415 lines
15 KiB
Python
415 lines
15 KiB
Python
# -*- coding: utf-8 -*-
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"""
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Script for parsing the Fitbit data into graphs.
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@author M.F. Dijkhof
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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 numpy as np
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import matplotlib.pyplot as plt
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# Disable copy overwrite warning
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pd.options.mode.chained_assignment = None # default='warn'
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#%% Define filenames and path
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FilenameComp = 'SurgeryAndAdmission2.csv' #Surg and Adm + Complications
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FilenamePA = 'PA_Data.csv'
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FilenameSteps = 'StepData.csv'
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FilenameComplete = 'Complete.csv'
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FilenameOutcome = 'Complications.csv'
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Path = 'I:\Mike Dijkhof\Connecare MGP\Data'
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# Set path
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os.chdir(Path)
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#%% Create DF from files
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DFComp = pd.DataFrame(pd.read_csv(FilenameComp))
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DFPA = pd.DataFrame(pd.read_csv(FilenamePA))
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DFSteps = pd.DataFrame(pd.read_csv(FilenameSteps))
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DFComplete = pd.DataFrame(pd.read_csv(FilenameComplete))
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DFOutcome = pd.DataFrame(pd.read_csv(FilenameOutcome))
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DFComp = DFComp.set_index('Study ID')
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DFPA = DFPA.set_index('Study ID')
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DFSteps = DFSteps.set_index('Study ID')
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DFComplete = DFComplete.set_index('Study ID')
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DFOutcome = DFOutcome.set_index('Study ID')
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#%%
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# Clear all uncomplete cases
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CompleteCheck= DFComplete['Has patient completed study?'] == 'Yes'
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DFComp = DFComp[CompleteCheck]
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DFPA = DFPA[CompleteCheck]
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DFOutcome = DFOutcome[CompleteCheck]
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DFSteps = DFSteps[CompleteCheck]
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# Transpose PA data into the right format
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NewDF= pd.DataFrame(DFPA.iloc[0]).transpose()
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counter = range(1, len(DFPA))
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for i in counter:
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NewRow = DFPA.iloc[i].transpose()
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NewDF = NewDF.append(NewRow)
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NewDF = NewDF.drop(['Complete?'], axis=1)
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# Do the same for Step data
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NewStepDF = pd.DataFrame(DFSteps.iloc[0]).transpose()
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counter = range(1, len(DFSteps))
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for i in counter:
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NewRow = DFSteps.iloc[i].transpose()
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NewStepDF = NewStepDF.append(NewRow)
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NewStepDF = NewStepDF.drop(['Complete?'], axis=1)
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#%% Create DF with important dates
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DFDates = DFComp [['Date of surgery','Date of hospital discharge',
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'Date first complication at home', 'Date (first) readmission',
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'Date discharge after first readmission', 'Date second readmission',
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'Date discharge second readmission']]
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for i in DFDates:
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DFDates[i] = pd.to_datetime(DFDates[i]).dt.date
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DFDates['LOS'] = DFDates['Date of hospital discharge'] - DFDates['Date of surgery'] #LOS = Length of stay
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DFDates['TTC'] = DFDates['Date first complication at home'] - DFDates['Date of surgery'] #TTC = Time to complication
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DFDates['TTR'] = DFDates['Date (first) readmission'] - DFDates['Date of surgery'] #TTR = Time to readmission
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DFDates['TT2R'] = DFDates['Date second readmission'] - DFDates['Date of surgery'] #TT2R = Time to second readmission
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#%% Create coordinates from the dates for the plots
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AXVcoord = pd.DataFrame(columns= ['LOS', 'TTC', 'TTR', 'TT2R'])
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for rows, index in DFDates.iterrows():
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AXVcoord.loc[rows, 'LOS'] = DFDates['LOS'].loc[rows].days
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AXVcoord.loc[rows, 'TTC'] = DFDates['TTC'].loc[rows].days
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AXVcoord.loc[rows, 'TTR'] = DFDates['TTR'].loc[rows].days
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AXVcoord.loc[rows, 'TT2R'] = DFDates['TT2R'].loc[rows].days
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AXVcomb = AXVcoord.values.tolist()
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AXVArray = np.array(AXVcomb)
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#%% Create DFs for each PA level
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NoActDF = NewDF.loc[:, :'No activity After Surgery: 90']
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LowActDF = NewDF.loc[:, 'Low activity Before Surgery: -1 ':'Low activity After Surgery: 90']
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MedActDF = NewDF.loc[:, 'Medium activity Before Surgery: -1':'Medium activity After Surgery: 90']
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HighActDF = NewDF.loc[:, 'High activity Before Surgery: -1 ':'High activity After Surgery: 90']
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def MakeStepDF(NewDF):
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StepDF = NewDF.iloc[:,321:427]
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StepDF = StepDF.drop('Days Fitbit prescribed after surgery', axis=1)
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StepDF = StepDF.replace(' ', '')
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StepDF = StepDF.replace('N.A.', np.nan)
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StepDF = StepDF.replace('N.A. ', np.nan)
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StepDF = StepDF.replace('NA.', np.nan)
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StepDF = StepDF.replace('n.a.', np.nan)
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StepDF = StepDF.replace('N.A', np.nan)
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StepDF = StepDF.replace('NaN', np.nan)
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StepDF = StepDF.astype('float64')
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return StepDF
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StepDF = MakeStepDF(NewStepDF)
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#%% Day -14 to surgery were in the wrong order so we have to flip the first 14 days
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def DayFlipper(DF):
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ListCol = DF.columns.tolist()
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ListCol[0:14] = ListCol[0:14][::-1]
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DF = DF[ListCol]
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return(DF)
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NoActDF = DayFlipper(NoActDF)
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print(NoActDF.columns)
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LowActDF = DayFlipper(LowActDF)
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print(LowActDF.columns)
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MedActDF = DayFlipper(MedActDF)
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print(MedActDF.columns)
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HighActDF = DayFlipper(HighActDF)
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print(HighActDF.columns)
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StepDF = DayFlipper(StepDF)
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print(StepDF.columns)
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#%%
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OldColumns = LowActDF.columns
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NewColumns = range(-14, 91)
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LowActDF.columns = NewColumns
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MedActDF.columns = NewColumns
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HighActDF.columns = NewColumns
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StepDF.columns = NewColumns
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# Set NaN to zeroes in order to calculate the total amount of activity
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LowActDFZeroes = LowActDF.fillna(0)
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MedActDFZeroes = MedActDF.fillna(0)
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HighActDFZeroes = HighActDF.fillna(0)
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StepDFZeroes = StepDF.fillna(0)
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TotActDF = LowActDF + MedActDF + HighActDF
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TotActDFZeroes = LowActDFZeroes + MedActDFZeroes + HighActDFZeroes
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# Remove pts that reported less than threshold PA days
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Threshold = 200
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NaNCount = LowActDF.isnull().sum(axis=1) # Count days without data per patient
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NaNRowDrop = (LowActDF.isnull().sum(axis=1)) < Threshold
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NoActDFClean = NoActDF[NaNRowDrop]
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LowActDFClean = LowActDFZeroes[NaNRowDrop]
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MedActDFClean = MedActDFZeroes[NaNRowDrop]
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HighActDFClean = HighActDFZeroes[NaNRowDrop]
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TotActDFClean = TotActDFZeroes[NaNRowDrop]
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#%%
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# NoActDFClean['Group'] = 'Complication'
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# LowActDFClean['Group'] = 'Complication'
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# MedActDFClean['Group'] = 'Complication'
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# HighActDFClean['Group'] = 'Complication'
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# TotActDFClean['Group'] = 'Complication'
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# StepDF['Group'] = 'Complication'
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def Grouper(DF):
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DF['Group'] = 'Complication'
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DF['Group'] = DF['Group'].where(DFOutcome['Complications at home during monitoring ? '] == 'Yes', other='No Comp')
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return DF
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NoActDFClean = Grouper(NoActDFClean)
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LowActDFClean = Grouper(LowActDFClean)
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MedActDFClean = Grouper(MedActDFClean)
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HighActDFClean = Grouper(HighActDFClean)
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TotActDFClean = Grouper(TotActDFClean)
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StepDF = Grouper(StepDF)
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# #%% Divide Comps, Non-comps and Unknown-Comps
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# LowActComp = LowActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
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# MedActComp = MedActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
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# HighActComp = HighActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
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# TotActComp = TotActDFClean.loc[NewDF['Complications at Home'] == 'Yes']
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# LowActNoComp = LowActDFClean.loc[NewDF['Complications at Home'] == 'No']
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# MedActNoComp = MedActDFClean.loc[NewDF['Complications at Home'] == 'No']
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# HighActNoComp = HighActDFClean.loc[NewDF['Complications at Home'] == 'No']
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# TotActNoComp = TotActDFClean.loc[NewDF['Complications at Home'] == 'No']
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# LowActUnk = LowActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
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# MedActUnk = MedActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
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# HighActUnk = HighActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
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# TotActUnk = TotActDFClean.loc[(NewDF['Complications at Home'] != 'Yes') & (NewDF['Complications at Home'] != 'No')]
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#%% Plot comps, non-comps amd unknown patient data with event-dates
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colors = ['k','c','r', 'r'] # k=discharge, c=complication, r=readmissions
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def PAPlotter(Low, Med, High, Tot, Step, AXV):
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for index, row in Tot.iterrows():
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counter = index-1
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fig, ax1 = plt.subplots(figsize=(20,8))
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ax1.plot(Low.loc[index], 'b:')
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ax1.plot(Med.loc[index], 'r:')
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ax1.plot(High.loc[index], 'y:')
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ax1.plot(Tot.loc[index])
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ax1.set_ylabel('Minutes of PA')
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ax1.set_xlabel('Days')
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plt.ylim(0,1440)
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plt.vlines(x=0, ymin=0, ymax=1440, linestyle='dashed')
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plt.vlines(AXV[counter], ymin= 0, ymax= 1440, colors=colors, linestyle='dotted')
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ax2 = ax1.twinx()
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ax2.plot(Step.loc[index], 'k')
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ax2.set_ylabel('Steps per day')
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plt.title('PA levels comp pt' + str(index))
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plt.ylim(0,25000)
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PAPlotter(LowActDFClean, MedActDFClean, HighActDFClean, TotActDFClean, StepDF, AXVcomb)
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#PAPlotter(LowActNoComp, MedActNoComp, HighActNoComp,TotActNoComp, StepDF, AXVcomb, 'No Complication')
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#PAPlotter(LowActUnk, MedActUnk, HighActUnk, TotActUnk, StepDF, AXVcomb, 'Unknown Complication')
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#%% Calculate differences between comp PA and no comp PA
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def PAStats(DF, group):
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MeanTotPA = DF.mean().mean()
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StdTotPA = DF.std().std()
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PreMean= DF.loc[:,-14:-1].mean().mean()
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PreStd = DF.loc[:,-14:-1].std().std()
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Post30Mean = DF.loc[:,0:30].mean().mean()
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Post30Std = DF.loc[:,0:30].std().std()
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Post60Mean = DF.loc[:,0:60].mean().mean()
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Post60Std = DF.loc[:,0:60].std().std()
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Post90Mean = DF.loc[:,0:90].mean().mean()
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Post90Std = DF.loc[:,0:90].std().std()
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print('Stats '+ group + ':', '\n')
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print('Total Mean min PA ='+ str(MeanTotPA),'Std=' + str(StdTotPA))
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print('Preoperative Mean min PA =' + str(PreMean), 'Std=' + str(PreStd))
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print('30 days Postop. Mean min PA =' + str(Post30Mean), 'Std=' + str(Post30Std))
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print('60 days Postop. Mean min PA =' + str(Post60Mean), 'Std=' + str(Post60Std))
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print('90 days Postop. Mean min PA =' + str(Post90Mean), 'Std=' + str(Post90Std),'\n')
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PAStats(TotActComp, 'complication')
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PAStats(TotActNoComp, 'no complication')
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PAStats(TotActUnk, 'unkown')
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#%% Plot histogram number of missing values
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CountDF = pd.DataFrame(NaNCount)
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CountDF['Complication'] = DFCompl['Complications at home during monitoring ? ']
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CountDF.columns = ['Count', 'Complication']
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sns.displot(CountDF, x='Count', bins=[10, 20, 30, 40, 50, 60, 70, 80, 90], hue='Complication')
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sns.color_palette ('colorblind')
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#%%
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def RollingAvAct(DF, windowsize):
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AvDF = pd.DataFrame()
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for index, row in DF.iterrows():
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AvDF = AvDF.append(row.rolling(windowsize, min_periods=1).mean())
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return(AvDF)
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AvTotActComp =pd.DataFrame(RollingAvAct(TotActComp, 3))
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AvTotActNoComp = pd.DataFrame(RollingAvAct(TotActNoComp, 3))
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#%%
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def Trendliner(DF, Dates, group):
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newPASlopePre = pd.DataFrame(columns=['Slope', 'Int', 'Group'])
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newPASlopeLOS = pd.DataFrame(columns=['Slope', 'Int', 'Group'])
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newPASlopePost = pd.DataFrame(columns=['Slope', 'Int', 'Group'])
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for index, row in DF.iterrows():
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counter = index-1
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DisDay = int(AXVArray[counter,0])
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DisDay2 = int(DisDay+15)
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DisDay3 = int(DisDay2-1)
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# Calculate trendline pre-op
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Xpre = DF.columns[0:15]
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Ypre = DF.loc[index,-14:0]
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z_pre = np.polyfit(Xpre, Ypre, 1)
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p_pre = np.poly1d(z_pre)
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newPASlopePre.loc[index,'Slope'] = z_pre[0]
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newPASlopePre.loc[index,'Int'] = z_pre[1]
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newPASlopePre.loc[index, 'Group'] = group
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# Calculate trendline LOS
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Xlos = DF.columns[14:DisDay2]
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Ylos = DF.loc[index,0:DisDay]
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z_los = np.polyfit(Xlos, Ylos, 1)
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p_los = np.poly1d(z_los)
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newPASlopeLOS.loc[index,'Slope'] = z_los[0]
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newPASlopeLOS.loc[index,'Int'] = z_los[1]
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newPASlopeLOS.loc[index, 'Group'] = group
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# Calculate trendline post-op
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Xpost = DF.columns[DisDay3:]
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Ypost = DF.loc[index,DisDay:]
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z_post = np.polyfit(Xpost, Ypost, 1)
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p_post = np.poly1d(z_post)
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newPASlopePost.loc[index,'Slope'] = z_post[0]
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newPASlopePost.loc[index,'Int'] = z_post[1]
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newPASlopePost.loc[index, 'Group'] = group
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# Plot figures
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plt.figure(figsize=(24,8))
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plt.plot(DF.loc[index])
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plt.plot(Xpost,p_post(Xpost),'r--')
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plt.plot(Xpre, p_pre(Xpre), 'b--')
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plt.plot(Xlos, p_los(Xlos), 'k--')
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plt.vlines(x=0, ymin=0, ymax=1440, linestyle='dashed')
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plt.vlines(Dates[counter], ymin= 0, ymax= 1440, colors=colors, linestyle='dotted')
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plt.xlim(-14,105)
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plt.ylim(0,1440)
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plt.ylabel('Minutes of PA')
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plt.xlabel('Days')
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plt.title('Mov Avg PA levels pt' + str(index) + '_' + group)
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d = {'Pre': newPASlopePre, 'LOS':newPASlopeLOS, 'Post': newPASlopePost}
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return(d)
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TrendDictComp = Trendliner(AvTotActComp, AXVcomb, 'complication')
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TrendDictNoComp= Trendliner(AvTotActNoComp, AXVcomb, 'no complication')
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#%%
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# def SlopeStats(SlopeDict, group):
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# MeanSlopePre, MeanIntPre = SlopeDict['Pre'].mean()
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# StdSlopePre, StdIntPre = SlopeDict['Pre'].std()
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# MeanSlopeLOS, MeanIntLOS = SlopeDict['LOS'].mean()
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# StdSlopeLOS, StdIntLOS = SlopeDict['LOS'].std()
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# MeanSlopePost, MeanIntPost = SlopeDict['Post'].mean()
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# StdSlopePost, StdIntPost = SlopeDict['Post'].std()
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# print('Stats '+ group + ':', '\n')
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# print('Mean slope PA Pre-op = '+ str(MeanSlopePre),'Std= ' + str(StdSlopePre))
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# print('Mean slope PA hospitalization = '+ str(MeanSlopeLOS),'Std= ' + str(StdSlopeLOS))
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# print('Mean slope PA Post-op = '+ str(MeanSlopePost),'Std= ' + str(StdSlopePost))
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# print('Mean intersept PA Pre-op = '+ str(MeanIntPre),'Std= ' + str(StdIntPre))
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# print('Mean intercept PA hospitalization = '+ str(MeanIntLOS),'Std= ' + str(StdIntLOS))
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# print('Mean intercept PA Post-op = '+ str(MeanIntPre),'Std= ' + str(StdIntPre), '\n')
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# return(MeanSlopePre, StdSlopePre, MeanSlopeLOS, StdSlopeLOS, MeanSlopePost, StdSlopePost)
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# MeanSlopePreComp, StdSlopePreComp, MeanSlopeLOSComp, StdSlopLOSComp, MeanSlopePostComp, StdSlopeComp, = SlopeStats(TrendDictComp, 'complications')
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# MeanSlopePreNoComp, StdSlopePreNoComp, MeanSlopeLOSNoComp, StdSlopLOSNoComp, MeanSlopePostNoComp, StdSlopeNoComp = SlopeStats(TrendDictNoComp, 'no complications')
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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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# 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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# 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))
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# sns.set_theme(style="darkgrid")
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# 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.)
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#%%
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#fig, axes = plt.subplots(1,2, sharey=True)
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#sns.violinplot(data=newPASlopeComp['Intercept'], ax=axes[0], color='b')
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#sns.violinplot(data=newPASlopeNoComp['Intercept'], ax=axes[1], color='r') |