File size: 3,146 Bytes
3f98d52 | 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 | """Ubuntu plots from explicit evaluation records; never invent missing measurements."""
from pathlib import Path
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib import font_manager
GOLD = "#C68B25"
INK = "#302B25"
def configure_fonts(font_dir=None):
if font_dir is None:
font_dir = Path(__file__).resolve().parent/"fonts"
for path in Path(font_dir).glob("*.ttf"):
font_manager.fontManager.addfont(str(path))
available = {f.name for f in font_manager.fontManager.ttflist}
if "Ubuntu" not in available:
raise ValueError("Ubuntu is unavailable; provide --font-dir pointing to the included fonts")
plt.rcParams.update({"font.family": "Ubuntu", "font.size": 10, "mathtext.fontset": "cm",
"pdf.fonttype": 42, "ps.fonttype": 42, "axes.spines.top": False,
"axes.spines.right": False, "axes.labelcolor": INK, "text.color": INK,
"savefig.bbox": "tight", "figure.dpi": 160})
def plot_summary(summary, output, font_dir=None, title="Retrospective endpoint evaluation"):
configure_fonts(font_dir)
frame = pd.read_csv(summary) if not isinstance(summary, pd.DataFrame) else summary
output = Path(output); output.mkdir(parents=True, exist_ok=True)
for metric in frame.metric.unique():
group = frame[frame.metric == metric].sort_values("mean", ascending=metric != "success")
fig, ax = plt.subplots(figsize=(6.6, max(2.8, .38*len(group))))
y = np.arange(len(group))
colors = [GOLD if m == "remedi" else "#8C8982" for m in group.method]
ax.barh(y, group["mean"], color=colors, height=.65)
ax.errorbar(group["mean"], y, xerr=np.vstack([np.maximum(0, group["mean"]-group.ci_low), np.maximum(0, group.ci_high-group["mean"])]),
fmt="none", ecolor=INK, capsize=2, linewidth=.8)
ax.set_yticks(y, group.method); ax.invert_yaxis()
ax.set_xlabel(metric.replace("_", " ")); ax.set_title(title, loc="left", fontsize=11)
fig.savefig(output/f"{metric}.pdf"); fig.savefig(output/f"{metric}.png"); plt.close(fig)
def plot_curve(csv, output, x, y, hue="method", lower=None, upper=None, font_dir=None):
configure_fonts(font_dir)
frame = pd.read_csv(csv)
for field in [x, y, hue]:
if field not in frame: raise ValueError(f"Missing plot column {field}")
fig, ax = plt.subplots(figsize=(5.2, 3.4))
palette = [GOLD, "#74716B", "#9E4A35", "#457679", "#756589"]
for i, (name, group) in enumerate(frame.groupby(hue, sort=True)):
group = group.sort_values(x)
ax.plot(group[x], group[y], marker="o", markersize=3.5, label=name,
color=GOLD if name == "remedi" else palette[(i+1) % len(palette)])
if lower and upper:
ax.fill_between(group[x], group[lower], group[upper], alpha=.13)
ax.set_xlabel(x.replace("_", " ")); ax.set_ylabel(y.replace("_", " ")); ax.legend(frameon=False)
output = Path(output); output.parent.mkdir(parents=True, exist_ok=True)
fig.savefig(output); plt.close(fig)
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