# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Endometrioid_Cancer"
cohort = "GSE94524"

# Input paths
in_trait_dir = "../DATA/GEO/Endometrioid_Cancer"
in_cohort_dir = "../DATA/GEO/Endometrioid_Cancer/GSE94524"

# Output paths
out_data_file = "./output/preprocess/1/Endometrioid_Cancer/GSE94524.csv"
out_gene_data_file = "./output/preprocess/1/Endometrioid_Cancer/gene_data/GSE94524.csv"
out_clinical_data_file = "./output/preprocess/1/Endometrioid_Cancer/clinical_data/GSE94524.csv"
json_path = "./output/preprocess/1/Endometrioid_Cancer/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. Gene Expression Data Availability
is_gene_available = True  # Based on the background info, assume gene expression data is available.

# 2. Variable Availability and Data Type Conversion

# 2.1 Identify row keys (None if not available or constant)
#    The sample characteristics dictionary only has one key, 0, whose value is
#    "tissue: endometrioid adenocarcinoma" (single unique value).
#    Therefore, no meaningful variation for trait, age, or gender.
trait_row = None
age_row = None
gender_row = None

# 2.2 Define conversion functions
def convert_trait(value: str) -> int:
    # No data available, but if needed, here's a placeholder.
    return None

def convert_age(value: str) -> float:
    # No data available, but if needed, here's a placeholder.
    return None

def convert_gender(value: str) -> int:
    # No data available, but if needed, here's a placeholder.
    return None

# 3. Save Metadata (initial filtering) - trait is unavailable if trait_row is None
is_trait_available = (trait_row is not None)

is_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. Clinical Feature Extraction
#    Skip because trait_row is None (no available clinical variation).