# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Hepatitis"
cohort = "GSE152738"

# Input paths
in_trait_dir = "../DATA/GEO/Hepatitis"
in_cohort_dir = "../DATA/GEO/Hepatitis/GSE152738"

# Output paths
out_data_file = "./output/preprocess/3/Hepatitis/GSE152738.csv"
out_gene_data_file = "./output/preprocess/3/Hepatitis/gene_data/GSE152738.csv"
out_clinical_data_file = "./output/preprocess/3/Hepatitis/clinical_data/GSE152738.csv"
json_path = "./output/preprocess/3/Hepatitis/cohort_info.json"

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

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# 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")
# 1. Gene Expression Data Availability
# The series title and design mentions gene expression microarray analysis using Affymetrix arrays
is_gene_available = True

# 2.1 Data Availability
# age_stage info is in row 0 
trait_row = 0  # We can infer trait status from age stage
age_row = 0    # Age data is available in age stage
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert age stage to binary trait (0=young, 1=old)"""
    if not isinstance(x, str):
        return None
    x = x.split(": ")[1].lower() if ":" in x else x.lower()
    if "young" in x:
        return 0
    elif "old" in x:
        return 1
    return None

def convert_age(x):
    """Convert age stage to continuous age values"""
    if not isinstance(x, str):
        return None
    x = x.split(": ")[1].lower() if ":" in x else x.lower()
    if "young" in x:
        return 30  # Midpoint of 18-40 range
    elif "old" in x:
        return 50  # Conservative estimate for >40
    return None

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

# 4. Clinical Feature Extraction
selected_clinical_df = geo_select_clinical_features(clinical_data, trait, 
                                                  trait_row=trait_row,
                                                  convert_trait=convert_trait,
                                                  age_row=age_row,
                                                  convert_age=convert_age)

# Preview and save clinical data
preview_df(selected_clinical_df)
selected_clinical_df.to_csv(out_clinical_data_file)
# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 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)
requires_gene_mapping = True
# Extract gene annotation data
gene_metadata = get_gene_annotation(soft_file)

# Preview the annotation data 
print("Column names:", gene_metadata.columns.tolist())
print("\nFirst few rows preview:")
print(preview_df(gene_metadata))
# 1. Identify the mapping columns
# 'ID' in gene_metadata maps to the probe IDs in gene_data
# 'Gene Symbol' in gene_metadata contains the gene symbols
mapping_df = get_gene_mapping(gene_metadata, 'ID', 'Gene Symbol')

# 2. Apply the mapping to convert probe data to gene expression data
gene_data = apply_gene_mapping(gene_data, mapping_df)

# 3. Normalize gene symbols using synonym information to standardize format
gene_data = normalize_gene_symbols_in_index(gene_data)
# First verify data validity
if gene_data.empty or selected_clinical_df.empty:
    is_usable = validate_and_save_cohort_info(
        is_final=True, 
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False,
        is_biased=None,
        df=None,
        note="Data preprocessing failed due to invalid gene or clinical data."
    )
else:
    # 1. Save gene expression data 
    gene_data.to_csv(out_gene_data_file)

    # 2. Link clinical and genetic data
    selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
    
    # Print data info for debugging
    print("Clinical data shape:", selected_clinical_df.shape)
    print("Gene data shape:", gene_data.shape)
    print("Linked data shape:", linked_data.shape)
    print("\nLinked data preview:")
    print(linked_data.head())

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

    # 4. Evaluate bias in features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # 5. Validate and save cohort information
    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="Contains normalized gene expression data and clinical information."
    )

    # 6. Save data if usable 
    if is_usable:
        linked_data.to_csv(out_data_file)
# First validate both the gene and clinical data
if gene_data.empty or gene_data.isnull().all().all():
    print("Gene expression data is invalid")
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False,
        df=None,
        is_biased=None,
        note="Gene expression data preprocessing failed"
    )
elif selected_clinical_df.empty or selected_clinical_df.isnull().all().all():
    print("Clinical data is invalid") 
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=False,
        df=None,
        is_biased=None,
        note="Clinical data preprocessing failed"
    )
else:
    # Save normalized gene data
    gene_data.to_csv(out_gene_data_file)
    
    # Link clinical and genetic data
    linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)

    # Print data info for debugging
    print("Clinical data shape:", selected_clinical_df.shape)
    print("Gene data shape:", gene_data.shape) 
    print("Linked data shape:", linked_data.shape)
    print("\nLinked data preview:")
    print(linked_data.head())

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

    # Evaluate bias in features
    is_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

    # Validate and save cohort information
    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="Contains normalized gene expression data and clinical information."
    )

    # Save linked data if usable
    if is_usable:
        linked_data.to_csv(out_data_file)