# Path Configuration
from tools.preprocess import *

# Processing context
trait = "COVID-19"
cohort = "GSE227080"

# Input paths
in_trait_dir = "../DATA/GEO/COVID-19"
in_cohort_dir = "../DATA/GEO/COVID-19/GSE227080"

# Output paths
out_data_file = "./output/preprocess/3/COVID-19/GSE227080.csv"
out_gene_data_file = "./output/preprocess/3/COVID-19/gene_data/GSE227080.csv"
out_clinical_data_file = "./output/preprocess/3/COVID-19/clinical_data/GSE227080.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  # Yes, contains immunological gene expression data from NanoString nCounter

# 2.1 Data Availability
trait_row = 2  # Severity information
age_row = 1    # Age information 
gender_row = 0 # Gender information

# 2.2 Data Type Conversion Functions
def convert_trait(value: str) -> int:
    """Convert COVID-19 severity to binary: 1 for positive cases (MILD or MOD_SEV), 0 for negative"""
    if not value or ':' not in value:
        return None
    severity = value.split(':')[1].strip().upper()
    if severity == 'NEG':
        return 0
    elif severity in ['MILD', 'MOD_SEV']:
        return 1
    return None

def convert_age(value: str) -> float:
    """Convert age string to float"""
    if not value or ':' not in value:
        return None
    try:
        return float(value.split(':')[1].strip())
    except:
        return None

def convert_gender(value: str) -> int:
    """Convert gender to binary: 0 for female, 1 for male"""
    if not value or ':' not in value:
        return None
    gender = value.split(':')[1].strip().upper()
    if gender == 'F':
        return 0
    elif gender == 'M':
        return 1
    return None

# 3. Save Metadata
is_trait_available = trait_row is not None
validate_and_save_cohort_info(is_final=False, cohort=cohort, info_path=json_path,
                            is_gene_available=is_gene_available,
                            is_trait_available=is_trait_available)

# 4. Clinical Feature Extraction
clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                         age_row, convert_age,
                                         gender_row, convert_gender)

# Preview the processed clinical data
preview_df(clinical_df)

# Save clinical data
clinical_df.to_csv(out_clinical_data_file)
# Extract genetic data matrix
genetic_data = get_genetic_data(matrix_file_path)

# Print first few rows with column names to examine data structure
print("Data preview:")
print("\nColumn names:")
print(list(genetic_data.columns)[:5])
print("\nFirst 5 rows:")
print(genetic_data.head())
print("\nShape:", genetic_data.shape)

# Verify this is gene expression data and check identifiers
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)
)

# Save gene expression data 
genetic_data.to_csv(out_gene_data_file)
# The identifiers in the index appear to be standard human gene symbols (e.g. ABCB1, ABL1, ADA)
# so no mapping is needed
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data.T)
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=is_gene_available, 
    is_trait_available=True,
    is_biased=trait_biased,
    df=linked_data,
    note="Dataset contains immunological gene expression data from 60 COVID-19 positive cases (mild and moderate/severe) and 59 COVID-negative 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)