# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE73637"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE73637"

# Output paths
out_data_file = "./output/preprocess/3/Endometrioid_Cancer/GSE73637.csv"
out_gene_data_file = "./output/preprocess/3/Endometrioid_Cancer/gene_data/GSE73637.csv"
out_clinical_data_file = "./output/preprocess/3/Endometrioid_Cancer/clinical_data/GSE73637.csv"
json_path = "./output/preprocess/3/Endometrioid_Cancer/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
# From series title and design, this appears to be gene expression data from cell lines
is_gene_available = True

# 2. Variable Availability and Data Type Conversion
# 2.1 Data Rows
# Trait (Endometrioid) can be determined from histopathology in row 3
trait_row = 3

# Age and gender not available for cell lines
age_row = None  
gender_row = None

# 2.2 Conversion Functions
def convert_trait(value: str) -> Optional[int]:
    """Convert histopathology to binary trait"""
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip().lower()
    if 'endometrioid' in value:
        return 1
    # For cases where we can be sure it's not endometrioid
    if any(x in value for x in ['serous', 'clear cell', 'undifferentiated']):
        return 0
    return None

def convert_age(value: str) -> Optional[float]:
    """Placeholder function since age data not available"""
    return None

def convert_gender(value: str) -> Optional[int]:
    """Placeholder function since gender data not available"""
    return None

# 3. Save metadata
is_usable = 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
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])
# Given that the row identifiers are simply numerical indices (1, 2, 3, etc) rather than 
# recognizable gene symbols like BRCA1, TP53, etc., we need to perform gene mapping 
requires_gene_mapping = True
# Extract gene annotation data with proper handling
filtered_lines = []
with gzip.open(soft_file, 'rt') as f:
    for line in f:
        if not any(line.startswith(prefix) for prefix in ['^', '!', '#']):
            filtered_lines.append(line.strip())

# Preview the structure of filtered lines
print("Sample of filtered lines:")
for line in filtered_lines[:5]:
    print(line)

if filtered_lines:
    # Try to create DataFrame from filtered lines
    try:
        df_text = '\n'.join(filtered_lines)
        gene_metadata = pd.read_csv(io.StringIO(df_text), sep='\t', 
                                  engine='python', on_bad_lines='skip')
        print("\nColumn names:")
        print(gene_metadata.columns)
        print("\nPreview:")
        print(preview_df(gene_metadata))
    except Exception as e:
        print(f"Error creating DataFrame: {str(e)}")
# The gene expression data uses numerical IDs that match the 'ID' column in gene annotation
# The 'GeneSymbol' column contains the gene symbols we want to map to
mapping_df = get_gene_mapping(gene_metadata, prob_col='ID', gene_col='GeneSymbol')

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

# Print shape and preview to verify the mapping
print("Gene expression data shape:", gene_data.shape)
print("\nPreview of first 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 ERα-chromatin interactions in endometrial tumors from patients with/without tamoxifen treatment history"
)

# 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)