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Boxplots.ipynb
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{
"cells": [
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nimport pandas as pd\nimport numpy as np\nfrom pylab import *",
"execution_count": 17,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "# kernel density estimation\nfrom scipy.stats import gaussian_kde",
"execution_count": 18,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Apply grayscale first, seaborn whitegrid second to get greyscale plots."
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "grayscale = True\n\nif grayscale:\n plt.style.use('grayscale')",
"execution_count": 19,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "print(style.available)",
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": "['seaborn-darkgrid', 'seaborn-whitegrid', 'ggplot', 'seaborn-pastel', 'seaborn-poster', 'seaborn-deep', 'seaborn-notebook', 'seaborn-white', 'seaborn-dark-palette', 'seaborn-paper', 'bmh', 'seaborn-dark', 'seaborn-colorblind', 'dark_background', 'grayscale', 'classic', 'seaborn-ticks', 'seaborn-talk', 'fivethirtyeight', 'seaborn-bright', 'seaborn-muted']\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "%matplotlib inline\nimport seaborn as sns\nsns.set_style(\"whitegrid\")",
"execution_count": 21,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "CHRIS = True",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "from redcap import Project\napi_url = 'https://redcap.vanderbilt.edu/api/'\nif not CHRIS: \n api_key = open(\"/Users/alicetoll/Documents/OPTION/token.txt\").read()\nelse:\n api_key = open(\"/Users/fonnescj/Dropbox/Collaborations/LSL-DR/api_token.txt\").read()\n\nlsl_dr_project = Project(api_url, api_key)",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "articulation_fields = ['study_id','redcap_event_name', 'age_test_aaps','aaps_ss','age_test_gf2','gf2_ss']\narticulation = lsl_dr_project.export_records(fields=articulation_fields, format='df', \n df_kwargs={'index_col':None,\n 'na_values':[999, 9999]})",
"execution_count": 24,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "expressive_fields = ['study_id','redcap_event_name','age_test_eowpvt','eowpvt_ss','age_test_evt','evt_ss']\nexpressive = lsl_dr_project.export_records(fields=expressive_fields, format='df', \n df_kwargs={'index_col':None,\n 'na_values':[999, 9999]})",
"execution_count": 25,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive_fields = ['study_id','redcap_event_name','age_test_ppvt','ppvt_ss','age_test_rowpvt','rowpvt_ss']\nreceptive = lsl_dr_project.export_records(fields=receptive_fields, format='df', \n df_kwargs={'index_col':None,\n 'na_values':[999, 9999]})",
"execution_count": 26,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"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']\nlanguage_raw = lsl_dr_project.export_records(fields=language_fields, format='df', \n df_kwargs={'index_col':None, \n 'na_values':[999, 9999]})",
"execution_count": 27,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "demographic_fields = ['study_id','redcap_event_name','redcap_data_access_group', 'academic_year',\n'hl','prim_lang','mother_ed','father_ed','premature_age', 'synd_cause', 'age_disenrolled', 'race',\n'onset_1','age_int','age','age_amp', 'age_ci', 'age_ci_2', 'degree_hl_ad','type_hl_ad','tech_ad','degree_hl_as',\n'type_hl_as','tech_as','etiology','etiology_2', 'sib', 'gender', 'time', 'ad_250', 'as_250', 'ae',\n'ad_500', 'as_500', 'fam_age', 'family_inv', 'demo_ses', 'school_lunch', 'medicaid', 'hearing_changes',\n'slc_fo', 'sle_fo', 'a_fo', 'funct_out_age',\n'att_days_hr', 'att_days_sch', 'att_days_st2_417']\ndemographic_raw = lsl_dr_project.export_records(fields=demographic_fields, format='df', \n df_kwargs={'index_col':None, \n 'na_values':[888, 999, 9999]})",
"execution_count": 28,
"outputs": [
{
"output_type": "stream",
"text": "/Users/fonnescj/anaconda3/envs/dev/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2821: DtypeWarning: Columns (25,31,41) have mixed types. Specify dtype option on import or set low_memory=False.\n if self.run_code(code, result):\n",
"name": "stderr"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Language"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "5 language measures:\n* 3 versions of CELF\n* PLS\n * pls_ac_rs: PLS: Auditory Comprehension Raw Score\n * pls_ac_ss: PLS: Auditory Comprehension Standard Score\n * pls_ec_rs: PLS: Expressive Communication Raw Score\n * pls_ec_ss: PLS: Expressive Communication Standard Score\n * pls_tl_rs: PLS: Total Language Score Standard Score Total\n * pls_tl_ss: PLS: Total Language Score Standard Score\n* OWLS\n * age_test_owls: Age at time of testing (OWLS)\n * owls_lc_rs: OWLS: Listening Comprehension Raw Score\n * owls_lc_ss: OWLS: Listening Comprehension Standard Score\n * owls_oe_rs: OWLS: Oral Expression Raw Score\n * owls_oe_ss: OWLS: Oral Expression Standard Score\n * owls_oc_sss: OWLS: Oral Composite Sum of Listening Comprehension and Oral Expression Standard Scores\n * owls_oc_ss: OWLS: Oral Composite Standard Score\n * owls_wes_trs: OWLS: Written Expression Scale Total Raw Score\n * owls_wes_as: OWLS: Written Expression Scale Ability Score\n * owls_wes_ss: OWLS: Written Expression Scale Standard Score\n * owsl_lc: OWLS: 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: OWLS: Language Composite Standard Score"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# Test type\nlanguage_raw[\"test_name\"] = None\nlanguage_raw[\"test_type\"] = None\nlanguage_raw[\"score\"] = None\nCELP = language_raw.age_test_celp.notnull()\nCELF = language_raw.age_test_celf.notnull()\nPLS = language_raw.age_test_pls.notnull()\nOWLS = language_raw.age_test_owls.notnull()\n\nlanguage_raw['age_test'] = None\nlanguage_raw.loc[CELP, 'age_test'] = language_raw.age_test_celp\nlanguage_raw.loc[CELF, 'age_test'] = language_raw.age_test_celf\nlanguage_raw.loc[PLS, 'age_test'] = language_raw.age_test_pls\nlanguage_raw.loc[OWLS, 'age_test'] = language_raw.age_test_owls\n\nlanguage1 = language_raw[CELP | CELF | PLS | OWLS].copy()\nlanguage2 = language1.copy()\n\nlanguage1[\"test_type\"] = \"receptive\"\n\nlanguage1.loc[CELP, \"test_name\"] = \"CELF-P2\"\nlanguage1.loc[CELF, \"test_name\"] = \"CELF-4\"\nlanguage1.loc[PLS, \"test_name\"] = \"PLS\"\nlanguage1.loc[OWLS, \"test_name\"] = \"OWLS\"\n\nlanguage1.loc[CELP, \"score\"] = language1.celfp_rl_ss\nlanguage1.loc[CELF, \"score\"] = language1.celf_rlss\nlanguage1.loc[PLS, \"score\"] = language1.pls_ac_ss\nlanguage1.loc[OWLS, \"score\"] = language1.owls_lc_ss\n\n\nlanguage2[\"test_type\"] = \"expressive\"\n\nlanguage2.loc[CELP, \"test_name\"] = \"CELF-P2\"\nlanguage2.loc[CELF, \"test_name\"] = \"CELF-4\"\nlanguage2.loc[PLS, \"test_name\"] = \"PLS\"\nlanguage2.loc[OWLS, \"test_name\"] = \"OWLS\"\n\nlanguage2.loc[CELP, \"score\"] = language1.celfp_el_ss\nlanguage2.loc[CELF, \"score\"] = language1.celf_elss\nlanguage2.loc[PLS, \"score\"] = language1.pls_ec_ss\nlanguage2.loc[OWLS, \"score\"] = language1.owls_oe_ss\n\nlanguage = pd.concat([language1, language2])\nlanguage = language[language.score.notnull()]\nprint(pd.crosstab(language.test_name, language.test_type))\nprint(\"There are {0} null values for score\".format(sum(language[\"score\"].isnull())))",
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"text": "test_type expressive receptive\ntest_name \nCELF-4 659 565\nCELF-P2 1722 1728\nOWLS 1259 1267\nPLS 4072 4083\nThere are 0 null values for score\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "language[\"school\"] = language.study_id.str.slice(0,4)\nlanguage = language[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"test_name\", \"school\", \"age_test\"]]\nlanguage[\"domain\"] = \"Language\"\nlanguage.head()",
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " study_id redcap_event_name score test_type test_name \\\n0 0101-2002-0101 initial_assessment_arm_1 51 receptive PLS \n5 0101-2002-0101 year_5_complete_71_arm_1 61 receptive OWLS \n9 0101-2003-0102 initial_assessment_arm_1 55 receptive PLS \n10 0101-2003-0102 year_1_complete_71_arm_1 77 receptive PLS \n11 0101-2003-0102 year_2_complete_71_arm_1 93 receptive CELF-P2 \n\n school age_test domain \n0 0101 54 Language \n5 0101 113 Language \n9 0101 44 Language \n10 0101 54 Language \n11 0101 68 Language ",
"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>"
},
"metadata": {},
"execution_count": 30
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "language['ageGroup'] = None # initial variable to none\nlanguage.loc[(language.age_test >= 36) & (language.age_test < 48), 'ageGroup'] = 3 \nlanguage.loc[(language.age_test >= 48) & (language.age_test < 60), 'ageGroup'] = 4 \nlanguage.loc[(language.age_test >= 60) & (language.age_test < 72), 'ageGroup'] = 5 \nlanguage.head()",
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " study_id redcap_event_name score test_type test_name \\\n0 0101-2002-0101 initial_assessment_arm_1 51 receptive PLS \n5 0101-2002-0101 year_5_complete_71_arm_1 61 receptive OWLS \n9 0101-2003-0102 initial_assessment_arm_1 55 receptive PLS \n10 0101-2003-0102 year_1_complete_71_arm_1 77 receptive PLS \n11 0101-2003-0102 year_2_complete_71_arm_1 93 receptive CELF-P2 \n\n school age_test domain ageGroup \n0 0101 54 Language 4 \n5 0101 113 Language None \n9 0101 44 Language 3 \n10 0101 54 Language 4 \n11 0101 68 Language 5 ",
"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 <th>ageGroup</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 <td>4</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 <td>None</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 <td>3</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 <td>4</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 <td>5</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 31
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Expressive Language"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "if CHRIS:\n !mkdir DescriptiveFigures",
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"text": "mkdir: DescriptiveFigures: File exists\r\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "expressive_lang = language.loc[language.test_type=='expressive'].copy()\nexpressive_lang.head()",
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " study_id redcap_event_name score test_type test_name \\\n0 0101-2002-0101 initial_assessment_arm_1 60 expressive PLS \n5 0101-2002-0101 year_5_complete_71_arm_1 82 expressive OWLS \n9 0101-2003-0102 initial_assessment_arm_1 68 expressive PLS \n10 0101-2003-0102 year_1_complete_71_arm_1 77 expressive PLS \n11 0101-2003-0102 year_2_complete_71_arm_1 85 expressive CELF-P2 \n\n school age_test domain ageGroup \n0 0101 54 Language 4 \n5 0101 113 Language None \n9 0101 44 Language 3 \n10 0101 54 Language 4 \n11 0101 68 Language 5 ",
"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 <th>ageGroup</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>60</td>\n <td>expressive</td>\n <td>PLS</td>\n <td>0101</td>\n <td>54</td>\n <td>Language</td>\n <td>4</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>82</td>\n <td>expressive</td>\n <td>OWLS</td>\n <td>0101</td>\n <td>113</td>\n <td>Language</td>\n <td>None</td>\n </tr>\n <tr>\n <th>9</th>\n <td>0101-2003-0102</td>\n <td>initial_assessment_arm_1</td>\n <td>68</td>\n <td>expressive</td>\n <td>PLS</td>\n <td>0101</td>\n <td>44</td>\n <td>Language</td>\n <td>3</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>expressive</td>\n <td>PLS</td>\n <td>0101</td>\n <td>54</td>\n <td>Language</td>\n <td>4</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>85</td>\n <td>expressive</td>\n <td>CELF-P2</td>\n <td>0101</td>\n <td>68</td>\n <td>Language</td>\n <td>5</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 33
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Create models for converting scores"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive_scores = (expressive_lang.groupby(['study_id','ageGroup'])\n .apply(lambda x: x.pivot(columns='test_name', values='score')))",
"execution_count": 34,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive_scores = (expressive_scores.set_index(expressive_scores.index.droplevel(2))\n .reset_index()\n .groupby(['study_id','ageGroup'])).apply(max)",
"execution_count": 35,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive_owls_pls = expressive_scores[['OWLS', 'PLS']].dropna()\nexpressive_owls_pls.plot.scatter('OWLS', 'PLS')",
"execution_count": 36,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x111e49908>"
},
"metadata": {},
"execution_count": 36
},
{
"output_type": "display_data",
"data": {
"image/png": 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Q0qVLdfToUUnSv//9b2VnZ2vkyJG69dZbtW3btrAUCQAAokOdg8m+ffs0cuRIPf744zpy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\nAACMQTABAADGIJgAAABj/H+0JfUXW+PFKAAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x111e320b8>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": true
},
"cell_type": "code",
"source": "from sklearn import linear_model\nreg_owls = linear_model.LinearRegression()\nreg_owls.fit(expressive_owls_pls.OWLS.values.reshape(-1,1), expressive_owls_pls.PLS.values)",
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
},
"metadata": {},
"execution_count": 37
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive_lang.test_name.value_counts()",
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "PLS 4072\nCELF-P2 1722\nOWLS 1259\nCELF-4 659\nName: test_name, dtype: int64"
},
"metadata": {},
"execution_count": 38
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive_lang['old_score'] = expressive_lang.score.copy()\npred_vals = reg_owls.predict(expressive_lang[expressive_lang.test_name=='OWLS'].score.values.reshape(-1,1))\nexpressive_lang.loc[expressive_lang.test_name=='OWLS', 'score'] = pred_vals",
"execution_count": 39,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive_celf_pls = expressive_scores[['CELF-P2', 'PLS']].dropna()\nexpressive_celf_pls.plot.scatter('CELF-P2', 'PLS')",
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1134b3fd0>"
},
"metadata": {},
"execution_count": 40
},
{
"output_type": "display_data",
"data": {
"image/png": 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D9+NwOGQYRtCKEMMw5HA4lJ6eHvbni6brE029SPRjZdHUi3T2F6KG+KXe0sGkVatWqqys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\n",
"text/plain": "<matplotlib.figure.Figure at 0x1134b3748>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "reg_celf = linear_model.LinearRegression()\nreg_celf.fit(expressive_celf_pls['CELF-P2'].values.reshape(-1,1), expressive_celf_pls.PLS.values)",
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
},
"metadata": {},
"execution_count": 41
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "pred_vals = reg_celf.predict(expressive_lang[expressive_lang.test_name=='CELF-P2'].score.values.reshape(-1,1))\nexpressive_lang.loc[expressive_lang.test_name=='CELF-P2', 'score'] = pred_vals",
"execution_count": 42,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive_scores[['CELF-4', 'PLS']].dropna().plot.scatter('CELF-4', 'PLS')",
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1122b26a0>"
},
"metadata": {},
"execution_count": 43
},
{
"output_type": "display_data",
"data": {
"image/png": 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Wrlyp7Oxs3+vq6mrZ7Xa/fex2u7xeb8DH9ng8qqqqqvOMjYHb7fb7iNoht8CR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\nxqCYAAAAY/w/i9QTBHChsp8AAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x115f332e8>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "There arent enough points to fit a model for CELF-2, so we will combine these directly."
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# Test type\nexpressive_lang[\"test_type\"] = None\nARIZ = articulation.aaps_ss.notnull()\nGF = articulation.gf2_ss.notnull()\narticulation = articulation[ARIZ | GF]\narticulation.loc[(ARIZ & GF), \"test_type\"] = \"Arizonia and Goldman\"\narticulation.loc[(ARIZ & ~GF), \"test_type\"] = \"Arizonia\"\narticulation.loc[(~ARIZ & GF), \"test_type\"] = \"Goldman\"\n\nprint(articulation.test_type.value_counts())\nprint(\"There are {0} null values for test_type\".format(sum(articulation[\"test_type\"].isnull())))\n\n# Test score (Arizonia if both)\narticulation[\"score\"] = articulation.aaps_ss\narticulation.loc[(~ARIZ & GF), \"score\"] = articulation.gf2_ss[~ARIZ & GF]",
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"text": "Goldman 5728\nArizonia 525\nArizonia and Goldman 91\nName: test_type, dtype: int64\nThere are 0 null values for test_type\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "plt.style.use('grayscale')\nsns.set_style(\"whitegrid\")\nbp = expressive_lang.boxplot(column='score', by='ageGroup', grid=False, sym='')\nplt.xlabel('Age (years)'); plt.ylabel('Standard score');\nplt.suptitle('Expressive Language')\nfor i in [1,2,3]:\n y = expressive_lang.score[expressive_lang.ageGroup==i+2].dropna()\n # Add some random \"jitter\" to the x-axis\n x = np.random.normal(i, 0.04, size=len(y))\n plt.plot(x, y.values, 'k.', alpha=0.05)\nplt.savefig('DescriptiveFigures/expLang.png', dpi=300)\n \nexpressive_lang.ageGroup.value_counts()\n# expressive_lang.groupby('ageGroup')['score'].agg([np.mean, np.median, np.std, len])",
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "3 1590\n4 1572\n5 1118\nName: ageGroup, dtype: int64"
},
"metadata": {},
"execution_count": 45
},
{
"output_type": "display_data",
"data": {
"image/png": 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BYDDAarUik8lwArfJZEJ5eTmcTieArudVCQLQ8bzR6/VIJpNIJpPQarXIZDLw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\nOnS47v47o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"text/plain": "<matplotlib.figure.Figure at 0x1122831d0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "bp = expressive_lang.boxplot(column='score', by='ageGroup', grid=False, sym='')\nplt.xlabel('Age (years)'); plt.ylabel('Standard score');\nplt.suptitle('Expressive Language')\nfor i in [1,2,3]:\n y = expressive_lang.score[expressive_lang.ageGroup==i+2].dropna()\n # Add some random \"jitter\" to the x-axis\n x = np.random.normal(i, 0.04, size=len(y))\n plt.plot(x, y.values, 'k.', alpha=0.05)\nplt.savefig('DescriptiveFigures/expLang.png', dpi=300)",
"execution_count": 46,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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fB2muWSwWGAwGNj4ymQyMRiMAcIVPW0+x0DuIkSIwe8oT6U4eieScCHuiM28a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\nR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2vu3Lnk5+ezbt06li9fjoeHh9N+Dw8Px0219VFTUwPAyZMnGzSniIiInFvt527t5/C5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\nRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x1122836a0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# need to do this for some reason...\nexpressive_lang['scoreInt'] = np.array(expressive_lang.score, dtype = 'int')\nexpressive_lang.groupby('ageGroup')['scoreInt'].agg([np.mean, np.median, np.std, len])",
"execution_count": 47,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " mean median std len\nageGroup \n3 82.952201 82 15.582549 1590\n4 81.650127 81 16.563240 1572\n5 80.762970 80 18.071186 1118",
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mean</th>\n <th>median</th>\n <th>std</th>\n <th>len</th>\n </tr>\n <tr>\n <th>ageGroup</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>82.952201</td>\n <td>82</td>\n <td>15.582549</td>\n <td>1590</td>\n </tr>\n <tr>\n <th>4</th>\n <td>81.650127</td>\n <td>81</td>\n <td>16.563240</td>\n <td>1572</td>\n </tr>\n <tr>\n <th>5</th>\n <td>80.762970</td>\n <td>80</td>\n <td>18.071186</td>\n <td>1118</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 47
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Receptive Language"
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "receptive_lang = language.loc[language.test_type=='receptive'].copy()\nreceptive_lang.head()",
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " study_id redcap_event_name score test_type test_name \\\n0 0101-2002-0101 initial_assessment_arm_1 51 receptive PLS \n5 0101-2002-0101 year_5_complete_71_arm_1 61 receptive OWLS \n9 0101-2003-0102 initial_assessment_arm_1 55 receptive PLS \n10 0101-2003-0102 year_1_complete_71_arm_1 77 receptive PLS \n11 0101-2003-0102 year_2_complete_71_arm_1 93 receptive CELF-P2 \n\n school age_test domain ageGroup \n0 0101 54 Language 4 \n5 0101 113 Language None \n9 0101 44 Language 3 \n10 0101 54 Language 4 \n11 0101 68 Language 5 ",
"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 <th>ageGroup</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 <td>4</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 <td>None</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 <td>3</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 <td>4</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 <td>5</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 48
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive_lang.test_name.value_counts()",
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "PLS 4083\nCELF-P2 1728\nOWLS 1267\nCELF-4 565\nName: test_name, dtype: int64"
},
"metadata": {},
"execution_count": 49
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "receptive_scores = (receptive_lang.groupby(['study_id','ageGroup'])\n .apply(lambda x: x.pivot(columns='test_name', values='score')))",
"execution_count": 50,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "receptive_scores = (receptive_scores.set_index(receptive_scores.index.droplevel(2))\n .reset_index()\n .groupby(['study_id','ageGroup'])).apply(max)",
"execution_count": 51,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive_owls_pls = receptive_scores[['OWLS', 'PLS']].dropna()\nreceptive_owls_pls.plot.scatter('OWLS', 'PLS')",
"execution_count": 52,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1157340b8>"
},
"metadata": {},
"execution_count": 52
},
{
"output_type": "display_data",
"data": {
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Bq9Vi2bJlOHfunMMZFyIiIuo8hAgmJSUlSExMREFBgcNK5/LycgwePBgqlco+Fhsba3+I\nTXl5OeLj4+3bAgMDMWjQIJSVlXmueCIiInIZIR5J/+STT95wvKGhAeHh4Q5jISEhuHDhAgCgvr6+\nzfbQ0FD7diIiIupchDhjcjMWiwVKpdJhTKlUwmazAQCam5tvuZ2IiIg6FyHOmNyMSqWC2Wx2GLPZ\nbPaH2KhUqjYhxGaz4c4773TqfaxWK5qamjpWrMAsFovDV1/FPn2Lv/QJ+E+v7NO3WK1WtxxX6GDS\nu3fvNnfpmEwmhIWF2bc3NDS02R4dHe3U+xiNRhiNxo4V2wlUV1d7uwSPYJ++xV/6BPynV/ZJtyJ0\nMImJiUFubi5sNpv9kk1paSni4uLs248ePWrf32KxoKKiApmZmU69T0REBDQajesKF4zFYkF1dTUi\nIyOhVqu9XY7bsE/f4i99Av7TK/v0LY2NjW75o17oYJKQkICIiAi89NJL+PWvf40vvvgCx48fR3Z2\nNgAgNTUV77//PnJzc/GLX/wCa9aswYABA5CQkODU+6hUKgQFBbmjBaGo1Wr26UPYp+/xl17Zp29w\n16Uq4Ra/Xv+JjV26dMG7776LhoYGpKam4i9/+QvWrl2LPn36AAD69euH1atXo6ioCFOmTMGlS5ew\nZs0ab5VOREREHSTcGZPKykqH1/3798eWLVtuuv/YsWNRXFzs7rKIiIjIA4Q7Y0JERET+i8GEiIiI\nhMFgQkRERMJgMCEiIiJhMJgQERGRMBhMiIiISBgMJkRERCQMBhMiIiISBoMJERERCYPBhIiIiITB\nYEJERETCYDAhIiIiYQj3IX5EnqDX65GXlwer1QqVSoW0tDRIkuTtsshDOP9E4uIZE/I7er0eOTk5\n0Gg06NOnDzQaDXJycqDX671dGnkA559IbAwm5Hfy8vKg1WqhUCgAAAqFAlqtFvn5+V6ujDyB808k\nNgYT8jtWq9X+S6mVQqFAc3OzlyoiT+L8E4mNwYT8jkqlgizLDmOyLCMwMNBLFZEncf6JxMZgQn4n\nLS0NBoPB/stJlmUYDAbMmDHDy5WRJ3D+icTGYEJ+R5Ik6HQ6mM1m1NXVwWw2Q6fT8a4MP8H5JxIb\nbxcmvyRJEn8R+THOP5G4eMaEiIiIhMFgQkRERMJgMCEiIiJhCL/G5Ntvv8Ubb7yBgwcPolevXkhP\nT8djjz0GAKitrcWrr74KvV6Pfv36YfHixRg9erSXK6bOgI8k9y2cTyLfIfwZk1//+teor6/Hli1b\n8PLLLyM7Oxu7du2ybwsPD0dRUREmTpyI+fPno66uzssVk+j4SHLfwvkk8i1CB5MTJ07g2LFjyMnJ\nQVRUFB566CE8//zz2LhxI/75z3+itrYWb775JgYOHIg5c+ZAkiRs377d22WT4PhIct/C+STyLUIH\nk5qaGvTq1Qv9+vWzjz3wwAM4ceIEjhw5gsGDB0OlUtm3xcbG8q8k+kl8JLlv4XwS+Rahg0loaCi+\n++47WK1W+5jRaMTVq1dx8eJFhIeHO+wfEhKCCxcueLpM6mT4SHLfwvkk8i1CL36NiYlBWFgY3nzz\nTbzyyiuor69HXl4eFAoFrFYrlEqlw/5KpRI2m83p97FarWhqanJV2cKxWCwOX31Ve/ucPn061qxZ\ng3vvvRcKhQKyLKOqqgrz58/vFD8HnE9HnX0+Ac6pr/GXPq8/aeBKQgcTpVKJd955BwsWLEBsbCxC\nQkLw/PPPY9myZejSpUubSbfZbLf1V5LRaITRaHRV2cKqrq72dgke8VN9BgQEIDk5GcXFxbh27Rq6\ndu2K5ORkBAQEoLKy0jNFugDn8we+Mp8A59TX+EufriZ0MAGAIUOGYNeuXbh48SJ69uyJvXv3olev\nXhgwYAD27dvnsK/JZEJYWJjT7xEREQGNRuOqkoVjsVhQXV2NyMhIqNVqb5fjNs70GR0djYkTJ3qo\nMtfifLbVmecT4Jz6Gn/ps7Gx0S1/1AsdTMxmM+bNm4d169YhJCQEAPDll18iISEBw4YNw4YNG2Cz\n2eyXdEpLSxEXF+f0+6hUKgQFBbm0dhGp1Wr26UPYp+/xl17Zp29w16UqoRe/9ujRAxaLBcuXL0dN\nTQ0KCwuxc+dOzJ49GwkJCejbty9eeuklVFVV4b333sPx48fxxBNPeLtsIiIiuk1CBxMAWLlyJc6c\nOYOJEydi8+bNWLVqFQYPHowuXbrg3XffRUNDA1JTU/GXv/wFa9euRZ8+fbxdMhEREd0moS/lAEBk\nZCS2bNlyw239+/e/6TYiIiLqfIQ/Y0JERET+w6lg8q9//Qtbt27FpUuXAADXrl1DTk4OUlJS8Nxz\nz+HQoUNuKZKIiIj8Q7uDSU1NDVJSUrB8+XJ8++23AICsrCxs3LgRAwcOxF133YW5c+eitLTUbcUS\nERGRb2v3GpM1a9bgnnvuwbp16xAcHIzGxkYUFBRg3LhxWLVqFQCgX79+WLduHTZu3Oi2gomIiMh3\ntfuMyYEDB/DCCy8gODjY/vrq1auYPHmyfZ8xY8agvLzc9VUSERGRX2h3MPnXv/7l8Cm/R44cQZcu\nXZCQkGAf69mzp9uenU9ERES+r93BpFevXqivr7e/PnDgAKKjo9GjRw/7WGVlJUJDQ11bIREREfmN\ndgeTsWPHYv369bh8+TL++7//G9XV1UhKSrJvb2pqwrvvvovRo0e7pVAiIiLyfe1e/PrCCy/gmWee\nQXx8PGRZxpAhQ/Dss88CALZt24a1a9dCoVAgIyPDbcUSERGRb2t3MAkPD8df/vIXHDhwAAqFAg8+\n+CACAgJ+OEi3bkhOTsZzzz2H3r17u61YIiIi8m1OPZJeqVTi5z//eZvxKVOmuKoeIiIi8mMufST9\n3r17ER0d7cpDEhERkR8R/kP8iPyRXq9HXl4erFYrVCoV0tLSIEmSy/YnIhIVP8SPSDB6vR45OTnQ\naDTo06cPNBoNcnJyoNfrXbI/EZHIGEyIBJOXlwetVguFQgEAUCgU0Gq1yM/Pd8n+REQiYzAhEozV\narWHjFYKhQLNzc0u2Z+ISGTtXmOyePHin9znwoULHSqGiACVSgVZlh3ChizLCAwMdMn+REQia/cZ\nk9ra2p/858qVK4iLi3NnvUQ+Ly0tDQaDAbIsA/ghZBgMBsyYMcMl+xMRiazdZ0y2bNkCo9GIXbt2\nQaVS4Wc/+xn69OnjztqI/JIkSdDpdMjPz0dzczMCAwOh0+luepeNs/sTEYms3cHkyJEjmD17NiwW\nCwCge/fuWLVqFcaMGeO24oj8lSRJTgULZ/cnIhJVuy/lrFq1CqNGjcKePXuwf/9+jBkzBtnZ2e6s\njYiIiPxMu8+YVFRUoKCgAOHh4QCAl19+GT//+c9x+fJlBAcHu61AIiIi8h/tPmPS1NQEjUZjf927\nd28EBATAbDa7pbBWdXV1SE9PR2xsLB5++GGHZzNUVFRg6tSpkCQJU6ZMwcmTJ91aCxEREblXu4PJ\nj29HBICuXbuipaXF5UVd74UXXkD37t2xc+dOvPzyy/jjH/+IXbt2wWKxYM6cOYiPj8eOHTsgSRLm\nzp3LZzcQERF1YkI/YO27777DsWPHMG/ePAwYMAAPP/wwxo4di3/+85/47LPPoFarsWjRIgwcOBBL\nlixB9+7dUVxc7O2yiYiI6DY59SF+77//PtRqtf311atXsXnzZvTo0cNhv/nz57ukuMDAQKjVahQV\nFUGn0+Hs2bM4evQo/uM//gPHjh1DbGysw/4jRoxAWVkZJk+e7JL3JyIiIs9qdzDp27cv/vrXvzqM\nhYWF4e9//7vDmEKhcFkwUSqVeO211/Dmm29i8+bNuHbtGh5//HGkpqbi888/x/333++wf0hICKqq\nqlzy3kREROR57Q4mX3zxhTvruCmDwYBx48Zh1qxZ+Prrr/H73/8eiYmJaG5uhlKpdNhXqVTCZrN5\npU4iIiLqOKcu5XjawYMHsX37duzZswdKpRKDBg1CXV0d1q1bhwEDBrQJITab7bY+H8RqtaKpqclV\nZQun9aF4rV99Ffv0Lf7SJ+A/vbJP32K1Wt1yXKGDycmTJxEZGelwZiQ6Ohrr169HXFwcGhoaHPY3\nmUwICwtz+n2MRiOMRmOH6xVddXW1t0vwCPbpW/ylT8B/emWfdCtCB5Pw8HCcOXMGV69eRbduP5R6\n6tQp9O/fH5IkYcOGDQ77l5WVIT093en3iYiIcHhGi6+xWCyorq5GZGSkw+JlX8M+fYu/9An4T6/s\n07c0Nja65Y96oYPJuHHjsHz5crzyyitIT0/HqVOnsGHDBuh0OkyYMAF/+MMfkJWVhWnTpmHbtm1o\nampCUlKS0++jUqkQFBTkhg7Eolar2acPYZ++x196ZZ++wV2XqoR+jklwcDDy8vLQ0NCAKVOm4O23\n30ZGRgamTJmC4OBgbNiwAUeOHEFqaiqOHz+O3Nzc21pjQkRERGIQ+owJAGi1WmzatOmG24YOHYod\nO3Z4uCIiIiJyF6HPmBAREZF/YTAhIiIiYTCYEBERkTAYTIiIiEgYDCZEREQkDAYTIiIiEgaDCRER\nEQmDwYSIiIiEwWBCREREwmAwISIiImEwmBAREZEwGEyIiIhIGMJ/iB/5Pr1ej7y8PFitVqhUKqSl\npUGSJG+XRUREXsAzJuRVer0eOTk50Gg06NOnDzQaDXJycqDX671dGhEReQGDCXlVXl4etFotFAoF\nAEChUECr1SI/P9/LlRERkTcwmJBXWa1WeyhppVAo0Nzc7KWKiIjIm7jGhLxKpVJBlmWHcCLLMgID\nA71Sz43WuwDgGhgiIg/hGRPyqrS0NBgMBsiyDOCHUGIwGDBjxgyP13Kj9S7Lli3Da6+9xjUwREQe\nwmBCXiVJEnQ6HcxmM+rq6mA2m6HT6bxyRuJG612io6PR3NzMNTBERB7CSznkdZIkeTyI3OiSzc3W\nuwQEBLQZE20NjLO3XPMWbSISFc+YkN+52S3Kzc3N9ktKrWRZxpUrV9qMeWsNzI04e8s1b9EmIpEx\nmJDfudktyteuXWuz3qWyshKBgYFCrIG5GWdvueYt2kQkMl7KIb9zs0s23bt3x9y5c5Gfn4/m5mYE\nBgZi8eLFAOAw5q01MDfj7C3XvEWbiEQmdDDZuXMnFi9eDIVCYb+lVJZldOnSBRUVFaioqMAbb7yB\nr7/+Gvfddx/eeOMNDB482Ntlk4u4ax3ErW5Rvtl6F5GCyI85e8u1aLdoExFdT+hLOb/85S+xf/9+\n7Nu3D/v378c//vEP3H333ZgxYwYsFgvmzJmD+Ph47NixA5IkYe7cufyrz0e4cx2ESLcou4Kz/fha\n/0TkW4QOJkqlEiEhIfZ/Pv74YwDAwoUL8emnn0KtVmPRokUYOHAglixZgu7du6O4uNjLVZMruHMd\nhEi3KLuCs/34Wv9E5FuEvpRzPbPZjI0bNyIrKwsBAQEoLy9HbGyswz4jRoxAWVkZJk+e7KUqyVXc\nvQ7CG7cou5Oz/fha/0TkO4Q+Y3K9Dz/8EL1798b48eMBAPX19QgPD3fYJyQkBBcuXPBGeeRiresg\nrsd1EEREvq/TnDHZvn075syZY3/d3NwMpVLpsI9SqYTNZnP62FarFU1NTR2uUVQWi8Xha2cwffp0\nrFmzBvfee6990XNVVRXmz59/07nqjH3eDvbpe/ylV/bpW6xWq1uO2ymCSXl5OS5cuIBHH33UPqZS\nqdqEEJvNdlt/URuNRhiNxg7XKbrq6mpvl9BuAQEBSE5ORnFxMa5du4auXbsiOTkZAQEBqKysvOX3\nervPb775xqHuf//3f8d9993n8mN4u09P8Zc+Af/plX3SrXSKYLJv3z7Ex8fjjjvusI/17t0bDQ0N\nDvuZTCaEhYU5ffyIiAhoNJoO1ykqi8WC6upqREZGQq1We7ucdouOjsbEiRPbvb8IfZaXl+OTTz5x\nONPzySefYP78+Rg2bJhLjiFCn57gL30C/tMr+/QtjY2NbvmjvlMEkxstdI2JiUFubq7DWFlZGdLT\n050+vkqlQlBQUIdq7AzUajX7dLP/+q//sgcK4IcFu/feey8KCgowatQolx6D8+l7/KVX9ukb3HWp\nqlMsfv36668xcOBAh7FHHnkEly5dQlZWFgwGA5YuXYqmpiYkJSV5qUoi19xNxCezEpE/6xTB5Ntv\nv0WPHj3G1qd/AAAd1klEQVQcxoKDg7F+/XocOXIEqampOH78OHJzc3nXBnmVK+4m4h1JROTPOkUw\n0ev1GD16dJvxoUOHYseOHdDr9SgoKEBUVJQXqiP6P654qiqfzEpE/qxTBBOizsIVT1Xlk1mJyJ91\nisWvRJ2JK56qyiezEpG/4hkTIiIiEgaDCREREQmDwYSIiIiEwWBCREREwmAwISIiImEwmBAREZEw\nGEyIiIhIGAwmREREJAwGEyIiIhIGgwkREREJg4+kJwd6vR55eXmwWq1QqVRIS0vjo9GJiMhjeMaE\n7PR6PXJycqDRaNCnTx9oNBrk5ORAr9d7uzQiIvITDCZkl5eXB61WC4VCAQBQKBTQarXIz8/3cmVE\nROQvGEzIzmq12kNJK4VCgebmZi9VRERE/obBhOxUKhVkWXYYk2UZgYGBXqqIiIj8DYMJ2aWlpcFg\nMNjDiSzLMBgMmDFjhpcrIyIif8FgQnaSJEGn08FsNqOurg5msxk6nY535RARkcfwdmFyIEmSx4MI\nb1HunNw5b3q9Hps2bUJjYyM0Gg1mzZrFnwkiP8EzJuRVvEW5c3LnvLUeOyQkBPfeey9CQkL4M0Hk\nRxhMyKt4i3Ln5M55488EkX8TPpjYbDb87ne/Q0JCAsaMGYOVK1fat1VUVGDq1KmQJAlTpkzByZMn\nvVgp3Q7RblHW6/VYsGAB5s2bhwULFvCv9Jtw57yJ9jNBRJ4lfDBZunQpDh48iPfffx9/+MMf8NFH\nH+Gjjz6CxWLBnDlzEB8fjx07dkCSJMydO5f/8epkRLpFmZeV2s+d8ybSzwQReZ7QwcRsNmPHjh1Y\nunQphgwZglGjRmHmzJk4duwYPvvsM6jVaixatAgDBw7EkiVL0L17dxQXF3u7bHKCSLco8xJC+7lz\n3kT6mSAizxM6mJSWluKOO+5AXFycfWz27Nl46623cOzYMcTGxjrsP2LECJSVlXm6TOoAkW5R5iWE\n9nPnvLUe+9tvv0VVVRUuXrzI29aJ/IjQtwvX1NSgX79++POf/4wNGzbgypUrePzxxzFv3jzU19fj\n/vvvd9g/JCQEVVVVXqqWbpc3blG+kdZLCNeHE15CuDl3zpskScjOzkZlZSWio6MRFBTklvchIvEI\nHUyamppQXV2NwsJCZGdno6GhAa+99hqCgoLQ3NwMpVLpsL9SqYTNZnP6faxWK5qamlxVtnAsFovD\nV1/V0T6nT5+ONWvW4N5774VCoYAsy6iqqsL8+fOF+vngfPoef+mVffoWq9XqluMKHUy6du2K77//\nHjk5OejTpw8A4Ny5c/jwww9xzz33tAkhNpvttv66NRqNMBqNLqlZZNXV1d4uwSNut8+AgAAkJyej\nuLgY165dQ9euXZGcnIyAgABUVla6tkgX4Hz6Hn/plX3SrQgdTMLDw6FSqeyhBADuuece1NXVYeTI\nkWhoaHDY32QyISwszOn3iYiIgEaj6XC9orJYLKiurkZkZCTUarW3y3EbV/QZHR2NiRMnurgy1+J8\n+h5/6ZV9+pbGxka3/FEvdDCRJAlWqxVnzpzB3XffDQAwGAy46667IEkSNmzY4LB/WVkZ0tPTnX4f\nlUrlF9ew1Wo1+/Qh7NP3+Euv7NM3uOtSldB35URGRuKhhx7CSy+9hK+++gp79+5Fbm4unnrqKUyY\nMAGXLl1CVlYWDAYDli5diqamJiQlJXm7bCIiIrpNQp8xAYA//OEPWLp0KX71q19BrVbj6aefxq9+\n9SsAwIYNG/D666/jo48+wgMPPIDc3NxOeQcFP8SOiIjoB8IHk+DgYGRnZyM7O7vNtqFDh2LHjh1e\nqMp1Wp822vpgL1mWkZOTw+c2EBGRXxL6Uo4/4NNGiYiI/g+DiZfxaaNERET/R/hLOb6OTxv1Dq7r\nISISE8+YeBk/sMzz+CnCRETiYjDxMpE+xM5fcF0PEZG4eClHAKJ8iJ2/4LoeIiJx8YwJ+Z3WdT3X\n47oeIiIxMJiQ3+G6HiIicTGYkN/huh4iInFxjQn5Ja7rISISE8+YEBERkTAYTIiIiEgYDCZEREQk\nDAYTIiIiEgaDCREREQmDwYSIiIiEwWBCREREwmAwISIiImEwmBAREZEw+ORXum16vR55eXmwWq1Q\nqVRIS0vj01SJiKhDeMaEboter0dOTg40Gg369OkDjUaDnJwc6PV6b5dGRESdGIMJ3Za8vDxotVoo\nFAoAgEKhgFarRX5+vpcrIyKizoyXcui2WK1WeyhppVAo0Nzc7KWKPI+XsoiIXE/4Mya7du1CVFQU\noqOj7V9feOEFAEBFRQWmTp0KSZIwZcoUnDx50svV+g+VSgVZlh3GZFlGYGCglyryLF7KIiJyD+GD\nSVVVFcaNG4f9+/dj//792LdvH9566y1YLBbMmTMH8fHx2LFjByRJwty5c/3qL3ZvSktLg8FgsIcT\nWZZhMBgwY8YML1fmGbyURUTkHsIHE4PBgPvuuw+9evVCSEgIQkJCEBwcjE8//RRqtRqLFi3CwIED\nsWTJEnTv3h3FxcXeLtkvSJIEnU4Hs9mMuro6mM1m6HQ6v7mUwUtZRETuIfwaE4PBgNGjR7cZLy8v\nR2xsrMPYiBEjUFZWhsmTJ3uqvBvqDGsPnKnxZvu2/tORY3dWrZeyrg8n/nQpi4jIXYQ/Y3L69Gns\n3bsXjzzyCMaPH48VK1bgypUrqK+vR3h4uMO+ISEhuHDhgpcq/UFnWHvgTI3O9tMZ+ncFf7+URUTk\nLkKfMTl//jyam5uhUqmwatUq1NbW2teXNDc3Q6lUOuyvVCphs9mcfh+r1YqmpiaX1Lxp06Ybrj3Y\ntGkT3n77bZe8h7MsFovDV2dqdLYfb/b/4z7d6f7770dGRgY+/PBDWK1WKJVKZGRk4P7773fZz9LN\neLJPb/KXPgH/6ZV9+har1eqW4wodTPr27YtDhw7hzjvvBABERUWhpaUFixYtwsiRI9uEEJvNdlun\n0o1GI4xGo0tqbmxsRGhoqMOYQqFAY2MjKisrXfIet6u6uhqAczU6248I/bf26W4BAQFtzpB4co49\n1ae3+UufgP/0yj7pVoQOJgDsoaSVVquF1WpFaGgoGhoaHLaZTCaEhYU5/R4RERHQaDQdqrOVRqO5\n4doDjUaD6Ohol7yHsywWC6qrqxEZGQm1Wu1Ujc72483+f9ynr2KfvsdfemWfvqWxsdFlf9RfT+hg\nsm/fPuh0OuzZswcqlQrAD88u6dmzJ+Li4rBhwwaH/cvKypCenu70+6hUKgQFBbmk5lmzZiEnJ8d+\nOaN17YFOp3PZe9wutVqNoKAgp2p0th8R+m/t09exT9/jL72yT9/grktVQi9+HT58ONRqNZYsWYLT\np09j9+7dWL58OWbPno0JEybg0qVLyMrKgsFgwNKlS9HU1ISkpCSv1twZbqN1pkZn++kM/RMRkbiE\nPmPSvXt3bNq0CVlZWXjiiSfQvXt3TJ8+HTNnzgQAbNiwAa+//jo++ugjPPDAA8jNzRXids2b3UYr\nEmdqdLafztA/ERGJSehgAsB+R8eNDB06FDt27PBwRUREROQuQl/KISIiIv/CYEJERETCYDAhIiIi\nYTCYEBERkTAYTIiIiEgYDCZEREQkDAYTIiIiEgaDCREREQmDwYSIiIiEwWBCREREwmAwISIiImEw\nmBAREZEwGEyIiIhIGAwmREREJAwGEyIiIhIGgwkREREJg8GEiIiIhMFgQkRERMJgMCEiIiJhMJgQ\nERGRMBhMiIiISBgMJkRERCSMThVM5syZg8WLF9tfV1RUYOrUqZAkCVOmTMHJkye9WB0RERF1VKcJ\nJp9++in27Nljf22xWDBnzhzEx8djx44dkCQJc+fORXNzsxerJCIioo7oFMHEbDZj+fLlGDZsmH3s\n008/hVqtxqJFizBw4EAsWbIE3bt3R3FxsRcrJSIioo7oFMHk7bffxqRJk6DVau1j5eXliI2Nddhv\nxIgRKCsr83R5RERE5CLCB5ODBw+itLQUGRkZDuP19fUIDw93GAsJCcGFCxc8WR4RERG5UDdvF3Ar\nNpsNb7zxBl5//XUolUqHbc3NzW3GlEolbDZbu4/f0tICALh8+XLHixWY1WoFADQ2NsJisXi5Gvdh\nn77FX/oE/KdX9ulbWn93tv4udRWhg8nq1asxZMgQPPjgg222qVSqNiHEZrMhMDCw3cdv/eExmUww\nmUwdK7YTMBqN3i7BI9inb/GXPgH/6ZV9+har1Yrg4GCXHU/oYPLZZ5/h4sWLGD58OADgypUrAIC/\n/e1vSE5ORkNDg8P+JpMJYWFh7T5+jx49EBkZCZVKhS5dhL+qRUREJIyWlhZYrVb06NHDpccVOphs\n3boVV69etb9evnw5AGDRokUoKSlBbm6uw/5lZWVIT09v9/G7deuGkJAQ1xRLRETkZ1x5pqSV0MEk\nIiLC4XX37t0BAP3790fPnj2xYsUKZGVlYdq0adi2bRuampqQlJTkjVKJiIjIBTrt9Yvg4GCsX78e\nR44cQWpqKo4fP47c3Fyn1pgQERGRWBSyLMveLoKIiIgI6MRnTIiIiMj3MJgQERGRMBhMiIiISBgM\nJkRERCQMBhMiIiISht8Ek127diEqKgrR0dH2ry+88AIAoKKiAlOnToUkSZgyZQpOnjzp5Wpvn81m\nw+9+9zskJCRgzJgxWLlypX2br/S5c+fONnMZFRWFQYMGAfCdPgGgrq4O6enpiI2NxcMPP4z8/Hz7\nNl/q89tvv8VvfvMbxMfH45FHHsHOnTvt22pra/Hcc89h+PDhSE5Oxv79+71Y6e2x2WxISUnB4cOH\n7WM/1deBAweQkpICSZKQlpaGmpoaT5d9W27Ua6tTp07Zn+R9vX379iE5ORmSJGHmzJk4d+6cJ0rt\nkBv1qdfrMX36dAwfPhxJSUkoLCx0+J7OOKc36nPv3r2YNGkSYmJiMHnyZOzZs8fhezrcp+wn1q1b\nJ8+bN0++ePGibDKZZJPJJF+6dEluamqSR48eLf/nf/6nbDAY5KVLl8qjR4+WLRaLt0u+La+++qr8\nyCOPyMePH5cPHjwojxo1Si4oKPCpPq1Wq30OTSaTbDQa5QkTJsjZ2dk+1acsy/LUqVPlhQsXymfO\nnJF37dolS5Ikf/755z7X57Rp0+Rp06bJlZWV8pdffiknJCTIn3/+uSzLspySkiL/v//3/2SDwSBv\n2LBBliRJNhqNXq64/axWq5yRkSFHRUXJJSUl9vGJEyfetK/z58/LkiTJf/rTn+Sqqip5wYIFckpK\nirdaaLeb9SrLslxbWytPmDBBHjx4sMN4TU2NLEmSvHnzZrmqqkrOzMyUH3vsMU+W7bQb9dnQ0CDH\nx8fLK1eulM+cOSN/+umn8rBhw+Qvv/xSlmVZPnfuXKeb0xv1eebMGTkmJkbOz8+Xa2pq5D/96U/y\nkCFD5HPnzsmy7JqfXb8JJi+++KK8YsWKNuOFhYXyv/3bvzmMTZgwQd65c6enSnOZxsZGefDgwfLh\nw4ftY++995788ssvy9u3b/eZPn9s/fr18oQJE2SbzeZT82k2m+UHHnhA/uabb+xjmZmZ8u9//3uf\nms/jx4/LUVFRcm1trX3svffek6dNmyYfPHhQHj58uNzc3GzflpaWJq9evdobpTqtqqpKnjRpkjxp\n0iSH/7gfOHDgln398Y9/lJ955hn7NovFIo8YMaLNL3uR3KxXWZbl4uJiedSoUfKkSZPaBJMVK1bI\nzz33nP31999/L0uSJJeWlnqsdmfcrM9t27bJjz76qMO+r776qvziiy/Kstz55vRmfR46dEjOyspy\n2DchIUH+61//KsuyLK9atarDffrNpRyDwYB77rmnzXh5eTliY2MdxkaMGIGysjJPleYypaWluOOO\nOxAXF2cfmz17Nt566y0cO3bMZ/q8ntlsxsaNG/Hiiy8iICDAp+YzMDAQarUaRUVFuHr1Kk6dOoWj\nR48iOjrap+azpqYGvXr1Qr9+/exjDzzwAE6cOIEjR45g8ODBUKlU9m2xsbHQ6/XeKNVpJSUlSExM\nREFBAeTrnmVZXl5+y77Ky8sRHx9v3xYYGIhBgwYJPb836xUAdu/eDZ1Oh9/+9rdtvk+v1zv8Nyso\nKAjR0dHCzvHN+vzZz36GZcuWtdn/0qVLADrfnN6sz4SEBCxevBgAcPXqVRQWFsJmsyEmJgYAcOzY\nsQ73KfRn5bjS6dOnsXfvXqxbtw4tLS1ISkpCZmYm6uvrcf/99zvsGxISgqqqKi9VevtqamrQr18/\n/PnPf8aGDRtw5coVPP7445g3b55P9Xm9Dz/8EL1798b48eMBwKf6VCqVeO211/Dmm29i8+bNuHbt\nGh5//HGkpqbi888/95k+Q0ND8d1338Fqtdp/URuNRly9ehUXL15EeHi4w/4hISG4cOGCN0p12pNP\nPnnD8YaGhlv2VV9f32Z7aGio0H3frFcAyMrKAgAcPHiwzbYb/bsIDQ1FXV2dawt0kZv12bdvX/Tt\n29f++uLFi/jss8/wm9/8BkDnm9NbzScAnD17FklJSWhpaYFOp7N/tp0r+vSLYHL+/Hk0NzdDpVJh\n1apVqK2txVtvvQWLxYLm5mYolUqH/ZVKJWw2m5eqvX1NTU2orq5GYWEhsrOz0dDQgNdeew1BQUE+\n1ef1tm/fjjlz5thf+1qfBoMB48aNw6xZs/D111/j97//PRITE32qz5iYGISFheHNN9/EK6+8gvr6\neuTl5UGhUMBqtfpMn9ezWCy37MuX5ven3KjXgICATt2r1WpFZmYmwsPDMW3aNAC+N6e9evVCUVER\nysrKsGzZMtx9990YP368S/r0i2DSt29fHDp0CHfeeScAICoqCi0tLVi0aBFGjhzZ5l+YzWbrlB8G\n2LVrV3z//ffIyclBnz59AADnzp3Dhx9+iHvuucdn+mxVXl6OCxcu4NFHH7WPqVQqn+nz4MGD2L59\nO/bs2QOlUolBgwahrq4O69atw4ABA3ymT6VSiXfeeQcLFixAbGwsQkJC8Pzzz2PZsmXo0qULLBaL\nw/6dtc/rqVQqmM1mh7Hr+7rZz3Hrf8N8yY1+aV25cgVqtdpLFXVMU1MT5s2bh7Nnz2Lbtm32s4C+\nNqfBwcGIiopCVFQUqqqqsGXLFowfP94lffrNGpMf/0vRarWwWq0IDQ1FQ0ODwzaTyYSwsDBPlucS\n4eHhUKlU9lACAPfccw/q6uoQHh7uM3222rdvH+Lj43HHHXfYx3r37u0zfZ48eRKRkZEOf31ER0fj\n/PnzPjefQ4YMwa5du7B3717s3r0bkZGR6NWrFwYMGOBTfbb6qZ9TX/o5/im9e/eGyWRyGGtoaOiU\nvV6+fBkzZ86EwWBAfn4++vfvb9/mK3NaVVWFI0eOOIxptVr861//AuCaPv0imOzbtw8jR46E1Wq1\nj1VUVKBnz56Ii4vD0aNHHfYvKyuDJEmeLrPDJEmC1WrFmTNn7GMGgwF33XUXJEnymT5b3Wiha0xM\nTJtFVp21z/DwcJw5cwZXr161j506dQr9+/f3qfk0m8146qmnYDabERISgi5duuDLL79EQkIChg0b\nhpMnTzr8BVZaWtop+7xeTEwMKioqbtpXTEyMw/xaLBZUVFR0+r5vRJIklJaW2l9///33+Oqrr+yL\nKTsLWZYxf/58nDt3Dlu3boVWq3XY7itz+sUXX+DVV191GDtx4oS9X1f06RfBZPjw4VCr1ViyZAlO\nnz6N3bt3Y/ny5Zg9ezYmTJiAS5cuISsrCwaDAUuXLkVTUxOSkpK8XbbTIiMj8dBDD+Gll17CV199\nhb179yI3NxdPPfWUT/XZ6uuvv8bAgQMdxh555BGf6XPcuHHo1q0bXnnlFVRXV+OLL77Ahg0b8Oyz\nz/rUfPbo0QMWiwXLly9HTU0NCgsLsXPnTsyePRsJCQno27cvXnrpJVRVVeG9997D8ePH8cQTT3i7\n7A5JSEhARETETftKTU3F0aNHkZubi6qqKixevBgDBgxAQkKClyt3vSeeeAIlJSV4//337b1qtdo2\nf3SIrrCwECUlJVi6dCmCg4NhMplgMpnsl+x8ZU4nTZoEk8mEFStW4MyZM/jggw/wySefID09HYCL\n+uzYnc6dR1VVlTxz5kx5xIgR8tixY+W1a9fat5WXl8uPPfaYHBMTI0+dOlWurKz0YqUdc+nSJfm3\nv/2tPGLECHn06NE+26csy3JMTIy8b9++NuO+1Gfrz21cXJw8YcIEefPmzfZtvtTn6dOn5aefflqW\nJElOTk62P5RKlmX57Nmz8tNPPy0PGzZMTk5Olg8ePOjFSm/fj5/t8VN97dmzR37kkUdkSZLkmTNn\nOjznRXQ3esCaLP/w/JYfP8dElmX5yy+/lCdMmCBLkiTPmjVLPn/+vCfK7LCoqCj7c6NmzZolR0VF\ntfnn+md6dNY5/fF86vV6eerUqbIkSfIvf/lL+R//+IfD/h3tUyHLP7rhnIiIiMhL/OJSDhEREXUO\nDCZEREQkDAYTIiIiEgaDCREREQmDwYSIiIiEwWBCREREwmAwISIiImEwmBAREZEwGEyIiIhIGAwm\nRORSLS0t+PDDDzFlyhQMHz4c8fHxmD59OoqKiuz7PPbYY9DpdG2+d8yYMYiOjobRaHQYX7duHRIS\nEiDLMl566SU8++yzt6zh0qVLyM7OxsMPP4whQ4YgMTERmZmZqKysdE2TROQ2DCZE5DJXr15Feno6\n1qxZg8ceewwff/wxCgoKkJSUhOzsbMyfPx8tLS1ITExs8+nIX331FRobGxEaGoq9e/c6bDty5AhG\njRoFhUIBhULxk3Wkp6fj2LFjyM7Oxueff4733nsPCoUCTz31FE6dOuXSnonItRhMiMhl1q9fj6NH\nj2Lbtm146qmnMGDAAAwcOBAzZszA5s2bsXv3bmzatAmJiYmoq6tDXV2d/Xv37t2LoUOHYuzYsQ7B\npKWlBXq9Hg8++GC7avjmm29QWlqK119/HfHx8YiIiMDQoUOxYsUKaDQaFBYWurxvInIdBhMicglZ\nlrF161Y8/vjjuPvuu9tsj46OxqRJk7B161bExcWhW7duDmdN9u7di9GjR2P06NH45z//iZaWFgDA\nyZMn0dTUhNGjR7erji5dfvjP2u7dux3Gu3Xrhq1bt2L27Nm32yIReQCDCRG5xOnTp9HY2IgRI0bc\ndJ/ExETU19fDZDJBkiR7MGlqakJZWRnGjh2LBx98EJcvX7ZvKy0tRb9+/dC/f/921aHVajFu3Dis\nXLkSv/jFL7BkyRLs3LkTFy5cQL9+/dCrV6+ON0tEbsNgQkQuYTabAQAajeam+/Ts2RMA8O2332LU\nqFH28HHw4EEEBQVh2LBh6NmzJwYNGoR9+/YB+GF9SXvPlrRau3Yt3njjDfTt2xcff/wxXn75Zfz8\n5z/HwoULcfny5dtpj4g8hMGEiFyiNXTc6hd/a3jp1asXEhMT8fXXX8NisWD//v0YOXKkfWHrmDFj\ncOjQIQA/nDFxNpgoFApMnz4dH3zwAUpKSrB+/XpMnjwZxcXFeO21126nPSLyEAYTInKJAQMGICws\nDIcPH77pPocOHUJYWBjuuusuxMTEIDAwEMeOHcP+/fsxduxY+35jxozBiRMncOLECXz33XdITExs\ndx2ff/451q1bZ38dFBSEhx56CMuWLUNaWhq+/PLL2+qPiDyDwYSIXKJLly5IS0tDYWEhDAZDm+3f\nfPMNPv74Yzz99NNQKBTo2rUr4uLisGvXLpw9e9bhrMjw4cMREBCAbdu2YciQIbjjjjvaXUddXR3e\nffddXLhwoc22O+64A6GhobfXIBF5RDdvF0BEvmPmzJk4ceIEnnnmGcyfPx9jxowB8MMdN6tXr8aD\nDz7ocFfMqFGj8M477yAyMhJ9+/a1j3fr1g0jR47EZ599hueee67N+zQ2NrZ51gkAjBw5Eo8//jgK\nCgrwzDPPIDMzE5Ik4fvvv0dpaSk2btzISzlEglPIsix7uwgi8i0ff/wxPvroI3zzzTeQZRn33Xcf\nUlNTkZqa6rDf//7v/2Ly5Ml4+umnsWTJEodtH3zwAZYuXYotW7YgLi7OPr548WL8+c9/vuH7/v3v\nf0ffvn3x3XffYd26dfjHP/6Buro6dOnSBYMGDcLMmTMxbtw41zdMRC7DYEJERETC4BoTIiIiEgaD\nCREREQmDwYSIiIiEwWBCREREwmAwISIiImEwmBAREZEwGEyIiIhIGAwmREREJAwGEyIiIhIGgwkR\nEREJg8GEiIiIhPH/AY6FMderqYOkAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x1157346a0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive_celfp2_pls = receptive_scores[['CELF-P2', 'PLS']].dropna()\nreceptive_celfp2_pls.plot.scatter('CELF-P2', 'PLS')",
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x113525668>"
},
"metadata": {},
"execution_count": 53
},
{
"output_type": "display_data",
"data": {
"image/png": 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krDgng0mtAC6HYIKgstIcikALpTkZnEcAgcKlHASN1eZQBFqozMngPAIIJIIJ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\nANMgmAAAANP4/+/9O2Wz8jJRAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x11573d208>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive_celf4_pls = receptive_scores[['CELF-4', 'PLS']].dropna()\nreceptive_celf4_pls.plot.scatter('CELF-4', 'PLS')",
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x115449828>"
},
"metadata": {},
"execution_count": 54
},
{
"output_type": "display_data",
"data": {
"image/png": 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2LiJ169aV2+3W9OnTde+99+rLL7/Uu+++qzfeeEMnTpzQyy+/rNdee03du3fX559/rpUr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\nAIyhiAAAAGP+H6eefhp2hhzqAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x1154804e0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "reg_owls = linear_model.LinearRegression()\nreg_owls.fit(receptive_owls_pls.OWLS.values.reshape(-1,1), receptive_owls_pls.PLS.values)",
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
},
"metadata": {},
"execution_count": 55
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "receptive_lang['old_score'] = receptive_lang.score.copy()\npred_vals = reg_owls.predict(receptive_lang[receptive_lang.test_name=='OWLS'].score.values.reshape(-1,1))\nreceptive_lang.loc[receptive_lang.test_name=='OWLS', 'score'] = pred_vals",
"execution_count": 56,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "reg_celf = linear_model.LinearRegression()\nreg_celf.fit(receptive_celfp2_pls['CELF-P2'].values.reshape(-1,1), receptive_celfp2_pls.PLS.values)",
"execution_count": 57,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
},
"metadata": {},
"execution_count": 57
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "pred_vals = reg_celf.predict(receptive_lang[receptive_lang.test_name=='CELF-P2'].score.values.reshape(-1,1))\nreceptive_lang.loc[receptive_lang.test_name=='CELF-P2', 'score'] = pred_vals",
"execution_count": 58,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive_lang['scoreInt'] = np.array(receptive_lang.score, dtype = 'int')",
"execution_count": 59,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "bp = receptive_lang.boxplot(column='scoreInt', by='ageGroup', grid=False, sym='')\nplt.xlabel('Age (years)'); plt.ylabel('Standard score');\nplt.suptitle('Receptive Language')\nfor i in [1,2,3]:\n y = receptive_lang.score[receptive_lang.ageGroup==i+2].dropna()\n # Add some random \"jitter\" to the x-axis\n x = np.random.normal(i, 0.04, size=len(y))\n plt.plot(x, y.values, 'k.', alpha=0.05)\nplt.savefig('DescriptiveFigures/recLang.png', dpi=300)\n\nreceptive_lang.groupby('ageGroup')['scoreInt'].agg([np.mean, np.median, np.std, len])",
"execution_count": 60,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " mean median std len\nageGroup \n3 83.541509 84 18.573121 1590\n4 83.525649 84 17.976144 1579\n5 82.148858 81 17.876704 1095",
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mean</th>\n <th>median</th>\n <th>std</th>\n <th>len</th>\n </tr>\n <tr>\n <th>ageGroup</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>83.541509</td>\n <td>84</td>\n <td>18.573121</td>\n <td>1590</td>\n </tr>\n <tr>\n <th>4</th>\n <td>83.525649</td>\n <td>84</td>\n <td>17.976144</td>\n <td>1579</td>\n </tr>\n <tr>\n <th>5</th>\n <td>82.148858</td>\n <td>81</td>\n <td>17.876704</td>\n <td>1095</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 60
},
{
"output_type": "display_data",
"data": {
"image/png": 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g17reiNlshq7rIpgVhGGABCmCIOwyJkyYgNtvvx26ruPTTz/F888/j9/97ndobGyEyWTa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\ngg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x114005080>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Articulation"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# Test type\narticulation[\"test_type\"] = None\nARIZ = articulation.aaps_ss.notnull()\nGF = articulation.gf2_ss.notnull()\narticulation = articulation[ARIZ | GF]\narticulation.loc[(ARIZ & GF), \"test_type\"] = \"Arizonia and Goldman\"\narticulation.loc[(ARIZ & ~GF), \"test_type\"] = \"Arizonia\"\narticulation.loc[(~ARIZ & GF), \"test_type\"] = \"Goldman\"\n\nprint(articulation.test_type.value_counts())\nprint(\"There are {0} null values for test_type\".format(sum(articulation[\"test_type\"].isnull())))\n\n# Test score (Arizonia if both)\narticulation[\"score\"] = articulation.aaps_ss\narticulation.loc[(~ARIZ & GF), \"score\"] = articulation.gf2_ss[~ARIZ & GF]",
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"text": "Goldman 5728\nArizonia 525\nArizonia and Goldman 91\nName: test_type, dtype: int64\nThere are 0 null values for test_type\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Map Arizonia onto Goldman"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# create indicator variable if a student took either test\narticulation.loc[articulation.aaps_ss.notnull() | articulation.gf2_ss.notnull(), 'test'] = 1\n# drop observations when neither test was taken\ntemp = articulation.dropna(subset = ['test'])\n# Can drop test variable\ntemp = temp.drop('test', 1)\n# Create new variable if student took both tests in one observation\ntemp['both'] = 0\ntemp.loc[temp.aaps_ss.notnull() & temp.gf2_ss.notnull(), 'both'] = 1\nprint(temp.both.value_counts()) # 73 students took both tests, 5716 took only one\n# temp.head()",
"execution_count": 62,
"outputs": [
{
"output_type": "stream",
"text": "0 6253\n1 91\nName: both, dtype: int64\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "AAPS = temp.aaps_ss.notnull()\nGF2 = temp.gf2_ss.notnull()\n\ntemp.loc[AAPS, \"test_name\"] = \"AAPS\"\ntemp.loc[GF2, \"test_name\"] = \"GF2\"",
"execution_count": 63,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# One test\nsingle = temp.loc[temp.both==0,]\na = single.shape[0]\nsingle = single.groupby('study_id').last()\nb = single.shape[0]\nprint('We have', a, 'observations where a student took one test in a single year, but only', b, 'unique students')\n\n# Both tests\nboth = temp.loc[temp.both==1,]\na = both.shape[0]\nboth = both.groupby('study_id').last()\nb = both.shape[0]\nprint('We have', a, 'observations where a student took both test in a single year, but only', b, 'unique students')",
"execution_count": 64,
"outputs": [
{
"output_type": "stream",
"text": "We have 6253 observations where a student took one test in a single year, but only 2932 unique students\nWe have 91 observations where a student took both test in a single year, but only 62 unique students\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "reg = linear_model.LinearRegression()\nreg.fit(both.aaps_ss.values.reshape(-1,1), both.gf2_ss.values)",
"execution_count": 65,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
},
"metadata": {},
"execution_count": 65
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "articulation['old_score'] = articulation.score.copy()\npred_vals = reg.predict(articulation[articulation.test_type=='Arizonia'].score.values.reshape(-1,1))\narticulation.loc[articulation.test_type=='Arizonia', 'score'] = pred_vals",
"execution_count": 66,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "articulation[\"school\"] = articulation.study_id.str.slice(0,4)\narticulation[\"age_test\"] = articulation.age_test_aaps\narticulation.loc[articulation.age_test.isnull(), 'age_test'] = articulation.age_test_gf2[articulation.age_test.isnull()]\nprint(articulation.age_test.describe())",
"execution_count": 67,
"outputs": [
{
"output_type": "stream",
"text": "count 6340.000000\nmean 68.697950\nstd 30.557327\nmin 23.000000\n25% 47.000000\n50% 60.000000\n75% 81.000000\nmax 243.000000\nName: age_test, dtype: float64\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": true
},
"cell_type": "code",
"source": "articulation = articulation.drop([\"age_test_aaps\", \"age_test_gf2\", \"aaps_ss\", \"gf2_ss\"], axis=1)\narticulation[\"domain\"] = \"Articulation\"\narticulation.head()",
"execution_count": 68,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " study_id redcap_event_name test_type score test \\\n1 0101-2002-0101 year_1_complete_71_arm_1 Goldman 78.0 1.0 \n9 0101-2003-0102 initial_assessment_arm_1 Goldman 72.0 1.0 \n10 0101-2003-0102 year_1_complete_71_arm_1 Goldman 97.0 1.0 \n14 0101-2004-0101 year_2_complete_71_arm_1 Goldman 75.0 1.0 \n15 0101-2004-0101 year_3_complete_71_arm_1 Goldman 80.0 1.0 \n\n old_score school age_test domain \n1 78.0 0101 80.0 Articulation \n9 72.0 0101 44.0 Articulation \n10 97.0 0101 54.0 Articulation \n14 75.0 0101 53.0 Articulation \n15 80.0 0101 66.0 Articulation ",
"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>test</th>\n <th>old_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>1.0</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>1.0</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>1.0</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>1.0</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>1.0</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>"
},
"metadata": {},
"execution_count": 68
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "articulation['ageGroup'] = None # initial variable to none\narticulation.loc[(articulation.age_test >= 36) & (articulation.age_test < 48), 'ageGroup'] = 3 \narticulation.loc[(articulation.age_test >= 48) & (articulation.age_test < 60), 'ageGroup'] = 4 \narticulation.loc[(articulation.age_test >= 60) & (articulation.age_test < 72), 'ageGroup'] = 5 \n\nbp = articulation.boxplot(column='score', by='ageGroup', grid=False, sym='')\nplt.xlabel('Age (years)'); plt.ylabel('Standard score');\nplt.suptitle('Articulation')\nfor i in [1,2,3]:\n y = articulation.score[articulation.ageGroup==i+2].dropna()\n # Add some random \"jitter\" to the x-axis\n x = np.random.normal(i, 0.04, size=len(y))\n plt.plot(x, y.values, 'k.', alpha=0.05)\nplt.savefig('DescriptiveFigures/artic.png', dpi=300)\n\narticulation.groupby('ageGroup')['score'].agg([np.mean, np.median, np.std, len])",
"execution_count": 69,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " mean median std len\nageGroup \n3 83.422264 84.0 18.680816 1281.0\n4 82.868699 85.0 21.087332 1489.0\n5 82.219630 86.0 21.043017 1131.0",
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mean</th>\n <th>median</th>\n <th>std</th>\n <th>len</th>\n </tr>\n <tr>\n <th>ageGroup</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>83.422264</td>\n <td>84.0</td>\n <td>18.680816</td>\n <td>1281.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>82.868699</td>\n <td>85.0</td>\n <td>21.087332</td>\n <td>1489.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>82.219630</td>\n <td>86.0</td>\n <td>21.043017</td>\n <td>1131.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 69
},
{
"output_type": "display_data",
"data": {
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MRIIUQRAEQRBmJBKkCIIgCIIwI5EgRRAEQRCEGYkEKYIgCIIgzEgkSBEEQRAEYUYiQYog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\nAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x114018b70>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Expressive Vocabulary"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# Test type\nexpressive[\"test_type\"] = None\nEOWPVT = expressive.eowpvt_ss.notnull()\nEVT = expressive.evt_ss.notnull()\nexpressive = expressive[EOWPVT | EVT]\nexpressive.loc[EOWPVT & EVT, \"test_type\"] = \"EOWPVT and EVT\"\nexpressive.loc[EOWPVT & ~EVT, \"test_type\"] = \"EOWPVT\"\nexpressive.loc[~EOWPVT & EVT, \"test_type\"] = \"EVT\"\nprint(\"There are {0} null values for test_type\".format(sum(expressive[\"test_type\"].isnull())))\n\nexpressive[\"score\"] = expressive.eowpvt_ss\nexpressive.loc[~EOWPVT & EVT, \"score\"] = expressive.evt_ss[~EOWPVT & EVT]",
"execution_count": 70,
"outputs": [
{
"output_type": "stream",
"text": "There are 0 null values for test_type\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive.test_type.value_counts()",
"execution_count": 71,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "EVT 4271\nEOWPVT 3087\nEOWPVT and EVT 176\nName: test_type, dtype: int64"
},
"metadata": {},
"execution_count": 71
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Map EVT to EOWPVT"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# create indicator variable if a student took either test\nexpressive.loc[expressive.evt_ss.notnull() | expressive.eowpvt_ss.notnull(), 'test'] = 1\n# drop observations when neither test was taken\ntemp = expressive.dropna(subset = ['test'])\n# Can drop test variable\ntemp = temp.drop('test', 1)\n# Create new variable if student took both tests in one observation\ntemp['both'] = 0\ntemp.loc[temp.evt_ss.notnull() & temp.eowpvt_ss.notnull(), 'both'] = 1\nprint(temp.both.value_counts()) # 73 students took both tests, 5716 took only one\n# temp.head()",
"execution_count": 72,
"outputs": [
{
"output_type": "stream",
"text": "0 7358\n1 176\nName: both, dtype: int64\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "EVT = temp.evt_ss.notnull()\nEOWPVT = temp.eowpvt_ss.notnull()\n\ntemp.loc[EVT, \"test_name\"] = \"EVT\"\ntemp.loc[EOWPVT, \"test_name\"] = \"EOWPVT\"",
"execution_count": 73,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# One test\nsingle = temp.loc[temp.both==0,]\na = single.shape[0]\nsingle = single.groupby('study_id').last()\nb = single.shape[0]\nprint('We have', a, 'observations where a student took one test in a single year, but only', b, 'unique students')\n\n# Both tests\nboth = temp.loc[temp.both==1,]\na = both.shape[0]\nboth = both.groupby('study_id').last()\nb = both.shape[0]\nprint('We have', a, 'observations where a student took both test in a single year, but only', b, 'unique students')",
"execution_count": 74,
"outputs": [
{
"output_type": "stream",
"text": "We have 7358 observations where a student took one test in a single year, but only 3333 unique students\nWe have 176 observations where a student took both test in a single year, but only 127 unique students\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "del temp",
"execution_count": 75,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "reg = linear_model.LinearRegression()\nreg.fit(both.evt_ss.values.reshape(-1,1), both.eowpvt_ss.values)",
"execution_count": 76,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
},
"metadata": {},
"execution_count": 76
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive['old_score'] = expressive.score.copy()\npred_vals = reg.predict(expressive[expressive.test_type=='EVT'].score.values.reshape(-1,1))\nexpressive.loc[expressive.test_type=='EVT', 'score'] = pred_vals",
"execution_count": 77,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive[\"school\"] = expressive.study_id.str.slice(0,4)",
"execution_count": 78,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive[\"age_test\"] = expressive.age_test_eowpvt\nexpressive.loc[expressive.age_test.isnull(), 'age_test'] = expressive.age_test_evt[expressive.age_test.isnull()]",
"execution_count": 79,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "expressive = expressive[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"school\", \"age_test\"]]\nexpressive[\"domain\"] = \"Expressive Vocabulary\"\nexpressive.head()",
"execution_count": 80,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " study_id redcap_event_name score test_type school \\\n0 0101-2002-0101 initial_assessment_arm_1 58.0 EOWPVT 0101 \n2 0101-2002-0101 year_2_complete_71_arm_1 84.0 EOWPVT 0101 \n5 0101-2002-0101 year_5_complete_71_arm_1 90.0 EOWPVT 0101 \n14 0101-2004-0101 year_2_complete_71_arm_1 90.0 EOWPVT 0101 \n15 0101-2004-0101 year_3_complete_71_arm_1 87.0 EOWPVT 0101 \n\n age_test domain \n0 54.0 Expressive Vocabulary \n2 80.0 Expressive Vocabulary \n5 113.0 Expressive Vocabulary \n14 53.0 Expressive Vocabulary \n15 66.0 Expressive Vocabulary ",
"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>"
},
"metadata": {},
"execution_count": 80
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "expressive['ageGroup'] = None # initial variable to none\nexpressive.loc[(expressive.age_test >= 36) & (expressive.age_test < 48), 'ageGroup'] = 3 \nexpressive.loc[(expressive.age_test >= 48) & (expressive.age_test < 60), 'ageGroup'] = 4 \nexpressive.loc[(expressive.age_test >= 60) & (expressive.age_test < 72), 'ageGroup'] = 5 \n\nbp = expressive.boxplot(column='score', by='ageGroup', grid=False, sym='')\nplt.xlabel('Age (years)'); plt.ylabel('Standard score');\nplt.suptitle('Expressive Vocabulary')\nfor i in [1,2,3]:\n y = expressive.score[expressive.ageGroup==i+2].dropna()\n # Add some random \"jitter\" to the x-axis\n x = np.random.normal(i, 0.04, size=len(y))\n plt.plot(x, y.values, 'k.', alpha=0.05)\nplt.savefig('DescriptiveFigures/expVocab.png', dpi=300)\n\nexpressive.groupby('ageGroup')['score'].agg([np.mean, np.median, np.std, len])",
"execution_count": 81,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " mean median std len\nageGroup \n3 90.898049 92.770977 18.628653 1542.0\n4 90.023118 91.000000 18.967224 1726.0\n5 89.124085 90.000000 17.289723 1283.0",
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mean</th>\n <th>median</th>\n <th>std</th>\n <th>len</th>\n </tr>\n <tr>\n <th>ageGroup</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>90.898049</td>\n <td>92.770977</td>\n <td>18.628653</td>\n <td>1542.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>90.023118</td>\n <td>91.000000</td>\n <td>18.967224</td>\n <td>1726.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>89.124085</td>\n <td>90.000000</td>\n <td>17.289723</td>\n <td>1283.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 81
},
{
"output_type": "display_data",
"data": {
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+FIrpjuoxMWzYMIwcOVI8dD2EGClCARTKoYcGqXlaLBYkEgkkEgk+LhqNIpVK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\nRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x115843f98>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Receptive Vocabulary"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive.columns",
"execution_count": 82,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Index(['study_id', 'redcap_event_name', 'age_test_ppvt', 'ppvt_ss',\n 'age_test_rowpvt', 'rowpvt_ss'],\n dtype='object')"
},
"metadata": {},
"execution_count": 82
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# Test type\nreceptive[\"test_type\"] = None\nPPVT = receptive.ppvt_ss.notnull()\nROWPVT = receptive.rowpvt_ss.notnull()\nreceptive = receptive[PPVT | ROWPVT]\nreceptive.loc[PPVT & ROWPVT, \"test_type\"] = \"PPVT and ROWPVT\"\nreceptive.loc[PPVT & ~ROWPVT, \"test_type\"] = \"PPVT\"\nreceptive.loc[~PPVT & ROWPVT, \"test_type\"] = \"ROWPVT\"\nprint(\"There are {0} null values for test_type\".format(sum(receptive[\"test_type\"].isnull())))\n\nreceptive[\"score\"] = receptive.ppvt_ss\nreceptive.loc[~PPVT & ROWPVT, \"score\"] = receptive.rowpvt_ss[~PPVT & ROWPVT]",
"execution_count": 83,
"outputs": [
{
"output_type": "stream",
"text": "There are 0 null values for test_type\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Map PPVT onto ROWPVT"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# create indicator variable if a student took either test\nreceptive.loc[receptive.ppvt_ss.notnull() | receptive.rowpvt_ss.notnull(), 'test'] = 1\n# drop observations when neither test was taken\ntemp = receptive.dropna(subset = ['test'])\n# Can drop test variable\ntemp = temp.drop('test', 1)\n# Create new variable if student took both tests in one observation\ntemp['both'] = 0\ntemp.loc[temp.ppvt_ss.notnull() & temp.rowpvt_ss.notnull(), 'both'] = 1\nprint(temp.both.value_counts()) # 73 students took both tests, 5716 took only one\n# temp.head()",
"execution_count": 84,
"outputs": [
{
"output_type": "stream",
"text": "0 7497\n1 228\nName: both, dtype: int64\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "PPVT = temp.ppvt_ss.notnull()\nROWPVT = temp.rowpvt_ss.notnull()\n\ntemp.loc[PPVT, \"test_name\"] = \"PPVT\"\ntemp.loc[ROWPVT, \"test_name\"] = \"ROWPVT\"",
"execution_count": 85,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# One test\nsingle = temp.loc[temp.both==0,]\na = single.shape[0]\nsingle = single.groupby('study_id').last()\nb = single.shape[0]\nprint('We have', a, 'observations where a student took one test in a single year, but only', b, 'unique students')\n\n# Both tests\nboth = temp.loc[temp.both==1,]\na = both.shape[0]\nboth = both.groupby('study_id').last()\nb = both.shape[0]\nprint('We have', a, 'observations where a student took both test in a single year, but only', b, 'unique students')",
"execution_count": 86,
"outputs": [
{
"output_type": "stream",
"text": "We have 7497 observations where a student took one test in a single year, but only 3364 unique students\nWe have 228 observations where a student took both test in a single year, but only 157 unique students\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "del temp",
"execution_count": 87,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "rv_reg = linear_model.LinearRegression()\nrv_reg.fit(both.ppvt_ss.values.reshape(-1,1), both.rowpvt_ss.values)",
"execution_count": 88,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
},
"metadata": {},
"execution_count": 88
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive['old_score'] = receptive.score.copy()\npred_vals = reg.predict(receptive[receptive.test_type=='PPVT'].score.values.reshape(-1,1))\nreceptive.loc[receptive.test_type=='PPVT', 'score'] = pred_vals",
"execution_count": 89,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "receptive[\"school\"] = receptive.study_id.str.slice(0,4)",
"execution_count": 90,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "receptive[\"age_test\"] = receptive.age_test_ppvt\nreceptive.loc[receptive.age_test.isnull(), 'age_test'] = receptive.age_test_rowpvt[receptive.age_test.isnull()]",
"execution_count": 91,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "print(\"There are {0} null values for age_test\".format(sum(receptive.age_test.isnull())))",
"execution_count": 92,
"outputs": [
{
"output_type": "stream",
"text": "There are 23 null values for age_test\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive = receptive[[\"study_id\", \"redcap_event_name\", \"score\", \"test_type\", \"school\", \"age_test\"]]\nreceptive[\"domain\"] = \"Receptive Vocabulary\"\nreceptive.head()",
"execution_count": 93,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " study_id redcap_event_name score test_type school \\\n2 0101-2002-0101 year_2_complete_71_arm_1 86.408510 PPVT 0101 \n5 0101-2002-0101 year_5_complete_71_arm_1 101.000000 ROWPVT 0101 \n9 0101-2003-0102 initial_assessment_arm_1 58.572717 PPVT 0101 \n10 0101-2003-0102 year_1_complete_71_arm_1 78.455426 PPVT 0101 \n11 0101-2003-0102 year_2_complete_71_arm_1 95.156903 PPVT 0101 \n\n age_test domain \n2 80.0 Receptive Vocabulary \n5 113.0 Receptive Vocabulary \n9 44.0 Receptive Vocabulary \n10 54.0 Receptive Vocabulary \n11 68.0 Receptive Vocabulary ",
"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>86.408510</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>101.000000</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>58.572717</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>78.455426</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>95.156903</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>"
},
"metadata": {},
"execution_count": 93
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "receptive.study_id.unique().shape",
"execution_count": 94,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "(3404,)"
},
"metadata": {},
"execution_count": 94
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "receptive['ageGroup'] = None # initial variable to none\nreceptive.loc[(receptive.age_test >= 36) & (receptive.age_test < 48), 'ageGroup'] = 3 \nreceptive.loc[(receptive.age_test >= 48) & (receptive.age_test < 60), 'ageGroup'] = 4 \nreceptive.loc[(receptive.age_test >= 60) & (receptive.age_test < 72), 'ageGroup'] = 5 ",
"execution_count": 95,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "bp = receptive.boxplot(column='score', by='ageGroup', grid=False, sym='')\nplt.xlabel('Age (years)'); plt.ylabel('Standard score');\nplt.suptitle('Receptive Vocabulary')\nfor i in [1,2,3]:\n y = receptive.score[receptive.ageGroup==i+2].dropna()\n # Add some random \"jitter\" to the x-axis\n x = np.random.normal(i, 0.04, size=len(y))\n plt.plot(x, y.values, 'k.', alpha=0.05)\nplt.savefig('DescriptiveFigures/recVocab.png', dpi=300)\n\nreceptive.groupby('ageGroup')['score'].agg([np.mean, np.median, np.std, len])",
"execution_count": 96,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " mean median std len\nageGroup \n3 89.766914 90.385052 16.611238 1603.0\n4 88.455310 89.589744 17.323336 1756.0\n5 87.784735 88.794435 15.319930 1312.0",
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>mean</th>\n <th>median</th>\n <th>std</th>\n <th>len</th>\n </tr>\n <tr>\n <th>ageGroup</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>89.766914</td>\n <td>90.385052</td>\n <td>16.611238</td>\n <td>1603.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>88.455310</td>\n <td>89.589744</td>\n <td>17.323336</td>\n <td>1756.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>87.784735</td>\n <td>88.794435</td>\n <td>15.319930</td>\n <td>1312.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 96
},
{
"output_type": "display_data",
"data": {
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jj8WsWbPwm9/8Bt/5znf2xq9AEISdQMo9giDsNQ4++GBEIhFccskluOmmm7Bs2TLEYjFc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"text/plain": "<matplotlib.figure.Figure at 0x115c02a20>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "width = 3\nsns.kdeplot(np.array(receptive[receptive.ageGroup ==3].score), bw=width, label = \"3-year-olds\")\nsns.kdeplot(np.array(receptive[receptive.ageGroup ==4].score), bw=width, label = \"4-year-olds\")\nsns.kdeplot(np.array(receptive[receptive.ageGroup ==5].score), bw=width, label = \"5-year-olds\")",
"execution_count": 97,
"outputs": [
{
"output_type": "stream",
"text": "/Users/fonnescj/anaconda3/envs/dev/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n",
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x115c18cc0>"
},
"metadata": {},
"execution_count": 97
},
{
"output_type": "display_data",
"data": {
"image/png": 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myQ5qQghhprVr1wKGeWzK5M+qlJSUsGfPHsAwdNKrVy+srKzYsmWL+uHw3HPP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\nIYSwC0k6hBBCCGEX/x84CGGI/2cTVQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x115ff9cc0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "receptive[receptive.ageGroup ==3].score.hist(alpha=0.3, label='3-year-olds')\nreceptive[receptive.ageGroup ==4].score.hist(alpha=0.3, label='4-year-olds')\nreceptive[receptive.ageGroup ==5].score.hist(alpha=0.3, label='5-year-olds')\nplt.legend(loc=2)\n# plt.legend(bbox_to_anchor=(1.05, 1),loc=2, borderaxespad=0.)\n# plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.2),\n# ncol=3, fancybox=True, shadow=True)",
"execution_count": 98,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.legend.Legend at 0x1190c5320>"
},
"metadata": {},
"execution_count": 98
},
{
"output_type": "display_data",
"data": {
"image/png": 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PPw6z2Yxp06ZBEASkpqbWeZYHERGFNoWniUyLFwQBJ06cQKdOnRAbG3vF5ZvK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\nUaEmghBCCCFR+RdLMiYMfuFc7AAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x115fc0e80>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Merge Datasets"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "test_scores = pd.concat([articulation, expressive, receptive, language])\ntest_scores.head()",
"execution_count": 99,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": " ageGroup age_test domain old_score redcap_event_name \\\n1 None 80 Articulation 78.0 year_1_complete_71_arm_1 \n9 3 44 Articulation 72.0 initial_assessment_arm_1 \n10 4 54 Articulation 97.0 year_1_complete_71_arm_1 \n14 4 53 Articulation 75.0 year_2_complete_71_arm_1 \n15 5 66 Articulation 80.0 year_3_complete_71_arm_1 \n\n school score study_id test test_name test_type \n1 0101 78 0101-2002-0101 1.0 NaN Goldman \n9 0101 72 0101-2003-0102 1.0 NaN Goldman \n10 0101 97 0101-2003-0102 1.0 NaN Goldman \n14 0101 75 0101-2004-0101 1.0 NaN Goldman \n15 0101 80 0101-2004-0101 1.0 NaN Goldman ",
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>ageGroup</th>\n <th>age_test</th>\n <th>domain</th>\n <th>old_score</th>\n <th>redcap_event_name</th>\n <th>school</th>\n <th>score</th>\n <th>study_id</th>\n <th>test</th>\n <th>test_name</th>\n <th>test_type</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1</th>\n <td>None</td>\n <td>80</td>\n <td>Articulation</td>\n <td>78.0</td>\n <td>year_1_complete_71_arm_1</td>\n <td>0101</td>\n <td>78</td>\n <td>0101-2002-0101</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>Goldman</td>\n </tr>\n <tr>\n <th>9</th>\n <td>3</td>\n <td>44</td>\n <td>Articulation</td>\n <td>72.0</td>\n <td>initial_assessment_arm_1</td>\n <td>0101</td>\n <td>72</td>\n <td>0101-2003-0102</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>Goldman</td>\n </tr>\n <tr>\n <th>10</th>\n <td>4</td>\n <td>54</td>\n <td>Articulation</td>\n <td>97.0</td>\n <td>year_1_complete_71_arm_1</td>\n <td>0101</td>\n <td>97</td>\n <td>0101-2003-0102</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>Goldman</td>\n </tr>\n <tr>\n <th>14</th>\n <td>4</td>\n <td>53</td>\n <td>Articulation</td>\n <td>75.0</td>\n <td>year_2_complete_71_arm_1</td>\n <td>0101</td>\n <td>75</td>\n <td>0101-2004-0101</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>Goldman</td>\n </tr>\n <tr>\n <th>15</th>\n <td>5</td>\n <td>66</td>\n <td>Articulation</td>\n <td>80.0</td>\n <td>year_3_complete_71_arm_1</td>\n <td>0101</td>\n <td>80</td>\n <td>0101-2004-0101</td>\n <td>1.0</td>\n <td>NaN</td>\n <td>Goldman</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 99
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "print(test_scores.test_type.value_counts())\nprint(test_scores.domain.value_counts())",
"execution_count": 100,
"outputs": [
{
"output_type": "stream",
"text": "expressive 7712\nreceptive 7643\nGoldman 5728\nPPVT 4893\nEVT 4271\nEOWPVT 3087\nROWPVT 2604\nArizonia 525\nPPVT and ROWPVT 228\nEOWPVT and EVT 176\nArizonia and Goldman 91\nName: test_type, dtype: int64\nLanguage 15355\nReceptive Vocabulary 7725\nExpressive Vocabulary 7534\nArticulation 6344\nName: domain, dtype: int64\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# make a new domain variable that contains only 5 categories\ntest_scores['domain2'] = test_scores.domain.copy()\ntest_scores.loc[test_scores.test_type == 'expressive', 'domain2'] = 'Expressive Language'\ntest_scores.loc[test_scores.test_type == 'receptive', 'domain2'] = 'Receptive Language'\ntest_scores.domain2.value_counts()",
"execution_count": 101,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Receptive Vocabulary 7725\nExpressive Language 7712\nReceptive Language 7643\nExpressive Vocabulary 7534\nArticulation 6344\nName: domain2, dtype: int64"
},
"metadata": {},
"execution_count": 101
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# make a dataframe that contains only 3, 4, and 5-year-olds\ntest_scores_345 = test_scores.loc[(test_scores.age_test >= 36) & (test_scores.age_test < 72), ]\ntest_scores_345.domain2.value_counts()",
"execution_count": 102,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "Receptive Vocabulary 4671\nExpressive Vocabulary 4551\nExpressive Language 4280\nReceptive Language 4264\nArticulation 3901\nName: domain2, dtype: int64"
},
"metadata": {},
"execution_count": 102
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "# Attempt to make black and white\nbp = test_scores_345.boxplot(column='score', by='domain2', grid=False, sym='')\nplt.title('Test scores for 3, 4, and 5-year-olds by functional outcome')\nplt.suptitle(\"\")\nplt.xlabel(''); plt.ylabel('Standard score');\nplt.xticks([1, 2, 3, 4, 5], ['Articulation', 'Expressive\\nLanguage', 'Expressive\\nVocabulary',\n 'Receptive\\nLanguage', 'Receptive\\nVocabulary'])\n# Articulation\ny = test_scores_345.score[test_scores_345.domain2=='Articulation'].dropna()\nx = np.random.normal(1, 0.08, size=len(y))\nplt.plot(x, y.values, 'k.', alpha=0.02)\n\n# Expressive Language\ny = test_scores_345.score[test_scores_345.domain2=='Expressive Language'].dropna()\nx = np.random.normal(2, 0.08, size=len(y))\nplt.plot(x, y.values, 'k.', alpha=0.02) \n\n# Expressive Vocabulary\ny = test_scores_345.score[test_scores_345.domain2=='Expressive Vocabulary'].dropna()\nx = np.random.normal(3, 0.08, size=len(y))\nplt.plot(x, y.values, 'k.', alpha=0.02) \n\n# Receptive Language\ny = test_scores_345.score[test_scores_345.domain2=='Receptive Language'].dropna()\nx = np.random.normal(4, 0.08, size=len(y))\nplt.plot(x, y.values, 'k.', alpha=0.02) \n\n# Receptive Vocabulary\ny = test_scores_345.score[test_scores_345.domain2=='Receptive Vocabulary'].dropna()\nx = np.random.normal(5, 0.08, size=len(y))\nplt.plot(x, y.values, 'k.', alpha=0.02) \n\nplt.axhline(y=85)\n\n# for components in bp.keys():\n# for line in bp[components]:\n# line.set_color('black') # black lines",
"execution_count": 124,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.lines.Line2D at 0x114e37e10>"
},
"metadata": {},
"execution_count": 124
},
{
"output_type": "display_data",
"data": {
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rhg0b8NprrwGAwhSs0WiQn5+veG/r1q1RU1MDoDbjQKPRYMCAAYr31JfxYDKZ8NZbb2HF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\nhBAiIkkjRQghhBAR6T+aQg/QkYLk8gAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x112f86be0>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Histograms"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "* Blue = three year olds\n* Green = four year olds\n* Red = five year olds\n\nNeed to make in black and white. \nNeed to make legend"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Expressive Language"
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "sns.distplot(np.array(receptive[receptive.ageGroup ==3].score), kde=False, hist=True, norm_hist=False);",
"execution_count": 104,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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bdPDgQfOVAwCApDIVIgzD0L333qsDBw5ELJ82bZqysrK0fv16XX/99Zo+fbqqqqokSZWV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\nsTREbNq0Sdu2bdO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"text/plain": "<matplotlib.figure.Figure at 0x113faf6d8>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "fig = sns.distplot(np.array(receptive[receptive.ageGroup ==3].score), kde=True, hist=False)\nplt.yticks(fig.get_yticks(), fig.get_yticks() * 6735)\nplt.ylabel('Counts', fontsize=16)",
"execution_count": 105,
"outputs": [
{
"output_type": "stream",
"text": "/Users/fonnescj/anaconda3/envs/dev/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n",
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.text.Text at 0x11901ce10>"
},
"metadata": {},
"execution_count": 105
},
{
"output_type": "display_data",
"data": {
"image/png": 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3buXatWvUqVMHNze3LPtqULaIyK3NmjULgBIlStC8eXOTq7m/OnToAMClS5eM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\nV+plPVU9N2UWLVpU4TqeSUlJBAYG4ufnR2RkJDdv3tT5UqW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"text/plain": "<matplotlib.figure.Figure at 0x11906b0f0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "sns.kdeplot(np.array(receptive[receptive.ageGroup ==3].score), bw=width, label = \"3-year-olds\")\nplt.ylabel('Density')\nplt.yticks([0, 0.01, 0.02]);\n# plt.yticks(fig.get_yticks(), fig.get_yticks() * 100)",
"execution_count": 106,
"outputs": [
{
"output_type": "stream",
"text": "/Users/fonnescj/anaconda3/envs/dev/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n",
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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gwYIFdv98IsVNRUVExIY2bdpEWloaYJ/Lkv/J1dWVsLAwIO+OypcuXbL75xQpTioqIiI2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\nWPz9/WnevDnTpk3jyJEjBqctfVRURJx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"text/plain": "<matplotlib.figure.Figure at 0x1190372b0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "def kdeplot(domain, width, score='score'):\n sns.kdeplot(np.array(domain.loc[domain.ageGroup ==3, score]), bw=width, label = \"3-year-olds\")\n sns.kdeplot(np.array(domain.loc[domain.ageGroup ==4, score]), bw=width, label = \"4-year-olds\")\n sns.kdeplot(np.array(domain.loc[domain.ageGroup ==5, score]), bw=width, label = \"5-year-olds\")\n plt.ylabel('Density')\n",
"execution_count": 107,
"outputs": []
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "kdeplot(expressive_lang, 4)",
"execution_count": 108,
"outputs": [
{
"output_type": "stream",
"text": "/Users/fonnescj/anaconda3/envs/dev/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n",
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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zJxqNhkOHDinHdu/eTf/+/QFYsmQJJ0+eZM2aNWzYsIHMzEymTJkCQGZmJuPHj8fX15eQ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\n",
"text/plain": "<matplotlib.figure.Figure at 0x11548b5c0>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false,
"scrolled": false
},
"cell_type": "code",
"source": "expressive_lang[expressive_lang.ageGroup ==3].score.hist(alpha=0.3, label='3-year-olds')\nexpressive_lang[expressive_lang.ageGroup ==4].score.hist(alpha=0.3, label='4-year-olds')\nexpressive_lang[expressive_lang.ageGroup ==5].score.hist(alpha=0.3, label='5-year-olds')\nplt.legend(loc=1)",
"execution_count": 109,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.legend.Legend at 0x113fa9d30>"
},
"metadata": {},
"execution_count": 109
},
{
"output_type": "display_data",
"data": {
"image/png": 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j1apVUKlUGD16NA4cOCBfrdG3b1/MnDkTy5cvr/cSzd9SqVRYsWIFMjMzMXHi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\n8PBwj07LqNVqqNVqREVFXbM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"text/plain": "<matplotlib.figure.Figure at 0x115c18668>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Receptive Language"
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "kdeplot(receptive_lang, 4)",
"execution_count": 110,
"outputs": [
{
"output_type": "stream",
"text": "/Users/fonnescj/anaconda3/envs/dev/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n",
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
"image/png": 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sbCxjx46lW7duvPTSSxQWFqrXcnNzmTlzJkFBQQQHBzN79mzy8vLu/mbdQw0e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