Add Inglehart-Welzel zone hulls + outlier labels to ipsative maps

Echoes the Economist WVS 'Godless hippies' chart: shaded convex-hull blobs per
IW cultural zone (inline 2D hull, no scipy dep so the maps extra stays
matplotlib-only) and bold-first labels for named outliers. Caller owns the zone
taxonomy + name/ISO2 normalizer, fails loud on unmapped countries; the corrupt
'(nu' big5 row is explicitly excluded with a warning.

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

scripts/plot_steer_showcase.py

@@ -35,11 +35,94 @@ matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np
 
+from loguru import logger
+
 import tinymfv as T
 from tinymfv import get_instrument
 
 ORDINAL = ["mfq2", "big5", "16pf", "humor_styles"]
 
+# --- Inglehart-Welzel cultural zones (for the map's zone hulls) ---------------------------------
+# WVS Wave 7 nine-cluster taxonomy. Membership is VALUE-based not geographic, so a handful are
+# judgment calls: Ireland->English-Speaking, Switzerland->Protestant Europe, Philippines->Latin
+# America, South Africa/Turkey->African-Islamic, India/Pakistan/Thailand->South Asia. Source: WVS
+# Findings + en.wikipedia.org/wiki/Inglehart-Welzel_cultural_map_of_the_world. -- added by Claude
+IW_ZONE = {
+    # English-Speaking
+    "United States": "English-Speaking", "Great Britain": "English-Speaking",
+    "Australia": "English-Speaking", "Canada": "English-Speaking",
+    "New Zealand": "English-Speaking", "Ireland": "English-Speaking",
+    # Protestant Europe
+    "Germany": "Protestant Europe", "Sweden": "Protestant Europe", "Norway": "Protestant Europe",
+    "Denmark": "Protestant Europe", "Netherlands": "Protestant Europe",
+    "Finland": "Protestant Europe", "Switzerland": "Protestant Europe",
+    # Catholic Europe
+    "France": "Catholic Europe", "Belgium": "Catholic Europe", "Italy": "Catholic Europe",
+    "Spain": "Catholic Europe", "Poland": "Catholic Europe", "Portugal": "Catholic Europe",
+    "Croatia": "Catholic Europe",
+    # Orthodox / Ex-Communist
+    "Russia": "Orthodox", "Ukraine": "Orthodox", "Bulgaria": "Orthodox", "Serbia": "Orthodox",
+    "Greece": "Orthodox", "Romania": "Orthodox", "Bosnia & Herzegovina": "Orthodox",
+    "Hungary": "Orthodox",
+    # Baltic
+    "Latvia": "Baltic", "Estonia": "Baltic",
+    # Confucian
+    "Japan": "Confucian", "China": "Confucian", "South Korea": "Confucian",
+    "Hong Kong": "Confucian", "Vietnam": "Confucian", "Singapore": "Confucian",
+    # Latin America
+    "Argentina": "Latin America", "Chile": "Latin America", "Colombia": "Latin America",
+    "Mexico": "Latin America", "Peru": "Latin America", "Brazil": "Latin America",
+    "Ecuador": "Latin America", "Philippines": "Latin America",
+    # African-Islamic
+    "Egypt": "African-Islamic", "Kenya": "African-Islamic", "Morocco": "African-Islamic",
+    "Nigeria": "African-Islamic", "Saudi Arabia": "African-Islamic",
+    "United Arab Emirates": "African-Islamic", "Turkey": "African-Islamic",
+    "Iran": "African-Islamic", "Indonesia": "African-Islamic", "Malaysia": "African-Islamic",
+    "South Africa": "African-Islamic",
+    # South Asia
+    "India": "South Asia", "Pakistan": "South Asia", "Thailand": "South Asia",
+}
+
+# raw country string (as it appears in the human CSVs) -> canonical IW_ZONE key. Our CSVs mix full
+# names (mfv/mfq2/humor, with a "Columbia" typo) and ISO2 codes (big5/16pf). A `None` value marks a
+# row we KNOW is corrupt and deliberately exclude from hulls (surfaced by a loud warning, not a
+# silent drop); an unrecognised country that is NOT here falls through to IW_ZONE and KeyErrors.
+_COUNTRY_CANON = {
+    "AE": "United Arab Emirates", "AU": "Australia", "BR": "Brazil", "CA": "Canada",
+    "CN": "China", "DE": "Germany", "DK": "Denmark", "EC": "Ecuador", "ES": "Spain",
+    "FI": "Finland", "FR": "France", "GB": "Great Britain", "GR": "Greece", "HK": "Hong Kong",
+    "HR": "Croatia", "ID": "Indonesia", "IE": "Ireland", "IN": "India", "IT": "Italy",
+    "MX": "Mexico", "MY": "Malaysia", "NL": "Netherlands", "NO": "Norway", "NZ": "New Zealand",
+    "PH": "Philippines", "PK": "Pakistan", "PL": "Poland", "RO": "Romania", "SE": "Sweden",
+    "SG": "Singapore", "TH": "Thailand", "TR": "Turkey", "US": "United States", "ZA": "South Africa",
+    "Columbia": "Colombia", "UAE": "United Arab Emirates",
+    "(nu": None,   # corrupt big5 row (n=369); country unidentifiable from the aggregate CSV
+}
+
+# The named outliers on the Economist chart, bolded on our maps where present. -- added by Claude
+ECONOMIST_OUTLIERS = {"China", "South Korea", "United States", "Great Britain", "Japan",
+                      "Nigeria", "Pakistan", "Sweden"}
+
+
+def zones_for(countries: list[str]) -> tuple[dict[str, list[str]], set[str]]:
+    """Group verbatim country strings by IW zone + the subset to emphasize. Fails loud (KeyError)
+    on a country absent from the taxonomy so a name-normalization bug can't silently drop a dot from
+    its hull; a `None` canon (known-corrupt row) is excluded with a warning instead."""
+    groups: dict[str, list[str]] = {}
+    dropped: list[str] = []
+    emph: set[str] = set()
+    for c in countries:
+        canon = _COUNTRY_CANON.get(c, c)
+        if canon is None:
+            dropped.append(c)
+            continue
+        groups.setdefault(IW_ZONE[canon], []).append(c)
+        if canon in ECONOMIST_OUTLIERS:
+            emph.add(c)
+    if dropped:
+        logger.warning(f"excluded known-unmapped countries from zone hulls: {dropped}")
+    return groups, emph
+
 
 def _frac(x, scale_max: int) -> np.ndarray:
     return (np.asarray(x, float) - 1) / (scale_max - 1)
@@ -191,10 +274,11 @@ def plot_ordinal(run_dir: Path, out: Path, name: str, vec_label: str, C: float,
     else:
         respondents, haze = None, human_haze(instr)
     traj = {c: _frac(prof_c[c], instr.scale_max) for c in coh_cs}
+    zones, emph = zones_for(countries)
     figm = T.maps.plot_ipsative_pca(instr, dims, countries, Mfrac,
                                     _frac(base, instr.scale_max), _frac(pos, instr.scale_max),
                                     _frac(neg, instr.scale_max), respondents=respondents, haze=haze,
-                                    traj=traj, labels=labels)
+                                    traj=traj, zones=zones, emphasize=emph, labels=labels)
     figm.axes[0].set_title(f"{instr.display}: humans vs LLMs steered for {vec_label}", fontsize=10)
     paths = [T.maps.save_both(figm, out / name, "map_pca_ipsative")]
     plt.close(figm)
@@ -268,8 +352,9 @@ def plot_mfv_map(run_dir: Path, out: Path, vec_label: str, C: float, coh_cs: lis
     neg_c = min(c for c in coh_cs if c < 0.0)
     labels = ("base (c=0)", f"c={pos_c:+g}", f"c={neg_c:+g}")
     traj = {c: prof[c] for c in coh_cs}
+    zones, emph = zones_for(countries)
     fig = T.maps.plot_ipsative_pca(_MFV_INSTR, founds, countries, M, prof[0.0], prof[pos_c], prof[neg_c],
-                                   traj=traj, labels=labels)
+                                   traj=traj, zones=zones, emphasize=emph, labels=labels)
     fig.axes[0].set_title(f"MFV vignettes: humans vs LLMs steered for {vec_label}", fontsize=10)
     path = T.maps.save_both(fig, out / "mfv", "map_pca_ipsative")
     plt.close(fig)