# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Sarcoma"
cohort = "GSE233860"

# Input paths
in_trait_dir = "../DATA/GEO/Sarcoma"
in_cohort_dir = "../DATA/GEO/Sarcoma/GSE233860"

# Output paths
out_data_file = "./output/preprocess/1/Sarcoma/GSE233860.csv"
out_gene_data_file = "./output/preprocess/1/Sarcoma/gene_data/GSE233860.csv"
out_clinical_data_file = "./output/preprocess/1/Sarcoma/clinical_data/GSE233860.csv"
json_path = "./output/preprocess/1/Sarcoma/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  # Given the background information mentions gene expression quantification of 770 genes

# 2. Variable Availability
# The sample characteristics dictionary only has one key (0) with values: ["outcome: SD", "outcome: PD", "outcome: PR"].
# None of these correspond to the trait (Sarcoma), age, or gender. Therefore:
trait_row = None
age_row = None
gender_row = None

# 2.2 Data Type Conversion
def convert_trait(value: str):
    # Example parsing: split on ':' and use the second part if available
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    raw_val = parts[1].strip().lower()
    # This dataset doesn't actually provide trait info, so we return None
    return None

def convert_age(value: str):
    # Example parsing: split on ':' and convert to float if possible
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    raw_val = parts[1].strip()
    try:
        return float(raw_val)
    except ValueError:
        return None

def convert_gender(value: str):
    # Example parsing: split on ':' and map female -> 0, male -> 1
    parts = value.split(':', 1)
    if len(parts) < 2:
        return None
    raw_val = parts[1].strip().lower()
    if 'male' in raw_val:
        return 1
    elif 'female' in raw_val:
        return 0
    else:
        return None

# 3. Save Metadata (initial filtering)
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
)
print("Initial usability check:", is_usable)

# 4. Clinical Feature Extraction
# This step is skipped because trait_row is None (trait data not available).