# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Aniridia"
cohort = "GSE137997"

# Input paths
in_trait_dir = "../DATA/GEO/Aniridia"
in_cohort_dir = "../DATA/GEO/Aniridia/GSE137997"

# Output paths
out_data_file = "./output/preprocess/3/Aniridia/GSE137997.csv"
out_gene_data_file = "./output/preprocess/3/Aniridia/gene_data/GSE137997.csv"
out_clinical_data_file = "./output/preprocess/3/Aniridia/clinical_data/GSE137997.csv"
json_path = "./output/preprocess/3/Aniridia/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
# Based on the background info, this is an mRNA study, so gene expression data should be available
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# Trait (Aniridia) can be inferred from disease status (AAK vs control) in Feature 2
trait_row = 2

def convert_trait(value):
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip().lower()
    # AAK (aniridia-associated keratopathy) indicates aniridia
    if value == 'aak':
        return 1
    elif value == 'healthy control':
        return 0
    return None

# Age is available in Feature 0
age_row = 0

def convert_age(value):
    if not isinstance(value, str):
        return None
    try:
        age = int(value.split(': ')[-1])
        return age
    except:
        return None

# Gender is available in Feature 1
gender_row = 1

def convert_gender(value):
    if not isinstance(value, str):
        return None
    value = value.split(': ')[-1].strip().lower()
    if value in ['f', 'w']:  # 'w' likely means woman/weiblich(German)
        return 0
    elif value == 'm':
        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
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save to CSV
    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])
# Based on the identifiers having the format "hsa-miR-*" and "hsa-let-*", these are microRNA identifiers,  
# not standard human gene symbols. They need to be mapped to their target genes.
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file)

# Print findings about dataset nature
print("Dataset Analysis:")
print("-" * 50)
print("This dataset contains miRNA expression data (hsa-miR-* identifiers)")
print("Standard gene mapping is not applicable for miRNA data")
print("The dataset cannot be used for gene-level analysis without miRNA target information")
print("-" * 50)

# Set requires_gene_mapping to False since we cannot map miRNAs to genes
requires_gene_mapping = False

# Set is_gene_available to False since we don't have gene expression data
is_gene_available = False

# Save updated metadata about dataset usability
validate_and_save_cohort_info(
    is_final=False,
    cohort=cohort,
    info_path=json_path,
    is_gene_available=is_gene_available,
    is_trait_available=True,
    note="Dataset contains miRNA expression data instead of gene expression data"
)