# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Red_Hair"

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

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

# 1. Find directory for melanoma data - most relevant to red hair as both involve melanin pathways
selected_dir = 'TCGA_Melanoma_(SKCM)'
cohort_dir = os.path.join(tcga_root_dir, selected_dir)

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

# 3. 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')

# 4. Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())

# Record data availability
is_gene_available = len(genetic_df.columns) > 0 
is_trait_available = len(clinical_df.columns) > 0

validate_and_save_cohort_info(
    is_final=False,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)
# Identify candidate columns
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Load clinical data
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_root_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()
print("Age columns preview:", preview_df(age_preview))

# Extract and preview gender columns 
gender_preview = clinical_df[candidate_gender_cols].head()
print("Gender columns preview:", preview_df(gender_preview))
# 1. Find directory for melanoma data - most relevant to red hair as both involve melanin pathways
selected_dir = 'TCGA_Melanoma_(SKCM)'
cohort_dir = os.path.join(tcga_root_dir, selected_dir)

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

# 3. 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')

# 4. Print clinical data columns
print("Clinical data columns:")
print(clinical_df.columns.tolist())

# Record data availability
is_gene_available = len(genetic_df.columns) > 0 
is_trait_available = len(clinical_df.columns) > 0

validate_and_save_cohort_info(
    is_final=False,
    cohort="TCGA",
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)
# Define candidate columns for age and gender
candidate_age_cols = ['age_at_initial_pathologic_diagnosis', 'days_to_birth']
candidate_gender_cols = ['gender']

# Load clinical data from a previous step
clinical_file_path, _ = tcga_get_relevant_filepaths(tcga_root_dir)
clinical_df = pd.read_table(clinical_file_path, index_col=0)

# Preview age columns
age_preview = {}
for col in candidate_age_cols:
    age_preview[col] = clinical_df[col].head(5).tolist()
print("Age columns preview:")
print(age_preview)

# Preview gender columns  
gender_preview = {}
for col in candidate_gender_cols:
    gender_preview[col] = clinical_df[col].head(5).tolist()
print("\nGender columns preview:")
print(gender_preview)
# Select appropriate demographic columns
age_col = 'age_at_initial_pathologic_diagnosis'  # This is more directly usable than days_to_birth
gender_col = 'gender'

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

# 2. Normalize gene symbols in genetic data
normalized_genetic_df = normalize_gene_symbols_in_index(genetic_df)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_genetic_df.to_csv(out_gene_data_file)

# 3. Link clinical and genetic data
linked_data = pd.merge(selected_clinical_df, normalized_genetic_df.T, left_index=True, right_index=True)

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

# 5. Check for bias in trait and demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 6. Validate and save cohort info 
note = f"Sample size after preprocessing: {len(linked_data)}. Number of genes: {len(linked_data.columns) - 3}"
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_biased,
    df=linked_data,
    note=note
)

# 7. 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)
    print(f"Linked data saved to {out_data_file}")
    print("Shape of final linked data:", linked_data.shape)
else:
    print("Dataset was found to be unusable and was not saved")