# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE244266"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE244266"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE244266.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE244266.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE244266.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
# 1. Gene expression data availability
is_gene_available = True  # RNA-based molecular subtype analysis implies gene expression data is available.

# 2. Variable availability
# From the sample characteristics dictionary:
# 0: treatment arm
# 1: disease (muscle-invasive bladder cancer) -> constant, not useful
# 2: clinical stage stratification
trait_row = None     # Everyone has muscle-invasive bladder cancer -> constant feature
age_row = None       # No age field present
gender_row = None    # No gender field present

# 2.2 Conversion functions
def _extract_value(cell):
    if cell is None:
        return None
    s = str(cell)
    if ':' in s:
        return s.split(':', 1)[1].strip()
    return s.strip()

def convert_trait(cell):
    v = _extract_value(cell)
    if v is None or v == '':
        return None
    vl = v.lower()
    # Map common labels to binary case/control for bladder cancer
    if any(k in vl for k in ['normal', 'adjacent normal', 'healthy', 'control', 'benign', 'non-cancer']):
        return 0
    if any(k in vl for k in ['bladder cancer', 'urothelial', 'carcinoma', 'tumor', 'tumour', 'case', 'muscle-invasive']):
        return 1
    return None

def convert_age(cell):
    v = _extract_value(cell)
    if v is None or v == '':
        return None
    # extract first numeric token as age in years
    import re
    m = re.search(r'[-+]?\d*\.?\d+', v)
    if not m:
        return None
    try:
        age = float(m.group())
        if age < 0 or age > 120:
            return None
        return age
    except:
        return None

def convert_gender(cell):
    v = _extract_value(cell)
    if v is None or v == '':
        return None
    vl = v.lower()
    if vl in ['female', 'f', 'woman', 'women']:
        return 0
    if vl in ['male', 'm', 'man', 'men']:
        return 1
    # handle prefixes like 'gender: Male', 'sex: female'
    if 'female' in vl:
        return 0
    if 'male' in vl:
        return 1
    return None

# 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
)

# 4. Clinical feature extraction
# Skipped because trait_row is None (no usable trait variability in this dataset).