# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Telomere_Length"
cohort = "GSE16058"

# Input paths
in_trait_dir = "../DATA/GEO/Telomere_Length"
in_cohort_dir = "../DATA/GEO/Telomere_Length/GSE16058"

# Output paths
out_data_file = "./output/preprocess/3/Telomere_Length/GSE16058.csv"
out_gene_data_file = "./output/preprocess/3/Telomere_Length/gene_data/GSE16058.csv"
out_clinical_data_file = "./output/preprocess/3/Telomere_Length/clinical_data/GSE16058.csv"
json_path = "./output/preprocess/3/Telomere_Length/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 shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

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
# Yes, this is gene expression data studying cell proliferation and aging, not miRNA or methylation
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Row identification
trait_row = 3  # Growth status indicates senescence state and telomere length
age_row = 2  # Passage number indicates cellular age
gender_row = None  # No gender information available

# 2.2 Data type conversion functions
def convert_trait(value: str) -> float:
    """Convert growth status to binary: 1 for senescent/aged states, 0 for growing states"""
    if not value or ':' not in value:
        return None
    status = value.split(': ')[1].lower()
    if 'senescent' in status or 'stasis' in status or 'agonesence' in status:
        return 1.0
    elif 'growing' in status or 'intermediate' in status:
        return 0.0
    return None

def convert_age(value: str) -> float:
    """Extract passage number as continuous value"""
    if not value or ':' not in value:
        return None
    try:
        # Extract number before 'p'
        passage = value.split(': ')[1]
        return float(passage.replace('p',''))
    except:
        return None

def convert_gender(value: str) -> float:
    """Not used since gender data is unavailable"""
    return None

# 3. Save metadata 
is_usable = 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. Extract clinical features
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 extracted features
    print("\nExtracted clinical features preview:")
    print(preview_df(selected_clinical))
    
    # Save to file
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical.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])
# The identifiers in ID column are from the Affymetrix platform, ending in "_at" format
# They need to be mapped to official gene symbols for interpretability
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# 1. Identify relevant columns: 'ID' contains probe IDs and 'Gene Symbol' contains gene symbols
probe_col = 'ID'
gene_col = 'Gene Symbol'

# 2. Get gene mapping dataframe
gene_mapping = get_gene_mapping(gene_annotation, probe_col, gene_col)

# 3. Convert probe-level measurements to gene-level measurements
gene_data = apply_gene_mapping(genetic_data, gene_mapping)

# Preview results
print("\nGene expression data shape after mapping:", gene_data.shape)
print("\nFirst few rows of mapped gene expression data:")
print(gene_data.head())
print("\nFirst few gene symbols:", list(gene_data.index)[:10])
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_data.shape) 

# Load clinical data previously processed
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
print("\nClinical data shape:", selected_clinical_df.shape)

# 2. Link clinical and genetic data using normalized gene-level data
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, gene_data)
print("\nLinked data shape:", linked_data.shape)

# 3. Handle missing values systematically  
if trait in linked_data.columns:
    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 = "Data was successfully preprocessed from probe-level to gene-level expression using gene symbol normalization with NCBI Gene database."
    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 and not biased
    if is_usable and not trait_biased:
        os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
        linked_data.to_csv(out_data_file)