# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Depression"
cohort = "GSE208668"

# Input paths
in_trait_dir = "../DATA/GEO/Depression"
in_cohort_dir = "../DATA/GEO/Depression/GSE208668"

# Output paths
out_data_file = "./output/preprocess/3/Depression/GSE208668.csv"
out_gene_data_file = "./output/preprocess/3/Depression/gene_data/GSE208668.csv"
out_clinical_data_file = "./output/preprocess/3/Depression/clinical_data/GSE208668.csv"
json_path = "./output/preprocess/3/Depression/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
# From the background info, this dataset contains transcriptome data from PBMCs
# However, it mentions raw data was lost, so gene expression data is not available
is_gene_available = False

# 2.1 Data Availability
# Depression trait can be inferred from "history of depression" field (key 9)
trait_row = 9
# Age is available in key 1
age_row = 1  
# Gender is available in key 2
gender_row = 2

# 2.2 Data Type Conversion Functions
def convert_trait(x):
    if not isinstance(x, str):
        return None
    x = x.lower().strip()
    if 'history of depression:' not in x:
        return None
    value = x.split(':')[1].strip()
    if value == 'yes':
        return 1
    elif value == 'no':
        return 0
    return None

def convert_age(x):
    if not isinstance(x, str):
        return None
    if 'age:' not in x:
        return None
    try:
        return float(x.split(':')[1].strip())
    except:
        return None

def convert_gender(x):
    if not isinstance(x, str):
        return None
    if 'gender:' not in x:
        return None
    value = x.split(':')[1].strip().lower()
    if value == 'female':
        return 0
    elif value == '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=(trait_row is not None))

# 4. Clinical Feature Extraction
# Since trait_row is not None, extract clinical features
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 and save clinical data
print("Preview of clinical data:")
print(preview_df(clinical_df))
clinical_df.to_csv(out_clinical_data_file)