diff --git a/Sort_data.py b/Sort_data.py
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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Sep 1 14:13:55 2024
+
+@author: Maya Coulson Theodorsen (mcoth@dtu.dk)
+
+This script preprocesses questionnaire data by organizing questionnaire items
+into subscales, calculating derived variables, and standardizing the data.
+Computation of variables.
+
+
+"""
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+
+
+# Define sorting and organizing of data
+def sort_data(data_complete):
+ # DataFrame with only the 59 DASS & PCL items
+ data = data_complete.loc[:, 'q0010_0001': 'q0014_0007']
+
+ # Make new column names so easier to understand
+ def prepend(list, str):
+ str += '{0}'
+ list = [str.format(i) for i in list]
+ return(list)
+
+
+ #Make DASS1-DASS42 list
+ list = [*range(1,43)]
+ str = 'DASS'
+ DASScolumns = (prepend(list, str))
+
+ #Make PCL1-PCL17 list
+ list = [*range(1,18)]
+ str = 'PCL'
+ PCLcolumns = (prepend(list, str))
+
+ #Make PCA1-PCA59 list to use later
+ list = [*range(1,60)]
+ str = 'PCA'
+ PCAcolumns = (prepend(list, str))
+
+ # Add column names
+ columnNames = DASScolumns + PCLcolumns
+ data.columns = columnNames
+
+ # Make df with only DASS
+ DASS = data.loc[:,'DASS1':'DASS42']
+
+ # Make df with only PCL
+ PCL = data.loc[:,'PCL1':'PCL17']
+
+ # Questionnaire subscales
+ questionnaireClusters = data_complete[['PCL_t0', 'Intrusion_t0', 'Avoidance_t0',
+ 'Hyperarousal_t0', 'DASS_A_t0',
+ 'DASS_D_t0', 'DASS_S_t0']].copy()
+ # Rename columns
+ questionnaireClusters.columns=['PCL total','PCL Intrusion', 'PCL Avoidance', 'PCL Hyperarousal',
+ 'DASS Anxiety', 'DASS Depression', 'DASS Stress']
+
+ # Separate PCL Avoidance and Numbing
+ Avoidance = data_complete.loc[:,['q0013_0006', 'q0013_0007']]
+ Avoidance = Avoidance.sum(axis=1)
+ Numb = data_complete.loc[:,['q0013_0008','q0013_0009', 'q0013_0010','q0014_0001', 'q0014_0002']]
+ Numb = Numb.sum(axis=1)
+
+ # Add to questionnaire subscales df
+ questionnaireClusters['PCL Avoidance'] = Avoidance
+ questionnaireClusters['PCL Numbing'] = Numb
+
+ questionnaireClusters = questionnaireClusters[['PCL total','PCL Intrusion',
+ 'PCL Avoidance','PCL Numbing',
+ 'PCL Hyperarousal',
+ 'DASS Anxiety', 'DASS Depression',
+ 'DASS Stress',]]
+ # Standardize questionnaires
+ questionnaireClusters_std = StandardScaler().fit_transform(questionnaireClusters)
+ questionnaireClusters_std = pd.DataFrame(questionnaireClusters_std)
+ questionnaireClusters_std.columns = questionnaireClusters.columns
+
+ # Standardize items to a scale of 0 to 1
+ std_data = data.copy()
+ std_PCL = std_data.loc[:,'PCL1':'PCL17'] - 1
+ std_PCL = std_PCL.div(4)
+ std_DASS = DASS.div(3)
+ std_data = pd.concat([std_DASS, std_PCL], axis=1)
+
+ # Adjust weights since DASS has 42 items and PCL has 17
+ weightDASS = 50/42
+ weightPCL = 50/17
+ std_data = pd.concat([std_data.loc[:,'DASS1':'DASS42'].mul(weightDASS),
+ std_data.loc[:,'PCL1':'PCL17'].mul(weightPCL)], axis=1)
+
+
+ # Transform a few variables for comparison of clusters
+
+ # Transform marital status into binary variable
+ data_complete['civil_status'] = data_complete['q0004'].apply(lambda x: 0 if x in [2, 3] else 1)
+
+ # Flip 0 & 1 in self-rated health for better interpretability
+ data_complete['Selv_vur_helbred'] = data_complete['Selv_vur_helbred'].apply(lambda x: 0 if x in [1] else 1)
+
+ # Replace NAs with 0 for units per week to those who answered q0020_0001 as "Never drink alcohol"
+ data_complete['q0021_0001'] = data_complete['q0021_0001'].fillna(0)
+
+ # Age as numeric
+ data_complete['q0003_0001'] = pd.to_numeric(data_complete['q0003_0001'], errors='coerce')
+
+ # Extract military trauma
+ data_complete['Military_trauma'] = data_complete['Traume_t0_4'].apply(lambda x: 1 if x > 0 else 0)
+
+ # Calculate unemployment (study/work active vs not)
+ data_complete['Unemployed'] = data_complete[['q0005_0010','q0005_0011','q0005_0012','q0005_0013','q0005_0014','q0005_0015']].fillna(0).sum(axis=1)
+ data_complete['Unemployed'] = data_complete['Unemployed'].apply(lambda x: 1 if x > 0 else 0)
+
+ # Drugs
+ data_complete['hash'] = data_complete['q0024_0001'].apply(lambda x: 1 if x > 1 else 0)
+ data_complete['drugs'] = data_complete[['q0026_0001', 'hash']].fillna(0).sum(axis=1)
+ data_complete['drugs'] = data_complete['drugs'].apply(lambda x: 1 if x > 0 else 0)
+
+ # Alcohol - convert to units per week
+ alc = data_complete.loc[:,['q0020_0001', 'q0021_0001']]
+ alc = alc.dropna()
+ alc.columns=['AlcFreq', 'Units']
+ # Pairs to compare in all following barplots
+ pairs=[(0, 1), (0, 2), (1, 2)]
+
+ #
+ # Mapping drinks per occasion to average values
+ drinks_map = {
+ 1: 1.5,
+ 2: 3.5,
+ 3: 5.5,
+ 4: 8.0,
+ 5: 10.0
+ }
+
+ # Mapping drinking frequency to weekly frequency
+ frequency_map = {
+ 1: 0,
+ 2: 0.25,
+ 3: 0.75,
+ 4: 2.5,
+ 5: 4.5
+ }
+ #
+ # Apply the mappings
+ data_complete['drinks_per_occasion'] = data_complete['q0021_0001'].map(drinks_map)
+ data_complete['frequency_per_week'] = data_complete['q0020_0001'].map(frequency_map)
+
+ # Calculate the mean number of standard drinks per week
+ data_complete['units_per_week'] = data_complete['drinks_per_occasion'] * data_complete['frequency_per_week']
+
+ data_complete['units_per_week'] = data_complete['units_per_week'].fillna(0)
+
+ # Excessive drinking
+ data_complete['alcohol_over_5'] = data_complete['q0021_0001'].apply(lambda x: 1 if x > 2 else 0)
+ data_complete['alcohol_over_4'] = data_complete['q0021_0001'].apply(lambda x: 1 if x > 1 else 0)
+
+ data_complete['alcohol_over_14'] = data_complete['units_per_week'].apply(lambda x: 1 if x > 14 else 0)
+ data_complete['alcohol_over_7'] = data_complete['units_per_week'].apply(lambda x: 1 if x > 7 else 0)
+
+ # Excessive drinking by sex
+ data_complete['binge'] = (
+ (((data_complete['alcohol_over_5'] == 1) | (data_complete['alcohol_over_14'] == 1)) & (data_complete['q0002'] == 1)) |
+ (((data_complete['alcohol_over_4'] == 1) | (data_complete['alcohol_over_7'] == 1)) & (data_complete['q0002'] == 2))
+ ).astype(int)
+
+
+ return data, DASS, PCL, questionnaireClusters, questionnaireClusters_std, std_data, columnNames, PCAcolumns, data_complete
+
+