import os
import subprocess
import numpy as np
import pandas as pd
import  glob
import sys
import matplotlib
import matplotlib.pyplot as plt
#from matplotlib_venn import venn3
import matplotlib.patches as mpatches
import argparse
import re
from tkinter import *
matplotlib.use('TkAgg')

parser = argparse.ArgumentParser(description="Venn Diagramm of gff and bed files")
parser.add_argument('-bed1', type=str, help="Path to the first bed file")
parser.add_argument('-bed2', type=str, help="Path to the second bed file")
parser.add_argument('-bed3', type=str, help="Path to the third bed file")

args = parser.parse_args()

bed1 = args.bed1
bed2 = args.bed2
bed3 = args.bed3

filename1 = os.path.splitext(os.path.basename(bed1))[0]
filename2 = os.path.splitext(os.path.basename(bed2))[0]
filename3 = os.path.splitext(os.path.basename(bed3))[0]

# Running awk for filtring gff file.



# Utilisation de la sortie de la première commande comme entrée pour la deuxième commande
command2 = subprocess.Popen(f"multiIntersectBed -i  {bed1} {bed2} {bed3} ", shell=True, stdout=subprocess.PIPE)
output, error = command2.communicate()
results = output.decode('utf-8').strip()
#print(results)
columns = ["Chro", "Start", "End", "nb_overlap_file", "label_overlap_file","None1", "None2", "None3"]
rows= [line.split('\t') for line in results.split('\n') if line]

raw_dataframe=pd.DataFrame(rows, columns=columns)
#print(raw_dataframe)
df= raw_dataframe.loc[:, ["Chro", "Start", "End", "nb_overlap_file", "label_overlap_file"]]
#print(df)
df['nb_overlap_file'] = df['nb_overlap_file'].astype(int)
df['label_overlap_file'] = df['label_overlap_file'].astype(str)
filtre1 = df[df['nb_overlap_file'] == 3]
count1 = filtre1.shape[0]
#print(count1)


filtre2 = df.loc[(df['nb_overlap_file'] == 2) & (df['label_overlap_file'] == '1,3')]
#print(len(filtre2))

filtre3 = df.loc[(df['nb_overlap_file'] == 2) & (df['label_overlap_file'] == '1,2')]
print(filtre3)

filtre4 = df[(df['nb_overlap_file'] == 1) & (df['label_overlap_file'] == '1')]
#print(len(filtre4))

filtre5 = df[(df['nb_overlap_file'] == 1) & (df['label_overlap_file'] == '2')]
print(filtre5)


filtre6 = df[(df['nb_overlap_file'] == 1) & (df['label_overlap_file'] == '3')]
#print(len(filtre6))


"""
# Création de la liste des colonnes pour le DataFrame:
columns = ["chromosome_f1", "start_f1", "end_f1", "feature", "chromosome_f2", "start_f2", "end_f2", "methylation", "overlap_length"]

# Création de liste de ligne pout le DataFrame:
rows= [line.split('\t') for line in intersection.split('\n') if line]

#Cette ligne de code divise la chaîne intersection en une liste de listes,
# où chaque sous-liste représente une ligne du tableau de l'intersection,
# en filtrant les lignes vides.

dataframe = pd.DataFrame(rows, columns=columns)
methylated_counts = dataframe.groupby(['start_f1', 'end_f1']).size().reset_index(name='Methylated_cytosine')

## Plotting pie Chart:

# Proportion de gènes avec et sans méthylation
num_feature_methylated = len(set(dataframe['start_f1']))
num_feature_unmethylated = nb_features - num_feature_methylated

# Création de la figure
fig, ax = plt.subplots(figsize=(6, 6))

# Données à représenter
data = [num_feature_methylated, num_feature_unmethylated]

# Labels pour le pie chart
labels = ['Methylated', 'Unmethylated']

# Couleurs pour chaque section du pie chart
colors = ['#ffc107', '#007bff']

# Création du pie chart
ax.pie(data, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)

# Ajout d'un titre
ax.set_title(f'Proportion of methylated and unmethylated {feature}')

# Affichage du graphique
plt.show()
"""