131 lines
3.9 KiB
Python
131 lines
3.9 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Report: DXA requisitions (Medicus → Dropbox)
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--------------------------------------------
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- Selects all histdoc records for year 2025 containing "DXA"
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- Finds matching PDF files in Dropbox (by rod_cis + "dxa" in name)
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- Outputs Excel report: datum, idpaci, rod_cis, prijmeni, jmeno, file
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"""
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import os
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import firebirdsql as fb
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import pandas as pd
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from pathlib import Path
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from datetime import datetime
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# ================== CONFIGURATION ==================
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FDB_PATH = r"z:\Medicus 3\data\MEDICUS.FDB"
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DOC_DIR = Path(r"U:\Dropbox\Ordinace\Dokumentace_zpracovaná")
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EXPORT_DIR = Path(r"U:\Dropbox\!!!Days\Downloads Z230")
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timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
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xlsx_path = EXPORT_DIR / f"{timestamp}_DXA_report.xlsx"
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# ================== DATABASE QUERY ==================
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conn = fb.connect(
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host="192.168.1.4",
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port=3050,
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database=FDB_PATH,
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user="SYSDBA",
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password="masterkey",
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charset="WIN1250",
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)
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sql = """
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SELECT
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h.datum,
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h.idpaci,
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k.rodcis,
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k.prijmeni,
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k.jmeno,
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(
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SELECT LIST(
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CAST(dd.datose AS DATE) || ':' || dd.kod, ', '
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)
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FROM dokladd dd
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WHERE dd.rodcis = k.rodcis
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AND EXTRACT(YEAR FROM dd.datose) = 2025
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AND dd.kod STARTING WITH '1132'
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) AS vykony_1132_2025
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FROM histdoc h
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JOIN kar k ON h.idpaci = k.idpac
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WHERE EXTRACT(YEAR FROM h.datum) = 2025
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AND h.data CONTAINING 'dxa'
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ORDER BY h.datum DESC
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"""
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df = pd.read_sql(sql, conn)
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conn.close()
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# ================== REMOVE DUPLICATES AND SORT ==================
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# Convert to datetime → drop duplicates → keep newest per patient → sort descending
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df["DATUM"] = pd.to_datetime(df["DATUM"], errors="coerce")
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# Drop duplicates, keep the newest by date
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df = (
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df.sort_values("DATUM", ascending=False)
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.drop_duplicates(subset="RODCIS", keep="first")
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.sort_values("DATUM", ascending=False)
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.reset_index(drop=True)
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)
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# Convert to pure date (no time portion)
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df["DATUM"] = df["DATUM"].dt.date
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# ================== FIND MATCHING PDF FILES ==================
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def find_dxa_file(rod_cis: str) -> str:
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"""Return first matching DXA file for given rod_cis or ''."""
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if not rod_cis:
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return ""
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rod_cis = rod_cis.strip()
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# Case-insensitive search for files like "1234567890 [DXA] something.pdf"
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for file in DOC_DIR.iterdir():
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name = file.name.lower()
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if (
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file.suffix.lower() == ".pdf"
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and name.startswith(rod_cis.lower())
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and "[dxa]" in name
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):
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return file.name
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return ""
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df["FILE"] = df["RODCIS"].apply(find_dxa_file)
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# ================== EXPORT TO EXCEL ==================
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with pd.ExcelWriter(xlsx_path, engine="openpyxl") as writer:
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df.to_excel(writer, index=False, sheet_name="DXA")
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ws = writer.sheets["DXA"]
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# Format header
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from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
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header_fill = PatternFill(start_color="FFFF00", end_color="FFFF00", fill_type="solid")
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for cell in ws[1]:
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cell.font = Font(bold=True, color="000000")
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cell.alignment = Alignment(horizontal="center", vertical="center")
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cell.fill = header_fill
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# Auto column width
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from openpyxl.utils import get_column_letter
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for col in ws.columns:
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col_letter = get_column_letter(col[0].column)
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max_len = max(len(str(cell.value)) if cell.value else 0 for cell in col)
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ws.column_dimensions[col_letter].width = min(max_len + 2, 80)
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# Borders
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thin = Side(border_style="thin", color="000000")
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border = Border(top=thin, left=thin, right=thin, bottom=thin)
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for row in ws.iter_rows(min_row=1, max_row=ws.max_row,
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min_col=1, max_col=ws.max_column):
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for cell in row:
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cell.border = border
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print(f"✅ Report created: {xlsx_path}")
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print(f"📂 Source folder scanned: {DOC_DIR}")
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print(f"🩻 {df['FILE'].astype(bool).sum()} matching DXA PDFs found")
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