# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE203149"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE203149"

# Output paths
out_data_file = "./output/preprocess/1/Bladder_Cancer/GSE203149.csv"
out_gene_data_file = "./output/preprocess/1/Bladder_Cancer/gene_data/GSE203149.csv"
out_clinical_data_file = "./output/preprocess/1/Bladder_Cancer/clinical_data/GSE203149.csv"
json_path = "./output/preprocess/1/Bladder_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
# According to the background information, this dataset indeed has gene expression data.
is_gene_available = True

# 2. Variable Availability and Data Type Conversion

# The sample characteristics dictionary shows only one row:
# 0: ['disease: Muscle-invasive bladder cancer']
# There's only one unique value for "disease" (i.e., "Muscle-invasive bladder cancer"), 
# so the trait does not vary and is considered unavailable for our association study.
trait_row = None

# No entries for age or gender are found in the sample dictionary.
age_row = None
gender_row = None

# Define conversion functions for trait, age, and gender (though not used, they must be declared):

def convert_trait(value: str):
    # Placeholder function since trait is actually unavailable (single value).
    # Typical approach: parse the substring after the colon, if present.
    parts = value.split(":")
    val = parts[1].strip() if len(parts) > 1 else value.strip()
    # Here, we'd convert to an integer or float if relevant,
    # but this dataset has no meaningful variation for the trait.
    return None

def convert_age(value: str):
    # No age data is available, so this is a placeholder.
    return None

def convert_gender(value: str):
    # No gender data is available, so this is a placeholder.
    return None

# 3. Save Metadata (Initial Filtering)
# Trait data is considered unavailable because it doesn't vary (only one unique value).
is_trait_available = False

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
# Since trait_row is None, we skip the clinical feature extraction step.