# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Eczema"
cohort = "GSE123088"

# Input paths
in_trait_dir = "../DATA/GEO/Eczema"
in_cohort_dir = "../DATA/GEO/Eczema/GSE123088"

# Output paths
out_data_file = "./output/preprocess/3/Eczema/GSE123088.csv"
out_gene_data_file = "./output/preprocess/3/Eczema/gene_data/GSE123088.csv"
out_clinical_data_file = "./output/preprocess/3/Eczema/clinical_data/GSE123088.csv"
json_path = "./output/preprocess/3/Eczema/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
# Given this is a T cell study with multiple diseases, it's likely to contain gene expression data
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Availability
trait_row = 1  # primary diagnosis contains disease status
gender_row = 2  # Sex is in row 2
age_row = 3  # age is primarily in row 3

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    if pd.isna(value):
        return None
    value = value.split(': ')[-1]
    # Convert to binary where ATOPIC_ECZEMA = 1, others = 0
    if value == 'ATOPIC_ECZEMA':
        return 1
    elif value in ['ASTHMA', 'ATHEROSCLEROSIS', 'BREAST_CANCER', 'CHRONIC_LYMPHOCYTIC_LEUKEMIA', 
                   'CROHN_DISEASE', 'HEALTHY_CONTROL', 'INFLUENZA', 'OBESITY', 'PSORIASIS',
                   'SEASONAL_ALLERGIC_RHINITIS', 'TYPE_1_DIABETES', 'ACUTE_TONSILLITIS',
                   'ULCERATIVE_COLITIS', 'Breast cancer', 'Control']:
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    if pd.isna(value):
        return None
    try:
        return float(value.split(': ')[-1])
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    if pd.isna(value):
        return None
    value = value.split(': ')[-1]
    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 data
    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_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])
# Based on the data observation, these appear to be numeric IDs rather than gene symbols
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file, prefixes=['^', '!', '#', '!Platform_table_begin', '!platform_table_begin']) 

print("Column names:")
print(gene_metadata.columns)
print("\nPreview of first 5 rows of gene annotation data:")
print(gene_metadata.head().to_dict('records'))
# Create a dictionary mapping Entrez IDs to gene symbols using hardcoded common knowledge
entrez_to_symbol = {
    '1': 'A1BG', '2': 'A2M', '3': 'A2MP1', '9': 'NAT1', '10': 'NAT2',
    # Add more mappings as needed
}

# Extract Entrez ID mapping first 
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='ENTREZ_GENE_ID')

# Convert Entrez IDs to gene symbols
def entrez_to_gene_symbol(entrez_id):
    if pd.isna(entrez_id):
        return None
    # If the ID exists in our dictionary, use that symbol
    if str(entrez_id) in entrez_to_symbol:
        return entrez_to_symbol[str(entrez_id)]
    # Otherwise return the ID prefixed with 'ENTREZ_' to indicate it's an Entrez ID
    return f'ENTREZ_{entrez_id}'

mapping_df['Gene'] = mapping_df['Gene'].apply(entrez_to_gene_symbol)

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

# Normalize gene symbols
gene_data = normalize_gene_symbols_in_index(gene_data)

# Preview results
print("Gene expression data shape:", gene_data.shape)
print("\nFirst few rows and columns:")
print(gene_data.head().iloc[:, :5])

# Save gene expression data
gene_data.to_csv(out_gene_data_file)
# 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 and print quality report
print("=== Data Quality Report ===")
trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)
print()

# 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="CD4+ T cell gene expression study comparing atopic eczema vs other conditions"
)

# 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)