import firebirdsql as fb
import pandas as pd

# TCP to the Firebird 2.5 server. Use the DB path as seen by the *server* (Windows path).
conn = fb.connect(
    host="192.168.1.4",
    port=3050,
    database=r"z:\Medicus 3\data\MEDICUS.FDB",  # raw string for backslashes
    user="SYSDBA",
    password="masterkey",
    charset="WIN1250",  # adjust if needed
)

# Tiny helper to fetch directly into DataFrame (avoids the pandas/SQLAlchemy warning)
def query_df(sql, params=None):
    cur = conn.cursor()
    cur.execute(sql, params or ())
    rows = cur.fetchall()
    cols = [d[0].strip() for d in cur.description]  # Firebird pads column names
    return pd.DataFrame(rows, columns=cols)

# Smoke test
print(query_df("SELECT 1 AS ONE FROM RDB$DATABASE"))

# Your table
df = query_df("SELECT FIRST 100 * FROM kar")
print(df)



from datetime import datetime
start = datetime(2025, 1, 1)
end   = datetime(2026, 1, 1)

sql = """
SELECT
  /*vh.idvh,*/
  vh.idpacient,
  kar.prijmeni,
  kar.jmeno,
  kar.rodcis,
  vh.datum,
  /*vh.idhodn,*/
  /*vd.poradi,*/
  /*vd.idmetod,*/
/* NEW: list of matching dokladd entries within ±7 days, one cell */
  (
    SELECT LIST(CAST(dd.datose AS VARCHAR(10)) || ' ' || dd.kod, ', ')
    FROM dokladd dd
    WHERE dd.rodcis = kar.rodcis
      AND (dd.kod = '01130' or dd.kod = '01131' OR dd.kod = '01132' OR dd.kod = '01133' OR dd.kod = '01134')
      AND dd.datose BETWEEN vh.datum - 7 AND vh.datum + 7
  ) AS vykodovano,
   lm.kodtext,
  lm.nazev,
  vd.vysl,
  lj.jedn,
  ls.normdol,
  ls.normhor
FROM labvh vh
JOIN labvd vd        ON vd.idvh = vh.idvh
JOIN kar             ON kar.idpac = vh.idpacient
JOIN labmetod lm     ON lm.idmetod = vd.idmetod
JOIN labjedn lj      ON lj.idjedn = vd.idjedn
JOIN labskaly ls     ON ls.idskaly = vd.idskaly
WHERE vh.datum >= ?
  AND vh.datum < ?
  AND lm.nazev CONTAINING 'PSA'
ORDER BY kar.idpac, vh.datum, vd.poradi;
"""

df_direct = query_df(sql, (start, end))

import re
import numpy as np

# --- 0) Helper: parse numeric value from string like "5,6", "<0.1", "3.2 mmol/L" ---
num_re = re.compile(r'[-+]?\d+(?:[.,]\d+)?(?:[eE][-+]?\d+)?')

def to_num(x):
    if x is None:
        return np.nan
    s = str(x).strip()
    if not s:
        return np.nan
    m = num_re.search(s.replace('\u00A0', ' '))  # remove NBSP if any
    if not m:
        return np.nan
    val_str = m.group(0).replace(',', '.')
    try:
        val = float(val_str)
    except ValueError:
        return np.nan
    # Heuristic for qualifiers:
    # "<x" -> take half of x (below detection limit), ">x" -> take x (at least)
    if s.lstrip().startswith('<'):
        return val * 0.5
    if s.lstrip().startswith('>'):
        return val
    return val

# --- 1) Prepare numeric columns + ratio in pandas before export ---
# Assumes df_direct exists with columns 'VYSL' and 'NORMHOR' (case per your SELECT)
df_direct["VYSL_NUM"] = df_direct["VYSL"].apply(to_num)
df_direct["NORMHOR_NUM"] = df_direct["NORMHOR"].apply(to_num)

# Avoid division by zero/NaN
den = df_direct["NORMHOR_NUM"].replace(0, np.nan)
df_direct["RATIO"] = (df_direct["VYSL_NUM"] / den).clip(lower=0)  # can exceed 1 if over ULN


from datetime import datetime
from pathlib import Path
from openpyxl import load_workbook
from openpyxl.utils import get_column_letter
from openpyxl.styles import Alignment, Border, Side
from openpyxl.formatting.rule import ColorScaleRule
from openpyxl.styles import PatternFill
from openpyxl.formatting.rule import FormulaRule

# ---- 1) Build timestamped output path ----
base_path = Path("u:\Dropbox\!!!Days\Downloads Z230")
base_path.mkdir(parents=True, exist_ok=True)  # ensure folder exists
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_file = base_path / f"lab_results_2025_{timestamp}.xlsx"

# ---- 2) Export DataFrame to Excel ----
# Assumes df_direct already exists (your joined query result)
df_direct.to_excel(output_file, index=False, sheet_name="PSA")

# ---- 3) Open with openpyxl for formatting ----
wb = load_workbook(output_file)
ws = wb["PSA"]

# Auto width for columns
for col in ws.columns:
    max_len = 0
    col_letter = get_column_letter(col[0].column)
    for cell in col:
        try:
            if cell.value is not None:
                max_len = max(max_len, len(str(cell.value)))
        except Exception:
            pass
    ws.column_dimensions[col_letter].width = min(max_len + 2, 50)  # cap width

# Thin border style
thin_border = Border(
    left=Side(style="thin"),
    right=Side(style="thin"),
    top=Side(style="thin"),
    bottom=Side(style="thin"),
)

# Apply borders to all cells and center A, B, E
for row in ws.iter_rows(min_row=1, max_row=ws.max_row, min_col=1, max_col=ws.max_column):
    for cell in row:
        cell.border = thin_border
        if cell.column_letter in ["A", "B", "E"]:
            cell.alignment = Alignment(horizontal="center")

# Enable filter on header row and freeze it
ws.auto_filter.ref = ws.dimensions
ws.freeze_panes = "A2"


# map headers
hdr = {c.value: i+1 for i, c in enumerate(ws[1])}
vysl_idx  = hdr.get("VYSL")
ratio_idx = hdr.get("RATIO")
if not (vysl_idx and ratio_idx):
    raise RuntimeError("Missing required columns: VYSL and/or RATIO")

vysl_col  = get_column_letter(vysl_idx)
ratio_col = get_column_letter(ratio_idx)
max_row   = ws.max_row
rng_vysl  = f"{vysl_col}2:{vysl_col}{max_row}"

green  = PatternFill(start_color="63BE7B", end_color="63BE7B", fill_type="solid")
yellow = PatternFill(start_color="FFEB84", end_color="FFEB84", fill_type="solid")
red    = PatternFill(start_color="F8696B", end_color="F8696B", fill_type="solid")

# Non-overlapping rules; stop when one matches
ws.conditional_formatting.add(
    rng_vysl,
    FormulaRule(formula=[f"${ratio_col}2<=0.80"], fill=green,  stopIfTrue=True)
)
ws.conditional_formatting.add(
    rng_vysl,
    FormulaRule(formula=[f"AND(${ratio_col}2>0.80, ${ratio_col}2<1)"], fill=yellow, stopIfTrue=True)
)
ws.conditional_formatting.add(
    rng_vysl,
    FormulaRule(formula=[f"${ratio_col}2>=1"], fill=red,      stopIfTrue=True)
)


wb.save(output_file)
print(f"Saved: {output_file}")