# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"

# Input paths
tcga_root_dir = "../DATA/TCGA"

# Output paths
out_data_file = "./output/preprocess/1/Anxiety_disorder/TCGA.csv"
out_gene_data_file = "./output/preprocess/1/Anxiety_disorder/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/1/Anxiety_disorder/clinical_data/TCGA.csv"
json_path = "./output/preprocess/1/Anxiety_disorder/cohort_info.json"

import os
import pandas as pd

# 1. Identify the relevant subdirectory for the trait "Obesity"
subdirectories = [
    'CrawlData.ipynb', '.DS_Store', 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)',
    'TCGA_Uterine_Carcinosarcoma_(UCS)', 'TCGA_Thyroid_Cancer_(THCA)', 'TCGA_Thymoma_(THYM)',
    'TCGA_Testicular_Cancer_(TGCT)', 'TCGA_Stomach_Cancer_(STAD)', 'TCGA_Sarcoma_(SARC)',
    'TCGA_Rectal_Cancer_(READ)', 'TCGA_Prostate_Cancer_(PRAD)', 'TCGA_Pheochromocytoma_Paraganglioma_(PCPG)',
    'TCGA_Pancreatic_Cancer_(PAAD)', 'TCGA_Ovarian_Cancer_(OV)', 'TCGA_Ocular_melanomas_(UVM)',
    'TCGA_Mesothelioma_(MESO)', 'TCGA_Melanoma_(SKCM)', 'TCGA_Lung_Squamous_Cell_Carcinoma_(LUSC)',
    'TCGA_Lung_Cancer_(LUNG)', 'TCGA_Lung_Adenocarcinoma_(LUAD)', 'TCGA_Lower_Grade_Glioma_(LGG)',
    'TCGA_Liver_Cancer_(LIHC)', 'TCGA_Large_Bcell_Lymphoma_(DLBC)', 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)',
    'TCGA_Kidney_Clear_Cell_Carcinoma_(KIRC)', 'TCGA_Kidney_Chromophobe_(KICH)', 'TCGA_Head_and_Neck_Cancer_(HNSC)',
    'TCGA_Glioblastoma_(GBM)', 'TCGA_Esophageal_Cancer_(ESCA)', 'TCGA_Endometrioid_Cancer_(UCEC)',
    'TCGA_Colon_and_Rectal_Cancer_(COADREAD)', 'TCGA_Colon_Cancer_(COAD)', 'TCGA_Cervical_Cancer_(CESC)',
    'TCGA_Breast_Cancer_(BRCA)', 'TCGA_Bladder_Cancer_(BLCA)', 'TCGA_Bile_Duct_Cancer_(CHOL)',
    'TCGA_Adrenocortical_Cancer_(ACC)', 'TCGA_Acute_Myeloid_Leukemia_(LAML)'
]

trait_keyword = trait
target_subdir = None

for sd in subdirectories:
    if trait_keyword.lower() in sd.lower():
        target_subdir = sd
        break

if target_subdir is None:
    # No suitable data found for this trait; mark as completed
    print("No TCGA subdirectory found for the trait. Skipping.")
else:
    # 2. Locate clinical and genetic data files
    cohort_dir = os.path.join(tcga_root_dir, target_subdir)
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # 3. Load the data
    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')
    genetic_df = pd.read_csv(genetic_file_path, index_col=0, sep='\t')

    # 4. Print column names of clinical data
    print(clinical_df.columns)