WVS map: on-map LLM labels (textalloc) + corner-extreme outlier labels

Replace the model legend with textalloc-placed on-map labels (leader lines, coloured
to each star), and route country labels through the same textalloc pass so Pakistan/
Nigeria/Egypt no longer collide. outlying_countries now returns the corner-most
society in each diagonal direction (top-right/bottom-left/...) instead of the n
farthest from the centroid, which bunched on one side and left the bottom-left corner
(Egypt) unlabelled.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>

scripts/wvs_map.py

@@ -223,17 +223,22 @@ def main() -> None:
     ax.axvline(med_x, color="#c9c4b4", lw=1.0, zorder=1)
     maps.draw_zone_hulls(ax, P, countries, zones)
     ax.scatter(P[:, 0], P[:, 1], s=28, c=dot_cols, alpha=0.85, edgecolors="white", linewidths=0.5, zorder=3)
-    for i, c in enumerate(countries):
-        if c in label_set:
-            ax.annotate(c, (P[i, 0], P[i, 1]), fontsize=9, xytext=(4, 3),
-                        textcoords="offset points", color="#111", fontweight="bold", zorder=6)
-    # LLM stars cluster in one corner (that IS the result), so their inline labels collide -- use a
-    # legend in the empty lower-right instead; the distinct colours tie each star to its name.
+    mnames = list(models)
+    mpts = np.array([models[k] for k in mnames])
     for (name, pt), col in zip(models.items(), MODEL_COLORS):
-        ax.scatter(*pt, s=190, marker="*", c=col, edgecolors="white", linewidths=1.0, zorder=8,
-                   label=name)
-    ax.legend(loc="lower right", fontsize=9, framealpha=0.92, title="LLMs (sampled)",
-              title_fontproperties={"weight": "bold"}, borderpad=0.7, labelspacing=0.5).set_zorder(11)
+        ax.scatter(*pt, s=190, marker="*", c=col, edgecolors="white", linewidths=1.0, zorder=8)
+    # All labels (country + LLM) placed by textalloc: non-overlapping, with leader lines back to the
+    # dot/star. LLM labels ON the map (not a legend), coloured to their star; country labels dark. The
+    # scatter set it avoids is every dot + star, so no label lands on a point.
+    import textalloc as ta
+    lab_i = [i for i, c in enumerate(countries) if c in label_set]
+    tx = [P[i, 0] for i in lab_i] + list(mpts[:, 0])
+    ty = [P[i, 1] for i in lab_i] + list(mpts[:, 1])
+    txt = [countries[i] for i in lab_i] + mnames
+    tcol = ["#111"] * len(lab_i) + list(MODEL_COLORS[:len(mnames)])
+    ta.allocate_text(fig, ax, tx, ty, txt,
+                     x_scatter=list(P[:, 0]) + list(mpts[:, 0]), y_scatter=list(P[:, 1]) + list(mpts[:, 1]),
+                     textsize=9, textcolor=tcol, linecolor="#aaa", linewidth=0.6, draw_lines=True)
     # Four pole signposts, each arrow sitting ON its neutral crosshair (x=0.5 for the vertical axis,
     # y=0.5 for the horizontal one -- these lines are NOT at the plot centre) and pointing out to its
     # pole, in the padded inner margin. All labels horizontal so they stay readable.

src/tinymfv/maps.py

@@ -142,11 +142,14 @@ def select_spread_zones(P: np.ndarray, countries: list[str], zones: dict[str, li
 
 
 def outlying_countries(P: np.ndarray, countries: list[str], n: int = 4) -> set[str]:
-    """The `n` countries farthest from the data centroid -- the automatic extreme labels for ANY map,
-    unioned with a named-major set (US/Japan/China...) so every map labels the same few landmarks plus
-    whatever its own extremes are."""
-    d = np.hypot(*(P - P.mean(0)).T)
-    return {countries[i] for i in np.argsort(d)[::-1][:n]}
+    """The corner-most country in each direction: the society that projects farthest along each of n
+    compass directions from the centroid (n=4 -> the four diagonal CORNERS top-right/bottom-left/
+    top-left/bottom-right; n=8 adds the axis extremes). Unlike 'n farthest from the centroid' (which
+    can bunch all on one side and miss a corner), this guarantees the top-right-most, bottom-left-most
+    etc. are each labelled -- the extremes a reader's eye goes to, on ANY map."""
+    Pc = P - P.mean(0)
+    dirs = [(1, 1), (-1, -1), (-1, 1), (1, -1), (1, 0), (-1, 0), (0, 1), (0, -1)][:n]
+    return {countries[int(np.argmax(Pc[:, 0] * dx + Pc[:, 1] * dy))] for dx, dy in dirs}
 
 
 def draw_zone_hulls(ax, P: np.ndarray, countries: list[str], zones: dict[str, list[str]],