# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Prostate_Cancer"
cohort = "GSE206793"

# Input paths
in_trait_dir = "../DATA/GEO/Prostate_Cancer"
in_cohort_dir = "../DATA/GEO/Prostate_Cancer/GSE206793"

# Output paths
out_data_file = "./output/preprocess/3/Prostate_Cancer/GSE206793.csv"
out_gene_data_file = "./output/preprocess/3/Prostate_Cancer/gene_data/GSE206793.csv"
out_clinical_data_file = "./output/preprocess/3/Prostate_Cancer/clinical_data/GSE206793.csv"
json_path = "./output/preprocess/3/Prostate_Cancer/cohort_info.json"

# Get file paths
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# Extract background info and clinical data using specified prefixes
background_info, clinical_data = get_background_and_clinical_data(
    matrix_file,
    prefixes_a=['!Series_title', '!Series_summary', '!Series_overall_design'],
    prefixes_b=['!Sample_geo_accession', '!Sample_characteristics_ch1']
)

# Get unique values per clinical feature
sample_characteristics = get_unique_values_by_row(clinical_data)

# Print background info
print("Dataset Background Information:")
print(f"{background_info}\n")

# Print sample characteristics 
print("Sample Characteristics:")
for feature, values in sample_characteristics.items():
    print(f"Feature: {feature}")
    print(f"Values: {values}\n")
# 1. Gene Expression Data Availability
# This dataset contains miRNA data, not gene expression data
is_gene_available = False

# 2.1 Data Availability & 2.2 Data Type Conversion
# Trait data is available in Feature 0, convert disease state to binary
trait_row = 0
def convert_trait(value):
    if not value or ":" not in value:
        return None
    value = value.split(":")[1].strip().lower()
    if "healthy" in value:
        return 0
    elif "prostate cancer" in value:
        return 1
    return None

# Age data is available in Feature 1
age_row = 1
def convert_age(value):
    if not value or ":" not in value:
        return None
    try:
        age = float(value.split(":")[1].strip())
        return age
    except:
        return None

# Gender data is not available in sample characteristics
gender_row = None
def convert_gender(value):
    return None

# 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 
if trait_row is not None:
    clinical_df = geo_select_clinical_features(clinical_data, trait, trait_row, convert_trait,
                                             age_row, convert_age,
                                             gender_row, convert_gender)
    print("Clinical data preview:")
    print(preview_df(clinical_df))
    clinical_df.to_csv(out_clinical_data_file)