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118 lines
2.6 KiB
Python
118 lines
2.6 KiB
Python
import numpy as np
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import os
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import time
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from tools.postprocessK.flow import ComputeVol, comp_Kdiss_Kaverage
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import subprocess
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#k[x,y,z]
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import json
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def comp_postKeff(parser,rundir,nr,PetscP):
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k=np.load(rundir+'k.npy')
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P=np.load(rundir+'P.npy')
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ref=P.shape[0]//k.shape[0]
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t0=time.time()
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k, diss, vx, Px, Py, Pz = ComputeVol(k,P) #refina k
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tDissVel=time.time()-t0
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P=0
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S_min_post = int(parser.get('K-Postprocess','MinBlockSize'))
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nimax =2** int(parser.get('K-Postprocess','Max_sample_size'))
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compKperm =parser.get('K-Postprocess','kperm')
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if compKperm=='yes':
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compKperm=True
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S_min_post=S_min_post*ref
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if S_min_post==0:
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sx=k.shape[0]
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else:
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sx = get_min_nbl(k,nimax,nr,S_min_post)
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kdiss,kave=getKpost(k, diss, vx, Px, Py, Pz,sx,rundir,ref)
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ttotal=time.time()-t0
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summary = np.array([kdiss,kave,ttotal,tDissVel/ttotal]).T
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np.savetxt(rundir + 'PosKeffSummary.txt',summary,header='K_diss, K_average,ttotal,tDiss/ttotal')
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return
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def getKpost(kf, diss, vx, Px, Py, Pz,sx,rundir,ref,compkperm):
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ex=int(np.log2(kf.shape[0]))
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esx=int(np.log2(sx))
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scales=2**np.arange(esx,ex)
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datadir=rundir+'KpostProcess/'
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try:
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os.makedirs(datadir)
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except:
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nada=0
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for l in scales:
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nblx, nbly, nblz = kf.shape[0]//l, kf.shape[1]//l, kf.shape[2]//l
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sx,sy,sz=l,l,l
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if kf.shape[2]==1:
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nblz=1
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sz=1
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Kdiss,Kave=np.zeros((nblx,nbly,nblz)),np.zeros((nblx,nbly,nblz))
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if compkperm==True:
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Kperm = np.zeros((nblx,nbly,nblz))
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for i in range(nblx):
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for j in range(nbly):
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for k in range(nblz):
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Kdiss[i,j,k],Kave[i,j,k]=comp_Kdiss_Kaverage(kf[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz], diss[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz], vx[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz], Px[i*sx:(i+1)*sx+1,j*sy:(j+1)*sy+1,k*sz:(k+1)*sz+1], Py[i*sx:(i+1)*sx+1,j*sy:(j+1)*sy+1,k*sz:(k+1)*sz+1], Pz[i*sx:(i+1)*sx+1,j*sy:(j+1)*sy+1,k*sz:(k+1)*sz+1])
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if compkperm==True:
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Kperm[i,j,k]=PetscP(datadir,ref,k)(kf[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz])
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np.save(datadir+'Kd'+str(l//ref)+'.npy',Kdiss)
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np.save(datadir+'Kv'+str(l//ref)+'.npy',Kave)
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if compkperm==True:
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np.save(datadir+'Kperm'+str(l//ref)+'.npy',Kperm)
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Kdiss,Kave = comp_Kdiss_Kaverage(kf, diss, vx, Px, Py, Pz)
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np.save(datadir+'Kd'+str(kf.shape[0]//ref)+'.npy',np.array([Kdiss]))
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np.save(datadir+'Kv'+str(kf.shape[0]//ref)+'.npy',np.array([Kave]))
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return Kdiss, Kave
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def get_min_nbl(kc,nimax,nr,smin):
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if kc.shape[2]==1:
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dim=2.0
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else:
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dim=3.0
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if nr>0:
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y=(1/dim)*np.log2(nr*kc.size/(nimax*(smin**dim)))
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else:
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y=0
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y=int(y)
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s=int((2**y) * smin)
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if s<smin:
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s=smin
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return s
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