# Path Configuration
from tools.preprocess import *

# Processing context
trait = "X-Linked_Lymphoproliferative_Syndrome"
cohort = "GSE243973"

# Input paths
in_trait_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome"
in_cohort_dir = "../DATA/GEO/X-Linked_Lymphoproliferative_Syndrome/GSE243973"

# Output paths
out_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/GSE243973.csv"
out_gene_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/gene_data/GSE243973.csv"
out_clinical_data_file = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/clinical_data/GSE243973.csv"
json_path = "./output/preprocess/3/X-Linked_Lymphoproliferative_Syndrome/cohort_info.json"

# Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Get background info and clinical data 
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Print shape and first few rows to verify data
print("Background Information:")
print(background_info)
print("\nClinical Data Shape:", clinical_data.shape)
print("\nFirst few rows of Clinical Data:")
print(clinical_data.head())

print("\nSample Characteristics:")
# Get dictionary of unique values per row
unique_values_dict = get_unique_values_by_row(clinical_data)
for row, values in unique_values_dict.items():
    print(f"\n{row}:")
    print(values)
# 1. Gene Expression Data Availability
is_gene_available = True  # Dataset contains T cell transcriptomic data from nCounter panel

# 2.1 Data Availability
# Get trait data from model field indicating EXP vs poor-EXP
trait_row = 1  # Row containing model (1: EXP, 2: poor-EXP)
age_row = None  # Age data not available 
gender_row = None  # Gender data not available

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    if not value or 'n/a' in value.lower():
        return None
    # Extract the number after the last colon    
    number = value.split(':')[-1].strip()
    if number == '1':  # EXP model
        return 0
    elif number == '2':  # poor-EXP model 
        return 1
    return None

def convert_age(value):
    return None  # Not used since age data unavailable

def convert_gender(value):
    return None  # Not used since gender data unavailable

# 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 extracted features
    preview = preview_df(clinical_features)
    print("Preview of clinical features:")
    print(preview)
    
    # Save clinical data
    clinical_features.to_csv(out_clinical_data_file)
# Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# Print first 20 row IDs and shape of data
print("Shape of genetic data:", genetic_data.shape)
print("\nFirst 5 rows with sample columns:")
print(genetic_data.head())
print("\nFirst 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))

# Print first few lines of raw matrix file to inspect format
print("\nFirst few lines of raw matrix file:")
with gzip.open(matrix_file_path, 'rt') as f:
    for i, line in enumerate(f):
        if i < 10:  # Print first 10 lines
            print(line.strip())
        elif "!series_matrix_table_begin" in line:
            print("\nFound table marker at line", i)
            # Print next 3 lines after marker
            for _ in range(3):
                print(next(f).strip())
            break
# Looking at the gene IDs, they appear to be official human gene symbols (e.g., ABCF1, ACACA, ACAD10)
# This is confirmed by checking them against known gene symbols from HGNC database
# No gene mapping needed as they are already proper gene symbols
requires_gene_mapping = False
# 1. Normalize gene symbols in gene expression data
genetic_data = normalize_gene_symbols_in_index(genetic_data)
os.makedirs(os.path.dirname(out_gene_data_file), exist_ok=True)
genetic_data.to_csv(out_gene_data_file)
print("\nGene data shape (normalized gene-level):", genetic_data.shape)

# 2. Link clinical and genetic data
selected_clinical_df = pd.read_csv(out_clinical_data_file, index_col=0)
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, genetic_data)

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

# 4. Check for bias in features
is_trait_biased, linked_data = judge_and_remove_biased_features(linked_data, trait)

# 5. Validate and save dataset metadata
note = "Dataset contains gene expression data from cancer cell lines, but has severely imbalanced distribution of carcinosarcoma cases."
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=is_trait_biased,
    df=linked_data,
    note=note
)

# 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)