# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Insomnia"
cohort = "GSE208668"

# Input paths
in_trait_dir = "../DATA/GEO/Insomnia"
in_cohort_dir = "../DATA/GEO/Insomnia/GSE208668"

# Output paths
out_data_file = "./output/preprocess/3/Insomnia/GSE208668.csv"
out_gene_data_file = "./output/preprocess/3/Insomnia/gene_data/GSE208668.csv"
out_clinical_data_file = "./output/preprocess/3/Insomnia/clinical_data/GSE208668.csv"
json_path = "./output/preprocess/3/Insomnia/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# From background info, it's genome-wide transcriptional profiling of PBMCs
# Though raw data was lost, it's still gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion 
# 2.1 Data Availability
trait_row = 0  # 'insomnia' is in row 0
age_row = 1    # 'age' is in row 1
gender_row = 2 # 'gender' is in row 2

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1].strip()
    if value == "yes":
        return 1
    elif value == "no":
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    if not isinstance(value, str):
        return None
    try:
        age = float(value.split(": ")[-1].strip())
        return age
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    if not isinstance(value, str):
        return None
    value = value.lower().split(": ")[-1].strip()
    if value == "female":
        return 0
    elif value == "male":
        return 1
    return None

# 3. Save Metadata - Initial Filtering
is_trait_available = trait_row is not None
initial_validation = validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available
)

# 4. Clinical Feature Extraction
if trait_row is not None:
    selected_clinical = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    
    # Preview the processed data
    preview = preview_df(selected_clinical)
    print("Preview of processed clinical data:")
    print(preview)
    
    # Save to CSV
    selected_clinical.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(genetic_data.index[:20].tolist())
requires_gene_mapping = False  # The gene identifiers are already in human gene symbol format. No mapping needed.
# 1. Normalize gene symbols
genetic_data = normalize_gene_symbols_in_index(genetic_data)
genetic_data.to_csv(out_gene_data_file)

# Get clinical features
clinical_features = geo_select_clinical_features(
    clinical_data,
    trait=trait,
    trait_row=trait_row,
    convert_trait=convert_trait,
    age_row=age_row, 
    convert_age=convert_age,
    gender_row=gender_row,
    convert_gender=convert_gender
)

# 2. Link clinical and genetic data
linked_data = geo_link_clinical_genetic_data(clinical_features, genetic_data)

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

# 4. Judge whether features are biased and remove biased demographic features
is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "Dataset contains genome-wide transcriptional profiling of PBMCs from older adults with and without insomnia disorder."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note=note
)

# 6. Save the linked data only if it's usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)