# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Height"
cohort = "GSE97475"

# Input paths
in_trait_dir = "../DATA/GEO/Height"
in_cohort_dir = "../DATA/GEO/Height/GSE97475"

# Output paths
out_data_file = "./output/preprocess/3/Height/GSE97475.csv"
out_gene_data_file = "./output/preprocess/3/Height/gene_data/GSE97475.csv"
out_clinical_data_file = "./output/preprocess/3/Height/clinical_data/GSE97475.csv"
json_path = "./output/preprocess/3/Height/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 title and background information, this appears to be a microarray study of blood PBMCs
# Not pure miRNA or methylation data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Height data is explicitly recorded in row 5 
trait_row = 5

# Age data is recorded in row 81
age_row = 81

# Gender data can be inferred from row 118 
gender_row = 118

def convert_trait(x):
    # Extract height value after colon
    if pd.isna(x) or ':' not in x:
        return None
    height = x.split(': ')[1]
    try:
        # Convert to float for continuous data
        return float(height)
    except:
        if height == 'NA':
            return None
        return None

def convert_age(x):
    if pd.isna(x) or ':' not in x:
        return None
    age = x.split(': ')[1]  
    try:
        # Convert to float for continuous data
        return float(age)
    except:
        if age == 'NA':
            return None
        return None

def convert_gender(x):
    if pd.isna(x) or ':' not in x:
        return None
    gender = x.split(': ')[1]
    # Convert to binary (0=female, 1=male)
    if gender.lower() == 'female':
        return 0
    elif gender.lower() == 'male':
        return 1
    return None

# 3. Save metadata
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=(trait_row is not None))

# 4. Clinical feature extraction
if trait_row is not None:
    clinical_features_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 data
    print("Preview of extracted clinical features:")
    print(preview_df(clinical_features_df))
    
    # Save to CSV
    clinical_features_df.to_csv(out_clinical_data_file)
# Extract gene expression data from the matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print information about the data structure
print("First few rows of the genetic data:")
print(genetic_data.head())
print("\nShape of genetic data:", genetic_data.shape)
print("\nColumn names:", genetic_data.columns.tolist())
# Looking at the gene identifiers in the index (A1BG, A26C3, A2LD1, etc.)
# These appear to be standard HGNC gene symbols, so no mapping is needed
requires_gene_mapping = False
# Get gene expression data from matrix file
gene_data = get_genetic_data(matrix_file_path)
is_gene_available = len(gene_data.columns) > 1

# Load clinical data 
clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
# Check if there are any non-NaN height values
is_trait_available = not clinical_data.loc[trait].isna().all()

# 1. Normalize gene symbols 
gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
gene_data.to_csv(out_gene_data_file)

# 2-4. Skip data linking and processing if trait is not available
linked_data = pd.DataFrame()
is_biased = True
if is_trait_available:
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)
    linked_data = handle_missing_values(linked_data, trait)
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
note = "This dataset contains valid gene expression data and demographic information (age and gender), but all height measurements are missing."
is_usable = validate_and_save_cohort_info(
    is_final=True,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=is_trait_available,
    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)