# Path Configuration
from tools.preprocess import *

# Processing context
trait = "Allergies"
cohort = "GSE169149"

# Input paths
in_trait_dir = "../DATA/GEO/Allergies"
in_cohort_dir = "../DATA/GEO/Allergies/GSE169149"

# Output paths
out_data_file = "./output/z1/preprocess/Allergies/GSE169149.csv"
out_gene_data_file = "./output/z1/preprocess/Allergies/gene_data/GSE169149.csv"
out_clinical_data_file = "./output/z1/preprocess/Allergies/clinical_data/GSE169149.csv"
json_path = "./output/z1/preprocess/Allergies/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) Determine gene expression availability
is_gene_available = True  # Blood tissue and treatment context imply gene expression profiling (not miRNA/methylation only)

# 2) Identify availability rows in the sample characteristics
# Sample Characteristics Dictionary given:
# 0: ['subject status: Sarcoidosis patient', 'subject status: healthy control']
# 1: ['treatment: none', 'treatment: tofacitinib']
# 2: ['tissue: Blood']
trait_row = None   # No Allergies-related info present
age_row = None     # No age info present
gender_row = None  # No gender info present

# 2.2) Conversion functions
def _after_colon(x):
    if x is None:
        return None
    if not isinstance(x, str):
        x = str(x)
    parts = x.split(":", 1)
    v = parts[1] if len(parts) > 1 else parts[0]
    return v.strip()

def convert_trait(x):
    # Map allergy-related status to binary: 1 = has allergies/atopy; 0 = no allergies/controls; unknown -> None
    v = _after_colon(x)
    if v is None or v == "":
        return None
    s = v.lower()

    # Strong positive indicators
    pos_terms = [
        "allergy", "allergies", "allergic", "atopy", "atopic", "asthma",
        "hay fever", "rhinitis", "eczema", "urticaria"
    ]
    if any(term in s for term in pos_terms):
        return 1

    # Strong negative indicators
    neg_terms = [
        "non-atopic", "healthy control", "control", "no allergy", "without allergies",
        "none", "negative", "neg", "absent"
    ]
    if any(term in s for term in neg_terms):
        return 0

    # Generic yes/no
    if s in {"yes", "y", "true", "1", "positive", "pos", "present"}:
        return 1
    if s in {"no", "n", "false", "0"}:
        return 0

    return None

def convert_age(x):
    v = _after_colon(x)
    if v is None or v == "":
        return None
    s = v.lower().replace("years", "").replace("year", "").replace("yrs", "").replace("yr", "").strip()
    # Remove common non-numeric placeholders
    if s in {"na", "n/a", "unknown", "none"}:
        return None
    # Extract leading numeric if present
    try:
        return float(s.split()[0].replace(",", ""))
    except Exception:
        # Try to find a number within the string
        import re
        m = re.search(r"[-+]?\d*\.?\d+", s)
        if m:
            try:
                return float(m.group(0))
            except Exception:
                return None
        return None

def convert_gender(x):
    # 0 = female, 1 = male
    v = _after_colon(x)
    if v is None or v == "":
        return None
    s = v.strip().lower()
    # Standard labels
    if s in {"female", "f", "woman", "women"}:
        return 0
    if s in {"male", "m", "man", "men"}:
        return 1
    # Handle common encodings
    if s in {"0", "1"}:
        return 1 if s == "1" else 0
    if s in {"na", "n/a", "unknown", "none"}:
        return None
    return None

# 3) Initial filtering and save metadata
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,
        gender_row=gender_row,
        convert_gender=convert_gender
    )
    _ = preview_df(selected_clinical_df)
    # Save selected clinical data
    os.makedirs(os.path.dirname(out_clinical_data_file), exist_ok=True)
    selected_clinical_df.to_csv(out_clinical_data_file)