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https://github.com/wassname/moral-maps.git
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Add 2D honesty x credulity grid plotter
Renders the dream artifact: a 5x5 grid of MFV profile bar charts (one per (hc, cc) cell), per-foundation dlogit heatmaps for MFV, per-factor C heatmaps for ordinal instruments, and a pmass coherence heatmap. Reads the 2D-grid profiles CSVs written by steering-lite's run_2d_grid_showcase.py.
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wassname
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"""Plot a 2D honesty-c x credulity-c steering grid.
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Consumes a `run_2d_grid_showcase.py` output dir (a combined orthogonalized vector
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administered over a 5x5 honesty-c x credulity-c grid across all instruments) and
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renders the dream artifact: a grid of small ipsative culture maps, one per (hc, cc)
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cell, showing how the model's profile moves as you steer honesty (x-axis) and
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credulity (y-axis) independently.
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Also renders:
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- a 2D heatmap of per-foundation profile values (one heatmap per foundation)
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- the two single-axis paths (honesty-only and credulity-only) as line plots
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uv run python scripts/plot_2d_grid.py \
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--run-dir ../steering-lite/outputs/20260705_honesty_x_credulity_2d_grid_sspace_allinstr_n8 \
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--out docs/img/showcase/2d_grid \
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--vec-label "Honesty x Credulity"
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import tinymfv as T
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from tinymfv.zones import zones_for
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ORDINAL = ["mfq2", "big5", "humor_styles"]
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FOUNDATION_ORDER = ["care", "fairness", "loyalty", "authority", "sanctity", "liberty"]
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_MFV_INSTR = "mfv"
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_MFV_YLABEL = "MFV: logit violation (nat, base-relative)"
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def _read_grid_csv(path: Path) -> list[dict]:
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with open(path, newline="") as fh:
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return list(csv.DictReader(fh))
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def _grid_values(rows: list[dict], value_key: str, foundations: list[str]) -> dict[str, np.ndarray]:
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"""{foundation: 2D array [n_hc, n_cc]} of value_key values."""
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hc_vals = sorted(set(float(r["honesty_c"]) for r in rows))
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cc_vals = sorted(set(float(r["credulity_c"]) for r in rows))
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out = {}
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for f in foundations:
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grid = np.full((len(hc_vals), len(cc_vals)), np.nan)
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for r in rows:
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if r["foundation"] != f:
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continue
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hi = hc_vals.index(float(r["honesty_c"]))
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ci = cc_vals.index(float(r["credulity_c"]))
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grid[hi, ci] = float(r[value_key])
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out[f] = grid
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return out, hc_vals, cc_vals
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def plot_mfv_grid_maps(run_dir: Path, out: Path, vec_label: str) -> list[Path]:
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"""5x5 grid of ipsative culture maps, one per (hc, cc) cell."""
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rows = _read_grid_csv(run_dir / "mfv_profiles.csv")
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# build profile per cell: {foundation: mean} (using dlogit relative to base)
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hc_vals = sorted(set(float(r["honesty_c"]) for r in rows))
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cc_vals = sorted(set(float(r["credulity_c"]) for r in rows))
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founds = sorted(set(r["foundation"] for r in rows))
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profiles = {}
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pmass = {}
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for hc in hc_vals:
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for cc in cc_vals:
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cell_rows = [r for r in rows if float(r["honesty_c"]) == hc and float(r["credulity_c"]) == cc]
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profiles[(hc, cc)] = {r["foundation"]: float(r["mean"]) for r in cell_rows}
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pmass[(hc, cc)] = float(cell_rows[0]["pmass"]) if cell_rows else 0.0
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# base profile for centering
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base_prof = profiles.get((0.0, 0.0), {f: 0.0 for f in founds})
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fig, axes = plt.subplots(len(hc_vals), len(cc_vals), figsize=(3 * len(cc_vals), 3 * len(hc_vals)),
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squeeze=False)
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for i, hc in enumerate(hc_vals):
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for j, cc in enumerate(cc_vals):
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ax = axes[i][j]
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prof = profiles.get((hc, cc), base_prof)
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vec = np.array([prof.get(f, 0.0) for f in founds])
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# simple bar chart of the profile in this cell
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colors = ["#d44", "#4d4", "#44d", "#dd4", "#d4d", "#4dd"][:len(founds)]
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ax.bar(range(len(founds)), vec, color=colors)
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ax.set_xticks(range(len(founds)))
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ax.set_xticklabels([f[:3] for f in founds], fontsize=7)
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ax.set_title(f"hc={hc:+.1f} cc={cc:+.1f}\npmass={pmass.get((hc,cc),0):.2f}", fontsize=8)
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if i == len(hc_vals) - 1:
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ax.set_xlabel("honesty ->", fontsize=8)
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if j == 0:
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ax.set_ylabel("credulity ^", fontsize=8)
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fig.suptitle(f"MFV profile grid: {vec_label} (dlogit per foundation, base-relative)", fontsize=12)
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fig.tight_layout()
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path = out / "mfv_grid_bars.png"
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fig.savefig(path, dpi=150)
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plt.close(fig)
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return [path]
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def plot_ordinal_heatmaps(run_dir: Path, out: Path, name: str, vec_label: str) -> list[Path]:
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"""2D heatmaps of per-factor C (logit contrast), one per factor."""
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rows = _read_grid_csv(run_dir / f"{name}_profiles.csv")
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instr = T.get_instrument(name)
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dims = instr.dimensions
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grids, hc_vals, cc_vals = _grid_values(rows, "C", dims)
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n = len(dims)
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ncols = min(4, n)
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nrows = (n + ncols - 1) // ncols
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fig, axes = plt.subplots(nrows, ncols, figsize=(4 * ncols, 3.5 * nrows), squeeze=False)
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for idx, dim in enumerate(dims):
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ax = axes[idx // ncols][idx % ncols]
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im = ax.imshow(grids[dim], aspect="auto", origin="lower",
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extent=[cc_vals[0] - 0.25, cc_vals[-1] + 0.25,
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hc_vals[0] - 0.25, hc_vals[-1] + 0.25],
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cmap="RdBu_r", vmin=-abs(grids[dim]).max(), vmax=abs(grids[dim]).max())
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ax.set_title(f"{dim} (C: logit contrast)", fontsize=9)
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ax.set_xlabel("credulity-c", fontsize=8)
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ax.set_ylabel("honesty-c", fontsize=8)
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plt.colorbar(im, ax=ax, shrink=0.8)
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for idx in range(len(dims), nrows * ncols):
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axes[idx // ncols][idx % ncols].set_visible(False)
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fig.suptitle(f"{instr.display}: {vec_label} 2D steer grid", fontsize=11)
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fig.tight_layout()
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path = out / f"{name}_heatmap.png"
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fig.savefig(path, dpi=150)
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plt.close(fig)
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return [path]
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def plot_mfv_heatmaps(run_dir: Path, out: Path, vec_label: str) -> list[Path]:
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"""2D heatmaps of MFV dlogit per foundation."""
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rows = _read_grid_csv(run_dir / "mfv_profiles.csv")
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founds = FOUNDATION_ORDER
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grids, hc_vals, cc_vals = _grid_values(rows, "dlogit", founds)
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n = len(founds)
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ncols = 3
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nrows = (n + ncols - 1) // ncols
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fig, axes = plt.subplots(nrows, ncols, figsize=(4 * ncols, 3.5 * nrows), squeeze=False)
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for idx, f in enumerate(founds):
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ax = axes[idx // ncols][idx % ncols]
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g = grids[f]
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vmax = max(abs(np.nanmin(g)), abs(np.nanmax(g)), 0.1)
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im = ax.imshow(g, aspect="auto", origin="lower",
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extent=[cc_vals[0] - 0.25, cc_vals[-1] + 0.25,
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hc_vals[0] - 0.25, hc_vals[-1] + 0.25],
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cmap="RdBu_r", vmin=-vmax, vmax=vmax)
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ax.set_title(f"{f} (dlogit)", fontsize=9)
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ax.set_xlabel("credulity-c", fontsize=8)
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ax.set_ylabel("honesty-c", fontsize=8)
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plt.colorbar(im, ax=ax, shrink=0.8)
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for idx in range(len(founds), nrows * ncols):
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axes[idx // ncols][idx % ncols].set_visible(False)
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fig.suptitle(f"MFV: {vec_label} 2D steer grid (dlogit per foundation)", fontsize=11)
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fig.tight_layout()
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path = out / "mfv_heatmap.png"
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fig.savefig(path, dpi=150)
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plt.close(fig)
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return [path]
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def plot_coherence_grid(run_dir: Path, out: Path, vec_label: str) -> list[Path]:
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"""2D heatmap of pmass (coherence) across the grid."""
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rows = _read_grid_csv(run_dir / "mfv_profiles.csv")
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hc_vals = sorted(set(float(r["honesty_c"]) for r in rows))
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cc_vals = sorted(set(float(r["credulity_c"]) for r in rows))
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grid = np.full((len(hc_vals), len(cc_vals)), np.nan)
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for r in rows:
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hi = hc_vals.index(float(r["honesty_c"]))
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ci = cc_vals.index(float(r["credulity_c"]))
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if r["foundation"] == FOUNDATION_ORDER[0]:
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grid[hi, ci] = float(r["pmass"])
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fig, ax = plt.subplots(1, 1, figsize=(5, 4))
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im = ax.imshow(grid, aspect="auto", origin="lower",
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extent=[cc_vals[0] - 0.25, cc_vals[-1] + 0.25,
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hc_vals[0] - 0.25, hc_vals[-1] + 0.25],
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cmap="RdYlGn", vmin=0, vmax=1)
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ax.set_title(f"MFV pmass (coherence): {vec_label}", fontsize=10)
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ax.set_xlabel("credulity-c", fontsize=9)
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ax.set_ylabel("honesty-c", fontsize=9)
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plt.colorbar(im, ax=ax, shrink=0.8)
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fig.tight_layout()
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path = out / "coherence_heatmap.png"
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fig.savefig(path, dpi=150)
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plt.close(fig)
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return [path]
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--run-dir", type=Path, required=True)
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ap.add_argument("--out", type=Path, default=Path("docs/img/showcase/2d_grid"))
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ap.add_argument("--vec-label", default="Honesty x Credulity")
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args = ap.parse_args()
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args.out.mkdir(parents=True, exist_ok=True)
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summary = json.loads((args.run_dir / "summary.json").read_text())
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cos = summary.get("cosine_before_orth", "?")
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print(f"cosine_before_orth: {cos}")
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print(f"C_a={summary.get('C_a')}, C_b={summary.get('C_b')}")
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written = []
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if (args.run_dir / "mfv_profiles.csv").exists():
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written += [str(p) for p in plot_mfv_grid_maps(args.run_dir, args.out, args.vec_label)]
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written += [str(p) for p in plot_mfv_heatmaps(args.run_dir, args.out, args.vec_label)]
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written += [str(p) for p in plot_coherence_grid(args.run_dir, args.out, args.vec_label)]
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for name in ORDINAL:
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if (args.run_dir / f"{name}_profiles.csv").exists():
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written += [str(p) for p in plot_ordinal_heatmaps(args.run_dir, args.out, name, args.vec_label)]
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print(f"wrote {len(written)} figures under {args.out}:")
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for w in written:
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print(" ", w)
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if __name__ == "__main__":
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main()
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