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WVS map uses shared plot_value_map; big5 value axes bipolar
The WVS map's ~70 lines of pole-signpost/hull/textalloc rendering were a copy of what plot_value_map now does -- call the shared renderer instead (WVS + instruments one code path). big5 value-axis poles named at both ends (Reserved<->Exploratory, Volatile<->Stable) so every value map reads as a bipolar contrast, not a unipolar low<->high. Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
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wassname
+10
-74
@@ -43,12 +43,6 @@ from tinymfv.read import read_items, resolve_answer_ids
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from tinymfv.read_api import read_items_sampled
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from tinymfv.iw_axes import AXIS_ITEMS, X_AXIS, Y_AXIS, SKIP, resolve_items, positiveness
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# model-star palette: saturated/dark tones, distinct from the muted ZONE_COLORS. Long enough for a
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# big model panel (zip truncates silently, so a short list would just drop stars). The coloured
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# textalloc label ties each star to its name, so near-collisions between star colours are tolerable.
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MODEL_COLORS = ["#111111", "#d81b9a", "#5b2c86", "#008b8b", "#b8860b", "#8b0000",
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"#c2185b", "#00429d", "#5d1451", "#1a5e1a", "#7a3b00", "#444444",
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"#a80000", "#006d6d"]
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# option labels are single digits 0..n-1 -- single-token (unlike '10' on the justifiable scale) and
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# the format the answer-token reader is tuned for (a bare digit, not a letter the model ignores in
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# favour of the option word).
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@@ -208,74 +202,16 @@ def main() -> None:
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models = {k: model_axis_scores(v, meta, resolved) for k, v in vecs.items()}
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# Generic legibility rule (same on every map): draw only the zones that cover the most separate
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# space (farthest-first over macro-zone centroids), colour dots by their drawn zone (grey if their
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# zone wasn't selected), and label the named landmarks (US/Japan/China...) plus the 4 most-outlying
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# countries.
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zones_all, emph = zones_for(countries) # 6 macro zones
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zones = maps.select_spread_zones(P, countries, zones_all, 4)
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zone_of_c = {c: z for z, members in zones.items() for c in members}
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dot_cols = [maps.ZONE_COLORS.get(zone_of_c.get(c), "#888888") for c in countries]
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# Labels: named landmarks + the 4 most-outlying + one representative per drawn zone (its most
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# central member) so every region has at least one identifiable country.
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cidx = {c: i for i, c in enumerate(countries)}
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reps = set()
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for members in zones.values():
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mem = [c for c in members if c in cidx]
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pts = P[[cidx[c] for c in mem]]
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reps.add(mem[int(np.argmin(np.hypot(*(pts - pts.mean(0)).T)))])
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label_set = emph | maps.outlying_countries(P, countries, 4) | reps
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med_x, med_y = float(np.median(P[:, 0])), float(np.median(P[:, 1])) # the typical human society
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fig, ax = plt.subplots(figsize=(11, 9))
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ax.set_facecolor("#faf8f2")
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ax.grid(True, color="#eceadf", lw=0.3, zorder=0)
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ax.axhline(med_y, color="#c9c4b4", lw=1.0, zorder=1) # crosshair through the human median (Economist)
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ax.axvline(med_x, color="#c9c4b4", lw=1.0, zorder=1)
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maps.draw_zone_hulls(ax, P, countries, zones)
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ax.scatter(P[:, 0], P[:, 1], s=28, c=dot_cols, alpha=0.85, edgecolors="white", linewidths=0.5, zorder=3)
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mnames = list(models)
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mpts = np.array([models[k] for k in mnames])
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for (name, pt), col in zip(models.items(), MODEL_COLORS):
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ax.scatter(*pt, s=190, marker="*", c=col, edgecolors="white", linewidths=1.0, zorder=8)
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# All labels (country + LLM) placed by textalloc: non-overlapping, with leader lines back to the
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# dot/star. LLM labels ON the map (not a legend), coloured to their star; country labels dark. The
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# scatter set it avoids is every dot + star, so no label lands on a point.
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import textalloc as ta
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lab_i = [i for i, c in enumerate(countries) if c in label_set]
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tx = [P[i, 0] for i in lab_i] + list(mpts[:, 0])
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ty = [P[i, 1] for i in lab_i] + list(mpts[:, 1])
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txt = [countries[i] for i in lab_i] + mnames
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tcol = ["#111"] * len(lab_i) + list(MODEL_COLORS[:len(mnames)])
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ta.allocate_text(fig, ax, tx, ty, txt,
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x_scatter=list(P[:, 0]) + list(mpts[:, 0]), y_scatter=list(P[:, 1]) + list(mpts[:, 1]),
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textsize=9, textcolor=tcol, linecolor="#aaa", linewidth=0.6, draw_lines=True)
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# Four pole signposts, each arrow sitting ON its neutral crosshair (x=0.5 for the vertical axis,
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# y=0.5 for the horizontal one -- these lines are NOT at the plot centre) and pointing out to its
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# pole, in the padded inner margin. All labels horizontal so they stay readable.
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from matplotlib.transforms import blended_transform_factory
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import matplotlib.patheffects as pe
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ax.margins(0.13)
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tX = blended_transform_factory(ax.transData, ax.transAxes) # x = data (on x=0.5 line), y = axes frac
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tY = blended_transform_factory(ax.transAxes, ax.transData) # x = axes frac, y = data (on y=0.5 line)
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pkw = dict(fontsize=11, fontweight="bold", color="#555", zorder=10, ha="center", va="center",
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path_effects=[pe.withStroke(linewidth=3.0, foreground="white")])
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awp = dict(arrowstyle="-|>", color="#999", lw=1.3)
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ax.annotate("Secular-Rational", xy=(med_x, 0.995), xytext=(med_x, 0.945), xycoords=tX, arrowprops=awp, **pkw)
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ax.annotate("Traditional", xy=(med_x, 0.005), xytext=(med_x, 0.055), xycoords=tX, arrowprops=awp, **pkw)
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ax.annotate("Survival", xy=(0.006, med_y), xytext=(0.08, med_y), xycoords=tY, arrowprops=awp, **pkw)
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ax.annotate("Self-expression", xy=(0.994, med_y), xytext=(0.9, med_y), xycoords=tY, arrowprops=awp, **pkw)
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ax.set_xlabel("")
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ax.set_ylabel("")
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ax.set_xticks([]) # Economist: no ticks; the crosshair is the reference
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ax.set_yticks([])
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ax.set_title(f"WVS Inglehart-Welzel map: LLMs among {len(countries)} human societies "
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f"(approximate IW axes)", fontsize=12)
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ax.text(0.01, 0.01,
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"Approximate IW: axes built from GlobalOpinionQA WVS items (3 themes/axis, not the\n"
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"canonical 5; national pride / authority / materialism absent). Not a verbatim WVS "
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"factor score.",
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transform=ax.transAxes, fontsize=6.5, color="#888", va="bottom", ha="left", zorder=10)
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fig.tight_layout()
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# Render through the SHARED value-map renderer (same one the instrument value maps use): pole
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# signposts through the human median, 4 auto-selected zone hulls, textalloc labels, model stars.
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_, emph = zones_for(countries)
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fig = maps.plot_value_map(
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"WVS Inglehart-Welzel", countries, P,
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("Survival", "Self-expression", "Traditional", "Secular-Rational"),
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models=models, emphasize=emph,
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title=f"WVS Inglehart-Welzel map: LLMs among {len(countries)} human societies (approximate IW axes)",
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note=("Approximate IW: axes built from GlobalOpinionQA WVS items (3 themes/axis, not the\n"
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"canonical 5; national pride / authority / materialism absent). Not a verbatim WVS factor score."))
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fig.savefig(args.out, dpi=200, bbox_inches="tight")
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logger.info(f"wrote {args.out}")
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