# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Liver_cirrhosis"
cohort = "GSE163211"

# Input paths
in_trait_dir = "../DATA/GEO/Liver_cirrhosis"
in_cohort_dir = "../DATA/GEO/Liver_cirrhosis/GSE163211"

# Output paths
out_data_file = "./output/preprocess/3/Liver_cirrhosis/GSE163211.csv"
out_gene_data_file = "./output/preprocess/3/Liver_cirrhosis/gene_data/GSE163211.csv"
out_clinical_data_file = "./output/preprocess/3/Liver_cirrhosis/clinical_data/GSE163211.csv"
json_path = "./output/preprocess/3/Liver_cirrhosis/cohort_info.json"

# Step 1: Get file paths
soft_file_path, matrix_file_path = geo_get_relevant_filepaths(in_cohort_dir)

# Step 2: Extract background info and clinical data from matrix file
background_info, clinical_data = get_background_and_clinical_data(matrix_file_path)

# Step 3: Get dictionary of unique values for each clinical feature
unique_values_dict = get_unique_values_by_row(clinical_data)

# Step 4: Print background info and sample characteristics
print("Dataset Background Information:")
print("-" * 80)
print(background_info)
print("\nSample Characteristics:")
print("-" * 80)
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# From the background info, we can see this dataset contains gene expression data from Nanostring nCounter assay
is_gene_available = True

# 2.1 Data Availability
# Cirrhosis status can be inferred from NAFLD stage in row 8
trait_row = 8  

# Age is available in row 3
age_row = 3  

# Gender is available in row 4
gender_row = 4

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not x or ':' not in x:
        return None
    stage = x.split(': ')[1].strip()
    # NASH with fibrosis stage 1-4 indicates cirrhosis, others are non-cirrhosis
    return 1 if stage == 'NASH_F1_F4' else 0

def convert_age(x):
    if not x or ':' not in x:
        return None
    try:
        return float(x.split(': ')[1])
    except:
        return None

def convert_gender(x):
    if not x or ':' not in x:
        return None
    gender = x.split(': ')[1].strip().lower()
    if gender == 'female':
        return 0
    elif gender == 'male':
        return 1
    return None

# 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=True)

# 4. Clinical Feature Extraction
selected_clinical_df = 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 clinical data
preview_result = preview_df(selected_clinical_df)

# Save clinical data
selected_clinical_df.to_csv(out_clinical_data_file)
# 1. Extract gene expression data from matrix file
genetic_data = get_genetic_data(matrix_file_path)

# 2. Print first 20 row IDs
print("First 20 gene/probe identifiers:")
print(genetic_data.index[:20])
requires_gene_mapping = False
# 1. Normalize gene symbols and save gene data
normalized_gene_data = normalize_gene_symbols_in_index(genetic_data)
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
linked_data = geo_link_clinical_genetic_data(selected_clinical_df, normalized_gene_data)
linked_data = linked_data.rename(columns={'nafld stage': trait})

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

# 4. Check for biased features and remove biased demographic ones
is_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=True,
    is_trait_available=True,
    is_biased=is_biased,
    df=linked_data,
    note="Contains gene expression data from Nanostring nCounter assay measuring 795 genes in liver tissue from NAFLD patients."
)

# 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)