# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Cystic_Fibrosis"
cohort = "GSE53543"

# Input paths
in_trait_dir = "../DATA/GEO/Cystic_Fibrosis"
in_cohort_dir = "../DATA/GEO/Cystic_Fibrosis/GSE53543"

# Output paths
out_data_file = "./output/preprocess/3/Cystic_Fibrosis/GSE53543.csv"
out_gene_data_file = "./output/preprocess/3/Cystic_Fibrosis/gene_data/GSE53543.csv"
out_clinical_data_file = "./output/preprocess/3/Cystic_Fibrosis/clinical_data/GSE53543.csv"
json_path = "./output/preprocess/3/Cystic_Fibrosis/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
is_gene_available = True  # Yes, dataset contains genome-wide gene expression data from Illumina array

# 2. Variable Availability and Data Type Conversion
# Trait (RV infection status)
trait_row = 2  # 'sample group' row contains infection status

def convert_trait(value):
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'RV_infected':
        return 1
    elif value == 'Uninfected':
        return 0
    return None

# Gender 
gender_row = 1  # 'gender' row contains gender info

def convert_gender(value):
    if not value or ':' not in value:
        return None
    value = value.split(':')[1].strip()
    if value == 'Female':
        return 0
    elif value == 'Male':
        return 1
    return None

# Age
age_row = None  # Age information not available in sample characteristics

def convert_age(value):
    return None  # Not used but defined for completeness

# 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
selected_features = geo_select_clinical_features(clinical_df=clinical_data,
                                               trait=trait,
                                               trait_row=trait_row,
                                               convert_trait=convert_trait,
                                               gender_row=gender_row,
                                               convert_gender=convert_gender)

# Preview the extracted features
print("Preview of extracted clinical features:")
print(preview_df(selected_features))

# Save clinical features
selected_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])
# ILMN_ prefixes indicate these are Illumina probe IDs
# They need to be mapped to human gene symbols for analysis
requires_gene_mapping = True
# Extract gene annotation data, excluding control probe lines
gene_metadata = get_gene_annotation(soft_file) 

# Preview filtered annotation data
print("Column names:")
print(gene_metadata.columns)
print("\nPreview of gene annotation data:")
print(preview_df(gene_metadata))
# 1. From inspection: 'ID' column in annotation matches probe IDs, 'Symbol' column has gene symbols
probe_col = 'ID'
gene_col = 'Symbol'

# 2. Get mapping between probe IDs and gene symbols
mapping_df = get_gene_mapping(gene_metadata, probe_col, gene_col)

# 3. Convert probe measurements 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 gene expression data:")  
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
linked_data = geo_link_clinical_genetic_data(selected_features, 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="Cell line study comparing deltaF508 CFTR mutant with wildtype CFTR in cystic fibrosis bronchial epithelial cells"
)

# 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)