path: root/xembu.py

#!/usr/bin/env python3
import argparse
import os
import tarfile
import subprocess
from typing import Dict, List, Optional

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from matplotlib import font_manager

from modules.base import Frame, BigFrame, Module, ModuleResult
from modules.general import GeneralModule

from datetime import datetime

CSV_COLUMNS = [
    "Datum",
    "Nutzer",
    "Distributionsgruppe",
    "Distributionsflag",
    "Positionsbezeichnung",
    "Positionswert",
    "Modules",
    "Parameters",
    "Beleg",
]


def _pick_mono_font(size: int = 8) -> font_manager.FontProperties:
    for fam in ["Inconsolata", "DejaVu Sans Mono", "monospace"]:
        try:
            return font_manager.FontProperties(family=fam, size=size)
        except Exception:
            pass
    return font_manager.FontProperties(size=size)

def _decorate_figure(fig, mono_font, title: str, generated_at: str, page: int, total_pages: int):
    # Margins: links/rechts 2cm, oben/unten 1cm
    margin_lr_cm = 2.0
    margin_tb_cm = 1.0

    # Zusätzlicher Abstand (Bänder) zwischen Header/Footer und Content
    header_gap_cm = 1.3   # mehr Abstand nach unten
    footer_gap_cm = 2.0   # mehr Abstand nach oben (2-zeiliger Footer)

    cm_to_in = 1 / 2.54
    margin_lr_in = margin_lr_cm * cm_to_in
    margin_tb_in = margin_tb_cm * cm_to_in
    header_gap_in = header_gap_cm * cm_to_in
    footer_gap_in = footer_gap_cm * cm_to_in

    w_in, h_in = fig.get_size_inches()

    mx = min(0.45, margin_lr_in / w_in)
    my = min(0.45, margin_tb_in / h_in)
    header_gap = header_gap_in / h_in
    footer_gap = footer_gap_in / h_in

    # Content-Bereich: innerhalb der Margins + zusätzlich Platz für Header/Footer
    top = 1 - my - header_gap
    bottom = my + footer_gap
    if top <= bottom:
        # Fallback, falls es zu eng wird
        top = 1 - my
        bottom = my

    fig.subplots_adjust(left=mx, right=1 - mx, top=top, bottom=bottom)

    # Header/Footer Positionen: jeweils an der inneren Kante der Margins
    left_x = mx
    right_x = 1 - mx
    header_y = 1 - my
    footer_y = my

    # Kopfzeile
    fig.text(left_x,  header_y, title,        ha="left",  va="top", fontproperties=mono_font, fontsize=9)
    fig.text(right_x, header_y, generated_at, ha="right", va="top", fontproperties=mono_font, fontsize=9)

    # Fußzeile links (zweizeilig)
    footer_left = (
        "xembu - eXtensiblE Multiuser Bookkeeping Utility\n"
        "Copyright (C) 2024 Leonard Kugis\n"
        "This program comes with ABSOLUTELY NO WARRANTY; for details see LICENSE.txt"
    )
    fig.text(left_x, footer_y, footer_left, ha="left", va="bottom",
             fontproperties=mono_font, fontsize=7, linespacing=1.1)

    # Fußzeile rechts
    fig.text(right_x, footer_y, f"{page} / {total_pages}", ha="right", va="bottom",
             fontproperties=mono_font, fontsize=8)

def _read_csv_flexible(path: str) -> pd.DataFrame:
    df = pd.read_csv(path, sep=";", encoding="utf-8", header=0)
    if not set(CSV_COLUMNS).issubset(set(df.columns)):
        df = pd.read_csv(path, sep=";", encoding="utf-8", header=None, names=CSV_COLUMNS)
    return df


def parse_value_unit(s: str):
    if s is None or (isinstance(s, float) and pd.isna(s)):
        return 0.0, ""
    txt = str(s).strip()
    if not txt:
        return 0.0, ""
    parts = txt.split()
    if len(parts) < 2:
        num = txt.replace(",", ".").replace("€", "").strip()
        return float(num), ""
    unit = parts[-1].strip()
    num_str = " ".join(parts[:-1]).strip().replace(",", ".").replace("€", "").strip()
    return float(num_str), unit


def parse_modules_list(s: str) -> List[str]:
    if s is None or (isinstance(s, float) and pd.isna(s)):
        return []
    mods = [m.strip() for m in str(s).split(",")]
    return [m for m in mods if m]


def parse_groups_list(s: str) -> List[str]:
    if s is None or (isinstance(s, float) and pd.isna(s)):
        return []
    gs = [g.strip() for g in str(s).split(",")]
    return [g for g in gs if g]


def parse_parameters_list(s: str) -> List[tuple]:
    if s is None or (isinstance(s, float) and pd.isna(s)):
        return []
    txt = str(s).strip()
    if not txt:
        return []
    import re

    tuples = []
    for m in re.finditer(r"\(([^()]*)\)", txt):
        inner = m.group(1).strip()
        if inner == "":
            tuples.append(tuple())
            continue
        parts = [p.strip() for p in inner.split(",") if p.strip() != ""]
        vals = []
        for p in parts:
            try:
                if "." in p:
                    vals.append(float(p))
                else:
                    vals.append(int(p))
            except Exception:
                try:
                    vals.append(float(p))
                except Exception:
                    vals.append(p)
        tuples.append(tuple(vals))
    return tuples


def parse_csv(path: str) -> pd.DataFrame:
    df = _read_csv_flexible(path)

    df["Datum"] = pd.to_datetime(df["Datum"], format="%Y-%m-%d-%H-%M-%S", errors="coerce")
    df["Nutzer"] = df["Nutzer"].astype(str).str.strip()
    df["Distributionsflag"] = df["Distributionsflag"].astype(str).str.strip().str.upper()
    df["Positionsbezeichnung"] = df["Positionsbezeichnung"].astype(str).str.strip()

    df["dist_groups"] = df["Distributionsgruppe"].apply(parse_groups_list)
    df["modules_list"] = df["Modules"].apply(parse_modules_list)
    df["params_list"] = df["Parameters"].apply(parse_parameters_list)

    vals_units = df["Positionswert"].apply(parse_value_unit)
    df["value"] = vals_units.apply(lambda x: x[0])
    df["unit"] = vals_units.apply(lambda x: x[1])

    df["Beleg"] = df["Beleg"].where(df["Beleg"].notna(), "")
    return df


def compute_hash(filepath: str, base_dir: str = ".") -> Optional[str]:
    import hashlib
    try:
        if not filepath:
            return None
        full_path = os.path.join(base_dir, filepath)
        with open(full_path, "rb") as f:
            return hashlib.sha1(f.read()).hexdigest()
    except Exception:
        return None

def _build_positions_table_figs(df: pd.DataFrame, base_dir: str, mono_font):
    figures = []

    columns = [
        "Datum", "Nutzer", "Distributionsgruppe", "Flag",
        "Positionsbezeichnung", "Positionswert",
        "Modules", "Parameters", "Beleg", "SHA1",
    ]

    table_data = []
    for _, row in df.sort_values("Datum").iterrows():
        sha1 = compute_hash(str(row["Beleg"]), base_dir=base_dir) if row["Beleg"] else None
        sha1_fmt = ""
        if sha1:
            sha1_fmt = sha1[: len(sha1) // 2] + "\n" + sha1[len(sha1) // 2 :]

        groups_str = ", ".join(row["dist_groups"]) if isinstance(row["dist_groups"], list) else str(row["Distributionsgruppe"])
        mods_str = ", ".join(row["modules_list"]) if isinstance(row["modules_list"], list) else str(row["Modules"])
        params_str = str(row["params_list"]) if isinstance(row["params_list"], list) else str(row["Parameters"])

        table_data.append([
            row["Datum"].strftime("%Y-%m-%d %H:%M:%S") if pd.notna(row["Datum"]) else "INVALID",
            row["Nutzer"],
            groups_str,
            row["Distributionsflag"],
            row["Positionsbezeichnung"],
            f"{row['value']:.4f} {row['unit']}".strip(),
            mods_str,
            params_str,
            str(row["Beleg"]) if row["Beleg"] else "",
            sha1_fmt,
        ])

    chunk_size = 16
    fontprops = font_manager.FontProperties(size=8)

    for start in range(0, len(table_data), chunk_size):
        fig, ax = plt.subplots(figsize=(8.27, 11.69))
        ax.axis("off")
        chunk = table_data[start:start + chunk_size]

        renderer = fig.canvas.get_renderer()

        def get_text_width(text, prop):
            t = plt.text(0, 0, str(text), fontproperties=prop)
            bb = t.get_window_extent(renderer=renderer)
            t.remove()
            return bb.width

        col_widths = []
        for col_idx in range(len(columns)):
            max_w = get_text_width(columns[col_idx], fontprops)
            for r in chunk:
                max_w = max(max_w, get_text_width(r[col_idx], fontprops))
            col_widths.append(max_w)

        col_widths_inches = [w / fig.dpi for w in col_widths]
        total_w = sum(col_widths_inches) if sum(col_widths_inches) else 1.0
        scaled = [w / total_w for w in col_widths_inches]

        table = ax.table(cellText=chunk, colLabels=columns, loc="center", cellLoc="left")
        table.auto_set_font_size(False)

        for cell in table.get_celld().values():
            cell.get_text().set_fontproperties(mono_font)
            cell.PAD = 0.03

        for (_, c), cell in table.get_celld().items():
            if c < len(scaled):
                cell.set_width(scaled[c])

        table.scale(1, 2.0)
        figures.append(fig)

    return figures


def _separator_page(pdf: PdfPages, title: str, mono_font):
    fig, ax = plt.subplots(figsize=(8.27, 11.69))
    ax.axis("off")
    ax.text(0.5, 0.5, title, ha="center", va="center", fontproperties=mono_font, fontsize=18)
    pdf.savefig(fig)
    plt.close(fig)

def _build_frame_figs(frames: List[Frame], mono_font):
    figures = []
    idx = 0
    while idx < len(frames):
        fig, axs = plt.subplots(3, 2, figsize=(8.27, 11.69))
        axs = axs.flatten()

        for i in range(6):
            ax = axs[i]
            ax.clear()
            ax.axis("off")
            if idx + i < len(frames):
                fr = frames[idx + i]
                ax.set_title(fr.title, fontproperties=mono_font, fontsize=10)
                ax.set_title(fr.title, fontsize=10)
                ax.title.set_fontproperties(mono_font)

                fr.render(ax, mono_font)

        figures.append(fig)
        idx += 6
    return figures

def _build_bigframe_figs(bigframes: List[BigFrame], mono_font):
    figures = []
    idx = 0
    while idx < len(bigframes):
        fig, axs = plt.subplots(2, 1, figsize=(8.27, 11.69))
        axs = axs.flatten() if hasattr(axs, "flatten") else [axs]

        for i in range(2):
            ax = axs[i]
            ax.clear()
            ax.axis("off")
            if idx + i < len(bigframes):
                bf = bigframes[idx + i]
                ax.set_title(bf.title, fontproperties=mono_font, fontsize=11)
                ax.set_title(bf.title, fontsize=11)
                ax.title.set_fontproperties(mono_font)

                bf.render(ax, mono_font)

        figures.append(fig)
        idx += 2
    return figures

def _render_pages(pdf: PdfPages, pages: List[plt.Figure]):
    for fig in pages:
        pdf.savefig(fig)
        plt.close(fig)

def create_pdf(
    df: pd.DataFrame,
    module_frames: List[Frame],
    module_bigframes: List[BigFrame],
    module_pages: List[plt.Figure],
    pdf_path: str,
    mono_font,
    base_dir: str,
    title: str,
):
    os.makedirs(os.path.dirname(os.path.abspath(pdf_path)) or ".", exist_ok=True)

    generated_at = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")

    # 1) Alle Seiten als Figures sammeln (damit wir total_pages kennen)
    figs: List[plt.Figure] = []
    figs.extend(_build_positions_table_figs(df, base_dir=base_dir, mono_font=mono_font))
    figs.extend(_build_frame_figs(module_frames, mono_font=mono_font))
    figs.extend(_build_bigframe_figs(module_bigframes, mono_font=mono_font))
    figs.extend(module_pages)  # bereits fertige Figures aus Modulen

    total_pages = len(figs)

    # 2) Speichern mit Header/Footer + Seitenzählung
    with PdfPages(pdf_path) as pdf:
        for i, fig in enumerate(figs, start=1):
            _decorate_figure(fig, mono_font, title=title, generated_at=generated_at, page=i, total_pages=total_pages)
            pdf.savefig(fig)
            plt.close(fig)

def create_bundle(archive_path: str, csv_path: str, df: pd.DataFrame, base_dir: str, pdf_path: Optional[str] = None):
    """
    Bundle enthält: CSV, optional PDF, und alle Belege (relative Pfade aus 'Beleg' relativ zu base_dir).
    Ausgabe: .tar.zst (über externes zstd).
    """
    os.makedirs(os.path.dirname(os.path.abspath(archive_path)) or ".", exist_ok=True)

    # Wir bauen ein temporäres .tar daneben und komprimieren danach.
    tar_path = archive_path
    if tar_path.endswith(".zst"):
        tar_path = tar_path[:-4]  # strip ".zst"
    if not tar_path.endswith(".tar"):
        tar_path = tar_path + ".tar"

    # Sammle Belege
    beleg_paths = []
    for p in df["Beleg"].astype(str).tolist():
        p = p.strip()
        if p:
            beleg_paths.append(p)

    with tarfile.open(tar_path, "w") as tar:
        # CSV
        tar.add(csv_path, arcname=os.path.basename(csv_path))

        # PDF optional
        if pdf_path and os.path.exists(pdf_path):
            tar.add(pdf_path, arcname=os.path.basename(pdf_path))

        # Belege
        missing = []
        for rel in sorted(set(beleg_paths)):
            abs_path = rel if os.path.isabs(rel) else os.path.join(base_dir, rel)
            if os.path.exists(abs_path):
                # arcname: möglichst den relativen Pfad behalten
                arcname = os.path.basename(rel) if os.path.isabs(rel) else rel
                tar.add(abs_path, arcname=arcname)
            else:
                missing.append(rel)

    # zstd komprimieren → archive_path
    # zstd -o <archive> <tar>
    subprocess.run(["zstd", "-T0", "-o", archive_path, tar_path], check=True)

    # tar löschen (zstd bekommt eine Kopie)
    try:
        os.remove(tar_path)
    except Exception:
        pass

    if missing:
        print("\n[WARN] Fehlende Belege (nicht im Bundle):")
        for m in missing:
            print(f"  - {m}")


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("csv", help="Pfad zur CSV-Datei")
    parser.add_argument("--title", "-t", help="Titel für PDF-Kopfzeile (optional)")
    parser.add_argument("--pdf", "-p", help="Pfad zur Ziel-PDF (optional)")
    parser.add_argument("--bundle", "-b", help="Pfad zum Bundle (.tar.zst), enthält CSV, PDF (falls erzeugt) und Belege (optional)")
    args = parser.parse_args()

    csv_path = os.path.abspath(args.csv)
    base_dir = os.path.dirname(csv_path) or "."

    title = args.title if args.title else os.path.basename(csv_path)

    df = parse_csv(csv_path)
    if df["Datum"].isna().any():
        bad = df[df["Datum"].isna()][CSV_COLUMNS]
        raise ValueError(f"Ungültige Datumsangaben in folgenden Zeilen:\n{bad}")

    want_pdf = bool(args.pdf)
    mono_font = _pick_mono_font(size=8)

    # Module-Registry
    modules: Dict[str, Module] = {
        "general": GeneralModule(),
        # weitere Module später hier registrieren
    }

    # Modulzuordnung aus CSV
    rows_for_module: Dict[str, List[int]] = {}
    for idx, row in df.iterrows():
        for m in row["modules_list"]:
            rows_for_module.setdefault(m, []).append(idx)

    results: List[ModuleResult] = []

    # General immer
    results.append(modules["general"].process(df, context={"base_dir": base_dir, "want_pdf": want_pdf, "mono_font": mono_font}))

    # weitere Module optional
    for mod_name, indices in rows_for_module.items():
        if mod_name == "general":
            continue
        mod = modules.get(mod_name)
        if not mod:
            print(f"[INFO] Unbekanntes Modul '{mod_name}' – ignoriert (noch nicht registriert).")
            continue
        subdf = df.loc[indices].copy()
        results.append(mod.process(subdf, context={"base_dir": base_dir, "want_pdf": want_pdf, "mono_font": mono_font}))

    # ---- NEU: Konsolen-Auswertung je Modul
    print("\n===== Auswertung =====")
    for r in results:
        print(r.summary_text)
        print("")

    # PDF optional
    if args.pdf:
        module_frames: List[Frame] = []
        module_bigframes: List[BigFrame] = []   # NEU
        module_pages: List[plt.Figure] = []
        for r in results:
            module_frames.extend(r.frames)
            module_bigframes.extend(r.bigframes)  # NEU
            module_pages.extend(r.pages)

        create_pdf(df, module_frames, module_bigframes, module_pages, args.pdf, mono_font, base_dir=base_dir, title=title)
        print(f"[OK] PDF geschrieben: {args.pdf}")

    # Bundle optional (enthält CSV + ggf. PDF + Belege)
    if args.bundle:
        create_bundle(args.bundle, csv_path, df, base_dir=base_dir, pdf_path=args.pdf if args.pdf else None)
        print(f"[OK] Bundle geschrieben: {args.bundle}")


if __name__ == "__main__":
    main()