# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE162253"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE162253"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE162253.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE162253.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE162253.csv"
json_path = "./output/z1/preprocess/Bladder_Cancer/cohort_info.json"


# Step 1: Initial Data Loading
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)

# Step 2: Dataset Analysis and Clinical Feature Extraction
# Step 1: Determine availability
is_gene_available = True  # Series title suggests gene expression; not miRNA/methylation-only
trait_row = None          # No bladder cancer trait in sample characteristics
age_row = None            # No age information for human subjects
gender_row = None         # No gender information for human subjects

# Step 2: Converters
def _after_colon(val):
    if val is None:
        return None
    s = str(val)
    parts = s.split(":", 1)
    s = parts[1] if len(parts) > 1 else parts[0]
    s = s.strip()
    return s if s != "" else None

def convert_trait(val):
    # Generic fallback: try to infer bladder cancer status if ever provided.
    v = _after_colon(val)
    if v is None:
        return None
    v_low = v.lower()
    pos_tokens = {"bladder cancer", "urothelial carcinoma", "blca"}
    neg_tokens = {"normal", "healthy", "control", "benign", "adjacent normal", "non-cancer"}
    if any(tok in v_low for tok in pos_tokens):
        return 1
    if any(tok in v_low for tok in neg_tokens):
        return 0
    return None

def convert_age(val):
    v = _after_colon(val)
    if v is None:
        return None
    # extract first integer/float found
    import re
    m = re.search(r"(\d+(\.\d+)?)", v)
    if not m:
        return None
    try:
        age = float(m.group(1))
    except Exception:
        return None
    # sanity bounds for human age
    if 0 <= age <= 120:
        return age
    return None

def convert_gender(val):
    v = _after_colon(val)
    if v is None:
        return None
    v_low = v.lower()
    if v_low in {"female", "f", "woman", "women"}:
        return 0
    if v_low in {"male", "m", "man", "men"}:
        return 1
    return None

# Step 3: Save metadata (initial filtering)
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
)

# Step 4: Clinical feature extraction (skip because trait_row is None)
# Intentionally no extraction or saving of clinical CSV as clinical data for the target trait is unavailable.