#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Sep  1 13:50:11 2024

@author: Maya Coulson Theodorsen (mcoth@dtu.dk)

This is the main script used for the analysis. Runninng this file alone is
sufficient, as long as the custom functions below are also imported. 

"""

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)