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fc329a3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | """Plot PBMC sensitivity analyses for appendix use."""
import argparse
import json
from pathlib import Path
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams.update({
"font.size": 9,
"font.family": "sans-serif",
"font.sans-serif": ["DejaVu Sans", "Arial"],
"axes.labelsize": 10,
"axes.titlesize": 10,
"legend.fontsize": 8,
"xtick.labelsize": 8,
"ytick.labelsize": 8,
"figure.dpi": 150,
"savefig.dpi": 300,
"savefig.bbox": "tight",
"axes.spines.top": False,
"axes.spines.right": False,
})
METHODS = ["global", "partition", "twostage", "fullcp"]
METHOD_LABELS = {
"global": "Global",
"partition": "Mondrian",
"twostage": "TwoStage",
"fullcp": "FullCP",
}
SETTING_GROUPS = {
"Stratification": [
("Boundary", "results/tables/pbmc_sensitivity_exp2_1_bulk_deconv_boundary_fixed.json"),
("Entropy", "results/tables/pbmc_sensitivity_exp2_1_bulk_deconv_entropy_fixed.json"),
("KMeans", "results/tables/pbmc_sensitivity_exp2_1_bulk_deconv_kmeans_fixed.json"),
],
"Mixture concentration": [
("0.5", "results/tables/pbmc_concentration_sparse.json"),
("1.0", ["results/tables/real_bulk_deconv.json", "results/tables/real_bulk_deconv_fullcp.json"]),
("2.0", "results/tables/pbmc_concentration_balanced.json"),
],
}
LINE_COLORS = {
"Boundary": "#0072B2",
"Entropy": "#D55E00",
"KMeans": "#009E73",
"0.5": "#CC79A7",
"1.0": "#0072B2",
"2.0": "#E69F00",
}
LINE_MARKERS = {
"Boundary": "o",
"Entropy": "s",
"KMeans": "D",
"0.5": "o",
"1.0": "s",
"2.0": "D",
}
REPO_ROOT = Path(__file__).resolve().parents[1]
PAPER_FIG_DIR = REPO_ROOT / "paper" / "rewrite_2026" / "latex" / "figures"
def load_summary(path_or_paths: str | list[str]) -> dict:
if isinstance(path_or_paths, str):
path_or_paths = [path_or_paths]
merged = {}
for path in path_or_paths:
with open(path) as f:
data = json.load(f)
summary = data["aggregated"] if "aggregated" in data else data["summary"]
merged.update(summary)
return merged
def extract_metric(summary: dict, method: str, metric: str) -> tuple[float, float]:
entry = summary[method][metric]
return float(entry["mean"]), float(entry["std"])
def plot_panel(ax, title: str, settings: list[tuple[str, str]]):
x = np.arange(len(METHODS))
offsets = np.linspace(-0.22, 0.22, num=len(settings))
for offset, (label, path) in zip(offsets, settings):
summary = load_summary(path)
y = []
yerr = []
for method in METHODS:
mean, std = extract_metric(summary, method, "max_disparity")
y.append(mean)
yerr.append(std)
ax.errorbar(
x + offset,
y,
yerr=yerr,
color=LINE_COLORS[label],
marker=LINE_MARKERS[label],
markersize=5.5,
linewidth=1.6,
elinewidth=1.0,
capsize=2.5,
label=label,
)
ax.set_title(title)
ax.set_xticks(x)
ax.set_xticklabels([METHOD_LABELS[m] for m in METHODS])
ax.set_ylabel("Max disparity")
ax.set_ylim(0.0, 0.55)
ax.grid(axis="y", color="#d9d9d9", linewidth=0.8)
def save_figure(fig: plt.Figure, output: Path) -> None:
"""Save the appendix figure to both the results tree and the paper tree."""
output.parent.mkdir(parents=True, exist_ok=True)
PAPER_FIG_DIR.mkdir(parents=True, exist_ok=True)
fig.savefig(output)
mirror = PAPER_FIG_DIR / output.name
fig.savefig(mirror)
print(f"Saved {output}")
print(f"Mirrored {mirror}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--output",
default="results/figures/fig8_pbmc_sensitivity.pdf",
help="Output figure path",
)
args = parser.parse_args()
fig, axes = plt.subplots(1, 2, figsize=(7.1, 2.9), constrained_layout=True)
for ax, (title, settings) in zip(axes, SETTING_GROUPS.items()):
plot_panel(ax, title, settings)
handles, labels = axes[1].get_legend_handles_labels()
fig.legend(handles, labels, loc="upper center", ncol=3, frameon=False, bbox_to_anchor=(0.5, 1.05))
out = Path(args.output)
save_figure(fig, out)
plt.close(fig)
if __name__ == "__main__":
main()
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