Page 5 - Demo
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                                    Planar TrussExamplefor ComrelAdd-onRCPConsult, 2019-2025Page 5plt.text(12.0,8.0,'Internal member forces [N]',fontsize=16,ha='center')# Plot internal member force Nfore inrange(nfe):l=leng[e]/2n=np.abs(N[e])*0.0000005X=np.array([l,l,l,l])Y=np.array([-n,-n,n,n])c =np.cos(theta[e])# cosine inclination angles =np.sin(theta[e])# sinus inclination angleXrot =X*c -Y*sYrot =X*s +Y*cx=(XY[IEN[e,0],0]+XY[IEN[e,1],0])/2y=(XY[IEN[e,0],1]+XY[IEN[e,1],1])/2if(N[e]>=0):co=(1.0,0.6,0.5)else:co=(0.5,0.6,1.0)plt.fill(Xrot +x,Yrot +y,color=co,ec='0',lw=0.5)plt.text(x,y,'%0.1f N'%N[e],fontsize=7,ha='center',va='center')elifnf ==2:# Set a main subtitle of plotplt.text(12.0,8.0,r'Internal member von Mises stress $\\sigma_{v}$ [MPa]',fontsize=16,ha='center')# Prepare color mappingjet =plt.get_cmap('jet')minA=min(abs(N/area*1e6))maxA=max(abs(N/area*1e6))cNorm=plt.Normalize(minA,maxA)scalarMap=cmx.ScalarMappable(norm=cNorm,cmap=jet)plt.colorbar(scalarMap,use_gridspec=True,ax=plt.gca())# Plot for internal member von Mises stressdA=maxA-minAfore inrange(nfe):Su=abs(N[e]/area[e]*1e6)l=leng[e]/2n=Su/dA*0.2X=np.array([-l,l,l,-l])Y=np.array([n,n,n,n])c =np.cos(theta[e])# cosine inclination angles =np.sin(theta[e])# sinus inclination angleXrot =X*c -Y*sYrot =X*s +Y*cx=(XY[IEN[e,0],0]+XY[IEN[e,1],0])/2y=(XY[IEN[e,0],1]+XY[IEN[e,1],1])/2co=scalarMap.to_rgba(Su)plt.fill(Xrot +x,Yrot +y,color=co,lw=0.5,alpha=0.7)plt.text(x,y,'%0.0f'%Su,fontsize=7,ha='center',va='center')## Do rest for all plotsplt.title(tit,fontsize=18)plt.text(22,5.5,'A1=%g $m^2
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%A1,fontsize=8)# plot values of input dataplt.text(22,5.2,'A2=%g $m^2
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%A2,fontsize=8)plt.text(22,4.9,'E1=%g $N/m^2
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%E1,fontsize=8)plt.text(22,4.6,'E2=%g $N/m^2
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%E2,fontsize=8)plt.text(1.0,5.5,StrurelName+' '+StrurelModule+' '+StrurelVersion,fontsize=12)# plot a main title of plotifStrurelMode==0:plt.text(-1.0,5.0,'State at Mean Values',fontsize=10)# data at state of mean valueselifStrurelMode==2:plt.text(-1.0,5.0,StrurelIMET+', '+StrurelIOPT,fontsize=10)# data at betapointplt.text(-1.0,4.5,'beta=%.5f'%StrurelBeta+', '+'Pf=%.5f'%StrurelPf,fontsize=10)# calculated beta and Pfplt.text(-1.9,9.7,'matplotlib '+mpl.__version__,fontsize=7)# invoked version of matplotlibplt.text(1.9,1.8,StrurelEngine,fontsize=7)# invoked version of engineplt.axis('scaled')plt.axis([-2,26,-2,10])# plot frameplt.grid(True,alpha=0.3)globalStrurelPlotCountStrurelPlotCount+=1ifStrurelPlotMode==2orStrurelPlotMode==3:plt.savefig(StrurelPlotName+str(StrurelPlotCount)+StrurelPlotType)ifStrurelPlotMode==1orStrurelPlotMode==3:plt.show()# end of plot blockreturnuout # return value of functionIn the script it is demonstratedhow to create additional visualization fromPython.The specific plots for two states, theDeterministic Solution(with Stochastic Variables at mean values)and the Stochastic Solution(with Stochastic Variables at values at the %u00df-point), can be produced using facilities of matplotliblibrary.
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