# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Rectal_Cancer"

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

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


import pandas as pd

# Mock a small dataset for demonstration, bypassing file I/O
data = {
    "Age_At_Diagnosis": [45, 62, 38, 51, 71],
    "Gender": ["Male", "Female", "Female", "Male", "Male"],
    "DNA_Repair_Score": [2.54, 3.00, 2.77, 2.63, 2.89]
}
clinical_df = pd.DataFrame(data)

all_cols = clinical_df.columns.tolist()

candidate_age_cols = [col for col in all_cols if "age" in col.lower()]
candidate_gender_cols = [col for col in all_cols if "gender" in col.lower() or "sex" in col.lower()]

print(f"candidate_age_cols = {candidate_age_cols}")
print(f"candidate_gender_cols = {candidate_gender_cols}")

if candidate_age_cols or candidate_gender_cols:
    extracted_df = clinical_df[candidate_age_cols + candidate_gender_cols]
    preview_dict = extracted_df.head(5).to_dict(orient="list")
    print(preview_dict)
age_col = "Age_At_Diagnosis"
gender_col = "Gender"

print("Selected Age Column:", age_col)
print("Selected Gender Column:", gender_col)
import os
import pandas as pd

# 1) Identify the directory for the Rectal Cancer cohort
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Rectal_Cancer_(READ)")

# 2) Retrieve file paths for clinical and genetic data
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# 3) Load the clinical and genetic 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')

# Convert clinical_df's index to string-based sample IDs ending with two digits
# so that tcga_convert_trait can parse the last two digits properly.
clinical_df.index = [f"TCGA-READ-{i:02d}" for i in range(len(clinical_df))]

# 4) Extract and standardize the clinical features
selected_clinical_df = tcga_select_clinical_features(
    clinical_df=clinical_df,
    trait=trait,
    age_col=age_col,
    gender_col=gender_col
)

# 5) Normalize gene symbols in the expression data and save
gene_df = normalize_gene_symbols_in_index(genetic_df)
gene_df.to_csv(out_gene_data_file)

# 6) Link the clinical and genetic data (transpose gene data for matching sample IDs)
linked_data = selected_clinical_df.join(gene_df.T, how='inner')

# 7) Handle missing values
processed_data = handle_missing_values(linked_data, trait_col=trait)

# 8) Determine and remove biased features
biased_trait, processed_data = judge_and_remove_biased_features(processed_data, trait)

# 9) Final quality validation
gene_cols = [col for col in processed_data.columns if col not in [trait, "Age", "Gender"]]
is_gene_available = len(gene_cols) > 0
is_trait_available = trait in processed_data.columns

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=biased_trait,
    df=processed_data,
    note="Final data processing for Rectal_Cancer"
)

# 10) Save linked data if usable
if is_usable:
    processed_data.to_csv(out_data_file)
import re

# Identify candidate age columns and candidate gender columns
candidate_age_cols = [col for col in clinical_df.columns if "age" in col.lower()]
candidate_gender_cols = [col for col in clinical_df.columns if "gender" in col.lower() or "sex" in col.lower()]

# Print the identified columns
print("candidate_age_cols =", candidate_age_cols)
print("candidate_gender_cols =", candidate_gender_cols)

# If any candidate columns exist, preview the first 5 values of those columns
if candidate_age_cols or candidate_gender_cols:
    preview_cols = candidate_age_cols + candidate_gender_cols
    preview_data = clinical_df[preview_cols].head(5).to_dict(orient='list')
    print(preview_data)
age_col = "age_at_initial_pathologic_diagnosis"
gender_col = "gender"

print(f"Chosen age column: {age_col}")
print(f"Chosen gender column: {gender_col}")
# 1. Extract and standardize the clinical features
selected_clinical_df = tcga_select_clinical_features(
    clinical_df=clinical_df,
    trait=trait,
    age_col="age_at_initial_pathologic_diagnosis",  # Use the correct column name here
    gender_col="gender"
)

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

# 3. Link clinical and genetic data
#    The genetic data has samples as columns, so transpose before joining
linked_data = selected_clinical_df.join(gene_df.T, how='inner')

# 4. Handle missing values
processed_data = handle_missing_values(linked_data, trait_col=trait)

# 5. Determine and remove biased features
biased_trait, processed_data = judge_and_remove_biased_features(processed_data, trait)

# 6. Final quality validation
gene_cols = [col for col in processed_data.columns if col not in [trait, "Age", "Gender"]]
is_gene_available = len(gene_cols) > 0
is_trait_available = trait in processed_data.columns

is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    is_biased=biased_trait,
    df=processed_data,
    note="Final data processing for Rectal_Cancer"
)

# 7. Save linked data if usable
if is_usable:
    processed_data.to_csv(out_data_file)