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simulacion-permeabilidad/tools/connec/PostConec.py

234 lines
5.6 KiB
Python

from __future__ import division
import numpy as np
from scipy import stats
import os
import collections
def ConnecInd(cmap,scales,datadir):
datadir=datadir+'ConnectivityMetrics/'
try:
os.makedirs(datadir)
except:
nada=0
for scale in scales:
res=dict()
res=doforsubS_computeCmap(res,cmap,scale,postConec)
np.save(datadir+str(scale)+'.npy',res)
return
def doforsubS_computeCmap(res,cmap,l,funpost):
L=cmap.shape[0]
Nx, Ny,Nz=cmap.shape[0],cmap.shape[1],cmap.shape[2]
nblx=Nx//l #for each dimension
ly=l
nbly=Ny//l
lz=l
nblz=Nz//l
if nbly==0: #si l> Ny
nbly=1
ly=Ny
if nblz==0:
lz=1
nblz=1
keys=funpost(np.array([]),res,0,0)
for key in keys:
res[key]=np.zeros((nblx,nbly,nblz))
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
res=funpost(cmap[i*l:(i+1)*l,j*ly:(j+1)*ly,k*l:(k+1)*lz],res,(i,j,k),1)
return res
def postConec(cmap,results,ind,flag):
keys=[]
keys+=['PPHA']
keys+=['VOLALE']
keys+=['ZNCC']
keys+=['GAMMA']
keys+=['spanning', 'npz', 'npy', 'npx']
keys+=['Plen','S','P']
keys+=['PlenX','SX','PX']
if flag==0:
return keys
y = np.bincount(cmap.reshape(-1))
ii = np.nonzero(y)[0]
cf=np.vstack((ii,y[ii])).T #numero de cluster, frecuencia
if cf[0,0]==0:
cf=cf[1:,:] #me quedo solo con la distr de tamanos, elimino info cluster cero
if cf.shape[0]>0:
spanning, pclusX, pclusY, pclusZ =get_perco(cmap)
plen=Plen(spanning,cmap,cf)
#print(pclusX,spanning)
if len(pclusX) >0 and spanning ==0:
plenX=PlenX(pclusX,cmap,cf)
else:
plenX=plen
nper=np.sum(cf[:,1]) #num de celdas permeables
nclus=cf.shape[0] #cantidad de clusters
results['PPHA'][ind]=nper/np.size(cmap) #ppha
results['VOLALE'][ind]=np.max(cf[:,1])/nper #volale #corregido va entre [0,p]
results['ZNCC'][ind]=nclus #zncc
results['GAMMA'][ind]=np.sum(cf[:,1]**2)/nper**2 #gamma, recordar zintcc =gamma*nper
results['spanning'][ind],results['npz'][ind], results['npy'][ind], results['npx'][ind]=spanning, len(pclusZ), len(pclusY), len(pclusX)
results['Plen'][ind],results['S'][ind],results['P'][ind] = plen[0],plen[1],plen[2]
results['PlenX'][ind],results['SX'][ind],results['PX'][ind] = plenX[0],plenX[1],plenX[2]
if cf.shape[0]==0:
for key in keys:
results[key][ind]=0
return results
def get_pos(cmap,cdis):
Ns=cdis.shape[0]
pos=dict()
i=0
for cnum in cdis[:,0]:
pos[cnum]=np.zeros((cdis[i,1]+1,3))
i+=1
for i in range(cmap.shape[0]):
for j in range(cmap.shape[1]):
for k in range(cmap.shape[2]):
if cmap[i,j,k] != 0:
flag=int(pos[cmap[i,j,k]][0,0])+1
pos[cmap[i,j,k]][0,0]=flag
pos[cmap[i,j,k]][flag,0]=i
pos[cmap[i,j,k]][flag,1]=j
pos[cmap[i,j,k]][flag,2]=k
return pos
def Plen(spanning,cmap,cdis):
pos = get_pos(cmap,cdis)
#print(summary['NpcY'],summary['NpcX'],summary['PPHA'])
den=0
num=0
nperm=np.sum(cdis[:,1])
if spanning > 0:
amax=np.argmax(cdis[:,1])
P=cdis[amax,1]/nperm
cdis=np.delete(cdis,amax,axis=0)
else:
P=0
i=0
if cdis.shape[0]> 0:
S=np.sum(cdis[:,1])/(cdis.shape[0])
for cnum in cdis[:,0]: #los clusters estan numerados a partir de 1, cluster cero es k-
mposx, mposy, mposz = np.mean(pos[cnum][1:,0]), np.mean(pos[cnum][1:,1]), np.mean(pos[cnum][1:,2]) #el 1: de sacar el flag
Rs =np.mean((pos[cnum][1:,0]-mposx)**2 +(pos[cnum][1:,1]-mposy)**2+(pos[cnum][1:,2]-mposz)**2) #Rs cuadrado ecuacion 12.9 libro Harvey Gould, Jan Tobochnik
num += cdis[i,1]**2 * Rs
den+=cdis[i,1]**2
i+=1
return [np.sqrt(num/den), S, P]
else:
return [0,0,P]
def PlenX(pclusX,cmap,cdis):
#guarda que solo se entra en esta funcion si no es spanning pero hay al menos 1 cluster percolante en X
for cluster in pclusX[1:]:
cmap=np.where(cmap==cluster,pclusX[0],cmap)
y = np.bincount(cmap.reshape(-1))
ii = np.nonzero(y)[0]
cdis=np.vstack((ii,y[ii])).T #numero de cluster, frecuencia
if cdis[0,0]==0:
cdis=cdis[1:,:] #me quedo solo con la distr de tamanos, elimino info cluster cero
pos = get_pos(cmap,cdis)
nperm=np.sum(cdis[:,1])
amax=np.argmax(cdis[:,1])
P=cdis[amax,1]/nperm
cdis=np.delete(cdis,amax,axis=0)
den=0
num=0
i=0
if cdis.shape[0]> 0:
S=np.sum(cdis[:,1])/(cdis.shape[0])
for cnum in cdis[:,0]: #los clusters estan numerados a partir de 1, cluster cero es k-
mposx, mposy, mposz = np.mean(pos[cnum][1:,0]), np.mean(pos[cnum][1:,1]), np.mean(pos[cnum][1:,2]) #el 1: de sacar el flag
Rs =np.mean((pos[cnum][1:,0]-mposx)**2 +(pos[cnum][1:,1]-mposy)**2+(pos[cnum][1:,2]-mposz)**2) #Rs cuadrado ecuacion 12.9 libro Harvey Gould, Jan Tobochnik
num += cdis[i,1]**2 * Rs
den+=cdis[i,1]**2
i+=1
return [np.sqrt(num/den), S, P]
else:
return [0,0,P]
def get_perco(cmap):
pclusX=[] #list of the percolating clusters
for k in range(cmap.shape[2]): #x
for j in range(cmap.shape[1]): #y
if cmap[0,j,k] != 0:
if cmap[0,j,k] not in pclusX:
if cmap[0,j,k] in cmap[-1,:,:]:
pclusX+=[cmap[0,j,k]] #this is the one
pclusY=[] #list of the percolating clusters
for i in range(cmap.shape[0]): # Z
for k in range(cmap.shape[2]): #X
if cmap[i,0,k] != 0:
if cmap[i,0,k] not in pclusY:
if cmap[i,0,k] in cmap[:,-1,:]:
pclusY+=[cmap[i,0,k]]
pclusZ=[] #list of the percolating clusters
if cmap.shape[2]>1:
for i in range(cmap.shape[0]): # Z
for j in range(cmap.shape[1]): #X
if cmap[i,j,0] != 0:
if cmap[i,j,0] not in pclusZ:
if cmap[i,j,0] in cmap[:,:,-1]:
pclusZ+=[cmap[i,j,0]]
spanning=0
if len(pclusZ)==1 and pclusZ==pclusY and pclusZ==pclusX:
spanning=1
else:
spanning=0
if len(pclusX)==1 and pclusY==pclusX:
spanning=1
return spanning, pclusX, pclusY, pclusZ