# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Duchenne_Muscular_Dystrophy"
cohort = "GSE13608"

# Input paths
in_trait_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy"
in_cohort_dir = "../DATA/GEO/Duchenne_Muscular_Dystrophy/GSE13608"

# Output paths
out_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/GSE13608.csv"
out_gene_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/gene_data/GSE13608.csv"
out_clinical_data_file = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/clinical_data/GSE13608.csv"
json_path = "./output/preprocess/3/Duchenne_Muscular_Dystrophy/cohort_info.json"

# Get paths to the SOFT and matrix files
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

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

# Get unique values for each feature (row) in clinical data 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background info
print("=== Dataset Background Information ===")
print(background_info)
print("\n=== Sample Characteristics ===")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
is_gene_available = True  # Dataset contains muscle biopsy gene expression data

# 2.1 Data Availability
trait_row = 1  # Disease status in row 1
age_row = 2    # Age in row 2
gender_row = 3 # Gender in row 3

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not isinstance(value, str):
        return None
    # Extract value after colon if present
    if ':' in value:
        value = value.split(':')[1].strip()
    if 'Duchenne Muscular Dystrophy' in value:
        return 1
    elif 'Normal' in value:
        return 0
    return None

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

def convert_gender(value):
    if not isinstance(value, str):
        return None
    if ':' in value:
        value = value.split(':')[1].strip()
    if 'F' == value.strip():
        return 0
    elif 'M' == value.strip():
        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:
    selected_clinical_df = 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 data
    print("Preview of selected clinical features:")
    print(preview_df(selected_clinical_df))
    
    # Save to CSV
    selected_clinical_df.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_df = get_genetic_data(matrix_file)

# Print DataFrame shape and first 20 row IDs
print("DataFrame shape:", genetic_df.shape)
print("\nFirst 20 row IDs:")
print(genetic_df.index[:20])

print("\nPreview of first few rows and columns:")
print(genetic_df.head().iloc[:, :5])
# The identifiers in this dataset appear to be probe IDs from Affymetrix array
# These need to be mapped to standard gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# The gene expression data uses probe IDs stored in the 'ID' column of gene_metadata
# The gene symbols are stored in the 'Gene Symbol' column
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='Gene Symbol')

# Convert probe-level measurements to gene-level expression
gene_data = apply_gene_mapping(genetic_df, mapping_df)

# Preview the mapped gene expression data
print("Gene expression data shape after mapping:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])
# 1. Normalize gene symbols and save
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
clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(clinical_df, gene_data)

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

# 4. Check for biased features
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Final validation and metadata saving
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="Study comparing gene expression in healthy vs DMD myoblasts and myotubes, including immortalized cell lines"
)

# 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)