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pancettapancetta
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postprocessing for boussinesq
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‎examples/fault_tolerance/postproc_boussinesq.py‎

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import numpy as np
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import os
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import matplotlib.pyplot as plt
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from pylab import rcParams
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axis_font = {'fontname':'Arial', 'size':'8', 'family':'serif'}
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fs = 8
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ms = 8
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lw = 2
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if __name__ == "__main__":
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ref = 'PFASST_BOUSSINESQ_stats_hf_NOFAULT_P16.npz'
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list = [ ('PFASST_BOUSSINESQ_stats_hf_SPREAD_P16.npz','SPREAD','1-sided','red','s'),
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('PFASST_BOUSSINESQ_stats_hf_INTERP_P16.npz','INTERP','2-sided','orange','o'),
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('PFASST_BOUSSINESQ_stats_hf_SPREAD_PREDICT_P16.npz','SPREAD_PREDICT','1-sided+corr','blue','^'),
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('PFASST_BOUSSINESQ_stats_hf_INTERP_PREDICT_P16.npz','INTERP_PREDICT','2-sided+corr','green','d') ]
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nprocs = 16
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xtick_dist = 8
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minstep = 128
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maxstep = 176
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# minstep = 0
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# maxstep = 320
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nblocks = int(320/nprocs)
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# maxiter = 14
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nsteps = 0
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maxiter = 0
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for file,strategy,label,color,marker in list:
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data = np.load(file)
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iter_count = data['iter_count'][minstep:maxstep]
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residual = data['residual'][:,minstep:maxstep]
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residual = np.where(residual > 0, np.log10(residual), -99)
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vmin = -9
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vmax = int(np.amax(residual))
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maxiter = max(maxiter,int(max(iter_count)))
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nsteps = max(nsteps,len(iter_count))
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data = np.load(ref)
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ref_iter_count = data['iter_count'][nprocs-1::nprocs]
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rcParams['figure.figsize'] = 6.0, 2.5
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fig, ax = plt.subplots()
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plt.plot(range(nblocks),[0]*nblocks,'k-',linewidth=2)
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ymin = 99
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ymax = 0
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for file,strategy,label,color,marker in list:
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if not file is ref:
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data = np.load(file)
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iter_count = data['iter_count'][nprocs-1::nprocs]
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ymin = min(ymin,min(iter_count-ref_iter_count))
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ymax = max(ymax,max(iter_count-ref_iter_count))
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plt.plot(range(nblocks),iter_count-ref_iter_count,color=color,label=label,marker=marker,linestyle='',linewidth=lw,markersize=ms)
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plt.xlabel('block', **axis_font)
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plt.ylabel('$K_\\mathrm{add}$', **axis_font)
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plt.xlim(-1,nblocks)
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plt.ylim(-1+ymin,ymax+1)
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plt.legend(loc=2,numpoints=1,fontsize=fs)
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plt.tick_params(axis='both', which='major', labelsize=fs)
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ax.xaxis.labelpad = -0.5
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ax.yaxis.labelpad = -1
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plt.tight_layout()
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fname = 'BOUSSINESQ_Kadd_vs_NOFAULT_hf.pdf'
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plt.savefig(fname, bbox_inches='tight')
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os.system('pdfcrop '+fname+' '+fname)
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# exit()
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for file,strategy,label,color,marker in list:
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data = np.load(file)
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iter_count = data['iter_count'][minstep:maxstep]
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residual = data['residual'][:,minstep:maxstep]
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stats = data['hard_stats']
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residual = np.where(residual > 0, np.log10(residual), -99)
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rcParams['figure.figsize'] = 6.0, 2.5
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fig, ax = plt.subplots()
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cmap = plt.get_cmap('Reds',vmax-vmin+1)
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pcol = plt.pcolor(residual,cmap=cmap,vmin=vmin,vmax=vmax)
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pcol.set_edgecolor('face')
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for item in stats:
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if item[0] in range(minstep,maxstep):
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plt.text(item[0]+0.5-(maxstep-nsteps),item[1]-1+0.5,'x',horizontalalignment='center',verticalalignment='center')
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plt.axis([0,nsteps,0,maxiter])
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ticks = np.arange(vmin,vmax+1,2)
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tickpos = np.linspace(ticks[0]+0.5, ticks[-1]-0.5, len(ticks))
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cax = plt.colorbar(pcol, ticks=tickpos, pad=0.02)
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cax.set_ticklabels(ticks)
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cax.ax.tick_params(labelsize=fs)
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cax.set_label('log10(residual)', **axis_font)
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plt.tick_params(axis='both', which='major', labelsize=fs)
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ax.xaxis.labelpad = -0.5
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ax.yaxis.labelpad = -0.5
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ax.set_xlabel('step', **axis_font)
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ax.set_ylabel('iteration', **axis_font)
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ax.set_yticks(np.arange(1,maxiter,2)+0.5, minor=False)
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ax.set_xticks(np.arange(0,nsteps,xtick_dist)+0.5, minor=False)
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ax.set_yticklabels(np.arange(1,maxiter,2)+1, minor=False)
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ax.set_xticklabels(np.arange(minstep,maxstep,xtick_dist), minor=False)
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plt.tight_layout()
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fname = 'BOUSSINESQ_steps_vs_iteration_hf_'+strategy+'.pdf'
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plt.savefig(fname, bbox_inches='tight')
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os.system('pdfcrop '+fname+' '+fname)
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exit()

‎examples/fault_tolerance/postproc_grayscott.py‎

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if __name__ == "__main__":
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ref = 'PFASST_GRAYSCOTT_stats_hf_NOFAULT_P32.npz'
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# ref = 'PFASST_GRAYSCOTT_stats_hf_SPREAD_P32.npz'
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# list = [('PFASST_GRAYSCOTT_stats_hf_INTERP_PREDICT_P32.npz','2-sided+corr','green','o')]
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list = [ ('PFASST_GRAYSCOTT_stats_hf_SPREAD_P32.npz','SPREAD','1-sided','red','s'),
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('PFASST_GRAYSCOTT_stats_hf_INTERP_P32.npz','INTERP','2-sided','orange','o'),
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('PFASST_GRAYSCOTT_stats_hf_SPREAD_PREDICT_P32.npz','SPREAD_PREDICT','1-sided+corr','blue','^'),
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plt.savefig(fname, bbox_inches='tight')
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os.system('pdfcrop '+fname+' '+fname)
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exit()
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fig, ax = plt.subplots(figsize=(20,7))
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# nblocks = int((maxstep-minstep)/nprocs)
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nblocks = 20
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data = np.load('PFASST_GRAYSCOTT_stats_hf_NOFAULT_P32.npz')
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iter_count = data['iter_count'][:]
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iterblocks = np.zeros(nblocks)
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iterblocks[:] = iter_count[nprocs-1::nprocs]
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# for i in range(nblocks):
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# iterblocks[i] = np.sum(iter_count[i*nprocs:(i+1)*nprocs])/nprocs
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miniter = np.amin(iterblocks)
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plt.plot(range(1,nblocks+1),iterblocks,color='k',label='no fault',marker='',linestyle='--',linewidth=lw,markersize=12)
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for file,strategy,label,color,marker in list:
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data = np.load(file)
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iter_count = data['iter_count']
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iterblocks = np.zeros(nblocks)
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iterblocks[:] = iter_count[nprocs-1::nprocs]
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# for i in range(nblocks):
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# iterblocks[i] = np.sum(iter_count[i*nprocs:(i+1)*nprocs])/nprocs
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plt.plot(range(1,nblocks+1),iterblocks,color=color,label=label,marker=marker,linestyle='-',linewidth=lw,markersize=12)
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plt.xlabel('block')
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plt.ylabel('number of iterations')
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plt.xlim(0.5,nblocks+0.5)
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plt.ylim(miniter-0.5,maxiter+0.5)
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ax.set_xticks(np.arange(1,nblocks+1), minor=False)
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# ax.set_xticklabels(np.arange(minstep,maxstep,xtick_dist), minor=False)
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plt.legend(loc=2,numpoints=1)
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plt.tight_layout()
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fname = 'GRAYSCOTT_iteration_count_hf.png'
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plt.savefig(fname, rasterized=True, transparent=True, bbox_inches='tight')
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# plt.show()
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exit()

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