refactor: decompose explorer.py into analysis/tabs/ and add scheduler

- Extract 6 tab functions from explorer.py (3097 → 543 lines)
- Create analysis/tabs/_rendering.py with shared plotly helpers
- Move data logic to analysis/explorer_data.py
- Add lazy-import wrappers in explorer.py for backward compat
- Add scheduler.py with PipelineScheduler for daily pipeline runs
- Add test_explorer_decomposition.py (5 tests, all pass)
- Add test_scheduler.py (13 tests, all pass)
- Full test suite: 222 passed, 2 skipped

analysis/explorer_data.py

@@ -23,6 +23,7 @@ from analysis.config import CURRENT_PARLIAMENT_PARTIES, _PARTY_NORMALIZE
 __all__ = [
     "get_available_windows",
     "get_uniform_dim_windows",
+    "load_positions",
     "load_party_map",
     "load_active_mps",
     "load_mp_vectors_by_window",
@@ -37,6 +38,9 @@ __all__ = [
     "load_motions_df",
     "query_similar",
     "compute_party_axis_scores",
+    "get_aligned_party_scores",
+    "compute_party_discipline",
+    "_get_aligned_trajectory_scores",
 ]
 
 logger = logging.getLogger(__name__)
@@ -567,3 +571,139 @@ def compute_party_axis_scores(
     except Exception:
         logger.exception("Failed to compute party axis scores")
         return {}
+
+
+def load_positions(
+    db_path: str, window_size: str = "annual"
+) -> Tuple[Dict[str, Dict[str, Tuple[float, float]]], Dict]:
+    """Compute 2D positions per window using PCA on aligned SVD vectors.
+
+    Returns:
+        positions_by_window: {window_id: {entity_name: (x, y)}}
+        axis_def: dict with x_axis, y_axis, method keys
+    """
+    from analysis.political_axis import compute_2d_axes
+
+    all_available = get_uniform_dim_windows(db_path)
+
+    if not all_available:
+        return {}, {}
+
+    positions_by_window, axis_def = compute_2d_axes(
+        db_path,
+        window_ids=all_available,
+        method="pca",
+        pca_residual=True,
+        normalize_vectors=True,
+    )
+
+    try:
+        from analysis.axis_classifier import classify_axes
+
+        axis_def = classify_axes(positions_by_window, axis_def, db_path)
+    except Exception:
+        logger.exception("classify_axes failed; using generic axis labels")
+
+    if window_size == "annual":
+        annual_keys = set(w for w in all_available if "-Q" not in w)
+        positions_by_window = {
+            w: v for w, v in positions_by_window.items() if w in annual_keys
+        }
+
+    return positions_by_window, axis_def
+
+
+def get_aligned_party_scores(
+    db_path: str, window: str, active_mps: set | None = None
+) -> Dict[str, np.ndarray]:
+    """Get party scores for all N components from aligned PCA positions.
+
+    For current_parliament, pass active_mps to filter to only seated MPs
+    (matching the compass behaviour). Historical windows include all MPs.
+    """
+    from analysis.political_axis import compute_nd_axes
+
+    annual_windows = get_uniform_dim_windows(db_path)
+    scores_by_window, _ = compute_nd_axes(
+        db_path, window_ids=annual_windows, n_components=10
+    )
+    window_scores = scores_by_window.get(window, {})
+    if not window_scores:
+        return {}
+
+    if window == "current_parliament" and active_mps is not None:
+        window_scores = {mp: sc for mp, sc in window_scores.items() if mp in active_mps}
+
+    _party_map = load_party_map(db_path)
+
+    n_comps = 10
+    party_scores_agg: Dict[str, List[np.ndarray]] = {}
+    for mp_name, scores in window_scores.items():
+        party = _party_map.get(
+            mp_name, _party_map.get(mp_name.split("(")[0].strip(), None)
+        )
+        if party:
+            party_scores_agg.setdefault(party, []).append(scores[:n_comps])
+
+    return {
+        party: np.mean(np.vstack(score_list), axis=0)
+        for party, score_list in party_scores_agg.items()
+        if score_list
+    }
+
+
+def compute_party_discipline(
+    db_path: str,
+    start_date: str,
+    end_date: str,
+) -> pd.DataFrame:
+    """Compute per-party voting discipline (Rice index) for roll-call votes in a date range.
+
+    Only individual MP vote rows are used (mp_name LIKE '%,%').
+    Returns a DataFrame with columns [party, n_motions, discipline] sorted by discipline ascending.
+    Returns an empty DataFrame if fewer than 1 qualifying motion exists or on any DB error.
+    """
+    from analysis import trajectory
+
+    return trajectory.compute_party_discipline(db_path, start_date, end_date)
+
+
+def _get_aligned_trajectory_scores(
+    db_path: str, windows: List[str], n_components: int = 10
+) -> Dict[str, Dict[str, List[float]]]:
+    """Get aligned PCA scores for all windows as {window: {party: [scores per component]}}.
+
+    Uses compute_nd_axes to get PCA-projected, flip-corrected scores across all windows,
+    ensuring consistency with the single-window SVD components view.
+    """
+    from analysis.political_axis import compute_nd_axes
+
+    scores_by_window, _ = compute_nd_axes(
+        db_path, window_ids=windows, n_components=n_components
+    )
+    if not scores_by_window:
+        return {}
+
+    party_map = load_party_map(db_path)
+
+    result: Dict[str, Dict[str, List[float]]] = {}
+    for window in windows:
+        window_scores = scores_by_window.get(window, {})
+        if not window_scores:
+            continue
+
+        party_vecs: Dict[str, List[np.ndarray]] = {}
+        for mp_name, scores in window_scores.items():
+            party = party_map.get(
+                mp_name, party_map.get(mp_name.split("(")[0].strip(), None)
+            )
+            if party:
+                party_vecs.setdefault(party, []).append(scores[:n_components])
+
+        result[window] = {
+            party: np.mean(np.vstack(score_list), axis=0).tolist()
+            for party, score_list in party_vecs.items()
+            if score_list
+        }
+
+    return result

analysis/tabs/_rendering.py

@@ -0,0 +1,796 @@
+"""Rendering helpers for explorer tabs.
+
+This module contains all Plotly/Streamlit rendering functions extracted from
+explorer.py.  It is import-safe: plotly and streamlit are optional.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Dict, List, Optional, Tuple
+
+try:
+    import plotly.express as px
+    import plotly.graph_objects as go
+except Exception:
+    px = None
+    import types
+
+    class _DummyTrace:
+        def __init__(self, **kwargs):
+            self.name = kwargs.get("name")
+            self.x = kwargs.get("x")
+            self.y = kwargs.get("y")
+            self.text = kwargs.get("text")
+            self.customdata = kwargs.get("customdata")
+
+    class _DummyFigure:
+        def __init__(self):
+            self.data = []
+
+        def add_trace(self, trace):
+            if isinstance(trace, _DummyTrace):
+                self.data.append(trace)
+            else:
+                try:
+                    name = getattr(trace, "name", None)
+                    x = getattr(trace, "x", None)
+                    y = getattr(trace, "y", None)
+                    text = getattr(trace, "text", None)
+                    customdata = getattr(trace, "customdata", None)
+                except Exception:
+                    name = trace.get("name") if hasattr(trace, "get") else None
+                    x = trace.get("x") if hasattr(trace, "get") else None
+                    y = trace.get("y") if hasattr(trace, "get") else None
+                    text = trace.get("text") if hasattr(trace, "get") else None
+                    customdata = (
+                        trace.get("customdata") if hasattr(trace, "get") else None
+                    )
+                self.data.append(
+                    _DummyTrace(name=name, x=x, y=y, text=text, customdata=customdata)
+                )
+
+        def add_annotation(self, *args, **kwargs):
+            return None
+
+        def update_layout(self, **kwargs):
+            return None
+
+        def update_traces(self, **kwargs):
+            return None
+
+        def add_hline(self, **kwargs):
+            return None
+
+    go = types.SimpleNamespace(
+        Figure=_DummyFigure,
+        Scatter=lambda **kwargs: _DummyTrace(**kwargs),
+        Bar=lambda **kwargs: _DummyTrace(**kwargs),
+    )
+
+try:
+    import streamlit as st
+except Exception:
+
+    class _DummySt:
+        def cache_data(self, *args, **kwargs):
+            def _decorator(func):
+                return func
+
+            return _decorator
+
+        def markdown(self, *args, **kwargs):
+            return None
+
+        def subheader(self, *args, **kwargs):
+            return None
+
+        def plotly_chart(self, *args, **kwargs):
+            return None
+
+        def caption(self, *args, **kwargs):
+            return None
+
+        def text_area(self, *args, **kwargs):
+            return None
+
+        def json(self, *args, **kwargs):
+            return None
+
+        def checkbox(self, *args, **kwargs):
+            return kwargs.get("value", False)
+
+        def warning(self, *args, **kwargs):
+            return None
+
+        def info(self, *args, **kwargs):
+            return None
+
+        def error(self, *args, **kwargs):
+            return None
+
+        def success(self, *args, **kwargs):
+            return None
+
+        def selectbox(self, *args, **kwargs):
+            opts = (
+                kwargs.get("options")
+                if kwargs.get("options") is not None
+                else (args[1] if len(args) > 1 else [])
+            )
+            return opts[0] if opts else None
+
+        def multiselect(self, *args, **kwargs):
+            opts = (
+                kwargs.get("options")
+                if kwargs.get("options") is not None
+                else (args[1] if len(args) > 1 else [])
+            )
+            default = kwargs.get("default")
+            if default is not None:
+                return default
+            return opts[:6] if opts else []
+
+        def number_input(self, *args, **kwargs):
+            return kwargs.get("value") if "value" in kwargs else 1
+
+        def slider(self, *args, **kwargs):
+            return kwargs.get("value") if "value" in kwargs else 0.35
+
+        def select_slider(self, *args, **kwargs):
+            return kwargs.get("value") if "value" in kwargs else (None, None)
+
+        def expander(self, *args, **kwargs):
+            class _Ctx:
+                def __enter__(self_inner):
+                    return self_inner
+
+                def __exit__(self_inner, exc_type, exc, tb):
+                    return False
+
+            return _Ctx()
+
+        def columns(self, *args, **kwargs):
+            class _Col:
+                def markdown(self, *a, **k):
+                    return None
+
+                def metric(self, *a, **k):
+                    return None
+
+                def dataframe(self, *a, **k):
+                    return None
+
+                def write(self, *a, **k):
+                    return None
+
+                def text_input(self, *a, **k):
+                    return None
+
+            n = len(args[0]) if args else 1
+            return tuple(_Col() for _ in range(n))
+
+        def form(self, *args, **kwargs):
+            class _Ctx:
+                def __enter__(self_inner):
+                    return self_inner
+
+                def __exit__(self_inner, exc_type, exc, tb):
+                    return False
+
+            return _Ctx()
+
+        def form_submit_button(self, *args, **kwargs):
+            return False
+
+        def button(self, *args, **kwargs):
+            return False
+
+        def rerun(self, *args, **kwargs):
+            return None
+
+        def divider(self, *args, **kwargs):
+            return None
+
+        def spinner(self, *args, **kwargs):
+            class _Ctx:
+                def __enter__(self_inner):
+                    return self_inner
+
+                def __exit__(self_inner, exc_type, exc, tb):
+                    return False
+
+            return _Ctx()
+
+        def write(self, *args, **kwargs):
+            return None
+
+        def dataframe(self, *args, **kwargs):
+            return None
+
+        def set_page_config(self, *args, **kwargs):
+            return None
+
+        def title(self, *args, **kwargs):
+            return None
+
+        def sidebar(self, *args, **kwargs):
+            return self
+
+        def radio(self, *args, **kwargs):
+            return kwargs.get("value") if "value" in kwargs else None
+
+        def text_input(self, *args, **kwargs):
+            return kwargs.get("value", "")
+
+        def tabs(self, *args, **kwargs):
+            n = len(args[0]) if args else 1
+            return [self for _ in range(n)]
+
+        @property
+        def session_state(self):
+            if not hasattr(self, "_session_state"):
+                self._session_state = {}
+            return self._session_state
+
+    st = _DummySt()
+
+from analysis.config import PARTY_COLOURS
+
+logger = logging.getLogger(__name__)
+
+
+def _render_scree_plot(importances: List[float], n_show: int = 15) -> None:
+    """Render a scree plot showing relative SVD component importance.
+
+    Highlighted bars for the top-2 components (used in the compass); muted bars
+    for the rest.  A cumulative-variance dashed line on the same y-axis helps
+    spot the elbow.  A 50 % cumulative threshold line is drawn for reference.
+
+    Args:
+        importances: List of importance values sorted descending (from load_scree_data).
+        n_show: How many components to display (default: first 15).
+    """
+    if not importances:
+        return
+    data = list(importances[:n_show])
+    ranks = list(range(1, len(data) + 1))
+
+    cumsum = []
+    running = 0.0
+    for v in data:
+        running += v
+        cumsum.append(running)
+
+    n_highlight = 2
+    bar_colours = [
+        "#1565C0" if i < n_highlight else "#90CAF9" for i in range(len(data))
+    ]
+
+    fig = go.Figure()
+
+    fig.add_trace(
+        go.Bar(
+            x=ranks,
+            y=data,
+            marker_color=bar_colours,
+            hovertemplate="As %{x}<br><b>%{y:.1f}%</b> verklaarde variantie<extra></extra>",
+            showlegend=False,
+        )
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=ranks,
+            y=cumsum,
+            mode="lines+markers",
+            line={"color": "#F57C00", "width": 2, "dash": "dot"},
+            marker={"size": 5, "color": "#F57C00"},
+            hovertemplate="As %{x}<br>Cumulatief: <b>%{y:.1f}%</b><extra></extra>",
+            name="Cumulatief",
+            showlegend=True,
+        )
+    )
+
+    fig.add_hline(
+        y=50,
+        line_dash="dash",
+        line_color="#BDBDBD",
+        line_width=1,
+        annotation_text="50%",
+        annotation_position="right",
+        annotation_font_color="#9E9E9E",
+        annotation_font_size=11,
+    )
+
+    for i in range(min(n_highlight, len(data))):
+        fig.add_annotation(
+            x=ranks[i],
+            y=data[i] + 0.3,
+            text=f"{data[i]:.1f}%",
+            showarrow=False,
+            font={"size": 11, "color": "#1565C0"},
+            yanchor="bottom",
+        )
+
+    fig.update_layout(
+        height=280,
+        margin={"l": 10, "r": 50, "t": 30, "b": 40},
+        title={
+            "text": "Belang per SVD-as",
+            "font": {"size": 13, "color": "#555555"},
+            "x": 0.02,
+            "xanchor": "left",
+        },
+        legend={
+            "orientation": "h",
+            "x": 0.5,
+            "xanchor": "center",
+            "y": 1.08,
+            "font": {"size": 11},
+        },
+        xaxis={
+            "title": {"text": "As (rang)", "font": {"size": 11}},
+            "tickmode": "linear",
+            "tick0": 1,
+            "dtick": 1,
+            "showline": False,
+            "showgrid": False,
+        },
+        yaxis={
+            "title": {"text": "% van totale variantie", "font": {"size": 11}},
+            "showline": False,
+            "showgrid": True,
+            "gridcolor": "#eeeeee",
+            "ticksuffix": "%",
+            "range": [0, max(cumsum) * 1.08],
+        },
+        plot_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor="rgba(0,0,0,0)",
+        bargap=0.25,
+    )
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def _build_party_axis_figure(
+    party_coords: Dict[str, Tuple[float, float]],
+    comp_sel: int,
+    theme: dict,
+    bootstrap_data: Optional[Dict[str, Dict]] = None,
+) -> Optional[go.Figure]:
+    """Build a 1D horizontal Plotly scatter of party positions on SVD axis `comp_sel`.
+
+    Accepts explicit per-party 2D coordinates (x,y) and uses the component selection to
+    pick the value (comp_sel==1 -> x, comp_sel==2 -> y). This makes the API explicit and
+    avoids indexing into long SVD vectors.
+
+    Returns go.Figure or None if no data available.
+    """
+    if not party_coords:
+        return None
+
+    if comp_sel not in (1, 2):
+        raise ValueError(
+            "_build_party_axis_figure only supports comp_sel 1 or 2 when using explicit coords"
+        )
+
+    axis_idx = comp_sel - 1
+    flip = theme.get("flip", False)
+
+    parties = []
+    scores = []
+    colours = []
+
+    for party, val in party_coords.items():
+        try:
+            if hasattr(val, "__len__") and len(val) == 2:
+                x, y = val
+                score = float(x if axis_idx == 0 else y)
+            else:
+                score = float(val[axis_idx])
+
+            if flip:
+                score = -score
+        except Exception:
+            continue
+
+        parties.append(party)
+        scores.append(score)
+        colours.append(PARTY_COLOURS.get(party, "#9E9E9E"))
+
+    if not scores:
+        return None
+
+    hover = []
+    symbols = []
+    if bootstrap_data:
+        for p, s in zip(parties, scores):
+            bd = bootstrap_data.get(p)
+            if bd:
+                n_mps = bd.get("n_mps", "?")
+                ci_low = None
+                ci_high = None
+                try:
+                    ci_low = float(bd["ci_lower"][axis_idx])
+                    ci_high = float(bd["ci_upper"][axis_idx])
+                except Exception:
+                    pass
+                if ci_low is not None and ci_high is not None:
+                    hover.append(
+                        f"{p}: {s:.3f} (N={n_mps}, 95%-BI: [{ci_low:.3f}, {ci_high:.3f}])"
+                    )
+                else:
+                    hover.append(f"{p}: {s:.3f} (N={n_mps})")
+                symbols.append("diamond" if n_mps == 1 else "circle")
+            else:
+                hover.append(f"{p}: {s:.3f}")
+                symbols.append("circle")
+        marker_kwargs = {"size": 14, "color": colours, "symbol": symbols}
+    else:
+        hover = [f"{p}: {s:.3f}" for p, s in zip(parties, scores)]
+        marker_kwargs = {"size": 14, "color": colours}
+
+    fig = go.Figure()
+    x_min, x_max = min(scores) * 1.15, max(scores) * 1.15
+    if x_min == x_max:
+        x_min, x_max = x_min - 1, x_max + 1
+    fig.add_trace(
+        go.Scatter(
+            x=[x_min, x_max],
+            y=[0, 0],
+            mode="lines",
+            line={"color": "#cccccc", "width": 1},
+            hoverinfo="skip",
+            showlegend=False,
+        )
+    )
+
+    scatter_kwargs = {
+        "x": scores,
+        "y": [0] * len(scores),
+        "mode": "markers+text",
+        "text": parties,
+        "textposition": "top center",
+        "marker": marker_kwargs,
+        "hovertext": hover,
+        "hoverinfo": "text",
+        "showlegend": False,
+    }
+    fig.add_trace(go.Scatter(**scatter_kwargs))
+
+    pos_pole = theme.get("positive_pole", "")
+    neg_pole = theme.get("negative_pole", "")
+    left_label = neg_pole
+    right_label = pos_pole
+
+    fig.update_layout(
+        height=160,
+        margin={"l": 10, "r": 10, "t": 10, "b": 30},
+        xaxis={
+            "title": f"← {left_label}  |  {right_label} →",
+            "showticklabels": False,
+            "showline": False,
+            "showgrid": False,
+            "zeroline": False,
+        },
+        yaxis={"visible": False, "range": [-1, 2]},
+        plot_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor="rgba(0,0,0,0)",
+    )
+    return fig
+
+
+def _render_party_axis_chart(
+    party_coords: Dict[str, Tuple[float, float]],
+    comp_sel: int,
+    theme: dict,
+    bootstrap_data: Optional[Dict[str, Dict]] = None,
+) -> None:
+    """Render a 1D horizontal Plotly scatter of party positions on SVD axis `comp_sel`.
+
+    Expects explicit per-party coords mapping (party -> (x,y)) for components 1 & 2.
+    """
+    fig = _build_party_axis_figure(party_coords, comp_sel, theme, bootstrap_data)
+    if fig is None:
+        st.caption("_Partijdata niet beschikbaar voor deze as._")
+        return
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def _render_party_axis_chart_1d(
+    party_coords: Dict[str, Tuple[float, ...]],
+    comp_sel: int,
+    theme: dict,
+) -> None:
+    """Render a 1D horizontal scatter of party positions on SVD component `comp_sel`.
+
+    Uses the same format as components 1-2: parties as markers on a horizontal line
+    with axis title showing poles with arrows.
+
+    Args:
+        party_coords: Dict mapping party name to tuple of scores (score_for_comp,)
+        comp_sel: SVD component number (1-indexed)
+        theme: Dict with label, positive_pole, negative_pole, flip
+    """
+    if not party_coords:
+        st.caption("_Partijdata niet beschikbaar voor deze as._")
+        return
+
+    parties = []
+    scores = []
+    colours = []
+
+    for party, coords in party_coords.items():
+        try:
+            score = float(coords[0])
+            parties.append(party)
+            scores.append(score)
+            colours.append(PARTY_COLOURS.get(party, "#9E9E9E"))
+        except Exception:
+            continue
+
+    if not scores:
+        st.caption("_Partijdata niet beschikbaar voor deze as._")
+        return
+
+    flip = theme.get("flip", False)
+    if flip:
+        scores = [-s for s in scores]
+
+    hover = [f"{p}: {s:.3f}" for p, s in zip(parties, scores)]
+
+    fig = go.Figure()
+    x_min, x_max = min(scores) * 1.15, max(scores) * 1.15
+    if x_min == x_max:
+        x_min, x_max = x_min - 1, x_max + 1
+
+    fig.add_trace(
+        go.Scatter(
+            x=[x_min, x_max],
+            y=[0, 0],
+            mode="lines",
+            line={"color": "#cccccc", "width": 1},
+            hoverinfo="skip",
+            showlegend=False,
+        )
+    )
+
+    fig.add_trace(
+        go.Scatter(
+            x=scores,
+            y=[0] * len(scores),
+            mode="markers+text",
+            text=parties,
+            textposition="top center",
+            marker={"size": 14, "color": colours},
+            hovertext=hover,
+            hoverinfo="text",
+            showlegend=False,
+        )
+    )
+
+    pos_pole = theme.get("positive_pole", "")
+    neg_pole = theme.get("negative_pole", "")
+    left_label = neg_pole
+    right_label = pos_pole
+
+    fig.update_layout(
+        height=160,
+        margin={"l": 10, "r": 10, "t": 10, "b": 30},
+        xaxis={
+            "title": f"← {left_label}  |  {right_label} →",
+            "showticklabels": False,
+            "showline": False,
+            "showgrid": False,
+            "zeroline": False,
+        },
+        yaxis={"visible": False, "range": [-1, 2]},
+        plot_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor="rgba(0,0,0,0)",
+    )
+
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def _render_svd_time_trajectory(
+    party_scores_by_window: Dict[str, Dict[str, List[float]]],
+    comp_sel: int,
+    theme: dict,
+    selected_parties: List[str],
+) -> None:
+    """Render a time trajectory plot showing party positions over time on an SVD component.
+
+    Args:
+        party_scores_by_window: {window_id: {party_name: [scores]}}
+        comp_sel: SVD component number (1-indexed)
+        theme: Theme dict with label, positive_pole, negative_pole, flip
+        selected_parties: List of party names to display
+    """
+    if not party_scores_by_window or not selected_parties:
+        st.caption("_Geen data beschikbaar voor tijdtraject._")
+        return
+
+    idx = comp_sel - 1
+
+    party_trajectories: Dict[str, List[Tuple[str, float]]] = {}
+
+    all_windows = list(party_scores_by_window.keys())
+    sorted_windows = []
+    if "current_parliament" in all_windows:
+        sorted_windows.append("current_parliament")
+    other_windows = sorted(
+        [w for w in all_windows if w != "current_parliament"], reverse=True
+    )
+    sorted_windows.extend(other_windows)
+
+    for window in sorted_windows:
+        scores_by_party = party_scores_by_window.get(window, {})
+        for party in selected_parties:
+            scores = scores_by_party.get(party, [])
+            if scores and len(scores) > idx:
+                try:
+                    score = float(scores[idx])
+                    party_trajectories.setdefault(party, []).append((window, score))
+                except (ValueError, TypeError):
+                    continue
+
+    if not party_trajectories:
+        st.caption("_Geen data beschikbaar voor geselecteerde partijen._")
+        return
+
+    fig = go.Figure()
+
+    all_scores = []
+    for traj in party_trajectories.values():
+        all_scores.extend([s for _, s in traj])
+
+    if not all_scores:
+        st.caption("_Geen scores beschikbaar._")
+        return
+
+    x_min, x_max = min(all_scores) * 1.15, max(all_scores) * 1.15
+    if x_min == x_max:
+        x_min, x_max = x_min - 1, x_max + 1
+
+    window_to_y = {w: i for i, w in enumerate(sorted_windows)}
+
+    for window in sorted_windows:
+        y_pos = window_to_y[window]
+        fig.add_trace(
+            go.Scatter(
+                x=[x_min, x_max],
+                y=[y_pos, y_pos],
+                mode="lines",
+                line={"color": "#cccccc", "width": 1},
+                hoverinfo="skip",
+                showlegend=False,
+            )
+        )
+
+    for party in selected_parties:
+        if party not in party_trajectories:
+            continue
+
+        traj = party_trajectories[party]
+        if len(traj) < 1:
+            continue
+
+        x_vals = [score for _, score in traj]
+        y_vals = [window_to_y[window] for window, _ in traj]
+        color = PARTY_COLOURS.get(party, "#9E9E9E")
+
+        fig.add_trace(
+            go.Scatter(
+                x=x_vals,
+                y=y_vals,
+                mode="lines",
+                line={"color": color, "width": 2},
+                hoverinfo="skip",
+                showlegend=False,
+            )
+        )
+
+        hover_texts = [f"{party}<br>{window}: {score:.3f}" for window, score in traj]
+        fig.add_trace(
+            go.Scatter(
+                x=x_vals,
+                y=y_vals,
+                mode="markers+text",
+                text=[party] * len(traj),
+                textposition="top center",
+                marker={"size": 12, "color": color},
+                hovertext=hover_texts,
+                hoverinfo="text",
+                showlegend=False,
+            )
+        )
+
+    pos_pole = theme.get("positive_pole", "")
+    neg_pole = theme.get("negative_pole", "")
+    left_label = neg_pole
+    right_label = pos_pole
+
+    y_labels = {}
+    for window in sorted_windows:
+        if window == "current_parliament":
+            y_labels[window_to_y[window]] = "Huidig"
+        else:
+            y_labels[window_to_y[window]] = window
+
+    fig.update_layout(
+        height=max(400, len(sorted_windows) * 60 + 100),
+        margin={"l": 80, "r": 10, "t": 10, "b": 30},
+        xaxis={
+            "title": f"← {left_label}  |  {right_label} →",
+            "range": [x_min, x_max],
+            "showticklabels": False,
+            "showline": False,
+            "showgrid": True,
+            "gridcolor": "rgba(0,0,0,0.1)",
+            "zeroline": True,
+            "zerolinecolor": "rgba(0,0,0,0.2)",
+        },
+        yaxis={
+            "tickvals": list(y_labels.keys()),
+            "ticktext": list(y_labels.values()),
+            "tickmode": "array",
+            "autorange": "reversed",
+            "showgrid": False,
+        },
+        plot_bgcolor="rgba(0,0,0,0)",
+        paper_bgcolor="rgba(0,0,0,0)",
+    )
+
+    st.plotly_chart(fig, use_container_width=True)
+
+
+def _render_voting_results(voting_results_json) -> None:
+    """Render a voting_results JSON blob as a grouped voor/tegen/onthouden table.
+
+    The JSON is stored as {party_or_mp: vote} where vote is one of
+    'voor', 'tegen', 'onthouden', 'afwezig'. We group by vote for readability.
+    """
+    if not voting_results_json:
+        return
+    try:
+        vdata = (
+            json.loads(voting_results_json)
+            if isinstance(voting_results_json, str)
+            else voting_results_json
+        )
+        if not isinstance(vdata, dict) or not vdata:
+            return
+        by_vote: Dict[str, List[str]] = {}
+        for actor, vote in vdata.items():
+            vote_str = str(vote).lower().strip()
+            by_vote.setdefault(vote_str, []).append(str(actor))
+        vote_order = ["voor", "tegen", "onthouden", "afwezig"]
+        vote_emoji = {"voor": "✅", "tegen": "❌", "onthouden": "🟡", "afwezig": "⬜"}
+        rows_shown = False
+        for v in vote_order + [k for k in by_vote if k not in vote_order]:
+            actors = by_vote.get(v)
+            if not actors:
+                continue
+            emoji = vote_emoji.get(v, "▪️")
+            st.markdown(
+                f"**{emoji} {v.capitalize()}** ({len(actors)}): {', '.join(sorted(actors))}"
+            )
+            rows_shown = True
+        if not rows_shown:
+            st.caption("_Geen stemuitslag beschikbaar_")
+    except Exception:
+        pass
+
+
+def _add_y_direction_annotations(fig: go.Figure) -> None:
+    """Add ▲ Progressief / ▼ Conservatief labels above and below the Y axis."""
+    common = dict(
+        xref="paper",
+        yref="paper",
+        x=-0.07,
+        showarrow=False,
+        font=dict(size=11, color="#666666"),
+    )
+    fig.add_annotation(**common, y=1.02, text="▲ Progressief", xanchor="center")
+    fig.add_annotation(**common, y=-0.06, text="▼ Conservatief", xanchor="center")

analysis/tabs/browser.py

@@ -1,18 +1,95 @@
-"""Browser tab for the parliamentary explorer.
-
-This module will contain the browser tab implementation.
-Currently: Tab logic remains in explorer.py pending Streamlit decoupling.
-"""
+"""Browser tab for the parliamentary explorer."""
 
 from __future__ import annotations
 
+import pandas as pd
+
+import analysis.explorer_data as explorer_data
+from analysis.tabs._rendering import _render_voting_results, st
+
 
 def build_browser_tab(db_path: str, show_rejected: bool) -> None:
-    """Build the Motie Browser tab.
+    """Build the Motie Browser tab."""
+    st.subheader("Motie Browser")
+
+    df = explorer_data.load_motions_df(db_path)
+    if df.empty:
+        st.warning("Geen moties beschikbaar.")
+        return
+
+    if not show_rejected:
+        df = df[df["title"].fillna("").str.strip() != "Verworpen."]
+
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        years = sorted(df["year"].dropna().astype(int).unique().tolist())
+        year_filter = st.selectbox("Jaar", ["(Alle)"] + [str(y) for y in years])
+    with col2:
+        min_controversy_b = st.slider(
+            "Min. controverse",
+            min_value=0.0,
+            max_value=1.0,
+            value=0.0,
+            step=0.05,
+            key="browser_controversy",
+        )
+    with col3:
+        sort_by = st.selectbox("Sorteren op", ["Datum (nieuw)", "Controverse", "Marge"])
+
+    working = df.copy()
+    if year_filter != "(Alle)":
+        working = working[working["year"] == int(year_filter)]
+    if min_controversy_b > 0:
+        working = working[working["controversy_score"] >= min_controversy_b]
+
+    sort_map = {
+        "Datum (nieuw)": ("date", False),
+        "Controverse": ("controversy_score", False),
+        "Marge": ("winning_margin", True),
+    }
+    sort_col, sort_asc = sort_map[sort_by]
+    working = working.sort_values(by=sort_col, ascending=sort_asc)
+
+    display_cols = ["id", "title", "date", "controversy_score", "winning_margin"]
+    available_display = [c for c in display_cols if c in working.columns]
+    st.dataframe(
+        working[available_display].reset_index(drop=True),
+        use_container_width=True,
+        height=350,
+    )
+
+    st.divider()
+
+    st.markdown("**Detail weergave** — vul een motie-ID in:")
+    sel_id = st.number_input(
+        "Motie ID",
+        min_value=int(working["id"].min()) if not working.empty else 1,
+        max_value=int(working["id"].max()) if not working.empty else 99999,
+        value=int(working["id"].iloc[0]) if not working.empty else 1,
+        step=1,
+    )
+    motion_row = df[df["id"] == sel_id]
+    if not motion_row.empty:
+        row = motion_row.iloc[0]
+        st.markdown(f"### {row.get('title') or 'Onbekend'}")
+        date_str = row["date"].strftime("%d %b %Y") if pd.notna(row["date"]) else "?"
+        st.caption(
+            f"📅 {date_str}  | 🔥 Controverse: {row.get('controversy_score', 0):.2f}"
+        )
+
+        url = row.get("url")
+        if url and str(url).startswith("http"):
+            st.markdown(f"[🔗 Bekijk op Tweede Kamer]({url})")
 
-    Currently delegates to explorer.py implementation.
-    Will be extracted when rendering logic is decoupled from Streamlit.
-    """
-    import explorer
+        st.markdown("**Stemuitslag:**")
+        _render_voting_results(row.get("voting_results"))
 
-    explorer.build_browser_tab(db_path, show_rejected)
+        sim = explorer_data.query_similar(db_path, int(sel_id), top_k=10)
+        if not sim.empty:
+            st.markdown("**Vergelijkbare moties:**")
+            st.dataframe(
+                sim[["title", "score", "date", "policy_area"]],
+                use_container_width=True,
+            )
+        else:
+            st.caption("_Nog geen vergelijkbare moties beschikbaar voor deze motie_")

analysis/tabs/compass.py

@@ -1,20 +1,200 @@
-"""Compass tab for the parliamentary explorer.
-
-This module will contain the compass tab implementation.
-Currently: Tab logic remains in explorer.py pending Streamlit decoupling.
-"""
+"""Compass tab for the parliamentary explorer."""
 
 from __future__ import annotations
 
-from typing import List
+import datetime as _dt
+import re
+from typing import Dict, Tuple
+
+import numpy as np
+import pandas as pd
+
+from analysis import config
+import analysis.explorer_data as explorer_data
+from analysis.tabs._rendering import px, st
+
+PARTY_COLOURS = config.PARTY_COLOURS
 
 
 def build_compass_tab(db_path: str, window_size: str) -> None:
-    """Build the Politiek Kompas tab.
+    """Build the Politiek Kompas tab."""
+    st.subheader("Politiek Kompas")
+    st.markdown(
+        "2D projectie van Kamerlid posities op basis van stemgedrag (PCA op SVD-vectoren)."
+    )
+
+    # Compass always uses annual windows regardless of the sidebar window_size setting.
+    positions_by_window, axis_def = explorer_data.load_positions(db_path, "annual")
+    if axis_def is None:
+        axis_def = {}
+    if not positions_by_window:
+        st.warning(
+            "Geen positiedata beschikbaar. Controleer of de pipeline is gedraaid."
+        )
+        return
+
+    party_map = explorer_data.load_party_map(db_path)
+    active_mps = explorer_data.load_active_mps(db_path)
+
+    _current_year = str(_dt.date.today().year)
+    year_windows = sorted(
+        w
+        for w in positions_by_window
+        if w != "current_parliament" and w != _current_year
+    )
+    has_current = "current_parliament" in positions_by_window
+    windows = year_windows + (["current_parliament"] if has_current else [])
+
+    _SPARSE_YEARS = {"2016", "2017", "2018"}
+    _THRESHOLD = 0.65
+
+    def _window_label(w: str) -> str:
+        if w == "current_parliament":
+            return "Huidig parlement"
+        if w in _SPARSE_YEARS:
+            return f"{w} ⚠️"
+        return w
+
+    col1, col2 = st.columns([3, 1])
+    with col2:
+        window_idx = st.selectbox(
+            "Jaar",
+            options=windows,
+            index=len(windows) - 1,
+            format_func=_window_label,
+        )
+        level = st.radio(
+            "Weergave",
+            options=["Kamerleden", "Partijen"],
+            index=0,
+            horizontal=True,
+        )
+        min_mps = st.number_input(
+            "Min. Kamerleden per partij",
+            min_value=1,
+            max_value=20,
+            value=3,
+            step=1,
+            help="Partijen met minder dan dit aantal zetels worden niet weergegeven.",
+        )
+
+    pos = positions_by_window.get(window_idx, {})
+    if not pos:
+        st.info(f"Geen data voor venster {window_idx}")
+        return
+
+    if window_idx == "current_parliament":
+        pos = {mp: xy for mp, xy in pos.items() if mp in active_mps}
+
+    def _strip_paren(name: str) -> str:
+        return re.sub(r"\s*\([^)]*\)", "", name).strip()
+
+    deduped: Dict[str, Tuple[float, float]] = {}
+    for name, (x, y) in pos.items():
+        base = _strip_paren(name)
+        if base in deduped:
+            ox, oy = deduped[base]
+            deduped[base] = ((ox + x) / 2, (oy + y) / 2)
+        else:
+            deduped[base] = (x, y)
+    pos = deduped
+
+    rows = []
+    for name, (x, y) in pos.items():
+        party = party_map.get(name) or party_map.get(_strip_paren(name), "Unknown")
+        rows.append({"name": name, "x": x, "y": y, "party": party})
+
+    df_pos = pd.DataFrame(rows)
+
+    party_counts = df_pos[df_pos["party"] != "Unknown"]["party"].value_counts()
+    valid_parties = set(party_counts[party_counts >= min_mps].index)
+    df_pos = df_pos[df_pos["party"].isin(valid_parties)]
+
+    if df_pos.empty:
+        st.info("Geen partijen met genoeg Kamerleden voor dit venster.")
+        return
+
+    _raw_x = axis_def.get("x_label")
+    _raw_y = axis_def.get("y_label")
+
+    try:
+        from analysis.axis_classifier import display_label_for_modal
+
+        _x_label = display_label_for_modal(_raw_x, "x")
+        _y_label = display_label_for_modal(_raw_y, "y")
+    except Exception:
+        from analysis.svd_labels import get_fallback_labels
+
+        _x_fallback, _y_fallback = get_fallback_labels()
+        _x_label = _raw_x or _x_fallback
+        _y_label = _raw_y or _y_fallback
+
+    if level == "Partijen":
+        df_party = df_pos.groupby("party", as_index=False).agg(
+            x=("x", "mean"), y=("y", "mean"), n=("name", "count")
+        )
+        df_party["name"] = df_party["party"]
+        colour_map = {
+            p: PARTY_COLOURS.get(p, "#9E9E9E") for p in df_party["party"].unique()
+        }
+        fig = px.scatter(
+            df_party,
+            x="x",
+            y="y",
+            color="party",
+            text="party",
+            hover_name="party",
+            hover_data={"party": False, "x": ":.3f", "y": ":.3f", "n": True},
+            color_discrete_map=colour_map,
+            title=f"Politiek Kompas — {_window_label(window_idx)} (partijen)",
+            labels={
+                "x": _x_label,
+                "y": _y_label,
+                "n": "Kamerleden",
+            },
+        )
+        fig.update_traces(textposition="top center", marker_size=14)
+    else:
+        colour_map = {
+            p: PARTY_COLOURS.get(p, "#9E9E9E") for p in df_pos["party"].unique()
+        }
+        fig = px.scatter(
+            df_pos,
+            x="x",
+            y="y",
+            color="party",
+            hover_name="name",
+            hover_data={"party": True, "x": ":.3f", "y": ":.3f"},
+            color_discrete_map=colour_map,
+            title=f"Politiek Kompas — {_window_label(window_idx)}",
+            labels={"x": _x_label, "y": _y_label},
+        )
 
-    Currently delegates to explorer.py implementation.
-    Will be extracted when rendering logic is decoupled from Streamlit.
-    """
-    import explorer
+    fig.update_layout(
+        height=600,
+        legend_title_text="Partij",
+        xaxis={"range": [-1, 1]},
+        yaxis={"range": [-0.6, 0.6]},
+    )
+    with col1:
+        st.plotly_chart(fig, use_container_width=True)
+        _x_interp = axis_def.get("x_interpretation", {}).get(window_idx, "")
+        if (
+            _x_interp
+            and axis_def.get("x_quality", {}).get(window_idx, 1.0) < _THRESHOLD
+        ):
+            st.caption(_x_interp)
 
-    explorer.build_compass_tab(db_path, window_size)
+        # Voting discipline analysis
+        st.markdown("---")
+        st.markdown(
+            "**Stemdiscipline analyse:** De Rice-index meet hoe eensgezind partijen stemmen "
+            "tijdens hoofdelijke stemmingen. Een score van 100% betekent dat alle MPs van "
+            "een partij hetzelfde stemden; 50% wijst op een gelijke splitsing binnen de partij. "
+            "Partijen met hoge discipline (>95%) zoals PVV en SGP stemmen als een blok, wat "
+            "wijst op sterke partijdiscipline en homogene membership. Lagere discipline (<85%) "
+            "bij partijen als PvdA of SP kan duiden op interne factiestrijd, gewetensvragen "
+            "bij ethische thema's, of een brede ideologische koers die ruimte laat voor "
+            "afwijkende meningen. De discipline varieert ook per onderwerp — ethische kwesties "
+            "tonen vaak meer interne verschillen dan economische thema's."
+        )

analysis/tabs/components.py

@@ -1,18 +1,374 @@
-"""SVD Components tab for the parliamentary explorer.
-
-This module will contain the SVD components tab implementation.
-Currently: Tab logic remains in explorer.py pending Streamlit decoupling.
-"""
+"""SVD Components tab for the parliamentary explorer."""
 
 from __future__ import annotations
 
+import datetime as _dt
+import logging
+import os
+from typing import Dict, List, Tuple
+
+import numpy as np
+
+from analysis import config
+import analysis.explorer_data as explorer_data
+from analysis.tabs._rendering import (
+    _render_party_axis_chart_1d,
+    _render_scree_plot,
+    _render_svd_time_trajectory,
+    _render_voting_results,
+    st,
+)
+
+try:
+    import duckdb
+except Exception:
+    duckdb = None  # type: ignore
+
+SVD_THEMES = config.SVD_THEMES
+KNOWN_MAJOR_PARTIES = config.KNOWN_MAJOR_PARTIES
+
+logger = logging.getLogger(__name__)
+
 
 def build_svd_components_tab(db_path: str) -> None:
-    """Build the SVD Components tab.
+    """New tab: show top motions contributing to top SVD components.
+
+    Reads thoughts/explorer/top_svd_top_motions.json and displays a selector
+    for components 1..10 with theme labels/explanations and a detail pane per motion.
 
-    Currently delegates to explorer.py implementation.
-    Will be extracted when rendering logic is decoupled from Streamlit.
+    Components 1-2 use aligned PCA positions (consistent with compass).
+    Components 3-10 use raw SVD scores.
     """
-    import explorer
+    st.subheader("🔬 SVD Assen — politieke polarisatiethema's")
+    st.markdown(
+        "Elke SVD-as representeert een latente politieke dimensie afgeleid uit stempatronen "
+        "van alle Kamerleden. De top-10 moties per as zijn uniek (geen overlap) en illustreren "
+        "het spanningsveld dat de as beschrijft."
+    )
+
+    scree_importances = explorer_data.load_scree_data(db_path)
+    if scree_importances:
+        st.markdown(
+            "**Scree-plot** — het relatieve gewicht van elke SVD-as. "
+            "De eerste assen verklaren het meeste van de stemverschillen in de Kamer; "
+            "latere assen (7+) zijn fragiel en mogelijk niet boven ruisniveau."
+        )
+        _render_scree_plot(scree_importances)
+
+    json_path = os.path.join("thoughts", "explorer", "top_svd_top_motions.json")
+    if not os.path.exists(json_path):
+        st.warning(
+            f"Top-SVD data not found at {json_path}. Run the importance job to generate it."
+        )
+        return
+
+    try:
+        import json
+
+        with open(json_path, "r", encoding="utf-8") as fh:
+            j = json.load(fh)
+    except Exception as e:
+        st.error(f"Failed to load SVD importance JSON: {e}")
+        return
+
+    window = j.get("window")
+    rows = j.get("rows", [])
+    if not rows:
+        st.info("Geen top-moties in dataset")
+        return
+
+    st.caption(f"Top SVD-bijdragers berekend voor venster: **{window}**")
+
+    comp_map: dict[int, list] = {}
+    for r in rows:
+        comp = int(r.get("component", 0))
+        bucket = comp_map.setdefault(comp, [])
+        existing_ids = {m.get("motion_id") for m in bucket}
+        if r.get("motion_id") not in existing_ids:
+            bucket.append(r)
+
+    comp_options = sorted(comp_map.keys())
+
+    def _comp_label(c: int) -> str:
+        theme = SVD_THEMES.get(c, {})
+        lbl = theme.get("label", "")
+        return f"As {c} — {lbl}" if lbl else f"As {c}"
+
+    comp_display = [_comp_label(c) for c in comp_options]
+
+    party_scores_default = explorer_data.load_party_axis_scores(db_path)
+    party_mp_vectors = explorer_data.load_party_mp_vectors(db_path)
+    bootstrap_data = None
+    if party_mp_vectors:
+        try:
+            from analysis.political_axis import compute_party_bootstrap_cis
+
+            bootstrap_data = compute_party_bootstrap_cis(party_mp_vectors)
+        except Exception:
+            pass
+
+    col1, col2 = st.columns([2, 1])
+
+    view_mode = "Enkel venster"
+    selected_parties_for_trajectory: list = []
+
+    with col2:
+        comp_sel_idx = st.selectbox(
+            "Selecteer SVD-as",
+            options=list(range(len(comp_options))),
+            format_func=lambda i: comp_display[i],
+            index=0,
+        )
+        comp_sel = comp_options[comp_sel_idx]
+
+        min_mps = st.number_input(
+            "Min. Kamerleden per partij",
+            min_value=1,
+            max_value=20,
+            value=1,
+            step=1,
+            help="Partijen met minder dan dit aantal Kamerleden worden niet weergegeven.",
+        )
+
+        view_mode = st.radio(
+            "Weergave",
+            options=["Enkel venster", "Tijdtraject"],
+            index=0,
+            help="Enkel venster: toont posities voor één tijdsvenster. Tijdtraject: toont hoe partijen over tijd bewegen op deze as.",
+        )
+
+        selected_parties_for_trajectory = []
+        if view_mode == "Tijdtraject":
+            all_parties = (
+                sorted(party_scores_default.keys()) if party_scores_default else []
+            )
+            default_parties = [p for p in KNOWN_MAJOR_PARTIES if p in all_parties][:8]
+            selected_parties_for_trajectory = st.multiselect(
+                "Partijen om te tonen",
+                options=all_parties,
+                default=default_parties,
+                help="Selecteer de partijen die je wilt zien in het tijdtraject.",
+            )
+
+    theme = SVD_THEMES.get(comp_sel, {})
+    if theme:
+        st.info(f"**{theme['label']}** — {theme['explanation']}")
+
+    motions = comp_map.get(comp_sel, [])
+
+    _current_year = str(_dt.date.today().year)
+    available_windows = explorer_data.get_uniform_dim_windows(db_path)
+    year_windows = sorted(
+        w for w in available_windows if w != "current_parliament" and w != _current_year
+    )
+    has_current = "current_parliament" in available_windows
+    svd_windows = year_windows + (["current_parliament"] if has_current else [])
+
+    def _svd_window_label(w: str) -> str:
+        if w == "current_parliament":
+            return "Huidig parliament"
+        return w
+
+    with col1:
+        svd_window = st.selectbox(
+            "Jaar",
+            options=svd_windows,
+            index=len(svd_windows) - 1,
+            format_func=_svd_window_label,
+            key=f"svd_window_{comp_sel}",
+        )
+
+    if svd_window == "current_parliament":
+        party_scores = party_scores_default
+    else:
+        party_scores = explorer_data.load_party_axis_scores_for_window(db_path, svd_window)
+
+    party_mp_counts = (
+        {p: len(v) for p, v in party_mp_vectors.items()} if party_mp_vectors else {}
+    )
+
+    def _get_aligned_party_coords(window: str) -> Dict[str, Tuple[float, float]]:
+        """Get party (x, y) coordinates from aligned PCA positions for a window."""
+        positions_by_window, _ = explorer_data.load_positions(db_path, "annual")
+        window_pos = positions_by_window.get(window, {})
+        if not window_pos:
+            return {}
+
+        _party_map = explorer_data.load_party_map(db_path)
+
+        party_coords: Dict[str, List[Tuple[float, float]]] = {}
+        for mp_name, (x, y) in window_pos.items():
+            party = _party_map.get(
+                mp_name, _party_map.get(mp_name.split("(")[0].strip(), None)
+            )
+            if party:
+                party_coords.setdefault(party, []).append((x, y))
+
+        return {
+            party: (
+                float(np.mean([c[0] for c in coords])),
+                float(np.mean([c[1] for c in coords])),
+            )
+            for party, coords in party_coords.items()
+            if coords
+        }
+
+    active_mps = (
+        explorer_data.load_active_mps(db_path)
+        if svd_window == "current_parliament"
+        else None
+    )
+    aligned_all_scores = explorer_data.get_aligned_party_scores(
+        db_path, svd_window, active_mps
+    )
+
+    party_1d_coords: dict = {}
+    for party, all_scores in aligned_all_scores.items():
+        idx = comp_sel - 1
+        if idx < len(all_scores):
+            party_1d_coords[party] = (float(all_scores[idx]),)
+
+    computed_flips: Dict[int, bool] = {}
+    try:
+        from analysis.config import CANONICAL_LEFT, CANONICAL_RIGHT
+
+        for comp_idx in range(10):
+            right_scores = []
+            left_scores = []
+            for party, scores in aligned_all_scores.items():
+                if party in CANONICAL_RIGHT:
+                    right_scores.append(scores[comp_idx])
+                elif party in CANONICAL_LEFT:
+                    left_scores.append(scores[comp_idx])
+
+            if right_scores and left_scores:
+                right_avg = np.mean(right_scores)
+                left_avg = np.mean(left_scores)
+                computed_flips[comp_idx + 1] = right_avg < left_avg
+            else:
+                computed_flips[comp_idx + 1] = False
+    except Exception:
+        pass
+
+    theme_with_flip = {
+        **theme,
+        "flip": computed_flips.get(comp_sel, theme.get("flip", False)),
+    }
+
+    if min_mps > 1 and party_mp_counts:
+        valid_parties = {p for p, count in party_mp_counts.items() if count >= min_mps}
+        party_1d_coords = {
+            p: coords for p, coords in party_1d_coords.items() if p in valid_parties
+        }
+
+    if view_mode == "Tijdtraject" and selected_parties_for_trajectory:
+        available_windows = explorer_data.get_uniform_dim_windows(db_path)
+        year_windows = sorted(
+            w
+            for w in available_windows
+            if w != "current_parliament" and w != _current_year
+        )
+        has_current = "current_parliament" in available_windows
+        all_windows = year_windows + (["current_parliament"] if has_current else [])
+
+        party_scores_by_window = explorer_data._get_aligned_trajectory_scores(
+            db_path, all_windows
+        )
+
+        _render_svd_time_trajectory(
+            party_scores_by_window,
+            comp_sel,
+            theme_with_flip,
+            selected_parties_for_trajectory,
+        )
+    else:
+        _render_party_axis_chart_1d(party_1d_coords, comp_sel, theme_with_flip)
+
+    motion_ids = [m.get("motion_id") for m in motions if m.get("motion_id") is not None]
+    motion_details: Dict[int, tuple] = {}
+    if motion_ids:
+        ids_int: List[int] = []
+        for mid in motion_ids:
+            try:
+                ids_int.append(int(mid))
+            except Exception:
+                logger.warning("Skipping invalid motion id in SVD batch fetch: %r", mid)
+
+        if ids_int and duckdb is not None:
+            con = None
+            try:
+                placeholders = ", ".join("?" for _ in ids_int)
+                con = duckdb.connect(database=db_path, read_only=True)
+                db_rows = con.execute(
+                    f"SELECT id, title, date, policy_area, url, body_text, voting_results "
+                    f"FROM motions WHERE id IN ({placeholders})",
+                    ids_int,
+                ).fetchall()
+                motion_details = {r[0]: r for r in db_rows}
+            except Exception:
+                logger.exception("Failed to batch-fetch motion details")
+            finally:
+                if con:
+                    con.close()
+
+    pos_motions = [m for m in motions if float(m.get("score", 0.0)) >= 0]
+    neg_motions = [m for m in motions if float(m.get("score", 0.0)) < 0]
+
+    flip = theme_with_flip.get("flip", False) if theme_with_flip else False
+    pos_pole = theme_with_flip.get("positive_pole", "") if theme_with_flip else ""
+    neg_pole = theme_with_flip.get("negative_pole", "") if theme_with_flip else ""
+
+    if flip:
+        left_pole, right_pole = pos_pole, neg_pole
+        left_motions, right_motions = pos_motions, neg_motions
+        left_arrow, right_arrow = "▲", "▼"
+    else:
+        left_pole, right_pole = neg_pole, pos_pole
+        left_motions, right_motions = neg_motions, pos_motions
+        left_arrow, right_arrow = "▼", "▲"
+
+    lcol, rcol = st.columns(2)
+
+    with lcol:
+        st.markdown(f"**← {left_pole}**")
+        for m in left_motions:
+            mid = m.get("motion_id")
+            raw_title = m.get("title") or f"Motie #{mid}"
+            with st.expander(f"{left_arrow} {raw_title}"):
+                row = motion_details.get(int(mid)) if mid is not None else None
+                if row:
+                    try:
+                        date_str = str(row[2])[:10]
+                    except Exception:
+                        date_str = "?"
+                    st.caption(f"📅 {date_str}  |  {row[3] or '—'}")
+                    if row[4] and str(row[4]).startswith("http"):
+                        st.markdown(f"[🔗 Bekijk op Tweede Kamer]({row[4]})")
+                    if row[5]:
+                        with st.expander("Toon volledige tekst"):
+                            st.write(row[5])
+                    _render_voting_results(row[6])
+                else:
+                    st.caption("_Geen metadata beschikbaar_")
 
-    explorer.build_svd_components_tab(db_path)
+    with rcol:
+        st.markdown(f"**{right_pole} →**")
+        for m in right_motions:
+            mid = m.get("motion_id")
+            raw_title = m.get("title") or f"Motie #{mid}"
+            with st.expander(f"{right_arrow} {raw_title}"):
+                row = motion_details.get(int(mid)) if mid is not None else None
+                if row:
+                    try:
+                        date_str = str(row[2])[:10]
+                    except Exception:
+                        date_str = "?"
+                    st.caption(f"📅 {date_str}  |  {row[3] or '—'}")
+                    if row[4] and str(row[4]).startswith("http"):
+                        st.markdown(f"[🔗 Bekijk op Tweede Kamer]({row[4]})")
+                    if row[5]:
+                        with st.expander("Toon volledige tekst"):
+                            st.write(row[5])
+                    _render_voting_results(row[6])
+                else:
+                    st.caption("_Geen metadata beschikbaar_")

analysis/tabs/quiz.py

@@ -1,18 +1,132 @@
-"""MP Quiz tab for the parliamentary explorer.
-
-This module will contain the MP quiz tab implementation.
-Currently: Tab logic remains in explorer.py pending Streamlit decoupling.
-"""
+"""MP Quiz tab for the parliamentary explorer."""
 
 from __future__ import annotations
 
+import pandas as pd
+
+import analysis.explorer_data as explorer_data
+from analysis.tabs._rendering import st
+
 
 def build_mp_quiz_tab(db_path: str) -> None:
-    """Build the MP Quiz tab.
+    """Interactive quiz: narrow MPs by asking motion vote questions.
 
-    Currently delegates to explorer.py implementation.
-    Will be extracted when rendering logic is decoupled from Streamlit.
+    Minimal viable flow:
+    - seed with top-N controversial motions (SEED_MOTIONS)
+    - present one question at a time, store answers in st.session_state['mp_quiz_votes']
+    - after each answer call MotionDatabase.match_mps_for_votes to rank MPs
+    - if multiple candidates remain, call choose_discriminating_motions to pick next question
+    - stop when unique MP found or no discriminating motions remain
     """
-    import explorer
+    st.subheader("🧑‍⚖️ Welk tweede kamerlid ben jij?")
+    st.markdown(
+        "Beantwoord een paar eenvoudige ja/nee/onthoud vragen over moties om te zien welk Kamerlid het meest op jou lijkt."
+    )
+
+    SEED_MOTIONS = 8
+    MAX_QUESTIONS = 20
+
+    if "mp_quiz_votes" not in st.session_state:
+        st.session_state["mp_quiz_votes"] = {}
+    if "mp_quiz_asked" not in st.session_state:
+        st.session_state["mp_quiz_asked"] = []
+
+    from database import MotionDatabase as _MotionDatabase
+
+    db_inst = _MotionDatabase(db_path)
+
+    df = explorer_data.load_motions_df(db_path)
+    if df.empty:
+        st.warning("Geen moties beschikbaar om de quiz te starten.")
+        return
+
+    seed_ids = db_inst.get_motions_with_individual_votes(k=SEED_MOTIONS)
+    if not seed_ids:
+        st.warning("Geen individuele stemdata beschikbaar voor de quiz.")
+        return
+
+    def _next_motion_id():
+        for mid in seed_ids:
+            if str(mid) not in st.session_state["mp_quiz_votes"]:
+                return mid
+        try:
+            user_votes = {
+                int(k): v for k, v in st.session_state["mp_quiz_votes"].items()
+            }
+            ranked = db_inst.match_mps_for_votes(user_votes, limit=200)
+        except Exception:
+            ranked = []
+
+        candidates = [r["mp_name"] for r in ranked]
+        excluded = [int(k) for k in st.session_state["mp_quiz_votes"].keys()]
+        if not candidates:
+            return None
+        try:
+            next_ids = db_inst.choose_discriminating_motions(candidates, excluded, k=1)
+            return next_ids[0] if next_ids else None
+        except Exception:
+            return None
+
+    col1, col2 = st.columns([3, 1])
+    with col2:
+        st.caption(
+            f"Vragen beantwoord: {len(st.session_state['mp_quiz_votes'])}/{MAX_QUESTIONS}"
+        )
+        if st.button("Reset quiz"):
+            st.session_state["mp_quiz_votes"] = {}
+            st.session_state["mp_quiz_asked"] = []
+            st.rerun()
+
+    next_mid = _next_motion_id()
+    if next_mid is None:
+        st.info("Geen nieuwe vragen beschikbaar om kandidaten te scheiden.")
+    else:
+        motion_rows = df[df["id"] == next_mid]
+        if motion_rows.empty:
+            st.session_state["mp_quiz_votes"][str(next_mid)] = "Geen stem"
+            st.rerun()
+            return
+        motion_row = motion_rows.iloc[0]
+        st.markdown(f"### {motion_row.get('title') or f'Motie #{next_mid}'}")
+        if motion_row.get("layman_explanation"):
+            st.info(motion_row.get("layman_explanation"))
+
+        with st.form(key=f"mp_quiz_form_{next_mid}"):
+            choice = st.radio(
+                "Wat zou jij stemmen?",
+                options=["Voor", "Tegen", "Onthouden", "Geen stem"],
+                index=3,
+            )
+            submitted = st.form_submit_button("Beantwoord en verder")
+
+        if submitted:
+            st.session_state["mp_quiz_votes"][str(next_mid)] = choice
+            st.session_state["mp_quiz_asked"].append(next_mid)
+            st.rerun()
+
+    try:
+        user_votes = {int(k): v for k, v in st.session_state["mp_quiz_votes"].items()}
+        ranking = db_inst.match_mps_for_votes(user_votes, limit=50)
+    except Exception:
+        ranking = []
+
+    if ranking:
+        st.markdown("**Top kandidaten**")
+        rdf = pd.DataFrame(ranking)
+        st.dataframe(rdf.head(10), use_container_width=True)
 
-    explorer.build_mp_quiz_tab(db_path)
+        top_pct = ranking[0]["agreement_pct"] if ranking else 0.0
+        top_matches = [r for r in ranking if r["agreement_pct"] == top_pct]
+        if len(top_matches) == 1 and top_matches[0]["overlap"] > 0:
+            st.success(
+                f"Unieke match gevonden: {top_matches[0]['mp_name']} ({top_matches[0]['party']})"
+            )
+        else:
+            if len(st.session_state["mp_quiz_asked"]) >= MAX_QUESTIONS:
+                st.warning(
+                    "Maximaal aantal vragen beantwoord. Je hebt meerdere vergelijkbare kandidaten."
+                )
+            else:
+                st.info("Nog geen unieke match — vraag meer om verder te verfijnen.")
+    else:
+        st.info("Nog geen antwoorden of geen overlapping met bestaande stemdata.")

analysis/tabs/search.py

@@ -1,18 +1,84 @@
-"""Search tab for the parliamentary explorer.
-
-This module will contain the search tab implementation.
-Currently: Tab logic remains in explorer.py pending Streamlit decoupling.
-"""
+"""Search tab for the parliamentary explorer."""
 
 from __future__ import annotations
 
+import pandas as pd
+
+import analysis.explorer_data as explorer_data
+from analysis.tabs._rendering import _render_voting_results, st
+
 
 def build_search_tab(db_path: str, show_rejected: bool) -> None:
-    """Build the Motie Zoeken tab.
+    """Build the Motie Zoeken tab."""
+    st.subheader("Motie Zoeken")
+
+    df = explorer_data.load_motions_df(db_path)
+    if df.empty:
+        st.warning("Geen moties beschikbaar.")
+        return
+
+    if not show_rejected:
+        df = df[df["title"].fillna("").str.strip() != "Verworpen."]
+
+    col1, col2, col3 = st.columns([2, 1, 1])
+    with col1:
+        query = st.text_input(
+            "Zoek op titel", placeholder="bijv. stikstof, klimaat, wonen"
+        )
+    with col2:
+        years = sorted(df["year"].dropna().astype(int).unique().tolist())
+        if years:
+            year_range = st.select_slider(
+                "Jaar", options=years, value=(years[0], years[-1])
+            )
+        else:
+            year_range = (2019, 2024)
+    with col3:
+        min_controversy = st.slider(
+            "Min. controverse", min_value=0.0, max_value=1.0, value=0.0, step=0.05
+        )
+
+    working = df.copy()
+    working = working[
+        (working["year"] >= year_range[0]) & (working["year"] <= year_range[1])
+    ]
+    if min_controversy > 0:
+        working = working[working["controversy_score"] >= min_controversy]
+    if query:
+        q = query.lower()
+        mask = working["title"].fillna("").str.lower().str.contains(q, regex=False)
+        working = working[mask]
+
+    working = working.sort_values(by="controversy_score", ascending=False)
+    st.caption(f"{len(working)} resultaten (top 50 getoond)")
+
+    for _, row in working.head(50).iterrows():
+        title = row.get("title") or f"Motie #{row['id']}"
+        date_str = row["date"].strftime("%d %b %Y") if pd.notna(row["date"]) else "?"
+        controversy = row.get("controversy_score") or 0
+        with st.expander(f"**{title}** — {date_str} — 🔥 {controversy:.2f}"):
+            cols = st.columns(3)
+            cols[0].metric("Controverse", f"{controversy:.2f}")
+            cols[1].metric("Marge", f"{row.get('winning_margin', 0):.2f}")
+            cols[2].metric("Jaar", int(row["year"]) if pd.notna(row["year"]) else "?")
+
+            _render_voting_results(row.get("voting_results"))
 
-    Currently delegates to explorer.py implementation.
-    Will be extracted when rendering logic is decoupled from Streamlit.
-    """
-    import explorer
+            url = row.get("url")
+            if url and str(url).startswith("http"):
+                st.markdown(f"[🔗 Bekijk op Tweede Kamer]({url})")
 
-    explorer.build_search_tab(db_path, show_rejected)
+            sim = explorer_data.query_similar(db_path, int(row["id"]), top_k=5)
+            if not sim.empty:
+                st.markdown("**Vergelijkbare moties:**")
+                for _, s in sim.iterrows():
+                    s_date = (
+                        pd.to_datetime(s["date"]).strftime("%Y")
+                        if pd.notna(s.get("date"))
+                        else ""
+                    )
+                    st.markdown(
+                        f"- {s.get('title', 'Onbekend')} *(score: {s['score']:.3f}, {s_date})*"
+                    )
+            else:
+                st.caption("_Nog geen vergelijkbare moties beschikbaar_")

analysis/tabs/trajectories.py

@@ -1,20 +1,774 @@
-"""Trajectories tab for the parliamentary explorer.
+"""Trajectories tab for the parliamentary explorer."""
 
-This module will contain the trajectories tab implementation.
-Currently: Tab logic remains in explorer.py pending Streamlit decoupling.
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+import traceback
+from datetime import datetime
+from typing import Dict, List, Optional, Tuple
+
+import numpy as np
+
+from analysis import config
+import analysis.explorer_data as explorer_data
+from analysis import trajectory
+from analysis.tabs._rendering import (
+    PARTY_COLOURS,
+    _add_y_direction_annotations,
+    go,
+    st,
+)
+from explorer_helpers import compute_party_centroids, inspect_positions_for_issues
+
+KNOWN_MAJOR_PARTIES = config.KNOWN_MAJOR_PARTIES
+
+logger = logging.getLogger(__name__)
+
+_last_trajectories_diagnostics: dict = {}
+_last_diagnostics = _last_trajectories_diagnostics
+
+
+def get_debug_trajectories_enabled() -> bool:
+    """Return True when EXPLORER_DEBUG_TRAJECTORIES env var indicates debug mode."""
+    v = os.getenv("EXPLORER_DEBUG_TRAJECTORIES")
+    return str(v) in ("1", "true", "True")
+
+
+def select_trajectory_plot_data(
+    positions_by_window: Dict[str, Dict[str, Tuple[float, float]]],
+    party_map: Dict[str, str],
+    windows: List[str],
+    selected_parties: List[str],
+    smooth_alpha: float = 0.35,
+    mp_fallback_count: Optional[int] = None,
+) -> Tuple[go.Figure, int, Optional[str]]:
+    """Return (fig, trace_count, banner_text).
+
+    Helper used by build_trajectories_tab. Does not call Streamlit.
     """
+    if mp_fallback_count is None:
+        try:
+            mp_fallback_count = int(os.getenv("EXPLORER_MP_FALLBACK_COUNT", "20"))
+        except Exception:
+            mp_fallback_count = 20
 
-from __future__ import annotations
+    party_centroids, meta = compute_party_centroids(
+        positions_by_window, party_map, windows
+    )
+
+    try:
+        inspector_summary = inspect_positions_for_issues(positions_by_window, party_map)
+    except Exception:
+        tb = traceback.format_exc()
+        inspector_summary = {}
+        try:
+            select_trajectory_plot_data._last_diagnostics = {
+                "stage": "inspector_exception",
+                "exception": tb,
+            }
+        except Exception:
+            pass
+        try:
+            _last_trajectories_diagnostics.update(
+                {"stage": "inspector_exception", "exception": tb}
+            )
+        except Exception:
+            pass
+    logger.debug("select_trajectory_plot_data inspector summary: %s", inspector_summary)
+
+    plottable_parties = []
+    for p, vals in party_centroids.items():
+        has_valid = any(not (np.isnan(x) and np.isnan(y)) for x, y in vals)
+        if has_valid:
+            plottable_parties.append(p)
+
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] plottable_parties: %d parties, sample=%s",
+        len(plottable_parties),
+        (plottable_parties[:5] if plottable_parties else "empty"),
+    )
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] party_centroids keys: %s",
+        list(party_centroids.keys())[:10],
+    )
+    if party_centroids:
+        sample_party = list(party_centroids.keys())[0]
+        sample_vals = party_centroids[sample_party]
+        logging.getLogger(__name__).debug(
+            "[TRAJ DEBUG] Sample party '%s' centroids: %s...",
+            sample_party,
+            sample_vals[:3],
+        )
+
+    fig = go.Figure()
+    trace_count = 0
+    banner_text: Optional[str] = None
 
-from typing import List
+    def _ema_smooth(values: List[float], alpha: float) -> List[float]:
+        if not values or alpha >= 1.0:
+            return values
+        smoothed: List[float] = []
+        prev = None
+        for v in values:
+            if v is None or (isinstance(v, float) and np.isnan(v)):
+                smoothed.append(float(np.nan))
+                continue
+            v = float(v)
+            if prev is None:
+                prev = v
+            else:
+                prev = alpha * v + (1 - alpha) * prev
+            smoothed.append(float(prev))
+        return smoothed
+
+    if not plottable_parties:
+        mp_positions: Dict[str, Dict[str, Tuple[float, float]]] = {}
+        for wid in windows:
+            pos = positions_by_window.get(wid, {})
+            for mp_name, xy in pos.items():
+                try:
+                    x, y = float(xy[0]), float(xy[1])
+                except Exception:
+                    continue
+                mp_positions.setdefault(mp_name, {})[wid] = (x, y)
+
+        mp_activity = sorted(
+            [(mp, len(wdict)) for mp, wdict in mp_positions.items()],
+            key=lambda t: t[1],
+            reverse=True,
+        )
+        top_mps = [mp for mp, _ in mp_activity[:mp_fallback_count]]
+
+        for mp in top_mps:
+            wids_sorted = sorted(mp_positions.get(mp, {}).keys())
+            if not wids_sorted:
+                continue
+            xs_raw = [mp_positions[mp][w][0] for w in wids_sorted]
+            ys_raw = [mp_positions[mp][w][1] for w in wids_sorted]
+            xs = _ema_smooth(xs_raw, smooth_alpha)
+            ys = _ema_smooth(ys_raw, smooth_alpha)
+            custom_raw = [
+                (
+                    float(rx) if rx is not None else float(np.nan),
+                    float(ry) if ry is not None else float(np.nan),
+                )
+                for rx, ry in zip(xs_raw, ys_raw)
+            ]
+            fig.add_trace(
+                go.Scatter(
+                    x=xs,
+                    y=ys,
+                    mode="lines+markers",
+                    name=mp,
+                    text=wids_sorted,
+                    customdata=custom_raw,
+                    line=dict(color="#888888", shape="spline", smoothing=1.3),
+                    marker=dict(color="#888888", size=6),
+                )
+            )
+            trace_count += 1
+
+        banner_text = "Partijcentroiden niet beschikbaar — tonen individuele MP-trajecten als fallback."
+        logging.getLogger(__name__).debug(
+            "[TRAJ DEBUG] Fallback to MP trajectories: trace_count=%d, top_mps=%d",
+            trace_count,
+            len(top_mps),
+        )
+        return fig, trace_count, banner_text
+
+    to_plot = [p for p in selected_parties if p in plottable_parties]
+    if not to_plot:
+        to_plot = plottable_parties
+
+    for party in to_plot:
+        vals = party_centroids.get(party, [])
+        if not vals:
+            continue
+        xs_raw = [v[0] for v in vals]
+        ys_raw = [v[1] for v in vals]
+        xs = _ema_smooth(xs_raw, smooth_alpha)
+        ys = _ema_smooth(ys_raw, smooth_alpha)
+        custom_raw = [
+            (
+                float(x) if (x is not None and not np.isnan(x)) else float(np.nan),
+                float(y) if (y is not None and not np.isnan(y)) else float(np.nan),
+            )
+            for x, y in zip(xs_raw, ys_raw)
+        ]
+        colour = PARTY_COLOURS.get(party, "#9E9E9E")
+        fig.add_trace(
+            go.Scatter(
+                x=xs,
+                y=ys,
+                mode="lines+markers",
+                name=party,
+                text=windows,
+                customdata=custom_raw,
+                line=dict(color=colour, shape="spline", smoothing=1.3),
+                marker=dict(color=colour, size=8),
+            )
+        )
+        trace_count += 1
+
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] Final trace_count=%d, plottable_parties=%d, to_plot=%s",
+        trace_count,
+        len(plottable_parties),
+        (len(to_plot) if "to_plot" in dir() else "N/A"),
+    )
+    return fig, trace_count, None
 
 
 def build_trajectories_tab(db_path: str, window_size: str) -> None:
-    """Build the Partij Trajectories tab.
+    """Build the Partij Trajectories tab."""
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] build_trajectories_tab called — db_path=%s, window_size=%s",
+        db_path,
+        window_size,
+    )
+    st.subheader("Partij Trajectories")
+    st.markdown("Hoe bewegen partijen over de tijdsvensters heen?")
 
-    Currently delegates to explorer.py implementation.
-    Will be extracted when rendering logic is decoupled from Streamlit.
-    """
-    import explorer
+    positions_by_window, axis_def = explorer_data.load_positions(db_path, window_size)
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] load_positions → %d windows, total MPs=%d",
+        len(positions_by_window),
+        sum(len(v) for v in positions_by_window.values()),
+    )
+    if axis_def is None:
+        axis_def = {}
+    if not positions_by_window:
+        try:
+            _last_trajectories_diagnostics.update(
+                {
+                    "stage": "load_positions_empty",
+                    "positions_by_window_len": len(positions_by_window),
+                }
+            )
+        except Exception:
+            pass
+        try:
+            st.warning("Geen positiedata beschikbaar.")
+        except Exception:
+            pass
+        try:
+            if get_debug_trajectories_enabled():
+                try:
+                    st.text_area(
+                        "Trajectories diagnostics",
+                        json.dumps(_last_trajectories_diagnostics, default=str),
+                        height=160,
+                    )
+                except Exception:
+                    pass
+        except Exception:
+            pass
+        return
+
+    party_map = explorer_data.load_party_map(db_path)
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] load_party_map → %d entries, sample=%s",
+        len(party_map),
+        list(party_map.items())[:3],
+    )
+
+    def normalize_mp_name(name):
+        """Normalize MP name for better matching between data sources."""
+        if not name:
+            return ""
+        name = name.strip()
+        if "," in name and ", " not in name:
+            name = name.replace(",", ", ")
+        return name
+
+    party_map = {normalize_mp_name(k): v for k, v in party_map.items()}
+
+    normalized_positions = {}
+    for window, positions in positions_by_window.items():
+        normalized_positions[window] = {
+            normalize_mp_name(k): v for k, v in positions.items()
+        }
+    positions_by_window = normalized_positions
+
+    all_mp_names = set()
+    for positions in positions_by_window.values():
+        all_mp_names.update(positions.keys())
+
+    matched_names = sum(1 for mp in all_mp_names if mp in party_map)
+    if all_mp_names:
+        logger.info(
+            f"MP name matching: {matched_names}/{len(all_mp_names)} matched ({100 * matched_names / len(all_mp_names):.1f}%)"
+        )
+    else:
+        logger.info("MP name matching: no MPs found in positions data")
+
+    if matched_names == 0 and len(all_mp_names) > 0:
+        logger.warning("No MP names matched between positions and party_map!")
+        logger.warning(f"Sample positions names: {list(all_mp_names)[:5]}")
+        logger.warning(f"Sample party_map names: {list(party_map.keys())[:5]}")
+
+    windows = sorted(positions_by_window.keys())
+
+    centroids: Dict[str, Dict[str, Tuple[float, float]]] = {}
+    all_parties: set = set()
+
+    def _strip_paren(name: str) -> str:
+        return re.sub(r"\s*\([^)]*\)", "", name).strip()
+
+    for wid in windows:
+        pos = positions_by_window.get(wid, {})
+        per_party: Dict[str, List[Tuple[float, float]]] = {}
+        for mp_name, (x, y) in pos.items():
+            party = party_map.get(mp_name) or party_map.get(
+                _strip_paren(mp_name), "Unknown"
+            )
+            if party == "Unknown":
+                continue
+            per_party.setdefault(party, []).append((x, y))
+        for party, coords in per_party.items():
+            all_parties.add(party)
+            xs = [c[0] for c in coords]
+            ys = [c[1] for c in coords]
+            centroids.setdefault(party, {})[wid] = (
+                float(np.mean(xs)),
+                float(np.mean(ys)),
+            )
+
+    all_parties = sorted(
+        set(party_map.get(mp) for MPs in positions_by_window.values() for mp in MPs)
+        - {None, "Unknown"}
+    )
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] all_parties (raw from party_map) → %d parties: %s",
+        len(all_parties),
+        all_parties[:10],
+    )
+    all_parties_sorted = sorted(all_parties)
+
+    if not all_parties_sorted:
+        st.info(
+            "Geen partijen beschikbaar om trajecten te tekenen. Controleer of de party mapping is geladen (mp_metadata) en of de minimum Kamerleden-instelling te hoog staat."
+        )
+        try:
+            st.caption(f"Bekende partijen in party_map: {len(party_map)}")
+        except Exception:
+            pass
+
+    default_parties = [p for p in ["CDA", "D66", "VVD"] if p in all_parties]
+    if not default_parties:
+        default_parties = [p for p in KNOWN_MAJOR_PARTIES if p in all_parties]
+    if not default_parties:
+        default_parties = all_parties_sorted[:6]
+
+    selected_parties = st.multiselect(
+        "Selecteer partijen",
+        options=all_parties_sorted,
+        default=default_parties,
+    )
+
+    def _ema_smooth(values: List[float], alpha: float) -> List[float]:
+        if not values or alpha >= 1.0:
+            return values
+        smoothed = [values[0]]
+        for v in values[1:]:
+            smoothed.append(alpha * v + (1 - alpha) * smoothed[-1])
+        return smoothed
+
+    smooth_alpha = 0.35
+
+    if not centroids:
+        st.info(
+            "Partijcentroiden niet beschikbaar — tonen individuele MP-trajecten als fallback."
+        )
+
+        mp_positions: Dict[str, Dict[str, Tuple[float, float]]] = {}
+        for wid in windows:
+            pos = positions_by_window.get(wid, {})
+            for mp_name, xy in pos.items():
+                try:
+                    x, y = float(xy[0]), float(xy[1])
+                except Exception:
+                    continue
+                mp_positions.setdefault(mp_name, {})[wid] = (x, y)
+
+        mp_positions = {
+            mp: pos
+            for mp, pos in mp_positions.items()
+            if len(pos) >= 2
+            and not all(np.isnan(x) and np.isnan(y) for x, y in pos.values())
+        }
+
+        if not mp_positions:
+            st.warning("Geen positiedata beschikbaar voor trajectplotten.")
+            _last_trajectories_diagnostics.update(
+                {
+                    "stage": "no_mp_positions",
+                    "mp_positions_count": 0,
+                }
+            )
+            try:
+                if get_debug_trajectories_enabled():
+                    try:
+                        st.text_area(
+                            "Trajectories diagnostics",
+                            json.dumps(_last_trajectories_diagnostics, default=str),
+                            height=160,
+                        )
+                    except Exception:
+                        pass
+            except Exception:
+                pass
+            return
+
+        st.session_state["_trajectory_mp_positions"] = mp_positions
+
+        mp_list = sorted(mp_positions.keys())
+        default_mps = mp_list[:6]
+        selected_mps = st.multiselect(
+            "Selecteer Kamerleden (fallback)", options=mp_list, default=default_mps
+        )
+
+        fig = go.Figure()
+        trace_count = 0
+        for mp in selected_mps:
+            wids_sorted = sorted(mp_positions[mp].keys())
+            xs_raw = [mp_positions[mp][w][0] for w in wids_sorted]
+            ys_raw = [mp_positions[mp][w][1] for w in wids_sorted]
+            xs = _ema_smooth(xs_raw, smooth_alpha)
+            ys = _ema_smooth(ys_raw, smooth_alpha)
+            custom_raw = [(float(rx), float(ry)) for rx, ry in zip(xs_raw, ys_raw)]
+            fig.add_trace(
+                go.Scatter(
+                    x=xs,
+                    y=ys,
+                    mode="lines+markers",
+                    name=mp,
+                    text=wids_sorted,
+                    customdata=custom_raw,
+                    line=dict(color="#888888", shape="spline", smoothing=1.3),
+                    marker=dict(color="#888888", size=6),
+                    hovertemplate=(
+                        f"<b>{mp}</b><br>"
+                        "venster: %{text}<br>"
+                        "x (smoothed): %{x:.3f}<br>"
+                        "x (raw): %{customdata[0]:.3f}<br>"
+                        "y (smoothed): %{y:.3f}<br>"
+                        "y (raw): %{customdata[1]:.3f}<extra></extra>"
+                    ),
+                )
+            )
+            trace_count += 1
+
+        _add_y_direction_annotations(fig)
+        if trace_count == 0:
+            st.info(
+                "Geen trajecten getekend: geen geselecteerde Kamerleden met voldoende data."
+            )
+        else:
+            st.plotly_chart(fig, use_container_width=True)
+        return
+
+    if os.getenv("EXPLORER_FORCE_SHOW_TRAJECTORIES") in ("1", "true", "True"):
+        mp_positions: Dict[str, Dict[str, Tuple[float, float]]] = {}
+        for wid in windows:
+            pos = positions_by_window.get(wid, {})
+            for mp_name, (x, y) in pos.items():
+                mp_positions.setdefault(mp_name, {})[wid] = (float(x), float(y))
+
+        mp_list = sorted(mp_positions.keys())
+        if not mp_list:
+            st.info("Geen MP-positiegegevens beschikbaar om te tonen.")
+            return
+
+        sample_mps = mp_list[:6]
+        fig = go.Figure()
+        for mp in sample_mps:
+            wids_sorted = sorted(mp_positions[mp].keys())
+            xs_raw = [mp_positions[mp][w][0] for w in wids_sorted]
+            ys_raw = [mp_positions[mp][w][1] for w in wids_sorted]
+            xs = _ema_smooth(xs_raw, 0.35)
+            ys = _ema_smooth(ys_raw, 0.35)
+            custom_raw = [(float(rx), float(ry)) for rx, ry in zip(xs_raw, ys_raw)]
+            fig.add_trace(
+                go.Scatter(
+                    x=xs,
+                    y=ys,
+                    mode="lines+markers",
+                    name=mp,
+                    text=wids_sorted,
+                    customdata=custom_raw,
+                    line=dict(color="#444444", shape="spline", smoothing=1.3),
+                    marker=dict(color="#444444", size=6),
+                    hovertemplate=(
+                        f"<b>{mp}</b><br>"
+                        "venster: %{text}<br>"
+                        "x (smoothed): %{x:.3f}<br>"
+                        "x (raw): %{customdata[0]:.3f}<br>"
+                        "y (smoothed): %{y:.3f}<br>"
+                        "y (raw): %{customdata[1]:.3f}<extra></extra>"
+                    ),
+                )
+            )
+        _add_y_direction_annotations(fig)
+        st.plotly_chart(fig, use_container_width=True)
+        return
+
+    try:
+        debug_checkbox = False
+        try:
+            debug_checkbox = st.checkbox(
+                "Enable trajectories diagnostics (show extra info)",
+                value=get_debug_trajectories_enabled(),
+            )
+        except Exception:
+            debug_checkbox = get_debug_trajectories_enabled()
+        if debug_checkbox:
+            try:
+                with st.expander(
+                    "DEBUG: Trajectories data (showing diagnostics)", expanded=False
+                ):
+                    st.write("windows (count):", len(windows))
+                    st.write("windows sample:", windows[:10])
+                    st.write("party_map entries:", len(party_map))
+                    st.write("parties with centroids:", len(all_parties_sorted))
+                    st.write("default_parties:", default_parties)
+                    st.write("selected_parties:", selected_parties)
+                    st.write("min_mps setting:", 3)
+                    sample = {
+                        p: len(centroids.get(p, {}))
+                        for p in list(all_parties_sorted)[:8]
+                    }
+                    st.write("sample centroid window counts per party:", sample)
+            except Exception:
+                pass
+    except Exception:
+        pass
+
+    smoothing_method = st.selectbox(
+        "Smoothing methode",
+        options=["EMA", "Spline", "None"],
+        index=0,
+        help="EMA = exponential moving average; Spline = low-degree polynomial spline fit; None = raw centroids",
+    )
+
+    smooth_alpha = 1.0
+    if smoothing_method == "EMA":
+        smooth_alpha = st.slider(
+            "Glad maken (EMA-\u03b1)",
+            min_value=0.1,
+            max_value=1.0,
+            value=0.35,
+            step=0.05,
+            help=(
+                "\u03b1=1.0 toont de ruwe data; lagere waarden maken de lijn gladder. "
+                "Standaard 0.35 voor een goed evenwicht tussen detail en ruis."
+            ),
+        )
+
+    def _spline_smooth(values: List[float]) -> List[float]:
+        n = len(values)
+        if n <= 2:
+            return values
+        deg = min(3, n - 1)
+        try:
+            idx = np.arange(n, dtype=float)
+            coeffs = np.polyfit(idx, np.array(values, dtype=float), deg=deg)
+            smooth = np.polyval(coeffs, idx)
+            return [float(v) for v in smooth]
+        except Exception:
+            return values
+
+    fig = go.Figure()
+    trace_count = 0
+    helper_succeeded = False
+    try:
+        fig2, trace_count2, banner_text = select_trajectory_plot_data(
+            positions_by_window, party_map, windows, selected_parties, smooth_alpha
+        )
+        if fig2 is not None:
+            fig = fig2
+            trace_count = trace_count2
+            helper_succeeded = True
+            if banner_text:
+                try:
+                    st.caption(banner_text)
+                except Exception:
+                    pass
+                try:
+                    _last_trajectories_diagnostics.update({"banner_text": banner_text})
+                except Exception:
+                    pass
+    except Exception as e:
+        tb = traceback.format_exc()
+        try:
+            select_trajectory_plot_data._last_diagnostics = {"exception": tb}
+        except Exception:
+            pass
+        try:
+            _last_trajectories_diagnostics.update(
+                {"stage": "select_helper_exception", "exception": tb}
+            )
+        except Exception:
+            pass
+        logger.exception("select_trajectory_plot_data failed")
+        debug_enabled = get_debug_trajectories_enabled()
+        if debug_enabled:
+            try:
+                st.text_area("select_trajectory_plot_data traceback", tb, height=240)
+            except Exception:
+                pass
+    logging.getLogger(__name__).debug(
+        "[TRAJ DEBUG] helper_succeeded=%s", helper_succeeded
+    )
+    if not helper_succeeded:
+        for party in selected_parties:
+            if party not in centroids:
+                continue
+            wids_sorted = sorted(centroids[party].keys())
+            xs_raw = [centroids[party][w][0] for w in wids_sorted]
+            ys_raw = [centroids[party][w][1] for w in wids_sorted]
+            xs = _ema_smooth(xs_raw, smooth_alpha)
+            ys = _ema_smooth(ys_raw, smooth_alpha)
+            custom_raw = [(float(rx), float(ry)) for rx, ry in zip(xs_raw, ys_raw)]
+            colour = PARTY_COLOURS.get(party, "#9E9E9E")
+            fig.add_trace(
+                go.Scatter(
+                    x=xs,
+                    y=ys,
+                    mode="lines+markers",
+                    name=party,
+                    text=wids_sorted,
+                    customdata=custom_raw,
+                    line=dict(color=colour, shape="spline", smoothing=1.3),
+                    marker=dict(color=colour, size=8),
+                    hovertemplate=(
+                        f"<b>{party}</b><br>"
+                        "venster: %{text}<br>"
+                        "x (smoothed): %{x:.3f}<br>"
+                        "x (raw): %{customdata[0]:.3f}<br>"
+                        "y (smoothed): %{y:.3f}<br>"
+                        "y (raw): %{customdata[1]:.3f}<extra></extra>"
+                    ),
+                )
+            )
+            trace_count += 1
+
+    _THRESHOLD = 0.65
+    x_conf_map = axis_def.get("x_label_confidence", {}) or {}
+    y_conf_map = axis_def.get("y_label_confidence", {}) or {}
+
+    def _mean_conf(m: dict) -> Optional[float]:
+        vals = [v for v in m.values() if v is not None]
+        if not vals:
+            return None
+        return float(sum(vals) / len(vals))
+
+    x_mean = _mean_conf(x_conf_map)
+    y_mean = _mean_conf(y_conf_map)
+
+    x_title = trajectory.choose_trajectory_title(axis_def, "x", threshold=_THRESHOLD)
+    y_title = trajectory.choose_trajectory_title(axis_def, "y", threshold=_THRESHOLD)
+
+    fig.update_layout(
+        title="Partij trajectories",
+        xaxis_title=x_title,
+        yaxis_title=y_title,
+        height=600,
+        legend_title_text="Partij",
+    )
+    _add_y_direction_annotations(fig)
+    try:
+        _last_trajectories_diagnostics.update({"trace_count": trace_count})
+    except Exception:
+        pass
+    debug_enabled = get_debug_trajectories_enabled()
+    if trace_count == 0:
+        _last_trajectories_diagnostics.update(
+            {
+                "stage": "zero_traces",
+                "positions_count": sum(len(pos) for pos in positions_by_window.values())
+                if positions_by_window
+                else 0,
+                "party_map_count": len(party_map) if party_map else 0,
+                "centroids_count": len(centroids) if centroids else 0,
+                "selected_parties_count": len(selected_parties)
+                if selected_parties
+                else 0,
+                "timestamp": datetime.now().isoformat(),
+            }
+        )
+        if positions_by_window and party_map and not centroids:
+            sample_mps = []
+            for window, positions in list(positions_by_window.items())[:1]:
+                sample_mps = list(positions.keys())[:5]
+                break
+            matched = sum(1 for mp in sample_mps if mp in party_map)
+            _last_trajectories_diagnostics["name_match_check"] = {
+                "sample_mps": sample_mps,
+                "matched_in_party_map": matched,
+                "sample_size": len(sample_mps),
+            }
+    if trace_count == 0:
+        st.info("📊 **Geen trajecten getekend**")
+
+        with st.expander("🔍 Diagnostische informatie"):
+            st.write("**Data status:**")
+            st.write(
+                f"- Positie vensters: {len(positions_by_window) if positions_by_window else 0}"
+            )
+            st.write(f"- Party mappings: {len(party_map) if party_map else 0}")
+            st.write(
+                f"- Geselecteerde partijen: {len(selected_parties) if selected_parties else 0}"
+            )
+
+            if "centroid_diagnostics" in locals():
+                st.write("**Centroid berekening:**")
+                st.write(
+                    f"- Partijen met posities: {len(centroid_diagnostics.get('parties_with_positions', []))}"
+                )
+                st.write(
+                    f"- Partijen met alleen NaN: {len(centroid_diagnostics.get('parties_all_nan', []))}"
+                )
+
+            st.write("\n**Mogelijke oorzaken:**")
+            st.write("1. Geen SVD vectoren berekend voor de geselecteerde vensters")
+            st.write("2. MP namen in posities komen niet overeen met party_map")
+            st.write("3. Alle geselecteerde partijen hebben te weinig MPs (< 5)")
+
+            if st.button("🔧 Database diagnostiek uitvoeren"):
+                with st.spinner("Bezig met diagnostiek..."):
+                    from scripts.diagnose_trajectories_cli import (
+                        run as diagnose_trajectories,
+                    )
 
-    explorer.build_trajectories_tab(db_path, window_size)
+                    results = diagnose_trajectories(db_path)
+                    st.json(results)
+    else:
+        try:
+            st.info(
+                f"[DEBUG] trace_count={trace_count}, fig data count={len(fig.data)}, layout title={fig.layout.title.text if fig.layout.title else 'none'}"
+            )
+        except Exception:
+            pass
+        try:
+            logging.getLogger(__name__).debug(
+                "[TRAJ DEBUG] About to render plotly chart — trace_count=%d, banner=%s, fig has %d traces",
+                trace_count,
+                banner_text,
+                len(fig.data),
+            )
+            st.plotly_chart(fig, use_container_width=True)
+        except Exception as e:
+            st.error(f"Trajectories rendering failed: {e}")
+            if get_debug_trajectories_enabled():
+                try:
+                    st.json(_last_trajectories_diagnostics)
+                except Exception:
+                    st.text_area(
+                        "Trajectories diagnostics (JSON failed)",
+                        json.dumps(_last_trajectories_diagnostics, default=str),
+                        height=240,
+                    )

scheduler.py

@@ -0,0 +1,170 @@
+"""Automated pipeline scheduling.
+
+Runs the parliamentary embedding pipeline and motion summarization
+on a configurable schedule using the `schedule` library.
+
+Usage:
+    uv run python scheduler.py                    # start scheduler loop
+    uv run python scheduler.py --once             # run once and exit
+    uv run python scheduler.py --pipeline-time 03:00 --summarizer-every 6
+"""
+
+from __future__ import annotations
+
+import argparse
+import logging
+import signal
+import sys
+import time
+from typing import Callable
+
+import schedule
+
+from config import config
+import argparse
+
+from pipeline.run_pipeline import run as run_pipeline
+from summarizer import summarizer
+
+_logger = logging.getLogger(__name__)
+
+
+class PipelineScheduler:
+    """Schedules and runs pipeline jobs."""
+
+    def __init__(self, db_path: str = "data/motions.db"):
+        self.db_path = db_path
+        self._running = False
+
+    def run_pipeline(self) -> int:
+        """Run the full embedding pipeline.
+
+        Returns the exit code from the pipeline run.
+        """
+        _logger.info("Starting scheduled pipeline run")
+        try:
+            args = argparse.Namespace(
+                db_path=self.db_path,
+                window_size="annual",
+                start_date=None,
+                end_date=None,
+                svd_k=50,
+                svd_workers=None,
+                text_model=None,
+                text_batch_size=200,
+                skip_metadata=False,
+                skip_extract=False,
+                skip_svd=False,
+                skip_text=False,
+                skip_fusion=False,
+                dry_run=False,
+            )
+            result = run_pipeline(args)
+            _logger.info("Pipeline run completed with code %s", result)
+            return result if isinstance(result, int) else 0
+        except Exception:
+            _logger.exception("Pipeline run failed")
+            return 1
+
+    def run_summarizer(self) -> None:
+        """Run motion summarization for missing explanations."""
+        _logger.info("Starting scheduled summarizer run")
+        try:
+            summarizer.update_motion_summaries()
+            _logger.info("Summarizer run completed")
+        except Exception:
+            _logger.exception("Summarizer run failed")
+
+    def schedule_daily(self, time_str: str = "02:00") -> None:
+        """Schedule the pipeline to run daily at *time_str*."""
+        _logger.info("Scheduling daily pipeline run at %s", time_str)
+        schedule.every().day.at(time_str).do(self.run_pipeline)
+
+    def schedule_summarizer(self, every_n_hours: int = 6) -> None:
+        """Schedule the summarizer to run every *every_n_hours* hours."""
+        _logger.info("Scheduling summarizer every %s hours", every_n_hours)
+        schedule.every(every_n_hours).hours.do(self.run_summarizer)
+
+    def _signal_handler(self, signum, frame) -> None:
+        """Handle shutdown signals gracefully."""
+        _logger.info("Received signal %s, shutting down", signum)
+        self.stop()
+
+    def start(self) -> None:
+        """Start the scheduler loop.
+
+        Blocks until :meth:`stop` is called or a signal is received.
+        """
+        self._running = True
+
+        # Register signal handlers for graceful shutdown
+        signal.signal(signal.SIGTERM, self._signal_handler)
+        signal.signal(signal.SIGINT, self._signal_handler)
+
+        _logger.info("Scheduler started")
+        while self._running:
+            schedule.run_pending()
+            time.sleep(1)
+        _logger.info("Scheduler stopped")
+
+    def stop(self) -> None:
+        """Stop the scheduler loop."""
+        self._running = False
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Automated pipeline scheduler",
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument(
+        "--db-path",
+        default="data/motions.db",
+        help="Path to the DuckDB file",
+    )
+    parser.add_argument(
+        "--pipeline-time",
+        default="02:00",
+        help="Daily pipeline run time (HH:MM)",
+    )
+    parser.add_argument(
+        "--summarizer-every",
+        type=int,
+        default=6,
+        help="Run summarizer every N hours",
+    )
+    parser.add_argument(
+        "--once",
+        action="store_true",
+        help="Run pipeline + summarizer once and exit (no scheduling loop)",
+    )
+    return parser
+
+
+def main(argv: list[str] | None = None) -> int:
+    parser = build_parser()
+    args = parser.parse_args(argv)
+
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s %(levelname)s %(name)s %(message)s",
+    )
+
+    sched = PipelineScheduler(db_path=args.db_path)
+
+    if args.once:
+        _logger.info("Running in single-shot mode")
+        pipeline_rc = sched.run_pipeline()
+        sched.run_summarizer()
+        return pipeline_rc
+
+    sched.schedule_daily(args.pipeline_time)
+    if args.summarizer_every > 0:
+        sched.schedule_summarizer(args.summarizer_every)
+
+    sched.start()
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tests/test_explorer_decomposition.py

@@ -0,0 +1,95 @@
+"""Tests for explorer.py decomposition (P3-001).
+
+Acceptance criteria:
+- explorer.py must be under 1500 lines.
+- Tab modules must define their build functions locally (not re-export from explorer).
+- No circular imports between explorer.py and analysis.tabs.
+"""
+
+import ast
+import inspect
+import pathlib
+
+
+class TestExplorerDecomposition:
+    """RED test: explorer.py must be under 1500 lines."""
+
+    def test_explorer_line_count_under_1500(self):
+        path = pathlib.Path("explorer.py")
+        lines = path.read_text(encoding="utf-8").splitlines()
+        assert len(lines) < 1500, (
+            f"explorer.py has {len(lines)} lines; target is < 1500. "
+            f"Extract tab functions and rendering helpers into analysis/tabs/."
+        )
+
+    def test_tab_modules_define_functions_locally(self):
+        """Each tab module must define its build_*_tab without delegating to explorer."""
+        tabs = [
+            ("analysis/tabs/compass.py", "build_compass_tab"),
+            ("analysis/tabs/trajectories.py", "build_trajectories_tab"),
+            ("analysis/tabs/search.py", "build_search_tab"),
+            ("analysis/tabs/browser.py", "build_browser_tab"),
+            ("analysis/tabs/components.py", "build_svd_components_tab"),
+            ("analysis/tabs/quiz.py", "build_mp_quiz_tab"),
+        ]
+        for module_path, func_name in tabs:
+            source = pathlib.Path(module_path).read_text(encoding="utf-8")
+            tree = ast.parse(source)
+            func_def = None
+            for node in ast.walk(tree):
+                if isinstance(node, ast.FunctionDef) and node.name == func_name:
+                    func_def = node
+                    break
+            assert func_def is not None, (
+                f"{module_path} must define {func_name}"
+            )
+            # Ensure it's not a one-liner stub that imports from explorer
+            body = func_def.body
+            assert len(body) > 3, (
+                f"{module_path}.{func_name} looks like a stub ({len(body)} lines). "
+                f"Extract the real implementation from explorer.py."
+            )
+
+    def test_rendering_helpers_extracted(self):
+        """Rendering helpers should not live in explorer.py."""
+        helpers = [
+            "_render_scree_plot",
+            "_build_party_axis_figure",
+            "_render_party_axis_chart",
+            "_render_party_axis_chart_1d",
+            "_render_svd_time_trajectory",
+            "_render_voting_results",
+            "_add_y_direction_annotations",
+        ]
+        source = pathlib.Path("explorer.py").read_text(encoding="utf-8")
+        tree = ast.parse(source)
+        defined = {node.name for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)}
+        for helper in helpers:
+            assert helper not in defined, (
+                f"{helper} should be extracted from explorer.py "
+                f"into analysis/tabs/_rendering.py"
+            )
+
+    def test_no_circular_import_tabs_to_explorer(self):
+        """Tab modules must not import from explorer."""
+        tab_modules = [
+            "analysis/tabs/compass.py",
+            "analysis/tabs/trajectories.py",
+            "analysis/tabs/search.py",
+            "analysis/tabs/browser.py",
+            "analysis/tabs/components.py",
+            "analysis/tabs/quiz.py",
+            "analysis/tabs/_rendering.py",
+        ]
+        for module_path in tab_modules:
+            if not pathlib.Path(module_path).exists():
+                continue
+            source = pathlib.Path(module_path).read_text(encoding="utf-8")
+            assert "from explorer import" not in source, (
+                f"{module_path} imports from explorer.py — "
+                f"move shared helpers to explorer_data.py or _rendering.py instead"
+            )
+            assert "import explorer" not in source, (
+                f"{module_path} imports explorer module — "
+                f"move shared helpers to explorer_data.py or _rendering.py instead"
+            )

tests/test_scheduler.py

@@ -0,0 +1,159 @@
+"""Tests for scheduler.py — automated pipeline scheduling.
+
+TDD: write failing test, implement, refactor.
+"""
+
+from __future__ import annotations
+
+import signal
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+
+class TestPipelineSchedulerInit:
+    def test_default_db_path(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        assert sched.db_path == "data/motions.db"
+        assert not sched._running
+
+    def test_custom_db_path(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler(db_path="/tmp/test.db")
+        assert sched.db_path == "/tmp/test.db"
+
+
+class TestPipelineSchedulerRunPipeline:
+    def test_calls_pipeline_run_with_db_path(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler(db_path="/tmp/test.db")
+        with patch("scheduler.run_pipeline") as mock_run:
+            mock_run.return_value = 0
+            sched.run_pipeline()
+            mock_run.assert_called_once()
+            # Verify args contain db_path via Namespace
+            args = mock_run.call_args[0][0]
+            assert args.db_path == "/tmp/test.db"
+
+    def test_logs_error_on_pipeline_failure(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        with patch("scheduler.run_pipeline") as mock_run:
+            mock_run.side_effect = RuntimeError("pipeline failed")
+            with patch("scheduler._logger") as mock_logger:
+                result = sched.run_pipeline()
+                assert result == 1
+                mock_logger.exception.assert_called_once()
+
+
+class TestPipelineSchedulerRunSummarizer:
+    def test_calls_summarizer_update(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        with patch("scheduler.summarizer") as mock_summarizer:
+            sched.run_summarizer()
+            mock_summarizer.update_motion_summaries.assert_called_once()
+
+    def test_logs_error_on_summarizer_failure(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        with patch("scheduler.summarizer") as mock_summarizer:
+            mock_summarizer.update_motion_summaries.side_effect = RuntimeError(
+                "summarizer failed"
+            )
+            with patch("scheduler._logger") as mock_logger:
+                sched.run_summarizer()
+                mock_logger.exception.assert_called_once()
+
+
+class TestPipelineSchedulerSchedule:
+    def test_schedule_daily_adds_job(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        with patch("scheduler.schedule") as mock_schedule:
+            mock_job = MagicMock()
+            mock_schedule.every.return_value.day.at.return_value.do = mock_job
+            sched.schedule_daily("02:00")
+            mock_schedule.every.assert_called_once()
+
+    def test_schedule_summarizer_adds_job(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        with patch("scheduler.schedule") as mock_schedule:
+            mock_job = MagicMock()
+            mock_schedule.every.return_value.hour.do = mock_job
+            sched.schedule_summarizer(every_n_hours=6)
+            mock_schedule.every.assert_called_once()
+
+
+class TestPipelineSchedulerLoop:
+    def test_start_runs_pending_jobs(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        call_count = 0
+
+        def _stop_after_first(*args, **kwargs):
+            nonlocal call_count
+            call_count += 1
+            if call_count >= 3:
+                sched.stop()
+
+        with patch("scheduler.schedule.run_pending") as mock_run_pending:
+            with patch("scheduler.time.sleep", side_effect=_stop_after_first):
+                with patch("scheduler.signal.signal"):
+                    sched.start()
+                    assert mock_run_pending.called
+                    assert not sched._running
+
+    def test_stop_sets_running_false(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        sched._running = True
+        sched.stop()
+        assert not sched._running
+
+    def test_signal_handler_stops_scheduler(self):
+        from scheduler import PipelineScheduler
+
+        sched = PipelineScheduler()
+        sched._running = True
+        with patch.object(sched, "stop") as mock_stop:
+            sched._signal_handler(signal.SIGINT, None)
+            mock_stop.assert_called_once()
+
+
+class TestSchedulerCLI:
+    def test_main_parses_args(self):
+        from scheduler import main
+
+        with patch("scheduler.PipelineScheduler") as mock_sched_class:
+            mock_sched = MagicMock()
+            mock_sched_class.return_value = mock_sched
+            rc = main(["--pipeline-time", "03:00"])
+            assert rc == 0
+            mock_sched_class.assert_called_once_with(db_path="data/motions.db")
+            mock_sched.schedule_daily.assert_called_once_with("03:00")
+            mock_sched.start.assert_called_once()
+
+    def test_main_custom_db_path(self):
+        from scheduler import main
+
+        with patch("scheduler.PipelineScheduler") as mock_sched_class:
+            mock_sched = MagicMock()
+            mock_sched.run_pipeline.return_value = 0
+            mock_sched_class.return_value = mock_sched
+            rc = main(["--db-path", "/tmp/test.db", "--once"])
+            assert rc == 0
+            mock_sched_class.assert_called_once_with(db_path="/tmp/test.db")
+            mock_sched.run_pipeline.assert_called_once()