from pathlib import Path import json,math import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.colors import ListedColormap,BoundaryNorm from scipy.stats import beta R=Path(__file__).resolve().parents[1];F=R/'figures';F.mkdir(exist_ok=True) plt.rcParams.update({'font.size':11,'axes.titlesize':12,'axes.labelsize':11, 'figure.dpi':160,'savefig.dpi':210,'axes.spines.top':False,'axes.spines.right':False, 'font.family':'DejaVu Sans'}) blue='#186B8C';orange='#CF763C';grey='#8B929C';dark='#243344' a=np.genfromtxt(R/'results/access_redundancy.csv',delimiter=',',names=True) fig,axs=plt.subplots(1,2,figsize=(9,3.5)) axs[0].plot(a['b'],a['exponent'],color=orange,lw=2.4,label='Supply distance grows') axs[0].plot(a['b'],a['channel_exponent'],color=blue,lw=2.4,label='Local supply maintained') axs[0].set(xlabel='Redundant subcomponents b',ylabel='Reliability exponent E',title='Redundancy can starve repair',xlim=(0,1100),ylim=(0,190)) axs[0].axhline(math.log(1e6/.05),color=grey,ls='--',lw=1) axs[0].text(570,19,'Example required exponent',fontsize=8,color=grey) axs[0].legend(fontsize=10,loc='upper left') axs[1].plot(a['b'],a['raw_error'],color=orange,lw=2.4,label='Conversion-inclusive error') axs[1].axhline(.2,color=dark,ls='--',label='Assumed tolerance = 0.20') axs[1].set(xlabel='Redundant subcomponents b',ylabel='Raw subcomponent error',title='Depletion closes the window',xlim=(0,1100),ylim=(0,.8)) axs[1].legend(fontsize=10) fig.text(.5,.01,'Illustrative analytical model; no chemical rates or device tolerance were measured.',ha='center',fontsize=8,color=grey) fig.tight_layout(rect=(0,.055,1,1));fig.savefig(F/'reliability_access.png');plt.close(fig) cmap=ListedColormap(['#CA4560','#F0F1F2','#7387A2','#E2A844']) norm=BoundaryNorm([-1.5,-.5,.5,1.5,2.5],4) fig,axs=plt.subplots(2,3,figsize=(8.2,5.7)) for row,dim in enumerate([2,3]): ref=np.load(R/f'results/snapshot_{dim}d_reference.npz') no=np.load(R/f'results/snapshot_{dim}d_no_repair.npz') vals=[ref['target'],ref['final'],no['final']] for col,v in enumerate(vals): if dim==3:v=v[:,:,v.shape[2]//2] axs[row,col].imshow(v.T,origin='lower',cmap=cmap,norm=norm,interpolation='nearest') axs[row,col].set_xticks([]);axs[row,col].set_yticks([]) if row==0:axs[row,col].set_title(['Target','Repair + delayed lock','No repair'][col]) if col==0:axs[row,col].set_ylabel('2-D device' if dim==2 else '3-D central slice') fig.text(.5,.025,'Light: void / sacrificial role Blue: support role Gold: recruited phase',ha='center',fontsize=9) fig.tight_layout(rect=(0,.055,1,1));fig.savefig(F/'growth_snapshots.png');plt.close(fig) summary=json.loads((R/'results/growth_summary.json').read_text());labels=['reference','no_repair','early_lock','no_internal_supply','common_mode','conversion_damage'] short=['Reference','No repair','Early lock','Boundary\nsupply','Wrong\nreference','Conversion\ndamage'] fig,axs=plt.subplots(1,2,figsize=(8.2,4.1),gridspec_kw={'width_ratios':[1.3,1]},sharey=True) x=np.arange(6) short=['Reference','No repair','Early lock','Boundary supply','Wrong reference','Conversion damage'] for dim,color,offset in [(2,blue,-.18),(3,orange,.18)]: vals=[next(r for r in summary if r['dim']==dim and r['case']==lab) for lab in labels] axs[0].barh(x+offset,[v['material_fidelity_mean'] for v in vals],height=.34,color=color,label=f'{dim}-D', xerr=[v['material_fidelity_sd'] for v in vals],capsize=2,error_kw={'lw':1}) axs[0].set_yticks(x,short,fontsize=10);axs[0].set_xlim(.7,1.005);axs[0].set_xlabel('Material fidelity');axs[0].set_title('Local repair improves labels');axs[0].legend(fontsize=10,loc='lower left') vals=[next(r for r in summary if r['dim']==2 and r['case']==lab) for lab in labels] axs[1].barh(x,[v['functional_passes'] for v in vals],color=[blue]+[grey]*5,height=.6) axs[1].set_xlim(0,8.6);axs[1].set_xlabel('Functional passes / 8');axs[1].set_title('Function remains difficult') axs[0].invert_yaxis() fig.text(.5,.012,'Coarse stochastic model. Error bars: between-run SD; eight runs per condition.',ha='center',fontsize=9,color=grey) fig.tight_layout(rect=(0,.075,1,1));fig.savefig(F/'ablation_results.png');plt.close(fig) stress=json.loads((R/'results/transport_stress.json').read_text());fig,axs=plt.subplots(1,2,figsize=(8,3.5)) for i,spacing in enumerate([5,0]): vals=[v for v in stress if v['config']['channel_spacing']==spacing] for ax,key in zip(axs,['completed_fraction','material_fidelity']): yy=np.array([v[key] for v in vals]);ax.bar(i,yy.mean(),color=[blue,orange][i],alpha=.8) ax.scatter(i+np.linspace(-.1,.1,len(yy)),yy,color=dark,s=19,zorder=3) for ax,title in zip(axs,['Completed fraction','Material fidelity']): ax.set_xticks([0,1],['Internal feed planes','Boundary supply only'],fontsize=9) ax.set_ylim(0,1.);ax.set_title(title);ax.axhline(1,color=grey,ls='--',lw=1) fig.text(.5,.015,'Four runs each; both conditions fail to complete by the declared deadline.',ha='center',fontsize=8,color=grey) fig.tight_layout(rect=(0,.075,1,1));fig.savefig(F/'transport_stress.png');plt.close(fig) nums=json.loads((R/'results/numerical_summary.json').read_text());mc=nums['monte_carlo'] fig,ax=plt.subplots(figsize=(6.5,3.8));b=np.array([v['b'] for v in mc]);yy=np.array([v['empirical'] for v in mc]) lo=np.array([beta.ppf(.025,v['failures'],v['trials']-v['failures']+1) if v['failures'] else 0 for v in mc]) hi=np.array([beta.ppf(.975,v['failures']+1,v['trials']-v['failures']) if v['failures']