# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"

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

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

# Get GBMLGG cohort directory path (contains eczema data based on column names)
cohort_dir = os.path.join(tcga_root_dir, "TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)")

# Get clinical and genetic file paths
clinical_file, genetic_file = tcga_get_relevant_filepaths(cohort_dir)

# Load clinical and genetic data
clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)
genetic_df = pd.read_csv(genetic_file, sep='\t', index_col=0)

# Print clinical data columns
print("\nClinical data columns:")
print(clinical_df.columns.tolist())

# Record data availability in metadata
validate_and_save_cohort_info(
    is_final=False,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=True,
    is_trait_available='eczema_history' in clinical_df.columns
)
# Step 1: Define candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth', 'first_diagnosis_age_asth_ecz_hay_fev_mold_dust', 'first_diagnosis_age_of_animal_insect_allergy', 'first_diagnosis_age_of_food_allergy']
candidate_gender_cols = ['gender']

# Step 2: Choose BRCA cohort and preview data
cohort_dir = os.path.join(tcga_root_dir, "TCGA_Breast_Cancer_(BRCA)")
clinical_file_path, genetic_file_path = tcga_get_relevant_filepaths(cohort_dir)
clinical_df = pd.read_csv(clinical_file_path, index_col=0)

# Extract and preview age columns
age_preview = clinical_df[candidate_age_cols].head(5).to_dict(orient='list')
print("\nAge columns preview:")
print(age_preview)

# Extract and preview gender columns 
gender_preview = clinical_df[candidate_gender_cols].head(5).to_dict(orient='list')
print("\nGender columns preview:")
print(gender_preview)
# Debug directory contents first
print("Directory contents:")
print(os.listdir(tcga_root_dir))

# Try printing sample files from each subdirectory
for subdir in os.listdir(tcga_root_dir):
    full_path = os.path.join(tcga_root_dir, subdir)
    if os.path.isdir(full_path):
        print(f"\nFiles in {subdir}:")
        print(os.listdir(full_path)[:5])  # Show first 5 files
# Look at first cohort to examine column names
first_cohort_dir = os.path.join(tcga_root_dir, 'TCGA_lower_grade_glioma_and_glioblastoma_(GBMLGG)')
clinical_file, _ = tcga_get_relevant_filepaths(first_cohort_dir)
clinical_df = pd.read_csv(clinical_file, sep='\t', index_col=0)

# Preview clinical data columns
candidate_age_cols = ['_age_at_initial_pathologic_diagnosis', 'age_at_initial_pathologic_diagnosis']
candidate_gender_cols = ['gender']

# Preview values
age_df = clinical_df[candidate_age_cols[0] if candidate_age_cols[0] in clinical_df.columns else candidate_age_cols[1]]
gender_df = clinical_df[candidate_gender_cols[0]]

print("Age column preview:")
print(preview_df(pd.DataFrame(age_df)))
print("\nGender column preview:")
print(preview_df(pd.DataFrame(gender_df)))
# Select columns for demographic features
age_col = "age_at_initial_pathologic_diagnosis"  # Contains valid numeric age values
gender_col = "gender"  # Contains valid gender values

# Print chosen columns
print(f"Selected age column: {age_col}")
print(f"Selected gender column: {gender_col}")
# Extract clinical features (trait labels from sample IDs, age, and gender) 
clinical_features = tcga_select_clinical_features(
    clinical_df,
    trait=trait,
    age_col="age_at_initial_pathologic_diagnosis",
    gender_col="gender"
)

# Save processed clinical data
clinical_features.to_csv(out_clinical_data_file)

# Normalize gene symbols in genetic data and save 
normalized_gene_data = normalize_gene_symbols_in_index(genetic_df)
normalized_gene_data.to_csv(out_gene_data_file)

# Link clinical and genetic data
linked_data = pd.merge(
    clinical_features,
    normalized_gene_data.T, 
    left_index=True,
    right_index=True,
    how='inner'
)

# Handle missing values
linked_data = handle_missing_values(linked_data, trait)

# Check if trait or demographic features are biased and remove biased demographics
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# Final validation and save metadata
notes = "Using TCGA lower grade glioma and glioblastoma (GBMLGG) data. Normal samples serve as controls, tumor samples as disease cases."
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=is_trait_biased,
    df=linked_data,
    note=notes
)

# Save processed data if usable
if is_usable:
    linked_data.to_csv(out_data_file)