import pandas as pd
import numpy as np

QC_FILE = "BEA25P077_RP.xlsx"
PATIENTS_FILE = "patients.xlsx"
THRESHOLDS = [0, 0.1, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 5.0]

# Load patient data
patients = pd.read_excel(PATIENTS_FILE, usecols=["Number", "Exf"])
patients = patients.dropna(subset=["Number"])
patients["Number"] = patients["Number"].astype(int)
patients["Exf"] = patients["Exf"].astype(int)

def parse_conc(val):
    """Return float concentration, or NaN for 'Too low' / missing."""
    if isinstance(val, str):
        return np.nan
    try:
        return float(val)
    except (TypeError, ValueError):
        return np.nan

def analyse_sheet(sheet_name, conc_col):
    df = pd.read_excel(QC_FILE, sheet_name=sheet_name, usecols=["ID", conc_col])
    df["conc_val"] = df[conc_col].apply(parse_conc)
    # Extract patient number from ID like "4A_G2" or "4B_G2"
    extracted = df["ID"].str.extract(r"^(\d+)[AB]")[0]
    df["Number"] = pd.to_numeric(extracted, errors="coerce")
    df = df.dropna(subset=["Number"])
    df["Number"] = df["Number"].astype(int)
    # Merge with patient diagnosis
    df = df.merge(patients, on="Number", how="left")
    return df

sheets = {
    "A (Schirmer strips)": ("A-samples", "conc (ng/ul)"),
    "B (Lens tissues)":    ("B-samples", "Conc [ng/ul]"),
}

print(f"{'Threshold':>10}  {'Sheet':<22}  {'Total':>6}  {'Healthy (Exf=0)':>15}  {'Diseased (Exf=1)':>16}")
print("-" * 80)

for label, (sheet_name, conc_col) in sheets.items():
    df = analyse_sheet(sheet_name, conc_col)
    total_samples = len(df)
    n_too_low = df["conc_val"].isna().sum()
    print(f"\n  {label}  ({total_samples} samples total, {n_too_low} 'Too low')")
    print(f"  {'Threshold':>10}  {'Passing':>7}  {'Healthy':>8}  {'Diseased':>9}  {'Unknown dx':>10}")
    print(f"  {'-'*55}")
    for t in THRESHOLDS:
        if t == 0:
            # threshold=0: include all samples (Too low counts as passing)
            passing = df.copy()
        else:
            # Only samples with a numeric conc >= threshold pass
            passing = df[df["conc_val"] >= t]
        healthy  = (passing["Exf"] == 0).sum()
        diseased = (passing["Exf"] == 1).sum()
        unknown  = passing["Exf"].isna().sum()
        print(f"  {t:>10.2f}  {len(passing):>7}  {healthy:>8}  {diseased:>9}  {unknown:>10}")

print()