# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Kidney_stones"

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

# Output paths
out_data_file = "./output/preprocess/3/Kidney_stones/TCGA.csv"
out_gene_data_file = "./output/preprocess/3/Kidney_stones/gene_data/TCGA.csv"
out_clinical_data_file = "./output/preprocess/3/Kidney_stones/clinical_data/TCGA.csv"
json_path = "./output/preprocess/3/Kidney_stones/cohort_info.json"

# Find relevant trait directory
trait_dir = 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)'
cohort_dir = os.path.join(tcga_root_dir, trait_dir)

# Get clinical and genetic data file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# Load the 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')

# Print clinical data columns
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']

# Load clinical data file 
tcga_brca_dir = os.path.join(tcga_root_dir, 'BRCA')
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_brca_dir)
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Preview age columns
age_preview = clinical_df[candidate_age_cols].head()
print("\nAge columns preview:", preview_df(age_preview))

# Preview gender columns 
gender_preview = clinical_df[candidate_gender_cols].head()
print("\nGender columns preview:", preview_df(gender_preview))
import pandas as pd

# Get file paths
cohort_dir = os.path.join(tcga_root_dir, "KIRC")
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# Load clinical data to get column names
clinical_df = pd.read_csv(clinical_file_path, sep='\t', index_col=0)

# Define candidate columns based on examination of clinical data columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth'] 
candidate_gender_cols = ['gender']

# Preview age columns
age_preview = {}
for col in candidate_age_cols:
    if col in clinical_df.columns:
        age_preview[col] = clinical_df[col].head().tolist()

# Preview gender columns        
gender_preview = {}
for col in candidate_gender_cols:
    if col in clinical_df.columns:
        gender_preview[col] = clinical_df[col].head().tolist()
# Examined candidate demographic columns from previous step output missing
candidate_age_cols = []  
candidate_gender_cols = [] 

# Cannot preview data since previous output is missing
# Will need to wait for output containing clinical dataset column names before continuing
age_col = None
gender_col = None

print(f"Selected age column: {age_col}")  
print(f"Selected gender column: {gender_col}")
# Find relevant trait directory
trait_dir = 'TCGA_Kidney_Papillary_Cell_Carcinoma_(KIRP)'
cohort_dir = os.path.join(tcga_root_dir, trait_dir)

# Get clinical and genetic data file paths
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

# Load the 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')

# Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())
# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Print column names only per the instructions
print("\nIdentified candidate columns:")
print(f"candidate_age_cols = {candidate_age_cols}")
print(f"candidate_gender_cols = {candidate_gender_cols}")

# Load clinical data
clinical_file_path, _ = tcga_get_relevant_filepaths(os.path.join(tcga_root_dir, 'KIRP/')) 
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Preview age and gender data
age_preview = preview_df(clinical_df[candidate_age_cols])
print("\nAge columns preview:")
print(age_preview)

gender_preview = preview_df(clinical_df[candidate_gender_cols])
print("\nGender columns preview:")
print(gender_preview)
# Define candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Get available cohorts
available_cohorts = os.listdir(tcga_root_dir)

# Get kidney-related cohorts
kidney_cohorts = [c for c in available_cohorts if "TCGA_Kidney" in c]

if kidney_cohorts:
    # Get cohort directory path
    cohort_dir = os.path.join(tcga_root_dir, kidney_cohorts[0])

    # Get file paths
    clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)

    # Read clinical data 
    clinical_df = pd.read_csv(clinical_file_path, index_col=0, sep='\t')

    # Preview age columns
    age_preview = {}
    for col in candidate_age_cols:
        if col in clinical_df.columns:
            age_preview[col] = clinical_df[col].head().tolist()
    print("Age columns preview:", age_preview)

    # Preview gender columns        
    gender_preview = {}
    for col in candidate_gender_cols:
        if col in clinical_df.columns:
            gender_preview[col] = clinical_df[col].head().tolist()
    print("Gender columns preview:", gender_preview)
else:
    print("No kidney-related cohorts found")
# Choose age column by inspecting the previews
# 'age_at_initial_pathologic_diagnosis' has direct age values whereas 'days_to_birth' needs conversion
age_col = 'age_at_initial_pathologic_diagnosis'

# Choose gender column by inspecting the previews
# 'gender' column has standard values MALE/FEMALE
gender_col = 'gender'

# Print chosen columns
print(f"Chosen age column: {age_col}")
print(f"Chosen gender column: {gender_col}")
# Get trait information from sample IDs using TCGA ID format
clinical_df['Kidney_stones'] = clinical_df.index.map(tcga_convert_trait)

# Extract standardized clinical features
selected_clinical_df = tcga_select_clinical_features(clinical_df, 'Kidney_stones', age_col, gender_col)

# Normalize gene symbols and save gene data
normalized_gene_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_df.to_csv(out_gene_data_file)

# Link clinical and genetic data 
linked_data = pd.concat([selected_clinical_df, normalized_gene_df.T], axis=1, join='inner')

# Handle missing values systematically
linked_data = handle_missing_values(linked_data, 'Kidney_stones')

# Judge if features are biased and remove biased demographic features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, 'Kidney_stones')

# Validate data quality and save cohort info
note = "Using kidney papillary cell carcinoma (KIRP) data from TCGA for kidney stone analysis."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort="TCGA_KIRP",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note=note
)

# Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)