# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Bladder_Cancer"
cohort = "GSE245953"

# Input paths
in_trait_dir = "../DATA/GEO/Bladder_Cancer"
in_cohort_dir = "../DATA/GEO/Bladder_Cancer/GSE245953"

# Output paths
out_data_file = "./output/z1/preprocess/Bladder_Cancer/GSE245953.csv"
out_gene_data_file = "./output/z1/preprocess/Bladder_Cancer/gene_data/GSE245953.csv"
out_clinical_data_file = "./output/z1/preprocess/Bladder_Cancer/clinical_data/GSE245953.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
import re

# 1) Gene expression availability based on background info
is_gene_available = True  # Clariom S microarray full transcriptome -> gene expression data

# 2) Variable availability
# Sample characteristics show only a constant condition: Muscle-invasive bladder cancer
trait_row = None     # Constant disease status (all cases) -> not usable for association
age_row = None       # Not available in the sample characteristics
gender_row = None    # Not available in the sample characteristics

# 2.2) Conversion functions
def _extract_after_colon(x):
    if x is None:
        return None
    s = str(x)
    parts = s.split(":", 1)
    return parts[1].strip() if len(parts) == 2 else s.strip()

def convert_trait(x):
    val = _extract_after_colon(x)
    if val is None or val == "":
        return None
    s = val.strip().lower()
    # Map disease presence to 1, absence to 0
    positive_tokens = ["bladder cancer", "muscle-invasive bladder cancer", "mibc", "cancer", "tumor", "tumour", "case"]
    negative_tokens = ["normal", "control", "healthy", "benign", "adjacent normal", "non-cancer", "no cancer"]
    if any(tok in s for tok in positive_tokens):
        return 1
    if any(tok in s for tok in negative_tokens):
        return 0
    if s in {"na", "n/a", "unknown", "not available", "missing"}:
        return None
    # Default: None if unrecognized
    return None

def convert_age(x):
    val = _extract_after_colon(x)
    if val is None or val == "":
        return None
    s = val.lower()
    # Extract first numeric occurrence
    m = re.search(r"[-+]?\d*\.?\d+", s)
    if not m:
        return None
    num = float(m.group())
    # If months indicated, convert to years
    if "month" in s:
        return round(num / 12.0, 2)
    return num

def convert_gender(x):
    val = _extract_after_colon(x)
    if val is None or val == "":
        return None
    s = val.strip().lower()
    if s in {"male", "m"}:
        return 1
    if s in {"female", "f"}:
        return 0
    if s in {"na", "n/a", "unknown", "not available", "missing"}:
        return None
    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 (skip because trait_row is None)
if trait_row is not None:
    selected_clinical_df = geo_select_clinical_features(
        clinical_df=clinical_data,
        trait=trait,
        trait_row=trait_row,
        convert_trait=convert_trait,
        age_row=age_row,
        convert_age=convert_age if age_row is not None else None,
        gender_row=gender_row,
        convert_gender=convert_gender if gender_row is not None else None
    )
    preview = preview_df(selected_clinical_df)
    selected_clinical_df.to_csv(out_clinical_data_file)