# -*- coding: utf-8 -*-
"""Untitled17.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1GwdSjrwh3f6QCzOa8KHr_XkWy0KmZvdV
"""

import pandas as pd
import numpy as np
from ast import literal_eval
import re

df_disprot = pd.read_parquet(PATHS['disprot'] / 'disprot_data.parquet')
df_uniprot = pd.read_parquet(PATHS['uniprot'] / 'uniprot_mitochondrial.parquet')
df_clinvar = pd.read_parquet(PATHS['clinvar'] / 'clinvar_variants.parquet')
df_mobidb = pd.read_parquet(PATHS['mobidb'] / 'mobidb_data.parquet')

print(f"    DisProt: {len(df_disprot)} ")
print(f"    UniProt: {len(df_uniprot)} ")
print(f"    ClinVar: {len(df_clinvar)} ")
print(f"    MobiDB: {len(df_mobidb)} ")


mito_accs = set(df_uniprot['uniprot_acc'].unique())

df_disprot_mito = df_disprot[df_disprot['uniprot_acc'].isin(mito_accs)].copy()
print(f"   ✓ DisProt : {len(df_disprot_mito)} régions ({df_disprot_mito['uniprot_acc'].nunique()} protéines)")

idp_mito_accs = set(df_disprot_mito['uniprot_acc'].unique())







def parse_protein_change(change_str: str) -> dict:
    if not change_str or pd.isna(change_str):
        return None


    aa_map = {
        'Ala': 'A', 'Arg': 'R', 'Asn': 'N', 'Asp': 'D', 'Cys': 'C',
        'Gln': 'Q', 'Glu': 'E', 'Gly': 'G', 'His': 'H', 'Ile': 'I',
        'Leu': 'L', 'Lys': 'K', 'Met': 'M', 'Phe': 'F', 'Pro': 'P',
        'Ser': 'S', 'Thr': 'T', 'Trp': 'W', 'Tyr': 'Y', 'Val': 'V',
        'Ter': '*'
    }

    pattern = r'([A-Z][a-z]{2})(\d+)([A-Z][a-z]{2})'
    match = re.match(pattern, change_str)

    if match:
        wt_3 = match.group(1)
        pos = int(match.group(2))
        mut_3 = match.group(3)

        wt_1 = aa_map.get(wt_3, '?')
        mut_1 = aa_map.get(mut_3, '?')

        if wt_1 != '?' and mut_1 != '?' and mut_1 != '*':
            return {
                'position': pos - 1,
                'wt_aa': wt_1,
                'mut_aa': mut_1
            }

    return None

parsed_mutations = []
for idx, row in df_clinvar.iterrows():
    parsed = parse_protein_change(row['protein_change'])
    if parsed:
        parsed['clinvar_id'] = row['clinvar_id']
        parsed['gene'] = row['gene']
        parsed['is_pathogenic'] = row['is_pathogenic']
        parsed['is_benign'] = row['is_benign']
        parsed['clinical_significance'] = row['clinical_significance']
        parsed_mutations.append(parsed)

df_mutations = pd.DataFrame(parsed_mutations)
print(f"   ✓ {len(df_mutations)} mutations parsées avec succès")

n_pathogenic = df_mutations['is_pathogenic'].sum()
n_benign = df_mutations['is_benign'].sum()
print(f"   ✓ Pathognes {n_pathogenic}")
print(f"   ✓ Bénin: {n_benign}")






gene_to_seq = {}
gene_to_acc = {}
for _, row in df_uniprot.iterrows():
    gene = row['gene_name']
    if gene and row['sequence']:
        gene_to_seq[gene] = row['sequence']
        gene_to_acc[gene] = row['uniprot_acc']

df_mutations['sequence'] = df_mutations['gene'].map(gene_to_seq)
df_mutations['uniprot_acc'] = df_mutations['gene'].map(gene_to_acc)

df_mutations_valid = df_mutations.dropna(subset=['sequence']).copy()

def validate_mutation(row):
    seq = row['sequence']
    pos = row['position']
    wt = row['wt_aa']

    if pos < 0 or pos >= len(seq):
        return False

    actual_aa = seq[pos]
    return actual_aa == wt

df_mutations_valid['is_valid'] = df_mutations_valid.apply(validate_mutation, axis=1)
df_mutations_final = df_mutations_valid[df_mutations_valid['is_valid']].copy()

print(f"   ✓ {len(df_mutations_final)} mutations validé")


df_pathogenic = df_mutations_final[df_mutations_final['is_pathogenic']].copy()
df_benign = df_mutations_final[df_mutations_final['is_benign']].copy()

print(f"   Pathogènes : {len(df_pathogenic)}")
print(f"   Bénins : {len(df_benign)}")

df_pathogenic['label'] = 1
df_benign['label'] = 0

df_dataset = pd.concat([df_pathogenic, df_benign], ignore_index=True)
df_dataset = df_dataset.sample(frac=1, random_state=42).reset_index(drop=True)

print(f"   ✓ : {len(df_dataset)} mutations")



disorder_regions_by_acc = {}
for acc in df_disprot['uniprot_acc'].unique():
    regions = df_disprot[df_disprot['uniprot_acc'] == acc][['region_start', 'region_end']].values.tolist()
    disorder_regions_by_acc[acc] = regions

def is_in_disorder_region(row):
    acc = row['uniprot_acc']
    pos = row['position'] + 1

    if acc not in disorder_regions_by_acc:
        return None
    for start, end in disorder_regions_by_acc[acc]:
        if start <= pos <= end:
            return True
    return False

df_dataset['in_disorder_region'] = df_dataset.apply(is_in_disorder_region, axis=1)

n_in_disorder = df_dataset['in_disorder_region'].sum()
n_annotated = df_dataset['in_disorder_region'].notna().sum()
print(f"   ✓ {n_in_disorder}/{n_annotated} ")