Mike Dijkhof
7 months ago
5 changed files with 895 additions and 0 deletions
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54DatesParser.py
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136DemoParser.py
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217FinalDF_Parser.py
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415PAParser.py
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73ScatterBoxplotter.py
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# -*- coding: utf-8 -*- |
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""" |
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Created on Fri Apr 2 10:57:40 2021 |
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@author: Dijkhofmf |
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""" |
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import os |
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import pandas as pd |
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pd.options.mode.chained_assignment = None # default='warn' |
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Path = r'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles' |
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os.chdir(Path) |
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FilenameOutc = 'SurgAdmComp.csv' |
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FilenameSACM = 'DataSACM.csv' |
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FilenameComplet = 'Complete.csv' |
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DFComp = pd.DataFrame(pd.read_csv(FilenameOutc)) |
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DFComp = DFComp.set_index('Study ID') |
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DFSACM = pd.DataFrame(pd.read_csv(FilenameSACM)) |
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DFSACM = DFSACM.set_index('Study ID') |
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DFComplet = pd.DataFrame(pd.read_csv(FilenameComplet)) |
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DFComplet = DFComplet.set_index('Study ID') |
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Startdate = pd.to_datetime(DFSACM['Start date Fitbit']).dt.date |
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Enddate = pd.to_datetime(DFSACM['End date Fitbit']).dt.date |
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DFDates = pd.DataFrame() |
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DFDates['Study ID'] = DFComp.index |
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DFDates = DFDates.set_index('Study ID') |
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DFDates['Start'] = Startdate |
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DFDates['Surgery'] = pd.to_datetime(DFComp['Date of surgery']).dt.date |
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DFDates['Preop'] = DFDates['Surgery'] - DFDates['Start'] |
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DFDates['Discharge'] = pd.to_datetime(DFComp['Date of hospital discharge']).dt.date |
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DFDates['LOS'] = DFDates['Discharge'] - DFDates['Surgery'] |
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DFDates['St2Dis'] = DFDates['Discharge'] - DFDates['Start'] |
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DFDates['First Comp'] = pd.to_datetime(DFComp['Date first complication at home']).dt.date |
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DFDates['T2C'] = DFDates['First Comp'] - DFDates['Discharge'] |
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DFDates['First Read'] = pd.to_datetime(DFComp['Date (first) readmission']).dt.date |
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DFDates['T2R'] = DFDates['First Read'] - DFDates['Discharge'] |
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DFDates['Sec Read'] = pd.to_datetime(DFComp['Date second readmission']).dt.date |
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DFDates['T2SR'] = DFDates['Sec Read'] - DFDates['Discharge'] |
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DFDates['End'] = Enddate |
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DFDates['Length'] = DFDates['End'] - DFDates['Start'] |
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DFDates = DFDates[DFComplet['Has patient completed study?']=='Yes'] |
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DFDates.to_csv('Dates.csv') |
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# -*- coding: utf-8 -*- |
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""" |
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Created on Mon Mar 8 10:38:31 2021 |
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@author: Dijkhofmf |
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""" |
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# Import stuff |
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import os |
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import pandas as pd |
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import seaborn as sns |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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pd.options.mode.chained_assignment = None # default='warn' |
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#%% Define filenames and path |
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FilenameComplete = 'Complete.csv' |
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FilenameDemo = 'DemoData.csv' |
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Filename_T0 = 'FinalDF_T0.csv' |
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Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles' |
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# Set path |
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os.chdir(Path) |
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DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete)) |
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DFDemo = pd.DataFrame(pd.read_csv(FilenameDemo)) |
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DFDemo['Complete data'] = DFComplete['Has patient completed study?'] |
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DFDemo = DFDemo.drop(DFDemo[DFDemo['Complete data'] !='Yes'].index) |
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DFDemo['ASA-classification'] = DFDemo['ASA-classification'].str.replace('ASA ', '').astype('float64') |
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DFDemo = DFDemo.replace('Unchecked', 0) |
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DFDemo = DFDemo.replace('Checked', 1) |
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Dropcols = ['Year of birth', 'Subject ID Connecare', 'Subject ID Connecare (version 2.0)','Date subject signed consent', 'Nationality', 'Language', 'Former occupation', |
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'Does the patient have a smartphone that they use?', 'How many days a week is the smartphone used?', |
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'Does the patient have a tablet that they use?','How many days a week is the tablet used?','Does the patient have a computer/pc that they use?', |
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'How many days a week is the computer/pc used?','Smart device at home', 'Smart device at inclusion? (check all that apply) (choice=Fitbit)', |
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'Smart device at inclusion? (check all that apply) (choice=Weight scale)','Indication Surgery', 'Comments', 'Complete?', 'Complete data'] |
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DFDemo = DFDemo.drop(Dropcols, axis=1) |
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DFDemo = DFDemo.set_index('Study ID') |
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# Calculate CCI score |
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DFDemo.iloc[:,20:26] = DFDemo.iloc[:,20:26]*2 |
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DFDemo.iloc[:,26] = DFDemo.iloc[:,26]*3 |
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DFDemo.iloc[:,26:28] = DFDemo.iloc[:,26:28]*6 |
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ColMask = DFDemo.columns[10:29] |
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DFDemo['Comorb'] = DFDemo[ColMask].sum(axis=1) |
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DFDemo = DFDemo.drop(ColMask, axis=1) |
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#%% |
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DF_T0 = pd.DataFrame(pd.read_csv(Filename_T0)) |
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DF_T0 = DF_T0.set_index('Study ID') |
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DFDemo['Type'] = DF_T0['Pt Type'] |
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#%% code variables |
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DFDemo['Gender'] = DFDemo['Gender'].replace('Female', 0) |
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DFDemo['Gender'] = DFDemo['Gender'].replace('Male', 1) |
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Housing = pd.get_dummies(DFDemo['Housing'], drop_first=True) |
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Education = pd.get_dummies(DFDemo['Education'], drop_first=True) |
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Smoking = pd.get_dummies(DFDemo['Smoking'], drop_first=True) |
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Med_Dif = pd.get_dummies(DFDemo['Difficulty preparing medication?'], drop_first=True) |
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Loc_Tu = pd.get_dummies(DFDemo['Location tumour'], drop_first=True) |
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Prim_Mal = pd.get_dummies(DFDemo['Primary Malignancy'], drop_first=True) |
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DFDemo['Recurrent disease?'] = DFDemo['Recurrent disease?'].replace('No', 0) |
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DFDemo['Recurrent disease?'] = DFDemo['Recurrent disease?'].replace('Yes', 1) |
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DFDemo = DFDemo.drop(['Marital State', 'Housing', 'Education', 'Tumour Stage', 'Smoking', 'Difficulty preparing medication?', |
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'Location tumour', 'Primary Malignancy'], axis=1) |
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#%% |
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DFDemo = pd.concat([DFDemo, Housing, Education, Smoking, Med_Dif, Loc_Tu, Prim_Mal], axis=1) |
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#%% Create Neoadjuvant therapy variable |
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for i,r in DFDemo.iterrows(): |
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if (DFDemo.loc[i,'Neo-adjuvant therapy (choice=Chemotherapy)'] == 1) & (DFDemo.loc[i,'Neo-adjuvant therapy (choice=Radiotherapy)'] == 1): |
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DFDemo.loc[i,'Neo'] = 1 |
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elif DFDemo.loc[i, 'Neo-adjuvant therapy (choice=Chemotherapy)'] == 1: |
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DFDemo.loc[i,'Neo'] = 2 |
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=Immunotherapy)'] == 1: |
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DFDemo.loc[i,'Neo'] = 3 |
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=Radiotherapy)'] == 1: |
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DFDemo.loc[i,'Neo'] = 4 |
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=Targeted Therapy)'] == 1: |
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DFDemo.loc[i,'Neo'] = 5 |
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elif DFDemo.loc[i,'Neo-adjuvant therapy (choice=None)'] == 1: |
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DFDemo.loc[i,'Neo'] = 0 |
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Neo = pd.get_dummies(DFDemo['Neo'], drop_first=True) |
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NeoDrop = ['Neo-adjuvant therapy (choice=Chemotherapy)','Neo-adjuvant therapy (choice=Chemotherapy)','Neo-adjuvant therapy (choice=Immunotherapy)', |
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'Neo-adjuvant therapy (choice=Radiotherapy)', 'Neo-adjuvant therapy (choice=None)', 'Neo-adjuvant therapy (choice=Targeted Therapy)', 'Neo'] |
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DFDemo = DFDemo.drop(NeoDrop, axis=1) |
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DFDemo = pd.concat([DFDemo, Neo], axis=1) |
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#%% |
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plt.figure() |
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sns.displot(DFDemo['Age (years)']) |
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#%% |
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DemoComp = DFDemo[DFDemo['Type'] != 'Healthy'] |
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DemoComp = DemoComp.drop('Type', axis=1) |
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DemoNoComp = DFDemo[DFDemo['Type'] == 'Healthy'] |
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DemoNoComp = DemoNoComp.drop('Type', axis=1) |
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from scipy import stats |
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#outcome = pd.DataFrame(index=['stat', 'p-value']) |
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outcomeT = stats.ttest_ind(DemoNoComp, DemoComp, nan_policy='omit') |
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OutcomeT = outcomeT[1].tolist() |
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OutcomeMW = [] |
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for column in DemoComp: |
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print(column) |
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outcomeMW = stats.mannwhitneyu(DemoNoComp[column], DemoComp[column]) |
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OutcomeMW.append(outcomeMW[1]) |
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#DFDemo.to_csv('FinalDemo.csv') |
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# -*- coding: utf-8 -*- |
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""" |
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Created on Thu Feb 25 11:13:35 2021 |
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@author: Dijkhofmf |
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""" |
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# Import stuff |
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import os |
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import pandas as pd |
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import numpy as np |
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#import seaborn as sns |
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#import matplotlib.pyplot as plt |
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pd.options.mode.chained_assignment = None # default='warn' |
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#%% Define filenames and path |
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Filename_T0 = 'BaselineT0.csv' |
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Filename_T1 = 'DischargeT1.csv' |
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#Filename_T2 = 'FollowUpT2Data.csv' |
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FilenameOutc = 'SurgAdmComp.csv' |
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FilenameComplete = 'Complete.csv' |
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Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles' |
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# Set path |
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os.chdir(Path) |
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DFT0 = pd.DataFrame(pd.read_csv(Filename_T0)) |
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DFT1 = pd.DataFrame(pd.read_csv(Filename_T1)) |
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#DFT2 = pd.DataFrame(pd.read_csv(Filename_T2)) |
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DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete)) |
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DFCompl = pd.DataFrame(pd.read_csv(FilenameOutc)) |
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#%% |
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DFT0['Complete'] = DFComplete['Has patient completed study?'] |
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DFT0 = DFT0.drop(DFT0[DFT0['Complete'] !='Yes'].index) |
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DFT0 = DFT0.astype('str') |
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DFT0 = DFT0.set_index(['Study ID']) |
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DFT1['Complete'] = DFComplete['Has patient completed study?'] |
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DFT1 = DFT1.drop(DFT1[DFT1['Complete'] !='Yes'].index) |
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DFT1 = DFT1.astype('str') |
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DFT1 = DFT1.set_index(['Study ID']) |
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# DFT2['Complete data'] = DFComplete['Has patient completed study?'] |
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# DFT2 = DFT2.drop(DFT2[DFT2['Complete data'] !='Yes'].index) |
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# DFT2 = DFT2.astype('str') |
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# DFT2 = DFT2.set_index(['Study ID']) |
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DFCompl['Complete'] = DFComplete['Has patient completed study?'] |
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DFCompl = DFCompl.drop(DFCompl[DFCompl['Complete'] !='Yes'].index) |
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DFCompl = DFCompl.set_index(['Study ID']) |
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#%% |
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DFT0 = DFT0.apply(lambda x: x.str.replace(',','.'), axis=1) |
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DFT1 = DFT1.apply(lambda x: x.str.replace(',','.'), axis=1) |
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#DFT2 = DFT2.apply(lambda x: x.str.replace(',','.'), axis=1) |
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#%% |
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FinalDF_T0 = pd.DataFrame() |
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FinalDF_T0[['BMI','GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT0[['BMI', 'Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64') |
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FinalDF_T1 = pd.DataFrame() |
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FinalDF_T1[['GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT1[['Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64') |
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FinalDF_T2 = pd.DataFrame() |
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#FinalDF_T2[['GFI', 'HADS_A', 'HADS_D', 'ADL', 'iADL']] = DFT2[['Groningen Frailty Index', 'Anxiety - Hospital Anxiety Depression Scale', 'Depression - Hospital Anxiety Depression Scale', 'ADL', 'iADL']].astype('float64') |
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#%% TUG_T0 |
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FinalDF_T0['TUG1'] = DFT0['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(0) |
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FinalDF_T0['TUG2'] = DFT0['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(0) |
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for i, r in FinalDF_T0.iterrows(): |
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if FinalDF_T0.loc[i,'TUG1'] != 0 and FinalDF_T0.loc[i,'TUG2'] != 0: |
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FinalDF_T0.loc[i,'TUGTot'] = (FinalDF_T0.loc[i,'TUG1']+FinalDF_T0.loc[i,'TUG2'])/2 |
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else: |
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FinalDF_T0.loc[i,'TUGTot'] = (FinalDF_T0.loc[i,'TUG1']+FinalDF_T0.loc[i,'TUG2'])/1 |
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FinalDF_T0['TUG1'] = FinalDF_T0['TUG1'].replace(0, np.nan) |
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FinalDF_T0['TUG2'] = FinalDF_T0['TUG2'].replace(0, np.nan) |
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FinalDF_T0['TUGTot'] = FinalDF_T0['TUGTot'].replace(0, np.nan) |
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FinalDF_T0 = FinalDF_T0.drop(['TUG1', 'TUG2'], axis=1) |
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# TUG_T1 Asuming that all missing data were due to physical disabilties --> NaNs to 30 seconds |
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FinalDF_T1['TUG1'] = DFT1['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(30) |
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FinalDF_T1['TUG2'] = DFT1['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(30) |
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FinalDF_T1['TUGTot'] = (FinalDF_T1['TUG1']+FinalDF_T1['TUG2'])/2 |
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FinalDF_T1 = FinalDF_T1.drop(['TUG1', 'TUG2'], axis=1) |
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# TUG_T2 |
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#FinalDF_T2['TUG1'] = DFT2['Timed to Up&Go - attempt 1 (sec)'].astype('float64').fillna(0) |
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#FinalDF_T2['TUG2'] = DFT2['Timed to Up&Go - attempt 2 (sec)'].astype('float64').fillna(0) |
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# for i, r in FinalDF_T2.iterrows(): |
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# if FinalDF_T2.loc[i,'TUG1'] != 0 and FinalDF_T2.loc[i,'TUG2'] != 0: |
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# FinalDF_T2.loc[i,'TUGTot'] = (FinalDF_T2.loc[i,'TUG1']+FinalDF_T2.loc[i,'TUG2'])/2 |
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# else: |
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# FinalDF_T2.loc[i,'TUGTot'] = (FinalDF_T2.loc[i,'TUG1']+FinalDF_T2.loc[i,'TUG2'])/1 |
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# FinalDF_T2['TUG1'] = FinalDF_T2['TUG1'].replace(0, np.nan) |
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# FinalDF_T2['TUG2'] = FinalDF_T2['TUG2'].replace(0, np.nan) |
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# FinalDF_T2['TUGTot'] = FinalDF_T2['TUGTot'].replace(0, np.nan) |
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#%% |
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FinalDF_T0[['HGSR1','HGSR2','HGSR3']] = DFT0[['Handgrip Strength test Attempt 1 rigth','Handgrip Strength test Attempt 2 rigth','Handgrip Strength test Attempt 3 right']].astype('float64') |
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FinalDF_T0['HGSRAvg'] = (FinalDF_T0['HGSR1'] + FinalDF_T0['HGSR2'] + FinalDF_T0['HGSR3'])/3 |
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FinalDF_T0[['HGSL1','HGSL2','HGSL3']] = DFT0[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64') |
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FinalDF_T0['HGSLAvg'] = (FinalDF_T0['HGSL1'] + FinalDF_T0['HGSL2'] + FinalDF_T0['HGSL3'])/3 |
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FinalDF_T0['Dominance'] = DFT0['Hand dominance'] |
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FinalDF_T1[['HGSR1','HGSR2','HGSR3']] = DFT1[['Handgrip Strength test Attempt 1 rigth','Handgrip Strength test Attempt 2 rigth','Handgrip Strength test Attempt 3 right']].astype('float64') |
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FinalDF_T1['HGSRAvg'] = (FinalDF_T1['HGSR1'] + FinalDF_T1['HGSR2'] + FinalDF_T1['HGSR3'])/3 |
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FinalDF_T1[['HGSL1','HGSL2','HGSL3']] = DFT1[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64') |
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FinalDF_T1['HGSLAvg'] = (FinalDF_T1['HGSL1'] + FinalDF_T1['HGSL2'] + FinalDF_T1['HGSL3'])/3 |
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for i, r in DFT1.iterrows(): |
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if DFT1.loc[i,'Handgrip Strength Test'] == 'No': |
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FinalDF_T1.loc[i,['HGSR1','HGSR2','HGSR3','HGSRAvg','HGSL1','HGSL2','HGSL3','HGSLAvg']] = 0 |
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for index, rows in FinalDF_T1.iterrows(): |
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if FinalDF_T0.loc[index, 'Dominance'] == 'Rigth': |
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FinalDF_T0.loc[index, 'HGSDom'] = FinalDF_T0.loc[index,'HGSRAvg'] |
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FinalDF_T1.loc[index, 'HGSDom'] = FinalDF_T1.loc[index,'HGSRAvg'] |
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elif FinalDF_T0.loc[index, 'Dominance'] == 'Left': |
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FinalDF_T0.loc[index, 'HGSDom'] = FinalDF_T0.loc[index,'HGSLAvg'] |
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FinalDF_T1.loc[index, 'HGSDom'] = FinalDF_T1.loc[index,'HGSLAvg'] |
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else: |
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FinalDF_T0.loc[index, 'HGSDom'] = (FinalDF_T0.loc[index,'HGSRAvg']+FinalDF_T0.loc[index,'HGSLAvg'])/2 |
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FinalDF_T1.loc[index, 'HGSDom'] = (FinalDF_T1.loc[index,'HGSRAvg']+FinalDF_T1.loc[index,'HGSLAvg'])/2 |
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FinalDF_T0 = FinalDF_T0.drop(['HGSR1', 'HGSR2', 'HGSR3', 'HGSRAvg','HGSL1', 'HGSL2', 'HGSL3', 'HGSLAvg', 'Dominance'], axis=1) |
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FinalDF_T1 = FinalDF_T1.drop(['HGSR1', 'HGSR2', 'HGSR3', 'HGSRAvg','HGSL1', 'HGSL2', 'HGSL3', 'HGSLAvg'], axis=1) |
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# FinalDF_T2[['HGSR1','HGSR2','HGSR3']] = DFT2[['Handgrip Strength test Attempt 1 right','Handgrip Strength test Attempt 2 right','Handgrip Strength test Attempt 3 right']].astype('float64') |
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# FinalDF_T2['HGSRAvg'] = (FinalDF_T2['HGSR1'] + FinalDF_T2['HGSR2'] + FinalDF_T2['HGSR3'])/3 |
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# FinalDF_T2[['HGSL1','HGSL2','HGSL3']] = DFT2[['Handgrip Strength test Attempt 1 left', 'Handgrip Strength test Attempt 2 left', 'Handgrip Strength test Attempt 3 left']].astype('float64') |
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# FinalDF_T2['HGSLAvg'] = (FinalDF_T2['HGSL1'] + FinalDF_T2['HGSL2'] + FinalDF_T2['HGSL3'])/3 |
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# if FinalDF_T0['Dominance'] == 'Right': |
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# FinalDF_T2['HSGDom'] = FinalDF_T0['HGSRAvg'] |
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# elif FinalDF_T0['Dominance'] == 'Left': |
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# FinalDF_T2['HSGDom'] = FinalDF_T0['HGSLAvg'] |
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# else: |
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# FinalDF_T2['HGSDom'] = (FinalDF_T0['HGSRAvg']+FinalDF_T0['HGSLAvg'])/2 |
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#%% |
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EORTCCols = DFT0.columns[15:59].tolist() |
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EORTCScoresT0 = DFT0[EORTCCols] |
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#EORTCScoresT2 = DFT2[EORTCCols] |
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#%% |
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os.chdir('I:\Mike Dijkhof\Python') |
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import EORTC as eor |
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import SQUASH as sq |
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NewEORTCScoresT0 = eor.EORTCCalculator(EORTCScoresT0, EORTCCols) |
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#NewEORTCScoresT2 = eor.EORTCCalculator(EORTCScoresT2, EORTCCols) |
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EORTCT0 = eor.EORTCScore(EORTCScoresT0) |
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#EORTCT2 = eor.EORTCScore(EORTCScoresT2) |
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os.chdir(Path) |
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#%% plaatjes |
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|
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# for index, row in EORTC.iterrows(): |
|||
# plt.figure(figsize=(20,8)) |
|||
# plt.title('EORTC preoperative outcomes pt ' + str(index)) |
|||
# sns.barplot(x=EORTC.columns, y=EORTC.loc[index,:]) |
|||
|
|||
#%% |
|||
|
|||
SQUASHScoresT0 = sq.SQUASHParse(DFT0) |
|||
#SQUASHScoresT2 = sq.SQUASHParse(DFT2) |
|||
|
|||
ColsToDrop = ['SQUASH baseline afgenomen?', 'Woon werkverkeer?', 'Werk?', 'Huishoudelijk werk?'] |
|||
|
|||
SQUASHScoresT0 = SQUASHScoresT0.drop(ColsToDrop, axis=1) |
|||
SQUASHScoresT0 = SQUASHScoresT0.astype('float64') |
|||
|
|||
#SQUASHScoresT2 = SQUASHScoresT2.drop(ColsToDrop, axis=1) |
|||
#SQUASHScoresT2 = SQUASHScoresT2.astype('float64') |
|||
|
|||
SQUASHT0 = sq.SQUASHScore(SQUASHScoresT0) |
|||
#SQUASHT2 = sq.SQUASHScore(SQUASHScoresT2) |
|||
|
|||
#%% |
|||
FinalDF_T0['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values |
|||
FinalDF_T1['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values |
|||
#FinalDF_T2['Pt Type'] = DFCompl.loc[:,'Complications at home during monitoring ? '].values |
|||
|
|||
|
|||
FinalDF_T0['Pt Type'] = FinalDF_T0['Pt Type'].str.replace('Yes', 'Complication') |
|||
FinalDF_T0['Pt Type'] = FinalDF_T0['Pt Type'].str.replace('No', 'Healthy') |
|||
|
|||
FinalDF_T1['Pt Type'] = FinalDF_T1['Pt Type'].str.replace('Yes', 'Complication') |
|||
FinalDF_T1['Pt Type'] = FinalDF_T1['Pt Type'].str.replace('No', 'Healthy') |
|||
|
|||
#FinalDF_T2['Pt Type'] = FinalDF_T2['Pt Type'].str.replace('Yes', 'Complication') |
|||
#FinalDF_T2['Pt Type'] = FinalDF_T2['Pt Type'].str.replace('No', 'Healthy') |
|||
|
|||
|
|||
#%% Save FinalDF to .csv file |
|||
|
|||
FinalDF_T0.to_csv('FinalDF_T0.csv') |
|||
FinalDF_T1.to_csv('FinalDF_T1.csv') |
|||
#FinalDF_T2.to_csv('FinalDF_T2.csv') |
|||
@ -0,0 +1,415 @@ |
|||
# -*- coding: utf-8 -*- |
|||
""" |
|||
Script for parsing the Fitbit data into graphs. |
|||
@author M.F. Dijkhof |
|||
""" |
|||
# Import stuff |
|||
import os |
|||
import pandas as pd |
|||
import seaborn as sns |
|||
import numpy as np |
|||
import matplotlib.pyplot as plt |
|||
|
|||
# Disable copy overwrite warning |
|||
pd.options.mode.chained_assignment = None # default='warn' |
|||
|
|||
#%% Define filenames and path |
|||
|
|||
FilenameComp = 'SurgeryAndAdmission2.csv' #Surg and Adm + Complications |
|||
FilenamePA = 'PA_Data.csv' |
|||
FilenameSteps = 'StepData.csv' |
|||
FilenameComplete = 'Complete.csv' |
|||
FilenameOutcome = 'Complications.csv' |
|||
|
|||
Path = 'I:\Mike Dijkhof\Connecare MGP\Data' |
|||
|
|||
# Set path |
|||
os.chdir(Path) |
|||
|
|||
#%% Create DF from files |
|||
|
|||
DFComp = pd.DataFrame(pd.read_csv(FilenameComp)) |
|||
DFPA = pd.DataFrame(pd.read_csv(FilenamePA)) |
|||
DFSteps = pd.DataFrame(pd.read_csv(FilenameSteps)) |
|||
DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete)) |
|||
DFOutcome = pd.DataFrame(pd.read_csv(FilenameOutcome)) |
|||
|
|||
DFComp = DFComp.set_index('Study ID') |
|||
DFPA = DFPA.set_index('Study ID') |
|||
DFSteps = DFSteps.set_index('Study ID') |
|||
DFComplete = DFComplete.set_index('Study ID') |
|||
DFOutcome = DFOutcome.set_index('Study ID') |
|||
|
|||
#%% |
|||
# Clear all uncomplete cases |
|||
CompleteCheck= DFComplete['Has patient completed study?'] == 'Yes' |
|||
|
|||
DFComp = DFComp[CompleteCheck] |
|||
DFPA = DFPA[CompleteCheck] |
|||
DFOutcome = DFOutcome[CompleteCheck] |
|||
DFSteps = DFSteps[CompleteCheck] |
|||
|
|||
|
|||
# Transpose PA data into the right format |
|||
NewDF= pd.DataFrame(DFPA.iloc[0]).transpose() |
|||
|
|||
counter = range(1, len(DFPA)) |
|||
|
|||
for i in counter: |
|||
NewRow = DFPA.iloc[i].transpose() |
|||
NewDF = NewDF.append(NewRow) |
|||
|
|||
NewDF = NewDF.drop(['Complete?'], axis=1) |
|||
|
|||
# Do the same for Step data |
|||
NewStepDF = pd.DataFrame(DFSteps.iloc[0]).transpose() |
|||
|
|||
counter = range(1, len(DFSteps)) |
|||
|
|||
for i in counter: |
|||
NewRow = DFSteps.iloc[i].transpose() |
|||
NewStepDF = NewStepDF.append(NewRow) |
|||
|
|||
NewStepDF = NewStepDF.drop(['Complete?'], axis=1) |
|||
|
|||
#%% Create DF with important dates |
|||
|
|||
DFDates = DFComp [['Date of surgery','Date of hospital discharge', |
|||
'Date first complication at home', 'Date (first) readmission', |
|||
'Date discharge after first readmission', 'Date second readmission', |
|||
'Date discharge second readmission']] |
|||
|
|||
for i in DFDates: |
|||
DFDates[i] = pd.to_datetime(DFDates[i]).dt.date |
|||
|
|||
DFDates['LOS'] = DFDates['Date of hospital discharge'] - DFDates['Date of surgery'] #LOS = Length of stay |
|||
DFDates['TTC'] = DFDates['Date first complication at home'] - DFDates['Date of surgery'] #TTC = Time to complication |
|||
DFDates['TTR'] = DFDates['Date (first) readmission'] - DFDates['Date of surgery'] #TTR = Time to readmission |
|||
DFDates['TT2R'] = DFDates['Date second readmission'] - DFDates['Date of surgery'] #TT2R = Time to second readmission |
|||
|
|||
#%% Create coordinates from the dates for the plots |
|||
|
|||
AXVcoord = pd.DataFrame(columns= ['LOS', 'TTC', 'TTR', 'TT2R']) |
|||
|
|||
for rows, index in DFDates.iterrows(): |
|||
AXVcoord.loc[rows, 'LOS'] = DFDates['LOS'].loc[rows].days |
|||
AXVcoord.loc[rows, 'TTC'] = DFDates['TTC'].loc[rows].days |
|||
AXVcoord.loc[rows, 'TTR'] = DFDates['TTR'].loc[rows].days |
|||
AXVcoord.loc[rows, 'TT2R'] = DFDates['TT2R'].loc[rows].days |
|||
|
|||
AXVcomb = AXVcoord.values.tolist() |
|||
AXVArray = np.array(AXVcomb) |
|||
|
|||
|
|||
#%% Create DFs for each PA level |
|||
|
|||
NoActDF = NewDF.loc[:, :'No activity After Surgery: 90'] |
|||
LowActDF = NewDF.loc[:, 'Low activity Before Surgery: -1 ':'Low activity After Surgery: 90'] |
|||
MedActDF = NewDF.loc[:, 'Medium activity Before Surgery: -1':'Medium activity After Surgery: 90'] |
|||
HighActDF = NewDF.loc[:, 'High activity Before Surgery: -1 ':'High activity After Surgery: 90'] |
|||
|
|||
def MakeStepDF(NewDF): |
|||
StepDF = NewDF.iloc[:,321:427] |
|||
StepDF = StepDF.drop('Days Fitbit prescribed after surgery', axis=1) |
|||
StepDF = StepDF.replace(' ', '') |
|||
StepDF = StepDF.replace('N.A.', np.nan) |
|||
StepDF = StepDF.replace('N.A. ', np.nan) |
|||
StepDF = StepDF.replace('NA.', np.nan) |
|||
StepDF = StepDF.replace('n.a.', np.nan) |
|||
StepDF = StepDF.replace('N.A', np.nan) |
|||
StepDF = StepDF.replace('NaN', np.nan) |
|||
StepDF = StepDF.astype('float64') |
|||
return StepDF |
|||
|
|||
StepDF = MakeStepDF(NewStepDF) |
|||
|
|||
#%% Day -14 to surgery were in the wrong order so we have to flip the first 14 days |
|||
|
|||
def DayFlipper(DF): |
|||
ListCol = DF.columns.tolist() |
|||
ListCol[0:14] = ListCol[0:14][::-1] |
|||
DF = DF[ListCol] |
|||
return(DF) |
|||
|
|||
NoActDF = DayFlipper(NoActDF) |
|||
print(NoActDF.columns) |
|||
LowActDF = DayFlipper(LowActDF) |
|||
print(LowActDF.columns) |
|||
MedActDF = DayFlipper(MedActDF) |
|||
print(MedActDF.columns) |
|||
HighActDF = DayFlipper(HighActDF) |
|||
print(HighActDF.columns) |
|||
StepDF = DayFlipper(StepDF) |
|||
print(StepDF.columns) |
|||
|
|||
#%% |
|||
OldColumns = LowActDF.columns |
|||
NewColumns = range(-14, 91) |
|||
|
|||
LowActDF.columns = NewColumns |
|||
MedActDF.columns = NewColumns |
|||
HighActDF.columns = NewColumns |
|||
StepDF.columns = NewColumns |
|||
|
|||
# Set NaN to zeroes in order to calculate the total amount of activity |
|||
LowActDFZeroes = LowActDF.fillna(0) |
|||
MedActDFZeroes = MedActDF.fillna(0) |
|||
HighActDFZeroes = HighActDF.fillna(0) |
|||
StepDFZeroes = StepDF.fillna(0) |
|||
|
|||
TotActDF = LowActDF + MedActDF + HighActDF |
|||
TotActDFZeroes = LowActDFZeroes + MedActDFZeroes + HighActDFZeroes |
|||
|
|||
# Remove pts that reported less than threshold PA days |
|||
Threshold = 200 |
|||
|
|||
NaNCount = LowActDF.isnull().sum(axis=1) # Count days without data per patient |
|||
NaNRowDrop = (LowActDF.isnull().sum(axis=1)) < Threshold |
|||
|
|||
NoActDFClean = NoActDF[NaNRowDrop] |
|||
LowActDFClean = LowActDFZeroes[NaNRowDrop] |
|||
MedActDFClean = MedActDFZeroes[NaNRowDrop] |
|||
HighActDFClean = HighActDFZeroes[NaNRowDrop] |
|||
TotActDFClean = TotActDFZeroes[NaNRowDrop] |
|||
|
|||
#%% |
|||
|
|||
# NoActDFClean['Group'] = 'Complication' |
|||
# LowActDFClean['Group'] = 'Complication' |
|||
# MedActDFClean['Group'] = 'Complication' |
|||
# HighActDFClean['Group'] = 'Complication' |
|||
# TotActDFClean['Group'] = 'Complication' |
|||
# StepDF['Group'] = 'Complication' |
|||
|
|||
def Grouper(DF): |
|||
DF['Group'] = 'Complication' |
|||
DF['Group'] = DF['Group'].where(DFOutcome['Complications at home during monitoring ? '] == 'Yes', other='No Comp') |
|||
return DF |
|||
|
|||
NoActDFClean = Grouper(NoActDFClean) |
|||
LowActDFClean = Grouper(LowActDFClean) |
|||
MedActDFClean = Grouper(MedActDFClean) |
|||
HighActDFClean = Grouper(HighActDFClean) |
|||
TotActDFClean = Grouper(TotActDFClean) |
|||
StepDF = Grouper(StepDF) |
|||
|
|||
# #%% Divide Comps, Non-comps and Unknown-Comps |
|||
|
|||
# LowActComp = LowActDFClean.loc[NewDF['Complications at Home'] == 'Yes'] |
|||
# MedActComp = MedActDFClean.loc[NewDF['Complications at Home'] == 'Yes'] |
|||
# HighActComp = HighActDFClean.loc[NewDF['Complications at Home'] == 'Yes'] |
|||
# TotActComp = TotActDFClean.loc[NewDF['Complications at Home'] == 'Yes'] |
|||
|
|||
# LowActNoComp = LowActDFClean.loc[NewDF['Complications at Home'] == 'No'] |
|||
# MedActNoComp = MedActDFClean.loc[NewDF['Complications at Home'] == 'No'] |
|||
# HighActNoComp = HighActDFClean.loc[NewDF['Complications at Home'] == 'No'] |
|||
# TotActNoComp = TotActDFClean.loc[NewDF['Complications at Home'] == 'No'] |
|||
|
|||
# LowActUnk = LowActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')] |
|||
# MedActUnk = MedActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')] |
|||
# HighActUnk = HighActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')] |
|||
# TotActUnk = TotActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')] |
|||
|
|||
#%% Plot comps, non-comps amd unknown patient data with event-dates |
|||
|
|||
colors = ['k','c','r', 'r'] # k=discharge, c=complication, r=readmissions |
|||
|
|||
def PAPlotter(Low, Med, High, Tot, Step, AXV): |
|||
|
|||
for index, row in Tot.iterrows(): |
|||
|
|||
counter = index-1 |
|||
fig, ax1 = plt.subplots(figsize=(20,8)) |
|||
|
|||
ax1.plot(Low.loc[index], 'b:') |
|||
ax1.plot(Med.loc[index], 'r:') |
|||
ax1.plot(High.loc[index], 'y:') |
|||
ax1.plot(Tot.loc[index]) |
|||
ax1.set_ylabel('Minutes of PA') |
|||
ax1.set_xlabel('Days') |
|||
plt.ylim(0,1440) |
|||
|
|||
plt.vlines(x=0, ymin=0, ymax=1440, linestyle='dashed') |
|||
plt.vlines(AXV[counter], ymin= 0, ymax= 1440, colors=colors, linestyle='dotted') |
|||
|
|||
ax2 = ax1.twinx() |
|||
ax2.plot(Step.loc[index], 'k') |
|||
ax2.set_ylabel('Steps per day') |
|||
|
|||
plt.title('PA levels comp pt' + str(index)) |
|||
plt.ylim(0,25000) |
|||
|
|||
PAPlotter(LowActDFClean, MedActDFClean, HighActDFClean, TotActDFClean, StepDF, AXVcomb) |
|||
#PAPlotter(LowActNoComp, MedActNoComp, HighActNoComp,TotActNoComp, StepDF, AXVcomb, 'No Complication') |
|||
#PAPlotter(LowActUnk, MedActUnk, HighActUnk, TotActUnk, StepDF, AXVcomb, 'Unknown Complication') |
|||
|
|||
|
|||
#%% Calculate differences between comp PA and no comp PA |
|||
|
|||
def PAStats(DF, group): |
|||
MeanTotPA = DF.mean().mean() |
|||
StdTotPA = DF.std().std() |
|||
PreMean= DF.loc[:,-14:-1].mean().mean() |
|||
PreStd = DF.loc[:,-14:-1].std().std() |
|||
Post30Mean = DF.loc[:,0:30].mean().mean() |
|||
Post30Std = DF.loc[:,0:30].std().std() |
|||
Post60Mean = DF.loc[:,0:60].mean().mean() |
|||
Post60Std = DF.loc[:,0:60].std().std() |
|||
Post90Mean = DF.loc[:,0:90].mean().mean() |
|||
Post90Std = DF.loc[:,0:90].std().std() |
|||
|
|||
print('Stats '+ group + ':', '\n') |
|||
print('Total Mean min PA ='+ str(MeanTotPA),'Std=' + str(StdTotPA)) |
|||
print('Preoperative Mean min PA =' + str(PreMean), 'Std=' + str(PreStd)) |
|||
print('30 days Postop. Mean min PA =' + str(Post30Mean), 'Std=' + str(Post30Std)) |
|||
print('60 days Postop. Mean min PA =' + str(Post60Mean), 'Std=' + str(Post60Std)) |
|||
print('90 days Postop. Mean min PA =' + str(Post90Mean), 'Std=' + str(Post90Std),'\n') |
|||
|
|||
PAStats(TotActComp, 'complication') |
|||
PAStats(TotActNoComp, 'no complication') |
|||
PAStats(TotActUnk, 'unkown') |
|||
|
|||
|
|||
#%% Plot histogram number of missing values |
|||
CountDF = pd.DataFrame(NaNCount) |
|||
CountDF['Complication'] = DFCompl['Complications at home during monitoring ? '] |
|||
CountDF.columns = ['Count', 'Complication'] |
|||
|
|||
sns.displot(CountDF, x='Count', bins=[10, 20, 30, 40, 50, 60, 70, 80, 90], hue='Complication') |
|||
sns.color_palette ('colorblind') |
|||
|
|||
#%% |
|||
|
|||
def RollingAvAct(DF, windowsize): |
|||
AvDF = pd.DataFrame() |
|||
|
|||
for index, row in DF.iterrows(): |
|||
AvDF = AvDF.append(row.rolling(windowsize, min_periods=1).mean()) |
|||
return(AvDF) |
|||
|
|||
AvTotActComp =pd.DataFrame(RollingAvAct(TotActComp, 3)) |
|||
AvTotActNoComp = pd.DataFrame(RollingAvAct(TotActNoComp, 3)) |
|||
|
|||
|
|||
#%% |
|||
def Trendliner(DF, Dates, group): |
|||
newPASlopePre = pd.DataFrame(columns=['Slope', 'Int', 'Group']) |
|||
newPASlopeLOS = pd.DataFrame(columns=['Slope', 'Int', 'Group']) |
|||
newPASlopePost = pd.DataFrame(columns=['Slope', 'Int', 'Group']) |
|||
|
|||
for index, row in DF.iterrows(): |
|||
|
|||
counter = index-1 |
|||
DisDay = int(AXVArray[counter,0]) |
|||
DisDay2 = int(DisDay+15) |
|||
DisDay3 = int(DisDay2-1) |
|||
|
|||
# Calculate trendline pre-op |
|||
Xpre = DF.columns[0:15] |
|||
Ypre = DF.loc[index,-14:0] |
|||
z_pre = np.polyfit(Xpre, Ypre, 1) |
|||
p_pre = np.poly1d(z_pre) |
|||
newPASlopePre.loc[index,'Slope'] = z_pre[0] |
|||
newPASlopePre.loc[index,'Int'] = z_pre[1] |
|||
newPASlopePre.loc[index, 'Group'] = group |
|||
|
|||
# Calculate trendline LOS |
|||
Xlos = DF.columns[14:DisDay2] |
|||
Ylos = DF.loc[index,0:DisDay] |
|||
z_los = np.polyfit(Xlos, Ylos, 1) |
|||
p_los = np.poly1d(z_los) |
|||
newPASlopeLOS.loc[index,'Slope'] = z_los[0] |
|||
newPASlopeLOS.loc[index,'Int'] = z_los[1] |
|||
newPASlopeLOS.loc[index, 'Group'] = group |
|||
|
|||
# Calculate trendline post-op |
|||
Xpost = DF.columns[DisDay3:] |
|||
Ypost = DF.loc[index,DisDay:] |
|||
z_post = np.polyfit(Xpost, Ypost, 1) |
|||
p_post = np.poly1d(z_post) |
|||
newPASlopePost.loc[index,'Slope'] = z_post[0] |
|||
newPASlopePost.loc[index,'Int'] = z_post[1] |
|||
newPASlopePost.loc[index, 'Group'] = group |
|||
|
|||
# Plot figures |
|||
plt.figure(figsize=(24,8)) |
|||
plt.plot(DF.loc[index]) |
|||
plt.plot(Xpost,p_post(Xpost),'r--') |
|||
plt.plot(Xpre, p_pre(Xpre), 'b--') |
|||
plt.plot(Xlos, p_los(Xlos), 'k--') |
|||
plt.vlines(x=0, ymin=0, ymax=1440, linestyle='dashed') |
|||
plt.vlines(Dates[counter], ymin= 0, ymax= 1440, colors=colors, linestyle='dotted') |
|||
plt.xlim(-14,105) |
|||
plt.ylim(0,1440) |
|||
plt.ylabel('Minutes of PA') |
|||
plt.xlabel('Days') |
|||
plt.title('Mov Avg PA levels pt' + str(index) + '_' + group) |
|||
|
|||
d = {'Pre': newPASlopePre, 'LOS':newPASlopeLOS, 'Post': newPASlopePost} |
|||
|
|||
return(d) |
|||
|
|||
TrendDictComp = Trendliner(AvTotActComp, AXVcomb, 'complication') |
|||
TrendDictNoComp= Trendliner(AvTotActNoComp, AXVcomb, 'no complication') |
|||
|
|||
|
|||
#%% |
|||
|
|||
# def SlopeStats(SlopeDict, group): |
|||
# MeanSlopePre, MeanIntPre = SlopeDict['Pre'].mean() |
|||
# StdSlopePre, StdIntPre = SlopeDict['Pre'].std() |
|||
# MeanSlopeLOS, MeanIntLOS = SlopeDict['LOS'].mean() |
|||
# StdSlopeLOS, StdIntLOS = SlopeDict['LOS'].std() |
|||
# MeanSlopePost, MeanIntPost = SlopeDict['Post'].mean() |
|||
# StdSlopePost, StdIntPost = SlopeDict['Post'].std() |
|||
|
|||
# print('Stats '+ group + ':', '\n') |
|||
# print('Mean slope PA Pre-op = '+ str(MeanSlopePre),'Std= ' + str(StdSlopePre)) |
|||
# print('Mean slope PA hospitalization = '+ str(MeanSlopeLOS),'Std= ' + str(StdSlopeLOS)) |
|||
# print('Mean slope PA Post-op = '+ str(MeanSlopePost),'Std= ' + str(StdSlopePost)) |
|||
# print('Mean intersept PA Pre-op = '+ str(MeanIntPre),'Std= ' + str(StdIntPre)) |
|||
# print('Mean intercept PA hospitalization = '+ str(MeanIntLOS),'Std= ' + str(StdIntLOS)) |
|||
# print('Mean intercept PA Post-op = '+ str(MeanIntPre),'Std= ' + str(StdIntPre), '\n') |
|||
|
|||
# return(MeanSlopePre, StdSlopePre, MeanSlopeLOS, StdSlopeLOS, MeanSlopePost, StdSlopePost) |
|||
|
|||
# MeanSlopePreComp, StdSlopePreComp, MeanSlopeLOSComp, StdSlopLOSComp, MeanSlopePostComp, StdSlopeComp, = SlopeStats(TrendDictComp, 'complications') |
|||
# MeanSlopePreNoComp, StdSlopePreNoComp, MeanSlopeLOSNoComp, StdSlopLOSNoComp, MeanSlopePostNoComp, StdSlopeNoComp = SlopeStats(TrendDictNoComp, 'no complications') |
|||
|
|||
|
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#%% |
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|
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# SlopeIntPreComp = pd.DataFrame(TrendDictComp['Pre']) |
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# SlopeIntPreComp['Period'] = 'Pre' |
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# SlopeIntPreNoComp= pd.DataFrame(TrendDictNoComp['Pre']) |
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# SlopeIntPreNoComp['Period'] = 'Pre' |
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# SlopeIntLOSComp = pd.DataFrame(TrendDictComp['LOS']) |
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# SlopeIntLOSComp['Period'] = 'LOS' |
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# SlopeIntLOSNoComp= pd.DataFrame(TrendDictNoComp['LOS']) |
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# SlopeIntLOSNoComp['Period'] = 'LOS' |
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|
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# SlopeIntPostComp = pd.DataFrame(TrendDictComp['Post']) |
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# SlopeIntPostComp['Period'] = 'Post' |
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# SlopeIntPostNoComp= pd.DataFrame(TrendDictNoComp['Post']) |
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# SlopeIntPostNoComp['Period'] = 'Post' |
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|
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# Slope = pd.DataFrame() |
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# Slope = Slope.append([SlopeIntPreComp, SlopeIntPreNoComp, SlopeIntLOSComp, SlopeIntLOSNoComp, SlopeIntPostComp, SlopeIntPostNoComp]) |
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# Slope['Slope'] = Slope['Slope'].astype('float64') |
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# Slope['Int'] = Slope['Int'].astype('float64') |
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|
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#%% |
|||
# plt.figure(figsize=(12,8)) |
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# sns.set_theme(style="darkgrid") |
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# sns.violinplot(x=Slope['Period'], y=Slope['Slope'],hue=Slope['Group'], palette="muted", split=True) |
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# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) |
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|
|||
# plt.figure(figsize=(12,8)) |
|||
# sns.set_theme(style="darkgrid") |
|||
# sns.violinplot(x=Slope['Period'], y=Slope['Int'],hue=Slope['Group'], palette="muted", split=True) |
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# plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.) |
|||
|
|||
#%% |
|||
#fig, axes = plt.subplots(1,2, sharey=True) |
|||
#sns.violinplot(data=newPASlopeComp['Intercept'], ax=axes[0], color='b') |
|||
#sns.violinplot(data=newPASlopeNoComp['Intercept'], ax=axes[1], color='r') |
|||
@ -0,0 +1,73 @@ |
|||
# -*- coding: utf-8 -*- |
|||
""" |
|||
Created on Fri May 14 09:18:32 2021 |
|||
|
|||
@author: Dijkhofmf |
|||
""" |
|||
|
|||
# Import stuff |
|||
import os |
|||
import pandas as pd |
|||
import matplotlib.pyplot as plt |
|||
import seaborn as sns |
|||
from sklearn import preprocessing |
|||
|
|||
#%% Import data and path |
|||
|
|||
Path = 'I:\Mike Dijkhof\Connecare MGP\Data\FinalFiles' |
|||
|
|||
# Set path |
|||
os.chdir(Path) |
|||
|
|||
#%% Create DF |
|||
FinalDF = pd.DataFrame(pd.read_csv('FinalDataset.csv')) |
|||
|
|||
X = pd.DataFrame(FinalDF) |
|||
|
|||
cols = X.drop('Pt Type', axis=1) |
|||
|
|||
|
|||
ID = X['Study ID'] |
|||
y = X['Pt Type'] |
|||
y= y.replace('Healthy', 'No-complication') |
|||
X = X.drop(['Pt Type', 'Study ID'], axis=1) |
|||
|
|||
#%% |
|||
X1 = pd.DataFrame(preprocessing.scale(X), columns=X.columns) |
|||
|
|||
X1['Pt Type'] = y |
|||
X1.set_index(ID) |
|||
|
|||
#%% |
|||
|
|||
X1.columns = ['Age (years)', 'Gender', 'Daily alcohol use', 'Medication', |
|||
'ASA-classification', 'Recurrent disease?', 'Comorb', |
|||
'Independent, with others', 'Smokes cigarettes/sigar', 'BMI', 'GFI', |
|||
'HADS_A', 'HADS Depression', 'ADL', 'iADL', 'TUG', 'Handgrip strength', |
|||
'Avg. Steps/day', 'Avg. MVPA/day', 'Pt Type'] |
|||
|
|||
plots = X1.columns |
|||
|
|||
#%% |
|||
import matplotlib.pylab as pylab |
|||
|
|||
params = {'legend.fontsize': 'x-large', |
|||
'axes.labelsize': 'x-large', |
|||
'axes.titlesize':'x-large', |
|||
'xtick.labelsize':'x-large', |
|||
'ytick.labelsize':'x-large'} |
|||
|
|||
pylab.rcParams.update(params) |
|||
|
|||
plots = plots[1:] |
|||
namecount=0 |
|||
|
|||
for x in plots: |
|||
name = str(plots[namecount]) |
|||
plt.figure(dpi=720) |
|||
sns.boxplot(x='Pt Type', y=x, data=X1, boxprops=dict(alpha=0.5)) |
|||
sns.swarmplot(x='Pt Type', y=x, data=X1) |
|||
plt.title('Swarm-boxplot ' + name) |
|||
namecount = namecount +1 |
|||
|
|||
|
|||
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