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AsianPlayer/TriDrive / questionnaire /scripts /make_user_study_fig_paper.py

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"""Table 5 -> figure: three paired-bar panels (appropriateness, timeliness, annoyance) with participant points, paired lines,
95% participant-bootstrap CIs of each condition mean, and the paired Delta with its reported CI and Wilcoxon p.  Paper palette."""
import re, numpy as np, matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt, matplotlib.font_manager as fm
from matplotlib.lines import Line2D
import glob
for f in glob.glob("/usr/share/fonts/truetype/msttcorefonts/Times*.ttf"): fm.fontManager.addfont(f)
plt.rcParams.update({"font.family":"Times New Roman","font.size":7,"axes.linewidth":0.5,"xtick.major.width":0.5,"ytick.major.width":0.5,"pdf.fonttype":42})
GREY="#8C8C8C"; LAV="#DDD6E5"; LAVL="#DFEDF2"; INK="#2B2B2B"; PEACHL="#F5D8BF"; LINE="#C8C8C8"
# participant table (appendix H): ID, APP C, APP S, TIM C, TIM S, ANN C, ANN S
rows="""P01 4.00 2.50 7 1 6 --
P02 6.33 4.00 7 1 2 2
P03 4.00 1.67 4 2 2 1
P04 6.33 1.67 6 -- 3 --
P05 6.00 5.00 7 2 4 4
P06 4.33 3.67 6 4 5 1
P07 4.00 4.00 4 4 1 1
P08 3.33 3.00 4 3 5 1
P09 6.67 1.67 6 1 1 1
P10 5.67 1.67 6 1 2 1
P11 7.00 7.00 7 7 1 1
P12 3.67 3.67 5 5 2 1
P13 6.00 6.67 6 6 1 1
P14 6.50 2.67 -- 3 5 2""".splitlines()
def f(x): return np.nan if x=="--" else float(x)
D=np.array([[f(v) for v in r.split()[1:]] for r in rows])
panels=[("Appropriateness",D[:,0],D[:,1],"+1.79 [0.86, 2.75]","p = .008",True),
        ("Timeliness",D[:,2],D[:,3],"+2.67 [1.33, 4.08]","p = .008",True),
        ("Annoyance",D[:,4],D[:,5],"+1.17 [0.33, 2.08]","p = .031",False)]
rng=np.random.default_rng(20260922)
def ci(v):
    b=[rng.choice(v,len(v)).mean() for _ in range(2000)]; return np.percentile(b,2.5),np.percentile(b,97.5)
fig,axes=plt.subplots(1,3,figsize=(5.5,1.08),dpi=300,gridspec_kw=dict(wspace=0.5,left=0.065,right=0.995,top=0.69,bottom=0.26))
for ax,(name,c,s,dtxt,ptxt,higher_good) in zip(axes,panels):
    m=~np.isnan(c)&~np.isnan(s); c,s=c[m],s[m]; n=int(m.sum())
    means=[s.mean(),c.mean()]; cis=[ci(s),ci(c)]
    x=[0,1]
    ax.bar(x,means,width=0.62,color=[GREY,LAV],edgecolor=[GREY,"#B9AFC9"],linewidth=0.5,zorder=1)
    for xi,(lo,hi) in zip(x,cis): ax.plot([xi,xi],[lo,hi],color=INK,lw=0.8,zorder=4); ax.plot([xi-0.08,xi+0.08],[lo,lo],color=INK,lw=0.6,zorder=4); ax.plot([xi-0.08,xi+0.08],[hi,hi],color=INK,lw=0.6,zorder=4)
    jit=rng.uniform(-0.13,0.13,n)
    for k in range(n): ax.plot([0+jit[k],1+jit[k]],[s[k],c[k]],color=LINE,lw=0.4,zorder=2)
    ax.scatter(0+jit,s,s=5,color="white",edgecolor=INK,linewidth=0.4,zorder=3); ax.scatter(1+jit,c,s=5,color="white",edgecolor=INK,linewidth=0.4,zorder=3)
    ax.set_xticks(x); ax.set_xticklabels([f"openpilot\n{means[0]:.2f}",f"TriDrive\n{means[1]:.2f}"],fontsize=6.5,linespacing=1.1); ax.set_xlim(-0.55,1.55)
    ax.set_ylim(0.5,7.6); ax.set_yticks([1,4,7]); ax.tick_params(length=2,pad=1.5)
    for sp in ("top","right"): ax.spines[sp].set_visible(False)
    ax.spines["left"].set_color(GREY); ax.spines["bottom"].set_color(GREY); ax.tick_params(colors=INK)
    ax.text(0.5,1.37,f"{name} (n = {n})",ha="center",va="bottom",fontsize=7.2,color=INK,transform=ax.transAxes,fontweight="bold")
    ax.text(0.5,1.09,f"$\\Delta$ {dtxt}, {ptxt}",ha="center",va="bottom",fontsize=6.3,color=INK,transform=ax.transAxes,bbox=dict(boxstyle="round,pad=0.2",fc=PEACHL,ec="none"))
axes[0].set_ylabel("Rating (1–7)",fontsize=6.5,color=INK,labelpad=1)
out="figures/user_study_bars.pdf"
fig.savefig(out); fig.savefig(out.replace(".pdf",".png"),dpi=300); print("saved",out)
for name,c,s,*_ in panels:
    m=~np.isnan(c)&~np.isnan(s); print(name,int(m.sum()),round(c[m].mean(),2),round(s[m].mean(),2),round((c[m]-s[m]).mean(),2))

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	"""Table 5 -> figure: three paired-bar panels (appropriateness, timeliness, annoyance) with participant points, paired lines,
	95% participant-bootstrap CIs of each condition mean, and the paired Delta with its reported CI and Wilcoxon p. Paper palette."""
	import re, numpy as np, matplotlib
	matplotlib.use("Agg")
	import matplotlib.pyplot as plt, matplotlib.font_manager as fm
	from matplotlib.lines import Line2D
	import glob
	for f in glob.glob("/usr/share/fonts/truetype/msttcorefonts/Times*.ttf"): fm.fontManager.addfont(f)
	plt.rcParams.update({"font.family":"Times New Roman","font.size":7,"axes.linewidth":0.5,"xtick.major.width":0.5,"ytick.major.width":0.5,"pdf.fonttype":42})
	GREY="#8C8C8C"; LAV="#DDD6E5"; LAVL="#DFEDF2"; INK="#2B2B2B"; PEACHL="#F5D8BF"; LINE="#C8C8C8"
	# participant table (appendix H): ID, APP C, APP S, TIM C, TIM S, ANN C, ANN S
	rows="""P01 4.00 2.50 7 1 6 --
	P02 6.33 4.00 7 1 2 2
	P03 4.00 1.67 4 2 2 1
	P04 6.33 1.67 6 -- 3 --
	P05 6.00 5.00 7 2 4 4
	P06 4.33 3.67 6 4 5 1
	P07 4.00 4.00 4 4 1 1
	P08 3.33 3.00 4 3 5 1
	P09 6.67 1.67 6 1 1 1
	P10 5.67 1.67 6 1 2 1
	P11 7.00 7.00 7 7 1 1
	P12 3.67 3.67 5 5 2 1
	P13 6.00 6.67 6 6 1 1
	P14 6.50 2.67 -- 3 5 2""".splitlines()
	def f(x): return np.nan if x=="--" else float(x)
	D=np.array([[f(v) for v in r.split()[1:]] for r in rows])
	panels=[("Appropriateness",D[:,0],D[:,1],"+1.79 [0.86, 2.75]","p = .008",True),
	("Timeliness",D[:,2],D[:,3],"+2.67 [1.33, 4.08]","p = .008",True),
	("Annoyance",D[:,4],D[:,5],"+1.17 [0.33, 2.08]","p = .031",False)]
	rng=np.random.default_rng(20260922)
	def ci(v):
	b=[rng.choice(v,len(v)).mean() for _ in range(2000)]; return np.percentile(b,2.5),np.percentile(b,97.5)
	fig,axes=plt.subplots(1,3,figsize=(5.5,1.08),dpi=300,gridspec_kw=dict(wspace=0.5,left=0.065,right=0.995,top=0.69,bottom=0.26))
	for ax,(name,c,s,dtxt,ptxt,higher_good) in zip(axes,panels):
	m=~np.isnan(c)&~np.isnan(s); c,s=c[m],s[m]; n=int(m.sum())
	means=[s.mean(),c.mean()]; cis=[ci(s),ci(c)]
	x=[0,1]
	ax.bar(x,means,width=0.62,color=[GREY,LAV],edgecolor=[GREY,"#B9AFC9"],linewidth=0.5,zorder=1)
	for xi,(lo,hi) in zip(x,cis): ax.plot([xi,xi],[lo,hi],color=INK,lw=0.8,zorder=4); ax.plot([xi-0.08,xi+0.08],[lo,lo],color=INK,lw=0.6,zorder=4); ax.plot([xi-0.08,xi+0.08],[hi,hi],color=INK,lw=0.6,zorder=4)
	jit=rng.uniform(-0.13,0.13,n)
	for k in range(n): ax.plot([0+jit[k],1+jit[k]],[s[k],c[k]],color=LINE,lw=0.4,zorder=2)
	ax.scatter(0+jit,s,s=5,color="white",edgecolor=INK,linewidth=0.4,zorder=3); ax.scatter(1+jit,c,s=5,color="white",edgecolor=INK,linewidth=0.4,zorder=3)
	ax.set_xticks(x); ax.set_xticklabels([f"openpilot\n{means[0]:.2f}",f"TriDrive\n{means[1]:.2f}"],fontsize=6.5,linespacing=1.1); ax.set_xlim(-0.55,1.55)
	ax.set_ylim(0.5,7.6); ax.set_yticks([1,4,7]); ax.tick_params(length=2,pad=1.5)
	for sp in ("top","right"): ax.spines[sp].set_visible(False)
	ax.spines["left"].set_color(GREY); ax.spines["bottom"].set_color(GREY); ax.tick_params(colors=INK)
	ax.text(0.5,1.37,f"{name} (n = {n})",ha="center",va="bottom",fontsize=7.2,color=INK,transform=ax.transAxes,fontweight="bold")
	ax.text(0.5,1.09,f"$\\Delta$ {dtxt}, {ptxt}",ha="center",va="bottom",fontsize=6.3,color=INK,transform=ax.transAxes,bbox=dict(boxstyle="round,pad=0.2",fc=PEACHL,ec="none"))
	axes[0].set_ylabel("Rating (1–7)",fontsize=6.5,color=INK,labelpad=1)
	out="figures/user_study_bars.pdf"
	fig.savefig(out); fig.savefig(out.replace(".pdf",".png"),dpi=300); print("saved",out)
	for name,c,s,*_ in panels:
	m=~np.isnan(c)&~np.isnan(s); print(name,int(m.sum()),round(c[m].mean(),2),round(s[m].mean(),2),round((c[m]-s[m]).mean(),2))