# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Atrial_Fibrillation"
cohort = "GSE235307"

# Input paths
in_trait_dir = "../DATA/GEO/Atrial_Fibrillation"
in_cohort_dir = "../DATA/GEO/Atrial_Fibrillation/GSE235307"

# Output paths
out_data_file = "./output/preprocess/3/Atrial_Fibrillation/GSE235307.csv"
out_gene_data_file = "./output/preprocess/3/Atrial_Fibrillation/gene_data/GSE235307.csv"
out_clinical_data_file = "./output/preprocess/3/Atrial_Fibrillation/clinical_data/GSE235307.csv"
json_path = "./output/preprocess/3/Atrial_Fibrillation/cohort_info.json"

# Get file paths for SOFT and matrix files
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data from the matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Create dictionary of unique values for each feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print the information
print("Dataset Background Information:")
print(background_info)
print("\nSample Characteristics:")
for feature, values in unique_values_dict.items():
    print(f"\n{feature}:")
    print(values)
# 1. Gene Expression Data Availability
# From background info, this is a blood gene expression study
is_gene_available = True

# 2.1 Data Row Identification
trait_row = 5  # cardiac rhythm after 1 year follow-up
age_row = 2    # age is available
gender_row = 1 # gender is available

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert AF status to binary: 1 for AF, 0 for sinus rhythm"""
    if value is None:
        return None
    value = value.split(': ')[-1].lower().strip()
    if 'atrial fibrillation' in value:
        return 1
    elif 'sinus rhythm' in value:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Convert age to float"""
    if value is None:
        return None
    try:
        return float(value.split(': ')[-1])
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    """Convert gender to binary: 0 for female, 1 for male"""
    if value is None:
        return None
    value = value.split(': ')[-1].lower().strip()
    if value == 'female':
        return 0
    elif value == '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 = 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 of clinical features:")
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
# The gene identifiers in this dataset appear to be simple numeric values, 
# which are not standard human gene symbols.
# Standard gene symbols would be like "BRCA1", "TP53", etc
# Therefore mapping is required to convert these to proper gene symbols.

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

# Preview column names and first few values
preview = preview_df(gene_metadata)
print("\nGene annotation columns and sample values:")
print(preview)
# 1. From the preview, 'ID' contains numeric identifiers matching gene expression data,
# and 'GENE_SYMBOL' contains human gene symbols

# 2. Extract mapping between probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GENE_SYMBOL')

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

# Normalize gene symbols in the gene expression data
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview the mapped gene data
print("\nFirst 20 genes after mapping:")
print(list(gene_data.index[:20]))

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene 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)

# 2. Link clinical and genetic data 
linked_data = geo_link_clinical_genetic_data(clinical_features, gene_data)

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

# 4. Judge bias in features and remove biased ones
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and save metadata
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=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Sample size adequate. Gene expression data quality good. Trait is early vs late recurrence."
)

# 6. Save linked data if usable
if is_usable:
    os.makedirs(os.path.dirname(out_data_file), exist_ok=True)
    linked_data.to_csv(out_data_file)