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

Instrument maps: same clean hull/label treatment as the WVS map

Browse Source 
plot_ipsative_pca now draws the 4 most-separate zones as edge-only convex hulls
(select_spread_zones + draw_zone_hulls) instead of the disc-union blobs, colours
dots by drawn zone, and labels only landmarks + 4 most-outlying + one representative
per zone. One generic rule across every map (WVS + mfq2/big5/mfv), replacing the
per-country disc unions on these maps. Verified on a human-only big5 render.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-07-04 21:45:02 +08:00
co-authored by Claudypoo
parent 19ce742ad4
commit 294a6af3ce
1 changed files with 18 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/tinymfv/maps.py
+18 -4
View File
@@ -376,18 +376,32 @@ def plot_ipsative_pca(instr: Instrument, dims: list[str], countries: list[str],
Pi = (cloud @ Pc - mu) @ Vt[:2].T
ax.scatter(Pi[:, 0], Pi[:, 1], s=4, c="#8f8a7e", alpha=0.14, edgecolors="none",
zorder=1, rasterized=True)
if zones:
draw_zone_regions(ax, P, countries, zones, cloud_P=Pi, cloud_countries=cloud_countries)
ax.scatter(P[:, 0], P[:, 1], s=26, c=C_HUM, alpha=0.7, edgecolors="white", linewidths=0.5, zorder=3)
# Same clean treatment as the WVS map: draw only the 4 zones covering the most separate space, as
# edge-only hulls, and colour each dot by its drawn zone (grey if ungrouped).
sel_zones = select_spread_zones(P, countries, zones, 4) if zones else {}
if sel_zones:
draw_zone_hulls(ax, P, countries, sel_zones)
zone_of_c = {c: z for z, ms in sel_zones.items() for c in ms}
dot_cols = [ZONE_COLORS.get(zone_of_c.get(c), C_HUM) for c in countries]
ax.scatter(P[:, 0], P[:, 1], s=26, c=dot_cols, alpha=0.75, edgecolors="white", linewidths=0.5, zorder=3)
# Society labels: each name/ISO code is pinned RIGHT NEXT to its dot (small fixed offset, no
# leader line). A label is dropped if its box would collide with an already-placed one -- better an
# omitted code than one flung far from its point. `emphasize` countries are placed FIRST (so they
# win contested space) and drawn bold+dark, so the named outliers always survive the drop.
# Label only the landmarks (emphasize) + the 4 most-outlying + one representative (most-central
# member) per drawn zone -- the same de-clutter rule as the WVS map, so no map letters all N dots.
fig.canvas.draw()
renderer = fig.canvas.get_renderer()
placed_boxes = []
emph = emphasize or set()
order_lr = list(np.argsort(P[:, 0])) # left-to-right; leftmost wins contested space
cidx = {c: i for i, c in enumerate(countries)}
reps = set()
for ms in sel_zones.values():
mem = [c for c in ms if c in cidx]
mp = P[[cidx[c] for c in mem]]
reps.add(mem[int(np.argmin(np.hypot(*(mp - mp.mean(0)).T)))])
label_set = emph | outlying_countries(P, countries, 4) | reps
order_lr = [i for i in np.argsort(P[:, 0]) if countries[i] in label_set] # leftmost wins contested space
order = [i for i in order_lr if countries[i] in emph] + [i for i in order_lr if countries[i] not in emph]
for i in order:
is_e = countries[i] in emph