# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Obstructive_sleep_apnea"
cohort = "GSE75097"

# Input paths
in_trait_dir = "../DATA/GEO/Obstructive_sleep_apnea"
in_cohort_dir = "../DATA/GEO/Obstructive_sleep_apnea/GSE75097"

# Output paths
out_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/GSE75097.csv"
out_gene_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/gene_data/GSE75097.csv"
out_clinical_data_file = "./output/preprocess/3/Obstructive_sleep_apnea/clinical_data/GSE75097.csv"
json_path = "./output/preprocess/3/Obstructive_sleep_apnea/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)
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")

# Get dictionary of unique values per row 
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
# "Microarray gene expression profiles" indicates this is gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
# Row 1 contains AHI values which indicate OSA severity
trait_row = 1  
# Row 3 contains age values
age_row = 3   
# Row 2 contains gender values
gender_row = 2  

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert AHI value to binary OSA status"""
    if not value or ':' not in value:
        return None
    try:
        ahi = float(value.split(': ')[1])
        # AHI >= 15 indicates moderate to severe OSA
        return 1 if ahi >= 15 else 0
    except:
        return None

def convert_age(value: str) -> float:
    """Convert age string to float"""
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(': ')[1])
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary (0=female, 1=male)"""
    if not value or ':' not in value:
        return None
    value = value.split(': ')[1].lower()
    if value == 'female':
        return 0
    elif value == 'male':
        return 1
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
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_df = 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 extracted features
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)
# Get gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Examine data structure
print("Data structure and head:")
print(genetic_data.head())

print("\nShape:", genetic_data.shape)

print("\nFirst 20 row IDs (gene/probe identifiers):")
print(list(genetic_data.index)[:20])

# Get a few column names to verify sample IDs
print("\nFirst 5 column names:")
print(list(genetic_data.columns)[:5])
# Review gene identifiers
# Looking at the identifiers like A1BG, A1CF, A2M, etc.
# These appear to be official human gene symbols (HUGO nomenclature)
# No mapping needed as they are already in the correct format

requires_gene_mapping = False
# Reload clinical data that was processed earlier
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)

# 1. Normalize gene symbols
normalized_genetic_data = normalize_gene_symbols_in_index(genetic_data)
normalized_genetic_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_genetic_data)

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

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

# 5. Final validation and information saving
note = "Gene expression profiles of peripheral blood mononuclear cells in OSA patients with clinical info including AHI, age and gender"
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=trait_biased,
    df=linked_data,
    note=note
)

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