"""Figures for the BALLAST reproduction logbook (Plotly HTML + raw CSV).""" from __future__ import annotations import json import os import sys import numpy as np import plotly.graph_objects as go OUT = "outputs" COL = { "unif": "#7f8c8d", "sobol": "#2980b9", "dist_sep": "#8e44ad", "eig": "#e67e22", "ballast_opt": "#16a085", "ballast_true": "#c0392b", } NAME = { "unif": "UNIF", "sobol": "SOBOL", "dist_sep": "DIST-SEP", "eig": "EIG", "ballast_opt": "BALLAST-opt", "ballast_true": "BALLAST-true", } LAYOUT = dict( template="plotly_white", font=dict(size=13), height=420, margin=dict(l=60, r=20, t=50, b=50), ) def _save(fig, name, csv_rows=None, header=None): os.makedirs(OUT, exist_ok=True) fig.write_html(f"{OUT}/{name}.html", include_plotlyjs="cdn") if csv_rows is not None: with open(f"{OUT}/{name}.csv", "w") as f: f.write(",".join(header) + "\n") for r in csv_rows: f.write(",".join(str(x) for x in r) + "\n") print("wrote", name) def fig_spde_cost(path="outputs/raw/spde_cost/spde_cost.json"): d = json.load(open(path)) rows = d["rows"] n = [r["n_sampT"] for r in rows] sp = [r["spde_s"] for r in rows] nv = [r["naive_s"] for r in rows] ok = [i for i, v in enumerate(nv) if v is not None] fig = go.Figure() fig.add_trace(go.Scatter(x=n, y=sp, name="SPDE (Sec. 4.1)", mode="lines+markers", line=dict(color="#16a085", width=3))) fig.add_trace(go.Scatter(x=[n[i] for i in ok], y=[nv[i] for i in ok], name="naive dense", mode="lines+markers", line=dict(color="#c0392b", width=3))) # extrapolate the naive cubic to show where it goes i0 = ok[-1] ext = [x for x in n if x > n[i0]] if ext: fig.add_trace(go.Scatter( x=[n[i0]] + ext, y=[nv[i0]] + [nv[i0] * (x / n[i0]) ** 3 for x in ext], name="naive (cubic extrapolation, out of memory)", mode="lines", line=dict(color="#c0392b", width=2, dash="dot"))) fig.update_layout( title="Posterior sampling cost: SPDE stays linear, naive dense goes cubic then out of memory", xaxis=dict(title="N_sampT (sampled time slices)", type="log"), yaxis=dict(title="seconds for J=20 samples (A100, fp64)", type="log"), legend=dict(x=0.02, y=0.98), **LAYOUT) _save(fig, "claim2_spde_cost", [(r["n_sampT"], r["spde_s"], r["naive_s"], r["naive_cov_gb"]) for r in rows], ["n_sampT", "spde_seconds", "naive_seconds", "naive_cov_gb"]) def fig_ablation(path="outputs/claim5.json"): d = json.load(open(path)) fig = go.Figure() colors = {"3.0": "#2980b9", "5.0": "#16a085", "7.0": "#c0392b"} rows = [] for t, r in d.items(): J = np.array(r["J"]) m = np.array(r["gap_mc_mean"]) s = np.array(r["gap_mc_se2"]) c = colors.get(str(t), "#333") fig.add_trace(go.Scatter(x=J, y=m, name=f"BALLAST t={t}", mode="lines", line=dict(color=c, width=2.5))) fig.add_trace(go.Scatter( x=np.concatenate([J, J[::-1]]), y=np.concatenate([m + s, (m - s)[::-1]]), fill="toself", fillcolor=c.replace("#", "rgba(").replace("", "") if False else c, opacity=0.15, line=dict(width=0), showlegend=False, hoverinfo="skip")) fig.add_trace(go.Scatter(x=[J[0], J[-1]], y=[r["eig_gap_mc"]] * 2, name=f"EIG t={t}", mode="lines", line=dict(color=c, width=1.5, dash="dash"))) fig.add_trace(go.Scatter(x=[J[0], J[-1]], y=[r["unif_gap_mc"]] * 2, name=f"UNIF t={t}", mode="lines", line=dict(color=c, width=1.5, dash="dot"))) for j, v in zip(J, m): rows.append((t, j, v)) fig.add_hline(y=1.0, line=dict(color="black", width=1)) fig.add_annotation(x=np.log10(120), y=np.log10(1.0), text="1% cut-off", showarrow=False, yshift=10) fig.add_vline(x=20, line=dict(color="#888", width=1, dash="dash")) fig.update_layout( title="Percentage utility gap vs Monte Carlo sample number J (2 s.e. bands)", xaxis=dict(title="J (posterior field samples)", type="log"), yaxis=dict(title="Monte Carlo % utility gap", type="log"), **LAYOUT) _save(fig, "claim5_ablation", rows, ["decision_time", "J", "gap_mc_pct"]) def fig_policy(path, tag, title): d = json.load(open(path)) pols = d["policies"] n_dep = d["n_deploy"] x = np.arange(1, n_dep + 1) # --- average rank fig = go.Figure() for i, p in enumerate(pols): m = np.array(d["rank_mean"][i]) s = np.array(d["rank_se2"][i]) fig.add_trace(go.Scatter(x=x, y=m, name=NAME[p], mode="lines+markers", line=dict(color=COL[p], width=2.5), error_y=dict(type="data", array=s, visible=True, thickness=1))) fig.update_layout( title=f"{title}: average policy rank (1 = best), {d['n_runs']} runs", xaxis=dict(title="drifters deployed"), yaxis=dict(title="average rank"), **LAYOUT) _save(fig, f"{tag}_rank", [(p, i + 1, d["rank_mean"][j][i]) for j, p in enumerate(pols) for i in range(n_dep)], ["policy", "n_drifters", "mean_rank"]) # --- iso-performance fig = go.Figure() for p in pols: if p == "unif": continue m = np.array(d["iso"][p]["mean"]) s = np.array(d["iso"][p]["se2"]) fig.add_trace(go.Scatter(x=x, y=m, name=NAME[p], mode="lines+markers", line=dict(color=COL[p], width=2.5), error_y=dict(type="data", array=s, visible=True, thickness=1))) fig.add_hline(y=0, line=dict(color="#7f8c8d", width=1, dash="dash")) fig.update_layout( title=f"{title}: drifters saved vs UNIF (iso-performance), {d['n_runs']} runs", xaxis=dict(title="drifters deployed"), yaxis=dict(title="drifters saved (positive = better)"), **LAYOUT) _save(fig, f"{tag}_iso", [(p, i + 1, d["iso"][p]["mean"][i], d["iso"][p]["se2"][i]) for p in pols for i in range(n_dep)], ["policy", "n_drifters", "drifters_saved", "se2"]) # --- error curves fig = go.Figure() for p in pols: m = np.array(d["err_mean"][p]) s = np.array(d["err_se2"][p]) fig.add_trace(go.Scatter(x=x, y=m, name=NAME[p], mode="lines", line=dict(color=COL[p], width=2.5), error_y=dict(type="data", array=s, visible=True, thickness=1))) fig.update_layout( title=f"{title}: field error vs drifters deployed, {d['n_runs']} runs", xaxis=dict(title="drifters deployed"), yaxis=dict(title="mean L2 error of posterior mean field"), **LAYOUT) _save(fig, f"{tag}_error", [(p, i + 1, d["err_mean"][p][i]) for p in pols for i in range(n_dep)], ["policy", "n_drifters", "mean_l2_error"]) if __name__ == "__main__": which = sys.argv[1] if len(sys.argv) > 1 else "all" if which in ("all", "spde"): fig_spde_cost() if which in ("all", "ablation") and os.path.exists("outputs/claim5.json"): fig_ablation() if which in ("all", "synth") and os.path.exists("outputs/claim3.json"): fig_policy("outputs/claim3.json", "claim3", "Temporal Helmholtz ground truth") if which in ("all", "suntans") and os.path.exists("outputs/claim4.json"): fig_policy("outputs/claim4.json", "claim4", "SUNTANS ground truth")