Removed unneccessary overhead

1 files changed, 77 insertions, 137 deletions

diff --git a/modules/general.py b/modules/general.py
index f7eca38..3ad587a 100644
--- a/modules/general.py
+++ b/modules/general.py
@@ -12,57 +12,42 @@ from typing import Optional
 
 from .base import Frame, BigFrame, ModuleResult
 
-
 MONEY_UNITS = {"€", "eur", "EUR", "euro", "EURO"}
 
-
 def _is_money_unit(u: str) -> bool:
     return str(u).strip() in MONEY_UNITS
 
-
 def compute_group_distribution(df: pd.DataFrame):
-    """
-    Liefert:
-      group_summary: dict group -> info
-      per_person: DataFrame columns [person, contributed, share, balance]
-      per_group_person: DataFrame detail columns [group, person, contributed, usage, share, balance]
-    """
-    # Explode Gruppen
     work = df.copy()
     work = work.explode("dist_groups")
     work["group"] = work["dist_groups"].fillna("").astype(str).str.strip()
     work = work[work["group"] != ""]
 
-    # C/U Normalisierung
-    work["flag"] = work["Distributionsflag"].astype(str).str.strip().str.upper()
-    work["person"] = work["Nutzer"].astype(str).str.strip()
+    work["flag"] = work["group_flag"].astype(str).str.strip().str.upper()
+    work["debitor"] = work["debitor"].astype(str).str.strip()
 
-    # Contributions (Geld)
     contrib = work[work["flag"] == "C"].copy()
     if len(contrib) > 0:
         bad_units = contrib[~contrib["unit"].apply(_is_money_unit)]
         if len(bad_units) > 0:
             raise ValueError(
                 "Contribution (C) muss Geld-Einheit haben (z.B. € / EUR). "
-                f"Problemzeilen:\n{bad_units[['Datum','Nutzer','group','Positionsbezeichnung','Positionswert','unit']]}"
+                f"Problemzeilen:\n{bad_units[['date','debitor','group','position','val','unit']]}"
             )
 
-    # Usage (Beliebige Einheit, pro Gruppe sollte es sinnvoll einheitlich sein)
     usage = work[work["flag"] == "U"].copy()
 
-    # Summen
-    contrib_by_gp = contrib.groupby(["group", "person"])["value"].sum().rename("contributed").reset_index()
-    contrib_tot = contrib.groupby("group")["value"].sum().rename("total_contrib").reset_index()
+    contrib_by_gp = contrib.groupby(["group", "debitor"])["val"].sum().rename("contributed").reset_index()
+    contrib_tot = contrib.groupby("group")["val"].sum().rename("total_contrib").reset_index()
 
-    usage_by_gp = usage.groupby(["group", "person"])["value"].sum().rename("usage").reset_index()
-    usage_tot = usage.groupby("group")["value"].sum().rename("total_usage").reset_index()
+    usage_by_gp = usage.groupby(["group", "debitor"])["val"].sum().rename("usage").reset_index()
+    usage_tot = usage.groupby("group")["val"].sum().rename("total_usage").reset_index()
     usage_unit = usage.groupby("group")["unit"].agg(lambda s: s.dropna().astype(str).unique().tolist()).reset_index()
     usage_unit = usage_unit.rename(columns={"unit": "usage_units"})
 
-    participants = work.groupby("group")["person"].agg(lambda s: sorted(set(s.tolist()))).reset_index()
-    participants = participants.rename(columns={"person": "participants"})
+    participants = work.groupby("group")["debitor"].agg(lambda s: sorted(set(s.tolist()))).reset_index()
+    participants = participants.rename(columns={"debitor": "participants"})
 
-    # group_summary
     summary = (
         participants.merge(contrib_tot, on="group", how="left")
         .merge(usage_tot, on="group", how="left")
@@ -73,22 +58,19 @@ def compute_group_distribution(df: pd.DataFrame):
     summary["has_usage"] = summary["total_usage"].apply(lambda x: x > 0)
     summary["mode"] = summary.apply(lambda r: "usage" if r["has_usage"] else "equal", axis=1)
 
-    # Detail pro (group, person)
     detail = (
         pd.DataFrame({"group": work["group"].unique()})
         .assign(key=1)
-        .merge(pd.DataFrame({"person": work["person"].unique()}).assign(key=1), on="key")
+        .merge(pd.DataFrame({"debitor": work["debitor"].unique()}).assign(key=1), on="key")
         .drop(columns=["key"])
     )
-    # Nur relevante Paare, die in der Gruppe vorkommen
-    gp_person = work[["group", "person"]].drop_duplicates()
-    detail = detail.merge(gp_person, on=["group", "person"], how="inner")
+    gp_debitor = work[["group", "debitor"]].drop_duplicates()
+    detail = detail.merge(gp_debitor, on=["group", "debitor"], how="inner")
 
-    detail = detail.merge(contrib_by_gp, on=["group", "person"], how="left").merge(usage_by_gp, on=["group", "person"], how="left")
+    detail = detail.merge(contrib_by_gp, on=["group", "debitor"], how="left").merge(usage_by_gp, on=["group", "debitor"], how="left")
     detail["contributed"] = detail["contributed"].fillna(0.0)
     detail["usage"] = detail["usage"].fillna(0.0)
 
-    # Shares berechnen pro Gruppe
     shares = []
     for _, row in summary.iterrows():
         g = row["group"]
@@ -97,7 +79,6 @@ def compute_group_distribution(df: pd.DataFrame):
         n = len(parts) if parts else 0
 
         g_detail = detail[detail["group"] == g].copy()
-        # usage-mode, sobald es irgendeine U-Position gibt (auch wenn total_usage==0 → fallback)
         g_has_any_u = (usage["group"] == g).any()
 
         if g_has_any_u:
@@ -114,25 +95,22 @@ def compute_group_distribution(df: pd.DataFrame):
             mode = "equal"
 
         g_detail["mode"] = mode
-        shares.append(g_detail[["group", "person", "share", "mode"]])
+        shares.append(g_detail[["group", "debitor", "share", "mode"]])
 
-    shares_df = pd.concat(shares, ignore_index=True) if shares else pd.DataFrame(columns=["group","person","share","mode"])
-    detail = detail.merge(shares_df, on=["group", "person"], how="left")
+    shares_df = pd.concat(shares, ignore_index=True) if shares else pd.DataFrame(columns=["group","debitor","share","mode"])
+    detail = detail.merge(shares_df, on=["group", "debitor"], how="left")
     detail["share"] = detail["share"].fillna(0.0)
     detail["balance"] = detail["contributed"] - detail["share"]
 
-    # per_person totals
-    per_person = detail.groupby("person")[["contributed", "share", "balance"]].sum().reset_index()
-    per_person = per_person.sort_values("person")
+    per_debitor = detail.groupby("debitor")[["contributed", "share", "balance"]].sum().reset_index()
+    per_debitor = per_debitor.sort_values("debitor")
 
-    # summary erweitern
-    # "Sobald es eine Position mit U gibt" zählt, auch wenn total_usage==0 (fallback)
     has_any_u = usage.groupby("group").size().rename("u_count").reset_index()
     summary = summary.merge(has_any_u, on="group", how="left")
     summary["u_count"] = summary["u_count"].fillna(0).astype(int)
     summary["mode"] = summary["u_count"].apply(lambda c: "usage" if c > 0 else "equal")
 
-    return summary, per_person, detail
+    return summary, per_debitor, detail
 
 @dataclass
 class GroupTimeSeries:
@@ -142,14 +120,13 @@ class GroupTimeSeries:
     usage_units: List[str]
     xlim_start: pd.Timestamp
     xlim_end: pd.Timestamp
-    contrib_cum: Dict[str, pd.Series]   # € kumulativ
-    usage_cum: Dict[str, pd.Series]     # unit kumulativ (z.B. km, stk)
-    share_cum: Dict[str, pd.Series]     # € kumulativ (Anteil)
-    ratio: Dict[str, pd.Series]         # Anteil/Ausgelegt
+    contrib_cum: Dict[str, pd.Series]
+    usage_cum: Dict[str, pd.Series]
+    share_cum: Dict[str, pd.Series]
+    ratio: Dict[str, pd.Series]
 
 
 def _auto_time_limits(tmin: pd.Timestamp, tmax: pd.Timestamp) -> tuple[pd.Timestamp, pd.Timestamp]:
-    # +/- 5% Intervall, bei 0 Intervall fallback 30 Minuten
     dt = tmax - tmin
     if dt <= pd.Timedelta(0):
         margin = pd.Timedelta(minutes=30)
@@ -159,28 +136,25 @@ def _auto_time_limits(tmin: pd.Timestamp, tmax: pd.Timestamp) -> tuple[pd.Timest
 
 
 def _prepare_group_timeseries(df: pd.DataFrame, group: str) -> Optional[GroupTimeSeries]:
-    # explode Gruppen und filtere
     work = df.copy().explode("dist_groups")
     work["group"] = work["dist_groups"].fillna("").astype(str).str.strip()
     work = work[work["group"] == group].copy()
 
-    work = work[pd.notna(work["Datum"])]
+    work = work[pd.notna(work["date"])]
     if work.empty:
         return None
 
-    work["person"] = work["Nutzer"].astype(str).str.strip()
-    work["flag"] = work["Distributionsflag"].astype(str).str.strip().str.upper()
+    work["debitor"] = work["debitor"].astype(str).str.strip()
+    work["flag"] = work["group_flag"].astype(str).str.strip().str.upper()
 
-    participants = sorted(work["person"].unique().tolist())
+    participants = sorted(work["debitor"].unique().tolist())
 
-    # timeline: alle Zeitpunkte der Gruppe (unique, sortiert)
-    times = pd.DatetimeIndex(sorted(work["Datum"].unique()))
+    times = pd.DatetimeIndex(sorted(work["date"].unique()))
     tmin, tmax = times.min(), times.max()
     x0, x1 = _auto_time_limits(tmin, tmax)
 
     times = times.union(pd.DatetimeIndex([x0, x1])).sort_values()
 
-    # usage units (kann leer sein, oder mehrere – wir zeigen dann z.B. "km/stk")
     usage_units = sorted(
         work.loc[work["flag"] == "U", "unit"]
             .dropna()
@@ -190,25 +164,21 @@ def _prepare_group_timeseries(df: pd.DataFrame, group: str) -> Optional[GroupTim
             .tolist()
     )
 
-    # pro Person: Beiträge (C) und Nutzung (U) als kumulatives step-series auf timeline
     contrib_cum: Dict[str, pd.Series] = {}
     usage_cum: Dict[str, pd.Series] = {}
 
     for p in participants:
-        c = work[(work["person"] == p) & (work["flag"] == "C")].copy()
-        u = work[(work["person"] == p) & (work["flag"] == "U")].copy()
+        c = work[(work["debitor"] == p) & (work["flag"] == "C")].copy()
+        u = work[(work["debitor"] == p) & (work["flag"] == "U")].copy()
 
-        # Beiträge: nach Datum aggregieren, reindex auf timeline, kumulieren
-        c_by_t = c.groupby("Datum")["value"].sum() if not c.empty else pd.Series(dtype=float)
+        c_by_t = c.groupby("date")["val"].sum() if not c.empty else pd.Series(dtype=float)
         c_by_t = c_by_t.reindex(times, fill_value=0.0)
         contrib_cum[p] = c_by_t.cumsum()
 
-        # Nutzung: nach Datum aggregieren, reindex auf timeline, kumulieren
-        u_by_t = u.groupby("Datum")["value"].sum() if not u.empty else pd.Series(dtype=float)
+        u_by_t = u.groupby("date")["val"].sum() if not u.empty else pd.Series(dtype=float)
         u_by_t = u_by_t.reindex(times, fill_value=0.0)
         usage_cum[p] = u_by_t.cumsum()
 
-    # share über Zeit: kumulative total contributions verteilt
     total_contrib = sum((contrib_cum[p] for p in participants), start=pd.Series(0.0, index=times))
     total_usage = sum((usage_cum[p] for p in participants), start=pd.Series(0.0, index=times))
 
@@ -217,9 +187,7 @@ def _prepare_group_timeseries(df: pd.DataFrame, group: str) -> Optional[GroupTim
 
     share_cum: Dict[str, pd.Series] = {}
     if has_any_u:
-        # usage-mode sobald U existiert; solange total_usage==0 => equal fallback
         for p in participants:
-            # share = total_contrib * usage_p / total_usage, sonst total_contrib/n
             usage_p = usage_cum[p]
             with np.errstate(divide="ignore", invalid="ignore"):
                 share_usage = total_contrib * (usage_p / total_usage.replace(0.0, np.nan))
@@ -227,7 +195,6 @@ def _prepare_group_timeseries(df: pd.DataFrame, group: str) -> Optional[GroupTim
             share = share_usage.where(total_usage > 0, share_equal)
             share_cum[p] = share.fillna(0.0)
     else:
-        # equal-mode immer
         equal = total_contrib / float(n)
         for p in participants:
             share_cum[p] = equal
@@ -254,13 +221,6 @@ def _prepare_group_timeseries(df: pd.DataFrame, group: str) -> Optional[GroupTim
 
 @dataclass
 class GroupChartBigFrame(BigFrame):
-    """
-    kind:
-      - 'usage_cum'
-      - 'contrib_cum'
-      - 'share_cum'
-      - 'ratio'
-    """
     gts: GroupTimeSeries
     kind: str
 
@@ -271,48 +231,45 @@ class GroupChartBigFrame(BigFrame):
         formatter = mdates.ConciseDateFormatter(locator)
         ax.xaxis.set_major_locator(locator)
         ax.xaxis.set_major_formatter(formatter)
-        ax.xaxis.get_offset_text().set_visible(False)  # <-- "2025-Dec" weg
+        ax.xaxis.get_offset_text().set_visible(False)
 
         ax.set_xlim(self.gts.xlim_start, self.gts.xlim_end)
 
         if self.kind == "usage_cum":
             series_map = self.gts.usage_cum
             unit = "/".join(self.gts.usage_units) if self.gts.usage_units else ""
-            ax.set_ylabel(f"Verbrauch kumulativ {unit}".strip(), fontproperties=mono_font)
+            ax.set_ylabel(f"Usage cumulative {unit}".strip(), fontproperties=mono_font)
 
         elif self.kind == "contrib_cum":
             series_map = self.gts.contrib_cum
-            ax.set_ylabel("Contributions kumulativ €", fontproperties=mono_font)
+            ax.set_ylabel("Contribution cumulative €", fontproperties=mono_font)
 
         elif self.kind == "share_cum":
             series_map = self.gts.share_cum
-            ax.set_ylabel("Anteil kumulativ €", fontproperties=mono_font)
+            ax.set_ylabel("Share cumulative €", fontproperties=mono_font)
 
         elif self.kind == "ratio":
             series_map = self.gts.ratio
-            ax.set_ylabel("Anteil / Ausgelegt", fontproperties=mono_font)
-            ax.set_yscale("log")  # <-- LOG
+            ax.set_ylabel("Share / Contribution ratio (logarithmic)", fontproperties=mono_font)
+            ax.set_yscale("log")
 
         else:
             raise ValueError(f"Unknown kind: {self.kind}")
 
-        # Plot + Sammeln für robuste y-Limits
         all_vals = []
 
-        min_ratio = 1e-3  # „quasi 0“ für log, damit Kurven am Anfang nicht "mittendrin" starten
+        min_ratio = 1e-3
 
         for p in self.gts.participants:
             y = series_map[p].copy()
 
             if self.kind == "ratio":
-                # NaN/0/Inf behandeln, damit die Kurve von Anfang an existiert
                 y = y.replace([np.inf, -np.inf], np.nan)
                 y = y.fillna(min_ratio)
                 y = y.clip(lower=min_ratio)
             else:
                 y = y.replace([np.inf, -np.inf], np.nan).fillna(0.0)
 
-            # Steps für kumulative Kurven ist meist sauberer
             ax.plot(self.gts.times, y.values, label=p, linewidth=1, drawstyle="steps-post")
 
             v = y.values
@@ -320,7 +277,6 @@ class GroupChartBigFrame(BigFrame):
             if v.size:
                 all_vals.append(v)
 
-        # y-Limits so setzen, dass wirklich ALLE Werte sichtbar sind
         if all_vals:
             vv = np.concatenate(all_vals)
 
@@ -329,14 +285,14 @@ class GroupChartBigFrame(BigFrame):
                 if vmax <= 0:
                     ax.set_ylim(0, 1)
                 else:
-                    ax.set_ylim(0, vmax * 1.08)  # kleiner Puffer
+                    ax.set_ylim(0, vmax * 1.08)
 
             elif self.kind == "ratio":
                 vpos = vv[vv > 0]
                 if vpos.size:
                     vmin = float(np.nanmin(vpos))
                     vmax = float(np.nanmax(vpos))
-                    ax.set_ylim(vmin / 1.5, vmax * 1.5)  # log: multiplicative padding
+                    ax.set_ylim(vmin / 1.5, vmax * 1.5)
 
         ax.grid(True, alpha=0.2)
 
@@ -345,7 +301,6 @@ class GroupChartBigFrame(BigFrame):
             for t in leg.get_texts():
                 t.set_fontproperties(mono_font)
 
-        # Tick-Fonts monospace
         for tick in ax.get_xticklabels() + ax.get_yticklabels():
             tick.set_fontproperties(mono_font)
 
@@ -358,12 +313,11 @@ class TextFrame(Frame):
 
 @dataclass
 class PlotBigFrame(BigFrame):
-    per_person: pd.DataFrame  # erwartet Spalten: person, contributed, share
+    per_debitor: pd.DataFrame
 
     def render(self, ax: Axes, mono_font: FontProperties) -> None:
-        # Axes ist schon da, wir zeichnen direkt hinein
         ax.axis("on")
-        plot_df = self.per_person.set_index("person")[["contributed", "share"]]
+        plot_df = self.per_debitor.set_index("debitor")[["contributed", "share"]]
         plot_df.plot.bar(ax=ax)
         ax.tick_params(axis="x", rotation=0)
         leg = ax.legend(prop=mono_font)
@@ -386,38 +340,37 @@ class GeneralModule:
 
         mono_font = context.get("mono_font") or FontProperties(family="DejaVu Sans Mono", size=8)
 
-        group_summary, per_person, detail = compute_group_distribution(df)
+        group_summary, per_debitor, detail = compute_group_distribution(df)
 
-        balance = {r["person"]: float(r["balance"]) for _, r in per_person.iterrows()}
+        balance = {r["debitor"]: float(r["balance"]) for _, r in per_debitor.iterrows()}
         payments = self._minimize_payments(balance)
 
-        # ---- NEU: Textauswertung für Konsole
         summary_lines = []
-        summary_lines.append("General – Verteilung über Distributionsgruppen")
+        summary_lines.append("General")
         summary_lines.append("")
-        summary_lines.append("Gruppen:")
+        summary_lines.append("Goups:")
         for _, r in group_summary.sort_values("group").iterrows():
             g = r["group"]
             total_c = float(r.get("total_contrib", 0.0))
             u_count = int(r.get("u_count", 0))
             mode = "usage" if u_count > 0 else "equal"
             participants = r.get("participants", []) or []
-            summary_lines.append(f"  - {g}: {total_c:.2f} €; mode={mode}; teilnehmer={len(participants)}")
+            summary_lines.append(f"  - {g}: {total_c:.2f} €; mode={mode}; participants={len(participants)}")
 
         summary_lines.append("")
-        summary_lines.append("Personen (Summe über alle Gruppen):")
-        for _, r in per_person.sort_values("person").iterrows():
+        summary_lines.append("Debitors (total):")
+        for _, r in per_debitor.sort_values("debitor").iterrows():
             summary_lines.append(
-                f"  - {r['person']}: ausgelegt={r['contributed']:.2f} €; anteil={r['share']:.2f} €; saldo={r['balance']:.2f} €"
+                f"  - {r['debitor']}: contributed={r['contributed']:.2f} €; share={r['share']:.2f} €; balance={r['balance']:.2f} €"
             )
 
         summary_lines.append("")
-        summary_lines.append("Ausgleich (minimiert):")
+        summary_lines.append("Compensation (minimized):")
         if payments:
             for p, r, a in payments:
                 summary_lines.append(f"  - {p} → {r}: {a:.2f} €")
         else:
-            summary_lines.append("  (keine Zahlungen nötig)")
+            summary_lines.append("  (No compensation required)")
 
         summary_text = "\n".join(summary_lines)
 
@@ -426,45 +379,42 @@ class GeneralModule:
         pages: List[plt.Figure] = []
 
         if want_pdf:
-            frames.extend(self._make_frames(group_summary, per_person, payments))
+            frames.extend(self._make_frames(group_summary, per_debitor, payments))
 
-            # BigFrame: Gesamt-Balkenplot bleibt (wie vorher)
             bigframes.append(
                 PlotBigFrame(
-                    title="General – Ausgelegt vs Anteil (Summe über Gruppen)",
-                    per_person=per_person.copy(),
+                    title="General – Shares vs. Contributions (total)",
+                    per_debitor=per_debitor.copy(),
                 )
             )
 
-            # NEU: pro Distributionsgruppe 4 BigFrame-Charts
             for g in sorted(group_summary["group"].unique().tolist()):
                 gts = _prepare_group_timeseries(df, g)
                 if not gts:
                     continue
 
                 bigframes.append(GroupChartBigFrame(
-                    title=f"{g} – Kumulativer Verbrauch pro Person",
+                    title=f"{g} – Cumulative usage per debitor",
                     gts=gts,
                     kind="usage_cum",
                 ))
                 bigframes.append(GroupChartBigFrame(
-                    title=f"{g} – Kumulative Contributions pro Person",
+                    title=f"{g} – Cumulative contributions per debitor",
                     gts=gts,
                     kind="contrib_cum",
                 ))
                 bigframes.append(GroupChartBigFrame(
-                    title=f"{g} – Anteil pro Person (zeitlicher Verlauf)",
+                    title=f"{g} – Share per debitor",
                     gts=gts,
                     kind="share_cum",
                 ))
                 bigframes.append(GroupChartBigFrame(
-                    title=f"{g} – Verhältnis Anteil/Ausgelegt (zeitlicher Verlauf)",
+                    title=f"{g} – Share / Contribution ratio (logarithmic)",
                     gts=gts,
                     kind="ratio",
                 ))
 
-            # Pages: nur noch Detailseiten, keine Balkenplot-Seite mehr
-            pages.extend(self._make_pages(group_summary, per_person, detail, mono_font))
+            pages.extend(self._make_pages(group_summary, per_debitor, detail, mono_font))
 
         return ModuleResult(summary_text=summary_text, frames=frames, bigframes=bigframes, pages=pages)
 
@@ -493,57 +443,52 @@ class GeneralModule:
                 j += 1
         return out
 
-    def _make_frames(self, group_summary: pd.DataFrame, per_person: pd.DataFrame, payments: List[Tuple[str,str,float]]) -> List[Frame]:
-        # Frame 1: Gruppen-Übersicht
-        lines = ["Gruppenübersicht:"]
+    def _make_frames(self, group_summary: pd.DataFrame, per_debitor: pd.DataFrame, payments: List[Tuple[str,str,float]]) -> List[Frame]:
+        lines = ["Groups:"]
         for _, r in group_summary.sort_values("group").iterrows():
             g = r["group"]
             total_c = float(r.get("total_contrib", 0.0))
             u_count = int(r.get("u_count", 0))
             parts = r.get("participants", [])
             mode = "usage" if u_count > 0 else "equal"
-            lines.append(f"- {g}: {total_c:.2f} €; mode={mode}; teilnehmer={len(parts)}")
+            lines.append(f"- {g}: {total_c:.2f} €; mode={mode}; participants={len(parts)}")
 
-        f1 = TextFrame(title="General: Gruppen", text="\n".join(lines))
+        f1 = TextFrame(title="General: Groups", text="\n".join(lines))
 
-        # Frame 2: Personen-Totale
-        lines = ["Personen (Summe über alle Gruppen):", "Person | contributed | share | balance"]
-        for _, r in per_person.iterrows():
-            lines.append(f"{r['person']}: {r['contributed']:.2f} €; {r['share']:.2f} €; {r['balance']:.2f} €")
-        f2 = TextFrame(title="General: Personen", text="\n".join(lines))
+        lines = ["Debitor total:", "debitor | contributed | share | balance"]
+        for _, r in per_debitor.iterrows():
+            lines.append(f"{r['debitor']} | {r['contributed']:.2f} € | {r['share']:.2f} € | {r['balance']:.2f} €")
+        f2 = TextFrame(title="General: Debitors", text="\n".join(lines))
 
-        # Frame 3: Ausgleich
-        lines = ["Ausgleich (minimiert):"]
+        lines = ["Compensation (minimized):"]
         if payments:
             for p, r, a in payments:
                 lines.append(f"{p} → {r}: {a:.2f} €")
         else:
-            lines.append("(keine Zahlungen nötig)")
-        f3 = TextFrame(title="General: Ausgleich", text="\n".join(lines))
+            lines.append("(No compensation required)")
+        f3 = TextFrame(title="General: Compensation", text="\n".join(lines))
 
         return [f1, f2, f3]
 
-    def _make_pages(self, group_summary, per_person, detail, mono_font) -> List[plt.Figure]:
+    def _make_pages(self, group_summary, per_debitor, detail, mono_font) -> List[plt.Figure]:
         pages: List[plt.Figure] = []
 
-        # Textseiten: pro Gruppe Detail (ggf. mehrere)
-        # Wir machen je Gruppe eine Seite, wenn es nicht zu viele sind
         for g in sorted(detail["group"].unique().tolist()):
-            gdet = detail[detail["group"] == g].sort_values("person")
+            gdet = detail[detail["group"] == g].sort_values("debitor")
             total_c = float(group_summary[group_summary["group"] == g]["total_contrib"].iloc[0]) if (group_summary["group"] == g).any() else 0.0
             u_count = int(group_summary[group_summary["group"] == g]["u_count"].iloc[0]) if (group_summary["group"] == g).any() else 0
             mode = "usage" if u_count > 0 else "equal"
 
             lines = [
-                f"Gruppe: {g}",
+                f"Group: {g}",
                 f"Total Contribution: {total_c:.2f} €",
                 f"Mode: {mode}",
                 "",
-                "Person | contributed | usage | share | balance",
+                "debitor | contributed | usage | share | balance",
             ]
             for _, r in gdet.iterrows():
                 lines.append(
-                    f"{r['person']}: {r['contributed']:.2f} €; {r['usage']:.4f}; {r['share']:.2f} €; {r['balance']:.2f} €"
+                    f"{r['debitor']} | {r['contributed']:.2f} € | {r['usage']:.4f} | {r['share']:.2f} € | {r['balance']:.2f} €"
                 )
 
             fig, ax = plt.subplots(figsize=(8.27, 11.69))
@@ -551,10 +496,5 @@ class GeneralModule:
             ax.text(0, 1, "\n".join(lines), va="top", ha="left", fontproperties=mono_font)
             pages.append(fig)
 
-        # Optional: Nutzungsverläufe für Gruppen mit unit "km"
-        # (nur wenn U vorhanden und unit in den U-rows km ist)
-        # Dafür brauchen wir zeitliche Daten → aus detail nicht möglich, also direkt aus df wäre besser.
-        # Wenn du willst, ergänze ich das als eigene Seite pro km-Gruppe auf Basis der Original-DF.
-
         return pages