# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Aniridia"

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

# Output paths
out_data_file = "./output/z1/preprocess/Aniridia/TCGA.csv"
out_gene_data_file = "./output/z1/preprocess/Aniridia/gene_data/TCGA.csv"
out_clinical_data_file = "./output/z1/preprocess/Aniridia/clinical_data/TCGA.csv"
json_path = "./output/z1/preprocess/Aniridia/cohort_info.json"


# Step 1: Initial Data Loading
import os
import pandas as pd

# 1) Select the most relevant TCGA cohort directory for the trait "Aniridia"
subdirs = [d for d in os.listdir(tcga_root_dir) if os.path.isdir(os.path.join(tcga_root_dir, d))]

# Heuristic keyword scoring to approximate phenotypic overlap with "Aniridia"
keywords_weights = [
    ("aniridia", 10),
    ("iris", 6),
    ("ocular", 5),
    ("eye", 4),
    ("uveal", 4),
    ("uvea", 4),
    ("retina", 3),
    ("optic", 2),
    ("ophthalm", 2)
]

def score_dir(name: str) -> int:
    ln = name.lower()
    return sum(w for k, w in keywords_weights if k in ln)

scored = [(d, score_dir(d)) for d in subdirs]
scored.sort(key=lambda x: x[1], reverse=True)
selected_dir = scored[0][0] if scored and scored[0][1] > 0 else None

if selected_dir is None:
    # No suitable cohort; record and exit step gracefully
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
    print("No suitable TCGA cohort directory found for the trait. Skipping.")
else:
    cohort_dir = os.path.join(tcga_root_dir, selected_dir)

    # 2) Identify clinical and genetic file paths
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # 3) Load both files as DataFrames
    clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')
    genetic_df = pd.read_csv(genetic_file_path, sep='\t', index_col=0, low_memory=False, compression='infer')

    # Keep references for downstream steps
    SELECTED_TCGA_DIR = selected_dir
    SELECTED_CLINICAL_PATH = clinical_file_path
    SELECTED_GENETIC_PATH = genetic_file_path
    TCGA_CLINICAL_DF = clinical_df
    TCGA_GENETIC_DF = genetic_df

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