# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bile_Duct_Cancer"

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

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

import os
import pandas as pd

# Step 1: Check directories in tcga_root_dir for anything relevant to "Bile_Duct_Cancer"
search_terms = ["bile_duct", "bileduct", "chol"]
dir_list = os.listdir(tcga_root_dir)
matching_dir = None

for d in dir_list:
    d_lower = d.lower()
    if any(term in d_lower for term in search_terms):
        # Found a match, select this directory
        matching_dir = d
        break

if matching_dir is None:
    # No matching directory found. Mark the dataset as skipped.
    validate_and_save_cohort_info(
        is_final=False,
        cohort="TCGA",
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False
    )
else:
    # 2. Identify the clinicalMatrix and PANCAN files
    cohort_dir = os.path.join(tcga_root_dir, matching_dir)
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # 3. Load both data files
    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 the column names of the clinical data
    print("Clinical Data Columns:")
    print(clinical_df.columns.tolist())
# Identify candidate demographic columns
candidate_age_cols = ["age_at_initial_pathologic_diagnosis", "days_to_birth"]
candidate_gender_cols = ["gender"]

# Extract the columns and preview them
age_cols_in_data = [col for col in candidate_age_cols if col in clinical_df.columns]
gender_cols_in_data = [col for col in candidate_gender_cols if col in clinical_df.columns]

if age_cols_in_data:
    age_preview_df = clinical_df[age_cols_in_data]
    print("Age Data Preview:", preview_df(age_preview_df, n=5))
else:
    print("Age Data Preview:", {})

if gender_cols_in_data:
    gender_preview_df = clinical_df[gender_cols_in_data]
    print("Gender Data Preview:", preview_df(gender_preview_df, n=5))
else:
    print("Gender Data Preview:", {})
# Based on inspection of the supplied previews, we select "age_at_initial_pathologic_diagnosis" for age 
# (as it directly represents age in years) and "gender" for gender.

age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"

print("Chosen Age Column:", age_col)
print("Chosen Gender Column:", gender_col)
# 1) Extract and standardize clinical features (trait, age, gender) from the TCGA data
selected_clinical_df = tcga_select_clinical_features(
    clinical_df=clinical_df,
    trait=trait,
    age_col=age_col,
    gender_col=gender_col
)

# 2) Normalize gene symbols in the gene expression data
genetic_df_normalized = normalize_gene_symbols_in_index(genetic_df)
genetic_df_normalized.to_csv(out_gene_data_file)

# 3) Link clinical and genetic data on sample IDs
gene_expr_t = genetic_df_normalized.T
linked_data = selected_clinical_df.join(gene_expr_t, how='inner')

# 4) Handle missing values in the linked data
linked_data = handle_missing_values(linked_data, trait)

# 5) Determine whether the trait and some demographic features are severely biased
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6) Validate and save cohort information
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Prostate Cancer data from TCGA."
)

# 7) If usable, save the final linked data, including clinical and genetic features
if is_usable:
    linked_data.to_csv(out_data_file)
    # Save clinical subset if present
    clinical_cols = [col for col in [trait, "Age", "Gender"] if col in linked_data.columns]
    if clinical_cols:
        linked_data[clinical_cols].to_csv(out_clinical_data_file)