# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Anxiety_disorder"
cohort = "GSE68526"

# Input paths
in_trait_dir = "../DATA/GEO/Anxiety_disorder"
in_cohort_dir = "../DATA/GEO/Anxiety_disorder/GSE68526"

# Output paths
out_data_file = "./output/preprocess/3/Anxiety_disorder/GSE68526.csv"
out_gene_data_file = "./output/preprocess/3/Anxiety_disorder/gene_data/GSE68526.csv"
out_clinical_data_file = "./output/preprocess/3/Anxiety_disorder/clinical_data/GSE68526.csv"
json_path = "./output/preprocess/3/Anxiety_disorder/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# Gene Expression Data Availability
is_gene_available = True  # From the background info, this study contains peripheral blood transcriptome profiles

# Convert trait (anxiety score) - use feature 13 which contains Beck Anxiety Inventory scores
def convert_trait(value: str) -> Optional[float]:
    if not value or 'missing' in value:
        return None
    # Extract numeric value after colon
    try:
        return float(value.split(': ')[1])
    except:
        return None
trait_row = 13  # Index of anxiety scores

# Convert age 
def convert_age(value: str) -> Optional[int]:
    if not value:
        return None
    try:
        return int(value.split(': ')[1])
    except:
        return None
age_row = 0  # Index of age data

# Convert gender (female: 0, male: 1)
def convert_gender(value: str) -> Optional[int]:
    if not value:
        return None
    try:
        # Value is coded as "female: 0" or "female: 1"
        # female: 1 means female (should be 0)
        # female: 0 means male (should be 1)
        return 1 - int(value.split(': ')[1])  
    except:
        return None
gender_row = 1  # Index of gender data

# Save initial filtering info
is_trait_available = trait_row is not None
is_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
)

# Extract clinical features if trait data is available
if trait_row is not None:
    clinical_df = 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
    )
    
    # Preview the extracted features
    print("Preview of clinical features:")
    print(preview_df(clinical_df))
    
    # Save clinical data
    clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
gene_data = get_genetic_data(matrix_file)

# Print first 20 row IDs and shape of data to help debug
print("Shape of gene expression data:", gene_data.shape)
print("\nFirst few rows of data:")
print(gene_data.head())
print("\nFirst 20 gene/probe identifiers:")
print(gene_data.index[:20])

# Inspect a snippet of raw file to verify identifier format
import gzip
with gzip.open(matrix_file, 'rt', encoding='utf-8') as f:
    lines = []
    for i, line in enumerate(f):
        if "!series_matrix_table_begin" in line:
            # Get the next 5 lines after the marker
            for _ in range(5):
                lines.append(next(f).strip())
            break
print("\nFirst few lines after matrix marker in raw file:")
for line in lines:
    print(line)
# Looking at the gene identifiers, they appear to be human gene symbols (e.g., A1BG, A1CF, A2M)
# so no mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Save normalized gene data
gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
try:
    clinical_data = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_data, gene_data)

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

    # 4. Determine if features are biased
    is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save cohort info
    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_trait_biased,
        df=linked_data,
        note="Gene expression data successfully mapped and linked with clinical features"
    )

    # 6. Save linked data only if usable AND trait is not biased
    if is_usable and not is_trait_biased:
        linked_data.to_csv(out_data_file)

except Exception as e:
    print(f"Error in data linking and processing: {str(e)}")
    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=True,
        df=pd.DataFrame(),
        note=f"Data processing failed: {str(e)}"
    )