# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Ankylosing_Spondylitis"
cohort = "GSE25101"

# Input paths
in_trait_dir = "../DATA/GEO/Ankylosing_Spondylitis"
in_cohort_dir = "../DATA/GEO/Ankylosing_Spondylitis/GSE25101"

# Output paths
out_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/GSE25101.csv"
out_gene_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/gene_data/GSE25101.csv"
out_clinical_data_file = "./output/preprocess/3/Ankylosing_Spondylitis/clinical_data/GSE25101.csv"
json_path = "./output/preprocess/3/Ankylosing_Spondylitis/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")
# 1. Gene Expression Data Availability
# Dataset uses Illumina HT-12 Whole-Genome Expression BeadChips 
# and measures whole blood gene expression
is_gene_available = True

# 2.1 Data Availability
# Trait data in row 2 - disease status
trait_row = 2
# Age and gender not available in characteristics but mentioned as matched in design
age_row = None 
gender_row = None

# 2.2 Convert functions
def convert_trait(value):
    """Convert disease status to binary"""
    if 'disease status:' in value:
        if 'Ankylosing spondylitis patient' in value:
            return 1
        elif 'Normal control' in value:
            return 0
    return None

convert_age = None
convert_gender = 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. Extract clinical features
clinical_features = 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_df(clinical_features))

# Save clinical data
clinical_features.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)
# Based on the gene identifiers starting with "ILMN_", these are Illumina probe IDs
# which need to be mapped to HUGO gene symbols for interpretability
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Preview annotation dataframe structure
print("Gene Annotation Preview:")
print("Column names:", gene_annotation.columns.tolist())
print("\nFirst few rows as dictionary:")
print(preview_df(gene_annotation))
# Extract gene mapping from annotation data
# 'ID' column in annotation matches ILMN_* identifiers in expression data
# 'Symbol' column contains the target gene symbols
mapping_data = get_gene_mapping(gene_annotation, 'ID', 'Symbol')

# Apply gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(gene_data, mapping_data)

# Preview results
print("Shape of mapped gene expression data:", gene_data.shape)
print("\nFirst few rows of mapped data:")
print(gene_data.head())
print("\nFirst 20 gene symbols:")
print(gene_data.index[:20])
# 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)}"
    )