# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometriosis"
cohort = "GSE138297"

# Input paths
in_trait_dir = "../DATA/GEO/Endometriosis"
in_cohort_dir = "../DATA/GEO/Endometriosis/GSE138297"

# Output paths
out_data_file = "./output/preprocess/1/Endometriosis/GSE138297.csv"
out_gene_data_file = "./output/preprocess/1/Endometriosis/gene_data/GSE138297.csv"
out_clinical_data_file = "./output/preprocess/1/Endometriosis/clinical_data/GSE138297.csv"
json_path = "./output/preprocess/1/Endometriosis/cohort_info.json"

# STEP1
from tools.preprocess import *
# 1. Identify the paths to the SOFT file and the matrix file
soft_file, matrix_file = geo_get_relevant_filepaths(in_cohort_dir)

# 2. Read the matrix file to obtain background information and sample characteristics data
background_prefixes = ['!Series_title', '!Series_summary', '!Series_overall_design']
clinical_prefixes = ['!Sample_geo_accession', '!Sample_characteristics_ch1']
background_info, clinical_data = get_background_and_clinical_data(matrix_file, background_prefixes, clinical_prefixes)

# 3. Obtain the sample characteristics dictionary from the clinical dataframe
sample_characteristics_dict = get_unique_values_by_row(clinical_data)

# 4. Explicitly print out all the background information and the sample characteristics dictionary
print("Background Information:")
print(background_info)
print("Sample Characteristics Dictionary:")
print(sample_characteristics_dict)
# 1. Determine if gene expression data is available
#    From the background info, it's a microarray dataset for gene expression.
is_gene_available = True

# 2. Determine the availability and the corresponding row indices for trait, age, and gender
#    The trait is "Endometriosis", but the dictionary does not contain any row with that info.
#    => trait_row is None
trait_row = None

#    The 'age' data is found in row 3 with multiple distinct values.
age_row = 3

#    The 'gender' data is found in row 1 with values "sex (female=1, male=0): 1" and "sex (female=1, male=0): 0".
gender_row = 1

# 2.2 Define the data type conversion functions
def convert_trait(value: str) -> Optional[int]:
    # Since trait data is not actually available in this dataset, we return None
    return None

def convert_age(value: str) -> Optional[float]:
    # Example of input: "age (yrs): 49"
    # We'll parse out the part after the colon and convert to float
    try:
        val = value.split(":", 1)[1].strip()
        return float(val)
    except:
        return None

def convert_gender(value: str) -> Optional[int]:
    # Example of input: "sex (female=1, male=0): 1"
    # The part after the colon is '1' (meaning female in the original dataset),
    # but per instructions we want female -> 0, male -> 1.
    try:
        val = value.split(":", 1)[1].strip()
        if val == "1":
            return 0  # female
        elif val == "0":
            return 1  # male
        else:
            return None
    except:
        return None

# 3. Save metadata with initial filtering
#    Trait data is not available, so we expect the dataset to be filtered out.
is_trait_available = (trait_row is not None)
is_dataset_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
)

# 4. If trait_row is not None, extract and preview clinical features.
#    Since trait_row is None, we SKIP this step.