# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Vitamin_D_Levels"
cohort = "GSE76324"

# Input paths
in_trait_dir = "../DATA/GEO/Vitamin_D_Levels"
in_cohort_dir = "../DATA/GEO/Vitamin_D_Levels/GSE76324"

# Output paths
out_data_file = "./output/preprocess/3/Vitamin_D_Levels/GSE76324.csv"
out_gene_data_file = "./output/preprocess/3/Vitamin_D_Levels/gene_data/GSE76324.csv"
out_clinical_data_file = "./output/preprocess/3/Vitamin_D_Levels/clinical_data/GSE76324.csv"
json_path = "./output/preprocess/3/Vitamin_D_Levels/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)
# Check if the dataset contains gene expression data
# From background information, this is a microarray dataset for airway epithelium transcriptome analysis
is_gene_available = True

# Find row indices and define conversion functions for clinical features
trait_row = 3  # Row containing vitamin D levels
age_row = None  # Age information not available
gender_row = None  # Gender information not available

def convert_trait(value):
    if pd.isna(value):
        return None
    # Extract value after colon and convert to numeric values
    if 'serum 25-oh-d:' in value.lower():
        if 'low' in value.lower():
            return 0
        elif 'mid' in value.lower():
            return 1  
        elif 'high' in value.lower():
            return 2
    return None

def convert_age(value):
    return None

def convert_gender(value):
    return None

# Save cohort metadata
is_trait_available = trait_row is not None
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=is_trait_available)

# Extract clinical features if trait data is available
if is_trait_available:
    selected_clinical_df = geo_select_clinical_features(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 = preview_df(selected_clinical_df)
    print("Preview of processed clinical data:", preview)
    
    # Save processed clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.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 
print("First 20 gene/probe IDs:")
print(list(genetic_data.index[:20]))
requires_gene_mapping = True
# Extract gene annotation from SOFT file 
gene_annotation = get_gene_annotation(soft_file_path)

# Preview annotation structure
preview = preview_df(gene_annotation)
print("Gene annotation preview:")
print(preview)
# Extract probe-gene mapping from gene annotation
mapping_data = get_gene_mapping(gene_annotation, prob_col='ID', gene_col='Gene Symbol')

# Apply probe-to-gene mapping to convert probe-level data to gene-level data
gene_data = apply_gene_mapping(genetic_data, mapping_data)

# Preview the gene data
print("\nFirst 10 genes and their data shape:")
print("Shape:", gene_data.shape)
print("Gene symbols:", list(gene_data.index[:10]))
# 1. Normalize gene symbols in gene expression data
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)
print("\nGene data shape (normalized gene-level):", gene_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, gene_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)