# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Alcohol_Flush_Reaction"
cohort = "GSE133228"

# Input paths
in_trait_dir = "../DATA/GEO/Alcohol_Flush_Reaction"
in_cohort_dir = "../DATA/GEO/Alcohol_Flush_Reaction/GSE133228"

# Output paths
out_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/GSE133228.csv"
out_gene_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/gene_data/GSE133228.csv"
out_clinical_data_file = "./output/z1/preprocess/Alcohol_Flush_Reaction/clinical_data/GSE133228.csv"
json_path = "./output/z1/preprocess/Alcohol_Flush_Reaction/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 os
import re
import pandas as pd

# 1) Gene expression availability judgment based on provided background:
# SuperSeries focused on chromatin architecture (CTCF/STAG2, loop extrusion). No explicit gene expression evidence.
# Treat as not containing usable gene expression matrix for our pipeline.
is_gene_available = False

# 2) Identify variable availability from the Sample Characteristics Dictionary provided:
# Keys observed:
# 0: gender: Male/Female
# 1: age: integers
# 2: tumor type: primary tumor (constant -> not useful)
trait_row = None  # Alcohol Flush Reaction not present or inferable
age_row = 1
gender_row = 0

# 2.2 Converters
def _after_colon(x):
    if x is None:
        return None
    s = str(x)
    parts = s.split(":", 1)
    v = parts[1] if len(parts) > 1 else parts[0]
    return v.strip()

def convert_trait(x):
    # Binary: 1 = flusher/positive/yes/case, 0 = non-flusher/negative/no/control; unknown -> None
    v = _after_colon(x)
    if v is None or v == "":
        return None
    vl = v.strip().lower()
    if vl in {"na", "n/a", "not available", "unknown", "nan", "missing", "null"}:
        return None
    # Common synonyms
    positive_terms = {
        "yes", "y", "true", "positive", "pos", "case", "flusher", "with", "present", "af", "afr", "flush", "red face"
    }
    negative_terms = {
        "no", "n", "false", "negative", "neg", "control", "non-flusher", "without", "absent", "nonflusher", "none"
    }
    if vl in positive_terms:
        return 1
    if vl in negative_terms:
        return 0
    # Heuristics
    if "flusher" in vl or "flush" in vl or "red face" in vl:
        # infer flusher
        return 1
    if "non" in vl and ("flusher" in vl or "flush" in vl):
        return 0
    # Numeric fallback
    if vl.isdigit():
        if vl == "1":
            return 1
        if vl == "0":
            return 0
    return None

def convert_age(x):
    # Continuous age in years; extract first number
    v = _after_colon(x)
    if v is None or v == "":
        return None
    m = re.search(r"[-+]?\d*\.?\d+", v)
    if not m:
        return None
    try:
        return float(m.group())
    except Exception:
        return None

def convert_gender(x):
    # Binary: female=0, male=1
    v = _after_colon(x)
    if v is None or v == "":
        return None
    vl = v.strip().lower()
    if vl in {"na", "n/a", "not available", "unknown", "nan", "missing", "null"}:
        return None
    if vl in {"male", "m", "man", "boy"}:
        return 1
    if vl in {"female", "f", "woman", "girl"}:
        return 0
    if vl == "1":
        return 1
    if vl == "0":
        return 0
    return None

# 3) Initial filtering metadata save
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 (only if trait is available)
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,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    preview = preview_df(selected_clinical_df, n=5)
    print(preview)

    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)