fix: connection leak, Rice index excludes absences, per-party motion count guard

explorer.py

@@ -258,7 +258,10 @@ def compute_party_discipline(
 
     Rice index per motion per party = fraction of party MPs voting with the party majority.
     The per-party score is the average Rice index across all motions in the date range.
+    Only 'voor' and 'tegen' votes are counted; absent and abstaining MPs are excluded from the
+    Rice index calculation.
     """
+    conn = None
     try:
         conn = duckdb.connect(db_path, read_only=True)
         result = conn.execute(
@@ -272,7 +275,7 @@ def compute_party_discipline(
                 WHERE mp_name LIKE '%,%'
                   AND date >= CAST(? AS DATE)
                   AND date <= CAST(? AS DATE)
-                  AND vote IN ('voor', 'tegen', 'afwezig', 'onthouden')
+                  AND vote IN ('voor', 'tegen')
             ),
             vote_counts AS (
                 SELECT
@@ -313,11 +316,16 @@ def compute_party_discipline(
             """,
             [start_date, end_date],
         ).fetchdf()
-        conn.close()
         return result
     except Exception as exc:
         logger.warning("compute_party_discipline failed: %s", exc)
         return pd.DataFrame(columns=["party", "n_motions", "discipline"])
+    finally:
+        if conn is not None:
+            try:
+                conn.close()
+            except Exception:
+                pass
 
 
 @st.cache_data(show_spinner="Partijposities op SVD-assen laden…")
@@ -955,74 +963,79 @@ def build_compass_tab(db_path: str, window_size: str) -> None:
     disc_df = compute_party_discipline(db_path, start_date, end_date)
 
     st.subheader("Stemgedrag cohesie")
-    if disc_df.empty or disc_df["n_motions"].max() < _MIN_MOTIONS_FOR_DISCIPLINE:
+    if disc_df.empty:
         st.caption(
             "Te weinig hoofdelijke stemmingen in dit venster voor een cohesieanalyse."
         )
     else:
-        compass_parties = set(df_pos["party"].unique())
+        disc_df = disc_df[disc_df["n_motions"] >= _MIN_MOTIONS_FOR_DISCIPLINE].copy()
-        disc_df = disc_df[disc_df["party"].isin(compass_parties)].copy()
-
         if disc_df.empty:
-            st.caption("Geen overlappende partijen tussen kompas en stemmingsdata.")
+            st.caption(
+                "Te weinig hoofdelijke stemmingen in dit venster voor een cohesieanalyse."
+            )
         else:
-            disc_df["discipline_pct"] = (disc_df["discipline"] * 100).round(1)
+            compass_parties = set(df_pos["party"].unique())
-            disc_df["party_label"] = disc_df.apply(
+            disc_df = disc_df[disc_df["party"].isin(compass_parties)].copy()
-                lambda r: f"{r['party']} ({int(r['n_motions'])} moties)", axis=1
+            if disc_df.empty:
-            )
+                st.caption("Geen overlappende partijen tussen kompas en stemmingsdata.")
-
+            else:
-            bar_fig = px.bar(
+                disc_df["discipline_pct"] = (disc_df["discipline"] * 100).round(1)
-                disc_df.sort_values("discipline"),
+                disc_df["party_label"] = disc_df.apply(
-                x="discipline_pct",
+                    lambda r: f"{r['party']} ({int(r['n_motions'])} moties)", axis=1
-                y="party_label",
-                orientation="h",
-                color="discipline_pct",
-                color_continuous_scale="RdYlGn",
-                range_color=[80, 100],
-                labels={"discipline_pct": "Cohesie (%)", "party_label": "Partij"},
-                title="Cohesie bij hoofdelijke stemmingen",
-            )
-            bar_fig.update_layout(
-                height=max(300, len(disc_df) * 35 + 80),
-                showlegend=False,
-                coloraxis_showscale=False,
-                yaxis_title="",
-            )
-            st.plotly_chart(bar_fig, use_container_width=True)
-
-            top3 = disc_df.nlargest(3, "discipline")[
-                ["party", "discipline_pct", "n_motions"]
-            ]
-            bot3 = disc_df.nsmallest(3, "discipline")[
-                ["party", "discipline_pct", "n_motions"]
-            ]
-            col_a, col_b = st.columns(2)
-            with col_a:
-                st.markdown("**Meest eensgezind**")
-                st.dataframe(
-                    top3.rename(
-                        columns={
-                            "party": "Partij",
-                            "discipline_pct": "Cohesie (%)",
-                            "n_motions": "Moties",
-                        }
-                    ),
-                    hide_index=True,
-                    use_container_width=True,
                 )
-            with col_b:
+
-                st.markdown("**Meest verdeeld**")
+                bar_fig = px.bar(
-                st.dataframe(
+                    disc_df.sort_values("discipline"),
-                    bot3.rename(
+                    x="discipline_pct",
-                        columns={
+                    y="party_label",
-                            "party": "Partij",
+                    orientation="h",
-                            "discipline_pct": "Cohesie (%)",
+                    color="discipline_pct",
-                            "n_motions": "Moties",
+                    color_continuous_scale="RdYlGn",
-                        }
+                    range_color=[80, 100],
-                    ),
+                    labels={"discipline_pct": "Cohesie (%)", "party_label": "Partij"},
-                    hide_index=True,
+                    title="Cohesie bij hoofdelijke stemmingen",
-                    use_container_width=True,
                 )
+                bar_fig.update_layout(
+                    height=max(300, len(disc_df) * 35 + 80),
+                    showlegend=False,
+                    coloraxis_showscale=False,
+                    yaxis_title="",
+                )
+                st.plotly_chart(bar_fig, use_container_width=True)
+
+                top3 = disc_df.nlargest(3, "discipline")[
+                    ["party", "discipline_pct", "n_motions"]
+                ]
+                bot3 = disc_df.nsmallest(3, "discipline")[
+                    ["party", "discipline_pct", "n_motions"]
+                ]
+                col_a, col_b = st.columns(2)
+                with col_a:
+                    st.markdown("**Meest eensgezind**")
+                    st.dataframe(
+                        top3.rename(
+                            columns={
+                                "party": "Partij",
+                                "discipline_pct": "Cohesie (%)",
+                                "n_motions": "Moties",
+                            }
+                        ),
+                        hide_index=True,
+                        use_container_width=True,
+                    )
+                with col_b:
+                    st.markdown("**Meest verdeeld**")
+                    st.dataframe(
+                        bot3.rename(
+                            columns={
+                                "party": "Partij",
+                                "discipline_pct": "Cohesie (%)",
+                                "n_motions": "Moties",
+                            }
+                        ),
+                        hide_index=True,
+                        use_container_width=True,
+                    )
 
 
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