wassname/moral-maps
1
0
Fork 0
mirror of https://github.com/wassname/moral-maps.git synced 2026-09-09 11:27:22 +08:00
Code Issues Packages Projects Releases Wiki Activity

wvs map: zone labels hug the hull edge (no arrows) + flip X axis

Browse Source 
Replace the fling-to-open-space-with-a-leader zone placement with _hull_label_pos:
walk the hull's own boundary vertices, nudge each slightly outward, and put the
label on the vertex whose nearest dot/label is farthest -- so the name sits against
an uncrowded arc of its own outline, no leader line. And flip X (invert_x) so
Self-expression is on the left and Survival on the right: this is a better map than
the Economist's, and it puts the cultural West on the left / East Asia on the right.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-07-05 13:02:35 +08:00
co-authored by Claudypoo
parent 88d9994c96
commit aa675bc8de
2 changed files with 32 additions and 33 deletions
Download Patch File Download Diff File Expand all files Collapse all files
scripts/wvs_map.py
+5 -2
View File
@@ -298,10 +298,13 @@ def main() -> None:
def _ver(k: str) -> list[float]:
return [float(n) for n in re.findall(r"\d+(?:\.\d+)?", k)]
model_labels = {max(ks, key=_ver): max(ks, key=_ver).replace("claude-", "") for ks in fams.values()}
# Flip X so Self-expression is on the LEFT and Survival on the RIGHT (invert_x). We are building a
# better map than the Economist's, not xeroxing it, and this puts the cultural "West" on the left.
# poles are (x_left, x_right, y_bottom, y_top) as DRAWN, so the x pair is swapped to match.
fig = maps.plot_value_map(
"WVS Inglehart-Welzel", countries, P,
("Survival", "Self-expression", "Traditional", "Secular-Rational"),
models=plot_models, model_labels=model_labels, emphasize=emph)
("Self-expression", "Survival", "Traditional", "Secular-Rational"),
models=plot_models, model_labels=model_labels, emphasize=emph, invert_x=True)
fig.savefig(args.out, dpi=200, bbox_inches="tight")
logger.info(f"wrote {args.out}")
src/tinymfv/maps.py
+27 -31
View File
@@ -200,13 +200,12 @@ def draw_zone_hulls(ax, P: np.ndarray, countries: list[str], zones: dict[str, li
zcol = ZONE_COLORS.get(zname, "#888888")
ax.add_patch(MplPolygon(coords, closed=True, facecolor="none", edgecolor=zcol,
lw=1.8, alpha=0.9, zorder=1.5))
anchor = pts.mean(0) # zone centroid: the leader ties the label here
if label:
top = coords[np.argmax(coords[:, 1])] # ipsative maps draw it at the hull top vertex
ax.text(top[0], top[1], zname, fontsize=10, color=zcol, ha="center", va="bottom",
style="italic", fontweight="bold", zorder=5,
path_effects=[pe.withStroke(linewidth=3.0, foreground="white")])
specs.append((zname, (float(anchor[0]), float(anchor[1])), zcol))
specs.append((zname, coords, zcol))
return specs
@@ -258,26 +257,22 @@ def model_family_color(name: str) -> str:
return MODEL_RED
def _open_slot(anchor: tuple[float, float], obstacles: list[tuple[float, float]],
xlim: tuple[float, float], ylim: tuple[float, float], span: np.ndarray,
radii=(0.08, 0.14, 0.22, 0.32, 0.44)) -> tuple[float, float]:
"""A label position in the emptiest nearby space: scan a ring of candidate offsets (fractions of
the data span) around `anchor` and keep the one whose NEAREST obstacle (a dot or an already-placed
label) is farthest, with a mild penalty for straying from the anchor. Distances are normalised by
the data span so x/y crowding weigh equally. Used for the few big zone labels, which adjustText's
local force-relaxation otherwise parks in a crowded local minimum."""
ax0, ay0 = anchor
best, best_score = (ax0, ay0), -np.inf
for r in radii:
for deg in range(0, 360, 20):
a = np.radians(deg)
x, y = ax0 + r * span[0] * np.cos(a), ay0 + r * span[1] * np.sin(a)
if not (xlim[0] < x < xlim[1] and ylim[0] < y < ylim[1]):
continue
dmin = min(np.hypot((x - ox) / span[0], (y - oy) / span[1]) for ox, oy in obstacles)
score = dmin - 0.35 * r # prefer open space; mild pull toward the anchor
if score > best_score:
best_score, best = score, (x, y)
def _hull_label_pos(coords: np.ndarray, center: np.ndarray, obstacles: list[tuple[float, float]],
span: np.ndarray, out: float = 0.018) -> tuple[float, float]:
"""Place a zone label directly ON its hull's boundary, hugging the emptiest arc -- NO leader line. A
convex hull has plenty of perimeter, so rather than fling the label into open space with an arrow,
walk its boundary vertices, nudge each slightly OUTWARD (away from the plot centre so the text sits
just outside the edge), and keep the one whose NEAREST dot/label is farthest (distances normalised
by the data span so x/y crowding weigh equally). The label lands against an uncrowded stretch of
its own outline."""
best, best_score = tuple(coords[0]), -np.inf
for vx, vy in coords:
dn = np.array([(vx - center[0]) / span[0], (vy - center[1]) / span[1]])
u = dn / (np.hypot(*dn) or 1.0) # outward unit vector (normalised space)
cx, cy = vx + out * u[0] * span[0], vy + out * u[1] * span[1]
dmin = min(np.hypot((cx - ox) / span[0], (cy - oy) / span[1]) for ox, oy in obstacles)
if dmin > best_score:
best_score, best = dmin, (cx, cy)
return best
@@ -285,7 +280,8 @@ def plot_value_map(display: str, countries: list[str], P: np.ndarray,
poles: tuple[str, str, str, str], *, models: dict[str, tuple[float, float]] | None = None,
model_labels: dict[str, str] | None = None,
steer: dict[str, tuple[float, float, str]] | None = None,
emphasize: set[str] | None = None, title: str | None = None, note: str | None = None):
emphasize: set[str] | None = None, invert_x: bool = False,
title: str | None = None, note: str | None = None):
"""The interpretable "4-value map": two NAMED axes with four pole signposts through the human
MEDIAN crosshair, Economist-style zone hulls (the 4 most-separate zones), zone-coloured dots, and
textalloc labels (landmarks + corner outliers + one representative per zone + any models). NO
@@ -351,24 +347,24 @@ def plot_value_map(display: str, countries: list[str], P: np.ndarray,
obs_x.append(bx); obs_y.append(by)
ax.margins(0.13)
fig.canvas.draw()
xlim, ylim = ax.get_xlim(), ax.get_ylim()
span = P.max(0) - P.min(0)
center = P.mean(0)
obs_pts = list(zip(obs_x, obs_y))
zone_texts, zplaced = [], []
for zn, (zx, zy), zc in zone_specs: # big zone labels -> emptiest slot, leader to hull
lx, ly = _open_slot((zx, zy), obs_pts + zplaced, xlim, ylim, span)
for zn, coords, zc in zone_specs: # label hugs the emptiest arc of its OWN hull edge
lx, ly = _hull_label_pos(coords, center, obs_pts + zplaced, span)
zplaced.append((lx, ly))
zone_texts.append(ax.annotate(
zn, xy=(zx, zy), xytext=(lx, ly), color=zc, fontsize=10, fontweight="bold", fontstyle="italic",
ha="center", va="center", zorder=9, arrowprops=dict(arrowstyle="-", color=zc, lw=0.7, alpha=0.6),
path_effects=[pe.withStroke(linewidth=2.5, foreground="white")]))
zone_texts.append(ax.text(lx, ly, zn, color=zc, fontsize=10, fontweight="bold", fontstyle="italic",
ha="center", va="center", zorder=9,
path_effects=[pe.withStroke(linewidth=3.0, foreground="white")]))
texts = [ax.text(x, y, t, color=c, fontsize=fs, fontweight=fw, fontstyle=st, ha="center",
va="center", zorder=9, path_effects=[pe.withStroke(linewidth=2.5, foreground="white")])
for x, y, t, c, fw, st, fs in lab_specs]
adjust_text(texts, x=obs_x, y=obs_y, ax=ax, objects=zone_texts, expand=(1.15, 1.4),
arrowprops=dict(arrowstyle="-", color="#aaa", lw=0.6))
_pole_signposts(ax, med_x, med_y, poles)
if invert_x: # e.g. put Self-expression on the LEFT
ax.invert_xaxis()
ax.set_xticks([]); ax.set_yticks([]); ax.set_xlabel(""); ax.set_ylabel("")
if models: # legend: one star swatch per lab family present
from matplotlib.lines import Line2D