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"""Compass tab for the parliamentary explorer."""
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from __future__ import annotations
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import datetime as _dt
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import re
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from typing import Dict, Tuple
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import numpy as np
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import pandas as pd
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from analysis import config
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import analysis.explorer_data as explorer_data
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from analysis.tabs._rendering import px, st
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PARTY_COLOURS = config.PARTY_COLOURS
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def build_compass_tab(db_path: str, window_size: str) -> None:
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"""Build the Politiek Kompas tab."""
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st.subheader("Politiek Kompas")
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st.markdown(
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"2D projectie van Kamerlid posities op basis van stemgedrag (PCA op SVD-vectoren)."
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)
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# Compass always uses annual windows regardless of the sidebar window_size setting.
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positions_by_window, axis_def = explorer_data.load_positions(db_path, "annual")
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if axis_def is None:
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axis_def = {}
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if not positions_by_window:
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st.warning(
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"Geen positiedata beschikbaar. Controleer of de pipeline is gedraaid."
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)
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return
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party_map = explorer_data.load_party_map(db_path)
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active_mps = explorer_data.load_active_mps(db_path)
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_current_year = str(_dt.date.today().year)
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year_windows = sorted(
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w
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for w in positions_by_window
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if w != "current_parliament" and w != _current_year
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)
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has_current = "current_parliament" in positions_by_window
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windows = year_windows + (["current_parliament"] if has_current else [])
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_SPARSE_YEARS = {"2016", "2017", "2018"}
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_THRESHOLD = 0.65
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def _window_label(w: str) -> str:
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if w == "current_parliament":
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return "Huidig parlement"
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if w in _SPARSE_YEARS:
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return f"{w} ⚠️"
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return w
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col1, col2 = st.columns([3, 1])
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with col2:
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window_idx = st.selectbox(
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"Jaar",
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options=windows,
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index=len(windows) - 1,
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format_func=_window_label,
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)
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level = st.radio(
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"Weergave",
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options=["Kamerleden", "Partijen"],
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index=0,
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horizontal=True,
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)
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min_mps = st.number_input(
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"Min. Kamerleden per partij",
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min_value=1,
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max_value=20,
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value=3,
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step=1,
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help="Partijen met minder dan dit aantal zetels worden niet weergegeven.",
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)
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pos = positions_by_window.get(window_idx, {})
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if not pos:
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st.info(f"Geen data voor venster {window_idx}")
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return
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if window_idx == "current_parliament":
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pos = {mp: xy for mp, xy in pos.items() if mp in active_mps}
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def _strip_paren(name: str) -> str:
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return re.sub(r"\s*\([^)]*\)", "", name).strip()
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deduped: Dict[str, Tuple[float, float]] = {}
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for name, (x, y) in pos.items():
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base = _strip_paren(name)
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if base in deduped:
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ox, oy = deduped[base]
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deduped[base] = ((ox + x) / 2, (oy + y) / 2)
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else:
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deduped[base] = (x, y)
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pos = deduped
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rows = []
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for name, (x, y) in pos.items():
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party = party_map.get(name) or party_map.get(_strip_paren(name), "Unknown")
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rows.append({"name": name, "x": x, "y": y, "party": party})
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df_pos = pd.DataFrame(rows)
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party_counts = df_pos[df_pos["party"] != "Unknown"]["party"].value_counts()
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valid_parties = set(party_counts[party_counts >= min_mps].index)
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df_pos = df_pos[df_pos["party"].isin(valid_parties)]
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if df_pos.empty:
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st.info("Geen partijen met genoeg Kamerleden voor dit venster.")
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return
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_raw_x = axis_def.get("x_label")
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_raw_y = axis_def.get("y_label")
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try:
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from analysis.axis_classifier import display_label_for_modal
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_x_label = display_label_for_modal(_raw_x, "x")
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_y_label = display_label_for_modal(_raw_y, "y")
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except Exception:
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from analysis.svd_labels import get_fallback_labels
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_x_fallback, _y_fallback = get_fallback_labels()
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_x_label = _raw_x or _x_fallback
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_y_label = _raw_y or _y_fallback
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if level == "Partijen":
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df_party = df_pos.groupby("party", as_index=False).agg(
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x=("x", "mean"), y=("y", "mean"), n=("name", "count")
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)
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df_party["name"] = df_party["party"]
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colour_map = {
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p: PARTY_COLOURS.get(p, "#9E9E9E") for p in df_party["party"].unique()
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}
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fig = px.scatter(
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df_party,
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x="x",
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y="y",
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color="party",
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text="party",
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hover_name="party",
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hover_data={"party": False, "x": ":.3f", "y": ":.3f", "n": True},
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color_discrete_map=colour_map,
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title=f"Politiek Kompas — {_window_label(window_idx)} (partijen)",
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labels={
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"x": _x_label,
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"y": _y_label,
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"n": "Kamerleden",
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},
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)
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fig.update_traces(textposition="top center", marker_size=14)
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else:
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colour_map = {
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p: PARTY_COLOURS.get(p, "#9E9E9E") for p in df_pos["party"].unique()
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}
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fig = px.scatter(
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df_pos,
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x="x",
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y="y",
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color="party",
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hover_name="name",
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hover_data={"party": True, "x": ":.3f", "y": ":.3f"},
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color_discrete_map=colour_map,
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title=f"Politiek Kompas — {_window_label(window_idx)}",
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labels={"x": _x_label, "y": _y_label},
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)
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fig.update_layout(
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height=600,
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legend_title_text="Partij",
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xaxis={"range": [-1, 1]},
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yaxis={"range": [-0.6, 0.6]},
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)
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with col1:
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st.plotly_chart(fig, use_container_width=True)
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_x_interp = axis_def.get("x_interpretation", {}).get(window_idx, "")
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if (
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_x_interp
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and axis_def.get("x_quality", {}).get(window_idx, 1.0) < _THRESHOLD
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):
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st.caption(_x_interp)
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# Voting discipline analysis
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st.markdown("---")
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st.markdown(
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"**Stemdiscipline analyse:** De Rice-index meet hoe eensgezind partijen stemmen "
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"tijdens hoofdelijke stemmingen. Een score van 100% betekent dat alle MPs van "
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"een partij hetzelfde stemden; 50% wijst op een gelijke splitsing binnen de partij. "
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"Partijen met hoge discipline (>95%) zoals PVV en SGP stemmen als een blok, wat "
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"wijst op sterke partijdiscipline en homogene membership. Lagere discipline (<85%) "
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"bij partijen als PvdA of SP kan duiden op interne factiestrijd, gewetensvragen "
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"bij ethische thema's, of een brede ideologische koers die ruimte laat voor "
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"afwijkende meningen. De discipline varieert ook per onderwerp — ethische kwesties "
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"tonen vaak meer interne verschillen dan economische thema's."
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)
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