# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Huntingtons_Disease"
cohort = "GSE154141"

# Input paths
in_trait_dir = "../DATA/GEO/Huntingtons_Disease"
in_cohort_dir = "../DATA/GEO/Huntingtons_Disease/GSE154141"

# Output paths
out_data_file = "./output/preprocess/3/Huntingtons_Disease/GSE154141.csv"
out_gene_data_file = "./output/preprocess/3/Huntingtons_Disease/gene_data/GSE154141.csv"
out_clinical_data_file = "./output/preprocess/3/Huntingtons_Disease/clinical_data/GSE154141.csv"
json_path = "./output/preprocess/3/Huntingtons_Disease/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)

# Get unique values for each clinical feature 
unique_values_dict = get_unique_values_by_row(clinical_data)

# Print background information
print("Background Information:")
print(background_info)
print("\nSample Characteristics:")
print(json.dumps(unique_values_dict, indent=2))
# 1. Gene Expression Data Availability
# Not likely to contain gene expression data - sample characteristics show this is a sorted cell/lentivirus experiment
is_gene_available = False  

# 2. Clinical Feature Data
# 2.1 Data Availability
# Looking at sample characteristics, trait data can be inferred from lentivirus status (Q73 vs Q23)
trait_row = 1
# Age and gender are not available
age_row = None 
gender_row = None

# 2.2 Data Type Conversion Functions
def convert_trait(value):
    # Extract value after colon
    value = value.split(': ')[1].strip()
    # Q73 represents HD (1), Q23 is control (0), pTANK is None
    if value == 'Q73':
        return 1
    elif value == 'Q23':
        return 0
    return None

def convert_age(value):
    return None

def convert_gender(value):
    return None

# 3. Save Metadata
# trait_row is not None, so trait data is available
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. Extract Clinical Features
if trait_row is not None:
    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
    )
    print("Preview of clinical data:")
    print(preview_df(clinical_df))
    clinical_df.to_csv(out_clinical_data_file)