fig: plot physical grid bias crossover

1 files changed, 156 insertions, 0 deletions

diff --git a/experiments/plot_physical_grid_p5.py b/experiments/plot_physical_grid_p5.py
new file mode 100644
index 0000000..bc86647
--- /dev/null
+++ b/experiments/plot_physical_grid_p5.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+"""Plot the P5 physical-grid classification crossover from committed JSON."""
+
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+METHOD_STYLE = {
+    "raw": {
+        "label": "Raw local update",
+        "color": "#666666",
+        "marker": "x",
+        "linestyle": ":",
+    },
+    "constant": {
+        "label": "Constant calibration",
+        "color": "#E69F00",
+        "marker": "^",
+        "linestyle": "--",
+    },
+    "sdil": {
+        "label": "SDIL",
+        "color": "#0072B2",
+        "marker": "o",
+        "linestyle": "-",
+    },
+    "overclamp": {
+        "label": "Overclamping",
+        "color": "#222222",
+        "marker": "D",
+        "linestyle": "--",
+    },
+    "overclamp_sdil": {
+        "label": "Overclamping + SDIL",
+        "color": "#009E73",
+        "marker": "s",
+        "linestyle": "-",
+    },
+}
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input", type=Path,
+        default=Path("results/physical_bias/p5_grid_bias_crossover.json"))
+    parser.add_argument(
+        "--output-prefix", type=Path,
+        default=Path("results/figs/physical_grid_p5"))
+    return parser.parse_args()
+
+
+def values_by_diameter(records: list[dict], method: str) -> tuple[np.ndarray, list[np.ndarray]]:
+    diameters = np.asarray(sorted({
+        record["input_diameter_v"] for record in records
+    }))
+    values = []
+    for diameter in diameters:
+        values.append(np.asarray([
+            100.0 * record["methods"][method]["classification_error"]
+            for record in records
+            if record["input_diameter_v"] == diameter
+        ]))
+    return 1_000.0 * diameters, values
+
+
+def draw_panel(
+    axis: plt.Axes,
+    records: list[dict],
+    methods: tuple[str, ...],
+    title: str,
+) -> None:
+    offsets = np.linspace(-3.0, 3.0, len(methods))
+    for method_index, method in enumerate(methods):
+        diameters, values = values_by_diameter(records, method)
+        style = METHOD_STYLE[method]
+        for diameter, trial_values in zip(diameters, values):
+            trial_jitter = np.linspace(-1.5, 1.5, len(trial_values))
+            axis.scatter(
+                diameter + offsets[method_index] + trial_jitter,
+                trial_values,
+                color=style["color"],
+                marker=style["marker"],
+                s=18,
+                alpha=0.28,
+                linewidths=0.8,
+                zorder=2,
+            )
+        means = np.asarray([np.mean(trials) for trials in values])
+        axis.plot(
+            diameters,
+            means,
+            label=style["label"],
+            color=style["color"],
+            marker=style["marker"],
+            linestyle=style["linestyle"],
+            linewidth=1.8,
+            markersize=5.5,
+            markerfacecolor=(
+                "white" if method in {"sdil", "overclamp_sdil"}
+                else style["color"]),
+            markeredgewidth=1.2,
+            zorder=3,
+        )
+    axis.set_title(title, loc="left", fontsize=10, fontweight="bold")
+    axis.set_xlabel("Input diameter (mV)")
+    axis.set_xticks(diameters)
+    axis.set_ylim(-2.0, 80.0)
+    axis.set_yticks((0, 20, 40, 60, 80))
+    axis.grid(axis="y", color="#D9D9D9", linewidth=0.7, zorder=0)
+    axis.spines[["top", "right"]].set_visible(False)
+    axis.legend(frameon=False, fontsize=8.5, loc="upper right")
+
+
+def main() -> None:
+    args = parse_args()
+    report = json.loads(args.input.read_text())
+    records = report["records"]
+    plt.rcParams.update({
+        "font.family": "DejaVu Sans",
+        "font.size": 9,
+        "axes.labelsize": 9,
+        "xtick.labelsize": 8,
+        "ytick.labelsize": 8,
+        "svg.fonttype": "none",
+        "pdf.fonttype": 42,
+    })
+    figure, axes = plt.subplots(
+        1, 2, figsize=(7.1, 2.75), sharey=True, constrained_layout=True)
+    draw_panel(
+        axes[0], records, ("raw", "constant", "sdil"),
+        "(a) Standard local learning")
+    draw_panel(
+        axes[1], records, ("overclamp", "overclamp_sdil"),
+        "(b) Composition with overclamping")
+    axes[0].set_ylabel("Classification error (%)")
+    axes[1].text(
+        0.98, 0.82, "8 trials per input diameter",
+        transform=axes[1].transAxes, ha="right", va="top",
+        fontsize=8, color="#666666")
+    args.output_prefix.parent.mkdir(parents=True, exist_ok=True)
+    figure.savefig(args.output_prefix.with_suffix(".svg"))
+    figure.savefig(args.output_prefix.with_suffix(".pdf"))
+    figure.savefig(args.output_prefix.with_suffix(".png"), dpi=300)
+    plt.close(figure)
+    print(f"wrote {args.output_prefix}.{{svg,pdf,png}}")
+
+
+if __name__ == "__main__":
+    main()