diff --git a/Main_script.py b/Main_script.py
deleted file mode 100644
index 239f05cb643630719dca8c54b606f2494a63915b..0000000000000000000000000000000000000000
--- a/Main_script.py
+++ /dev/null
@@ -1,86 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Sep  1 13:50:11 2024
-
-@author: Maya Coulson Theodorsen (mcoth@dtu.dk)
-"""
-
-import os
-os.chdir('/Volumes/T7/')
-import sys
-sys.path.append('/Volumes/T7')  # Path
-
-# Import custom functions
-from Import_data import load_data
-from Sort_data import sort_data
-from Perform_pca import perform_pca
-from Perform_clustering import perform_clustering
-from Compare_clusters import compare_clusters
-from Descriptives import total_descriptives, cluster_descriptives
-
-#Import all necessary packages
-import numpy as np
-import pandas as pd
-
-# Plotting
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# PCA
-from sklearn.decomposition import PCA
-from sklearn.cluster import KMeans
-from sklearn import metrics
-from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity, calculate_kmo
-
-#Clustering
-from scipy.cluster.hierarchy import dendrogram, linkage
-from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
-
-# Statisticl tests
-import pingouin as pg
-import scipy.stats as stats
-import statsmodels.api as sm
-import scikit_posthocs as sp
-from sklearn.preprocessing import StandardScaler
-from scipy.stats import bartlett, levene, chi2_contingency
-from pingouin import normality, kruskal, homoscedasticity
-from itertools import combinations
-from statsmodels.stats.multitest import multipletests
-
-# Turn off warnings
-import warnings
-warnings.filterwarnings("ignore")
-
-#%% Import data using my Import_data function file
-data_complete = load_data("/Volumes/T7/data6_9_2023.csv")
-data = data_complete.loc[:, 'q0010_0001': 'q0014_0007']
-#%% Call the sort_data function
-data, DASS, PCL, questionnaireClusters, questionnaireClusters_std, std_data, columnNames, PCAcolumns, data_complete = sort_data(data_complete)
-
-#%% Call the perform_pca function
-pca, loadings, principleComponents = perform_pca(std_data, PCAcolumns, columnNames)
-
-#%% Call the perform_clustering function
-PC234, LABELS, clusterNames = perform_clustering(std_data, principleComponents, data_complete, questionnaireClusters, questionnaireClusters_std)
-
-#%% Call the function to compare clusters across all variables
-p_values, posthoc_p_values, categorical_variables, continuous_variables = compare_clusters(data_complete, questionnaireClusters)
-pd.options.display.float_format = '{:.10f}'.format
-p_values = pd.DataFrame(p_values)
-posthoc_p_values = pd.DataFrame(posthoc_p_values)
-
-#%% Descriptive stats for total N and each k
-cluster_column = 'clusters'
-sorter = ['Sex (male)', 'Age', 'Civil status (single)', 'Children', 'Unemployed', 
-          'Self-rated health', 'Psychoanaleptica', 'Psycholeptica', 'Excessive alcohol intake',
-          'Current drug usage', 'Suicidal history', 'Probable childhood ADHD', 'Exposed to war', 'combat',
-          'PCL Intrusion', 'PCL Avoidance', 'PCL Numbing', 'PCL Hyperarousal', 'DASS Anxiety',
-          'DASS Depression', 'DASS Stress', 'PCL total score', 'Probable PTSD diagnosis','Total traumas',
-          'Total unique traumas']
-
-binary_variables = ['PTSD_t0_DSMIV','q0002', 'q0006', 'civil_status', 'Psychoanaleptica', 'Psycholeptica', 'binge','q0033_0001', 'ADHD_total_GROUP_t0', 'drugs', 'Military_trauma', 'combat','Unemployed']
-
-descriptives_total = total_descriptives(data_complete, questionnaireClusters,categorical_variables, continuous_variables, binary_variables, sorter)
-
-descriptives_cluster = cluster_descriptives(data_complete, questionnaireClusters,categorical_variables, continuous_variables, cluster_column, binary_variables, sorter)