1"""Phase 7 — publication figures (vector PDF) → paper/figures/. Palette and mark rules follow the dataviz reference2(categorical slots in fixed order, one hue sequential ramp, thin marks, direct labels, recessive grid, no dual axes)."""3import json, numpy as np, pandas as pd, matplotlib4matplotlib.use("Agg")5import matplotlib.pyplot as plt6import matplotlib.dates as mdates7from matplotlib.colors import LinearSegmentedColormap8from src.common import ROOT, path, get_logger9log = get_logger("figures"); P = path("processed"); F = ROOT / "paper" / "figures"; E = ROOT / "experiments"; F.mkdir(exist_ok=True)10C = ["#2a78d6", "#eb6834", "#1baf7a", "#eda100", "#e87ba4", "#008300", "#6250d6", "#e34948"]11SEQ = LinearSegmentedColormap.from_list("seq", ["#cde2fb", "#86b6ef", "#3987e5", "#256abf", "#184f95", "#0d366b"])12TXT, TXT2, GRID = "#0b0b0b", "#52514e", "#e6e5e1"13plt.rcParams.update({"figure.constrained_layout.use": True, "font.size": 8.5, "axes.titlesize": 9, "axes.edgecolor": TXT2, "axes.labelcolor": TXT, "xtick.color": TXT2, "ytick.color": TXT2, "axes.spines.top": False, "axes.spines.right": False,14 "axes.grid": True, "grid.color": GRID, "grid.linewidth": 0.6, "axes.axisbelow": True, "legend.frameon": False, "pdf.fonttype": 42})1516def save(fig, name):17 fig.savefig(F / f"{name}.pdf", bbox_inches="tight"); plt.close(fig); log.info("wrote %s.pdf", name)1819def fig_coverage():20 wk = pd.read_csv(P / "coverage_by_week.csv", parse_dates=["week"]); feed = pd.read_csv(P / "coverage_by_feed.csv"); feed = feed[feed.active > 0]21 fig, axes = plt.subplots(1, 2, figsize=(7.2, 3.0), gridspec_kw={"width_ratios": [1.3, 1]})22 ax = axes[0]; ax.plot(wk.week, wk.online, color=C[0], lw=2, marker="o", ms=4); ax.plot(wk.week, wk.circular, color=C[1], lw=2, marker="o", ms=4)23 ax.text(wk.week.iloc[-1], wk.online.iloc[-1], " storefront", color=TXT, va="center"); ax.text(wk.week.iloc[-1], wk.circular.iloc[-1], " circular", color=TXT, va="center")24 ax.set_ylabel("Listings observed in week"); ax.set_title("A. Weekly coverage by channel", loc="left", color=TXT); ax.tick_params(axis="x", rotation=30); ax.set_ylim(0); ax.xaxis.set_major_formatter(mdates.DateFormatter("%d %b")); ax.set_xlim(right=wk.week.iloc[-1] + pd.Timedelta(days=20))25 ax = axes[1]; comp = feed.groupby("company").active.sum().sort_values(ascending=True).tail(10)26 ax.barh(comp.index, comp.values, color=C[0], height=0.6); ax.set_title("B. Active listings by company (top 10)", loc="left", color=TXT); ax.set_xlabel("Active listings, 1 Nov 2026")27 for i, v in enumerate(comp.values): ax.text(v, i, f" {v:,}", va="center", color=TXT2, fontsize=8)28 ax.grid(axis="y", visible=False)29 save(fig, "fig_coverage")3031def fig_attrition():32 att = pd.read_csv(P / "attrition_listings.csv"); v = pd.read_csv(P / "match_validation_summary.csv")33 fig, axes = plt.subplots(1, 2, figsize=(7.2, 3.0), gridspec_kw={"width_ratios": [1.4, 1]})34 ax = axes[0]; y = np.arange(len(att))[::-1]; ax.barh(y, att.listings, color=[C[0]] * (len(att) - 1) + [C[2]], height=0.6)35 labels = ["All listings", "excl. SAQ", "excl. pharmacies", "excl. non-food", "with a price", "excl. unrealistic", "parsable size", "matched (A–C)"]36 ax.set_yticks(y); ax.set_yticklabels(labels); ax.set_xlabel("Listings"); ax.set_title("A. Attrition flow", loc="left", color=TXT); ax.grid(axis="y", visible=False)37 for yi, n in zip(y, att.listings): ax.text(n, yi, f" {n:,}", va="center", color=TXT2, fontsize=8)38 ax = axes[1]; x = np.arange(4); ax.bar(x - 0.2, v.precision_strict, 0.38, color=C[0], label="Text review (240 pairs)"); ax.bar(x + 0.2, v.upc_precision.fillna(0), 0.38, color=C[1], label="UPC ground truth")39 ax.errorbar(x - 0.2, v.precision_strict, yerr=[np.maximum(0, v.precision_strict - v.ci_lo), np.maximum(0, v.ci_hi - v.precision_strict)], fmt="none", ecolor=TXT2, lw=1)40 ax.set_xticks(x); ax.set_xticklabels([f"Tier {t}" for t in v.tier]); ax.set_ylim(0, 1.05); ax.set_ylabel("Precision"); ax.set_title("B. Matching precision by tier", loc="left", color=TXT); ax.legend(loc="lower left", fontsize=7); ax.grid(axis="x", visible=False)41 save(fig, "fig_matching")4243def fig_gaps():44 st = pd.read_csv(P / "modelA_groupweek_stats.csv"); fig, axes = plt.subplots(1, 2, figsize=(7.2, 3.0))45 ax = axes[0]; bins = np.linspace(0, 1.0, 41)46 ax.hist(st[st.same_company].gap.clip(upper=1), bins=bins, color=C[0], alpha=0.85, density=True, label="same company"); ax.hist(st[~st.same_company].gap.clip(upper=1), bins=bins, color=C[1], alpha=0.6, density=True, label="different companies")47 ax.set_xlabel("Within-product max–min log price gap (group-week)"); ax.set_ylabel("Density"); ax.legend(); ax.set_title("A. Distribution of cross-banner gaps", loc="left", color=TXT); ax.grid(axis="x", visible=False)48 fe = pd.read_csv(P / "modelA_banner_effects.csv").sort_values("coef"); fe = fe[fe.n_listing_weeks >= 50]49 ax = axes[1]; y = np.arange(len(fe)); ax.errorbar(fe.coef, y, xerr=1.96 * fe.se, fmt="o", color=C[0], ecolor=TXT2, ms=4, lw=1); ax.axvline(0, color=TXT2, lw=0.8)50 ax.set_yticks(y); ax.set_yticklabels(fe.banner); ax.set_xlabel("Banner effect on log price (ref. Metro), 95% CI"); ax.set_title("B. Relative price levels", loc="left", color=TXT); ax.grid(axis="y", visible=False)51 save(fig, "fig_dispersion")5253def fig_heatmap():54 pt = pd.read_csv(P / "modelA_banner_pairs.csv"); pt = pt[pt.n >= 30]55 banners = sorted(set(pt.banner_r) | set(pt.banner_s)); M = pd.DataFrame(np.nan, index=banners, columns=banners)56 for _, r in pt.iterrows(): M.at[r.banner_r, r.banner_s] = r.median_gap; M.at[r.banner_s, r.banner_r] = -r.median_gap57 fig, ax = plt.subplots(figsize=(5.6, 4.8)); cm = LinearSegmentedColormap.from_list("div", ["#2a78d6", "#f0efec", "#e34948"])58 im = ax.imshow(M.values, cmap=cm, vmin=-0.3, vmax=0.3); ax.set_xticks(range(len(banners))); ax.set_xticklabels(banners, rotation=60, ha="right"); ax.set_yticks(range(len(banners))); ax.set_yticklabels(banners); ax.grid(False)59 for i in range(len(banners)):60 for j in range(len(banners)):61 if not np.isnan(M.values[i, j]): ax.text(j, i, f"{M.values[i, j]:+.2f}", ha="center", va="center", fontsize=6.5, color=TXT)62 cb = fig.colorbar(im, ax=ax, fraction=0.04); cb.set_label("Median log price gap, row banner minus column banner")63 ax.set_title("Median log gap, row minus column (pairs ≥ 30 group-weeks)", loc="left", color=TXT, fontsize=9)64 save(fig, "fig_heatmap")6566def fig_promotions():67 es = pd.read_csv(P / "modelB_event_study.csv"); es["k"] = es.term.str.extract(r"(-?\d+)$").astype(int); es = pd.concat([es, pd.DataFrame([{"term": "ref", "coef": 0, "se": 0, "k": -1}])]).sort_values("k")68 ep = pd.read_parquet(P / "modelB_episodes.parquet"); tm = pd.read_csv(P / "modelB_transition_matrix.csv", index_col=0)69 fig, axes = plt.subplots(1, 3, figsize=(7.6, 2.8), gridspec_kw={"width_ratios": [1.3, 1, 0.8]})70 ax = axes[0]; ax.fill_between(es.k, es.coef - 1.96 * es.se, es.coef + 1.96 * es.se, color=C[0], alpha=0.18, lw=0); ax.plot(es.k, es.coef, color=C[0], lw=2, marker="o", ms=4); ax.axhline(0, color=TXT2, lw=0.8); ax.axvline(-0.5, color=TXT2, lw=0.8, ls=":")71 ax.set_xlabel("Weeks relative to first promoted week"); ax.set_ylabel("Log price vs week −1"); ax.set_title("A. Price path, temporary sales", loc="left", color=TXT)72 ax = axes[1]; unc = ep[~ep.censored]; ax.hist(unc[unc.kind == "return"].depth, bins=np.linspace(0, 1.2, 31), color=C[0], alpha=0.85, label="return (temporary)"); ax.hist(unc[unc.kind == "reset_down"].depth, bins=np.linspace(0, 1.2, 31), color=C[1], alpha=0.6, label="reset down")73 ax.set_xlabel("Episode depth, log(reference / price)"); ax.set_ylabel("Episodes"); ax.legend(fontsize=7); ax.set_title("B. Depth by episode kind", loc="left", color=TXT); ax.grid(axis="x", visible=False)74 ax = axes[2]; im = ax.imshow(tm.values, cmap=SEQ, vmin=0, vmax=1); ax.set_xticks([0, 1]); ax.set_xticklabels(["regular", "promo"]); ax.set_yticks([0, 1]); ax.set_yticklabels(["regular", "promo"]); ax.set_xlabel("state in week w"); ax.set_ylabel("state in week w−1"); ax.grid(False)75 for i in range(2):76 for j in range(2): ax.text(j, i, f"{tm.values[i, j]:.2f}", ha="center", va="center", color="white" if tm.values[i, j] > 0.5 else TXT)77 ax.set_title("C. Weekly transitions", loc="left", color=TXT)78 save(fig, "fig_promotions")7980def fig_spillovers():81 d = pd.read_csv(P / "modelC_distributed_lags.csv"); fig, ax = plt.subplots(figsize=(5.2, 2.8))82 order = {"rival_all": 0, "rival_same": 1, "rival_cross": 2}; lab = {"rival_all": "all rivals", "rival_same": "same company", "rival_cross": "other company"}83 xs = ["_f2", "_f1", "", "_l1", "_l2", "_l3"]; xl = ["lead 2", "lead 1", "k=0", "lag 1", "lag 2", "lag 3"]84 for col, g in d.groupby("rival"):85 i = order[col]; vals = [g[g.term == col + s].coef.iloc[0] for s in xs]; ses = [g[g.term == col + s].se.iloc[0] for s in xs]86 ax.errorbar(np.arange(6) + (i - 1) * 0.22, vals, yerr=[1.96 * v for v in ses], fmt="o", color=C[i], ms=4, lw=1, label=lab[col])87 ax.axhline(0, color=TXT2, lw=0.8); ax.axvspan(-0.5, 1.5, color=GRID, alpha=0.5, lw=0); ax.set_xticks(range(6)); ax.set_xticklabels(xl); ax.set_ylabel("Response of own Δlog p"); ax.legend(fontsize=7, ncol=3, loc="upper left")88 ax.set_title("Own Δlog p on rivals' Δlog p (shaded = placebo leads)", loc="left", color=TXT, fontsize=9); ax.grid(axis="x", visible=False)89 save(fig, "fig_spillovers")9091def fig_indices():92 d = pd.read_csv(P / "modelD_indices.csv", parse_dates=["week"]); s = d[d["sample"] == "storefront_posted"]93 fig, ax = plt.subplots(figsize=(5.6, 3.0))94 ax.plot(s.week, s.index_mean, color=C[1], lw=2, marker="o", ms=3); ax.plot(s.week, s["index"], color=C[0], lw=2, marker="o", ms=3); ax.plot(s.week, s.jevons, color=C[2], lw=2, marker="o", ms=3, ls="--"); ax.plot(s.week, s.carli, color=C[3], lw=1.5, marker="o", ms=3, ls=":")95 for col, name in [("index_mean", "naive mean of posted prices"), ("index", "chained Jevons (matched model)"), ("jevons", "fixed-basket Jevons"), ("carli", "fixed-basket Carli")]:96 ax.text(s.week.iloc[-1], s[col].iloc[-1], " " + name, va="center", fontsize=7.5, color=TXT)97 ax.set_ylabel("Index (13 Aug 2026 = 100)"); ax.tick_params(axis="x", rotation=30); ax.xaxis.set_major_formatter(mdates.DateFormatter("%d %b")); ax.set_xlim(right=s.week.iloc[-1] + pd.Timedelta(days=45)); ax.set_title("Unweighted indices, storefront listings", loc="left", color=TXT, fontsize=9)98 save(fig, "fig_indices")99100def fig_qgpai():101 q = pd.read_csv(P / "qgpai_components.csv", parse_dates=["week"]); fig, axes = plt.subplots(2, 2, figsize=(7.2, 4.8)); axes = axes.ravel()102 ax = axes[0]; ax.fill_between(q.week, q.cpi_lo, q.cpi_hi, color=C[0], alpha=0.18, lw=0); ax.plot(q.week, q.comparable_price_index, color=C[0], lw=2, marker="o", ms=3); ax.set_title("1. Comparable price index", loc="left", color=TXT); ax.set_ylabel("13 Aug = 100")103 ax = axes[1]; ax.plot(q.week, q.promo_share, color=C[1], lw=2, marker="o", ms=3); ax.plot(q.week, q.circular_depth_median, color=C[3], lw=2, marker="s", ms=3)104 ax.text(q.week.iloc[-1], q.promo_share.iloc[-1], " share promoted\n (storefront, inferred)", fontsize=7, va="center", color=TXT); ax.text(q.week.iloc[-1], q.circular_depth_median.iloc[-1], " circular depth\n (observed, median log)", fontsize=7, va="center", color=TXT)105 ax.set_title("2. Promotion intensity", loc="left", color=TXT); ax.set_ylim(0)106 ax = axes[2]; ax.fill_between(q.week, q.disp_lo, q.disp_hi, color=C[2], alpha=0.18, lw=0); ax.plot(q.week, q.dispersion_median_gap, color=C[2], lw=2, marker="o", ms=3); ax.set_title("3. Dispersion (median log gap)", loc="left", color=TXT); ax.set_ylim(0)107 ax = axes[3]108 for col, name, c, dy in [("basket_K1", "best single banner", C[0], 1.5), ("basket_K2", "two banners", C[1], 0.0), ("basket_unconstrained", "unconstrained", C[2], -1.5)]:109 ax.plot(q.week, q[col], color=c, lw=2, marker="o", ms=3); ax.text(q.week.iloc[-1], q[col].iloc[-1] + dy, " " + name, fontsize=7, va="center", color=c)110 ax.set_title("4. Basket cost (CAD, fixed block)", loc="left", color=TXT); ax.set_ylim(0)111 for ax in axes: ax.tick_params(axis="x", rotation=30); ax.xaxis.set_major_formatter(mdates.DateFormatter("%d %b"))112 fig.suptitle("QGPAI components (component 5, access & affordability, not computable)", x=0.01, ha="left", color=TXT2, fontsize=9)113 save(fig, "fig_qgpai")114115def fig_basket():116 r = pd.read_csv(P / "modelE_results.csv"); r5 = r[(r.B == 5) & r.single.isna() & r.fixed_cost.isna()]; fr = r5.groupby("K").cost.mean(); ks = [1, 2, 3, 4, 5]; costs = [fr.get(k, fr.get(99)) if k < 5 else fr.get(99) for k in ks]117 tc = pd.read_csv(P / "modelE_travel_sensitivity.csv")118 fig, axes = plt.subplots(1, 2, figsize=(7.2, 2.8))119 ax = axes[0]; ax.plot(ks, costs, color=C[0], lw=2, marker="o", ms=5); ax.set_xlabel("Maximum number of banners visited (K)"); ax.set_ylabel("Weekly basket cost (CAD, mean of 11 weeks)"); ax.set_xticks(ks)120 for k, c in zip(ks, costs): ax.text(k, c, f" {c:.1f}", fontsize=7.5, color=TXT2, va="bottom")121 ax.set_title("A. Savings frontier (B = 5, 15 items)", loc="left", color=TXT)122 ax = axes[1]; ax.bar(tc.fixed_cost.astype(str), tc.cost_incl, color=C[1], width=0.55); ax.bar(tc.fixed_cost.astype(str), tc.cost_goods, color=C[0], width=0.55)123 for i, (g, t, n) in enumerate(zip(tc.cost_goods, tc.cost_incl, tc.n_used)): ax.text(i, t, f" {n:.1f} banners", ha="center", va="bottom", fontsize=7.5, color=TXT2)124 ax.set_xlabel("Illustrative fixed cost per banner visited (CAD)"); ax.set_ylabel("Goods (blue) + travel (orange)"); ax.set_title("B. Travel-cost sensitivity (assumption-based)", loc="left", color=TXT); ax.grid(axis="x", visible=False)125 save(fig, "fig_basket")126127def fig_forecast():128 r = pd.read_csv(P / "modelF_rolling_results.csv"); fig, axes = plt.subplots(1, 2, figsize=(7.2, 2.8))129 ax = axes[0]; d = r[r.target == "log_price"]130 for i, (m, g) in enumerate(d.groupby("model")): ax.plot(g.origin_t, g.mae, color=C[i % 8], lw=1.8, marker="o", ms=3, label=m)131 ax.set_xlabel("Forecast origin (week index)"); ax.set_ylabel("MAE of next-week log price"); ax.legend(fontsize=6.5, ncol=2); ax.set_title("A. Price forecasts", loc="left", color=TXT)132 ax = axes[1]; d = r[r.target == "promotion"]133 for i, (m, g) in enumerate(d.groupby("model")): ax.plot(g.origin_t, g.auc, color=C[i % 8], lw=1.8, marker="o", ms=3, label=m)134 ax.set_xlabel("Forecast origin (week index)"); ax.set_ylabel("AUC, next-week promotion"); ax.set_ylim(0.4, 1); ax.legend(fontsize=6.5, ncol=2, loc="lower right"); ax.set_title("B. Promotion forecasts", loc="left", color=TXT)135 save(fig, "fig_forecast")136137def fig_store_mixture():138 d = pd.read_parquet(P / "offers_daily.parquet", columns=["uid", "day", "p_min", "p_max", "p_mode", "n_distinct"]); x = d[d.uid == "metro:068400662600"].sort_values("day")139 pl = pd.read_parquet(path("interim") / "raw_price_log.parquet"); e = pl[pl.uid == "metro:068400662600"].copy(); e["ts"] = pd.to_datetime(e.ts, unit="s", utc=True).dt.tz_convert("America/Toronto")140 sm = pd.read_csv(P / "modelA_within_banner_mixture.csv").sort_values("share_multi", ascending=True).tail(12)141 fig, axes = plt.subplots(1, 2, figsize=(7.4, 2.9), gridspec_kw={"width_ratios": [1.4, 1]})142 ax = axes[0]; ax.step(e.ts, e.price, where="post", color=C[0], lw=1.2); ax.scatter(e.ts, e.price, color=C[0], s=8, zorder=3)143 ax.set_ylabel("Posted price (CAD)"); ax.set_title("A. One Metro listing: every logged price change", loc="left", color=TXT, fontsize=8.5); ax.tick_params(axis="x", rotation=30); ax.xaxis.set_major_formatter(mdates.DateFormatter("%d %b"))144 ax = axes[1]; ax.barh(sm.banner, sm.share_multi, color=C[0], height=0.6); ax.set_xlabel("Share of listing-weeks with > 1 price value"); ax.set_title("B. Multi-price weeks by banner", loc="left", color=TXT); ax.grid(axis="y", visible=False)145 for i, (v, r_) in enumerate(zip(sm.share_multi, sm.median_log_range_multi)): ax.text(v, i, f" {v:.2f} (range {r_:.2f})", va="center", fontsize=7, color=TXT2)146 save(fig, "fig_store_mixture")147148def main():149 for f in [fig_coverage, fig_attrition, fig_gaps, fig_heatmap, fig_promotions, fig_spillovers, fig_indices, fig_qgpai, fig_basket, fig_forecast, fig_store_mixture]:150 try: f()151 except Exception as ex: log.error("%s failed: %s", f.__name__, ex)152153if __name__ == "__main__":154 main()155