# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Melanoma"
cohort = "GSE144296"

# Input paths
in_trait_dir = "../DATA/GEO/Melanoma"
in_cohort_dir = "../DATA/GEO/Melanoma/GSE144296"

# Output paths
out_data_file = "./output/preprocess/3/Melanoma/GSE144296.csv"
out_gene_data_file = "./output/preprocess/3/Melanoma/gene_data/GSE144296.csv"
out_clinical_data_file = "./output/preprocess/3/Melanoma/clinical_data/GSE144296.csv"
json_path = "./output/preprocess/3/Melanoma/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
# Based on background info mentioning mRNA sequencing and gene expression analysis
is_gene_available = True

# 2.1 Data Availability
# Trait (melanoma vs non-melanoma) can be inferred from cell type field (row 1)
trait_row = 1
# Age not available in data
age_row = None  
# Gender not available in data
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    """Convert cell type to binary melanoma indicator"""
    if not isinstance(x, str):
        return None
    x = x.lower().split(': ')[-1]
    if 'melanoma' in x:
        return 1
    elif 'colorectal' in x:
        return 0
    return None

def convert_age(x):
    """Placeholder for age conversion"""
    return None

def convert_gender(x):
    """Placeholder for gender conversion"""
    return None

# 3. Save metadata for initial filtering
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 = 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 processed clinical data
    preview_df(selected_clinical)
    
    # Save clinical features
    selected_clinical.to_csv(out_clinical_data_file)
# Extract genetic data matrix with case-insensitive marker 
genetic_data = get_genetic_data(matrix_file_path, marker="!series_matrix_table_begin".lower())

# Verify data was loaded
if len(genetic_data.index) == 0:
    # Try alternative marker format
    genetic_data = get_genetic_data(matrix_file_path, marker="!Series_Matrix_Table_Begin")
    
if len(genetic_data.index) == 0:
    print("Warning: No data was extracted from the matrix file. Please check the matrix file formatting.")
    is_gene_available = False
else:
    print("First 20 row IDs:")
    print(list(genetic_data.index)[:20])
    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)
)

if is_gene_available:
    genetic_data.to_csv(out_gene_data_file)
# Examine file content before filtering
with gzip.open(soft_file_path, 'rt') as f:
    print("\nSample of unfiltered SOFT file content (first 20 lines):")
    for i, line in enumerate(f):
        if i < 20:  # Print more lines to better understand the structure
            print(line.strip())
        elif i == 20:
            print("...")
            break

# Try reading the matrix file for gene annotations since the SOFT file seems to lack them
with gzip.open(matrix_file_path, 'rt') as f:
    print("\nSample of matrix file content (first 20 lines):")
    for i, line in enumerate(f):
        if i < 20:
            print(line.strip())
        elif i == 20:
            print("...")
            break

# Since we can see the file content now, update the gene metadata extraction
probe_info_found = False
with gzip.open(matrix_file_path, 'rt') as f:
    lines = []
    for line in f:
        if line.startswith('!Platform_organism'):
            probe_info_found = True
            lines.append(line)
        elif probe_info_found and line.startswith('!'):
            lines.append(line)
        elif probe_info_found and not any(line.startswith(p) for p in ['!', '#', '^']):
            break
    gene_metadata_text = '\n'.join(lines)
    
print("\nExtracted probe/gene information:")
print(gene_metadata_text)
# Try reading gene expression data using the library function
genetic_data = get_genetic_data(matrix_file_path)

# Convert index to string type
genetic_data.index = genetic_data.index.astype(str)

# Print sample identifiers for verification
print("\nSample identifiers from genetic data:")
print(list(genetic_data.index)[:5])

# Extract gene mapping info
try:
    with gzip.open(matrix_file_path, 'rt') as f:
        for line in f:
            if '!series_matrix_table_begin' in line.lower():
                # Found start of expression data
                break
            if line.startswith('!Sample_platform_id'):
                # Save the platform ID if we find it
                platform_line = line.strip()

    # For RNA-seq data, create a 1:1 mapping using the original gene identifiers
    ids = genetic_data.index.tolist()
    annotation_df = pd.DataFrame({
        'ID': ids,
        'Gene': ids  # Use same IDs as gene symbols for now
    })

    print("\nSample rows from annotation mapping:")
    print(annotation_df.head())

    # Apply gene mapping using library function 
    gene_data = apply_gene_mapping(genetic_data, annotation_df)

    # Convert gene indices to string before normalization
    gene_data.index = gene_data.index.astype(str)

    # Normalize gene symbols
    gene_data = normalize_gene_symbols_in_index(gene_data)

    print("\nFinal gene data shape:", gene_data.shape)
    print("Sample gene names after normalization:")
    print(list(gene_data.index)[:5])

    # Save the processed gene data
    gene_data.to_csv(out_gene_data_file)

except Exception as e:
    print(f"Error during gene mapping: {str(e)}")
    # Save genetic data without mapping if error occurs
    genetic_data.to_csv(out_gene_data_file)
# Read the entire file first to find the exact line numbers of begin/end markers
with gzip.open(matrix_file_path, 'rt') as f:
    lines = f.readlines()
    
start_idx = None
end_idx = None
for i, line in enumerate(lines):
    if '!series_matrix_table_begin' in line.lower():
        start_idx = i + 1  # Skip the marker line
    elif '!series_matrix_table_end' in line.lower():
        end_idx = i
        break

genetic_data = None
if start_idx and end_idx:
    # Read only the data section
    genetic_data = pd.read_csv(io.StringIO(''.join(lines[start_idx:end_idx])), 
                              sep='\t', index_col=0)

# Print results
if genetic_data is not None and len(genetic_data) > 0:
    print("\nFirst 20 row IDs:")
    print(list(genetic_data.index)[:20])
    is_gene_available = True
else:
    print("\nWarning: No gene expression data could be extracted")
    is_gene_available = False

# 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)
)

if is_gene_available:
    genetic_data.to_csv(out_gene_data_file)