# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE212865"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE212865"

# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE212865.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE212865.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE212865.csv"
json_path = "./output/preprocess/3/COVID-19/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
is_gene_available = True  # Title mentions "microarrays" and summary discusses gene expression

# 2.1 Data Availability
trait_row = 0  # Disease state indicating COVID vs control
age_row = None  # Age not available
gender_row = None  # Gender not available 

# 2.2 Data Type Conversion
def convert_trait(value):
    """Convert COVID status to binary (0=Control, 1=COVID/COVID_SDRA)"""
    if not isinstance(value, str):
        return None
    val = value.split(': ')[-1].strip()
    if val == 'Control':
        return 0
    elif val in ['Covid19', 'Covid19_SDRA']:
        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
    )
    
    # Preview the results
    print(preview_df(clinical_features))
    
    # Save to CSV
    clinical_features.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs
print("First 20 row IDs:")
print(list(genetic_data.index)[:20])
# These row identifiers appear to be numeric IDs (not gene symbols).
# This kind of identifier pattern suggests probe IDs or similar platform-specific identifiers.
# Based on biomedical knowledge, human gene symbols would be text-based like "GAPDH", "IL6", etc.
# Therefore, these identifiers need to be mapped to standard 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)

# This is human gene data with proper annotations
is_gene_available = True

# Save updated 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)
)
# For each annotation row, extract gene symbols from descriptive text
gene_metadata['Gene_Symbol'] = gene_metadata['SPOT_ID.1'].apply(extract_human_gene_symbols)

# Get mapping data with probe IDs and gene symbols
mapping_data = get_gene_mapping(gene_metadata, 'ID', 'Gene_Symbol')

# Convert probe-level measurements to gene-level expression values
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Save processed gene data 
gene_data.to_csv(out_gene_data_file)
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
normalized_gene_data.to_csv(out_gene_data_file)

# 2. Link clinical and genetic data
clinical_features = pd.read_csv(out_clinical_data_file, index_col=0).T
linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_gene_data)

# Add debug prints
print("Clinical features shape:", clinical_features.shape)
print("Normalized gene data shape:", normalized_gene_data.shape)
print("Linked data shape:", linked_data.shape)
print("\nSample IDs in clinical data:", clinical_features.index.tolist()[:5])
print("Sample IDs in gene data:", normalized_gene_data.columns.tolist()[:5])

# Validate linking was successful
if len(linked_data) == 0 or linked_data[trait].isna().all():
    print(f"\nData linking failed - no valid samples found")
    is_gene_available = False
    linked_data = None
else:
    # 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="Dataset contains gene expression data comparing COVID-19 cases with healthy controls."
    )

    # 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)
# 1. Normalize gene symbols and save gene data
print("Gene data shape before normalization:", gene_data.shape)
if len(gene_data) == 0:
    # Create minimal DataFrame with clinical data for metadata
    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
    is_usable = validate_and_save_cohort_info(
        is_final=True,
        cohort=cohort,
        info_path=json_path,
        is_gene_available=False,
        is_trait_available=True,
        is_biased=True,  # Mark as biased since no gene data
        df=clinical_features,  # Pass clinical features as minimal DataFrame
        note="Gene mapping failed - no valid gene symbols found."
    )
else:
    # Continue with gene normalization and linking if gene data exists
    normalized_gene_data = normalize_gene_symbols_in_index(gene_data)
    os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
    normalized_gene_data.to_csv(out_gene_data_file)

    # 2. Link clinical and genetic data  
    clinical_features = pd.read_csv(out_clinical_data_file, index_col=0)
    linked_data = geo_link_clinical_genetic_data(clinical_features, normalized_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=True,
        is_trait_available=True,
        is_biased=trait_biased,
        df=linked_data,
        note="Dataset contains gene expression data comparing COVID-19 cases with healthy controls."
    )

    # 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)