fix: connection leak, Rice index excludes absences, per-party motion count guard
This commit is contained in:
+75
-62
@@ -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["party"].isin(compass_parties)].copy()
|
||||
|
||||
disc_df = disc_df[disc_df["n_motions"] >= _MIN_MOTIONS_FOR_DISCIPLINE].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)
|
||||
disc_df["party_label"] = disc_df.apply(
|
||||
lambda r: f"{r['party']} ({int(r['n_motions'])} moties)", axis=1
|
||||
)
|
||||
|
||||
bar_fig = px.bar(
|
||||
disc_df.sort_values("discipline"),
|
||||
x="discipline_pct",
|
||||
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,
|
||||
compass_parties = set(df_pos["party"].unique())
|
||||
disc_df = disc_df[disc_df["party"].isin(compass_parties)].copy()
|
||||
if disc_df.empty:
|
||||
st.caption("Geen overlappende partijen tussen kompas en stemmingsdata.")
|
||||
else:
|
||||
disc_df["discipline_pct"] = (disc_df["discipline"] * 100).round(1)
|
||||
disc_df["party_label"] = disc_df.apply(
|
||||
lambda r: f"{r['party']} ({int(r['n_motions'])} moties)", axis=1
|
||||
)
|
||||
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,
|
||||
|
||||
bar_fig = px.bar(
|
||||
disc_df.sort_values("discipline"),
|
||||
x="discipline_pct",
|
||||
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**")
|
||||
st.dataframe(
|
||||
bot3.rename(
|
||||
columns={
|
||||
"party": "Partij",
|
||||
"discipline_pct": "Cohesie (%)",
|
||||
"n_motions": "Moties",
|
||||
}
|
||||
),
|
||||
hide_index=True,
|
||||
use_container_width=True,
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
Reference in New Issue
Block a user