy/pu5LP\nyclRTk6OexslXzUaNGigvXv3llt1bu/evUZNvritbTXbtWvXz2678847qzEJ8Ov06dPnR8slt2nT\nxsJEZjpz5ow+//xzde3aVa+//rp+//vfKyAgwOpYRjh69KieeuopdenSRa1atdLRo0e1Y8cOLVmy\nxL2wVW1HyQOolOHDh7NQSjV44oknNGTIEPXo0UPvvPOO/vGPf2jp0qVWxzJGYWGhtmzZoiNHjqhZ\ns2a6//77jbpIl8P1ACqF5ZKrx8WLF90X5/bt21erV6+2OJFZrrnmGv32t7+1OobHUPIAKoXlkquH\n3W7Xtm3b1LFjR+3du9eoi8LgeRyuB3BF/vOf/6h58+buFbwuZcrKXTVJTk6O5syZo0OHDikoKEjj\nxo1T69atrY6FWoKSB3BFEhISNGHCBEVHR7sP03+/yBJX13vGl19+qa+++ko33nij2rdvb3Uc1CKU\nPIBKWbZsmZ588kmrYxgvJSVF69ev12233abdu3erV69eiomJsToWagkvqwMAqJ22bt2q0tJSq2MY\nb/369VqxYoUmTZqklStXGrUMKjyPC+8AVMrZs2fVrVs3XX/99bLZbLLZbCyB6gEul8t9Zza73c4y\nvrgilDyASnn55ZetjnBVCA0N1bPPPqvQ0FBlZGSoU6dOVkdCLcI5eQCVkpOTo3fffVfFxcWSpJMn\nTxqzcldNkZaWpgcffFDbtm3Tvn371KBBAw0ePNjqWKhFOCcPoFK+Xzb5k08+0dGjR/Xtt99anMgs\nCxcu1LZt21RSUqLu3burf//+2rlzpxYvXmx1NNQilDyASqlXr55GjBihZs2aKTExkZviVLGtW7dq\nwYIFqlu3rqTvbj40f/58bd682eJkqE0oeQCVYrPZdOrUKTmdTl24cEEXLlywOpJR6tWr574Pwffs\ndrv8/PwsSoTaiJIHUCkjR47Uv/71L/Xr1089e/bU3XffbXUko9SpU0dHjhwpN3bkyJEfFT9wOVx4\nB6DS8vPzdfToUbVq1YoZZhU7ePCg4uLidPfdd6tVq1Y6fvy4PvjgA82ZM0cdOnSwOh5qCUoeQKVs\n3LhRS5YsUWlpqSIiImSz2fT0009bHcsoeXl5eu+993Ty5Em1bNlS3bt3l8PhsDoWahFKHkClDBo0\nSCkpKYqJiVFKSooeeughrVmzxupYAC7BOXkAleLt7S1fX1/33e6+vwocQM1ByQOolNDQUMXFxenE\niROaOnWqQkJCrI4E4Ac4XA+g0rZu3aovv/xSQUFB6tGjh9VxAPwAJQ/giqxbt+5nt/Xv378akwD4\nJSxQA+CKZGVluR+vX79effr0kcvl4vfbQA3ETB5ApUVHRys1NdXqGAB+BhfeAag0Zu9AzUbJAwBg\nKA7XA7gicXFxstlscrlc2rlzZ7l71iclJVmYDMAPUfIArsiuXbt+dtudd95ZjUkA/BJKHgAAQ3FO\nHgAAQ1HyAAAYipIHAMBQlDwAAIai5AEAMNT/B33hfH/W5gdbAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb38630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_demo_data(unique_students.tech_left, tech_cats, rot=90, color=plot_color, ylim=(0, 3000))"
]
},
{
"cell_type": "code",
"execution_count": 288,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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fVHp6utWRAADwGpf0TD4vL09dunSRJN1www3asmWLxYl+rfjAbqsjuMWJ99XS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\nc3EAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12034fa58>"
]
},
"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": 289,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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mTRpLZuZml+a/Epc98C4rK4uKigqmT5/O3LlzsdvtANhsNmbOnMnGjRuvaGUzZ85k9uzZ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\nrWkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb13668>"
]
},
"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)\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": 290,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4742"
]
},
"execution_count": 290,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_right.count()"
]
},
{
"cell_type": "code",
"execution_count": 291,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4734"
]
},
"execution_count": 291,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.tech_left.count()"
]
},
{
"cell_type": "code",
"execution_count": 292,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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3ExQU5FoeEBDgWm61Wi96rYiIiFw5jw688/H539XZ7XbKly+P1Wq9qMAvXG63213LLvwg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\nRsfwmE2bNhEZGUlxcTHz589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DU11Kl9Bdo4EDB/LVV1/RtWtX7rnnHq+ZGGbRokVER0ez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\nIg8AwDRFkQcAYJqiyAMAME39D6Ow8KvAjxlQAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb13048>"
]
},
"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": 293,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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jx47ZrbAHpX7yIiKSW87eTz5XV/Lbt29/4IOLiIiIY+Qq5KdPn/674wMGDMjTYkRERCTv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\nXFujskvWT1kAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120110a20>"
]
},
"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": 294,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4642"
]
},
"execution_count": 294,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.degree_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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XfPvtN8rN3eV+HBf3WxUWHlNpael/XL68vFwZGX+rN+ZySb6+nKsKmO7Hvl+0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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120109630>"
]
},
"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": 296,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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q1bv85WxNhiMiImJSKnkRERGTUsmLiIiYlEpeRETEpFTyIiIiJqWSFxERMSmVvIiIiEmp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\n6JdMbyakAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x129eef080>"
]
},
"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": 297,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4563"
]
},
"execution_count": 297,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4660"
]
},
"execution_count": 298,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_as.count()"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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ClClTWL9+PTk5OaSnp3urXBEREZ/jteH6gQMHMm3aNABcLhf+/v7s27ePTZs2MWbMGDIy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\nHxlGAYo1K7j5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bbc0780>"
]
},
"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": 300,
"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>academic_year_rv</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>...</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>14665</th>\n",
" <td>initial_assessment_arm_1</td>\n",
" <td>2010-2011</td>\n",
" <td>2010.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</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>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14666</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>2011.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</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>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14667</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
" <td>2012-2013</td>\n",
" <td>2012.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</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>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14668</th>\n",
" <td>year_3_complete_71_arm_1</td>\n",
" <td>2013-2014</td>\n",
" <td>2013.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>3.0</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>51.0</td>\n",
" <td>Female</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year academic_year_rv hl male \\\n",
"14665 initial_assessment_arm_1 2010-2011 2010.0 0.0 0.0 \n",
"14666 year_1_complete_71_arm_1 2011-2012 2011.0 0.0 0.0 \n",
"14667 year_2_complete_71_arm_1 2012-2013 2012.0 0.0 0.0 \n",
"14668 year_3_complete_71_arm_1 2013-2014 2013.0 0.0 0.0 \n",
"\n",
" _race prim_lang sib _mother_ed father_ed ... bilateral_ci \\\n",
"14665 0.0 0.0 1.0 3.0 3.0 ... False \n",
"14666 0.0 0.0 1.0 3.0 3.0 ... False \n",
"14667 0.0 0.0 1.0 3.0 3.0 ... False \n",
"14668 0.0 0.0 1.0 3.0 3.0 ... False \n",
"\n",
" bilateral_ha bimodal tech implant_category age_diag sex \\\n",
"14665 True False 0 6 51.0 Female \n",
"14666 True False 0 6 51.0 Female \n",
"14667 True False 0 6 51.0 Female \n",
"14668 True False 0 6 51.0 Female \n",
"\n",
" known_synd synd_or_disab race \n",
"14665 0.0 0.0 0.0 \n",
"14666 0.0 0.0 0.0 \n",
"14667 0.0 0.0 0.0 \n",
"14668 0.0 0.0 0.0 \n",
"\n",
"[4 rows x 68 columns]"
]
},
"execution_count": 300,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic[demographic.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {
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},
"outputs": [
{
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" study_id redcap_event_name score test_type school \\\n",
"14665 1147-2010-0064 initial_assessment_arm_1 96.0 PPVT 1147 \n",
"14666 1147-2010-0064 year_1_complete_71_arm_1 91.0 PPVT 1147 \n",
"14667 1147-2010-0064 year_2_complete_71_arm_1 93.0 PPVT 1147 \n",
"\n",
" age_test domain \n",
"14665 63.0 Receptive Vocabulary \n",
"14666 73.0 Receptive Vocabulary \n",
"14667 85.0 Receptive Vocabulary "
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"receptive[receptive.study_id=='1147-2010-0064']"
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{
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"metadata": {
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},
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{
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" <td>87.0</td>\n",
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" <td>Bilateral HA</td>\n",
" <td>4.0</td>\n",
" <td>True</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15880</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
" <td>2011.0</td>\n",
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" <td>86.0</td>\n",
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" <td>EVT</td>\n",
" <td>2011</td>\n",
" <td>Bilateral HA</td>\n",
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" <td>True</td>\n",
" <td>6.0</td>\n",
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" <tr>\n",
" <th>15881</th>\n",
" <td>year_1_complete_71_arm_1</td>\n",
" <td>2011-2012</td>\n",
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" <td>Bilateral HA</td>\n",
" <td>4.0</td>\n",
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" <tr>\n",
" <th>23735</th>\n",
" <td>year_2_complete_71_arm_1</td>\n",
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" <td>2013</td>\n",
" <td>Bilateral HA</td>\n",
" <td>4.0</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>9 rows × 80 columns</p>\n",
"</div>"
],
"text/plain": [
" redcap_event_name academic_year academic_year_rv hl male \\\n",
"5947 initial_assessment_arm_1 2010-2011 2010.0 0.0 0.0 \n",
"5948 initial_assessment_arm_1 2010-2011 2010.0 0.0 0.0 \n",
"5949 initial_assessment_arm_1 2010-2011 2010.0 0.0 0.0 \n",
"5950 initial_assessment_arm_1 2010-2011 2010.0 0.0 0.0 \n",
"15880 year_1_complete_71_arm_1 2011-2012 2011.0 0.0 0.0 \n",
"15881 year_1_complete_71_arm_1 2011-2012 2011.0 0.0 0.0 \n",
"23735 year_2_complete_71_arm_1 2012-2013 2012.0 0.0 0.0 \n",
"23736 year_2_complete_71_arm_1 2012-2013 2012.0 0.0 0.0 \n",
"32791 year_3_complete_71_arm_1 2013-2014 2013.0 0.0 0.0 \n",
"\n",
" _race prim_lang sib _mother_ed father_ed ... school score \\\n",
"5947 0.0 0.0 1.0 3.0 3.0 ... 1147 91.0 \n",
"5948 0.0 0.0 1.0 3.0 3.0 ... 1147 96.0 \n",
"5949 0.0 0.0 1.0 3.0 3.0 ... 1147 101.0 \n",
"5950 0.0 0.0 1.0 3.0 3.0 ... 1147 87.0 \n",
"15880 0.0 0.0 1.0 3.0 3.0 ... 1147 86.0 \n",
"15881 0.0 0.0 1.0 3.0 3.0 ... 1147 91.0 \n",
"23735 0.0 0.0 1.0 3.0 3.0 ... 1147 95.0 \n",
"23736 0.0 0.0 1.0 3.0 3.0 ... 1147 93.0 \n",
"32791 0.0 0.0 1.0 3.0 3.0 ... NaN NaN \n",
"\n",
" test_name test_type academic_year_start tech_class age_year \\\n",
"5947 NaN EVT 2010 Bilateral HA 4.0 \n",
"5948 NaN PPVT 2010 Bilateral HA 4.0 \n",
"5949 PLS receptive 2010 Bilateral HA 4.0 \n",
"5950 PLS expressive 2010 Bilateral HA 4.0 \n",
"15880 NaN EVT 2011 Bilateral HA 4.0 \n",
"15881 NaN PPVT 2011 Bilateral HA 4.0 \n",
"23735 NaN EVT 2012 Bilateral HA 4.0 \n",
"23736 NaN PPVT 2012 Bilateral HA 4.0 \n",
"32791 NaN NaN 2013 Bilateral HA 4.0 \n",
"\n",
" non_profound age_test_year concerns \n",
"5947 True 5.0 NaN \n",
"5948 True 5.0 NaN \n",
"5949 True 4.0 NaN \n",
"5950 True 4.0 NaN \n",
"15880 True 6.0 NaN \n",
"15881 True 6.0 NaN \n",
"23735 True 7.0 NaN \n",
"23736 True 7.0 NaN \n",
"32791 True NaN NaN \n",
"\n",
"[9 rows x 80 columns]"
]
},
"execution_count": 302,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.study_id=='1147-2010-0064']"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4563"
]
},
"execution_count": 303,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.type_hl_ad.count()"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3108,)"
]
},
"execution_count": 304,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive[receptive.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 305,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5898,)"
]
},
"execution_count": 305,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"demographic.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3108,)"
]
},
"execution_count": 306,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive.study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3108,)"
]
},
"execution_count": 307,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[lsl_dr.domain==\"Receptive Vocabulary\"].study_id.unique().shape"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_ids = receptive.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 309,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"demographic_ids = demographic.study_id.unique()"
]
},
{
"cell_type": "code",
"execution_count": 310,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 310,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[s for s in receptive_ids if s not in demographic_ids]"
]
},
{
"cell_type": "code",
"execution_count": 311,
"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": 312,
"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>424</td>\n",
" <td>93.759434</td>\n",
" <td>17.998914</td>\n",
" <td>40.0</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1444</td>\n",
" <td>92.173823</td>\n",
" <td>19.124304</td>\n",
" <td>0.0</td>\n",
" <td>153.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1582</td>\n",
" <td>90.716814</td>\n",
" <td>20.243070</td>\n",
" <td>0.0</td>\n",
" <td>149.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1189</td>\n",
" <td>89.994113</td>\n",
" <td>18.050597</td>\n",
" <td>0.0</td>\n",
" <td>142.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>678</td>\n",
" <td>85.961652</td>\n",
" <td>16.160065</td>\n",
" <td>40.0</td>\n",
" <td>154.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>442</td>\n",
" <td>83.244344</td>\n",
" <td>16.113797</td>\n",
" <td>40.0</td>\n",
" <td>130.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>313</td>\n",
" <td>80.651757</td>\n",
" <td>17.500828</td>\n",
" <td>20.0</td>\n",
" <td>132.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>235</td>\n",
" <td>78.629787</td>\n",
" <td>17.568035</td>\n",
" <td>25.0</td>\n",
" <td>160.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>194</td>\n",
" <td>76.479381</td>\n",
" <td>17.488178</td>\n",
" <td>20.0</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>463</td>\n",
" <td>78.539957</td>\n",
" <td>18.944497</td>\n",
" <td>20.0</td>\n",
" <td>134.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 424 93.759434 17.998914 40.0 144.0\n",
"3 1444 92.173823 19.124304 0.0 153.0\n",
"4 1582 90.716814 20.243070 0.0 149.0\n",
"5 1189 89.994113 18.050597 0.0 142.0\n",
"6 678 85.961652 16.160065 40.0 154.0\n",
"7 442 83.244344 16.113797 40.0 130.0\n",
"8 313 80.651757 17.500828 20.0 132.0\n",
"9 235 78.629787 17.568035 25.0 160.0\n",
"10 194 76.479381 17.488178 20.0 123.0\n",
"11 463 78.539957 18.944497 20.0 134.0"
]
},
"execution_count": 312,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary = score_summary(\"Receptive Vocabulary\")\n",
"receptive_summary"
]
},
{
"cell_type": "code",
"execution_count": 313,
"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>696.400000</td>\n",
" <td>85.015106</td>\n",
" <td>17.919228</td>\n",
" <td>20.5000</td>\n",
" <td>142.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>515.522863</td>\n",
" <td>6.356443</td>\n",
" <td>1.280871</td>\n",
" <td>16.4063</td>\n",
" <td>12.068784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>194.000000</td>\n",
" <td>76.479381</td>\n",
" <td>16.113797</td>\n",
" <td>0.0000</td>\n",
" <td>123.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>340.750000</td>\n",
" <td>79.135280</td>\n",
" <td>17.491340</td>\n",
" <td>5.0000</td>\n",
" <td>132.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>452.500000</td>\n",
" <td>84.602998</td>\n",
" <td>17.783475</td>\n",
" <td>20.0000</td>\n",
" <td>143.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1061.250000</td>\n",
" <td>90.536139</td>\n",
" <td>18.721022</td>\n",
" <td>36.2500</td>\n",
" <td>152.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1582.000000</td>\n",
" <td>93.759434</td>\n",
" <td>20.243070</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 696.400000 85.015106 17.919228 20.5000 142.100000\n",
"std 515.522863 6.356443 1.280871 16.4063 12.068784\n",
"min 194.000000 76.479381 16.113797 0.0000 123.000000\n",
"25% 340.750000 79.135280 17.491340 5.0000 132.500000\n",
"50% 452.500000 84.602998 17.783475 20.0000 143.000000\n",
"75% 1061.250000 90.536139 18.721022 36.2500 152.000000\n",
"max 1582.000000 93.759434 20.243070 40.0000 160.000000"
]
},
"execution_count": 313,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary.describe()"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6964"
]
},
"execution_count": 314,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 315,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x129f16518>"
]
},
"execution_count": 315,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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LYGAg6enp3ooqV7HDh/OYsO4gwdfdeEXXW/RtPsk9IDQ07IquV0Tkx3i85Bs3bszKlSsB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\nR69ZRwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c4debe0>"
]
},
"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": 316,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sample Size</th>\n",
" <th>Mean</th>\n",
" <th>SD</th>\n",
" <th>Min</th>\n",
" <th>Max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>403</td>\n",
" <td>92.885856</td>\n",
" <td>21.971304</td>\n",
" <td>23.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1389</td>\n",
" <td>93.531317</td>\n",
" <td>21.386317</td>\n",
" <td>19.0</td>\n",
" <td>147.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1557</td>\n",
" <td>92.419396</td>\n",
" <td>21.762937</td>\n",
" <td>0.0</td>\n",
" <td>146.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1160</td>\n",
" <td>91.680172</td>\n",
" <td>19.999878</td>\n",
" <td>0.0</td>\n",
" <td>150.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>676</td>\n",
" <td>87.002959</td>\n",
" <td>18.252711</td>\n",
" <td>20.0</td>\n",
" <td>146.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>441</td>\n",
" <td>84.133787</td>\n",
" <td>15.653573</td>\n",
" <td>38.0</td>\n",
" <td>131.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>304</td>\n",
" <td>83.976974</td>\n",
" <td>16.415685</td>\n",
" <td>34.0</td>\n",
" <td>122.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>221</td>\n",
" <td>82.036199</td>\n",
" <td>16.163330</td>\n",
" <td>36.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>188</td>\n",
" <td>82.085106</td>\n",
" <td>15.380841</td>\n",
" <td>40.0</td>\n",
" <td>122.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>464</td>\n",
" <td>84.771552</td>\n",
" <td>17.333085</td>\n",
" <td>18.0</td>\n",
" <td>146.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 403 92.885856 21.971304 23.0 145.0\n",
"3 1389 93.531317 21.386317 19.0 147.0\n",
"4 1557 92.419396 21.762937 0.0 146.0\n",
"5 1160 91.680172 19.999878 0.0 150.0\n",
"6 676 87.002959 18.252711 20.0 146.0\n",
"7 441 84.133787 15.653573 38.0 131.0\n",
"8 304 83.976974 16.415685 34.0 122.0\n",
"9 221 82.036199 16.163330 36.0 145.0\n",
"10 188 82.085106 15.380841 40.0 122.0\n",
"11 464 84.771552 17.333085 18.0 146.0"
]
},
"execution_count": 316,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary = score_summary(\"Expressive Vocabulary\")\n",
"expressive_summary"
]
},
{
"cell_type": "code",
"execution_count": 317,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6803"
]
},
"execution_count": 317,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 318,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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HyM//kvnz5zJnTiaLFuVQUlJMdvabntwUERH3lnyLFi2YPXu2a3nWrFn84Q9/AMDhcBAQ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c4a82e8>"
]
},
"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, 800)\n",
"else:\n",
" plt.ylim(0, 1800)"
]
},
{
"cell_type": "code",
"execution_count": 319,
"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>306</td>\n",
" <td>85.254902</td>\n",
" <td>14.944281</td>\n",
" <td>50.0</td>\n",
" <td>122.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1215</td>\n",
" <td>83.656790</td>\n",
" <td>18.416468</td>\n",
" <td>40.0</td>\n",
" <td>126.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1407</td>\n",
" <td>83.461265</td>\n",
" <td>20.866057</td>\n",
" <td>0.0</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1089</td>\n",
" <td>82.844812</td>\n",
" <td>20.790949</td>\n",
" <td>39.0</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>638</td>\n",
" <td>79.460815</td>\n",
" <td>21.809311</td>\n",
" <td>39.0</td>\n",
" <td>115.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>415</td>\n",
" <td>78.101205</td>\n",
" <td>22.341971</td>\n",
" <td>3.0</td>\n",
" <td>112.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>268</td>\n",
" <td>79.313433</td>\n",
" <td>21.212468</td>\n",
" <td>40.0</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>195</td>\n",
" <td>81.497436</td>\n",
" <td>20.757901</td>\n",
" <td>39.0</td>\n",
" <td>109.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>149</td>\n",
" <td>81.516779</td>\n",
" <td>20.128507</td>\n",
" <td>40.0</td>\n",
" <td>107.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>326</td>\n",
" <td>81.733129</td>\n",
" <td>19.477465</td>\n",
" <td>39.0</td>\n",
" <td>105.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 306 85.254902 14.944281 50.0 122.0\n",
"3 1215 83.656790 18.416468 40.0 126.0\n",
"4 1407 83.461265 20.866057 0.0 123.0\n",
"5 1089 82.844812 20.790949 39.0 120.0\n",
"6 638 79.460815 21.809311 39.0 115.0\n",
"7 415 78.101205 22.341971 3.0 112.0\n",
"8 268 79.313433 21.212468 40.0 107.0\n",
"9 195 81.497436 20.757901 39.0 109.0\n",
"10 149 81.516779 20.128507 40.0 107.0\n",
"11 326 81.733129 19.477465 39.0 105.0"
]
},
"execution_count": 319,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary = score_summary(\"Articulation\")\n",
"articulation_summary"
]
},
{
"cell_type": "code",
"execution_count": 320,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6008"
]
},
"execution_count": 320,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 321,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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3bsLCwpzbfvLJJ66MKiIiYjouLfm77roLb29v52vDMJy/9vf3Jy8vD7vdTkBAgHPcz8/P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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120184c50>"
]
},
"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": 322,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['Expressive Vocabulary', 'Language', 'Articulation', nan,\n",
" 'Receptive Vocabulary'], dtype=object)"
]
},
"execution_count": 322,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.domain.unique()"
]
},
{
"cell_type": "code",
"execution_count": 323,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['EOWPVT', 'receptive', 'expressive', 'Goldman', nan, 'ROWPVT',\n",
" 'Arizonia', 'EVT', 'PPVT', 'Arizonia and Goldman', 'EOWPVT and EVT',\n",
" 'PPVT and ROWPVT'], dtype=object)"
]
},
"execution_count": 323,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.test_type.unique()"
]
},
{
"cell_type": "code",
"execution_count": 324,
"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>988</td>\n",
" <td>86.411943</td>\n",
" <td>22.293414</td>\n",
" <td>50.0</td>\n",
" <td>150.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1408</td>\n",
" <td>84.969460</td>\n",
" <td>19.728716</td>\n",
" <td>50.0</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1391</td>\n",
" <td>85.321352</td>\n",
" <td>19.453493</td>\n",
" <td>43.0</td>\n",
" <td>145.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>985</td>\n",
" <td>83.943147</td>\n",
" <td>18.823820</td>\n",
" <td>47.0</td>\n",
" <td>140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>515</td>\n",
" <td>78.081553</td>\n",
" <td>17.745640</td>\n",
" <td>11.0</td>\n",
" <td>127.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>331</td>\n",
" <td>76.129909</td>\n",
" <td>18.941810</td>\n",
" <td>40.0</td>\n",
" <td>123.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>201</td>\n",
" <td>74.880597</td>\n",
" <td>19.700652</td>\n",
" <td>40.0</td>\n",
" <td>127.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>55</td>\n",
" <td>70.363636</td>\n",
" <td>21.026759</td>\n",
" <td>40.0</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>47</td>\n",
" <td>79.617021</td>\n",
" <td>20.802961</td>\n",
" <td>40.0</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>69</td>\n",
" <td>77.101449</td>\n",
" <td>21.432620</td>\n",
" <td>40.0</td>\n",
" <td>139.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 988 86.411943 22.293414 50.0 150.0\n",
"3 1408 84.969460 19.728716 50.0 144.0\n",
"4 1391 85.321352 19.453493 43.0 145.0\n",
"5 985 83.943147 18.823820 47.0 140.0\n",
"6 515 78.081553 17.745640 11.0 127.0\n",
"7 331 76.129909 18.941810 40.0 123.0\n",
"8 201 74.880597 19.700652 40.0 127.0\n",
"9 55 70.363636 21.026759 40.0 120.0\n",
"10 47 79.617021 20.802961 40.0 120.0\n",
"11 69 77.101449 21.432620 40.0 139.0"
]
},
"execution_count": 324,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary = score_summary(\"Language\", \"receptive\")\n",
"receptive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5990"
]
},
"execution_count": 325,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 326,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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/lQYMeEqS5OPjo7FjX1RGxhpFRNyt+vUbSJK6du0uSfr004+8lhmoKHh2PQCv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\nAJOi5AEAMClKHgAAk6LkAQAwqf8Hp1e3YWEOJakAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb93c18>"
]
},
"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": 327,
"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>981</td>\n",
" <td>88.450561</td>\n",
" <td>18.587983</td>\n",
" <td>50.0</td>\n",
" <td>150.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1410</td>\n",
" <td>82.344681</td>\n",
" <td>17.569380</td>\n",
" <td>20.0</td>\n",
" <td>147.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1382</td>\n",
" <td>80.683792</td>\n",
" <td>19.533977</td>\n",
" <td>45.0</td>\n",
" <td>141.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1006</td>\n",
" <td>78.666998</td>\n",
" <td>20.106123</td>\n",
" <td>45.0</td>\n",
" <td>144.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>536</td>\n",
" <td>71.820896</td>\n",
" <td>19.421195</td>\n",
" <td>6.0</td>\n",
" <td>140.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>354</td>\n",
" <td>67.426554</td>\n",
" <td>21.096070</td>\n",
" <td>40.0</td>\n",
" <td>124.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>211</td>\n",
" <td>68.312796</td>\n",
" <td>21.588506</td>\n",
" <td>40.0</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>55</td>\n",
" <td>65.163636</td>\n",
" <td>21.369556</td>\n",
" <td>40.0</td>\n",
" <td>108.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>47</td>\n",
" <td>77.574468</td>\n",
" <td>23.968952</td>\n",
" <td>40.0</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>68</td>\n",
" <td>73.882353</td>\n",
" <td>22.531258</td>\n",
" <td>40.0</td>\n",
" <td>132.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sample Size Mean SD Min Max\n",
"2 981 88.450561 18.587983 50.0 150.0\n",
"3 1410 82.344681 17.569380 20.0 147.0\n",
"4 1382 80.683792 19.533977 45.0 141.0\n",
"5 1006 78.666998 20.106123 45.0 144.0\n",
"6 536 71.820896 19.421195 6.0 140.0\n",
"7 354 67.426554 21.096070 40.0 124.0\n",
"8 211 68.312796 21.588506 40.0 119.0\n",
"9 55 65.163636 21.369556 40.0 108.0\n",
"10 47 77.574468 23.968952 40.0 119.0\n",
"11 68 73.882353 22.531258 40.0 132.0"
]
},
"execution_count": 327,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary = score_summary(\"Language\", \"expressive\")\n",
"expressive_language_summary"
]
},
{
"cell_type": "code",
"execution_count": 328,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6050"
]
},
"execution_count": 328,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_summary['Sample Size'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 329,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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zcxk4cCBRUVE899xznD9/3mkZ77rrbp56qjcjRgyjd+8eeHp64ufnx8KFb1G5cmXee+8j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\npZIXERExqf8DEwi/AVHGYR4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x129f11e80>"
]
},
"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": 330,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 5381.000000\n",
"mean 2.441863\n",
"std 2.292325\n",
"min 0.000000\n",
"25% 0.666667\n",
"50% 2.000000\n",
"75% 3.333333\n",
"max 24.833333\n",
"Name: age, dtype: float64"
]
},
"execution_count": 330,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(unique_students.age/12.).describe()"
]
},
{
"cell_type": "code",
"execution_count": 331,
"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": 332,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"audition = pd.DataFrame(demographic.groupby('study_id').apply(calc_difference).dropna().values.tolist())"
]
},
{
"cell_type": "code",
"execution_count": 333,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11bb3a198>"
]
},
"execution_count": 333,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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H09TUNI4YIH2+DNXVbxGNFtHZmbv0Fo/bxj1o58/ShMPdvPrqO9x66zU4nRqgAyNniA4c\nOMC+fRZ03UEk0o/FEsNisZBIJCkpcRGP54JVR8fTOBy5fshk7KiqcUJbN26M0NpqQdctRCIugsH9\nZDL9uFxZHA7rmP2UX/f+/R1s3ryFhoYKnE4rfX0tWK1fQFEUNM2kvX0dMHoA8HojbNnyLqmUg1Do\nIJWVTqCU+voZbN/+PDt31o56aTE/XDqdOv39L2KxfG5wcO8D3qSiohFN24uuW/H53PT19WO1drBx\nY8OI4GOx2Ont3U9l5QIGBpJYrX7S6UY0zWTTphdpboauLk4aOmHiBpTXX48QDl89OHMHvb1RMhkn\nfX0DFBfPprr6knHNBufXAb5R9+Voj4/3tTQe41lXfh379h0kkbiK6uqySb+ncDLCtxid9P/kkmAm\nzrlly+pZs2bXcYPSiYYGChgZfPIHxXS6mEymHcjNmiUS8NZb/ScMkHV1u7j//tmsWgXxeO5ym6Io\ndHVZWb1675gDdTBYNDwYlZf7icW24vG0cPPNCaCdaDQ0oh2xmB2oRFFUFAUMow3TLCWbDaGq7297\n5swAmcwuUikHRUUHqapaeEJbjx3rJZmswGKxkEol0LQI6XSARCLJyy+vx2ZTB2+6r8Nud41YNr/u\nSETDMC4ZDjGx2H7q6oIkk1Zcrix1dTXA6AEgkUjxzjteUik7DsdMGhq24vHE6exs46qr7sZms2Ga\nJi++uIvly0cGs02bEkQiVQCk0xCNlg1vu7Q0DJRht/dSXt5AIrEOu70ORWnF47mDdHoGkYhCJpOm\nqQk+/OEZvPHGerzefny+Q1RU3D1cK9gBaG/vYGDgYyeETpjIAcU+3E+aFkfXA6TTAQYGwDTfO6H/\njneyOr7+9fJRw9Foj4/3tTQe41lXfh2JhA23+8Q3E5NhImcKxemT/p9cEszEOedyOcc1GA4NFJpW\njN8fzhso3h8US0stxOMGdnsv2WwnTqdnzAHy+AGuvf0ImnbrmAN1RUWEaDS3jKJYaWpSuP/+2aPW\nraoeKisrAMhmLyWTCVFfX0wo1E9x8QIgF75uuKEIuz1LKJTF5ysFjhGNhkcMijNmlBGJvEMm48Dl\nimK1WrHbW4hGD1JcfBPxeDmBwBx6e0dePju+btO0YLOlhvvD6VS57DL/cD9UVR07af8MBYBnngmj\nKDfidg/1Wxs///mJQffkJ+g0pvn+Op3O6PC233lHB2IsWJC7Bw8u5eqrr+A3v7GTzeZORzZbGtPM\nrdfpdHDffbUsXz6PX//awfr1EVKpBE6nzpIlbgDq6moIhU4MnTBxA8qSJW7Wr+8ilVLxeh2kUq/R\n1VWPYbTj9V43vI9PdzZ4tHA02uPjfS2Nx3jWlV9HTU0bgUD9Kdt6LkzkTKE4fdL/k0uCmSgYQwNF\nebmPnp7o8OP5g+Ls2Rn8/h4aG4vxetvRtEoymRaKiw+OCEGjzTA4HDNIp8ceqB966ApWrFg34pOA\nY8mvr6QkTFXVxTQ1VZFOX0QotBGPJzI4uM495WW0j3ykBF0vH7zHTKGqqp6mpovZutXE7c7VbbPZ\naGioPyEs5td98cWHcLuvwzR7cTp17rwzgMcz/gCgadkR6x76fTwn6CVLili/Pjcz6HRqfPjD1Xi9\nuW3cfPPQBxpahn82zfe4/PJjhEJVZDK9zJrlIxDYiccTH1HTPffMxW7Pvxw4F4CqquxJQ+d46x2P\n/G2/8MIOstm7UVUXun4F8Hs8nsvHnMEarY7RwtFEBrCzkV9HMjmDNWv2TMhs3emayJlCcfqk/yeX\nYpqmOdVFjEf+QD1dHB9Qpovj251Mpliz5sT7hFav3jt8eSidThIKnfrG5PxlTDN3yfNsB8D8+ny+\nCHBmn9grL/dx+HBP3rre/1Rm/tdUjKfu0fpsvL7+9ed4662L0XUHqqqxePF+/u//vW1c6z3dbR/f\n7tOtd6ztnW0/nMwPf7iPv/61aniG7gMf6OKBB+aedo21teXy+p5GpN3TS3m574yXlWBWwKbzAT2e\ndp/JoHsuBuqJMla7J7vuvr4BVqyYnO8PO9+O84kK9+dbuyeKtHt6mc7tPlMSzArYdD6gpd3Tx/nW\n7okKyedbuyeKtHt6mc7tPlNyj5kQQpyGQrn/SwhxYbJMdQFCCCGEECJHgpkQQgghRIGQYCaEEEII\nUSAkmAkhhBBCFAgJZkIIIYQQBUKCmRBCCCFEgZBgJoQQQghRICSYCSGEEEIUCAlmQgghhBAFQoKZ\nEEIIIUSBkGAmhBBCCFEgJJgJIYQQQhQICWZCCCGEEAVCneoChBBioiQSKdaubSMUcuD3ayxbVo/L\n5ZzqsoQQYtxkxkwIccFYu7aN9vaFxOONtLcvZM2atqkuSQghTovMmIkLgsyUCIBQyIGiKAAoikIo\n5JjiioQQ4vRMWTALhUJ88pOf5H/+53+or6+fqjLEBWJopkRRFGIxkzVrdrF8+bwxl5moMHe26xla\nXtOKcTjCp7X8RAbSyQ63Z9Pu0fj9GrGYiaIomKaJ369NULWFTd6YCHHhmJJgpus63/nOd3A65cRR\naEY7wZ/uiT8UGmDlym0Eg0VUVER46KErKCsrOWEbPl8MMIhGi4bXC75xrdfn60JRrEQi5XR3HyEQ\n0Mlmi3C50jgcY7cHRg9z+cs4HP3s3Bmkry9AUVEHimInHA5QVNQNKEQiFfT1tRKN+shkqlGUw/z7\nv7+KoszB4znEwoXlaNpMKioifPObl/Haa/0n1PL00y1s2ODGMMA0DTZvfoXGxktO2jfH93l+G3p6\nkjz55DP4fLUn9PlofVha2kdTUxma5qel5T36+maRTltxOq2k0y00NzeN61jJr9XrjaAoFqJR75jH\nylDtXq+Trq7UuML0ybadv41ly+pZs2bXiMdPtczpOtv1DPV/OFxGcXHfqPvpdJzJGxMhRGGakmD2\ngx/8gE9/+tP87Gc/m4rNizEMhYRUyoqq6mze/CqNjRfT2tpGIHATNptt1BCTP0itXLmNAwc+hqIo\nRKMmK1as4+GHlwIjB5EtW3qAbhYsaKS/P8O3v72BpqZ5KEovJxvon3tuG93dHwM8dHSsI5NJoCga\nphnHYoGiolJUNY3HsxWYN+aANdplr9/9bh/r19eRSqns329gt7uprv4A27c/B1zFxReXsW3bXwiH\nbdhs5aRSblS1HY+nknC4E/gEPt8c+vu3cPgwOJ0VqGqSbdvWEo0GSCQCOJ3dvPRSC01Ni/j979+j\no6MOUNC0AZxOG11dlQSDJorSRnl5KTbb+4Etv5+7uqzs3h0imbTS2tqFolxBTc08enqiXH/9rygt\nbcDv7+U735nLf/zHPkKhAP39+3A6b8FqrSAWC7Fp0wHmzr2UlpaDuN1zqarykkxmeeKJPxCNFo0a\nEPP79tVXj7J//+vY7XWkUvspLq6nsrJ4zIA3Wv+PFvigm+eeO0x/fxXZ7GGWLr2T4uJygsEoTz75\n51MG0vGEl/G8MRnttXC8U702HA4bvb2ZEa+NMyWXcMcmM4rifDLpwewPf/gDfr+f6667jp/+9KeT\nvXlxChs3hti/fw7ZrEo83kdJyWXU1MzmyBELoVCY+fMD6HqWl14KEwodpKWlld7eRei6F6dTJ53e\nR3PzQoLBouGBwjRNduywsGrVQfx+ja4u6/D/pVIqkBtE9u0Lk0jMoaHhEl5/3Up39x78/lJCoX4q\nKy/l8strOHjQQTbbhcezgEymF/gSpmkFujGMXYTDHhTFwZ49OsCI4OJyZbFYdFav3kso5GDHjnfY\nuvUImlaGw9HHVVcNsGoV/PGPx4hGL8MwVEKhajRtC0ePVqLrViyWLNu3h0mlNKCCbNYJqOi6jVgs\nAXiAPmIxB6aZASrJZKpIpw1aWqzY7TejKDb6+4+xbt12du1Kc+RIOeDD4/kA8XgHicTv2LmzjHj8\nIFZrE/F4BdGoQjrt5q23irDbI/zqV2soK5vNe+/tpqfnDkyziFTKgc22mVisj1jsALr+IdLpGoLB\nDHff/QTFxf+IxWIhFPogivI8Pt9VxOMRrNYs6XQATXMTi4UwjDSJRILi4lLi8cYR4Tk/iBw8mObp\np1tIJt1EIj1AKR5PJfF4NwMDl1JaWoGmmbzyynPY7XtPGBR9vhhbtvRgGE4MY4BAoJVVq6C1tQ2/\n/wbsdteIbf/mN53EYrfj81URDsd59tnn+fSn7+TVV9+hs3MJRUU+9u9P0dy8nttuu+KEAfjIEY0X\nXniReLwIl6uPmhp91NnLVGrkrOHjj+9k9eoE8XgRiUSS0tJ9VFbOwOXK4nBYT/payg+C/f1Jvv3t\njTQ01LNjh4nXmwVsKIpCMFh01q/bs72Ee6EHF5lRFOeTKQlmiqKwefNmWlpaePDBB/nJT36C3++f\n7FLESXR06GhaJYqikEoVEQy289ZbWXp7D5JO20kmrYRC/VRXFxOPN7J9u4dsNkpFxSzSadi0aS/N\nzVBRESEaNYcvsbndEI/nBvajR9cxMFBJKqUOrisBQDic5fDhFjo7M3R378Pnux6fr4qBAT89Pe+i\n617S6RiGYR+stoTcB4sVwACcmKaOaeq0tYUBOHDgAPv2WdB1B6qq0de3hyVLPouiKGzd2kF/fz12\nu594PMgrr2ynv99KW1uMTEbF4bCjaQ5AwzBqga0YhophlAEJcpdcqwe33wFcDmQAN6Z5ERAEwpjm\nXEzTBDyYZvVg7XvQ9RvJZGowzTpM8y0UJQNsxTSXk0z6yWRK0XWdbDZAJBIFZpBO19HZuR9FaWLR\noiaOHq3HMDZhtS4GXiGT+V9ks350fS7wV0zzckzTJJmcid2exDAsQATDmIdhNKLr/RjGNo4d20cm\n04lhzAfc6LqC3Z4CIB5XCAYHSKX2j7hMvGbNTnp6voyiWNH1GuBXGMbHMc1OdP392ZuOjvQog6IB\ndANeurvfQ1GuIB6v5siRCkKhg8yff9mI4J5MFmGaNgBUFRKJ3KxYV5dCLJYikXBhGFEikdnDx1r+\nALxp0wH6+j6LxWKhr6+LcHjXCWHz9dcjhMNXoygKmmayadOLNDfD73/fSV/fp7FYLITDx4hEWigt\nXYimmbS3vwXMPeG1lD+LtWfPXg4fLuPoUSsDAxlisWPMmjUH0zSpqIic1mv0ZMa6hDseF3pwkRlF\ncT6Z9GD2+OOPD//8uc99ju9+97vjCmXl5aPfd3Qhm+x2z57tJh5PDM4OHUJVrwcCpFKVZDLPY7df\nSTbbRnd3LTt3RonFktjtKna7immaOBwm5eU+fvSjG/iXf1lPd7eXdLqdQGAOO3cewu1OY5p2HI4+\nDMNBXV0Cv7+DyspientfAZpJp91kMlai0Qh2+0VkMlF0vQKooqzMSjD4JMnkUaCdXECyDVbfCswA\nkqTTEX71qw5aWzUslgpU1YGq6iQSfrze3ExAOl2C0xmguDhAMBghnb4UmE82ux9d34XT6Qd6gDiK\nsh1IAi9gsdQC+8gFizLgCFCDzZYmm60F1qMoCUwzCPSjKB4sFg1dT2CaEQzDClhRlCyxWBaLxQRM\nysrCRCIOIAtowACm6SOR6MYwBlCUKLFYikxGxWbTsdtVwAnMQlXnkc12ASlstjCKEkZRyrFYFAzD\nxGI5iq57sFgsKIoX02xB14+iqnux2a5HVSuw2xvweHYzd24dvb09FBfX4/E4iMXimGYAmE88btDd\n/Tzl5T7icT8WSwum6QAiQAVWq47d7sFm68TrteFy6dhsruE+B9C0YsrLfZhmBVdffQkAmzZl6egw\nUZQo0WgMVXXg8TgoLgYw8HgceDxxYjEdq9WCx+PC5TpAZWUtqdQ2dH05FoudbNZFIhHB43GM2BZA\nSUkd/f0pMhkLdjs4nYETnme1qvT17SWTcWCzacyYoVJe7iObLUVVczO9drsf0wzh9R7A7U5z6aWz\nT/o6nTXLQlubfTCcRoBrAR+1tRfT2flrfL5jNDTE+K//uoGysrN7nScSNnw+F5pmx+ezUF7uO60Z\nL00rPuk+Olcm+7yWvy9M02TWLMuUjCkyjonxmNKvyxh6BzMePT3Rc1hJYSov9016u6+5xkcsto9U\nyoHF4sThCAI6Vms/Vmsl6XSGSKQHRbkOj8eD3W4lk1kLKLhcGlde6Rqs2cr3v78EgH/8xz4OHFg8\nfL+Zpu1trr/6AAAgAElEQVRg6dJbhrfp8ah8/vMzeO65ObS3hzHNBB5PP4ZRDnTh85k4HIcAKx7P\nAaqrb+Kii/y89pod03wcqCUXjhqwWp0YRgqXayHd3bOIRvsxTYXKytzsSiqVIhZLoSgKTmeYWCxN\nNmtgGGCzJUmndez2AKaZoKiohGi0j2y2Ebf7cqJRDYvFSyBwGUePvgtcR242ZzbwKpWVjXR29qAo\nF1Fe/kHC4QGSyacoKgrjckUoLjY4fPhddN1FNtuGongxjAGczjjZ7B4cDgsOxx6s1g9itZpkMjoW\nSxder4NYrAtdj2Oa7cBbwPWk0zoWi45ptmOaLSjKUazWBi65xMvhwx66u18gne7D4QixeHEphw//\nnkSiBIfjAKp6LWVlDiKRIlwunZoaHaczQzo9i3S6CL/fgd+/DYCZM9vRtMvIZLrweLJUVlbS0xPF\n6RwgHr8Mi8WCYSQxzW2UlR3F4XBw2WX7uOwyK36/RjpdRGdnKu8yW5ienigOR5iurhRer5NgMEgq\ndTGxWAmq6iGReArQ+dCHhu4x28mXvmSybt06+vur8Pt7+elPlzJjRhX//d9eNC0IOFGUVqCceFwb\nsS0Avz/MwMDQzImJyxU74Xk2Wwxdr8MwrOh6FpttPz09UebN03jrrQjZrBW7PUFFhYNFi2ZhmiY+\n366Tvk5vvLGKNWveGvwQSRSfz0E6rQNWFi2axTPPfIienijZ7Nmf31av3js849XVZRKNnt6M19C+\nOH4fnQtTcV7L3xeBgMaNN9ZPeg1T0e5CMJ3bfaamNJj9+te/nsrNi5O4555G7PY2QqEsra0pAoFa\nbDYbzz/fTyJRTjrdiKKkgS3Y7TOZOzeJzRbggx/M4vdnWbas8YR11tXVEAoFh+/zUtUApnni/TDV\n1XESiQAOh41E4gqs1t9zww2Xj7jnaOtWE7e7hPnzS1GUerZtU7DZatB1E8PwYLdrZDImV1yRC2Kz\nZlk5dCiB3d6L06nz8Y9X4vXmLvn83d9ZWLfuWfr7q5gxo42amptRlF6qq30oSi/l5Qa1tS727NmM\nonRRU9NGaamDZLKdqiobicQBMpli7PYwM2aYXHTRu1x+eRdHjoSJRJLMmBHk9tvrMU0Pfr/KkSMf\n5J13XCSTdo4cKSca3YHXO0A63U5Nzd/w0Y828Oqrc9i//1ns9jocjj78/rlUVJRhs1mIRlvxeGJU\nVpbhcGzE653FggX76OgwSKcPYLdHmTFjHV7vXGbOPMTcuXdgGB6cTp2Skje59trchzH+8pcGwuF3\n8PsthEJBios/wOLFpWzfXkN39x5gNlarxuLFFTQ3z2b1ao329pnD+6uq6hgAf/u3tfziF4+TSAQI\nBIJceaWdpqYIfn+aZcs+Mjxjk0ymTnqZbejym6YVU1fXTzp9lHS6n+LiNB/4wKXcf//s446k2Xz1\nq9eccHxVV5eSTs/BNK2Ypp+iorV4PO4TLuk99NAVrFixjmCwiNraPhYtKkPTWkY8b86cWYTDseFj\ndc6cWQD8x39cyYoVrxAMFlFW1s+iRcUnLHs8l8s5HI58vhjr1wdJpVScTp0lS9ynfjGehrO9VHe2\nl0ILXf6+EKLQyRfMihHyT2DJ5AzWrNlDKOSgrm5o1qSXkpIoxcXzWbw4F7Dq6naxfPnxg+j7qqqy\nXHaZf3hgr64uwm4/cRAYGjjD4TIuuqiPhx5aSllZyWAd+wiFHNTUtBEI5J6/aFElM2e+Q0NDEp+v\nFIBoNElr6yH8/hsAaGy8mPLy3E3XuW0tyLvE8/5An0xexZo1bYRCGj5fHAgQjWbx+4tZtuwLJ1wW\nyp+hyPWB5ZQn/tWr92IYlw7O1pUA5SxYMJ+tW/243S4Ampouorh4Bk1NpbS2DhAIzMZms+F06kA1\nCxbMz+vzeSSTCwbrduD3lw/ftL1qlZ94/KLhbdvts6iqyvX5rFnvB910+iJCoY14PPV4vUeZP//D\n2GzOwb5sAUYftO+7bzFlZSdue6xj6mSPl5f7cDjCI/qzqmrXmH2Zb/nyCp544hiJhBO3O8W99zbw\nxS+eeDyWlZWc8tOPxx+rQyF0PMuO5Z575g6+4RnqwxPvSTsbZ3vzvwQXIQqHYubuSi5403UqtFDa\nnR9E0ukkoVB+2Bn7E1y5GZPxf+JrrHaPZ12nu70zcSbbyF8m/2sghr5+oaTESyyWygtdJ3/+eLZ3\nYnDclRe4T177WMucS+XlPg4f7jnjfTaR+3syjp0hE/n6nsy6z1Yhndcmk7R7ejmbS5kSzApYIR3Q\n5+uAdT4Y6tuJ/Ab8sw2Okzm4T7f9PUTaPb1Iu6cXCWYXqOl8QEu7pw9p9/Qi7Z5epnO7z5RlAusQ\nQgghhBBnQYKZEEIIIUSBkGAmhBBCCFEgJJgJIYQQQhQICWZCCCGEEAVCgpkQQgghRIGQYCaEEEII\nUSAkmAkhhBBCFAgJZkIIIYQQBWLMP2Le19fHE088wcsvv0x7ezsWi4Xa2lqWLl3Kpz/9acrKyiar\nTiGEEEKIC96oweyJJ57gxRdf5JZbbuE///M/mTlzJqqqcvToUd58803+4R/+gVtvvZXm5ubJrFcI\nIYQQ4oI1ajCrrKzkV7/61QmPNzQ00NDQwL333ssLL7xwTosTQgghhJhORr3H7Kabbhr+OZ1OA9De\n3s7GjRsxDAOAj370o+e4PCGEEEKI6WPMe8wAfvzjH3P48GEeeOAB7r33XhoaGtiwYQMrV66cjPqE\nEEIIIaaNU34q8+WXX2blypX8+c9/5s477+TRRx9lz549k1GbEEIIIcS0csoZM8MwsNvtvPLKKzzw\nwAMYhkEymZyM2sQ4JRIp1q5tIxRy4PdrLFtWj8vlPO+2cTY1eb0RFMVCNOo96/oKsa3i3JH9LYQo\nJKcMZtdccw133HEHTqeTxYsX89nPfpaPfOQjk1GbGKe1a9tob1+IoijEYiZr1uxi+fJ5I55zJoNP\nKDTAypXbCAaLGBg4gNd7KYbhxum0kk630NzcdC6bddLafb4YYBCNFrFjxza2blVIpSowjEOUl9cw\nc2YxNhts3vwKjY2XjNnW/PaVlfXS1FRBKlVKa2sbfv8N2O0ugsEoTz75Z3y+WioqIjz00BWUlZWM\nWtNYAfFcB4Cx1j/a/53u4xei8bx+hBBispwymD344IN87nOfo6qqCovFwkMPPcS8eXLSKiShkANF\nUQBQFIVQyHHCc44ffJ5+eht2u23MAfm557YRDC7DNB0Eg1kslmICgQogxe7df+aFFzKjhpWxBvlb\nbqnmxRc7T3jeUFAKh8twODqBLJFI1WAoXIBhWOnpSWOaGSoqLmbz5ii6fhvgAmYRDr/OoUMXoes9\nuN1dbN1agccTIRaL8KUvXQWMDCgvvLCdbPZTqKqNffve5vXXB2hosHLkiJdkch1O51wGBvbhdi+l\ntraaaNRkxYp1PPzwUp58cjdPPllJIuEkHI4TDr8BzAHamDGjhvr6+ScExHg8wcaNDaRSKjZbis2b\nX6OxsWG4D8A3rj7MD3/5obC1tY1A4CZsNtsJAeN3v9vH+vV1pFIqTqdOOr2P5uaFo4aSswkrx7dh\nyZJS/vf/3k0wWHTS40XTinE4wqcMf+cqLHZ1we7de0gm7bhcaRwnvnym1HQKyUKIMYLZt771rTEX\n/P73vz/hxYgz4/drxGImiqJgmiZ+v3bCc44Pbxs3RshkKkilrFgsGo899gwlJXPo7T3AsWNz0fUS\n+vsrcDjeweGoJJl8DzhAPD4DaAcS9PamUNUIsdh6fvGLe8Y9+H/jG0+webMTTfOjqh38v//3FpWV\n82ht3U02eweq6qOjw0U6vQe73UM6bWKaThyOYjTNCmygrU1H1x2Al9xhrACNGMblQJhE4iYOHSoH\nNP7P/3kaTSvH79dIpzU6OxejKApHjwbp6VmLolSQTu/Fal2CqlbQ1ZXFNK1UVFxBPO5E10NANaaZ\nZccOk1WrDvLLXx4mGr0KsBIO9wG1QAlQy+HD+4lEGkgmj5HJHMDhsOFwdBIIqITD1WQyDrLZAcrL\ny6mpaRyelQsEZhMMtuJ2X4dpFqOqseHwlj+Lt2XLu0AlCxaUs3lzF11dbfj9lXR2Jqmr62PRokp0\nXeOll3qGB/O1a4/w5ps+slk3VmuCw4cPEI162bEjTH19FlVV0fUsL70UJhQ6yJtvBjly5CDJpAun\nM0ltbXDcl4yPPw4eeWQdR49+GF13oqopUqnN/OQnHxs+LrxeJ11dqVPO9Ob3wXjD4nhCTXt7BwMD\nH0NRFDTNpL19HTBvSgPR2bZ7orYtQVCIyTdqMLvyyisnsw4xivGcJJctq2fNml0jnnP8si0trYRC\nxWQyTpxOnY6OTuLxRWSzKj09Gul0EV5vgIGBBPDu4JoPk0otxWabC7wO3EguCLmBUjStiVRK509/\n+hHXXfcqwWAXmUwNhmFDVU0gRHPziTMSL73USybztyiKDdPcxq5dMWprq+joiAM6Xm8xqVQSWEQq\nVQpYgSyZzExMcwCAVMoAIoBnsKY40I5h7AT6gUpME8BKd3cpGzdW4nTqwBYUJVdLV9cedP3L2O0u\nDGMWhtHK0aO1ZLNVQAu9va3o+j7S6Rq2bz+IYQxQV2cnHm+kv38HqZQFVbUCaaACmAdoQCcDA17A\nB8wlmWwgmZzBwMDz2O3VgJV02kE8/iq//KVGKnWQkpJbcDjq2b+/HlVto6FhDocPd/Duu410dflp\na1Po6XkKqMMwjlBdXUcqNYOWlk40TScS6SOZ7MEwSlm0CHbv3ktX12wGBkpxOnXeeOMVdP12wIqu\nZ9i79xU2bqwkFOonHu+lqamKlpZ+oIx4vJF33nmPWGwmPp+P3t4oHR3bSSY/SCjUT1VVLU1NVSNC\nQv6xtnZtJ3b7YiwWC+k0tLR4UNW5KIqCrpv85S/bgPHN9OaHvM5OJ3V177FoUdOIEDnWpeRoNMJr\nr5WSSllHvQRfV1dDKBQkmbTicmWpq6sBTv8S50QGmvxtHzrkZ+fO1/D7Z0/KjJ5c2hViao0azD7x\niU8M/3z06FFaW1u5/vrr6ezspKamZlKKE+M7SbpczpOeOPOX7e310N29C79/NqDR329impUoikIy\nmcU0+0kmG4H9wF3kZn8uA/6E1To0M1UF2MiFEAumaQAJDKOR7u6riES2ACZ2exmplMlbb3UA8N57\nbbz99pXouh1VTZPJ+IDAYHDKAFdhGBUYRgTDyGKatsEW+IBycqErhmHEgXeAm4FLgNnAi4M/vw0s\nBYqALnKXNwNAFgjR1taHzaYRiRwjHJ6PYbjQ9TkoSi/ZrAdIAW6y2QwQBXoxDAPDyALHME0Puq5x\n4MBefvlLO/H4MeBZstkyYBewnFxIzA72VQA4AFwNzABM4G0M4wCm6Rz8+U50fRaatoe+vl6gHkUx\nyGTcAPT3Z0inQ7S1KRw92ophNGG1ziebfZPDh6uorp5LNOrHNN/GMBqxWmtJpx/H47mGrq52uro+\nwtGjNmw2K7peMViPE0gCRaTTAdzuDxIK/QmP51KczqPMmnU9AIriJ5PZQCwWIJnsxuGoJp0OEIlU\nkMnoNDWNDFP5x1o8foxwOEllpQfTNNH1XjStHXAAGi5XHHh/phcYdaZ306YEkUgVANmsm/b2Iyxa\nxIgQuWVLD9DNggWNbN68na6uYvz+XCDt7NyL2/3h4eP8iSf+QDRaNCI4VVVluewy//CMc1XVMWB8\nwTHfRAaa/G1HIjEikTn4fJeMmNE7V0Zrt8ykCTE5Tvl1Gc8++yxf/epX+d73vkc4HGb58uWsWbNm\nMmoTnP7gMNqyuu7F75/N4sUXs2DBfHw+Dw5HDKs1gWmagD4YiLzkQo2dXG4vZdEiJxAefMxGLnwk\nyV0+BIhjmgGgCXgDeA9F+SvpdIZVqw7y5psamUwxplmCrvsH16EDBrnA8h7R6HvkZp5eJR5/HdgL\nFA9u0w60oSjHButwDW7XDZSRC5EzgYHBusqAvwLbgdeASsLhOnp75xAKga7PHKxXwzSduN1l5ELD\nAaB3cNsLUJRSYBFWaynl5fNQlFIM40bi8WuBS8ldvqwZ7IdScqHHNdgOP7kAG83bI1Yslnmo6hyG\nQpjFksBi6UPXVY4c6UVRMrhcu7DbW8hmd2EYCwmH6zCMa4DuwX0UANLY7b14vT04HGVYrb14PBEW\nLqzj/vtnMzAQIxIpJZksIhwuAfrIDeYNwNzBfgeLxUFtbRH33z+bpUvLUdXc8WWaIWy2m/B6l2Cx\n3AyEAFBVDdPUAUinM7S2trFq1UFeeilMNpsFoLa2FFXdid3eQnHxLlQ1jGn2Y5q5f7PZ3KznLbdU\n09X1PNu2vUZX1/Pcckv1SY7i9ODxCX6/A4+nB4+nBadzJ42NDQCkUiqpVK7uw4fjRCIXDYbIKnp7\nXcOvgZ6eOB0ddjZutLJunZWnn24BcjPOdXW78HhaqKvbNTzj7Pdrw9vOD46JRIrVq/eyatVBVq/e\nSzKZAs7utXq8/G0XFfkoKurFbu+lpCQ4PKN3rozW7qHgGY830t6+kDVr2s5pHUJMV6e8+f8Xv/gF\nv/nNb/jsZz+L3+/nj3/8I/fddx/Lli2bjPqmvfHcPzaeZXOX8XLLmqbJwoUWBgZaSaUchEIHSSQU\nFKUdCJILXjpgweE4yA031PPqqxHgVXKzWL3AO1itPWSz+4CPDW7RCei4XCbpdCc+3zXE442kUr0o\nSgqvtwTTNMlkBtD1tWSzJeh6K6p6J4pSBLhR1Va83nIGBhRgD1ZrKdlsP3AYu91PKrUHuI5cAPMA\ne8gFoKPkgmEZkAA6Bv/fAEKYZgKLRccwfNhsHhRFIZu9CvgtqnrJ4HouH2yHGyjB6Swlk4lgGBCJ\nhMjljiSKMjRr6MNiKcIwbMCb5EJtbHAdLcAhoA5oBWKoaidFRVvRdTfZ7A4UZT4+nwvDsKHr7Vit\nJpWVYebNczJ/fpYDB+xEo1lMMwEcA2agqjZ03cBm01i8uBSbTaOrqw+/30BVE3g8CVatOkg4HAPe\nwTTdKIoGJLFYNmEYPmAApzOB3d6C06mxZEkRMPKS+IIFbo4cOUYy6cLv78fp9GC3t1BfnyQQOIDH\nk6KzM3fvUzzuIpXS2bu3nwULyrn00nlUVm6koaEYvz/Le+9dRjDoJ5u1YbW6KS6+GIAXX+ykqupW\nGhqcxGIpXnxxF8uXl4w4hpcsKWL9+l2kUg6cTo2bb76I5ubZrF6t0d6eCz75xzZoqKoxfJwHAgmK\nirpIpVQSiS7c7lmk041omsmmTS/S3Dz6jPNotwiMNjN2Nq/VsbY9a9b795jlz+idK6O1eyKDpxBi\ndKcMZhaLBa/XO/x7RUUFFsspJ9rEBBntJHm6y95889B9OC34/Rpf/vI1g5+MzBIIJNmzp5RUqodg\nMIGmPYeqzkBV+1m6tIz775/NH//o4t13rRiGAtix2UqoqZlHLKaRSu1FVXuorOzDbu/B6TyApg1w\n223XAlBREaCraycWywCqqnHTTX68XivBoEIwWEEgYKLrGrt3R9H1hZSV1WCxzCWVilNZ6SKViuPz\nXUp19Xza2nrp6HgKmImuHwNsWK0a2awGbCM3m9QLHMRqrSSbbcVqvZTSUhdWaxZN6yeb3QU4sVqD\nuN3VfPGLH+KnP20jFls0eN9bGIigKB04HAOY5iFU1Y7Fsg9FWYLdbkPTUoAFm60UTasHesjNnPUA\nPlTVQFH8uN0vo6pzKS7u4u67L2PnTg+plINs9mKOHXsCVa3DYunntttup6rKTzyu4fG0cP/9s3n7\n7Xb27vWQzVqxWCqIxdZitfbidHbyoQ+peDxObr01BliJRrsHP5X5UeJxG05nCl0/ht3eABiUljoJ\nBGzE41ZcLjvz5vmZPz+L359l2bJGYGRA8fs12ttnoygKiUQpf/3rLux25+CnKq+irKyEVasgHs/N\nXjY2NnDo0Ot4PBdRW6vxjW9cN3yZ68kn95BOz8RisWAYBpWVrwDjG+jvuacRu72NUGhkraMd25df\nHqOv7z3SaTdOp8add16E19tBKOQgEtmLzfbx4e3lZmJHN1pgG63us3mtjrXtZHIGa9bsm5D1nu62\n801k8BRCjO6Uweziiy/m8ccfR9d19u7dy5NPPkljY+Nk1CYY/SQ5EcsOzU7kTvxthEJ2HI6L2bmz\nj74+CxUVNh56aCkAv/71rXzlK5sJhQKUlga5/fZZmGYEny8AWIhGPfj9KsuWXYXL5WT16r20t3sA\nuOGGOv761+34fI7Bgf1myspy2849r2pwgOsnkbBSU+PG778Cq/X3fPSjM/D5isgNvN2UlPj5m7/5\nOKqq8txzB+jsTOPzNRAKuUmltmKz+bDZMrjdbny+EOl0H3V1CRTlEE6nxj33zObpp98gHK7C5zvG\nZz87G6u1haVLVbZu/QOpVAWZzBGqq4uYMaOEzs5eKipu4Oqrq9m6tYwdO97Aaj2I3b4Hq/UYDscx\nLJZ2VLWEQMBGKpWgqKiCqioTp7OYm2++bvhm82QyNdjPWfz+UpYt+18n9FX+gPeRj5Si67lZTbs9\nQVlZBY2N1fj9ZSe9vycXlHL35y1YUM7u3Udxu7vxeCLcffellJW5CYXs+P1eli1bMOb9Qfkho7Oz\njSuvvGt4xmZoZit/oFZVB0uXlrN8+ewT1vXTn17HV77yOKFQAL+/l5/+9DpgfPeYjXYMj/b4+8fy\nUJBbNNxOny/G+vUDw58WXbLEPWr7xzJaQDmb1+pYztV6T9dEBk8hxOgUc+hmglEkEgl+8pOf8MYb\nb2AYBldffTX333//iFm0ydDTEz31ky4w5eW+87bd74eQsW8Uzn+e09nPjh1B4vEqiov7hr/vKl8u\nxCwcnMlJ8te/PoPPV0s0epgPfOAO3G4fpmlSV5e7vHQmdeR/PcE777z/1RSZTIbe3g00NNTjcIQG\nA2zZiC+n3bEjSH39tahq7j3P0OzXePrq+O/zGm/tJ+ub/FrP9kbtVasOEo+//2ZsqE2nW994232u\nnG29E72e8/n1fTak3dPLdG73mTplMHv00Ue54447CAQCZ7yRiTBdd6y0e6TRBsWJGiyP34bPFyE3\nIzi+P/WUH47yA+J4nO3+nsg+yHc2bRoPOc6nF2n39DKd232mThnMfvCDH/DCCy9QX1/PnXfeyS23\n3ILL5RprkXNiuu5Yaff55WzCUaG2+1wFviGF2u5zTdo9vUi7p5dzGsyGvP322zz77LNs3ryZhQsX\n8t///d9nvNEzMV13rLR7+pB2Ty/S7ulF2j29nE0wG9fHK3NfcZAhk8mgKAp2+9ifZhJCCCGEEKfv\nlJ/KXLFiBRs2bGDevHnceeed/Nu//RuOQvsrv0IIIYQQF4BTBrNZs2bxxz/+kbKyssmoRwghhBBi\n2jrlpcxPfepT/Pa3v+XBBx8kFovx4x//mHQ6PRm1CSGEEEJMK6cMZt/97ndJJBLs3r0bq9XK4cOH\n+dd//dfJqE0IIYQQYlo5ZTDbvXs3//RP/4SqqrhcLn7wgx+wd+/eyahNCCGEEGJaOWUwUxSFdDo9\n/Lfh+vv7h38WQgghhBAT55Q3/zc3N3PffffR09PD9773PTZs2MD9998/GbUJIYQQQkwrpwxmd911\nF/Pnz+fNN98km83yk5/8RP6IuRBCCCHEOTBqMHvmmWdG/O7xeABoaWmhpaWFu+6669xWJoQQQggx\nzYwazN58880xF5RgJoQQQggxsUYNZv/+7/9+ym/41zRN/gqAEEII8f/Ze/PoKK470f9TVb0v2lor\n+2YQiwBDsI0dHBsM3pLBvyTkkXjCccaTzLx4Mn5ZfN6M/ZwNJ3mT5LwcH8ZOJpszydhm7DgJeImD\nwQYv8YLB7AgEaEFo75Z67+qu5fdHleQWICQEyDK6n3M49FJV93tvVff99PfeuhIILhID3pX5jW98\ng6eeeopEInHGe4lEgscff5yvfe1rlzQ4gUAgEAgEgrHEgBmzhx9+mCeffJJPf/rTFBQUUFlZiaIo\nnDp1ip6eHtatW8fDDz88krEKBKOeVCrD5s31hMNuQiGV1aun4vV6PuiwBAKBQPAhYUAxk2WZO++8\nkzvvvJPa2loaGhqQZZlJkyaJuzLHCEIyBm6DgV7fvLmexsb5SJJEImGyadM+1q6dfcHlne82wzmu\nQCAQCD54Bl0uA6C6uvqiyZimadx///2cOnWKXC7HP/7jP7J8+fKLcuyxxMXsaMPhHh56aBcdHQUE\ngx1IkkksVkFLy0GOHatE1yuQ5RZ+8Yu3qayci8/XzsmT3cTjkwiFuvjZz65j3LjKfjEFgwnAIB4v\nIBCIIUky8XjgnLE2N7fx5S+/QTRaTjDYwu23T8IwKnC7u9m7t4NIpBSXq44DB7IkEuPx+RooLlZI\np6f3iyO/PuXlMR58cDElJUVntFt+jAPFNZBo5b/e3Z3m/vu3M2PGVPbs6WTKFBWn04Om6WzbFiUc\nPjHkczQUsetfdo7779/KjBlTz9nOjz++nyeflEkmJfz+FInEfv7+75cM63r5sCLkVCAQfBgYkphd\nTDZv3kxxcTE//OEPiUaj3HHHHULMhsH5ZmZO75RWrapiy5ZWwmE3zz33Dg0NK9B1N6lUEIejh7Ky\neTQ2dgEBwANI7N+fYP/+bqAViAOzqKtzsHDh73A65wL1VFUtweEoI5VSkaR6PB4P2WwrM2d+hMWL\nx58z1r/7u5c4cOB6wIemlbJ3717GjSums7OdRKIRh8OFpkWAf0JRXITDezh5MockjePYsQQf//hz\n3H33cn7/++0cPlyIabpRlARdXX/md7/77Bnt9tZbnUA7NTXVA8bV1gYHDx4inXYhyxFeeuk4zz6b\n4NSp44TDp8jlKlDVfeRyXhSlFcNoRVF+g9s9B9PsoKamgmTyzOOfTR7LyoK0tSkcPBgmnVbwenXc\nbuWM87drVxhVbSebddPZ2U4u56S5WSEc7qaycj4LF1adUd4TTzRw4sRcDMONLBs8/vjBIYtZftkD\nyZM7w3sAACAASURBVN+5pOdSC9FQ4oPz/8yMFoRQfvg42zmD4AcdluBDwoiL2a233sott9wCgGEY\nOBwjHsJlQb4wKEqyTxiKiyMsXFiCqob6dVJ79hzi7benks168XgM/vjHZ3nvPVDVKtJpHZBRlAJ0\nPUw2m6a5uQGIADcDTiAEeIFZQCVwCCgCVGAxudwyIEZT0/MUFo4jHu/EMCZRWDiHVMqkvb2DvXs1\nvN4Un/hEjK9+dRsdHQX4fA00NxvEYpXU1SXtMnUgg6oW0NQ0AV2XgEo0bTGQA5LoetZuiUJM0wlo\nNDePY8OGJJFIEPgYMAlNM9i2bUNeu70vPs3NceLxeo4ejSPLrXR1tfKd7zRTUHCKz31uKg7HRJ57\n7q/U1k7HMIrQ9RMUFl6L3z+dY8fGkcu143Jdjao2AjdgSWwMeANdv5pcLs3Onc/Q3FyI35/i1lud\nfXH8y79sZ9u2cWiaH4fDJJHYzp/+9HmOH2/g6NEZ6LqCaaY5cuQtdu6MEo83sWjRx/H5gjQ0dJNK\nOaioKKWtrQ5ZnkNp6URiMYlcLsvChZDJqDz2WBPPPpugvDxGU1MGVV2EJMkYhs6JE+/yyCMnhpTN\nfPrpWrZu9ZHJWPJXUTGHK6+cOGAG8XTpyd/f6TR4441XqK6e1a/DuhD5GEp8p5/7fOnNZ7RIUH4c\nx47VU1p6E06nc0ChHC1xCyzO9nn4ylfKPuiwBB8SBrWibDbLiRMnqK6u5tlnn+XQoUN84QtfoLy8\nfFgFer1ewLqz89577+WrX/3qsI4zFsn/8n355UMYxmdRFIW6ugjQgd9/PcePt3PoUB233FLNq6++\nx5EjbjyeAhobrwAkHI4pJJMGr7zyJnAXoAB7gQ5MczKQAiZimguBRkCy/7mwxKMAS8aqgflAAvgz\n0A3UAteTTk/CMPzAy0SjQWA3cA3RqJto1OCxx97A6fwomuZHVZPI8joCAS+QBQzAjXXDcBu6fso+\ntmn/awA+jnXpynb504AwMJlIpAbwYWX0VEBH00q4886dlJfHiES6eP31xWian0ymw66jC+gAlqAo\n0wmHk2zY8AZf+tJKjhx5k2z2VhRFxjQLiEaztLRk0DQXpgma1owlrD77ODKWVNYCHWQyDlpa0shy\njJdeqmfiRAfhsJstW2Ko6hpAJps1eOWV/wQgkfAgSScwTTeRyFEU5UYSiZmcOtVFInGQW265hqKi\nKWSz+3C5JuJ0duByzQPA6cximtbyNS+/XE9np5twuJLjx4tJpVqAjB1fGk2rJJms5q23DgAV1NSU\nDdjpv/56jGj0GiRJIhYrI5s9ypVXTkSSJMJhq7xw2N33N3Q1TWXbts4+Sdi+Pdy3/8mTHTQ1ZZk4\nsbpfhzXQ8OxQsnLbt4epq5uOrjuIxRy0t7+LpmXwerPkr+bT2HiSnh6rDFU1aWzcCczsV9dLlVU7\n3wxKfhwnT8qEw1HmzSvt1+YjEbdgeOR/HgY6ZwLBQAwqZvfddx/Tpk1DVVU2bNjA6tWr+Zd/+Rd+\n/etfD7vQ1tZW/umf/om//du/5bbbbhvSPmVlYzMNnF/v3/ymga6uJUiShM8n093dRGFhMdBOOh3k\nyJFTZLNpysq8+P1uDh1SiUSqUJRCdD0GyDgcLgzDwMqC9f6ingM8iWE4gX3A9ZhmK9CDJUV+oAtL\njEqx5KmFXgmBJNCMlS06RDYbA9qBcViZtjIggq47gQy6LpPLlSJJHkyzEF0/aAtcGPgIUGiXdx0w\nESsz9xdgvB3LX4Biu4w2LJk8BnzSrlMWS5R0LDlr5ZVXxqMoKrregK7PxjSx3wsCU+26SHbMWRKJ\n3fj9bnTdhyR12u0VwTQrgACGccou12+3k4H1cToKzLNjTQHzyGYXAjonTjT2nb90ehdw2I43TSJh\n8OMfH6GjI0Fl5XwcDoV4XENR3LhcDnw+CVUN4Pe7KShwkkgYuFxOKiocQDuBgE4w6COVeoPa2qm0\ntu7FMD6NphUiyzqy/DLQjGl6kKQoRUVO/H43hmENVfv9VsehqoVnfNbcbh8ulwNJkvB4wOGwtjdN\nkylTZMrKgkyZIlNf70KSJN577wiwGKikq8uks3MTHo+1v2m6cDp9/crr/T8QsK7HuroekslqZsyo\npqvL5OWXD3DXXTX9rv/81zs7QdOqbOGSyWZDwDySSYPm5s08/7yXri4X6bSDUGgf2WwAny/LnDnT\nzqhrfhwDtcdwOHvsZQMeOz+O4mJIJl1ntPlIxH2pGM2xXQzyPw+95wwu/3oPxFit93AZVMyam5t5\n+OGH+eEPf8inP/1pvvSlL/GpT31q2AV2dXVx9913881vfpNrrrlmyPt1dsaHXeaHlbKyYL96NzQY\npFLWEJ7LlSMYLGXBgiDvvHOCXK4IWZ5ENttKJFJHMqkSDkfQNAldL8cSpzim6USWM1jzxEwsIVGA\nHhQlhq73AOVYWaDZwA5gCrATuAo4jiUfpUAn8B7wUWAS8AIw136vCngDWGI/n4ksVwJgGDsxzRos\nEXoHSwwL7VhOYsmcG0t6AvZ25fbz8VjyVoWVFfsz1rBlD5bMSVhStg1LkNqAGgzjJnTdxDSjyPK1\nSBIYxl+Bm7DELATsxTRNwECW0ySTKk5nF5rmw/qozEaWf4Msd+Bw7EHTpqPrx7AyZX8EZtjtc6sd\nQwGgIElWljiVKuw7f4oSR9en2sdNIUn7SCRmUVAQpr39NUKhUny+WpzOa8hmNQIBH93db/PXv0aJ\nRhsoLLyabLaQsrK5lJbupbp6hj3k9SmcTic7dhSQzXbgclWiaSaK4iEQ6EDT/GhamJISF8mkiiwn\nAD/JpIppmoRC0TM+a1dd5eKll06RyTiYNClDKNQEuCgtVVm+fCqdnXGWL69k06addqagg6lTp5NM\nqvZ1LKNp1v5+fzdVVUa/8gDc7ihtbRk7Y2bg86X69m9oMOjsjFNXl2X37pa+oUhNy9LZGaesTKar\nK4auK7jd4HIlgDb8fp1wWObAgSuQJIlk0g+0s2DBFLLZHIcObeXb3zb7Zd/y4xioPYZD/me39zkM\n/L2WH8fkyRMJh7cDU/u1+UDbX8y4LwWnf69djuR/HnrPGYh+bCxxITI6qJjpuk4kEmHbtm1s2LCB\nzs5OMpnMsAv8j//4D2KxGI8++iiPPPIIkiTxy1/+EpfLNexjjhVCIZVEwkSSJGbOnEY4vB2/fyoV\nFUcJhyeh6/sJBuOEQt34/bXI8lFgNYbhxRKabfj9IXy+bk6dSgK/wRKeE8BinM7e+VzPYJrTgLeB\nNVjZqWP2MTzAFVhCpGLJ0NVYIlIEFCJJLkzTAwSRZT+GoQFRJMmHJOWQJAOIYl1+U4A6LImJY8li\nzD5usb2NCzCQpCpMsxN41Y4lgiVunXZMr2DJ2BFgFZI0BdPszaphd1o6oCNJsn3s3uHTIqyM0hso\nSg+zZvXg99dyzTUT2L37VbLZIiDCnDnXcOONS/ntb7uIx5fjcjmJRkuAWnuOngEEkKSAnZXLIUlJ\nQMPni2Ca1vkLBicSjzcDfkwzQTA4EYB58+YSCLzOwoXF3HxzKXv3vkMkUkI83sQtt3wGny/IO+/M\nxuttZd68YqAYvz/JPfdM45FHIJm05rFVVLhoaelBUVpwOLIUFxcyffoM0mkFl6sMt/sQfn8tK1em\ngEbi8XDeEFt/1qyZicuVPwx3/Rnzl7xeT9/Q2caNKo2N1vwt0zS58cZiXK6W0+6Ere1X3urVU9m0\naR/hsJuJE+sJhW7o2z8UsgRtoKHIG28sRtOOkcm4CYe7qKio4MorizFNk2PHvH1DSrNnF1Nffxi/\nv5bWVquMZNLbb+gvP46B2mM45H928+s0EPlxTJqk8vWvX3fOOWOXKm7B8Mj/PAgE58ugYnb33Xfz\nmc98huXLlzNz5kxuvvlm7r333mEX+MADD/DAAw8Me/+xzEBf1seO1XP8+NK+L/3p0zu5555p/PKX\nlbS1VWFlxAoJBgv5538uJxQq5C9/ibFjxwQ0rQBVDeN2f4TS0gCnTgUxzQzB4HLi8Qas+WEhLJHa\nD1RgZacySFIBphnGGs7sxprvpSDLQXTdA7Tgch0gl8vi8bxEeXkNfn+MiRM97N3bQCbjJxLpnds1\nHl2vBQ5SUlJIT08Yw0giSZ22jCm43R28/5ugVxavQ5IUTDOANXQJVuYtSWFhhJ6eDiCLJCUwzRxu\ndydO53NomjWECgvt9okDbUyZshC/P8DnPjefv//7aYRCKosWWTKQy+Xo6tqK319LZWUGvz+HYZgo\nSgBNayUUKqC5uQtNewtZLkbXO4EDBINJHI4Y110XZPJk6/zNnBmho8O6czSZVCkvt+TB4XCzYkUZ\na9dOw5o7Z/HIIyGSSesXmM9nkEpZApbfyed3/lOm+PB4jhEKFeDxqBQVuZgwIdR3jUye3G6XMTjn\n28mcKQnVg05Ezy8jnR7Hpk1Hz5CMyZPHEQ7vJ5124fVmmTx5HABr1lTb4qgTDDoBvU/8qqp8tLZa\nbaIoCitWFLJ2ba/EWpnM/DlAl6pDPV9xOt84hAgIBJcPg4rZJz7xCT7xiU8A1oT9f//3f+eKK664\n5IEJzmSgL98HH1zM+vXP91t6AWD+/AKy2Sy5nAOnU+MjHwlxzz1WZ7xqVRXr1++io8PJ4cM5HA4d\n00zhcGQxzW5kuZbeOWKKMhFdDwDv4nBoOBzNTJvmYvz4KIcOZeju3oJhVJDJnECWAzidIRQlQTDo\nYd48Fy5XiJKSONXVxYRCPlatutJeqiPNU09FaGp6A10vw+VqpagowuLFlfYdcxqxWJqCgjSgE4vV\n0dV1hJYWmWy2CZcrw7hxr1BaOpNI5DjxeIhcziCTMSgoiFBZ6UHXExw6tAtJ6qKwsI2f/vQmfvvb\nDjo6TJqb3Zw61U4uZ2Ka3cyb9zFuuGE+gD15/8wO9Wtfs2Q4GIzx0kvHyWTcuFwpSkqKqa4uRpLm\n88ILJ+nuVikoaGPChBCplE55uc6DD67sW0/ts58tsdu/gJKSCAsWlOB0HiEUip61086XrlmzCunq\n2onfbwyYdbrllgRQTDyuEwrprFq1lC1bRiajcqGSMND+lZUwd+6cPrmsrNw3aHnpdOasQnS+GawL\nRYiTQCAYKpJpTaoZkKeffprdu3dz3333cccdd+D3+1m1atWI3005VseoL6Tev/3tHl56yUcm48bj\nUVm5MsW6dQvPuV17ezvRaCsezywikSPI8nwKCiqRpAzl5Vu59dbF/ebkRCI9fYLR03OcQKAGwwjY\nstLQb1mEs2VNfvvbfbz00mQyGQcej8bKlY18/evXDVhvq6M98868/NeDQetGh8EWtN248XDfnWz7\n9/euaTbPziid+662geK4EM51vi9FeaOFoV7nF7MNRkN7juW5N6LeY4exXO/hMqiYffKTn+TXv/41\nmzdvpr6+ngceeIDPfOYz/OEPfxh2ocNhrJ7YC6n3UDuf/lLz/mr4bneYvXsjRCIlZ6ygfyHlDbbP\npEllI3K+B6q36KhHFlHvsYWo99hiLNd7uAxpddeioiJ27NjBunXrcDgcqOqlTfsLLg5DHT4ZeLuh\nzUE63/IudJ+LhRheEggEAsFoQx5sgxkzZvAP//APNDc3s3TpUu69915qampGIjaBQCAQCASCMcWg\nGbPvf//7vPfee8ycOROXy8Xq1au5/vrrRyI2gUAgEAgEgjHFoBkzwzB49913+f73v08ikeDQoUP2\nyvECgUAgEAgEgovJoGL23e9+l3Q6zcGDB1EUhaamJrEOmUAgEAgEAsElYFAxO3jwIF/72tdwOBx4\nvV7+7d/+jcOHD49EbAKBQCAQCARjikHFTJIkstls35816e7u7nssEAgEAoFAILh4DDr5f926dXzh\nC1+gs7OT733ve2zdupV77rlnJGITCAQCgUAgGFMMKmbXX3898+bN4+2330bXdX76059SXV09ErEJ\nBAKBQCAQjCkGFbM777yTP//5z8yYMWMk4hEIBAKBQCAYswwqZtXV1fzpT39i/vz5eDzv/4macePG\nXdLABAKBQCAQCMYag4rZ3r172bt3b7/XJEli27ZtlywogUAgEAgEgrHIoGL28ssvj0QcAoFAIBAI\nBGOeQcXsX//1X/s9lyQJj8fD9OnTWbNmDS6X65IFJxAIBAKBQDCWGHQdM0VRSCQS3HTTTdx0002o\nqko4HKa+vp5vfetbIxGjQCAQCAQCwZhg0IzZoUOH+MMf/tD3fPny5axZs4aHH36Yv/mbv7mkwQku\nD1KpDJs31xMOuwmFVFavnorX6xl8x0EIh3t46KFddHQUUF4e48EHF1NSUnQRIr40XKp2EAgEAsHl\nw6Bilk6n6ezspKysDIBwOIyqqgDoun5poxP0Y6COfTgd/lD2yd8mEIghSTLxeOC8y968uZ7GxvlI\nkkQiYbJp0z7Wrp19we3xf/7Pm+zYcQOa5sThyKGq23n00Vsv+LgDcb5tdvo2l6odLgeEtAoEAoHF\noGL2la98hU9+8pNceeWVGIbBgQMHeOCBB9iwYQPXXnvtSMQ4psnvsI4dqycUugGXy9uvY8/v8Lu7\n09x//3ZmzJh6ToFqa2vjP/8zQypVjMcTYdu2QyxceCVOZyf790eJRIrp6WnA55sK+Dh2bDenTuWA\nSUhSI//8z0/h812NLDdyxx23Ulk5g+7uHPffv/WMssNhd9+f8ZIkibY22LjxMOGwm2AwARjE4wV9\n+0CwX7z52+TH9/LLJ9H1LUAZ0MUf/9hKNLrznNmzCxGAp5+uZetWH5mMgiSl+a//2kRh4TSKiyMs\nXFiCqob6naOOjjhPPPEcweAkystjjBsX6tcO4bD7IlwhZ6e5uY0vf/kNwuFSQqEufvaz6xg3rvKS\nlXehCGkVCAQCC8k0TXOwjSKRCLt27UJRFBYuXEhJSQk9PT0UFY3csFFnZ3zEyhotlJUF2bDhnb4O\n6513Ivh8rcybNxcAv7+We+6Zxo9+dJAXXywimXSTyRzB45nCxIlFSFKSVGoHhYVT6Ow8RFtbKaoa\nwufrJhw+TCbzN0AA6AFewOudTyZzANO8CSgGOoHXgdnAfuDTQAGQBp4Erre3OUxx8TUYRgeFhW6m\nTq3GMNpobj6MwzGVXK6RiROXIcvleDwaXu92jh2bQDLpJ5XqBE7i883C4Wghm+3A55tJNLqfRKKa\nXK4E0+wkEDhFMFhDZ+cuVPVjKEoBmcwuYAFQCnQDz+LxLESWwziddZSXLzxDSn7+87fZuDFFMlmA\n292Nz9dKKFTdT+YGkrcvfvFV2ttXIUkShw+/RCyWw+UqQ9NakeUG/P5Z5HItVFa6GT9+EceOHQaW\nMGnSBKyP2eNcd91aJEnCNE0mT7bko7c8VS3E7Y6eVRaHMmybH/fvfvcGqdQ6HA4fhmFQVfUY1dVl\nF3XYdzgZ3LO9N2lSGd/+9l6Syff/okjvtX2+ZX+YKCsLjtnvNVHvscNYrvdwGdJQ5q9+9SvefPNN\ndF3nmmuu4d577x1RKRvL5GebfD6DVMoJQDLZzeuvv8df/xpm16536OkpRpImouvHcTi60bQFtLcf\nQlXB4TDRNBX4KBCgu1sFuoDpgAJ4gVmk05OBLFAIlADN9mMZqAD2AeOBqP1+ORADVtDdPRWIEI1u\noqPDRyZzGLgdRSlD1+fR1LQRt3sWbncrTmeWRGI2huFBVUuAJtzuElS1FZhGUdF0enqOA4uQJDem\nOYFMpoF4XEZVnUCQXM4D+Oy4fIAOzCWTuRpQgTCx2GyOHUvy8Y9v5u67byIUUnn88eM0NMzGMPxk\nszlMM4LHk8HhiJFIvMQvfrGmX/YmP+t18GADmcxmDKOUnp564NMYRhmath9oIps1AYVjxyI0NHjQ\ntJl4vftxOHw4HDoTJlQyefK+fjIB8MQTB3niiQpU1YPbbZBMHuTuuxf3uw4eemgXx4/fjiRJxOMm\n69c/z09+sqKfoBw4UMvhw/PIZHw0NV2H03kAl6sGWTaorfXR3b0EXXdy/HiOb33rTTZsuHlI1+BA\nEjRQlutc2a/HH9/Pk0/KJJMSXm+CHTu2cs01izh8uI5wOImmFeDxqKxcmTpnTCORYbsc5E8gEHz4\nGFTMvvvd7+L1evn+978PwFNPPcW3vvUtfvSjH13y4AQQDCZ4661OMhkHTqdGVdUR/H6DHTt20dn5\n/9Ha6iQSqQQ6kOVrgVI0TcYwqlHV/cAqNC0AuLDkpTDv6En79Zj93Gk/PoqVgXoXmAB4sGRnHpYI\ntQG7gYNY8ibZrx8DPk4mUwxMAo6i614ggWFUYxifIJXSyWZ/iizPxRK+DHAQVXUBE4HjpNO9sSQx\nTR1oAvyoqg6kgHas4cuIHa/DjkG1j2kAMqaZBNI0N7vZvl3B41FoaNDRtI/aWSvrWIZxK5mMwfbt\njwLQ1qZw8GCYdFrh6NFdZDLXUFBQQmdnglxuLm73OLu+h8hma4C9wOcAP6ABv0bTKoBm0ukYPT1h\nIENJyUnWrr3ujHP89NNddHcvw+FQSKV0nnrqIHff3X+bjo6CfsOgHR0FQH9BefXVEyQSCVwuGU1r\nRdOCGIYT09TR9TZOnuzBMLwYRjudnS1EIjspKelmwYJCVLVswKxXbe0xwuHF5HIePB6NbPYo69bN\nP2OIundo9syha6Vv6Pqxx46Ty30ORfHS3Z0gHN7GvHmziEQ8tLfXEwqV2m3YeEY75ZNfhqapbNvW\nedEFaijyN5R5mAKBQHA+DCpmBw8eZPPmzX3Pv/nNb3Lbbbdd0qAE+RhYIuJGUVSWLKlg3bppPPVU\nPdGoD8OQsITAj8PhJJsNIkkaitJl71+OJTlZLHnJYQlaDEuwvMDbwAos2TiIJWBFWMLhtfevwBK1\nVVjDmv+ANYTYBWzHErEWoMEuqwUrs5a2n59A0ySsS84P5JAkGYhjCdksYBpwwn78rP1cAd4DbseS\nykosAZxsb/cOcAXQYcfpAY5gDXHOxRK/v1JfL+NwyOi6B1k27LaQ7e1BlmUMoxyA48ePc/SojKa5\n6epK43CU4PeXYpoTkWUNj8dAVTV7/97/m7Eyd6odp2q/NhlVdeFw6ASDvj5Bye+4LeF8n9OfA5SX\nx4jHzb5h0PJyS6bzBSWdjqJpt+FwuJCkSZjmfyBJWZzOKIlEGl0vwBLxk8Tjq0kkxnP0aJjXX3+R\nGTMq8XgUstla1q1b2E9K3nsvja67KC8vJZuF1147zLp1EAqpJBLvxxQKWTcF5f+Y8Hg0iooaUNXb\nbcFxkE4fxOWai6pqKIolc7mcm1ColCVLigGIx8NntEE++WXX1h4DFpBMll3U7NlA4plPfju99dYB\noIKamosbh0AgGFsMKmamaRKLxSgosH6hx2IxFEW55IEJLOLxAmpqqvOe1wKQTHaSyzlsuckCcXQ9\njST14HIZKEoAS4xyWOIwDUt2arCkTLWPaGDJics+ThBLBFP2NssBt/34v7FEr9LeT8ISNxlLlvYA\nf4clKFVYwldsb1OCaWqYpoElUfUYhs9+7LHL1gENXW/CGirdb7+n23H1Zv7KsUQxjTU/zme/vxt4\nFQhjZfoi9uP5dgbRxOt9GV2vR9M8djuYSFIGUCkp6eCRR05w+HAKXS9EkgKAZMsvgIppOu3HRcBO\nLAlrBdZgSaQBvIQlrHFgFqWl1hyzY8feO2sGpqZGYufOfZimD7c7RU1Nb3nv8+CDi1m//vl+c8Sg\nv6D4fCXoeiuS5EdRkkhSiPLyChyOIhKJA1iiXWCfN8W+nhrR9evJZieiqiavvbaFdev6SwnkyOWs\n7a25clkAVq+eyqZNZw7N5v+YAJVEwtN3LI9HJZnEzlaGyeVU/vrXLsLhbqqqUn1l9EreQMOJ+WV7\nPBGmTrU+I0O9qWIow5QDiWc++e2Uybjp/Uo9VxxDmVP4QSKGcAWCD5ZBxeyuu+5izZo13HjjjYD1\nJ5q+9KUvXfLABBYDdQ6zZxewa9cLaFoBXm8zmtZMIJBG149RUTGLceOStLWZpFKbsUSmBUtiIvbj\nHFamyQHUYUlUOXAYSzJ89J+D5saSoqNYWbFeWTSwhkSn2v9asSQpBYSQpCCm6QLSyPILKEoriqJh\nmjkMQ7XnlZlYmbIUEGXSpFJOnEhhyaQLa25bDksINCyh6n1cCMzAyrrVYs2Bi9vH1OwYcyhKCofD\noKCgBEU5TDJZgNPZTjp9Eo9nG7rexIoVa0gmi8lk/DgcUcrLJ2Ga4+jpeRens9Ru5x4kKQzsAj6L\ny1VJNnsUOIQljgmgCknyYpo6DoeCoqRQFB2323nWDMx3vrOU9et3EY2WUFgY4cEHl55xHZSUFPGT\nn6w44/V8QVm2LM3hw0kyGZNwuANFmYAklQI5ZNmLYdxqX0cv5B3BhcuV7YvJau/+193EiVW0t7+N\nyzUNj0dl2TLrR5rX6zlrRuj0HxN1dW2YpnWsyZNLkaS3cbkkMpkOvF4DCFNZGaWkpAm/v7/kDTSc\nmF/2xo0qjY3vi+PZBOp0hjJMObB4vk9+O3k8Kr3X3Lni6C07EPDQ1pYZdZk1cYesQPDBMqiY3Xjj\njdTU1LBz504Mw2DDhg3MmjVrJGITMHDnsHLlOMBHJuOmtVVi0qRpLFy4iJ07JwPWkNCsWWW8+OLz\nuN1pwuFOdP0zSFIpDkeOdPrfSSZ/jzUcuR/4PFZ2qgUrAyZjSUYOKzOlYc07681Q/QZLiBrs91/G\nGkJcaB8nCnRRWNhBKtWOokyivHwJipIjHn8RSJLLmei6F01rx+Fosu/qrGD69JO0t0uk039AkqrQ\n9WN2POVYkjYTOI4lkU4sqQwDXchyG4bRArTh8STR9XqKi2czZUoCj0fD4Shm9uz3J7333v33yCMn\nSCatYbTJk100Nqq4XF1ccUUZpaUtVFe7ePddF6p6I7mcn/3748TjryHLZVhSORPr42QAClOnGqTT\nboqKaikvr8DjUSkqkvsEJb/j7pWu4dy9lC8o6fQ4Nm2qJxzWePfddlR1LrmcgdcLiUQV3d1hJkA2\nuwAAIABJREFUDMOBYcykoOCPBAJzmTGjAb//YxhGFx6PxrJlvjOuu1tvTQDFxOM6oZDO6tXV54jo\nzB8Ty5YV4HJZx5o2rZ4lS1bicnn77jK++upZJJMqfr/3jDsxhzKcOBSBOp2hHHcg8RyobOuGhUbi\n8fA54xhK2R8koz0+geByZ1Axu/POO/nzn//MzJkzRyIewWkM1DmsWVONy1VPOKxz7FiEUOgGgH6/\n2oPBQr7xjXmsXTubSGQB69e/v9xCUdFH2bHDTTLp4+TJaei6G3BimjLgRZICmOY8rOHL3kxYAdb8\nsAlAAw5HBYoisWJFjCVLxqNpBk888UdisfEEAieZNy9INnuC9vYWQqGrMAxLEg4cyGEY1+J2y5hm\nHEl6jgULFtjDWXGuvXYWU6YUsm3bH1AUg3TaQy5nkM1qaJoDn0/H59Po6HDY0ldgL1mxlIKC6chy\nKV7vG8yZI1NSUsjChRqZTDuhkEo2G6K19Uw5ypeJOXNKKC/fzYwZXruDvR6v12NnZsYjSRIuVzmt\nrUFCoWKOH3cQDr8KTMblamXxYh/LlsUIBosBuU9oVq1aypYt5ycQw71W8mM1TZNAwEFtbZhUyoPP\np3PnnTXcffdi0ukaW+YSdkwzz3ndDYUzRam6byjMksejhMNuJk6s77tuB8owDWU4cTixDuW4Q+FC\nyoahZ/hGkovVNgKBYHgMuo7ZV7/6VT72sY8xf/58PJ735xmMGzfukgeXz1hdB2Uo9U6nM3bn6iYY\njGHJwLnvDNu48XDfcMWTT24kkfgMwaCb9vY30LRO3O4qJKkT09yL3z8Xr7ceMEmnp1FQ0MrnPjcR\nh2PykOag5JdlmiYHD27l1KkZJJNuvN40Eyee4OqrJ/YtJGua5f3m3gxUP4+nmz17OohESolGG/D7\nb8AwfHg8GitXNrJu3fxztlV+7AO9PpR2zl9UNn99svPlYq73c3p9Vq2qYsuW1lE1b6g3xnPNtRrK\nebmQsj+I9hhKvT9ILnXbjOV1rUS9xw4Xso7ZoGK2fPnyM3eSJLZt2zbsQofDWD2xl6re+V++knSK\nF15oo7u7nIKCU0yY4CeVGnfRFiI9/Ys+m83R2rr4jIVWexlOvUdDR3uhZY/lLzBR77GDqPfYYizX\ne7gMaeX/0cBYPbGXY70HE5nLtd6DIeo9thD1HluIeo8tLsnK/+3t7axfv57GxkYWLVrE17/+9b4l\nMwSCC+FC5i8JBAKBQHA5Iw/0xv3338+0adO47777yGaz/OAHPxjJuAQCgUAgEAjGHOfMmP3qV78C\nYOnSpdxxxx0jFpRAIBAIBALBWGTAjJnT6ez3OP+5QCAQCAQCgeDiM6CYnc77f55FIBAIBAKBQHAp\nGHAos66ujhUr3v8TMO3t7axYsaJv5fKRXi5DIBAIBAKB4HJnQDH7y1/+MpJxCAQCgUAgEIx5BhSz\n8ePHj2QcAoFAIBAIBGOeIc8xEwgEAoFAIBBcWoSYCQQCgUAgEIwShJgJBAKBQCAQjBKEmAkEAoFA\nIBCMEoSYCQQCgUAgEIwShJgJBAKBQCAQjBIGXC5DIMgnlcqweXM94bCbUEhl9eqpeL2eS3JcCF54\nwAKBQCAQfAgRYjYK6ZUVVS3E7Y6elwQNJFCnv75sWTE//vFBOjoKCAY7kCSTWKyC8vIYDz64mJKS\non77HDtWTyh0Ay6Xl0TCZNOmfaxdO/uChW3z5noaG+cjSVLfcb/ylbJht9n5xHEpZDMc7uGhh3bR\n0VHQry2HUvalFNJLJdYCgUAguLiMuJiZpsm3v/1tjhw5gsvl4nvf+x4TJ04c6TBGNf/rf/2aP/0J\nYArQwKuvvsKjj3653zZ1dY18/vOvEo1WUljYxhNPXM+0aZN5/PH9PPmkTDIp4fFE2bFjK/PmzTlD\nrB577DfU1X0UTfOSzXrweFopLa2krq6Qz3/+RW677Spqa48RDi8ml/NQV5egre1XmOZkFOUUn//8\nRNaunX1aeT3s2PEy8+ZVEwwmAIN4vAC3u5u9ezuIREopL49x331zefXVbsJhN3v2RJk6VcfhcCBJ\nEuGw+4z2GEh28l/v7DxAW1sAVa3A5+siEuniy19eds52PpsUrl07u9825ys0Dz20i+PHb0eSJOJx\nk/Xrn+cnP1lxxnZDEdKhSLbLFWbfvgiRSMk5RXAodR1LjBZRHS1xCASC0cOIi9nWrVvJZrNs3LiR\nvXv38oMf/IBHH310pMMY1fzpTy3ANEABPPz+97VEozvxels4dSpJLDaeEyd2YpqLgSK6ux3ccMMf\nWLbso7z77hF0fR2S5CSZbODw4TqeeaYZ6AR+iCRdiaKcRNNkoBDwABFSKZmmpgzQQ0NDlHffPYpp\ntgFZYBzwJjAHKEPTvDz22Jv893/rpFJHUZQaHI4gmtbF4cPjeeEFF4YhUVgYIRCYQGdnAl1P4HJV\n4HAoHDz4Fyoq7iCTcdDW1so777yKx1OK1xtn9ux2fvxjD93ddTz5ZD2x2HgSib2oagGSNAVJamHj\nxrcoKrqKZPIAXu9cTFOhp8cAbgCK6e5O8f/+35OY5vh+ghgKqaxaVcWWLa2Ew27efbcNVQ2Ry/lR\nlAxbt9bz7LOJfoIzkNDkd6j54rl3bxzDOI6meXA6Vdzu90Uzf5+hCOnvf3+Ul16aTCbjQJZT/Nd/\nPUth4RTi8SYWLboDn8/LM8+8SSq1lIqKCsLhJLfd9jjTp1efIWnhsBtJkgAGLG8gzlceLlQ2RkJW\nnn66lq1bfWQyCh6PQjZby7p1Cy9qGUNBCLNAIDidERezXbt2sWyZlclYsGABBw4cGOkQPgQEgflA\nAAgBnRw9Oo+TJ3UMowlF8aPrxcAKLLHKkMmc4JVXnGhaAFlO4/f70LQwUAy47GMWY5q3oGka8FMg\nCRhAA5YIFgE6YGKafvv9MkACKoCpwAQgDXSRSi0FJqPrh9H1iYAb8JLJFGKaOh0dJ4lGU6hqDJiB\nql4JaESjJ4jHC9A0mY6OHKo6DperCF2XaGrqYt8+D3V1TcAVdkwm4Mc0P4JpdgCbCYeDgEQm04Ql\nsDLwNjAX6CIWq2b79grCYReVlVEWLqwmkTBZv/5FKitvQZIkjhzJcfLkW0jSNHK5CA5Hhp6eqzh6\nNMm6dc9z662LeeedUzQ2uslkCvD7VW65xQDg5z9/m0cfjdpi14Isp/D5KojFOjDNCQQCfnI5g5Mn\ntwEfBfp3wpmMxuHD3dTUlGGaJqGQesZVsHVrF7t36+RyPtLpTtzuKcydez0tLUdIJtu4+eapJJN+\nDMMJQGPjAZLJZeh6BbW1Sa699ilCoVmEQl3cdFMF+/eHyGQceDwaK1cmhnw15guix6ORzR5l3br5\nA24/VNkYaMh+JGTl9ddjRKPXIEkSqmry2mtbWLduaPteTHFsa4ODBw+RTrvwerO4h+7LAoHgMmXE\nxSyRSBAMvj+XxuFwYBgGsixuEH2fCmARlnAkgffo6QljGMeBReh6CIgBGpY09QA1aNpMIIVh1BKP\nl2JJVzWW5BnABuAUVhZMBjoAPxAFZgFOoAu4BSs7Vglk7P2rgFb7eF47Tq/9/kfs7YuBY5hmlV1G\nMaY5x67P65hmAIBczk9Lyx4Mw4+qxoGpOJ3FZDJt5HKHqK8/gSV/swAflpwesP9/E1iHJWyVQIvd\nVtOwBLMSKAC2snevTjar0N7eSC4XwOvNks36qaqyMkdtbU2o6nwUpQBdD6Lr+2lr242uNxKNLuT6\n66vZsydHT08TLtcMwuEM27fv57775vLTn9YTjf4jsiyj6wngcXS9FMOoBp4hkZiJLMdoa3Ny3XU7\nCIW6WLp0BopilV1dPYOGhtfx+yfkzTHrz4EDJ4lGlyBJTlS1Al1/EbgepzNLIlEIgMfTQ2dnkJYW\niEa7cTpnoush2to0dH0yuj6djo7xnDq1jTlzygE3qtrNU08d4S9/Uc85/NnLa6+liMUqAchm4bXX\nDp9TYoaanesVsEDAQ1tbpk/ALmZ2Lz9D2l+iXP3KsH68DI2LKY6NjS309NzeJ4iNjc8DImMmEIxl\nRlzMAoEAyWSy7/lQpaysbCzdqefCkguAE8AyMpkZWBIEloS8Zb8XwBKzDFCKJSYnsIYp92NJjIwl\ncGVYYmUAbwBX2u/tw5IyB+9Ll2z/6x3ulLAyV1H7XwxreDQLpOwYALqBo30x6HoT0AaUIklZwECS\nejCMK+wyLQnLZj32vlPR9euAOJZ8Fdl13GOX3xufbsdbZdc7DpTbbacDRZjmeFQ1iqYBzCOZNIAn\n8PlcdkfoQFGm4nAE0PVuYB5wPbregaq+hd+/CE1zA5VIUhmmGeXo0RR/93fv0dMTxDQ1DMOBJcjT\nMIzZdrx7cbmWoqoxUqlWstmPc+qUwXPP/ZzPfvY6JEnCNF3cccck7rqrpt+Zz7/ODaMEw/Bhmr0Z\nwQJcLgcVFbNQlD9QUbGExYuj7NvnIp1O4XSGcbudKIqMpqWRpApgNqZp0t39Hh7PBAxD4ciRViTp\n43i9lTQ1GfzoRy/yy1/exkC43SZOp2LHbeJ2m+f8PE6ZIlNf7+rbfsoU+azbq2ohgYCVaQoEPKhq\nIWVlwSHvfzZ+85sGurqWIEkSXV0mP/rRnxk//ta+5y+/fIC77qrh9ttLeO65LtJpB16vxu23lwy5\njPy4e58P9/tp7tzpJBIRUikFn09n7tzpY+K7bizU8WyIeguGwoiL2aJFi3jllVe45ZZb2LNnDzNn\nzhzSfp2d8Usc2WgiATRjiUsKS6QiWMLhxRIUGUuOHFhZtQ6s7JEJFCNJ8zDNLVjiZGDJQytQh5WN\nymJloyT7vb1YAnQUmA6oQA5LuCrs/9uw5O4oVpYsBLTb2+XsfZJ2jFYWwuEoRNdBlp8lGNRxOGKk\n035crgCGIaOqGlamK2zHNhtLMnX7mAn7mD04HK+jaQeApXYZubz20ezyA1hDqh3AYRyOCIpSBLTh\n9+vU1EyjtHQn4bCbQOAUyaTTLkuy26UHhyOO11tEMqna7dmOaQZIJg/gdN5OODwF0zwJxDDNoL1/\njx2DB0tcd9ntNQtdt4Y/db28r+zSUpXly6f2u67LyoL9nstyGln2I0kyiuJGUTpxuV5mwoQYDz74\nMUpKinjkkQyVldUA7N6d4uDBnchyEQ7HMSTpGgzDxDRNDEOnvb0ESZKIRstwuaJks6UANDV5zvn5\nuuoqLy+9tJtMxo3Xq3LVVd5zbr90aRFbt27qu1njc59bfNbt3e4obW0ZAgEPiUSGUChKZ2ec5csr\n2bRp4HY6Fw0NBqlUtu95U5OH4uJsv/c7O+PcfPMkMpn3M2s33zz0Mnrj7hXH3rjPl7KyIIFAmhkz\nCvuOFQg0Xvbfdadf52MFUe+xxYXI6IiL2cqVK3njjTdYu3YtAD/4wQ9GOoQPAUmgFmteWD1QRWWl\nRGNjAjiJJQI+YAqKEkLXi7AkLYolXy4kCUyzFNiINXm/FXDjcGRxOJJkMmHgBazhxxxWdm0clmjV\nYcnSYaysWr29fytWhkrHkiATa87ZC0iShmlay24UFtaTTrfgdscJhUw8nhiKUkxVlU55uU4iUcD+\n/Qa6LmGaU5CkLkpKXJw82YGmfRRZNtB1J/BXrCxYFGjigQeuYsMGN5HIFqzM1B47nixwDEvIdtvb\nuygqCpFMSpSXqyxZUoxpmkyceKpv2EmW2/n5z18hnS5Aljtwu0MUFjqQpCLKy3fg9xcyadJO6utX\nYRhBJCmJ368BEAwuIB5/HodjApp2ApiDy9WCqrYiyxMoK7uStrZ9dmxWZri8PHZeQ15z5wbJZN5E\n03w4HCkWLy7nd79b0m+bUEglkTCRJIl58+ZRVbWdGTPK2bsX3n47jKrm8HiSVFf7cbn2k067CATq\nkKRrAesu6fLy2DnjWLOmGpernnBYJxTSWb26+pzbb9nSSmXlLVRVWbKxZcs+1q49c6h09eqpbNq0\nD1UtJBSK9g3ner2eYQ8N5rdHb91M08yTKPWCy+iNu/9SJ8PjYh5LIBBcHoy4mEmSxHe+852RLvZD\nhoKVNXJjDc29wZQppcRitUSjKrIsoWl1SNJHCAQkolEnkMbtLsM03WSz21CULiSpiaKiOZhmKW63\nk6uvDrNggUYopPCNb5hksxqWZBVjZYwmYWWi3sThkNC0NFYGSMESwd4MVjtW5iwMdCPL4xk37qNo\nWg5N+ylXXqkTj8ssWrQSn8+LaZpMnvz+PJxIpIf161+ho6Og3x2Gb7/tZv/+Z1CUKYTDB7HmjjkB\nL6FQFffcM43a2jp27rwKXXfS2OjEyt71ZhJbKSqagCwn8Pt7mDnzACUlERYsKEFVa8/o+O6660qK\ni62sicdTxJ49HUQi79jzrlZSUlJELqexe7dCOq1y/HgYS16htLSIgoJxzJgxn7a2UmKxPXg8Bg7H\nKVS1E7c7Sk1NI6Ypk0x2EAp18bOfXXdeV8HKlVWAj0zGjccj2c/7k9+xT5qk8vWvX4fX6yGdHsem\nTfWEw2lCoRzZ7ERaW+cgSRJz51aye/dzBAKT+uaYnYvzlZihzhHrPe7F/EV9uuh88YuL2bLl4orP\nhUjdpTyWQCC4PBALzI5KYliyAZYURbnhBp3KylLC4SXkcm4MYxrNzX/C4ZiM01mHx3MHiuJHUTws\nWVLGhg2rSKeX252zm1DIwerVH+m7e6yjo5MNGwyyWRlVjQJeXK4OFCXCFVcEWb16Cq+8kmTnzkZy\nuXI07ShW9mwiUIjb/RrXX38Vra0N5HKVZLO7KCpKceutV3HffTWk0zVs2nT0rB1iSUlR39pe+dut\nXp1g9eoaTLOchx/uIh7/KKAgSQYTJnQC8J3vXM369Tvp6CigsLCW+vpyNM2B0+ljyhQnFRU65eUS\nDz74P845oR2G1ilWVurMnRuyhWZxn9BcdVWv8LUTDKaBK+wlOXysXr3qoizvMJRM1UB1OP31dDrT\nT+D+9/++/ZKtl3V61upsd5xeKs7WHmfL1gkEAsFoRTJN0/yggxgKY2mM+otffIZt2/zoegmKEmHF\niiS/+MWn7M71zNv0rQzU4KvN55N/LLc7zN69Zy5SunHj4b67z955p559+3Yhy1PxemN86Usy//N/\nLu23zemZseFSVhbk//7f7Tz+eAWplAefL8Odd7Zz9939MzsDtcfFZCTK6OVymYtxvm12udT7fBH1\nHluIeo8tLmSOmRCzUUivaEWjJRQWRoYkWpeC/A729IVaezvbSyEuZWVBmpo6R0yIRgtj+QtM1Hvs\nIOo9thjL9R4uQsxGMWP5ghb1HjuIeo8tRL3HFmO53sNFrOoqEAgEAoFAMEoQYiYQCAQCgUAwShBi\nJhAIBAKBQDBKEGImEAgEAoFAMEoQYiYQCAQCgUAwShBiJhAIBAKBQDBKEGImEAgEAoFAMEoQYiYQ\nCAQCgUAwShBiJhAIBAKBQDBKEGImEAgEAoFAMEoQYiYQCAQCgUAwShBiJhAIBAKBQDBKEGImEAgE\nAoFAMEoQYiYQCAQCgUAwShBiJhAIBAKBQDBKEGImEAgEAoFAMEpwfNABCD4YUqkMmzfXEw67CQYT\ngEE8XkAopLJ69VS8Xs8HHaJAIBAIBGMOIWZjhHC4h4ce2kVHRwHl5TGmTHGyadM0UikPmYzBrFkJ\nFi+uJpEw2bRpH2vXzu63f77IBQIxJEkmHg9cMpE7Pd4HH1xMSUkRzc1tfPnLbxAOlxIKdfGzn13H\nuHGVA8bqdHayf3+USKS433GGQ/5xh1PvC91fIBAIBJc/QszGCN/+9jvs3HkTuq5w/LjOc8/9F9ns\nAgzDSS6Xo6vrTRoadDyeLqZMgXDYjSR18PzzDfT0VJHJHCWT8aBpkzGMBsrLr2bcuAo8Ho1s9ijr\n1s0fVlz5AlZcHGHhwhKczvE888yb6Pr/wOFw0tOT5q67nuHmm6/kd797g3T671EUB42NGp/85C/5\n/Oev6yc6mzfX09g4H0mSePHFRlKpm6io8BOPm6xf/zw/+cmKc8Y0kEDlH3cggT0XF7q/QCAQCC5/\nhJhdZgyU2dq+PUY0msY0fSiKSSrlRpYrkSQZwzhMNuujpUVG12McOTKNt9/2Eov5kOU5eL1LiEYn\nAYdRlCnoukY0+hr19dNRlA7q6/VhZ88eemgXx4/fjiRJHD/ezqFDdXzqU7Noaemmu/sUihJC1w9R\nXHwdyeQkurs7ABW/30Emo5LJXEEy2T/TZ0mlBEAyWYBhKABIkkRHR8FZ22ooApZ/XE1T2batk3DY\njcsVZt//3969h0VZpn8A/w4zMJxBETNMEfOApZKpiXgIUNezlBHiAbK2Ns2zeMDN8KybWlZmvyu3\ntox0zfWwWGlll7oiElCmqKskiuIZHBVnOAwzzP37g2UCBTIU5g2+n+vqumBm3ue97+cdmW/P+85M\n+g3cuNEYbm45UKkEt28/dNcKXfntVSoVdDrtAzji9QNXE4mISjGY1TPlQ8UPPxwH8BA6dfLGrVuF\nMJmuQqMJQHGxBYAewAmIaAGcBhABs9kRJSV2AB6GydQaZrMXgJ+g1doDuAKgGUpK1ACMAPrDYukI\ns9mMs2f/765wdK9yctytYaWkxB75+S4AgFu3LiM/vwQajRlmcxFMphykpbkByIHJJAAAsxlwd7/1\nv59/DUqZmVnw8vKFg4MTXFxuo6CgBAAgImja9Halc1VVACsfoLy8jDAYBCqVCqdOZQIIQH6+N7Zt\nS0ZBQU889NBDOHw4A0bjaTRu3AxnzjTCggXJWLt28F3biwi8vIy/8+jWX1xNJCIqxWBWz5QPFUVF\nWpQdYhcXJ9y+fQYq1W3Y29+GxVIABwctRBxhNHoAcIBa7YCSEgt+fVqYABT/7+fbALoBcAPgCuAk\ngPZQqUqgUrUEULNVoKZNb0OvLw0rarUJzs75AACNxgl2di5QqRwBFEKkBYqLm8DXtz8uXvwHnJza\nwMEhG/36jQCACkGpSZNHcf3692jTxg9jxjjj6NHvK1xjVtlcVRXAygeosDA/JCSkQ6fTwtHxBvz8\n/AEA+fkusFjsAQAFBYDF0gYWiz+MRsGxY79Y91d++7JVISrF1UQiolIMZvVM+VDh6GgEYAYANGum\ngZ2dF1xc/KFWm+DufhZm8y/Iz3fH5cuXYTYboFKZAHhCpfoBdnYF0Gqvw2Q6AY1GBZUqFyIlUKkK\nIVIIwAH29mqYTGY4O+cCQI1Wgd54oyuWLPkaOTnuaNnyBgICGsPePgOurtfh6BgKOzt73LplBzu7\nFDg4FMPDoxhDhgRh+vQOKCwsQkJCFnQ6XYWgZG9vjzZt/DBpUmsAre9prqoKYOUDlJOTo3UVZ/Nm\nI86fLz1F6uKSj4ICEwBApQLs7H6dA61Wbf25/PZUEVcTiYhKMZjVM+VDxYABBQDOQ6/XISrqJo4c\nycGNGyVo2vQ2Zs8ejAMHbkKn0+L48SY4eTIJhYWe0GiuwWi8BK3WBY0a5WDIkACIPIxt23Jx5Uo+\nSkpMACwwGn+Cs7MO7u5XMGZMC2g0p2q0CtS4seddF+N7e7vBbL6CjRvPoqDAEe7ut9C+fQt07doW\nIoJmzdIBVB2U7vWF/V4C2L1sO2aMBUePJuPGjcbo0OEMDIauMJnOw9m5CBERTX7XfDRUXE0kIiql\nEhGxdRH3IjdXb+sS6py3t1ud9P3rylPVF15/9tkR7NnjjKIiLRwdjRgwoADR0U/USj3e3m7Izs61\n1nQvn7N2Lz3Uhfupo66Ot9Kw74aFfTcsDbnvmuKKGd3TCtHzz/vDwSELOl0JvLxKEBbmb/Oa7ufx\ntUUpdRAR0R8TgxndEwYOIiKi2sfvyiQiIiJSCAYzIiIiIoVgMCMiIiJSCAYzIiIiIoVgMCMiIiJS\nCAYzIiIiIoVgMCMiIiJSCAYzIiIiIoVgMCMiIiJSCAYzIiIiIoVgMCMiIiJSCAYzIiIiIoVgMCMi\nIiJSCAYzIiIiIoVgMCMiIiJSCAYzIiIiIoVgMCMiIiJSCE1d79BgMGDWrFnIz8+HyWRCbGwsnnji\niboug/5ACgqKsHNnFnQ6Lby8jAgL84OTk+MDezwREZFS1Hkw++STTxAUFITo6GhkZWUhJiYG27dv\nr+syGoQHGVCqGqsuQtDOnVk4f74zVCoVDAZBQkI6IiM7PLDH20Jl8wa42bosIiKysToPZi+++CIc\nHBwAAGazGVqttq5LaDDKB5ScnJvYtOlruLm1RNOmt/HGG13RuLHnPY+1desv2LPHF0VFGjg6mlFc\n/Auiozs/0BCk093C0qU/IS+vMTw8blhr1Om0UKlUAACVSgWdrvrnzO99vC1UNm9TpnjbuiwiIrKx\nWg1mW7duxYYNGyrctmLFCnTs2BG5ubmYM2cOXn/99dosoUErH1ASE1ORlzcSjzziDL1esGTJ11iz\npl+125df1dmx4yxu31bDYnGHWl2EK1fOQ693xZEjuWjVygh7e8f7DkELF6YiLa0/AHsAJixY8D3W\nrv0TvLyMMBgEKpUKIgIvL2O14/zex9vCHyE8EhFR3avVYBYeHo7w8PC7bs/IyMCsWbMwd+5cdOvW\n7Z7G8vZumKd57qfvVq3skJXlAJVKBaPRE87OdnBwKD3keXmNKx27oKAIW7acxvXrDjh16gyaNh0A\nBwd75OaqYDQK3Nwew61bRSgqOgkgACIPISvrPLp06QwRQatWdjWu+eRJLcxmd2ugOnlSC29vN7z8\ncmd88UVpTU2aFGPUqM7W06hltZbe3hZOTo5VPr46VY1VW8ofm7J5A/g8b2jYd8PCvule1PmpzMzM\nTEyfPh3vvPMO2rdvf8/b5ebqa7EqZfL2druvvkNDmyEhIe1/1zGdhcXSA8XFZogIHnnkRqVjb958\n0nqK7fTpYly6lIOOHZvA1dUJRuNNADlQqQrh6toE+flGtG7thqysYwCOokkTI0JD/Wpcs1p9E2Zz\nCeztNTCbS6BW37SONXRoK+vjDAYTDAZThVqvXhXo9b+eRq3s8dWpbqzaUP7YlM0bwOeHY2JZAAAW\ndElEQVR5Q8K+Gxb23bDcTxit82D29ttvo7i4GMuWLYOIwN3dHevWravrMhoEJydHa7gYPboxlizZ\njZwcd+s1ZpUpf4rN2dmEgoLSlRxfXwdotQXw8rJApytAs2YeAAC1Wo1+/TwQGdn6vut9/vmHsWnT\nLhiNnnBzu4Xnn3+42sc/yNOBdX1qsfyxISIiKlPnweyDDz6o610SgMaNPX/zmjKg4vVZ7dq1hk63\nHy4ufhg0yABADb3+GtzcDAAs0OtPlXtH4f0bOzYArq5ZMBo9oNU6/ua4D/Jasj/CdWlERFT/1Xkw\nI2ULC/NDQkI6dDotWrY0IiamV519BljZKtK9Ln2Xr/V+A+KDHIuIiKimGMyogj/SKbYHWesfqW8i\nIqq/+JVMRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERArB\nYEZERESkEAxmRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERE\nRArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERArBYEZERESkEBpbF0BED15BQRF27syCTqeF\nl5cRYWF+cHJytHVZRET0GxjMiOqhnTuzcP58Z6hUKhgMgoSEdERGdgDA0EZEpGQ8lUlUD+l0WqhU\nKgCASqWCTqe13lcW2vLz/XH+fGckJGTZqkwiIroDgxlRPeTlZYSIAABEBF5eRut91YU2IiKyLQYz\nonooLMwPvr7pcHE5BV/fdISF+Vnvqy60ERGRbfEaM6J6yMnJ0XpN2Z3CwvyQkJBe4RozIiJSBgYz\nogamutBGRES2xVOZRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERArBYEZERESkEAxm\nRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERArBYEZERESk\nEAxmRERERArBYEZERESkEAxmRERERArBYEZERESkEAxmRERERAphs2B25swZdOvWDcXFxbYqgYiI\niEhRbBLMDAYDVq5cCa1Wa4vdExERESmSTYJZXFwcZs6cCUdHR1vsnoiIiEiRNLU5+NatW7Fhw4YK\nt/n4+GDo0KFo3749RKQ2d09ERET0h6KSOk5HAwcOxEMPPQQRwdGjRxEQEID4+Pi6LIGIiIhIkeo8\nmJUXGhqKb7/9Fvb29rYqgYiIiEgxbPpxGSqViqcziYiIiP7HpitmRERERPQrfsAsERERkUIwmBER\nEREpBIMZERERkUIoNpgZDAZMmDABUVFRiIyMxNGjRwEAR44cQUREBMaMGYP333/fxlU+eCKCBQsW\nIDIyEtHR0bhw4YKtS6o1ZrMZc+bMwdixYxEREYG9e/ciOzsbY8aMwbhx47Bo0SJbl1irdDodgoOD\nkZWV1WD6Xr9+PSIjI/Hcc89h27ZtDaJvs9mMmJgYREZGYty4cQ3ieB89ehRRUVEAUGWvW7ZswXPP\nPYfIyEjs37/fRpU+WOX7PnnyJMaOHYvo6Gi8/PLLuHHjBoD633eZL7/8EpGRkdbf63vfN27cwGuv\nvYaoqCiMGTPG+tpdo75Fod577z3ZsGGDiIicPXtWnn32WRERCQsLkwsXLoiIyCuvvCInT560WY21\n4bvvvpPY2FgRETly5IhMnDjRxhXVnm3btsny5ctFRCQvL0+Cg4NlwoQJkpaWJiIicXFxsmfPHluW\nWGtMJpNMmjRJBg4cKGfPnm0QfaekpMiECRNERCQ/P1/Wrl3bIPr+/vvvZfr06SIikpSUJFOmTKnX\nff/973+XYcOGyahRo0REKu01NzdXhg0bJiaTSfR6vQwbNkyKi4ttWfZ9u7PvcePGyalTp0REZPPm\nzfK3v/2tQfQtInLixAl54YUXrLc1hL5jY2Nl9+7dIiLyww8/yP79+2vct2JXzF588UVr2jabzdBq\ntTAYDDCZTHjkkUcAAL1798ahQ4dsWeYD99NPP6FPnz4AgICAABw/ftzGFdWewYMHY9q0aQCAkpIS\nqNVq/Pe//0W3bt0AAH379kVycrItS6w1b775JkaPHo2mTZtCRBpE3wcPHkS7du3w2muvYeLEiQgO\nDm4Qfbdq1QolJSUQEej1emg0mnrdt6+vL9atW2f9/cSJExV6PXToENLT09G1a1doNBq4urqiVatW\nyMjIsFXJD8Sdfa9Zswbt27cHUPoa5uDg0CD6vnnzJt555x28/vrr1tsaQt+HDx/G1atX8eKLL+Kr\nr75Cjx49aty3IoLZ1q1bMXz48Ar/nTt3Dg4ODsjNzcWcOXMQExOD/Px8uLq6WrdzcXGBXq+3YeUP\nnsFggJubm/V3jUYDi8Viw4pqj5OTE5ydnWEwGDBt2jTMmDGjwufa1cfjCwDbt2+Hl5cXevXqZe23\n/DGur33fvHkTx48fx3vvvYeFCxdi1qxZDaJvFxcXXLx4EYMGDUJcXByioqLq9fN8wIABUKvV1t/v\n7NVgMCA/P7/C3zlnZ+c//Bzc2XeTJk0AlL5gb9q0CePHj7/r73t969tisWD+/PmIjY2Fk5OT9TH1\nvW8AuHTpEjw9PfHJJ5+gWbNmWL9+fY37rtXvyrxX4eHhCA8Pv+v2jIwMzJo1C3PnzkW3bt1gMBhg\nMBis9+fn58Pd3b0uS611rq6uyM/Pt/5usVhgZ6eI/Fwrrly5gsmTJ2PcuHEYOnQoVq1aZb2vPh5f\noDSYqVQqJCUlISMjA3PnzsXNmzet99fXvj09PfHoo49Co9HAz88PWq0W165ds95fX/v+9NNP0adP\nH8yYMQPXrl1DVFQUTCaT9f762neZ8n+/ynp1dXWt93/LAWDXrl348MMPsX79ejRq1Kje933ixAlk\nZ2dj4cKFMBqNOHPmDFasWIEePXrU676B0r9vISEhAEq/1WjNmjXo1KlTjfpW7Ct+ZmYmpk+fjtWr\nV6N3794ASkOLg4MDLly4ABHBwYMH0bVrVxtX+mA9+eST+M9//gOg9I0O7dq1s3FFtef69ev485//\njNmzZ+PZZ58FAHTo0AFpaWkAgAMHDtS74wsAn3/+OeLj4xEfHw9/f3+sXLkSffr0qfd9d+3aFYmJ\niQCAa9euobCwEIGBgUhNTQVQf/v28PCwrvS7ubnBbDbjscceq/d9l3nsscfuem536tQJP/30E4qL\ni6HX63H27Fm0bdvWxpU+WAkJCdi4cSPi4+PRvHlzAEDnzp3rbd8igk6dOuHLL7/EZ599hrfffhtt\n2rTBvHnz6nXfZbp27Wp97U5LS0Pbtm1r/DxXxIpZZd5++20UFxdj2bJlEBG4u7tj3bp1FU6B9OrV\nC507d7Z1qQ/UgAEDkJSUZL2+bsWKFTauqPZ8+OGHuH37Nj744AOsW7cOKpUKr7/+OpYuXQqTyYRH\nH30UgwYNsnWZdWLu3Ll444036nXfwcHB+PHHHxEeHg4RwcKFC9G8eXPMnz+/Xvf9wgsv4K9//SvG\njh0Ls9mMWbNm4fHHH6/3fZep7LmtUqms714TEcycORMODg62LvWBsVgsWL58OXx8fDBp0iSoVCo8\n9dRTmDx5cr3tW6VSVXlfkyZN6m3fZebOnYv58+fjn//8J9zc3PDWW2/Bzc2tRn3zK5mIiIiIFEKx\npzKJiIiIGhoGMyIiIiKFYDAjIiIiUggGMyIiIiKFYDAjIiIiUggGMyIiIiKFYDAjqiOXLl2Cv78/\nFixYUOH2kydPwt/fH//+979rNO6WLVuwa9cuAMC8efNqNM6lS5cQGhpa4/3+EaWmpiIqKgoAMH/+\nfJw4ceJ3j1FbcxAVFWX9UNYy5eutyr59+/Dpp5/WaJ/z5s3DlStXarTtndu/+uqryM3NrfFYRA0Z\ngxlRHfL09ERiYmKF7w/ctWsXvLy8ajzmzz//jOLi4vuurboPiKzN/dpSWc9Lly7F448//ru3r+s5\n+K1jdOLEiQpfAfN7pKSk4H4+1rL89h9++CG8vb1rPBZRQ6bYT/4nqo+cnZ2tX1Hz1FNPAQCSkpLQ\ns2dP62P27duHd999FyKCFi1aYPHixWjcuDFCQ0MRFhaGgwcPoqioCG+++Sby8vKwd+9epKSkWF8I\n9+3bh40bN0Kn02HixIl4/vnnkZycjFWrVsHOzg4eHh5466234OnpWaE2o9GI6dOnIysrC76+vli2\nbBnc3Nxw7NgxrFixAkVFRWjUqBEWLVqECxcuWPebm5uLPXv2YMuWLSgsLET37t2xadMmdO7cGQsW\nLEDPnj3RvXt3xMXF4erVq7Czs8PMmTPRs2dPFBQUYPHixTh9+jQsFgteeeUVDBkyBDt27EBiYiLy\n8vJw4cIF9OrV666VRgBYv349vvnmG1gsFvTu3RuzZs0CAKxZswY//PAD8vLy0KhRI7z//vvw8vJC\nYGAgOnbsCJ1Oh9mzZ1vHiYqKwtSpU9G9e/dKxzQYDIiJicH169cBAJMmTYKTk1OFue/Vq5d1vNOn\nT2PJkiUoLCyETqfDSy+9hHHjxuH999/HtWvXcO7cOVy5cgXh4eGYMGECiouLrat2Pj4+uHXrVrXP\no9TUVLzzzjsoKirC7du3MXv2bLRp0wabN28GADRv3hwDBw6sdG4zMjIQFxeHkpISaLVaLF++HN9+\n+y1ycnLwl7/8BRs3boSHh4d1X6GhoQgICMCpU6ewceNGbNiwocLcrl27Ftu3b7du//nnn2PkyJH4\n/PPPkZKSUuVxfOutt/Ddd9+hUaNG8Pb2Rr9+/fDMM8/8xr8gogZAiKhOXLx4UUJCQuSrr76SRYsW\niYhIenq6zJs3T2JjY2XHjh2i0+mkT58+cvnyZRER+eijj2TatGkiIhISEiKfffaZiIjEx8fLlClT\nRESs25b9PGHCBBER+eWXXyQwMFBERKKiouTYsWPWbZOSku6qzd/fXw4fPiwiIitXrpQVK1ZIcXGx\njBgxQq5cuSIiIomJiTJ+/Pi79hscHCx6vV4OHDggvXr1ko8++khERAYMGCB6vV5mzJghe/fuFRGR\nnJwc6d+/v+Tn58vq1aslPj5eRET0er0MGzZMLly4INu3b5eQkBApKCiQwsJCefrpp+WXX36pUPOB\nAwdk6tSpYrFYxGKxSExMjOzcuVPOnz9vnRsRkTlz5sgnn3wiIiLt27eXtLQ0ERFJSUmRqKgoEREZ\nN26cpKamVjpmQkKC7NixQxYvXiwiIpmZmbJy5cq75qC85cuXS3JysoiIZGdnS5cuXUREZO3atRIR\nESFms1l0Op106dJF9Hq9fPzxxzJnzhwRETl37px07txZUlNTK4xZvt6pU6fK2bNnRUQkOTlZhg8f\nbh1/7dq1IiKVzm12drbExsbKN998IyIiu3btkoSEBBEpfX6VPe/KCwkJsfZY3dyW3z40NFQuXbpU\n5XHcu3evjB07Vsxms+Tl5UloaGil80jUEHHFjKgOqVQqhISEYM2aNQBKT2MOGTIEX3/9NQAgPT0d\nAQEBePjhhwEAo0aNwvr1663b9+7dGwDQtm1b7Nmzp9J99OvXz/qYspWX0NBQTJo0Cf3790e/fv0Q\nFBR013atW7dGly5dAAAjRozAvHnzcO7cOWRnZ2PixInW01QFBQV3bdurVy+kpKTg8OHDiI6ORlpa\nGoKDg+Hj4wNXV1ccOnQIWVlZePfddwEAJSUlyM7OxqFDh2A0GrF161YAQFFRETIzMwEAXbp0gZOT\nEwCgRYsWyMvLq7DPQ4cO4dixYxg5ciREBEajEc2bN8fw4cMxd+5cbNmyBVlZWThy5Ahatmxp3a66\n79etasznnnsOa9aswdWrVxEcHIzXXnutyjGA0u/NS0xMxPr165GRkYHCwkLrfT169IBarUbjxo3h\n6ekJvV6P1NRU6/fj+vr64sknn6x2/FWrVmHfvn3YvXs3jh49WukxqWxuz5w5g5CQECxatAgHDhxA\nSEhIhe/plCpOZZbNWcuWLaud27Lty49T2XFMSkrC4MGDoVar4e7ujv79+1fbL1FDwmBGVMecnZ3R\noUMH/Pjjj0hJScHs2bOtwcxisVR4UbNYLCgpKbH+rtVqAZQGvKpeRDWau/9Zjx8/Hv369cO+ffuw\natUqDBo0CK+++mqFx6jVauvPIgKNRgOLxYKWLVtix44d1tvLTueV17dvXyQnJ+P48eP4+OOPsXnz\nZuzbtw/BwcHW7TZs2AB3d3cAQG5uLry8vGCxWLBq1Sp06NABAKDT6eDh4YEvv/zyri/7vbNfi8WC\n6OhojB8/HgBgMBigVqtx4sQJzJw5Ey+99BIGDRoEOzu7CttW9yXCVY3p5OSE3bt3IzExEXv37sU/\n/vEP7N69u8pxpk2bBk9PT4SEhGDIkCEV3iBQfv/lj6PFYrHebmdX/eW/o0ePRs+ePfHUU0+hZ8+e\n1lO4d/Zy59x6enpCrVbjiSeewP79+7FhwwYcOHAAixcvrnZ/jo6OAPCbc1uZyo6jWq2u0C8R/YoX\n/xPZwKBBg7B69Wp07NixwotwQEAAjh49isuXLwMAvvjiCwQGBlY7llqthtlsrvYxERERMBgMiI6O\nxgsvvFDpOxDPnDmDU6dOAQC2bduGoKAg+Pn5IS8vDz/++CMA4F//+hdiYmKs+zWZTACAoKAgJCYm\nQq1Ww8XFBY899hji4+MREhICoHSVaOPGjQCAzMxMDB8+HEVFRQgMDMSmTZsAADk5ORgxYsQ9vzMw\nMDAQO3fuREFBAcxmMyZOnIhvv/0WaWlp6NGjB0aNGoXWrVsjKSnpnkNAVWNu3LgR7733HgYOHIi4\nuDjcuHHDGtrK5qC85ORkTJ06FaGhoUhNTQVQ+WpU2W1BQUH46quvICK4dOkSfv755yprzMvLQ3Z2\nNqZOnYq+ffvi4MGD1v7UarU1yFc2t5cvX8aMGTOQnp6OiIgITJs2zfpc0Gg0Ff4noDLVze29bF8m\nKCgI3333HUwmEwwGA/bv339P2xE1BFwxI7KBkJAQzJ8/HzNmzKhwu5eXF5YsWYJJkybBbDbDx8cH\ny5YtA1D1O/KCgoKwZs0a62pUZWbMmIHY2Fjr6s+iRYvueoyvry/WrVuHc+fOoX379pg5cyYcHBzw\n7rvvYunSpSguLoarqyvefPPNCvv18PDAn/70J/j4+FhPeQUGBiIzMxO+vr4ASj+OIi4uDiNGjAAA\nrF69Gs7Ozpg0aRIWLVqE4cOHw2KxYM6cOWjRooU1CJaprPeQkBBkZGQgIiICFosFffv2xTPPPINr\n165hypQpCAsLg0ajgb+/Py5evFjtHJbdXtWYZRf/Dx8+HPb29pg6dSpcXV3vmoMykydPxujRo+Hu\n7g4/Pz888sgj1hoq2++YMWNw+vRpDBkyBD4+PmjXrl2Vx9LDwwPh4eEYOnQo3Nzc8MQTT6CwsBBF\nRUXo3r07YmNj0aRJE0yePBkLFy68a25fffVVzJ8/Hx988AE0Gg3mzZsHAAgODsYrr7yCjz/+GM2b\nN6907gcPHlzl3JZt/9FHH/3mPD/99NP4+eefMXLkSHh4eKBp06bWVTmihk4lv7UOTURE9AAdOXIE\n586dwzPPPAOz2YxRo0ZhxYoV1QZSooaCwYyIiOpUXl4eYmJikJubCxHByJEjrdf1ETV0DGZERERE\nCsGL/4mIiIgUgsGMiIiISCEYzIiIiIgUgsGMiIiISCEYzIiIiIgUgsGMiIiISCH+H0JKFBqTXFc+\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x129f115c0>"
]
},
"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": 334,
"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": 335,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11a9d55f8>"
]
},
"execution_count": 335,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RxGJRqqtDTJ4MOl2AXbt2UFMTJBhsRKkWotHMbgGxrs5OU1MzsZieQKAVu70E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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120094780>"
]
},
"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": 336,
"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": 337,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11a95cc50>"
]
},
"execution_count": 337,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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BY+71dtDQkMRi0dF1k+JiuPrqUWza1Jk+rq7H0fVUKHQ47CjVjN2+FafTYNas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\nIlKI/wNWL4Nju4Ax0QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b93f278>"
]
},
"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": 338,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6.0 17779\n",
"4.0 4722\n",
"3.0 4595\n",
"5.0 4336\n",
"2.0 1788\n",
"0.0 1273\n",
"1.0 307\n",
"Name: degree_hl, dtype: int64"
]
},
"execution_count": 338,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.degree_hl.dropna().value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 339,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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b1TgbAADXPedQC2bOnKmenp7441gsFv91WlqagsGgQqGQ3G53/Hpqamr8umVZ8bX9/f2X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\nGIyQAwBgMEIOAIDBCDkAAAb7/3QdD4/pUA4IAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a9e33c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = lsl_dr.degree_hl.hist(bins=7)"
]
},
{
"cell_type": "code",
"execution_count": 340,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x12056a5f8>"
]
},
"execution_count": 340,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AqYHXyAEAMBghBwDAYIQcAACDzdnXyMeS6B3t8fYBADDbpFzI472jXZIufPW5brljWZJX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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x129f162b0>"
]
},
"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": 341,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.20646594652570291"
]
},
"execution_count": 341,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age_int<6).mean()"
]
},
{
"cell_type": "code",
"execution_count": 342,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.13450292397660818"
]
},
"execution_count": 342,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(lsl_dr.age<6).mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Counts by year"
]
},
{
"cell_type": "code",
"execution_count": 343,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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TT/g+37Rpk5KSkiRJqampKikp0caNG5WYmCiXyyW32624uDiVlZWZHAsAgHbF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\nZQ4AgOUocwAALPd/EF3eTOOp7dMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x120162550>"
]
},
"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": 344,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x120162dd8>"
]
},
"execution_count": 344,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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RyIDDcPIV0D1RyIADWXHyFRN4ACcWhQzgX5jAAziRKGQAkpjAAzjROMsaAAAb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\n5XIpOztbd911lz7++GPdf//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