Fix empty paper-stats heatmap (matplotlib 2-panel); tighten passport gridspec; fan out sibling alignment labels

app.py

@@ -648,11 +648,11 @@ def _sensory_profile(name):
     return out
 
 def render_passport(name):
-    fig = plt.figure(figsize=(11.7, 16.5), facecolor=KAIKAKU_DARK)
+    fig = plt.figure(figsize=(12, 10.5), facecolor=KAIKAKU_DARK)
     fig.patch.set_facecolor(KAIKAKU_DARK)
-    gs = gridspec.GridSpec(6, 3, figure=fig,
-        height_ratios=[0.9, 0.4, 2.4, 2.0, 2.0, 0.4],
-        hspace=0.55, wspace=0.35, left=0.06, right=0.94, top=0.96, bottom=0.04)
+    gs = gridspec.GridSpec(4, 6, figure=fig,
+        height_ratios=[0.85, 2.0, 2.4, 1.8],
+        hspace=0.40, wspace=0.30, left=0.05, right=0.96, top=0.95, bottom=0.05)
     def styled(ax, title=None):
         ax.set_facecolor(KAIKAKU_DARK)
         for s in ax.spines.values():
@@ -662,22 +662,19 @@ def render_passport(name):
             ax.set_title(title, color=KAIKAKU_ACCENT_LIGHT, fontsize=10,
                          family="monospace", loc="left", pad=8)
         return ax
-    # Headline
+    # Headline (row 0)
     ax_h = fig.add_subplot(gs[0, :]); ax_h.axis("off")
     pretty = (name or "").replace("_", " ").upper()
-    ax_h.text(0.0, 0.55, pretty, color=KAIKAKU_ACCENT_LIGHT,
-              fontsize=42, fontweight="bold", va="center")
+    ax_h.text(0.0, 0.62, pretty, color=KAIKAKU_ACCENT_LIGHT,
+              fontsize=34, fontweight="bold", va="center")
     group = _NAME_TO_GROUP.get(name, "Other")
-    ax_h.text(0.0, 0.05, f"INGREDIENT PASSPORT  ·  FOOD GROUP: {group.upper()}",
-              color=KAIKAKU_ACCENT, fontsize=11, family="monospace", va="center")
-    ax_h.plot([0, 1], [0.0, 0.0], color=KAIKAKU_ACCENT, lw=2, transform=ax_h.transAxes)
-    # Section label
-    ax_s = fig.add_subplot(gs[1, :]); ax_s.axis("off")
-    ax_s.text(0.0, 0.5, "// NEAREST NEIGHBOURS PER SIBLING",
-              color=KAIKAKU_ACCENT_LIGHT, fontsize=11, family="monospace", va="center")
-    # Neighbours
+    ax_h.text(0.0, 0.15, f"INGREDIENT PASSPORT  ·  FOOD GROUP: {group.upper()}",
+              color=KAIKAKU_ACCENT, fontsize=10, family="monospace", va="center")
+    ax_h.plot([0, 1], [0.05, 0.05], color=KAIKAKU_ACCENT, lw=1.5, transform=ax_h.transAxes)
+    # Row 1: 3 neighbour panels (each spans 2 cols of a 6-col grid)
     for i, sib in enumerate(PASSPORT_SIBS):
-        ax = styled(fig.add_subplot(gs[2, i]), title=f"[{sib.upper()}]")
+        ax = styled(fig.add_subplot(gs[1, i*2:(i+1)*2]),
+                    title=f"[{sib.upper()}] NEAREST NEIGHBOURS")
         ax.set_xticks([]); ax.set_yticks([])
         m = MODELS[sib]
         if name not in m.vocab:
@@ -691,15 +688,15 @@ def render_passport(name):
                     fontsize=11, family="monospace", transform=ax.transAxes)
             ax.text(0.96, y, f"{sim:.3f}", color=KAIKAKU_ACCENT_LIGHT,
                     fontsize=10, family="monospace", ha="right", transform=ax.transAxes)
-    # Sensory radar (polar inset)
-    ax_radar_host = fig.add_subplot(gs[3, 0]); ax_radar_host.axis("off")
+    # Row 2: sensory radar (cols 0-1) + cuisine bar (cols 2-5)
+    ax_radar_host = fig.add_subplot(gs[2, 0:2]); ax_radar_host.axis("off")
     ax_radar_host.text(0.0, 1.02, "// SENSORY RADAR",
-                       color=KAIKAKU_ACCENT_LIGHT, fontsize=11,
+                       color=KAIKAKU_ACCENT_LIGHT, fontsize=10,
                        family="monospace", transform=ax_radar_host.transAxes)
     bb = ax_radar_host.get_position()
-    pad_x, pad_y = bb.width * 0.08, bb.height * 0.10
+    pad_x, pad_y = bb.width * 0.06, bb.height * 0.06
     ax_polar = fig.add_axes(
-        [bb.x0 + pad_x, bb.y0 + pad_y, bb.width - 2*pad_x, bb.height - 2*pad_y - 0.01],
+        [bb.x0 + pad_x, bb.y0 + pad_y, bb.width - 2*pad_x, bb.height - 2*pad_y - 0.012],
         projection="polar")
     sens = _sensory_profile(name)
     theta = np.linspace(0, 2*np.pi, len(PASSPORT_SENS), endpoint=False)
@@ -711,12 +708,12 @@ def render_passport(name):
     ax_polar.fill(theta_c, r_c, color=KAIKAKU_ACCENT, alpha=0.35)
     ax_polar.set_xticks(theta)
     ax_polar.set_xticklabels([a.upper() for a in PASSPORT_SENS],
-                             color=KAIKAKU_ACCENT_LIGHT, fontsize=9, family="monospace")
+                             color=KAIKAKU_ACCENT_LIGHT, fontsize=8, family="monospace")
     ax_polar.set_yticklabels([])
-    ax_polar.set_ylim(0, max(0.6, float(r.max()) + 0.05))
-    ax_polar.grid(color=KAIKAKU_ACCENT_LIGHT, alpha=0.20, lw=0.6)
-    # Cuisine bar
-    ax_c = styled(fig.add_subplot(gs[3, 1:]), title="// CUISINE AFFILIATION  (chem)")
+    ax_polar.set_ylim(0, max(0.5, float(r.max()) + 0.05))
+    ax_polar.grid(color=KAIKAKU_ACCENT_LIGHT, alpha=0.20, lw=0.5)
+    # Cuisine bar (row 2, cols 2-5)
+    ax_c = styled(fig.add_subplot(gs[2, 2:6]), title="// CUISINE AFFILIATION  (chem)")
     m = MODELS["chem"]
     if name in m.vocab:
         v = _unit(m.E[m.vocab[name]])
@@ -735,8 +732,8 @@ def render_passport(name):
     else:
         ax_c.text(0.5, 0.5, "(not in chem vocab)", ha="center", va="center",
                   color=KAIKAKU_ACCENT_LIGHT, transform=ax_c.transAxes)
-    # Closest 3 emergent modes
-    ax_m = styled(fig.add_subplot(gs[4, :]),
+    # Row 3: closest factor modes (all 6 cols)
+    ax_m = styled(fig.add_subplot(gs[3, :]),
                  title="// CLOSEST EMERGENT FACTOR MODES  (top 3 across siblings)")
     ax_m.set_xticks([]); ax_m.set_yticks([])
     scored = []
@@ -749,21 +746,16 @@ def render_passport(name):
             scored.append((float(_unit(md.pole) @ v), sib, md))
     scored.sort(key=lambda x: -x[0])
     for row, (sim, sib, md) in enumerate(scored[:3]):
-        y = 0.82 - row * 0.30
+        y = 0.85 - row * 0.30
         ax_m.text(0.01, y, f"[{sib.upper()}]", color=KAIKAKU_ACCENT,
-                  fontsize=11, family="monospace", transform=ax_m.transAxes)
-        ax_m.text(0.10, y, md.label, color="#FFFFFF",
-                  fontsize=13, fontweight="bold", transform=ax_m.transAxes)
+                  fontsize=10, family="monospace", transform=ax_m.transAxes)
+        ax_m.text(0.09, y, md.label, color="#FFFFFF",
+                  fontsize=12, fontweight="bold", transform=ax_m.transAxes)
         ax_m.text(0.99, y, f"cos {sim:.3f}", color=KAIKAKU_ACCENT_LIGHT,
                   fontsize=10, family="monospace", ha="right", transform=ax_m.transAxes)
         members = ", ".join(md.members[:6])
-        ax_m.text(0.10, y - 0.10, members, color=KAIKAKU_ACCENT_LIGHT,
+        ax_m.text(0.09, y - 0.09, members, color=KAIKAKU_ACCENT_LIGHT,
                   fontsize=9, family="monospace", transform=ax_m.transAxes)
-    # Footer
-    ax_f = fig.add_subplot(gs[5, :]); ax_f.axis("off")
-    ax_f.text(0.5, 0.5, "EPICURE  ·  300-D INGREDIENT EMBEDDING  ·  KAIKAKU",
-              ha="center", va="center", color=KAIKAKU_ACCENT,
-              fontsize=9, family="monospace", transform=ax_f.transAxes)
     return fig
 
 
@@ -890,17 +882,26 @@ def render_sibling_alignment(ingredient):
             except Exception: pass
         ax.scatter(coords[:,0], coords[:,1], s=2, c=GALLERY_DUST, alpha=0.5, linewidths=0, zorder=2)
         q = _unit(m.E[m.vocab[ingredient]])
-        nb = _topk(m, q, 5, exclude=[ingredient])
+        nb = _topk(m, q, 3, exclude=[ingredient])
         top1.append(nb[0][0] if nb else "-")
-        for nm, _s in nb:
+        # Label placement with vertical fan-out to avoid overlap
+        for j, (nm, _s) in enumerate(nb):
             p = coords[m.vocab[nm]]
-            ax.scatter([p[0]], [p[1]], s=110, c="#F4B86E", edgecolors="white", linewidths=0.7, zorder=4)
-            ax.text(p[0]+0.10, p[1]+0.10, nm, color=GALLERY_TEXT, fontsize=8.5, alpha=0.95, zorder=5)
+            ax.scatter([p[0]], [p[1]], s=130, c="#F4B86E", edgecolors="white", linewidths=0.8, zorder=4)
+            # Stagger labels vertically: top label up, second to right, third down
+            dx = 0.25 if j == 1 else 0.0
+            dy = (0.45, 0.0, -0.45)[j % 3]
+            ha = "left" if j == 1 else "center"
+            ax.text(p[0] + dx, p[1] + dy, nm.replace("_", " "), color=GALLERY_TEXT,
+                    fontsize=9.5, fontweight="bold", alpha=0.95, ha=ha,
+                    va=("bottom" if dy > 0 else "top" if dy < 0 else "center"), zorder=5)
         sp = coords[m.vocab[ingredient]]
-        ax.scatter([sp[0]], [sp[1]], s=380, c=KAIKAKU_ACCENT_LIGHT, marker="*",
-                   edgecolors="white", linewidths=1.0, zorder=6)
-        ax.text(sp[0], sp[1]+0.32, ingredient, color=KAIKAKU_ACCENT_LIGHT, ha="center",
-                fontsize=10.5, fontweight="bold", zorder=7)
+        ax.scatter([sp[0]], [sp[1]], s=420, c=KAIKAKU_ACCENT_LIGHT, marker="*",
+                   edgecolors="white", linewidths=1.2, zorder=6)
+        ax.text(sp[0], sp[1] - 0.45, ingredient.replace("_", " "), color=KAIKAKU_ACCENT_LIGHT,
+                ha="center", va="top", fontsize=11.5, fontweight="bold", zorder=7,
+                bbox=dict(boxstyle="round,pad=0.18", facecolor=GALLERY_BG,
+                          edgecolor=KAIKAKU_ACCENT_LIGHT, alpha=0.85, linewidth=0.6))
         ax.set_xlim(xmin, xmax); ax.set_ylim(ymin, ymax); ax.set_aspect("equal")
         ax.set_title(f"{sib.upper()} · {ingredient}", color=GALLERY_TXTDIM,
                      fontsize=11, family="monospace", pad=6)
@@ -996,31 +997,49 @@ def recipe_coherence(sibling, basket):
 
 
 def render_direction_quality_heatmap():
-    """Paper §3.2 table as a Plotly heatmap."""
-    probes = [
-        ("CF baked-in", "Compound feature (seen by Core/Chem walk schema)", [0.28, 0.40, 0.46]),
-        ("CF basic-taste (held-out)", "Sweet/sour/bitter/salt/umami", [0.32, 0.42, 0.47]),
-        ("USDA macros", "Protein/fat/carb/fibre/etc.", [0.41, 0.45, 0.49]),
-        ("Cuisine (Cohen's d)", "8 macro-regions, one-vs-rest", [2.43, 2.70, 3.07]),
+    """Paper §3.2 as a matplotlib heatmap (Plotly mixed-scale was breaking the render)."""
+    rho_probes = [
+        ("CF baked-in (Spearman ρ)",        [0.28, 0.40, 0.46]),
+        ("CF basic-taste held-out (ρ)",     [0.32, 0.42, 0.47]),
+        ("USDA macros (ρ)",                 [0.41, 0.45, 0.49]),
     ]
-    z = np.array([row[2] for row in probes])
-    text = [[f"{v:.2f}" for v in row] for row in z]
-    fig = go.Figure(go.Heatmap(
-        z=z, x=["Cooc", "Core", "Chem"], y=[r[0] for r in probes],
-        text=text, texttemplate="%{text}", textfont=dict(size=14, color="white"),
-        colorscale=[[0, GALLERY_BG], [0.3, GALLERY_DUST], [0.7, KAIKAKU_ACCENT], [1, KAIKAKU_ACCENT_LIGHT]],
-        showscale=True, colorbar=dict(title="score"),
-        hovertemplate="<b>%{y}</b><br>%{x}: %{z:.3f}<extra></extra>",
-    ))
-    fig.update_layout(
-        title=dict(text="DIRECTION QUALITY  ·  Cooc &lt; Core &lt; Chem (paper §3.2)",
-                   font=dict(size=14, color=GALLERY_TEXT, family="monospace")),
-        paper_bgcolor=GALLERY_BG, plot_bgcolor=GALLERY_BG,
-        height=380, margin=dict(l=180, r=40, t=70, b=40),
-        font=dict(color=GALLERY_TEXT),
-        xaxis=dict(tickfont=dict(color=GALLERY_TEXT, size=12)),
-        yaxis=dict(tickfont=dict(color=GALLERY_TEXT, size=11), autorange="reversed"),
-    )
+    d_probes = [
+        ("Cuisine, mean Cohen's d",         [2.43, 2.70, 3.07]),
+    ]
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(11, 5.6), facecolor=GALLERY_BG,
+                                   gridspec_kw=dict(height_ratios=[3, 1], hspace=0.35))
+    for ax in (ax1, ax2):
+        ax.set_facecolor(GALLERY_BG)
+        for s in ax.spines.values(): s.set_visible(False)
+    # Panel 1: rho (range 0.2-0.5)
+    z1 = np.array([r[1] for r in rho_probes])
+    im1 = ax1.imshow(z1, cmap="viridis", vmin=0.20, vmax=0.55, aspect="auto")
+    ax1.set_xticks([0, 1, 2]); ax1.set_xticklabels(["Cooc", "Core", "Chem"],
+                                                    color=GALLERY_TEXT, fontsize=12, fontweight="bold")
+    ax1.set_yticks(range(len(rho_probes)))
+    ax1.set_yticklabels([r[0] for r in rho_probes], color=GALLERY_TEXT, fontsize=11)
+    ax1.tick_params(colors=GALLERY_TEXT)
+    for i, row in enumerate(z1):
+        for j, v in enumerate(row):
+            ax1.text(j, i, f"{v:.2f}", ha="center", va="center",
+                     color=("white" if v < 0.38 else "#111111"),
+                     fontsize=15, fontweight="bold")
+    # Panel 2: Cohen's d (range 2-3)
+    z2 = np.array([r[1] for r in d_probes])
+    im2 = ax2.imshow(z2, cmap="viridis", vmin=2.2, vmax=3.2, aspect="auto")
+    ax2.set_xticks([0, 1, 2]); ax2.set_xticklabels(["Cooc", "Core", "Chem"],
+                                                    color=GALLERY_TEXT, fontsize=12, fontweight="bold")
+    ax2.set_yticks(range(len(d_probes)))
+    ax2.set_yticklabels([r[0] for r in d_probes], color=GALLERY_TEXT, fontsize=11)
+    ax2.tick_params(colors=GALLERY_TEXT)
+    for i, row in enumerate(z2):
+        for j, v in enumerate(row):
+            ax2.text(j, i, f"{v:.2f}", ha="center", va="center",
+                     color=("white" if v < 2.7 else "#111111"),
+                     fontsize=15, fontweight="bold")
+    fig.suptitle("DIRECTION QUALITY  ·  Cooc < Core < Chem  (paper §3.2)",
+                 color=GALLERY_TXTDIM, fontsize=12, family="monospace", y=0.97)
+    plt.tight_layout(rect=[0, 0, 1, 0.94])
     return fig