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milestone_5_without_improvements
chortas 3 years ago
parent 96db89d10c
commit 6d62c0e798
55 changed files with 5284 additions and 4315 deletions
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56
fftma_module/gen/FFTMA_TEST.py
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@ -3,34 +3,32 @@ from refine import refine as ref
import numpy as np
from random import randint as rdi
N=128
N = 128
for i in range(1):
nx, ny, nz = N,N,N
dx, dy, dz = 1.0, 1.0, 1.0
seed= 1548762 #rdi(10000,99999)
var=1
vario=2
alpha=1
lcx=2
lcy=4
lcz=16
ap1x=1
ap1y=0
ap1z=0
ap2x=0
ap2y=1
ap2z=0
v1 = (var, vario, alpha, lcx, lcy, lcz, ap1x, ap1y, ap1z, ap2x, ap2y, ap2z)
variograms = [v1]
mean=15.3245987
variance=3.5682389
typ=1
k=gen(nx, ny, nz, dx, dy, dz, seed, variograms, mean, variance, typ)
np.save("out"+str(i)+".npy",ref(k,4,4,4))
nx, ny, nz = N, N, N
dx, dy, dz = 1.0, 1.0, 1.0
seed = 1548762 # rdi(10000,99999)
var = 1
vario = 2
alpha = 1
lcx = 2
lcy = 4
lcz = 16
ap1x = 1
ap1y = 0
ap1z = 0
ap2x = 0
ap2y = 1
ap2z = 0
v1 = (var, vario, alpha, lcx, lcy, lcz, ap1x, ap1y, ap1z, ap2x, ap2y, ap2z)
variograms = [v1]
mean = 15.3245987
variance = 3.5682389
typ = 1
k = gen(nx, ny, nz, dx, dy, dz, seed, variograms, mean, variance, typ)
np.save("out" + str(i) + ".npy", ref(k, 4, 4, 4))

72
fftma_module/gen/setup.py
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@ -1,10 +1,74 @@
from distutils.core import setup, Extension
module_FFTMA = Extension('FFTMA', include_dirs = ['./include'],sources=["moduleFFTMA.c","./lib_src/Py_getvalues.c","./lib_src/Py_kgeneration.c","./lib_src/genlib.c","./lib_src/random.c","./lib_src/simpio.c","./lib_src/strlib.c","./lib_src/symtab.c","./lib_src/scanadt.c","./lib_src/stack.c","./lib_src/gammf.c","./lib_src/fftma.c","./lib_src/addstat.c","./lib_src/axes.c","./lib_src/cgrid.c","./lib_src/covariance.c","./lib_src/fourt.c","./lib_src/length.c","./lib_src/maxfactor.c","./lib_src/test_fact.c","./lib_src/cov_value.c","./lib_src/generate.c","./lib_src/gasdev.c","./lib_src/ran2.c","./lib_src/stable.c","./lib_src/gaussian.c","./lib_src/power.c","./lib_src/cubic.c","./lib_src/spherical.c","./lib_src/nugget.c","./lib_src/exponential.c","./lib_src/cardsin.c","./lib_src/nor2log.c","./lib_src/kgeneration.c","./lib_src/kgeneration2.c","./lib_src/fftma2.c","./lib_src/prebuild_gwn.c","./lib_src/build_real.c","./lib_src/addstat2.c","./lib_src/clean_real.c","./lib_src/pgeneration.c","./lib_src/pgeneration2.c","./lib_src/FFTPressure.c","./lib_src/FFTtest.c","./lib_src/build_pressure.c","./lib_src/build_velocity.c","./lib_src/total_pressure.c","./lib_src/total_velocity.c","./lib_src/clean_real2.c","./lib_src/waveVectorCompute3D.c","./lib_src/mat_vec.c","./lib_src/derivReal.c","./lib_src/inputdata.c","./lib_src/inputfiledata.c","./lib_src/debuginput.c","./lib_src/readdata.c","./lib_src/readfile_bin.c","./lib_src/writefile.c","./lib_src/writefile_bin.c","./lib_src/testmemory.c","./lib_src/testopenfile.c","./lib_src/readdata3.c"])
module_FFTMA = Extension(
"FFTMA",
include_dirs=["./include"],
sources=[
"moduleFFTMA.c",
"./lib_src/Py_getvalues.c",
"./lib_src/Py_kgeneration.c",
"./lib_src/genlib.c",
"./lib_src/random.c",
"./lib_src/simpio.c",
"./lib_src/strlib.c",
"./lib_src/symtab.c",
"./lib_src/scanadt.c",
"./lib_src/stack.c",
"./lib_src/gammf.c",
"./lib_src/fftma.c",
"./lib_src/addstat.c",
"./lib_src/axes.c",
"./lib_src/cgrid.c",
"./lib_src/covariance.c",
"./lib_src/fourt.c",
"./lib_src/length.c",
"./lib_src/maxfactor.c",
"./lib_src/test_fact.c",
"./lib_src/cov_value.c",
"./lib_src/generate.c",
"./lib_src/gasdev.c",
"./lib_src/ran2.c",
"./lib_src/stable.c",
"./lib_src/gaussian.c",
"./lib_src/power.c",
"./lib_src/cubic.c",
"./lib_src/spherical.c",
"./lib_src/nugget.c",
"./lib_src/exponential.c",
"./lib_src/cardsin.c",
"./lib_src/nor2log.c",
"./lib_src/kgeneration.c",
"./lib_src/kgeneration2.c",
"./lib_src/fftma2.c",
"./lib_src/prebuild_gwn.c",
"./lib_src/build_real.c",
"./lib_src/addstat2.c",
"./lib_src/clean_real.c",
"./lib_src/pgeneration.c",
"./lib_src/pgeneration2.c",
"./lib_src/FFTPressure.c",
"./lib_src/FFTtest.c",
"./lib_src/build_pressure.c",
"./lib_src/build_velocity.c",
"./lib_src/total_pressure.c",
"./lib_src/total_velocity.c",
"./lib_src/clean_real2.c",
"./lib_src/waveVectorCompute3D.c",
"./lib_src/mat_vec.c",
"./lib_src/derivReal.c",
"./lib_src/inputdata.c",
"./lib_src/inputfiledata.c",
"./lib_src/debuginput.c",
"./lib_src/readdata.c",
"./lib_src/readfile_bin.c",
"./lib_src/writefile.c",
"./lib_src/writefile_bin.c",
"./lib_src/testmemory.c",
"./lib_src/testopenfile.c",
"./lib_src/readdata3.c",
],
)
setup(ext_modules=[module_FFTMA])

2
fftma_module/ref/setup.py
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@ -1,7 +1,7 @@
from distutils.core import setup, Extension
module = Extension('refine', sources=['FINALrefine.c'])
module = Extension("refine", sources=["FINALrefine.c"])
setup(ext_modules=[module])

16
fftma_module/ref/test.py
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@ -2,15 +2,11 @@ from time import time
import numpy as np
import refine
size=420
a=np.arange(size**3).astype('f8').reshape((size,size,size))
ti=time()
b=refine.refine(a,2,2,2)
tf=time()
dt=tf-ti
print a
print b
print dt
size = 420
a = np.arange(size ** 3).astype("f8").reshape((size, size, size))
ti = time()
b = refine.refine(a, 2, 2, 2)
tf = time()
dt = tf - ti
raw_input("")

72
fftma_module/testRes.py
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@ -2,63 +2,55 @@ import numpy as np
import sys
from refine import refine as ref
def get_p(pn, pdir, pprefix):
p=np.load(pdir+pprefix+"0"+'.npy')
for i in range(1,pn):
p=np.concatenate((p,np.load(pdir+pprefix+str(i)+'.npy')),axis=0)
def get_p(pn, pdir, pprefix):
return p
p = np.load(pdir + pprefix + "0" + ".npy")
for i in range(1, pn):
p = np.concatenate((p, np.load(pdir + pprefix + str(i) + ".npy")), axis=0)
return p
def get_k(pn, kdir, kprefix):
k=(np.load(kdir+kprefix+'0'+'.npy'))[1:-1,:,:]
for i in range(1,pn):
k=np.concatenate((k,(np.load(kdir+kprefix+str(i)+'.npy'))[1:-1,:,:]),axis=0)
return ref(k,2,2,2)
k = (np.load(kdir + kprefix + "0" + ".npy"))[1:-1, :, :]
for i in range(1, pn):
k = np.concatenate(
(k, (np.load(kdir + kprefix + str(i) + ".npy"))[1:-1, :, :]), axis=0
)
return ref(k, 2, 2, 2)
def kef(P, K, i, j, k, pbc):
# tx=2*K[:,:,i]*K[:,:,i+1]/(K[:,:,i]+K[:,:,i+1])
# ty=2*K[:,j,:]*K[:,j+1,:]/(K[:,j,:]+K[:,j+1,:])
tz = 2 * K[k, :, :] * K[k + 1, :, :] / (K[k, :, :] + K[k + 1, :, :])
def kef(P,K,i,j,k,pbc):
#tx=2*K[:,:,i]*K[:,:,i+1]/(K[:,:,i]+K[:,:,i+1])
#ty=2*K[:,j,:]*K[:,j+1,:]/(K[:,j,:]+K[:,j+1,:])
tz=2*K[k,:,:]*K[k+1,:,:]/(K[k,:,:]+K[k+1,:,:])
# qx=tx*(P[:,:,i+1]-P[:,:,i])
# qy=ty*(P[:,j+1,:]-P[:,j,:])
qz = -tz * (P[k + 1, :, :] - P[k, :, :])
#qx=tx*(P[:,:,i+1]-P[:,:,i])
#qy=ty*(P[:,j+1,:]-P[:,j,:])
qz=-tz*(P[k+1,:,:]-P[k,:,:])
kz=qz.sum()*(K.shape[0]+1)/(pbc*K.shape[1]*K.shape[2])
return kz
kz = qz.sum() * (K.shape[0] + 1) / (pbc * K.shape[1] * K.shape[2])
return kz
def test(pn, kdir, pdir, kprefix, pprefix):
K=get_k(pn, kdir, kprefix)
print(K.shape)
P=get_p(pn, pdir, pprefix)
print(P)
print(P.shape)
print(kef(P,K,1,1,1,1000))
return
K = get_k(pn, kdir, kprefix)
print(K.shape)
P = get_p(pn, pdir, pprefix)
print(P)
print(P.shape)
print(kef(P, K, 1, 1, 1, 1000))
return
pn=4
kdir="./test/"
pdir="./test/"
kprefix="k"
pprefix="P"
pn = 4
kdir = "./test/"
pdir = "./test/"
kprefix = "k"
pprefix = "P"
test(pn, kdir, pdir, kprefix, pprefix)

93
mpirunner.py
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@ -1,6 +1,7 @@
import numpy as np
from mpi4py import MPI
#from tools.realization import realization
# from tools.realization import realization
from tools.generation.config import DotheLoop, get_config
import os
import sys
@ -8,59 +9,67 @@ from tools.Prealization import realization
from utilities.conditional_decorator import *
from memory_profiler import profile
CONFIG_FILE_PATH = 'config.ini' if 'CONFIG_FILE_PATH' not in os.environ else os.environ['CONFIG_FILE_PATH']
IS_TEST = False if 'TEST' not in os.environ else True
CONFIG_FILE_PATH = (
"config.ini"
if "CONFIG_FILE_PATH" not in os.environ
else os.environ["CONFIG_FILE_PATH"]
)
IS_TEST = False if "TEST" not in os.environ else True
def main():
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
pn = comm.Get_size()
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
pn = comm.Get_size()
if pn == 1:
sequential()
return
if rank == 0:
manager()
else:
worker()
return
if pn==1:
sequential()
return
if rank==0:
manager()
else:
worker()
return
@conditional_decorator(profile, IS_TEST)
def sequential():
comm = MPI.COMM_WORLD
conffile = CONFIG_FILE_PATH
parser,iterables = get_config(conffile)
njobs = DotheLoop(-1,parser,iterables)
start_job=0
for job in range(start_job,njobs):
realization(job)
return
comm = MPI.COMM_WORLD
conffile = CONFIG_FILE_PATH
parser, iterables = get_config(conffile)
njobs = DotheLoop(-1, parser, iterables)
start_job = 0
for job in range(start_job, njobs):
realization(job)
return
def manager():
comm = MPI.COMM_WORLD
conffile = CONFIG_FILE_PATH
parser,iterables = get_config(conffile)
njobs = DotheLoop(-1,parser,iterables)
start_job=0
for job in range(start_job,njobs):
dest=comm.recv(source=MPI.ANY_SOURCE)
comm.send(job,dest=dest)
for i in range(comm.Get_size()-1):
dest=comm.recv(source=MPI.ANY_SOURCE)
comm.send(-1,dest=dest)
comm = MPI.COMM_WORLD
conffile = CONFIG_FILE_PATH
parser, iterables = get_config(conffile)
njobs = DotheLoop(-1, parser, iterables)
start_job = 0
for job in range(start_job, njobs):
dest = comm.recv(source=MPI.ANY_SOURCE)
comm.send(job, dest=dest)
for i in range(comm.Get_size() - 1):
dest = comm.recv(source=MPI.ANY_SOURCE)
comm.send(-1, dest=dest)
return
return
@conditional_decorator(profile, IS_TEST)
def worker():
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
job=1
while job!=-1:
comm.send(rank,dest=0)
job = comm.recv(source=0)
realization(job)
return
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
job = 1
while job != -1:
comm.send(rank, dest=0)
job = comm.recv(source=0)
realization(job)
return
main()

35
tests/integration/test.py
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@ -4,6 +4,7 @@ import numpy as np
import unittest
from numpy.lib.function_base import diff
def find_relative_errors(path_original, path):
binary_original = np.load(path_original)
binary = np.load(path)
@ -18,22 +19,31 @@ def find_relative_errors(path_original, path):
for y in range(len(diffs)):
for z in range(len(diffs)):
if type(diffs[x][y][z]) != type([]):
relative_error = 0 if binary_original[x][y][z] == 0 else diffs[x][y][z] / binary_original[x][y][z]
relative_error = (
0
if binary_original[x][y][z] == 0
else diffs[x][y][z] / binary_original[x][y][z]
)
relative_errors.append(abs(relative_error))
else:
for w in range(len(diffs)):
relative_error = 0 if binary_original[x][y][z][w] == 0 else diffs[x][y][z][w] / binary_original[x][y][z][w]
relative_error = (
0
if binary_original[x][y][z][w] == 0
else diffs[x][y][z][w] / binary_original[x][y][z][w]
)
relative_errors.append(abs(relative_error))
return relative_errors
BINARIES = ['Cmap', 'D', 'P', 'V', 'k']
class TestIntegration(unittest.TestCase):
BINARIES = ["Cmap", "D", "P", "V", "k"]
class TestIntegration(unittest.TestCase):
@classmethod
def setUpClass(cls):
os.chdir('../..')
os.chdir("../..")
config_file = os.path.abspath("./tests/integration/conf_test.ini")
os.system(f"CONFIG_FILE_PATH={config_file} mpirun -np 1 python3 mpirunner.py")
@ -43,16 +53,21 @@ class TestIntegration(unittest.TestCase):
for i in range(90):
for binary in BINARIES:
path = './tests/integration/tmp_output/{}/{}.npy'.format(i, binary)
path_original = './test_loop/{}/{}.npy'.format(i, binary)
path = "./tests/integration/tmp_output/{}/{}.npy".format(i, binary)
path_original = "./test_loop/{}/{}.npy".format(i, binary)
relative_errors = find_relative_errors(path_original, path)
binary_results[binary].append(relative_errors)
cls.binary_stats = {}
for binary in binary_results:
binary_results[binary] = [item for sublist in binary_results[binary] for item in sublist]
binary_results[binary] = [
item for sublist in binary_results[binary] for item in sublist
]
if len(binary_results[binary]) != 0:
cls.binary_stats[binary] = {"max": max(binary_results[binary]), "avg": sum(binary_results[binary]) / len(binary_results[binary])}
cls.binary_stats[binary] = {
"max": max(binary_results[binary]),
"avg": sum(binary_results[binary]) / len(binary_results[binary]),
}
@classmethod
def tearDownClass(cls):
@ -84,5 +99,5 @@ class TestIntegration(unittest.TestCase):
self.assertLess(V_stats["avg"], 0.05)
if __name__ == '__main__':
if __name__ == "__main__":
unittest.main()

29
tests/performance/connectivity.py
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@ -1,7 +1,7 @@
import os
from benchmarker import Benchmarker
os.chdir('../..')
os.chdir("../..")
config_gen_file_64 = os.path.abspath("./tests/performance/conf_gen_64.ini")
config_conn_file_64 = os.path.abspath("./tests/performance/conf_conn_64.ini")
@ -13,7 +13,7 @@ index_1 = 0
index_8 = 0
'''
"""
Esta etapa tarda mucho tiempo y no es muy independiente de la generación de medios.
Si se generan medios con los parámetros dados:
[Iterables]
@ -26,32 +26,39 @@ Se generan 90 medios: 15 (p[2]) * 2 (seeds[1]) * 3 (len(connectivity)) * 1 (len(
Pero si se toman esos medios generados y se aplica solo la etapa de conectividad
Se calcula la conectividad sobre 6 medios: 2 (seeds[1]) * 3 (len(connectivity)) * 1 (len(variances))* 1 (len(lc))
Solucion: marcar en la etapa de generacion binary = yes -> esta bien esto?
'''
"""
with Benchmarker() as bench:
for i in range(len(CONN_CONFIG_FILES)):
size = 2**(6+i)
size = 2 ** (6 + i)
@bench(f"Connectivity 1 core with size {size}")
def _(bm):
global index_1
os.system(f"CONFIG_FILE_PATH={GEN_CONFIG_FILES[index_1]} TEST=True mpirun -oversubscribe -np 1 python3 mpirunner.py")
os.system(
f"CONFIG_FILE_PATH={GEN_CONFIG_FILES[index_1]} TEST=True mpirun -oversubscribe -np 1 python3 mpirunner.py"
)
with bm:
os.system(f"CONFIG_FILE_PATH={CONN_CONFIG_FILES[index_1]} TEST=True mpirun -oversubscribe -np 1 python3 mpirunner.py")
os.system(
f"CONFIG_FILE_PATH={CONN_CONFIG_FILES[index_1]} TEST=True mpirun -oversubscribe -np 1 python3 mpirunner.py"
)
## teardown
os.system("rm -rf ./tests/performance/tmp_gen_output")
index_1 +=1
index_1 += 1
@bench(f"Connectivity 8 core with size {size}")
def _(bm):
global index_8
os.system(f"CONFIG_FILE_PATH={GEN_CONFIG_FILES[index_8]} TEST=True mpirun -oversubscribe -np 8 python3 mpirunner.py")
os.system(
f"CONFIG_FILE_PATH={GEN_CONFIG_FILES[index_8]} TEST=True mpirun -oversubscribe -np 8 python3 mpirunner.py"
)
with bm:
os.system(f"CONFIG_FILE_PATH={CONN_CONFIG_FILES[index_8]} TEST=True mpirun -oversubscribe -np 8 python3 mpirunner.py")
os.system(
f"CONFIG_FILE_PATH={CONN_CONFIG_FILES[index_8]} TEST=True mpirun -oversubscribe -np 8 python3 mpirunner.py"
)
## teardown
os.system("rm -rf ./tests/performance/tmp_gen_output")
index_8 +=1
index_8 += 1

16
tests/performance/generation.py
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@ -1,7 +1,7 @@
import os
from benchmarker import Benchmarker
os.chdir('../..')
os.chdir("../..")
config_file_64 = os.path.abspath("./tests/performance/conf_gen_64.ini")
@ -18,26 +18,30 @@ index_8 = 0
with Benchmarker() as bench:
for i in range(len(CONFIG_FILES)):
size = 2**(6+i)
size = 2 ** (6 + i)
@bench(f"generation 1 core {size} tamaño")
def _(bm):
global index_1
config_file = CONFIG_FILES[index_1]
with bm:
os.system(f"CONFIG_FILE_PATH={config_file} TEST=True mpirun -oversubscribe -np 1 python3 mpirunner.py")
os.system(
f"CONFIG_FILE_PATH={config_file} TEST=True mpirun -oversubscribe -np 1 python3 mpirunner.py"
)
## teardown
os.system("rm -rf ./tests/performance/tmp_gen_output")
index_1 +=1
index_1 += 1
@bench(f"generation 8 core {size} tamaño")
def _(bm):
global index_8
config_file = CONFIG_FILES[index_8]
with bm:
os.system(f"CONFIG_FILE_PATH={config_file} TEST=True mpirun -oversubscribe -np 8 python3 mpirunner.py")
os.system(
f"CONFIG_FILE_PATH={config_file} TEST=True mpirun -oversubscribe -np 8 python3 mpirunner.py"
)
## teardown
os.system("rm -rf ./tests/performance/tmp_gen_output")
index_8 +=1
index_8 += 1

138
tools/Prealization.py
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@ -1,5 +1,5 @@
import numpy as np
from tools.generation.config import DotheLoop, get_config
from tools.generation.config import DotheLoop, get_config
import os
import sys
from mpi4py import MPI
@ -10,73 +10,75 @@ from tools.solver.comp_Kperm_scale import comp_kperm_sub
from tools.solver.Ndar import PetscP
from tools.generation.fftma_gen import fftmaGenerator
CONFIG_FILE_PATH = 'config.ini' if 'CONFIG_FILE_PATH' not in os.environ else os.environ['CONFIG_FILE_PATH']
def realization(job):
if job==-1:
return
conffile=CONFIG_FILE_PATH
parser, iterables = get_config(conffile)
start_job=int(parser.get('General',"startJob"))
if job<start_job:
return
rdir='./'+parser.get('General',"simDir")+'/'
datadir=rdir+str(job)+'/'
create_dir(datadir,job)
if job==0:
copyfile(conffile,rdir+"config.ini")
genera=parser.get('Generation',"genera")
if genera!='no':
fftmaGenerator(datadir, job, CONFIG_FILE_PATH)
#os.system('CONFIG_FILE_PATH=' + CONFIG_FILE_PATH + ' python3 ./tools/generation/fftma_gen.py ' + datadir +' ' + str(job))
nr= DotheLoop(job,parser, iterables)[3] -iterables['seeds'][0]
Cconec=parser.get('Connectivity',"conec")
if Cconec!='no':
comp_connec(parser,datadir,nr)
n_p=int(parser.get('Solver',"num_of_cores"))
ref=int(parser.get('Solver',"ref"))
solv=parser.get('Solver',"solve")
Rtol=parser.get('Solver',"rtol")
if solv!='no':
if n_p>1:
icomm=MPI.COMM_SELF.Spawn(sys.executable, args=['./tools/solver/Ndar.py',datadir,str(ref),'0',Rtol,'1'], maxprocs=n_p)
icomm.Disconnect()
else:
PetscP(datadir,ref,'0',True,float(Rtol),0)
compkperm=parser.get('K-Postprocess',"kperm")
if compkperm!='no':
#print('start kperm')
comp_kperm_sub(parser,datadir,nr)
#print('finished job ' +str(job))
postP=parser.get('K-Postprocess',"postprocess")
if postP!='no':
comp_postKeff(parser,datadir,nr)
return
def create_dir(datadir,job):
try:
os.makedirs(datadir)
except:
print('Warning: Unable to create dir job: '+str(job))
return
CONFIG_FILE_PATH = (
"config.ini"
if "CONFIG_FILE_PATH" not in os.environ
else os.environ["CONFIG_FILE_PATH"]
)
def realization(job):
if job == -1:
return
conffile = CONFIG_FILE_PATH
parser, iterables = get_config(conffile)
start_job = int(parser.get("General", "startJob"))
if job < start_job:
return
rdir = "./" + parser.get("General", "simDir") + "/"
datadir = rdir + str(job) + "/"
create_dir(datadir, job)
if job == 0:
copyfile(conffile, rdir + "config.ini")
genera = parser.get("Generation", "genera")
if genera != "no":
fftmaGenerator(datadir, job, CONFIG_FILE_PATH)
# os.system('CONFIG_FILE_PATH=' + CONFIG_FILE_PATH + ' python3 ./tools/generation/fftma_gen.py ' + datadir +' ' + str(job))
nr = DotheLoop(job, parser, iterables)[3] - iterables["seeds"][0]
Cconec = parser.get("Connectivity", "conec")
if Cconec != "no":
comp_connec(parser, datadir, nr)
n_p = int(parser.get("Solver", "num_of_cores"))
ref = int(parser.get("Solver", "ref"))
solv = parser.get("Solver", "solve")
Rtol = parser.get("Solver", "rtol")
if solv != "no":
if n_p > 1:
icomm = MPI.COMM_SELF.Spawn(
sys.executable,
args=["./tools/solver/Ndar.py", datadir, str(ref), "0", Rtol, "1"],
maxprocs=n_p,
)
icomm.Disconnect()
else:
PetscP(datadir, ref, "0", True, float(Rtol), 0)
compkperm = parser.get("K-Postprocess", "kperm")
if compkperm != "no":
# print('start kperm')
comp_kperm_sub(parser, datadir, nr)
# print('finished job ' +str(job))
postP = parser.get("K-Postprocess", "postprocess")
if postP != "no":
comp_postKeff(parser, datadir, nr)
return
def create_dir(datadir, job):
try:
os.makedirs(datadir)
except:
print("Warning: Unable to create dir job: " + str(job))
return

268
tools/connec/JoinCmaps.py
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@ -1,136 +1,142 @@
import numpy as np
def joinCmapX(cmap1,cmap2):
nclus1 = np.max(cmap1)
cmap2=np.where(cmap2!=0,cmap2+nclus1,0)
old_nclus=0
new_nclus=1
while new_nclus!= old_nclus:
old_nclus=new_nclus
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1,i,j] != 0 and cmap2[0,i,j] !=0:
if cmap1[-1,i,j] != cmap2[0,i,j]:
cmap2=np.where(cmap2==cmap2[0,i,j],cmap1[-1,i,j],cmap2)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1,i,j] != 0 and cmap2[0,i,j] !=0:
if cmap1[-1,i,j] != cmap2[0,i,j]:
cmap1=np.where(cmap1==cmap1[-1,i,j],cmap2[0,i,j],cmap1)
cmap=np.append(cmap1,cmap2,axis=0)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf=np.vstack((ii,y[ii])).T #numero de cluster, frecuencia
new_nclus=cf.shape[0] #cantidad de clusters
#print(new_nclus)
return cmap
def joinCmapY(cmap1,cmap2):
nclus1 = np.max(cmap1)
cmap2=np.where(cmap2!=0,cmap2+nclus1,0)
old_nclus=0
new_nclus=1
while new_nclus!= old_nclus:
old_nclus=new_nclus
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[2]):
if cmap1[i,-1,j] != 0 and cmap2[i,0,j] !=0:
if cmap1[i,-1,j] != cmap2[i,0,j]:
cmap2=np.where(cmap2==cmap2[i,0,j],cmap1[i,-1,j],cmap2)
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[2]):
if cmap1[i,-1,j] != 0 and cmap2[i,0,j] !=0:
if cmap1[i,-1,j] != cmap2[i,0,j]:
cmap1=np.where(cmap1==cmap1[i,-1,j],cmap2[i,0,j],cmap1)
cmap=np.append(cmap1,cmap2,axis=1)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf=np.vstack((ii,y[ii])).T #numero de cluster, frecuencia
new_nclus=cf.shape[0] #cantidad de clusters
#print(new_nclus)
return cmap
def joinCmapZ(cmap1,cmap2):
nclus1 = np.max(cmap1)
cmap2=np.where(cmap2!=0,cmap2+nclus1,0)
old_nclus=0
new_nclus=1
while new_nclus!= old_nclus:
old_nclus=new_nclus
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[1]):
if cmap1[i,j,-1] != 0 and cmap2[i,j,0] !=0:
if cmap1[i,j,-1] != cmap2[i,j,0]:
cmap2=np.where(cmap2==cmap2[i,j,0],cmap1[i,j,-1],cmap2)
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[1]):
if cmap1[i,j,-1] != 0 and cmap2[i,j,0] !=0:
if cmap1[i,j,-1] != cmap2[i,j,0]:
cmap1=np.where(cmap1==cmap1[i,j,-1],cmap2[i,j,0],cmap1)
cmap=np.append(cmap1,cmap2,axis=2)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf=np.vstack((ii,y[ii])).T #numero de cluster, frecuencia
new_nclus=cf.shape[0] #cantidad de clusters
#print(new_nclus)
return cmap
def joinBox(vec,join_y,join_z):
Nx, Ny,Nz=vec.shape[0],vec.shape[1],vec.shape[2]
nx = Nx//2
ny,nz=Ny, Nz
if join_y:
ny=Ny//2
if join_z:
nz=Nz//2
vec[:,:ny,:nz] = joinCmapX(vec[:nx,:ny,:nz],vec[nx:,:ny,:nz])
if not join_z and not join_y:
return vec
if join_y:
vec[:,ny:,:nz] = joinCmapX(vec[:nx,ny:,:nz],vec[nx:,ny:,:nz])
if join_z:
vec[:,:ny,nz:] = joinCmapX(vec[:nx,:ny,nz:],vec[nx:,:ny,nz:])
if join_z and join_y:
vec[:,ny:,nz:] = joinCmapX(vec[:nx,ny:,nz:],vec[nx:,ny:,nz:])
if join_y:
vec[:,:,:nz] = joinCmapY(vec[:,:ny,:nz],vec[:,ny:,:nz])
if join_z:
if join_y:
vec[:,:,nz:] = joinCmapY(vec[:,:ny,nz:],vec[:,ny:,nz:])
vec[:,:,:] = joinCmapZ(vec[:,:,:nz],vec[:,:,nz:])
return vec
def joinCmapX(cmap1, cmap2):
nclus1 = np.max(cmap1)
cmap2 = np.where(cmap2 != 0, cmap2 + nclus1, 0)
old_nclus = 0
new_nclus = 1
while new_nclus != old_nclus:
old_nclus = new_nclus
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1, i, j] != 0 and cmap2[0, i, j] != 0:
if cmap1[-1, i, j] != cmap2[0, i, j]:
cmap2 = np.where(
cmap2 == cmap2[0, i, j], cmap1[-1, i, j], cmap2
)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1, i, j] != 0 and cmap2[0, i, j] != 0:
if cmap1[-1, i, j] != cmap2[0, i, j]:
cmap1 = np.where(
cmap1 == cmap1[-1, i, j], cmap2[0, i, j], cmap1
)
cmap = np.append(cmap1, cmap2, axis=0)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf = np.vstack((ii, y[ii])).T # numero de cluster, frecuencia
new_nclus = cf.shape[0] # cantidad de clusters
# print(new_nclus)
return cmap
def joinCmapY(cmap1, cmap2):
nclus1 = np.max(cmap1)
cmap2 = np.where(cmap2 != 0, cmap2 + nclus1, 0)
old_nclus = 0
new_nclus = 1
while new_nclus != old_nclus:
old_nclus = new_nclus
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[2]):
if cmap1[i, -1, j] != 0 and cmap2[i, 0, j] != 0:
if cmap1[i, -1, j] != cmap2[i, 0, j]:
cmap2 = np.where(
cmap2 == cmap2[i, 0, j], cmap1[i, -1, j], cmap2
)
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[2]):
if cmap1[i, -1, j] != 0 and cmap2[i, 0, j] != 0:
if cmap1[i, -1, j] != cmap2[i, 0, j]:
cmap1 = np.where(
cmap1 == cmap1[i, -1, j], cmap2[i, 0, j], cmap1
)
cmap = np.append(cmap1, cmap2, axis=1)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf = np.vstack((ii, y[ii])).T # numero de cluster, frecuencia
new_nclus = cf.shape[0] # cantidad de clusters
# print(new_nclus)
return cmap
def joinCmapZ(cmap1, cmap2):
nclus1 = np.max(cmap1)
cmap2 = np.where(cmap2 != 0, cmap2 + nclus1, 0)
old_nclus = 0
new_nclus = 1
while new_nclus != old_nclus:
old_nclus = new_nclus
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[1]):
if cmap1[i, j, -1] != 0 and cmap2[i, j, 0] != 0:
if cmap1[i, j, -1] != cmap2[i, j, 0]:
cmap2 = np.where(
cmap2 == cmap2[i, j, 0], cmap1[i, j, -1], cmap2
)
for i in range(cmap1.shape[0]):
for j in range(cmap1.shape[1]):
if cmap1[i, j, -1] != 0 and cmap2[i, j, 0] != 0:
if cmap1[i, j, -1] != cmap2[i, j, 0]:
cmap1 = np.where(
cmap1 == cmap1[i, j, -1], cmap2[i, j, 0], cmap1
)
cmap = np.append(cmap1, cmap2, axis=2)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf = np.vstack((ii, y[ii])).T # numero de cluster, frecuencia
new_nclus = cf.shape[0] # cantidad de clusters
# print(new_nclus)
return cmap
def joinBox(vec, join_y, join_z):
Nx, Ny, Nz = vec.shape[0], vec.shape[1], vec.shape[2]
nx = Nx // 2
ny, nz = Ny, Nz
if join_y:
ny = Ny // 2
if join_z:
nz = Nz // 2
vec[:, :ny, :nz] = joinCmapX(vec[:nx, :ny, :nz], vec[nx:, :ny, :nz])
if not join_z and not join_y:
return vec
if join_y:
vec[:, ny:, :nz] = joinCmapX(vec[:nx, ny:, :nz], vec[nx:, ny:, :nz])
if join_z:
vec[:, :ny, nz:] = joinCmapX(vec[:nx, :ny, nz:], vec[nx:, :ny, nz:])
if join_z and join_y:
vec[:, ny:, nz:] = joinCmapX(vec[:nx, ny:, nz:], vec[nx:, ny:, nz:])
if join_y:
vec[:, :, :nz] = joinCmapY(vec[:, :ny, :nz], vec[:, ny:, :nz])
if join_z:
if join_y:
vec[:, :, nz:] = joinCmapY(vec[:, :ny, nz:], vec[:, ny:, nz:])
vec[:, :, :] = joinCmapZ(vec[:, :, :nz], vec[:, :, nz:])
return vec

567
tools/connec/PostConec (copy).py
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@ -6,272 +6,301 @@ 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
nbly=Ny//l
if cmap.shape[2]==1:
lz=1
nblz=1
else:
lz=l
nblz=Nz//l
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*l:(j+1)*l,k*l:(k+1)*lz],res,(i,j,k),1)
return res
def postConec(cmap,results,ind,flag):
if flag==0:
keys=[]
keys+=['PPHA']
keys+=['VOLALE']
keys+=['ZNCC']
keys+=['GAMMA']
keys+=['spanning', 'npz', 'npy', 'npx']
keys+=['Plen','S','P']
return keys
dim=3
if cmap.shape[2]==1:
cmap=cmap[:,:,0]
dim=2
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, pclusZ, pclusY, pclusX =get_perco(cmap,dim)
plen=Plen(spanning,cmap,cf,dim)
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)/np.size(cmap)/nper #gamma, recordar zintcc =gamma*p
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]
if cf.shape[0]==0:
for key in keys:
results[key][ind]=0
return results
#ZINTCC,VOLALE,ZGAMMA,ZIPZ,ZNCC,PPHA
def get_pos2D(cmap,cdis):
Ns=cdis.shape[0]
pos=dict()
i=0
for cnum in cdis[:,0]:
pos[cnum]=np.zeros((cdis[i,1]+1,2)) #+1 porque uso de flag
i+=1
for i in range(cmap.shape[0]):
for j in range(cmap.shape[1]):
if cmap[i,j] != 0:
flag=int(pos[cmap[i,j]][0,0])+1
pos[cmap[i,j]][0,0]=flag
pos[cmap[i,j]][flag,0]=i
pos[cmap[i,j]][flag,1]=j
return pos
def get_pos3D(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(spannng,cmap,cdis,dim):
if dim==2:
return P_len2D(spannng,cmap,cdis)
if dim==3:
return P_len3D(spannng,cmap,cdis)
return []
def P_len2D(spanning,cmap,cdis):
pos = get_pos2D(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 = np.mean(pos[cnum][1:,0]), np.mean(pos[cnum][1:,1]) #el 1: de sacar el flag
Rs =np.mean((pos[cnum][1:,0]-mposx)**2 +(pos[cnum][1:,1]-mposy)**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 P_len3D(spanning,cmap,cdis):
pos = get_pos3D(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 get_perco(cmap,dim):
if dim==2:
pclusY=[] #list of the percolating clusters
for i in range(cmap.shape[0]):
if cmap[i,0] != 0:
if cmap[i,0] not in pclusY:
if cmap[i,0] in cmap[:,-1]:
pclusY+=[cmap[i,0]]
pclusZ=[] #list of the percolating clusters Z direction, this one is the main flow in Ndar.py, the fixed dimension is the direction used to see if pecolates
for i in range(cmap.shape[1]):
if cmap[0,i] != 0:
if cmap[0,i] not in pclusZ:
if cmap[0,i] in cmap[-1,:]: #viendo sin en la primer cara esta el mismo cluster que en la ultima
pclusZ+=[cmap[0,i]]
pclusX=[]
spanning=0
if len(pclusZ)==1 and pclusZ==pclusY:
spanning=1
if dim==3:
pclusX=[] #list of the percolating clusters
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 pclusX:
if cmap[i,j,0] in cmap[:,:,-1]:
pclusX+=[cmap[i,j,0]]
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
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 pclusZ:
if cmap[0,j,k] in cmap[-1,:,:]:
pclusZ+=[cmap[0,j,k]] #this is the one
spanning=0
if len(pclusZ)==1 and pclusZ==pclusY and pclusZ==pclusX:
spanning=1
return spanning, pclusZ, pclusY, pclusX
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
nbly = Ny // l
if cmap.shape[2] == 1:
lz = 1
nblz = 1
else:
lz = l
nblz = Nz // l
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 * l : (j + 1) * l, k * l : (k + 1) * lz
],
res,
(i, j, k),
1,
)
return res
def postConec(cmap, results, ind, flag):
if flag == 0:
keys = []
keys += ["PPHA"]
keys += ["VOLALE"]
keys += ["ZNCC"]
keys += ["GAMMA"]
keys += ["spanning", "npz", "npy", "npx"]
keys += ["Plen", "S", "P"]
return keys
dim = 3
if cmap.shape[2] == 1:
cmap = cmap[:, :, 0]
dim = 2
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, pclusZ, pclusY, pclusX = get_perco(cmap, dim)
plen = Plen(spanning, cmap, cf, dim)
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) / np.size(cmap) / nper
) # gamma, recordar zintcc =gamma*p
(
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],
)
if cf.shape[0] == 0:
for key in keys:
results[key][ind] = 0
return results
# ZINTCC,VOLALE,ZGAMMA,ZIPZ,ZNCC,PPHA
def get_pos2D(cmap, cdis):
Ns = cdis.shape[0]
pos = dict()
i = 0
for cnum in cdis[:, 0]:
pos[cnum] = np.zeros((cdis[i, 1] + 1, 2)) # +1 porque uso de flag
i += 1
for i in range(cmap.shape[0]):
for j in range(cmap.shape[1]):
if cmap[i, j] != 0:
flag = int(pos[cmap[i, j]][0, 0]) + 1
pos[cmap[i, j]][0, 0] = flag
pos[cmap[i, j]][flag, 0] = i
pos[cmap[i, j]][flag, 1] = j
return pos
def get_pos3D(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(spannng, cmap, cdis, dim):
if dim == 2:
return P_len2D(spannng, cmap, cdis)
if dim == 3:
return P_len3D(spannng, cmap, cdis)
return []
def P_len2D(spanning, cmap, cdis):
pos = get_pos2D(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 = np.mean(pos[cnum][1:, 0]), np.mean(
pos[cnum][1:, 1]
) # el 1: de sacar el flag
Rs = np.mean(
(pos[cnum][1:, 0] - mposx) ** 2 + (pos[cnum][1:, 1] - mposy) ** 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 P_len3D(spanning, cmap, cdis):
pos = get_pos3D(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 get_perco(cmap, dim):
if dim == 2:
pclusY = [] # list of the percolating clusters
for i in range(cmap.shape[0]):
if cmap[i, 0] != 0:
if cmap[i, 0] not in pclusY:
if cmap[i, 0] in cmap[:, -1]:
pclusY += [cmap[i, 0]]
pclusZ = (
[]
) # list of the percolating clusters Z direction, this one is the main flow in Ndar.py, the fixed dimension is the direction used to see if pecolates
for i in range(cmap.shape[1]):
if cmap[0, i] != 0:
if cmap[0, i] not in pclusZ:
if (
cmap[0, i] in cmap[-1, :]
): # viendo sin en la primer cara esta el mismo cluster que en la ultima
pclusZ += [cmap[0, i]]
pclusX = []
spanning = 0
if len(pclusZ) == 1 and pclusZ == pclusY:
spanning = 1
if dim == 3:
pclusX = [] # list of the percolating clusters
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 pclusX:
if cmap[i, j, 0] in cmap[:, :, -1]:
pclusX += [cmap[i, j, 0]]
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
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 pclusZ:
if cmap[0, j, k] in cmap[-1, :, :]:
pclusZ += [cmap[0, j, k]] # this is the one
spanning = 0
if len(pclusZ) == 1 and pclusZ == pclusY and pclusZ == pclusX:
spanning = 1
return spanning, pclusZ, pclusY, pclusX

486
tools/connec/PostConec.py
Unescape Escape View File

@ -5,229 +5,271 @@ 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 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
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

742
tools/connec/PostConec_v2.py
Unescape Escape View File

@ -6,363 +6,395 @@ import os
import collections
def main():
#scales=[4,6,8,16,24,32]
#numofseeds=np.array([10,10,10,48,100,200])
#startseed=1
scales=[2,4,8,12,16,20,26,32]
numofseeds=np.array([1,2,12,16,20,25,30,50])
startseed=1
dim=3
numofseeds=numofseeds+startseed
mapa=np.loadtxt(('vecconec.txt')).astype(int)
if dim==2:
LL=int(np.sqrt(mapa.shape[0]))
mapa=mapa.reshape(LL,LL)
if dim==3:
LL=int(np.cbrt(mapa.shape[0]))
mapa=mapa.reshape(LL,LL,LL)
res, names=doforsubS_computeCmap(mapa,scales,postConec, compCon,dim,[],numofseeds)
with open('keysCon.txt', 'w') as f:
for item in names:
f.write("%s\n" % item)
np.save('ConResScales.npy',res)
return
def doforsubS_computeCmap(mapa,scales,funpost, funcompCmap,dim,args,numofseeds):
L=mapa.shape[0]
res=dict()
names=[]
with open('Kfield.don') as f:
seed = int(f.readline())
for iscale in range(len(scales)):
l=scales[iscale]
if numofseeds[iscale] > seed: #guarda aca
nblocks=L//l #for each dimension
if dim==2:
for i in range(nblocks):
for j in range(nblocks):
cmapa=funcompCmap(mapa[i*l:(i+1)*l,j*l:(j+1)*l],dim)
dats,names=funpost(cmapa,dim,args)
if i== 0 and j==0:
for icon in range(len(names)):
res[l,names[icon]]=[]
for icon in range(len(names)):
res[l,names[icon]]+=[dats[icon]]
if dim==3:
for i in range(nblocks):
for j in range(nblocks):
for k in range(nblocks):
cmapa=funcompCmap(mapa[i*l:(i+1)*l,j*l:(j+1)*l,k*l:(k+1)*l],dim)
dats, names=funpost(cmapa,dim,args)
if i== 0 and j==0 and k==0:
for icon in range(len(names)):
res[l,names[icon]]=[]
for icon in range(len(names)):
res[l,names[icon]]+=[dats[icon]]
return res, names
def ConConfig(L,dim):
params=[]
if dim==2:
params=['1','4','imap.txt',str(L)+' '+str(L),'1.0 1.0','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec2d'
if dim==3:
params=['1','6','imap.txt',str(L)+' '+str(L)+' ' +str(L),'1.0 1.0 1.0','30','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec3d'
return params, execCon
def compCon(mapa,dim):
exeDir='./'
L=mapa.shape[0]
params,execCon=ConConfig(L,dim)
with open(exeDir+'coninput.txt', 'w') as f:
for item in params:
f.write("%s\n" % item)
np.savetxt(exeDir+params[2],mapa.reshape(-1))
#wiam=os.getcwd()
#os.chdir(exeDir)
os.system('cp ../../../tools/conec3d ./')
os.system(' ./'+execCon +'>/dev/null') #'cd ' +exeDir+
cmapa=np.loadtxt(params[-2]).reshape(mapa.shape).astype(int) #exeDir+
#os.chdir(wiam)
return cmapa
def postConec(cmap,dim,args):
names=['PPHA','VOLALE','ZNCC','zintcc','spaninning','npz','npy','npx',]
L=cmap.shape[0]
results=[]
names=[]
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:
# headers=['N','p','Csize','CLenX','CLenY','CmaxVol','MaxLenX','MaxLenY','NpcX','NpcY']
nper=np.sum(cf[:,1]) #num de celdas permeables
nclus=cf.shape[0] #cantidad de clusters
#ZINTCC,VOLALE,ZGAMMA,ZIPZ,ZNCC,PPHA
results+=[nper/np.size(cmap)] #ppha
results+=[np.max(cf[:,1])/nper] #volale #corregido va entre [0,p]
results+=[nclus] #zncc
results+=[np.sum(cf[:,1]**2)/np.size(cmap)/nper] #gamma, recordar zintcc =gamma*p
spanning, pclusZ, pclusY, pclusX =get_perco(cmap,dim)
results+=[spanning, len(pclusZ), len(pclusY), len(pclusX)]
results+=Plen(spanning,cmap,cf,dim)
names+=['PPHA']
names+=['VOLALE']
names+=['ZNCC']
names+=['ZINTCC']
names+=['spanning', 'npz', 'npy', 'npx']
names+=['Plen','S','P']
if cf.shape[0]==0:
for i in range(len(names)):
results+=[0]
return results, names
#ZINTCC,VOLALE,ZGAMMA,ZIPZ,ZNCC,PPHA
def get_pos2D(cmap,cdis):
Ns=cdis.shape[0]
pos=dict()
i=0
for cnum in cdis[:,0]:
pos[cnum]=np.zeros((cdis[i,1]+1,2)) #+1 porque uso de flag
i+=1
for i in range(cmap.shape[0]):
for j in range(cmap.shape[1]):
if cmap[i,j] != 0:
flag=int(pos[cmap[i,j]][0,0])+1
pos[cmap[i,j]][0,0]=flag
pos[cmap[i,j]][flag,0]=i
pos[cmap[i,j]][flag,1]=j
return pos
def get_pos3D(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(spannng,cmap,cdis,dim):
if dim==2:
return P_len2D(spannng,cmap,cdis)
if dim==3:
return P_len3D(spannng,cmap,cdis)
return []
def P_len2D(spanning,cmap,cdis):
pos = get_pos2D(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 = np.mean(pos[cnum][1:,0]), np.mean(pos[cnum][1:,1]) #el 1: de sacar el flag
Rs =np.mean((pos[cnum][1:,0]-mposx)**2 +(pos[cnum][1:,1]-mposy)**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 P_len3D(spanning,cmap,cdis):
pos = get_pos3D(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 get_perco(cmap,dim):
if dim==2:
pclusY=[] #list of the percolating clusters
for i in range(cmap.shape[0]):
if cmap[i,0] != 0:
if cmap[i,0] not in pclusY:
if cmap[i,0] in cmap[:,-1]:
pclusY+=[cmap[i,0]]
pclusZ=[] #list of the percolating clusters Z direction, this one is the main flow in Ndar.py, the fixed dimension is the direction used to see if pecolates
for i in range(cmap.shape[1]):
if cmap[0,i] != 0:
if cmap[0,i] not in pclusZ:
if cmap[0,i] in cmap[-1,:]: #viendo sin en la primer cara esta el mismo cluster que en la ultima
pclusZ+=[cmap[0,i]]
pclusX=[]
spanning=0
if len(pclusZ)==1 and pclusZ==pclusY:
spanning=1
if dim==3:
pclusX=[] #list of the percolating clusters
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 pclusX:
if cmap[i,j,0] in cmap[:,:,-1]:
pclusX+=[cmap[i,j,0]]
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
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 pclusZ:
if cmap[0,j,k] in cmap[-1,:,:]:
pclusZ+=[cmap[0,j,k]] #this is the one
spanning=0
if len(pclusZ)==1 and pclusZ==pclusY and pclusZ==pclusX:
spanning=1
return spanning, pclusZ, pclusY, pclusX
# scales=[4,6,8,16,24,32]
# numofseeds=np.array([10,10,10,48,100,200])
# startseed=1
scales = [2, 4, 8, 12, 16, 20, 26, 32]
numofseeds = np.array([1, 2, 12, 16, 20, 25, 30, 50])
startseed = 1
dim = 3
numofseeds = numofseeds + startseed
mapa = np.loadtxt(("vecconec.txt")).astype(int)
if dim == 2:
LL = int(np.sqrt(mapa.shape[0]))
mapa = mapa.reshape(LL, LL)
if dim == 3:
LL = int(np.cbrt(mapa.shape[0]))
mapa = mapa.reshape(LL, LL, LL)
res, names = doforsubS_computeCmap(
mapa, scales, postConec, compCon, dim, [], numofseeds
)
with open("keysCon.txt", "w") as f:
for item in names:
f.write("%s\n" % item)
np.save("ConResScales.npy", res)
return
def doforsubS_computeCmap(mapa, scales, funpost, funcompCmap, dim, args, numofseeds):
L = mapa.shape[0]
res = dict()
names = []
with open("Kfield.don") as f:
seed = int(f.readline())
for iscale in range(len(scales)):
l = scales[iscale]
if numofseeds[iscale] > seed: # guarda aca
nblocks = L // l # for each dimension
if dim == 2:
for i in range(nblocks):
for j in range(nblocks):
cmapa = funcompCmap(
mapa[i * l : (i + 1) * l, j * l : (j + 1) * l], dim
)
dats, names = funpost(cmapa, dim, args)
if i == 0 and j == 0:
for icon in range(len(names)):
res[l, names[icon]] = []
for icon in range(len(names)):
res[l, names[icon]] += [dats[icon]]
if dim == 3:
for i in range(nblocks):
for j in range(nblocks):
for k in range(nblocks):
cmapa = funcompCmap(
mapa[
i * l : (i + 1) * l,
j * l : (j + 1) * l,
k * l : (k + 1) * l,
],
dim,
)
dats, names = funpost(cmapa, dim, args)
if i == 0 and j == 0 and k == 0:
for icon in range(len(names)):
res[l, names[icon]] = []
for icon in range(len(names)):
res[l, names[icon]] += [dats[icon]]
return res, names
def ConConfig(L, dim):
params = []
if dim == 2:
params = [
"1",
"4",
"imap.txt",
str(L) + " " + str(L),
"1.0 1.0",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec2d"
if dim == 3:
params = [
"1",
"6",
"imap.txt",
str(L) + " " + str(L) + " " + str(L),
"1.0 1.0 1.0",
"30",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec3d"
return params, execCon
def compCon(mapa, dim):
exeDir = "./"
L = mapa.shape[0]
params, execCon = ConConfig(L, dim)
with open(exeDir + "coninput.txt", "w") as f:
for item in params:
f.write("%s\n" % item)
np.savetxt(exeDir + params[2], mapa.reshape(-1))
# wiam=os.getcwd()
# os.chdir(exeDir)
os.system("cp ../../../tools/conec3d ./")
os.system(" ./" + execCon + ">/dev/null") #'cd ' +exeDir+
cmapa = np.loadtxt(params[-2]).reshape(mapa.shape).astype(int) # exeDir+
# os.chdir(wiam)
return cmapa
def postConec(cmap, dim, args):
names = [
"PPHA",
"VOLALE",
"ZNCC",
"zintcc",
"spaninning",
"npz",
"npy",
"npx",
]
L = cmap.shape[0]
results = []
names = []
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:
# headers=['N','p','Csize','CLenX','CLenY','CmaxVol','MaxLenX','MaxLenY','NpcX','NpcY']
nper = np.sum(cf[:, 1]) # num de celdas permeables
nclus = cf.shape[0] # cantidad de clusters
# ZINTCC,VOLALE,ZGAMMA,ZIPZ,ZNCC,PPHA
results += [nper / np.size(cmap)] # ppha
results += [np.max(cf[:, 1]) / nper] # volale #corregido va entre [0,p]
results += [nclus] # zncc
results += [
np.sum(cf[:, 1] ** 2) / np.size(cmap) / nper
] # gamma, recordar zintcc =gamma*p
spanning, pclusZ, pclusY, pclusX = get_perco(cmap, dim)
results += [spanning, len(pclusZ), len(pclusY), len(pclusX)]
results += Plen(spanning, cmap, cf, dim)
names += ["PPHA"]
names += ["VOLALE"]
names += ["ZNCC"]
names += ["ZINTCC"]
names += ["spanning", "npz", "npy", "npx"]
names += ["Plen", "S", "P"]
if cf.shape[0] == 0:
for i in range(len(names)):
results += [0]
return results, names
# ZINTCC,VOLALE,ZGAMMA,ZIPZ,ZNCC,PPHA
def get_pos2D(cmap, cdis):
Ns = cdis.shape[0]
pos = dict()
i = 0
for cnum in cdis[:, 0]:
pos[cnum] = np.zeros((cdis[i, 1] + 1, 2)) # +1 porque uso de flag
i += 1
for i in range(cmap.shape[0]):
for j in range(cmap.shape[1]):
if cmap[i, j] != 0:
flag = int(pos[cmap[i, j]][0, 0]) + 1
pos[cmap[i, j]][0, 0] = flag
pos[cmap[i, j]][flag, 0] = i
pos[cmap[i, j]][flag, 1] = j
return pos
def get_pos3D(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(spannng, cmap, cdis, dim):
if dim == 2:
return P_len2D(spannng, cmap, cdis)
if dim == 3:
return P_len3D(spannng, cmap, cdis)
return []
def P_len2D(spanning, cmap, cdis):
pos = get_pos2D(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 = np.mean(pos[cnum][1:, 0]), np.mean(
pos[cnum][1:, 1]
) # el 1: de sacar el flag
Rs = np.mean(
(pos[cnum][1:, 0] - mposx) ** 2 + (pos[cnum][1:, 1] - mposy) ** 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 P_len3D(spanning, cmap, cdis):
pos = get_pos3D(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 get_perco(cmap, dim):
if dim == 2:
pclusY = [] # list of the percolating clusters
for i in range(cmap.shape[0]):
if cmap[i, 0] != 0:
if cmap[i, 0] not in pclusY:
if cmap[i, 0] in cmap[:, -1]:
pclusY += [cmap[i, 0]]
pclusZ = (
[]
) # list of the percolating clusters Z direction, this one is the main flow in Ndar.py, the fixed dimension is the direction used to see if pecolates
for i in range(cmap.shape[1]):
if cmap[0, i] != 0:
if cmap[0, i] not in pclusZ:
if (
cmap[0, i] in cmap[-1, :]
): # viendo sin en la primer cara esta el mismo cluster que en la ultima
pclusZ += [cmap[0, i]]
pclusX = []
spanning = 0
if len(pclusZ) == 1 and pclusZ == pclusY:
spanning = 1
if dim == 3:
pclusX = [] # list of the percolating clusters
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 pclusX:
if cmap[i, j, 0] in cmap[:, :, -1]:
pclusX += [cmap[i, j, 0]]
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
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 pclusZ:
if cmap[0, j, k] in cmap[-1, :, :]:
pclusZ += [cmap[0, j, k]] # this is the one
spanning = 0
if len(pclusZ) == 1 and pclusZ == pclusY and pclusZ == pclusX:
spanning = 1
return spanning, pclusZ, pclusY, pclusX
main()

289
tools/connec/back/comp_cmap_scales.py
Unescape Escape View File

@ -2,129 +2,182 @@ import numpy as np
import os
import time
from JoinCmaps import *
#k[x,y,z]
def div_veccon(kc,kh,nbl,rundir):
t0=time.time()
kc=np.where(kc==kh,1,0).astype(int)
tcmaps=time.time()
kc=get_smallCmap(kc,nbl,rundir)
tcmaps=time.time()-tcmaps
#if s_scale<kc.shape[0]:
kc=join(kc,nbl)
y = np.bincount(kc.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
nclus=cf.shape[0] #cantidad de clusters
nper=np.sum(cf[:,1]) #num de celdas permeables
print(nbl,nclus,float(nper)/(kc.size), time.time()-t0)
return np.array([nbl,nclus,float(nper)/(kc.size),time.time()-t0, tcmaps,tcmaps/(time.time()-t0)])
def get_smallCmap(vec,nbl,rundir):
Nx, Ny,Nz=vec.shape[0],vec.shape[1],vec.shape[2]
sx,sy,sz = Nx//nbl,Ny//nbl,Nz//nbl
params, execCon = ConConfig(sx,sy,sz,Nz,rundir)
if Nz==1:
nblz=1
sz=1
else:
nblz=nbl
for i in range(nbl):
for j in range(nbl):
for k in range(nblz):
vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz]=connec(vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz],execCon,params,rundir)
return vec
def connec(vec,execCon,params,rundir):
np.savetxt(rundir+params[2],vec.reshape(-1))
os.system(rundir+execCon +'>/dev/null') #'cd ' +exeDir++'>/dev/null'
vec=np.loadtxt(params[-2]).reshape(vec.shape[0],vec.shape[1],vec.shape[2]).astype(int)
return vec
def ConConfig(sx,sy,sz,Nz,rundir):
params=[]
if Nz==1:
params=['1','4','vecconec.txt',str(sx)+' '+str(sy),'1.0 1.0','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec2d'
else:
params=['1','6','vecconec.txt',str(sx)+' '+str(sy)+' ' +str(sz),'1.0 1.0 1.0','30','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec3d'
with open(rundir+'coninput.txt', 'w') as f:
for item in params:
f.write("%s\n" % item)
return params, execCon
def join(vec,nbl):
Nx, Ny,Nz=vec.shape[0],vec.shape[1],vec.shape[2]
sx,sy,sz = Nx//nbl,Ny//nbl,Nz//nbl
ex,ey,ez=np.log2(Nx),np.log2(Ny),np.log2(Nz)
if Nz==1:
sz=1
nbz=1
ez=1
esz=1
else:
esz=np.log2(sz)
esx,esy=np.log2(sx),np.log2(sy)
for bs in range(0,int(ex-esx)):
nbx,nby = int(2**(ex-esx-bs-1)),int(2**(ey-esy-bs-1))
if Nz==1:
sz=1
nbz=1
else:
nbz=int(2**(ez-esz-bs-1))
sz=Nz//nbz
sx,sy=Nx//nbx,Ny//nby
for i in range(nbx):
for j in range(nby):
for k in range(nbz):
a=2
vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz]=joinBox(vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz],True,False)
return vec
'''
# k[x,y,z]
def div_veccon(kc, kh, nbl, rundir):
t0 = time.time()
kc = np.where(kc == kh, 1, 0).astype(int)
tcmaps = time.time()
kc = get_smallCmap(kc, nbl, rundir)
tcmaps = time.time() - tcmaps
# if s_scale<kc.shape[0]:
kc = join(kc, nbl)
y = np.bincount(kc.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
nclus = cf.shape[0] # cantidad de clusters
nper = np.sum(cf[:, 1]) # num de celdas permeables
print(nbl, nclus, float(nper) / (kc.size), time.time() - t0)
return np.array(
[
nbl,
nclus,
float(nper) / (kc.size),
time.time() - t0,
tcmaps,
tcmaps / (time.time() - t0),
]
)
def get_smallCmap(vec, nbl, rundir):
Nx, Ny, Nz = vec.shape[0], vec.shape[1], vec.shape[2]
sx, sy, sz = Nx // nbl, Ny // nbl, Nz // nbl
params, execCon = ConConfig(sx, sy, sz, Nz, rundir)
if Nz == 1:
nblz = 1
sz = 1
else:
nblz = nbl
for i in range(nbl):
for j in range(nbl):
for k in range(nblz):
vec[
i * sx : (i + 1) * sx, j * sy : (j + 1) * sy, k * sz : (k + 1) * sz
] = connec(
vec[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
],
execCon,
params,
rundir,
)
return vec
def connec(vec, execCon, params, rundir):
np.savetxt(rundir + params[2], vec.reshape(-1))
os.system(rundir + execCon + ">/dev/null") #'cd ' +exeDir++'>/dev/null'
vec = (
np.loadtxt(params[-2])
.reshape(vec.shape[0], vec.shape[1], vec.shape[2])
.astype(int)
)
return vec
def ConConfig(sx, sy, sz, Nz, rundir):
params = []
if Nz == 1:
params = [
"1",
"4",
"vecconec.txt",
str(sx) + " " + str(sy),
"1.0 1.0",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec2d"
else:
params = [
"1",
"6",
"vecconec.txt",
str(sx) + " " + str(sy) + " " + str(sz),
"1.0 1.0 1.0",
"30",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec3d"
with open(rundir + "coninput.txt", "w") as f:
for item in params:
f.write("%s\n" % item)
return params, execCon
def join(vec, nbl):
Nx, Ny, Nz = vec.shape[0], vec.shape[1], vec.shape[2]
sx, sy, sz = Nx // nbl, Ny // nbl, Nz // nbl
ex, ey, ez = np.log2(Nx), np.log2(Ny), np.log2(Nz)
if Nz == 1:
sz = 1
nbz = 1
ez = 1
esz = 1
else:
esz = np.log2(sz)
esx, esy = np.log2(sx), np.log2(sy)
for bs in range(0, int(ex - esx)):
nbx, nby = int(2 ** (ex - esx - bs - 1)), int(2 ** (ey - esy - bs - 1))
if Nz == 1:
sz = 1
nbz = 1
else:
nbz = int(2 ** (ez - esz - bs - 1))
sz = Nz // nbz
sx, sy = Nx // nbx, Ny // nby
for i in range(nbx):
for j in range(nby):
for k in range(nbz):
a = 2
vec[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
] = joinBox(
vec[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
],
True,
False,
)
return vec
"""
job=0
k=np.load('../../data/'+str(job)+'/k.npy')
div_veccon(k,100,1,'./')
div_veccon(k,100,2,'./')
div_veccon(k,100,4,'./')
'''
"""
for job in range(6):
k=np.load('../../data/'+str(job)+'/k.npy')
print(job)
res=div_veccon(k,100,4,'./')
np.savetxt('../../data/'+str(job)+'/Cmap_res.txt',res)
res=div_veccon(k,100,1,'./')
#div_veccon(k,100,64,'./')
#div_veccon(k,100,128,'./')
k = np.load("../../data/" + str(job) + "/k.npy")
print(job)
res = div_veccon(k, 100, 4, "./")
np.savetxt("../../data/" + str(job) + "/Cmap_res.txt", res)
res = div_veccon(k, 100, 1, "./")
# div_veccon(k,100,64,'./')
# div_veccon(k,100,128,'./')

242
tools/connec/back/get_cmap.py
Unescape Escape View File

@ -2,116 +2,136 @@ import numpy as np
import os
import time
def div_veccon(vec,kh,npartes,condir):
vec=np.where(vec==kh,1,0).astype(int)
Nx, Ny,Nz=k.shape[0],k.shape[1],k.shape[2]
rdir='./'
tt=0
t1=time.time()
nx=Nx//npartes
params,execCon=ConConfig(nx,Ny,Nz)
with open(condir+'coninput.txt', 'w') as f:
for item in params:
f.write("%s\n" % item)
wiam=os.getcwd()
os.chdir(condir)
i=0
np.savetxt(condir+params[2],vec[i*nx:(i+1)*nx,:,:].reshape(-1))
tcon=time.time()
os.system(' ./'+execCon +'>/dev/null') #'cd ' +exeDir+
tt=tt+(time.time()-tcon)
cmap=np.loadtxt(params[-2]).reshape(nx,Ny,Nz).astype(int)
for i in range(1,npartes):
np.savetxt(condir+params[2],vec[i*nx:(i+1)*nx,:,:].reshape(-1))
tcon=time.time()
os.system(' ./'+execCon +'>/dev/null') #'cd ' +exeDir++'>/dev/null'
tt=tt+(time.time()-tcon)
cmapb=np.loadtxt(params[-2]).reshape(nx,Ny,Nz).astype(int)
cmap=joinCmap(cmap,cmapb)
if npartes > 1:
np.savetxt(rdir+'cmap.txt',cmap.reshape(-1))
Ttotal, frac_solver = time.time()-t1, tt/(time.time()-t1)
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
nclus=cf.shape[0] #cantidad de clusters
nper=np.sum(cf[:,1]) #num de celdas permeables
print(nclus,float(nper)/(vec.size),Ttotal)
return np.array([npartes,nx*Ny*Nz,Ttotal, frac_solver ,nclus,float(nper)/(Nx*Nx)])
def ConConfig(nx,Ny,Nz):
params=[]
if Nz==1:
params=['1','4','vecconec.txt',str(nx)+' '+str(Ny),'1.0 1.0','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec2d'
else:
params=['1','6','vecconec.txt',str(nx)+' '+str(Nz)+' ' +str(Nz),'1.0 1.0 1.0','30','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec3d'
return params, execCon
def joinCmap(cmap1,cmap2):
nclus1 = np.max(cmap1)
cmap2=np.where(cmap2!=0,cmap2+nclus1,0)
old_nclus=0
new_nclus=1
while new_nclus!= old_nclus:
old_nclus=new_nclus
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1,i,j] != 0 and cmap2[0,i,j] !=0:
if cmap1[-1,i,j] != cmap2[0,i,j]:
cmap2=np.where(cmap2==cmap2[0,i,j],cmap1[-1,i,j],cmap2)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1,i,j] != 0 and cmap2[0,i,j] !=0:
if cmap1[-1,i,j] != cmap2[0,i,j]:
cmap1=np.where(cmap1==cmap1[-1,i,j],cmap2[0,i,j],cmap1)
cmap=np.append(cmap1,cmap2,axis=0)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf=np.vstack((ii,y[ii])).T #numero de cluster, frecuencia
new_nclus=cf.shape[0] #cantidad de clusters
#print(new_nclus)
return cmap
partes=[1,4]
def div_veccon(vec, kh, npartes, condir):
vec = np.where(vec == kh, 1, 0).astype(int)
Nx, Ny, Nz = k.shape[0], k.shape[1], k.shape[2]
rdir = "./"
tt = 0
t1 = time.time()
nx = Nx // npartes
params, execCon = ConConfig(nx, Ny, Nz)
with open(condir + "coninput.txt", "w") as f:
for item in params:
f.write("%s\n" % item)
wiam = os.getcwd()
os.chdir(condir)
i = 0
np.savetxt(condir + params[2], vec[i * nx : (i + 1) * nx, :, :].reshape(-1))
tcon = time.time()
os.system(" ./" + execCon + ">/dev/null") #'cd ' +exeDir+
tt = tt + (time.time() - tcon)
cmap = np.loadtxt(params[-2]).reshape(nx, Ny, Nz).astype(int)
for i in range(1, npartes):
np.savetxt(condir + params[2], vec[i * nx : (i + 1) * nx, :, :].reshape(-1))
tcon = time.time()
os.system(" ./" + execCon + ">/dev/null") #'cd ' +exeDir++'>/dev/null'
tt = tt + (time.time() - tcon)
cmapb = np.loadtxt(params[-2]).reshape(nx, Ny, Nz).astype(int)
cmap = joinCmap(cmap, cmapb)
if npartes > 1:
np.savetxt(rdir + "cmap.txt", cmap.reshape(-1))
Ttotal, frac_solver = time.time() - t1, tt / (time.time() - t1)
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
nclus = cf.shape[0] # cantidad de clusters
nper = np.sum(cf[:, 1]) # num de celdas permeables
print(nclus, float(nper) / (vec.size), Ttotal)
return np.array(
[npartes, nx * Ny * Nz, Ttotal, frac_solver, nclus, float(nper) / (Nx * Nx)]
)
def ConConfig(nx, Ny, Nz):
params = []
if Nz == 1:
params = [
"1",
"4",
"vecconec.txt",
str(nx) + " " + str(Ny),
"1.0 1.0",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec2d"
else:
params = [
"1",
"6",
"vecconec.txt",
str(nx) + " " + str(Nz) + " " + str(Nz),
"1.0 1.0 1.0",
"30",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec3d"
return params, execCon
def joinCmap(cmap1, cmap2):
nclus1 = np.max(cmap1)
cmap2 = np.where(cmap2 != 0, cmap2 + nclus1, 0)
old_nclus = 0
new_nclus = 1
while new_nclus != old_nclus:
old_nclus = new_nclus
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1, i, j] != 0 and cmap2[0, i, j] != 0:
if cmap1[-1, i, j] != cmap2[0, i, j]:
cmap2 = np.where(
cmap2 == cmap2[0, i, j], cmap1[-1, i, j], cmap2
)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1, i, j] != 0 and cmap2[0, i, j] != 0:
if cmap1[-1, i, j] != cmap2[0, i, j]:
cmap1 = np.where(
cmap1 == cmap1[-1, i, j], cmap2[0, i, j], cmap1
)
cmap = np.append(cmap1, cmap2, axis=0)
y = np.bincount(cmap.reshape(-1).astype(int))
ii = np.nonzero(y)[0]
cf = np.vstack((ii, y[ii])).T # numero de cluster, frecuencia
new_nclus = cf.shape[0] # cantidad de clusters
# print(new_nclus)
return cmap
partes = [1, 4]
for i in range(1):
t00=time.time()
res=np.array([])
rdir='../../data/'+str(i)+'/'
k=np.load('k643d.npy')
for npar in partes:
res=np.append(res,div_veccon(k,100,npar,'./'))
np.savetxt(rdir+'resTestCon.txt',res.reshape(len(partes),-1))
#np.savetxt(rdir+'resTestCon.txt',res.reshape(len(partes),-1))
print(i,time.time()-t00)
t00 = time.time()
res = np.array([])
rdir = "../../data/" + str(i) + "/"
k = np.load("k643d.npy")
for npar in partes:
res = np.append(res, div_veccon(k, 100, npar, "./"))
np.savetxt(rdir + "resTestCon.txt", res.reshape(len(partes), -1))
# np.savetxt(rdir+'resTestCon.txt',res.reshape(len(partes),-1))
print(i, time.time() - t00)

227
tools/connec/back/get_cmapOpt.py
Unescape Escape View File

@ -2,110 +2,127 @@ import numpy as np
import os
import time
def div_veccon(vec,kh,npartes,condir):
vec=np.where(vec==kh,1,0).astype(int)
Nx, Ny,Nz=k.shape[0],k.shape[1],k.shape[2]
rdir='./'
tt=0
t1=time.time()
nx=Nx//npartes
params,execCon=ConConfig(nx,Ny,Nz)
with open(condir+'coninput.txt', 'w') as f:
for item in params:
f.write("%s\n" % item)
wiam=os.getcwd()
os.chdir(condir)
i=0
np.savetxt(condir+params[2],vec[i*nx:(i+1)*nx,:,:].reshape(-1))
tcon=time.time()
os.system(' ./'+execCon +'>/dev/null') #'cd ' +exeDir+
tt=tt+(time.time()-tcon)
cmap=np.loadtxt(params[-2]).reshape(nx,Ny,Nz)
for i in range(1,npartes):
np.savetxt(condir+params[2],vec[i*nx:(i+1)*nx,:,:].reshape(-1))
tcon=time.time()
os.system(' ./'+execCon +'>/dev/null') #'cd ' +exeDir++'>/dev/null'
tt=tt+(time.time()-tcon)
cmapb=np.loadtxt(params[-2]).reshape(nx,Ny,Nz)
cmap=joinCmap(cmap,cmapb)
if npartes > 1:
np.savetxt(rdir+'cmap.txt',cmap.reshape(-1))
Ttotal, frac_solver = time.time()-t1, tt/(time.time()-t1)
y = np.bincount(cmap.reshape(-1).astype(int))
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
nclus=cf.shape[0] #cantidad de clusters
nper=np.sum(cf[:,1]) #num de celdas permeables
return np.array([npartes,nx*Ny*Nz,Ttotal, frac_solver ,nclus,float(nper)/(Nx*Nx)])
def ConConfig(nx,Ny,Nz):
params=[]
if Nz==1:
params=['1','4','vecconec.txt',str(nx)+' '+str(Ny),'1.0 1.0','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec2d'
else:
params=['1','6','vecconec.txt',str(nx)+' '+str(Nz)+' ' +str(Nz),'1.0 1.0 1.0','30','pardol.STA','pardol.CCO','pardol.COF']
execCon='conec3d'
return params, execCon
def joinCmap(cmap1,cmap2):
nclus1 = np.max(cmap1)
cmap2=np.where(cmap2!=0,cmap2+nclus1,0)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1,i,j] != 0 and cmap2[0,i,j] !=0:
if cmap1[-1,i,j] != cmap2[0,i,j]:
cmap2=np.where(cmap2==cmap2[0,i,j],cmap1[-1,i,j],cmap2)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1,i,j] != 0 and cmap2[0,i,j] !=0:
if cmap1[-1,i,j] != cmap2[0,i,j]:
cmap1=np.where(cmap1==cmap1[-1,i,j],cmap2[0,i,j],cmap1)
cmap=np.append(cmap1,cmap2,axis=0)
return cmap
njobs=2
partes=[1,4,8,16]
def div_veccon(vec, kh, npartes, condir):
vec = np.where(vec == kh, 1, 0).astype(int)
Nx, Ny, Nz = k.shape[0], k.shape[1], k.shape[2]
rdir = "./"
tt = 0
t1 = time.time()
nx = Nx // npartes
params, execCon = ConConfig(nx, Ny, Nz)
with open(condir + "coninput.txt", "w") as f:
for item in params:
f.write("%s\n" % item)
wiam = os.getcwd()
os.chdir(condir)
i = 0
np.savetxt(condir + params[2], vec[i * nx : (i + 1) * nx, :, :].reshape(-1))
tcon = time.time()
os.system(" ./" + execCon + ">/dev/null") #'cd ' +exeDir+
tt = tt + (time.time() - tcon)
cmap = np.loadtxt(params[-2]).reshape(nx, Ny, Nz)
for i in range(1, npartes):
np.savetxt(condir + params[2], vec[i * nx : (i + 1) * nx, :, :].reshape(-1))
tcon = time.time()
os.system(" ./" + execCon + ">/dev/null") #'cd ' +exeDir++'>/dev/null'
tt = tt + (time.time() - tcon)
cmapb = np.loadtxt(params[-2]).reshape(nx, Ny, Nz)
cmap = joinCmap(cmap, cmapb)
if npartes > 1:
np.savetxt(rdir + "cmap.txt", cmap.reshape(-1))
Ttotal, frac_solver = time.time() - t1, tt / (time.time() - t1)
y = np.bincount(cmap.reshape(-1).astype(int))
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
nclus = cf.shape[0] # cantidad de clusters
nper = np.sum(cf[:, 1]) # num de celdas permeables
return np.array(
[npartes, nx * Ny * Nz, Ttotal, frac_solver, nclus, float(nper) / (Nx * Nx)]
)
def ConConfig(nx, Ny, Nz):
params = []
if Nz == 1:
params = [
"1",
"4",
"vecconec.txt",
str(nx) + " " + str(Ny),
"1.0 1.0",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec2d"
else:
params = [
"1",
"6",
"vecconec.txt",
str(nx) + " " + str(Nz) + " " + str(Nz),
"1.0 1.0 1.0",
"30",
"pardol.STA",
"pardol.CCO",
"pardol.COF",
]
execCon = "conec3d"
return params, execCon
def joinCmap(cmap1, cmap2):
nclus1 = np.max(cmap1)
cmap2 = np.where(cmap2 != 0, cmap2 + nclus1, 0)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1, i, j] != 0 and cmap2[0, i, j] != 0:
if cmap1[-1, i, j] != cmap2[0, i, j]:
cmap2 = np.where(cmap2 == cmap2[0, i, j], cmap1[-1, i, j], cmap2)
for i in range(cmap1.shape[1]):
for j in range(cmap1.shape[2]):
if cmap1[-1, i, j] != 0 and cmap2[0, i, j] != 0:
if cmap1[-1, i, j] != cmap2[0, i, j]:
cmap1 = np.where(cmap1 == cmap1[-1, i, j], cmap2[0, i, j], cmap1)
cmap = np.append(cmap1, cmap2, axis=0)
return cmap
njobs = 2
partes = [1, 4, 8, 16]
for i in range(210):
t00=time.time()
res=np.array([])
rdir='../../data/'+str(i)+'/'
k=np.load(rdir+'k.npy')
for npar in partes:
res=np.append(res,div_veccon(k,100,npar,'./'))
res=res.reshape(len(partes),-1)
try:
rres=np.loadtxt(rdir+'resTestCon.txt')
res=np.append(rres,res,axis=0)
np.savetxt(rdir+'resTestCon.txt',res)
except:
np.savetxt(rdir+'resTestCon.txt',res)
print(i,time.time()-t00)
t00 = time.time()
res = np.array([])
rdir = "../../data/" + str(i) + "/"
k = np.load(rdir + "k.npy")
for npar in partes:
res = np.append(res, div_veccon(k, 100, npar, "./"))
res = res.reshape(len(partes), -1)
try:
rres = np.loadtxt(rdir + "resTestCon.txt")
res = np.append(rres, res, axis=0)
np.savetxt(rdir + "resTestCon.txt", res)
except:
np.savetxt(rdir + "resTestCon.txt", res)
print(i, time.time() - t00)

403
tools/connec/comp_cmap.py
Unescape Escape View File

@ -4,182 +4,233 @@ import time
from tools.connec.JoinCmaps import *
import subprocess
from tools.connec.PostConec import ConnecInd
#k[x,y,z]
# k[x,y,z]
import json
def comp_connec(parser,rundir,nr):
kc=np.load(rundir+'k.npy')
keep_aspect = parser.get('Connectivity','keep_aspect')
kh,sx = float(parser.get('Generation','kh')),int(parser.get('Connectivity','block_size'))
S_min_post = int(parser.get('Connectivity','indicators_MinBlockSize'))
nimax =2** int(parser.get('Connectivity','Max_sample_size'))
gcon =bool(parser.get('Connectivity','compGconec'))
if S_min_post ==-1 or S_min_post > kc.shape[0]:
S_min_post=kc.shape[0] #solo calcula indicadores para mayo escala
if S_min_post ==0:
S_min_post=sx #solo calcula indicadores para escalas a partir del optimo
if sx > S_min_post:
sx = get_min_nbl(kc,nimax,nr,S_min_post) #corta en mas artes para tener mediads de conec
nbl=kc.shape[0]//sx
if keep_aspect=='yes':
keep_aspect=True
else:
keep_aspect=False
t0=time.time()
kc=np.where(kc==kh,1,0).astype(int)
tcmaps=time.time()
kc=get_smallCmap(kc,nbl,rundir,keep_aspect)
tcmaps=time.time()-tcmaps
kc,PostConTime=join(kc,nbl,keep_aspect,rundir,S_min_post,gcon)
ttotal=time.time()-t0
summary = np.array([nbl,ttotal,tcmaps/ttotal,PostConTime/ttotal])
np.savetxt(rundir + 'ConnSummary.txt',summary,header='nbl,ttotal,tcmaps/ttotal,PostConTime/ttotal')
np.save(rundir+'Cmap.npy',kc)
return
def get_min_nbl(kc,nimax,nr,smin):
if kc.shape[2]==1:
dim=2.0
else:
dim=3.0
if nr>0:
y=(1/dim)*np.log2(nr*kc.size/(nimax*(smin**dim)))
else:
y=0
y=int(y)
s=int((2**y) * smin)
if s<smin:
s=smin
return s
def get_smallCmap(vec,nbl,rundir,keep_aspect):
Nx, Ny,Nz=vec.shape[0],vec.shape[1],vec.shape[2]
sx = Nx//nbl
if keep_aspect:
sy,sz = Ny//nbl,Nz//nbl
nblx, nbly,nblz = nbl, nbl, nbl
else:
sy,sz = sx,sx
nblx=nbl
nbly, nblz = Ny//sy, Nz//sz
params, execCon = ConConfig(sx,sy,sz,Nz,rundir)
if Nz==1:
nblz=1
sz=1
os.system('cp ./tools/connec/'+execCon +' '+rundir)
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz]=connec(vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz],execCon,params,rundir)
try:
temps=['pardol*','conec*d' ,'coninput.txt' ,'vecconec.txt']
for temp in temps:
os.system('rm '+rundir+temp)
except:
print('No connectivity temps to delete')
return vec
def connec(vec,execCon,params,rundir):
np.savetxt(rundir+params[2],vec.reshape(-1), fmt='%i')
wd = os.getcwd()
os.chdir(rundir)
os.system('nohup ./'+execCon +' > connec.out 2>&1') #subprocess.call(['./tools/connec/'+execCon],cwd=rundir) #, '>/dev/null' , cwd=rundir
os.chdir(wd)
vec=np.loadtxt(rundir+params[-1]).reshape(vec.shape[0],vec.shape[1],vec.shape[2]).astype(int)
return vec
def ConConfig(sx,sy,sz,Nz,rundir):
params=[]
if Nz==1:
params=['1','4','vecconec.txt',str(sx)+' '+str(sy),'1.0 1.0','pardol.CCO']
execCon='conec2d'
else:
params=['1','6','vecconec.txt',str(sx)+' '+str(sy)+' ' +str(sz),'1.0 1.0 1.0','pardol.CCO']
execCon='conec3d'
with open(rundir+'coninput.txt', 'w') as f:
for item in params:
f.write("%s\n" % item)
return params, execCon
def join(vec,nbl,keep_aspect,datadir,S_min_post,gcon):
Nx, Ny,Nz=vec.shape[0],vec.shape[1],vec.shape[2]
sx = Nx//nbl
if keep_aspect:
sy,sz = Ny//nbl,Nz//nbl
nblx, nbly,nblz = nbl, nbl, nbl
else:
sy,sz = sx,sx
nblx=nbl
nbly, nblz = Ny//sy, Nz//sz
ex=np.log2(Nx)
esx=np.log2(sx)
join_z=True
join_y=True
if Nz==1:
sz=1
nblz=1
post_time=0
sxL=[sx]
for bs in range(0,int(ex-esx)):
if vec.shape[0]==vec.shape[1] and sx>=S_min_post:
t0=time.time()
ConnecInd(vec,[sx],datadir)
post_time=time.time()-t0
sx,sy,sz = 2*sx,2*sy,2*sz
sxL+=[sx]
if sz > Nz:
sz=Nz
nblz=1
join_z=False
if sy > Ny:
sy=Ny
nbly=1
join_y=False
nblx,nbly,nblz = Nx//sx, Ny//sy, Nz//sz
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz]=joinBox(vec[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz],join_y,join_z)
if vec.shape[0]==vec.shape[1] and sx>=S_min_post: #
t0=time.time()
ConnecInd(vec,[sx],datadir)
post_time=post_time+(time.time()-t0)
if gcon:
ConnecInd(vec,sxL,datadir+'Global')
return vec, post_time
def comp_connec(parser, rundir, nr):
kc = np.load(rundir + "k.npy")
keep_aspect = parser.get("Connectivity", "keep_aspect")
kh, sx = float(parser.get("Generation", "kh")), int(
parser.get("Connectivity", "block_size")
)
S_min_post = int(parser.get("Connectivity", "indicators_MinBlockSize"))
nimax = 2 ** int(parser.get("Connectivity", "Max_sample_size"))
gcon = bool(parser.get("Connectivity", "compGconec"))
if S_min_post == -1 or S_min_post > kc.shape[0]:
S_min_post = kc.shape[0] # solo calcula indicadores para mayo escala
if S_min_post == 0:
S_min_post = sx # solo calcula indicadores para escalas a partir del optimo
if sx > S_min_post:
sx = get_min_nbl(
kc, nimax, nr, S_min_post
) # corta en mas artes para tener mediads de conec
nbl = kc.shape[0] // sx
if keep_aspect == "yes":
keep_aspect = True
else:
keep_aspect = False
t0 = time.time()
kc = np.where(kc == kh, 1, 0).astype(int)
tcmaps = time.time()
kc = get_smallCmap(kc, nbl, rundir, keep_aspect)
tcmaps = time.time() - tcmaps
kc, PostConTime = join(kc, nbl, keep_aspect, rundir, S_min_post, gcon)
ttotal = time.time() - t0
summary = np.array([nbl, ttotal, tcmaps / ttotal, PostConTime / ttotal])
np.savetxt(
rundir + "ConnSummary.txt",
summary,
header="nbl,ttotal,tcmaps/ttotal,PostConTime/ttotal",
)
np.save(rundir + "Cmap.npy", kc)
return
def get_min_nbl(kc, nimax, nr, smin):
if kc.shape[2] == 1:
dim = 2.0
else:
dim = 3.0
if nr > 0:
y = (1 / dim) * np.log2(nr * kc.size / (nimax * (smin ** dim)))
else:
y = 0
y = int(y)
s = int((2 ** y) * smin)
if s < smin:
s = smin
return s
def get_smallCmap(vec, nbl, rundir, keep_aspect):
Nx, Ny, Nz = vec.shape[0], vec.shape[1], vec.shape[2]
sx = Nx // nbl
if keep_aspect:
sy, sz = Ny // nbl, Nz // nbl
nblx, nbly, nblz = nbl, nbl, nbl
else:
sy, sz = sx, sx
nblx = nbl
nbly, nblz = Ny // sy, Nz // sz
params, execCon = ConConfig(sx, sy, sz, Nz, rundir)
if Nz == 1:
nblz = 1
sz = 1
os.system("cp ./tools/connec/" + execCon + " " + rundir)
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
vec[
i * sx : (i + 1) * sx, j * sy : (j + 1) * sy, k * sz : (k + 1) * sz
] = connec(
vec[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
],
execCon,
params,
rundir,
)
try:
temps = ["pardol*", "conec*d", "coninput.txt", "vecconec.txt"]
for temp in temps:
os.system("rm " + rundir + temp)
except:
print("No connectivity temps to delete")
return vec
def connec(vec, execCon, params, rundir):
np.savetxt(rundir + params[2], vec.reshape(-1), fmt="%i")
wd = os.getcwd()
os.chdir(rundir)
os.system(
"nohup ./" + execCon + " > connec.out 2>&1"
) # subprocess.call(['./tools/connec/'+execCon],cwd=rundir) #, '>/dev/null' , cwd=rundir
os.chdir(wd)
vec = (
np.loadtxt(rundir + params[-1])
.reshape(vec.shape[0], vec.shape[1], vec.shape[2])
.astype(int)
)
return vec
def ConConfig(sx, sy, sz, Nz, rundir):
params = []
if Nz == 1:
params = [
"1",
"4",
"vecconec.txt",
str(sx) + " " + str(sy),
"1.0 1.0",
"pardol.CCO",
]
execCon = "conec2d"
else:
params = [
"1",
"6",
"vecconec.txt",
str(sx) + " " + str(sy) + " " + str(sz),
"1.0 1.0 1.0",
"pardol.CCO",
]
execCon = "conec3d"
with open(rundir + "coninput.txt", "w") as f:
for item in params:
f.write("%s\n" % item)
return params, execCon
def join(vec, nbl, keep_aspect, datadir, S_min_post, gcon):
Nx, Ny, Nz = vec.shape[0], vec.shape[1], vec.shape[2]
sx = Nx // nbl
if keep_aspect:
sy, sz = Ny // nbl, Nz // nbl
nblx, nbly, nblz = nbl, nbl, nbl
else:
sy, sz = sx, sx
nblx = nbl
nbly, nblz = Ny // sy, Nz // sz
ex = np.log2(Nx)
esx = np.log2(sx)
join_z = True
join_y = True
if Nz == 1:
sz = 1
nblz = 1
post_time = 0
sxL = [sx]
for bs in range(0, int(ex - esx)):
if vec.shape[0] == vec.shape[1] and sx >= S_min_post:
t0 = time.time()
ConnecInd(vec, [sx], datadir)
post_time = time.time() - t0
sx, sy, sz = 2 * sx, 2 * sy, 2 * sz
sxL += [sx]
if sz > Nz:
sz = Nz
nblz = 1
join_z = False
if sy > Ny:
sy = Ny
nbly = 1
join_y = False
nblx, nbly, nblz = Nx // sx, Ny // sy, Nz // sz
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
vec[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
] = joinBox(
vec[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
],
join_y,
join_z,
)
if vec.shape[0] == vec.shape[1] and sx >= S_min_post: #
t0 = time.time()
ConnecInd(vec, [sx], datadir)
post_time = post_time + (time.time() - t0)
if gcon:
ConnecInd(vec, sxL, datadir + "Global")
return vec, post_time

139
tools/generation/config.py
Unescape Escape View File

@ -3,75 +3,74 @@ import configparser
import json
def get_config(conffile):
parser = configparser.ConfigParser()
parser.read(conffile)
cons=json.loads(parser.get('Iterables',"connectivity"))
ps=json.loads(parser.get('Iterables',"p"))
lcs=json.loads(parser.get('Iterables',"lc"))
variances=json.loads(parser.get('Iterables',"variances"))
seeds=json.loads(parser.get('Iterables',"seeds"))
seeds=np.arange(seeds[0],seeds[1]+seeds[0])
ps=np.linspace(ps[0],ps[1],ps[2])/100
iterables=dict()
iterables['ps'] = ps
iterables['seeds'] = seeds
iterables['lcs'] = lcs
iterables['variances'] = variances
iterables['cons'] = cons
return parser, iterables
def DotheLoop(job,parser,iterables):
ps = iterables['ps']
seeds = iterables['seeds']
lcs = iterables['lcs']
variances = iterables['variances']
cons = iterables['cons']
if job==-1:
if parser.get('Generation','binary')=='yes':
if 0 not in cons:
njobs=len(ps)*len(cons)*len(seeds)*len(lcs)
else:
njobs=len(ps)*(len(cons)-1)*len(seeds)*len(lcs)+len(ps)*len(seeds)
else:
if 0 not in cons:
njobs=len(variances)*len(cons)*len(seeds)*len(lcs)
else:
njobs=len(variances)*(len(cons)-1)*len(seeds)*len(lcs)+len(variances)*len(seeds)
return njobs
i=0
for con in cons:
if con == 0:
llcs=[0.000001]
else:
llcs=lcs
for lc in llcs:
if parser.get('Generation','binary')=='yes':
for p in ps:
for seed in seeds:
if i==job:
return [con,lc,p,seed]
i+=1
else:
for v in variances:
for seed in seeds:
if i==job:
return [con,lc,v,seed]
i+=1
return []
def get_config(conffile):
parser = configparser.ConfigParser()
parser.read(conffile)
cons = json.loads(parser.get("Iterables", "connectivity"))
ps = json.loads(parser.get("Iterables", "p"))
lcs = json.loads(parser.get("Iterables", "lc"))
variances = json.loads(parser.get("Iterables", "variances"))
seeds = json.loads(parser.get("Iterables", "seeds"))
seeds = np.arange(seeds[0], seeds[1] + seeds[0])
ps = np.linspace(ps[0], ps[1], ps[2]) / 100
iterables = dict()
iterables["ps"] = ps
iterables["seeds"] = seeds
iterables["lcs"] = lcs
iterables["variances"] = variances
iterables["cons"] = cons
return parser, iterables
def DotheLoop(job, parser, iterables):
ps = iterables["ps"]
seeds = iterables["seeds"]
lcs = iterables["lcs"]
variances = iterables["variances"]
cons = iterables["cons"]
if job == -1:
if parser.get("Generation", "binary") == "yes":
if 0 not in cons:
njobs = len(ps) * len(cons) * len(seeds) * len(lcs)
else:
njobs = len(ps) * (len(cons) - 1) * len(seeds) * len(lcs) + len(
ps
) * len(seeds)
else:
if 0 not in cons:
njobs = len(variances) * len(cons) * len(seeds) * len(lcs)
else:
njobs = len(variances) * (len(cons) - 1) * len(seeds) * len(lcs) + len(
variances
) * len(seeds)
return njobs
i = 0
for con in cons:
if con == 0:
llcs = [0.000001]
else:
llcs = lcs
for lc in llcs:
if parser.get("Generation", "binary") == "yes":
for p in ps:
for seed in seeds:
if i == job:
return [con, lc, p, seed]
i += 1
else:
for v in variances:
for seed in seeds:
if i == job:
return [con, lc, v, seed]
i += 1
return []

360
tools/generation/fftma_gen.py
Unescape Escape View File

@ -3,156 +3,238 @@ from scipy.special import erf, erfinv
from scipy.optimize import curve_fit
from scipy.stats import norm
from FFTMA import gen
from tools.generation.config import DotheLoop, get_config
from tools.generation.config import DotheLoop, get_config
from tools.generation.variograma import get_lc
from scipy.interpolate import interp1d
import sys
import time
import os
#from memory_profiler import profile
def fftmaGenerator(datadir,job,conffile):
# from memory_profiler import profile
def fftmaGenerator(datadir, job, conffile):
t0 = time.time()
parser, iterables = get_config(conffile)
params = DotheLoop(job, parser, iterables)
binary = parser.get("Generation", "binary")
uselc_bin = parser.get("Generation", "lcBin")
if binary == "yes":
logn = "no"
con, lc, p, seed = params[0], params[1], params[2], params[3]
variance = 0
else:
logn = "yes"
con, lc, variance, seed = params[0], params[1], params[2], params[3]
p = 0
Nx, Ny, Nz = (
int(parser.get("Generation", "Nx")),
int(parser.get("Generation", "Ny")),
int(parser.get("Generation", "Nz")),
)
# N=int(42.666666667*lc)
# Nx,Ny,Nz = N,N,N
# print(N)
kh, kl, vario = (
float(parser.get("Generation", "kh")),
float(parser.get("Generation", "kl")),
int(parser.get("Generation", "variogram_type")),
)
compute_lc = parser.get("Generation", "compute_lc")
generate_K(
Nx,
Ny,
Nz,
con,
lc,
p,
kh,
kl,
seed,
logn,
variance,
vario,
datadir,
compute_lc,
uselc_bin,
)
np.savetxt(
datadir + "GenParams.txt",
np.array(
[time.time() - t0, Nx, Ny, Nz, con, lc, p, kh, kl, seed, variance, vario]
),
header="Runtime,Nx,Ny,Nz,con,lc,p,kh,kl,seed,variance,vario",
)
return
def obtainLctobin(p, con, vario):
lc = np.load(
"./tools/generation/lc.npy", allow_pickle=True, encoding="latin1"
).item()
f = interp1d(lc["p"], lc[vario, con])
if p == 0 or p == 1:
return 1.0
return 1.0 / f(p)
def obtainLctobinBack(p, con, vario):
pb = np.linspace(0.0, 1.0, 11)
if vario == 2:
i = [0.0, 1.951, 2.142, 2.247, 2.301, 2.317, 2.301, 2.246, 2.142, 1.952, 0.0]
c = [0.0, 1.188, 1.460, 1.730, 2.017, 2.284, 2.497, 2.652, 2.736, 2.689, 0.0]
d = [0.0, 2.689, 2.736, 2.652, 2.497, 2.284, 2.017, 1.730, 1.460, 1.188, 0.0]
lcBin = np.array([i, c, d])
lcBin = lcBin / 3.0
if vario == 1:
i = [0.0, 3.13, 3.66, 3.94, 4.08, 4.10, 4.01, 3.84, 3.55, 3.00, 0.0]
c = [0.0, 0.85, 1.095, 1.312, 1.547, 1.762, 1.966, 2.149, 2.257, 2.186, 0.0]
d = [
0.0,
2.186,
2.2575,
2.1495,
1.9660,
1.7625,
1.5476,
1.3128,
1.0950,
0.8510,
0.0,
]
lcBin = np.array([i, c, d])
lcBin = lcBin / 6.0
f = interp1d(pb, lcBin[con - 1])
return 1.0 / f(p)
# @profile
def generate_K(
Nx,
Ny,
Nz,
con,
lc,
p,
kh,
kl,
seed,
logn,
LogVariance,
vario,
datadir,
compute_lc,
uselc_bin,
):
k = genGaussK(
Nx, Ny, Nz, con, lc, p, kh, kl, seed, logn, LogVariance, vario, uselc_bin
)
if compute_lc == "yes":
lcG = get_lc(k, vario)
lcNst = lcG
lcBin = np.nan
if con == 2:
k = -nst(k) # normal score transform
if compute_lc == "yes":
lcNst = get_lc(k, vario)
if con == 3:
k = nst(k)
if compute_lc == "yes":
lcNst = get_lc(k, vario)
if logn == "yes":
k = k * (LogVariance ** 0.5)
k = np.exp(k)
else:
k = binarize(k, kh, kl, p)
if compute_lc == "yes":
lcBin = get_lc(np.where(k > kl, 1, 0), vario)
np.save(datadir + "k.npy", k)
if compute_lc == "yes":
np.savetxt(
datadir + "lc.txt",
np.array([lcG, lcNst, lcBin]),
header="lcG, lcNst, lcBin",
)
return
def genGaussK(
Nx, Ny, Nz, con, lc, p, kh, kl, seed, logn, LogVariance, vario, uselc_bin
):
typ = 0 # structure du champ: 0=normal; 1=lognormal; 2=log-10
dx, dy, dz = 1.0, 1.0, 1.0
var = 1 # Nbr de structure du variogramme
alpha = 1 # valeur exposant
if con == 0:
lc = 0.000001
if (con == 2 or con == 3) and vario == 2:
lc = lc / 0.60019978939
if (con == 2 or con == 3) and vario == 1:
lc = lc / 0.38165155120015
if uselc_bin == "yes" and con != 0:
lc = lc * obtainLctobin(p, con, vario)
v1 = (
var,
vario,
alpha,
lc,
lc,
lc,
1,
0,
0,
0,
1,
0,
) # coord des vecteurs de base (1 0 0) y (0 1 0)
k = gen(
Nz, Ny, Nx, dx, dy, dz, seed, [v1], 0, 1, 0
) # 0, 1, 0 = mean, variance, typ #Generation of a correlated standard dsitribution N(0,1)
return k
t0=time.time()
parser, iterables = get_config(conffile)
params = DotheLoop(job,parser, iterables )
binary=parser.get('Generation','binary')
uselc_bin=parser.get('Generation','lcBin')
if binary=='yes':
logn='no'
con,lc,p,seed = params[0],params[1],params[2],params[3]
variance=0
else:
logn='yes'
con,lc,variance,seed = params[0],params[1],params[2],params[3]
p=0
Nx,Ny,Nz = int(parser.get('Generation','Nx')), int(parser.get('Generation','Ny')), int(parser.get('Generation','Nz'))
#N=int(42.666666667*lc)
#Nx,Ny,Nz = N,N,N
#print(N)
kh,kl,vario = float(parser.get('Generation','kh')),float(parser.get('Generation','kl')), int(parser.get('Generation','variogram_type'))
compute_lc=parser.get('Generation','compute_lc')
generate_K(Nx,Ny,Nz,con,lc,p,kh,kl,seed,logn,variance,vario,datadir,compute_lc,uselc_bin)
np.savetxt(datadir+'GenParams.txt',np.array([time.time()-t0,Nx,Ny,Nz,con,lc,p,kh,kl,seed,variance,vario]),header='Runtime,Nx,Ny,Nz,con,lc,p,kh,kl,seed,variance,vario')
return
def obtainLctobin(p,con,vario):
lc=np.load('./tools/generation/lc.npy',allow_pickle=True, encoding = 'latin1').item()
f=interp1d(lc['p'],lc[vario,con])
if p==0 or p==1:
return 1.0
return 1.0/f(p)
def obtainLctobinBack(p,con,vario):
pb=np.linspace(0.0,1.0,11)
if vario==2:
i=[0.0, 1.951, 2.142, 2.247, 2.301, 2.317, 2.301, 2.246, 2.142, 1.952, 0.0]
c=[0.0, 1.188, 1.460, 1.730, 2.017, 2.284, 2.497, 2.652, 2.736, 2.689, 0.0]
d=[0.0,2.689, 2.736,2.652, 2.497, 2.284, 2.017, 1.730, 1.460, 1.188, 0.0]
lcBin=np.array([i,c,d])
lcBin=lcBin/3.0
if vario==1:
i=[0.0,3.13, 3.66, 3.94, 4.08, 4.10, 4.01, 3.84, 3.55, 3.00,0.0]
c=[0.0,0.85, 1.095, 1.312, 1.547, 1.762, 1.966, 2.149, 2.257, 2.186,0.0]
d=[0.0,2.186, 2.2575,2.1495,1.9660,1.7625,1.5476,1.3128,1.0950,0.8510,0.0]
lcBin=np.array([i,c,d])
lcBin=lcBin/6.0
f=interp1d(pb,lcBin[con-1])
return 1.0/f(p)
#@profile
def generate_K(Nx,Ny,Nz,con,lc,p,kh,kl,seed,logn,LogVariance,vario,datadir,compute_lc,uselc_bin):
k=genGaussK(Nx,Ny,Nz,con,lc,p,kh,kl,seed,logn,LogVariance,vario,uselc_bin)
if compute_lc =='yes':
lcG=get_lc(k,vario)
lcNst=lcG
lcBin=np.nan
if con==2:
k = -nst(k) #normal score transform
if compute_lc =='yes':
lcNst=get_lc(k,vario)
if con==3:
k = nst(k)
if compute_lc =='yes':
lcNst=get_lc(k,vario)
if logn == 'yes':
k=k*(LogVariance**0.5)
k = np.exp(k)
else:
k = binarize(k,kh,kl,p)
if compute_lc =='yes':
lcBin=get_lc(np.where(k>kl,1,0),vario)
np.save(datadir+'k.npy',k)
if compute_lc =='yes':
np.savetxt(datadir+'lc.txt',np.array([lcG,lcNst,lcBin]),header='lcG, lcNst, lcBin')
return
def genGaussK(Nx,Ny,Nz,con,lc,p,kh,kl,seed,logn,LogVariance,vario,uselc_bin):
def nst(kc):
kc = np.abs(kc)
kc = np.sqrt(2) * erfinv(2 * erf(kc / np.sqrt(2)) - 1)
return kc
typ=0 #structure du champ: 0=normal; 1=lognormal; 2=log-10
dx, dy, dz = 1.0, 1.0, 1.0
var=1 #Nbr de structure du variogramme
alpha=1 #valeur exposant
if con==0:
lc=0.000001
if (con==2 or con==3) and vario==2:
lc=lc/0.60019978939
if (con==2 or con==3) and vario==1:
lc=lc/0.38165155120015
def binarize(kc, kh, kl, p):
if uselc_bin=='yes' and con!=0:
lc=lc*obtainLctobin(p,con,vario)
v1 = (var, vario, alpha, lc, lc, lc, 1, 0, 0, 0, 1, 0) # coord des vecteurs de base (1 0 0) y (0 1 0)
k=gen(Nz, Ny, Nx, dx, dy, dz, seed, [v1], 0, 1, 0) # 0, 1, 0 = mean, variance, typ #Generation of a correlated standard dsitribution N(0,1)
if kc.size < 100 ** 3:
if p > 0:
at = int((1 - p) * kc.size) #
else:
at = kc.size - 1
t1 = np.sort(kc.reshape(-1))[at] # get permeability treshold
kc = np.where(kc < t1, kl, kh) # Binarization
t1 = 0
else:
t1 = norm.ppf(1 - p)
kc = np.where(kc < t1, kl, kh)
return kc
return k
# CONFIG_FILE_PATH = 'config.ini' if 'CONFIG_FILE_PATH' not in os.environ else os.environ['CONFIG_FILE_PATH']
def nst(kc):
kc=np.abs(kc)
kc=np.sqrt(2)*erfinv(2*erf(kc/np.sqrt(2))-1)
return kc
def binarize(kc,kh,kl,p):
if kc.size < 100**3:
if p>0:
at=int((1-p)*kc.size) #
else:
at=kc.size-1
t1=np.sort(kc.reshape(-1))[at] #get permeability treshold
kc=np.where(kc<t1, kl,kh) #Binarization
t1=0
else:
t1=norm.ppf(1-p)
kc=np.where(kc<t1, kl,kh)
return kc
#CONFIG_FILE_PATH = 'config.ini' if 'CONFIG_FILE_PATH' not in os.environ else os.environ['CONFIG_FILE_PATH']
#fftmaGenerator(sys.argv[1],int(sys.argv[2]),CONFIG_FILE_PATH)
# fftmaGenerator(sys.argv[1],int(sys.argv[2]),CONFIG_FILE_PATH)

1
tools/generation/newPy.py
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@ -1,3 +1,2 @@
import numpy as np
import matplotlib.pyplot as plt

3
tools/generation/run_test.py
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@ -3,5 +3,4 @@ import os
for i in range(10):
os.system("python test.py "+str(i))
os.system("python test.py " + str(i))

50
tools/generation/test.py
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@ -4,27 +4,39 @@ import sys
from FFTMA import gen
def fftmaGenerator(seed):
typ=0 #structure du champ: 0=normal; 1=lognormal; 2=log-10
dx, dy, dz = 1.0, 1.0, 1.0
var=1 #Nbr de structure du variogramme
alpha=1 #valeur exposant
k=np.zeros(10)
v1 = (var, 2, alpha, 1.0, 1.0, 1.0, 1, 0, 0, 0, 1, 0) # coord des vecteurs de base (1 0 0) y (0 1 0)
kkc=gen(1 , 100, 100, dx, dy, dz, seed, [v1], 0, 1, 0) # 0, 1, 0 = mean, variance, typ #Generation of a correlated standard dsitribution N(0,1)
print(np.mean(kkc),np.var(kkc))
k=0
return
s=int(sys.argv[1])
typ = 0 # structure du champ: 0=normal; 1=lognormal; 2=log-10
dx, dy, dz = 1.0, 1.0, 1.0
var = 1 # Nbr de structure du variogramme
alpha = 1 # valeur exposant
k = np.zeros(10)
v1 = (
var,
2,
alpha,
1.0,
1.0,
1.0,
1,
0,
0,
0,
1,
0,
) # coord des vecteurs de base (1 0 0) y (0 1 0)
kkc = gen(
1, 100, 100, dx, dy, dz, seed, [v1], 0, 1, 0
) # 0, 1, 0 = mean, variance, typ #Generation of a correlated standard dsitribution N(0,1)
print(np.mean(kkc), np.var(kkc))
k = 0
return
s = int(sys.argv[1])
fftmaGenerator(s)
fftmaGenerator(s)

121
tools/generation/variograma.py
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@ -2,97 +2,90 @@ import numpy as np
from scipy.optimize import curve_fit
def covar2d(k):
x=[]
cov=[]
nx=k.shape[0]
for h in range(nx):
x.append(h)
kx,kh=k[:nx-h,:].reshape(-1),k[h:,:].reshape(-1)
cov.append(np.mean((kx*kh)-(np.mean(kx)*np.mean(kh))))
return cov,x
x = []
cov = []
nx = k.shape[0]
for h in range(nx):
def vario2d(k):
x=[]
vario=[]
nx=k.shape[0]
for h in range(nx):
x.append(h)
kx,kh=k[:nx-h,:].reshape(-1),k[h:,:].reshape(-1)
vario.append(np.mean((kh-kx)**2))
return vario,x
x.append(h)
kx, kh = k[: nx - h, :].reshape(-1), k[h:, :].reshape(-1)
cov.append(np.mean((kx * kh) - (np.mean(kx) * np.mean(kh))))
def vario3d(k):
x=[]
vario=[]
nx=k.shape[0]
for h in range(nx):
x.append(h)
kx,kh=k[:nx-h,:,:].reshape(-1),k[h:,:,:].reshape(-1)
vario.append(np.mean((kh-kx)**2))
return vario,x
return cov, x
def modelcovexp(h,a,c):
return c*(np.exp(-h/a))
def vario2d(k):
x = []
vario = []
nx = k.shape[0]
for h in range(nx):
x.append(h)
kx, kh = k[: nx - h, :].reshape(-1), k[h:, :].reshape(-1)
vario.append(np.mean((kh - kx) ** 2))
return vario, x
def modelcovexpLin(h,a,c):
return c-h/a
def vario3d(k):
x = []
vario = []
nx = k.shape[0]
for h in range(nx):
x.append(h)
kx, kh = k[: nx - h, :, :].reshape(-1), k[h:, :, :].reshape(-1)
vario.append(np.mean((kh - kx) ** 2))
def modelvarioexp(h,a,c):
return c*(1-np.exp(-h/a))
return vario, x
def modelcovgauss(h,a,c):
return c*(np.exp(-(h/a)**2))
def modelvariogauss(h,a,c):
return c*(1-np.exp(-(h/a)**2))
def modelcovexp(h, a, c):
return c * (np.exp(-h / a))
def get_CovPar2d(k,model):
def modelcovexpLin(h, a, c):
return c - h / a
cov,x=vario2d(k)
popt, pcov = curve_fit(model, x, cov)
return np.abs(popt[0]) #Ic,varianza
def modelvarioexp(h, a, c):
return c * (1 - np.exp(-h / a))
def get_varPar3d(k,model):
vario,x=vario3d(k)
popt, pcov = curve_fit(model, x, vario)
return np.abs(popt[0]) #Ic,varianza
def modelcovgauss(h, a, c):
return c * (np.exp(-((h / a) ** 2)))
def get_lc(k,vario):
def modelvariogauss(h, a, c):
return c * (1 - np.exp(-((h / a) ** 2)))
if vario==2:
model=modelvariogauss
mult=np.sqrt(3)
else:
model=modelvarioexp
mult=3
if k.shape[2]==1:
lc=get_CovPar2d(k,model)*mult
else:
lc=get_varPar3d(k,model)*mult
return lc
def get_CovPar2d(k, model):
cov, x = vario2d(k)
popt, pcov = curve_fit(model, x, cov)
return np.abs(popt[0]) # Ic,varianza
def get_varPar3d(k, model):
vario, x = vario3d(k)
popt, pcov = curve_fit(model, x, vario)
return np.abs(popt[0]) # Ic,varianza
def get_lc(k, vario):
if vario == 2:
model = modelvariogauss
mult = np.sqrt(3)
else:
model = modelvarioexp
mult = 3
if k.shape[2] == 1:
lc = get_CovPar2d(k, model) * mult
else:
lc = get_varPar3d(k, model) * mult
return lc

276
tools/postprocessK/2d_1darcy.py
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@ -1,190 +1,200 @@
import numpy as np
from scipy.sparse import diags
from scipy.stats import mstats
from scipy.sparse.linalg import spsolve, bicg, bicgstab, cg #,LinearOperator, spilu, bicgstab
from scipy.sparse.linalg import (
spsolve,
bicg,
bicgstab,
cg,
) # ,LinearOperator, spilu, bicgstab
from petsc4py import PETSc
import csv
import time
#[layer,columns,row]= [z,y,x]
# [layer,columns,row]= [z,y,x]
NNN=256
ref=2
NNN = 256
ref = 2
def computeT(k):
nx = k.shape[2]
ny = k.shape[1]
nz = k.shape[0]-2
tx = np.zeros((nz,ny, nx+1))
ty = np.zeros((nz,ny+1, nx))
tz = np.zeros((nz+1,ny, nx))
tx[:,:,1:-1] = 2*k[1:-1, :,:-1]*k[1:-1, :,1:]/(k[1:-1, :,:-1]+k[1:-1, :,1:])
ty[:,1:-1,:] = 2*k[1:-1, :-1,:]*k[1:-1, 1:,:]/(k[1:-1, :-1,:]+k[1:-1, 1:,:])
tz[:,:,:] = 2*k[:-1, :,:]*k[1:, :,:]/(k[:-1, :,:]+k[1:, :,:])
return tx, ty, tz, nx, ny, nz
def rafina(k,ref):
if ref==1:
return k
ny,nz=k.shape[1],k.shape[0]
krz=np.zeros((ref*nz,ny,1))
for i in range(ref):
krz[i::ref,:,:]=k
krzy=np.zeros((ref*nz,ny*ref,1))
for i in range(ref):
krzy[:,i::ref,:]=krz
return krzy
def get_kfield():
nx = k.shape[2]
ny = k.shape[1]
nz = k.shape[0] - 2
tx = np.zeros((nz, ny, nx + 1))
ty = np.zeros((nz, ny + 1, nx))
tz = np.zeros((nz + 1, ny, nx))
tx[:, :, 1:-1] = (
2 * k[1:-1, :, :-1] * k[1:-1, :, 1:] / (k[1:-1, :, :-1] + k[1:-1, :, 1:])
)
ty[:, 1:-1, :] = (
2 * k[1:-1, :-1, :] * k[1:-1, 1:, :] / (k[1:-1, :-1, :] + k[1:-1, 1:, :])
)
tz[:, :, :] = 2 * k[:-1, :, :] * k[1:, :, :] / (k[:-1, :, :] + k[1:, :, :])
#auxk=np.load('k.npy')
#auxk=auxk.reshape(nz,ny,nx)
#k=np.ones((nz,ny,nx))
#k = np.random.lognormal(0,3,(nz,ny,nx))
#k=np.load('./inp/k.npy')
#N=512
#k=np.loadtxt('./inp/out_rafine.dat')
#n=int(np.sqrt(k.size))
#k=k.reshape((n,n))
#k=k[:N,:N]
k=np.load('k.npy')
#kfiledir='../Modflow/bin/r'+str(ref)+'/'
#k=np.loadtxt(kfiledir+'out_fftma.txt')
#k=np.loadtxt(kfiledir+'out_rafine.dat')
#k=k.reshape(int(np.sqrt(k.size)),int(np.sqrt(k.size)),1)
#k=k[:NNN*ref,:NNN*ref,:]
#k=rafina(k,ref)
nx,ny,nz=k.shape[2],k.shape[1],k.shape[0]
#k=k.reshape((nz,ny,nx))
auxk=np.zeros((nz+2,ny,nx))
auxk[1:-1,:,:]=k
auxk[0,:,:]=k[0,:,:]
auxk[-1,:,:]=k[-1,:,:]
return tx, ty, tz, nx, ny, nz
return auxk
def rafina(k, ref):
def Rmat(k,pbc):
if ref == 1:
return k
ny, nz = k.shape[1], k.shape[0]
krz = np.zeros((ref * nz, ny, 1))
for i in range(ref):
krz[i::ref, :, :] = k
krzy = np.zeros((ref * nz, ny * ref, 1))
for i in range(ref):
krzy[:, i::ref, :] = krz
return krzy
tx, ty , tz , nx, ny, nz= computeT(k)
rh=np.zeros((nz,ny,nx))
rh[0,:,:]=pbc*tz[0,:,:]
rh=rh.reshape(-1)
d=(tx[:,:,:-1]+tx[:,:,1:]+ty[:,:-1,:]+ty[:,1:,:]+tz[:-1,:,:]+tz[1:,:,:]).reshape(-1)
a=(-tx[:,:,:-1].reshape(-1))[1:]
#a=(tx.reshape(-1))[:-1]
b=(-ty[:,1:,:].reshape(-1))[:-nx]
c=-tz[1:-1,:,:].reshape(-1)
def get_kfield():
# auxk=np.load('k.npy')
# auxk=auxk.reshape(nz,ny,nx)
# k=np.ones((nz,ny,nx))
# k = np.random.lognormal(0,3,(nz,ny,nx))
# k=np.load('./inp/k.npy')
# N=512
# k=np.loadtxt('./inp/out_rafine.dat')
# n=int(np.sqrt(k.size))
# k=k.reshape((n,n))
# k=k[:N,:N]
k = np.load("k.npy")
# kfiledir='../Modflow/bin/r'+str(ref)+'/'
# k=np.loadtxt(kfiledir+'out_fftma.txt')
# k=np.loadtxt(kfiledir+'out_rafine.dat')
# k=k.reshape(int(np.sqrt(k.size)),int(np.sqrt(k.size)),1)
# k=k[:NNN*ref,:NNN*ref,:]
# k=rafina(k,ref)
nx, ny, nz = k.shape[2], k.shape[1], k.shape[0]
# k=k.reshape((nz,ny,nx))
auxk = np.zeros((nz + 2, ny, nx))
auxk[1:-1, :, :] = k
auxk[0, :, :] = k[0, :, :]
auxk[-1, :, :] = k[-1, :, :]
return auxk
def Rmat(k, pbc):
tx, ty, tz, nx, ny, nz = computeT(k)
rh = np.zeros((nz, ny, nx))
rh[0, :, :] = pbc * tz[0, :, :]
rh = rh.reshape(-1)
d = (
tx[:, :, :-1]
+ tx[:, :, 1:]
+ ty[:, :-1, :]
+ ty[:, 1:, :]
+ tz[:-1, :, :]
+ tz[1:, :, :]
).reshape(-1)
a = (-tx[:, :, :-1].reshape(-1))[1:]
# a=(tx.reshape(-1))[:-1]
b = (-ty[:, 1:, :].reshape(-1))[:-nx]
c = -tz[1:-1, :, :].reshape(-1)
return a, b, c, d, rh
return a, b, c, d, rh
def imp(k):
for i in range(k.shape[1]):
for j in range(k.shape[0]):
if k[j,i]!=0:
print(i,j,k[j,i])
return
for i in range(k.shape[1]):
for j in range(k.shape[0]):
if k[j, i] != 0:
print(i, j, k[j, i])
return
def PysolveP(a, b, c, d, rh, nx, ny, nz, solver):
offset = [-nx*ny,-nx, -1, 0, 1, nx, nx*ny]
k=diags(np.array([c, b, a, d, a, b, c]), offset, format='csc')
offset = [-nx * ny, -nx, -1, 0, 1, nx, nx * ny]
k = diags(np.array([c, b, a, d, a, b, c]), offset, format="csc")
p = solver(k, rh)
return p
p = solver(k, rh)
return p
def PysolveP2d( b, c, d, rh, nx, ny, nz, solver):
def PysolveP2d(b, c, d, rh, nx, ny, nz, solver):
offset = [-ny, -1, 0, 1, ny]
k=diags(np.array([c, b, d, b, c]), offset, format='csc')
#imp(k.toarray())
k = diags(np.array([c, b, d, b, c]), offset, format="csc")
# imp(k.toarray())
p = solver(k, rh)
return p
def Pmat( pm, nx, ny, nz,pbc):
auxpm=np.zeros((nz+2,ny,nx))
auxpm[0,:,:]=pbc
auxpm[1:-1,:,:]=pm.reshape(nz,ny,nx)
return auxpm
def Pmat(pm, nx, ny, nz, pbc):
auxpm = np.zeros((nz + 2, ny, nx))
auxpm[0, :, :] = pbc
auxpm[1:-1, :, :] = pm.reshape(nz, ny, nx)
return auxpm
def getK(pm,k,pbc):
nx = k.shape[2]
ny = k.shape[1]
nz = k.shape[0]-2
def getK(pm, k, pbc):
tz = 2*k[2, :,:]*k[1, :,:]/(k[2, :,:]+k[1, :,:])
q=((pm[1,:,:]-pm[2,:,:])*tz).sum()
nx = k.shape[2]
ny = k.shape[1]
nz = k.shape[0] - 2
area=nx*ny
l=nz+1
tz = 2 * k[2, :, :] * k[1, :, :] / (k[2, :, :] + k[1, :, :])
q = ((pm[1, :, :] - pm[2, :, :]) * tz).sum()
keff=q*l/(pbc*area)
area = nx * ny
l = nz + 1
#print('Arit = ', np.mean(k),' Geom = ',mstats.gmean(k,axis=None),' Harm = ',mstats.hmean(k, axis=None))
keff = q * l / (pbc * area)
return keff
# print('Arit = ', np.mean(k),' Geom = ',mstats.gmean(k,axis=None),' Harm = ',mstats.hmean(k, axis=None))
return keff
def main():
pbc=1000
solver=spsolve
k=get_kfield()
nx,ny,nz=k.shape[2],k.shape[1],k.shape[0]-2
#print(k.shape)
a, b, c, d, rh=Rmat(k,pbc)
if nx==1:
p=PysolveP2d(b, c, d, rh, nx, ny, nz, solver)
else:
p=PysolveP(a, b, c, d, rh, nx, ny, nz, solver)
print(p.shape)
pbc = 1000
solver = spsolve
p=Pmat( p, nx, ny, nz,pbc)
p=p.reshape((nz+2,ny,nx))
keff=getK(p,k,pbc)
print(keff)
k = get_kfield()
#k=k.reshape((nz+2,ny,nx))
nx, ny, nz = k.shape[2], k.shape[1], k.shape[0] - 2
# print(k.shape)
a, b, c, d, rh = Rmat(k, pbc)
if nx == 1:
p = PysolveP2d(b, c, d, rh, nx, ny, nz, solver)
else:
p = PysolveP(a, b, c, d, rh, nx, ny, nz, solver)
print(p.shape)
auxp=np.zeros((nz+2,ny+2))
auxk=np.zeros((nz+2,ny+2))
auxp[:,0]=p[:,0]
auxp[:,-1]=p[:,-1]
auxp[:,1:-1]=p
p = Pmat(p, nx, ny, nz, pbc)
p = p.reshape((nz + 2, ny, nx))
keff = getK(p, k, pbc)
print(keff)
auxk[:,0]=0
auxk[:,-1]=0
auxk[:,1:-1]=k
# k=k.reshape((nz+2,ny,nx))
auxp = np.zeros((nz + 2, ny + 2))
auxk = np.zeros((nz + 2, ny + 2))
auxp[:, 0] = p[:, 0]
auxp[:, -1] = p[:, -1]
auxp[:, 1:-1] = p
np.save('./p',auxp)
np.save('./k',auxk)
#np.savetxt('./1p/k.txt',auxk)
np.savetxt('./keff.txt',np.array([keff]))
#print(p)
return
auxk[:, 0] = 0
auxk[:, -1] = 0
auxk[:, 1:-1] = k
np.save("./p", auxp)
np.save("./k", auxk)
# np.savetxt('./1p/k.txt',auxk)
np.savetxt("./keff.txt", np.array([keff]))
# print(p)
return
main()

245
tools/postprocessK/comp_PostKeff (copy).py
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@ -4,114 +4,143 @@ import time
from tools.postprocessK.flow import ComputeVol, comp_Kdiss_Kaverage
import subprocess
#k[x,y,z]
# k[x,y,z]
import json
def comp_postKeff(parser,rundir,nr,PetscP):
k=np.load(rundir+'k.npy')
P=np.load(rundir+'P.npy')
ref=P.shape[0]//k.shape[0]
t0=time.time()
k, diss, vx, Px, Py, Pz = ComputeVol(k,P) #refina k
tDissVel=time.time()-t0
P=0
S_min_post = int(parser.get('K-Postprocess','MinBlockSize'))
nimax =2** int(parser.get('K-Postprocess','Max_sample_size'))
compKperm =parser.get('K-Postprocess','kperm')
if compKperm=='yes':
compKperm=True
S_min_post=S_min_post*ref
if S_min_post==0:
sx=k.shape[0]
else:
sx = get_min_nbl(k,nimax,nr,S_min_post)
kdiss,kave=getKpost(k, diss, vx, Px, Py, Pz,sx,rundir,ref)
ttotal=time.time()-t0
summary = np.array([kdiss,kave,ttotal,tDissVel/ttotal]).T
np.savetxt(rundir + 'PosKeffSummary.txt',summary,header='K_diss, K_average,ttotal,tDiss/ttotal')
return
def getKpost(kf, diss, vx, Px, Py, Pz,sx,rundir,ref,compkperm):
ex=int(np.log2(kf.shape[0]))
esx=int(np.log2(sx))
scales=2**np.arange(esx,ex)
datadir=rundir+'KpostProcess/'
try:
os.makedirs(datadir)
except:
nada=0
for l in scales:
nblx, nbly, nblz = kf.shape[0]//l, kf.shape[1]//l, kf.shape[2]//l
sx,sy,sz=l,l,l
if kf.shape[2]==1:
nblz=1
sz=1
Kdiss,Kave=np.zeros((nblx,nbly,nblz)),np.zeros((nblx,nbly,nblz))
if compkperm==True:
Kperm = np.zeros((nblx,nbly,nblz))
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
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])
if compkperm==True:
Kperm[i,j,k]=PetscP(datadir,ref,k)(kf[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz])
np.save(datadir+'Kd'+str(l//ref)+'.npy',Kdiss)
np.save(datadir+'Kv'+str(l//ref)+'.npy',Kave)
if compkperm==True:
np.save(datadir+'Kperm'+str(l//ref)+'.npy',Kperm)
Kdiss,Kave = comp_Kdiss_Kaverage(kf, diss, vx, Px, Py, Pz)
np.save(datadir+'Kd'+str(kf.shape[0]//ref)+'.npy',np.array([Kdiss]))
np.save(datadir+'Kv'+str(kf.shape[0]//ref)+'.npy',np.array([Kave]))
return Kdiss, Kave
def get_min_nbl(kc,nimax,nr,smin):
if kc.shape[2]==1:
dim=2.0
else:
dim=3.0
if nr>0:
y=(1/dim)*np.log2(nr*kc.size/(nimax*(smin**dim)))
else:
y=0
y=int(y)
s=int((2**y) * smin)
if s<smin:
s=smin
return s
def comp_postKeff(parser, rundir, nr, PetscP):
k = np.load(rundir + "k.npy")
P = np.load(rundir + "P.npy")
ref = P.shape[0] // k.shape[0]
t0 = time.time()
k, diss, vx, Px, Py, Pz = ComputeVol(k, P) # refina k
tDissVel = time.time() - t0
P = 0
S_min_post = int(parser.get("K-Postprocess", "MinBlockSize"))
nimax = 2 ** int(parser.get("K-Postprocess", "Max_sample_size"))
compKperm = parser.get("K-Postprocess", "kperm")
if compKperm == "yes":
compKperm = True
S_min_post = S_min_post * ref
if S_min_post == 0:
sx = k.shape[0]
else:
sx = get_min_nbl(k, nimax, nr, S_min_post)
kdiss, kave = getKpost(k, diss, vx, Px, Py, Pz, sx, rundir, ref)
ttotal = time.time() - t0
summary = np.array([kdiss, kave, ttotal, tDissVel / ttotal]).T
np.savetxt(
rundir + "PosKeffSummary.txt",
summary,
header="K_diss, K_average,ttotal,tDiss/ttotal",
)
return
def getKpost(kf, diss, vx, Px, Py, Pz, sx, rundir, ref, compkperm):
ex = int(np.log2(kf.shape[0]))
esx = int(np.log2(sx))
scales = 2 ** np.arange(esx, ex)
datadir = rundir + "KpostProcess/"
try:
os.makedirs(datadir)
except:
nada = 0
for l in scales:
nblx, nbly, nblz = kf.shape[0] // l, kf.shape[1] // l, kf.shape[2] // l
sx, sy, sz = l, l, l
if kf.shape[2] == 1:
nblz = 1
sz = 1
Kdiss, Kave = np.zeros((nblx, nbly, nblz)), np.zeros((nblx, nbly, nblz))
if compkperm == True:
Kperm = np.zeros((nblx, nbly, nblz))
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
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,
],
)
if compkperm == True:
Kperm[i, j, k] = PetscP(datadir, ref, k)(
kf[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
]
)
np.save(datadir + "Kd" + str(l // ref) + ".npy", Kdiss)
np.save(datadir + "Kv" + str(l // ref) + ".npy", Kave)
if compkperm == True:
np.save(datadir + "Kperm" + str(l // ref) + ".npy", Kperm)
Kdiss, Kave = comp_Kdiss_Kaverage(kf, diss, vx, Px, Py, Pz)
np.save(datadir + "Kd" + str(kf.shape[0] // ref) + ".npy", np.array([Kdiss]))
np.save(datadir + "Kv" + str(kf.shape[0] // ref) + ".npy", np.array([Kave]))
return Kdiss, Kave
def get_min_nbl(kc, nimax, nr, smin):
if kc.shape[2] == 1:
dim = 2.0
else:
dim = 3.0
if nr > 0:
y = (1 / dim) * np.log2(nr * kc.size / (nimax * (smin ** dim)))
else:
y = 0
y = int(y)
s = int((2 ** y) * smin)
if s < smin:
s = smin
return s

251
tools/postprocessK/comp_PostKeff.py
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@ -2,120 +2,145 @@ import numpy as np
import os
import time
from tools.postprocessK.flow import ComputeVol, comp_Kdiss_Kaverage
#import subprocess
from tools.postprocessK.kperm.Ndar1P import PetscP
#k[x,y,z]
import json
def comp_postKeff(parser,rundir,nr):
k=np.load(rundir+'k.npy')
try:
P=np.load(rundir+'P.npy')
except:
print('no pressure file '+rundir)
return
ref=P.shape[0]//k.shape[0]
SaveV = parser.get('K-Postprocess','SaveVfield')
if SaveV=='yes':
SaveV=True
else:
SaveV=False
t0=time.time()
k, diss, vx,vy,vz, Px, Py, Pz = ComputeVol(k,P,SaveV) #refina k
tDissVel=time.time()-t0
P=0
S_min_post = int(parser.get('K-Postprocess','MinBlockSize'))
nimax =2** int(parser.get('K-Postprocess','Max_sample_size'))
compKperm =parser.get('K-Postprocess','kperm')
if compKperm=='yes':
compKperm=True
S_min_post=S_min_post*ref
if S_min_post==0:
sx=1 #k.shape[0]
else:
sx = get_min_nbl(k,nimax,nr,S_min_post)
kdiss,kave=getKpost(k, diss, vx, Px, Py, Pz,sx,rundir,ref,compKperm)
ttotal=time.time()-t0
summary = np.array([kdiss,kave,ttotal,tDissVel/ttotal]).T
np.savetxt(rundir + 'PosKeffSummary.txt',summary,header='K_diss, K_average,ttotal,tDiss/ttotal')
if SaveV:
np.save(rundir+'V.npy',np.array([vx,vy,vz]))
np.save(rundir+'D.npy',diss)
return
# import subprocess
from tools.postprocessK.kperm.Ndar1P import PetscP
# k[x,y,z]
import json
def getKpost(kf, diss, vx, Px, Py, Pz,sx,rundir,ref,compkperm):
ex=int(np.log2(kf.shape[0]))
esx=int(np.log2(sx))
scales=2**np.arange(esx,ex)
datadir=rundir+'KpostProcess/'
try:
os.makedirs(datadir)
except:
nada=0
for l in scales:
nblx, nbly, nblz = kf.shape[0]//l, kf.shape[1]//l, kf.shape[2]//l
sx,sy,sz=l,l,l
if kf.shape[2]==1:
nblz=1
sz=1
Kdiss,Kave=np.zeros((nblx,nbly,nblz)),np.zeros((nblx,nbly,nblz))
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
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])
np.save(datadir+'Kd'+str(l//ref)+'.npy',Kdiss)
np.save(datadir+'Kv'+str(l//ref)+'.npy',Kave)
Kdiss,Kave = comp_Kdiss_Kaverage(kf, diss, vx, Px, Py, Pz)
np.save(datadir+'Kd'+str(kf.shape[0]//ref)+'.npy',np.array([Kdiss]))
np.save(datadir+'Kv'+str(kf.shape[0]//ref)+'.npy',np.array([Kave]))
return Kdiss, Kave
def get_min_nbl(kc,nimax,nr,smin):
if kc.shape[2]==1:
dim=2.0
else:
dim=3.0
if nr>0:
y=(1/dim)*np.log2(nr*kc.size/(nimax*(smin**dim)))
else:
y=0
y=int(y)
s=int((2**y) * smin)
if s<smin:
s=smin
return s
def comp_postKeff(parser, rundir, nr):
k = np.load(rundir + "k.npy")
try:
P = np.load(rundir + "P.npy")
except:
print("no pressure file " + rundir)
return
ref = P.shape[0] // k.shape[0]
SaveV = parser.get("K-Postprocess", "SaveVfield")
if SaveV == "yes":
SaveV = True
else:
SaveV = False
t0 = time.time()
k, diss, vx, vy, vz, Px, Py, Pz = ComputeVol(k, P, SaveV) # refina k
tDissVel = time.time() - t0
P = 0
S_min_post = int(parser.get("K-Postprocess", "MinBlockSize"))
nimax = 2 ** int(parser.get("K-Postprocess", "Max_sample_size"))
compKperm = parser.get("K-Postprocess", "kperm")
if compKperm == "yes":
compKperm = True
S_min_post = S_min_post * ref
if S_min_post == 0:
sx = 1 # k.shape[0]
else:
sx = get_min_nbl(k, nimax, nr, S_min_post)
kdiss, kave = getKpost(k, diss, vx, Px, Py, Pz, sx, rundir, ref, compKperm)
ttotal = time.time() - t0
summary = np.array([kdiss, kave, ttotal, tDissVel / ttotal]).T
np.savetxt(
rundir + "PosKeffSummary.txt",
summary,
header="K_diss, K_average,ttotal,tDiss/ttotal",
)
if SaveV:
np.save(rundir + "V.npy", np.array([vx, vy, vz]))
np.save(rundir + "D.npy", diss)
return
def getKpost(kf, diss, vx, Px, Py, Pz, sx, rundir, ref, compkperm):
ex = int(np.log2(kf.shape[0]))
esx = int(np.log2(sx))
scales = 2 ** np.arange(esx, ex)
datadir = rundir + "KpostProcess/"
try:
os.makedirs(datadir)
except:
nada = 0
for l in scales:
nblx, nbly, nblz = kf.shape[0] // l, kf.shape[1] // l, kf.shape[2] // l
sx, sy, sz = l, l, l
if kf.shape[2] == 1:
nblz = 1
sz = 1
Kdiss, Kave = np.zeros((nblx, nbly, nblz)), np.zeros((nblx, nbly, nblz))
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
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,
],
)
np.save(datadir + "Kd" + str(l // ref) + ".npy", Kdiss)
np.save(datadir + "Kv" + str(l // ref) + ".npy", Kave)
Kdiss, Kave = comp_Kdiss_Kaverage(kf, diss, vx, Px, Py, Pz)
np.save(datadir + "Kd" + str(kf.shape[0] // ref) + ".npy", np.array([Kdiss]))
np.save(datadir + "Kv" + str(kf.shape[0] // ref) + ".npy", np.array([Kave]))
return Kdiss, Kave
def get_min_nbl(kc, nimax, nr, smin):
if kc.shape[2] == 1:
dim = 2.0
else:
dim = 3.0
if nr > 0:
y = (1 / dim) * np.log2(nr * kc.size / (nimax * (smin ** dim)))
else:
y = 0
y = int(y)
s = int((2 ** y) * smin)
if s < smin:
s = smin
return s

254
tools/postprocessK/flow.py
Unescape Escape View File

@ -2,158 +2,199 @@ import numpy as np
from scipy.sparse import diags
from scipy.stats import mstats
from scipy.sparse.linalg import bicg, bicgstab, cg, dsolve #,LinearOperator, spilu, bicgstab
#from scikits.umfpack import spsolve, splu
from scipy.sparse.linalg import (
bicg,
bicgstab,
cg,
dsolve,
) # ,LinearOperator, spilu, bicgstab
# from scikits.umfpack import spsolve, splu
import time
def getDiss(k,vx,vy,vz):
diss = (vx[1:,:,:]**2+vx[:-1,:,:]**2+vy[:,1:,:]**2+vy[:,:-1,:]**2+vz[:,:,1:]**2+vz[:,:,:-1]**2)/(2*k)
return diss
def getDiss(k, vx, vy, vz):
diss = (
vx[1:, :, :] ** 2
+ vx[:-1, :, :] ** 2
+ vy[:, 1:, :] ** 2
+ vy[:, :-1, :] ** 2
+ vz[:, :, 1:] ** 2
+ vz[:, :, :-1] ** 2
) / (2 * k)
return diss
def ComputeVol(k,P,saveV):
def ComputeVol(k, P, saveV):
k=refina(k, P.shape[0]//k.shape[0])
Px,Py,Pz = getPfaces(k,P)
vx,vy,vz = getVfaces(k,P, Px,Py, Pz)
diss = getDiss(k,vx,vy,vz)
if saveV==False:
vy, vz= 0, 0
else:
vy, vz= 0.5*(vy[:,1:,:]+vy[:,:-1,:]), 0.5*(vz[:,:,1:]+vz[:,:,:-1])
vx= 0.5*(vx[1:,:,:]+vx[:-1,:,:])
k = refina(k, P.shape[0] // k.shape[0])
Px, Py, Pz = getPfaces(k, P)
vx, vy, vz = getVfaces(k, P, Px, Py, Pz)
diss = getDiss(k, vx, vy, vz)
if saveV == False:
vy, vz = 0, 0
else:
vy, vz = 0.5 * (vy[:, 1:, :] + vy[:, :-1, :]), 0.5 * (
vz[:, :, 1:] + vz[:, :, :-1]
)
vx = 0.5 * (vx[1:, :, :] + vx[:-1, :, :])
return k, diss, vx, vy, vz, Px, Py, Pz
return k, diss, vx,vy,vz, Px, Py, Pz
def comp_Kdiss_Kaverage(k, diss, vx, Px, Py, Pz):
mgx, mgy, mgz = np.mean(Px[-1,:,:]-Px[0,:,:])/k.shape[0],np.mean(Py[:,-1,:]-Py[:,0,:])/k.shape[1],np.mean(Pz[:,:,-1]-Pz[:,:,0])/k.shape[2]
kave=np.mean(vx)/mgx
kdiss=np.mean(diss)/(mgx**2+mgy**2+mgz**2)
return kdiss, kave
mgx, mgy, mgz = (
np.mean(Px[-1, :, :] - Px[0, :, :]) / k.shape[0],
np.mean(Py[:, -1, :] - Py[:, 0, :]) / k.shape[1],
np.mean(Pz[:, :, -1] - Pz[:, :, 0]) / k.shape[2],
)
kave = np.mean(vx) / mgx
kdiss = np.mean(diss) / (mgx ** 2 + mgy ** 2 + mgz ** 2)
return kdiss, kave
def getKeff(pm, k, pbc, Nz):
def getKeff(pm,k,pbc,Nz):
nx = k.shape[2] # Pasar k sin bordes de k=0
ny = k.shape[1]
nx = k.shape[2] #Pasar k sin bordes de k=0
ny = k.shape[1]
tz = 2 * k[1, :, :] * k[0, :, :] / (k[0, :, :] + k[1, :, :])
q = ((pm[0, :, :] - pm[1, :, :]) * tz).sum()
area = ny * nx
l = Nz
keff = q * l / (pbc * area)
return keff, q
tz = 2*k[1,:,:]*k[0, :,:]/(k[0, :,:]+k[1,:,:])
q=((pm[0,:,:]-pm[1,:,:])*tz).sum()
area=ny*nx
l=Nz
keff=q*l/(pbc*area)
return keff,q
def getPfaces(k,P):
nx,ny,nz=k.shape[0],k.shape[1],k.shape[2]
Px,Py,Pz= np.zeros((nx+1,ny,nz)),np.zeros((nx,ny+1,nz)),np.zeros((nx,ny,nz+1))
def getPfaces(k, P):
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
Px, Py, Pz = (
np.zeros((nx + 1, ny, nz)),
np.zeros((nx, ny + 1, nz)),
np.zeros((nx, ny, nz + 1)),
)
Px[1:-1,:,:] = (k[:-1,:,:]*P[:-1,:,:]+k[1:,:,:]*P[1:,:,:])/(k[:-1,:,:]+k[1:,:,:])
Px[0,:,:]=nx
Px[1:-1, :, :] = (k[:-1, :, :] * P[:-1, :, :] + k[1:, :, :] * P[1:, :, :]) / (
k[:-1, :, :] + k[1:, :, :]
)
Px[0, :, :] = nx
Py[:,1:-1,:] = (k[:,:-1,:]*P[:,:-1,:]+k[:,1:,:]*P[:,1:,:])/(k[:,:-1,:]+k[:,1:,:])
Py[:,0,:],Py[:,-1,:] =P[:,0,:], P[:,-1,:]
Py[:, 1:-1, :] = (k[:, :-1, :] * P[:, :-1, :] + k[:, 1:, :] * P[:, 1:, :]) / (
k[:, :-1, :] + k[:, 1:, :]
)
Py[:, 0, :], Py[:, -1, :] = P[:, 0, :], P[:, -1, :]
Pz[:,:,1:-1] = (k[:,:,:-1]*P[:,:,:-1]+k[:,:,1:]*P[:,:,1:])/(k[:,:,:-1]+k[:,:,1:])
Pz[:,:,0],Pz[:,:,-1] =P[:,:,0], P[:,:,-1]
Pz[:, :, 1:-1] = (k[:, :, :-1] * P[:, :, :-1] + k[:, :, 1:] * P[:, :, 1:]) / (
k[:, :, :-1] + k[:, :, 1:]
)
Pz[:, :, 0], Pz[:, :, -1] = P[:, :, 0], P[:, :, -1]
return Px, Py, Pz
return Px, Py, Pz
def getVfaces(k,P, Px,Py, Pz):
nx,ny,nz=k.shape[0],k.shape[1],k.shape[2]
vx,vy,vz= np.zeros((nx+1,ny,nz)),np.zeros((nx,ny+1,nz)),np.zeros((nx,ny,nz+1))
vx[1:,:,:] = 2*k*(Px[1:,:,:]-P) #v= k*(deltaP)/(deltaX/2)
vx[0,:,:] = 2*k[0,:,:]*(P[0,:,:]-Px[0,:,:])
def getVfaces(k, P, Px, Py, Pz):
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
vx, vy, vz = (
np.zeros((nx + 1, ny, nz)),
np.zeros((nx, ny + 1, nz)),
np.zeros((nx, ny, nz + 1)),
)
vx[1:, :, :] = 2 * k * (Px[1:, :, :] - P) # v= k*(deltaP)/(deltaX/2)
vx[0, :, :] = 2 * k[0, :, :] * (P[0, :, :] - Px[0, :, :])
vy[:,1:,:] = 2*k*(Py[:,1:,:]-P)
vy[:,0,:] = 2*k[:,0,:]*(P[:,0,:]-Py[:,0,:])
vz[:,:,1:] = 2*k*(Pz[:,:,1:]-P)
vz[:,:,0] = 2*k[:,:,0]*(P[:,:,0]-Pz[:,:,0])
return vx,vy,vz
vy[:, 1:, :] = 2 * k * (Py[:, 1:, :] - P)
vy[:, 0, :] = 2 * k[:, 0, :] * (P[:, 0, :] - Py[:, 0, :])
vz[:, :, 1:] = 2 * k * (Pz[:, :, 1:] - P)
vz[:, :, 0] = 2 * k[:, :, 0] * (P[:, :, 0] - Pz[:, :, 0])
return vx, vy, vz
def refina(k, ref):
if ref==1:
return k
nx,ny,nz=k.shape[0],k.shape[1],k.shape[2]
if ref == 1:
return k
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
krx=np.zeros((ref*nx,ny,nz))
for i in range(ref):
krx[i::ref,:,:]=k
k=0
krxy=np.zeros((ref*nx,ny*ref,nz))
for i in range(ref):
krxy[:,i::ref,:]=krx
krx=0
if nz==1:
return krxy
krx = np.zeros((ref * nx, ny, nz))
for i in range(ref):
krx[i::ref, :, :] = k
k = 0
krxy = np.zeros((ref * nx, ny * ref, nz))
for i in range(ref):
krxy[:, i::ref, :] = krx
krx = 0
if nz == 1:
return krxy
krxyz=np.zeros((ref*nx,ny*ref,nz*ref))
for i in range(ref):
krxyz[:,:,i::ref]=krxy
krxy=0
krxyz = np.zeros((ref * nx, ny * ref, nz * ref))
for i in range(ref):
krxyz[:, :, i::ref] = krxy
krxy = 0
return krxyz
return krxyz
def computeT(k):
nx = k.shape[0]
ny = k.shape[1]
nz = k.shape[2]
tx = np.zeros((nx+1,ny, nz))
ty = np.zeros((nx,ny+1, nz))
tz = np.zeros((nx,ny, nz+1))
tx[1:-1,:,:] = 2*k[:-1,:,:]*k[1:,:,:]/(k[:-1,:,:]+k[1:,:,:])
ty[:,1:-1,:] = 2*k[:,:-1,:]*k[:,1:,:]/(k[:,:-1,:]+k[:,1:,:])
tz[:,:,1:-1] = 2*k[:,:,:-1]*k[:,:,1:]/(k[:,:,:-1]+k[:,:,1:])
nx = k.shape[0]
ny = k.shape[1]
nz = k.shape[2]
tx = np.zeros((nx + 1, ny, nz))
ty = np.zeros((nx, ny + 1, nz))
tz = np.zeros((nx, ny, nz + 1))
tx[1:-1, :, :] = 2 * k[:-1, :, :] * k[1:, :, :] / (k[:-1, :, :] + k[1:, :, :])
ty[:, 1:-1, :] = 2 * k[:, :-1, :] * k[:, 1:, :] / (k[:, :-1, :] + k[:, 1:, :])
tz[:, :, 1:-1] = 2 * k[:, :, :-1] * k[:, :, 1:] / (k[:, :, :-1] + k[:, :, 1:])
return tx, ty, tz
return tx, ty, tz
def Rmat(k):
pbc = k.shape[0]
tx, ty, tz = computeT(k)
pbc=k.shape[0]
tx, ty , tz = computeT(k)
tx[0, :, :], tx[-1, :, :] = 2 * tx[1, :, :], 2 * tx[-2, :, :]
tx[0,:,:],tx[-1,:,:] = 2*tx[1,:,:],2*tx[-2,:,:]
rh = np.zeros((k.shape[0], k.shape[1], k.shape[2]))
rh=np.zeros((k.shape[0],k.shape[1],k.shape[2]))
rh[0, :, :] = pbc * tx[0, :, :]
rh = rh.reshape(-1)
d = (
tz[:, :, :-1]
+ tz[:, :, 1:]
+ ty[:, :-1, :]
+ ty[:, 1:, :]
+ tx[:-1, :, :]
+ tx[1:, :, :]
).reshape(-1)
a = (-tz[:, :, :-1].reshape(-1))[1:]
# a=(tx.reshape(-1))[:-1]
b = (-ty[:, 1:, :].reshape(-1))[: -k.shape[2]]
c = -tx[1:-1, :, :].reshape(-1)
rh[0,:,:]=pbc*tx[0,:,:]
rh=rh.reshape(-1)
d=(tz[:,:,:-1]+tz[:,:,1:]+ty[:,:-1,:]+ty[:,1:,:]+tx[:-1,:,:]+tx[1:,:,:]).reshape(-1)
a=(-tz[:,:,:-1].reshape(-1))[1:]
#a=(tx.reshape(-1))[:-1]
b=(-ty[:,1:,:].reshape(-1))[:-k.shape[2]]
c=-tx[1:-1,:,:].reshape(-1)
return a, b, c, d, rh
return a, b, c, d, rh
def PysolveP(k, solver):
a, b, c, d, rh = Rmat(k)
nx, ny, nz = k.shape[0], k.shape[1],k.shape[2]
offset = [-nz*ny,-nz, -1, 0, 1, nz, nz*ny]
km=diags(np.array([c, b, a, d, a, b, c]), offset, format='csc')
a, b, c, d = 0, 0 ,0 , 0
lu = splu(km)
print(lu)
p = solver(km, rh)
p=p.reshape(nx, ny, nz)
keff,q = getKeff(p,k,nz,nz)
return keff
'''
a, b, c, d, rh = Rmat(k)
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
offset = [-nz * ny, -nz, -1, 0, 1, nz, nz * ny]
km = diags(np.array([c, b, a, d, a, b, c]), offset, format="csc")
a, b, c, d = 0, 0, 0, 0
lu = splu(km)
print(lu)
p = solver(km, rh)
p = p.reshape(nx, ny, nz)
keff, q = getKeff(p, k, nz, nz)
return keff
"""
solvers=[bicg, bicgstab, cg, dsolve, spsolve]
snames=['bicg', 'bicgstab',' cg',' dsolve',' spsolve']
@ -168,5 +209,4 @@ for job in range(jobs):
keff=PysolveP(kff, solvers[i])
print('Solver: '+snames[i]+' time: '+str(time.time()-t0))
'''
"""

117
tools/postprocessK/kperm/Ndar1P.py
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@ -2,88 +2,77 @@ import numpy as np
import petsc4py
import math
import time
#from mpi4py import MPI
# from mpi4py import MPI
from tools.postprocessK.kperm.computeFlows import *
from petsc4py import PETSc
petsc4py.init('-ksp_max_it 9999999999')
from tools.postprocessK.kperm.flow import getKeff
def PetscP(datadir,ref,k,saveres):
ref=1
rank=0
pn=1
t0=time.time()
pcomm=PETSc.COMM_SELF
if k.shape[2]==1:
refz=1
else:
refz=ref
nz, ny, nx=k.shape[0]*ref,k.shape[1]*ref,k.shape[2]*refz
n=nx*ny*nz
K = PETSc.Mat().create(comm=pcomm)
K.setType('seqaij')
K.setSizes(((n,None),(n,None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7,4)) # Idem anterior
K.setUp()
R = PETSc.Vec().createSeq((n,None),comm=pcomm) #PETSc.COMM_WORLD
R.setUp()
k2, Nz, nnz2=getKref(k,1,2,ref)
k, Nz, nnz=getKref(k,0,2,ref)
petsc4py.init("-ksp_max_it 9999999999")
from tools.postprocessK.kperm.flow import getKeff
pbc=float(Nz)
def PetscP(datadir, ref, k, saveres):
K,R = firstL(K,R,k,pbc)
r=(k.shape[1]-2)*(k.shape[2]-2)*nnz2 #start row
K,R =lastL(K,R,k2,r)
ref = 1
rank = 0
pn = 1
t0 = time.time()
k2=0
pcomm = PETSc.COMM_SELF
if k.shape[2] == 1:
refz = 1
else:
refz = ref
K.assemble()
R.assemble()
nz, ny, nx = k.shape[0] * ref, k.shape[1] * ref, k.shape[2] * refz
n = nx * ny * nz
K = PETSc.Mat().create(comm=pcomm)
K.setType("seqaij")
K.setSizes(((n, None), (n, None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7, 4)) # Idem anterior
K.setUp()
R = PETSc.Vec().createSeq((n, None), comm=pcomm) # PETSc.COMM_WORLD
R.setUp()
ksp = PETSc.KSP()
ksp.create(comm=pcomm)
ksp.setFromOptions()
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
k2, Nz, nnz2 = getKref(k, 1, 2, ref)
k, Nz, nnz = getKref(k, 0, 2, ref)
pc.create(comm=pcomm)
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1=time.time()
ksp.solve(R, P)
t2=time.time()
p=P.getArray().reshape(nz,ny,nx)
pbc = float(Nz)
if rank==0:
keff,Q=getKeff(p,k[1:-1,1:-1,1:-1],pbc,Nz)
print(keff,ref,nx,ny,nz)
return keff
K, R = firstL(K, R, k, pbc)
r = (k.shape[1] - 2) * (k.shape[2] - 2) * nnz2 # start row
K, R = lastL(K, R, k2, r)
k2 = 0
return
K.assemble()
R.assemble()
ksp = PETSc.KSP()
ksp.create(comm=pcomm)
ksp.setFromOptions()
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
pc.create(comm=pcomm)
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1 = time.time()
ksp.solve(R, P)
t2 = time.time()
p = P.getArray().reshape(nz, ny, nx)
#Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)
if rank == 0:
keff, Q = getKeff(p, k[1:-1, 1:-1, 1:-1], pbc, Nz)
print(keff, ref, nx, ny, nz)
return keff
return
# Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)

240
tools/postprocessK/kperm/Ndar1PBack.py
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@ -1,135 +1,123 @@
import numpy as np
#import petsc4py
# import petsc4py
import math
import time
#from mpi4py import MPI
# from mpi4py import MPI
from tools.postprocessK.kperm.computeFlows import *
from petsc4py import PETSc
#petsc4py.init('-ksp_max_it 9999999999',comm=PETSc.COMM_SELF)
from tools.postprocessK.flow import getKeff
def PetscP(datadir,ref,k,saveres):
#datadir='./data/'+str(job)+'/'
#comm=MPI.COMM_WORLD
#rank=comm.Get_rank()
'''
size=comm.Get_size()
print(rank,size)
pcomm = MPI.COMM_WORLD.Split(color=rank, key=rank)
#print(new_comm.Get_rank())
#pcomm=comm.Create(newgroup)
print('entro')
print pcomm.Get_rank()
print pcomm.Get_size()
pcomm=comm
rank=pcomm.rank
pn=pcomm.size
#PETSc.COMM_WORLD.PetscSubcommCreate(pcomm,PetscSubcomm *psubcomm)
print(rank,pn)
'''
#Optpetsc = PETSc.Options()
rank=0
pn=1
t0=time.time()
#comm=MPI.Comm.Create()
if k.shape[2]==1:
refz=1
else:
refz=ref
nz, ny, nx=k.shape[0]*ref,k.shape[1]*ref,k.shape[2]*refz
n=nx*ny*nz
print('algo')
K = PETSc.Mat().create(comm=PETSc.COMM_SELF)
print('algo2')
K.setType('seqaij')
print('algo3')
K.setSizes(((n,None),(n,None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7,4)) # Idem anterior
#K = PETSc.Mat('seqaij', m=n,n=n,nz=7,comm=PETSc.COMM_WORLD)
#K = PETSc.Mat('aij', ((n,None),(n,None)), nnz=(7,4),comm=PETSc.COMM_WORLD)
#K = PETSc.Mat().createAIJ(((n,None),(n,None)), nnz=(7,4),comm=PETSc.COMM_WORLD)
#K = PETSc.Mat().createSeqAIJ(((n,None),(n,None)), nnz=(7,4),comm=PETSc.COMM_WORLD)
#K.setPreallocationNNZ(nnz=(7,4))
print('ksetup')
#K.MatCreateSeqAIJ()
#K=PETSc.Mat().MatCreate(PETSc.COMM_WORLD)
#K = PETSc.Mat().createAIJ(((n,None),(n,None)), nnz=(7,4),comm=pcomm)
K.setUp()
print('entro2')
R = PETSc.Vec().createSeq((n,None),comm=PETSc.COMM_SELF) #PETSc.COMM_WORLD
R.setUp()
print('entro2')
k2, Nz, nnz2=getKref(k,1,2,ref)
k, Nz, nnz=getKref(k,0,2,ref)
pbc=float(Nz)
#print('entro3')
K,R = firstL(K,R,k,pbc)
r=(k.shape[1]-2)*(k.shape[2]-2)*nnz2 #start row
K,R =lastL(K,R,k2,r)
k2=0
K.assemble()
R.assemble()
print('entro3')
ksp = PETSc.KSP()
ksp.create(comm=PETSc.COMM_SELF)
ksp.setFromOptions()
print('entro4')
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
pc.create(comm=PETSc.COMM_SELF)
print('entro4')
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1=time.time()
ksp.solve(R, P)
t2=time.time()
p=P.getArray().reshape(nz,ny,nx)
if rank==0:
keff,Q=getKeff(p,k[1:-1,1:-1,1:-1],pbc,Nz)
return keff
return
#Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)
# petsc4py.init('-ksp_max_it 9999999999',comm=PETSc.COMM_SELF)
from tools.postprocessK.flow import getKeff
def PetscP(datadir, ref, k, saveres):
# datadir='./data/'+str(job)+'/'
# comm=MPI.COMM_WORLD
# rank=comm.Get_rank()
"""
size=comm.Get_size()
print(rank,size)
pcomm = MPI.COMM_WORLD.Split(color=rank, key=rank)
#print(new_comm.Get_rank())
#pcomm=comm.Create(newgroup)
print('entro')
print pcomm.Get_rank()
print pcomm.Get_size()
pcomm=comm
rank=pcomm.rank
pn=pcomm.size
#PETSc.COMM_WORLD.PetscSubcommCreate(pcomm,PetscSubcomm *psubcomm)
print(rank,pn)
"""
# Optpetsc = PETSc.Options()
rank = 0
pn = 1
t0 = time.time()
# comm=MPI.Comm.Create()
if k.shape[2] == 1:
refz = 1
else:
refz = ref
nz, ny, nx = k.shape[0] * ref, k.shape[1] * ref, k.shape[2] * refz
n = nx * ny * nz
print("algo")
K = PETSc.Mat().create(comm=PETSc.COMM_SELF)
print("algo2")
K.setType("seqaij")
print("algo3")
K.setSizes(((n, None), (n, None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7, 4)) # Idem anterior
# K = PETSc.Mat('seqaij', m=n,n=n,nz=7,comm=PETSc.COMM_WORLD)
# K = PETSc.Mat('aij', ((n,None),(n,None)), nnz=(7,4),comm=PETSc.COMM_WORLD)
# K = PETSc.Mat().createAIJ(((n,None),(n,None)), nnz=(7,4),comm=PETSc.COMM_WORLD)
# K = PETSc.Mat().createSeqAIJ(((n,None),(n,None)), nnz=(7,4),comm=PETSc.COMM_WORLD)
# K.setPreallocationNNZ(nnz=(7,4))
print("ksetup")
# K.MatCreateSeqAIJ()
# K=PETSc.Mat().MatCreate(PETSc.COMM_WORLD)
# K = PETSc.Mat().createAIJ(((n,None),(n,None)), nnz=(7,4),comm=pcomm)
K.setUp()
print("entro2")
R = PETSc.Vec().createSeq((n, None), comm=PETSc.COMM_SELF) # PETSc.COMM_WORLD
R.setUp()
print("entro2")
k2, Nz, nnz2 = getKref(k, 1, 2, ref)
k, Nz, nnz = getKref(k, 0, 2, ref)
pbc = float(Nz)
# print('entro3')
K, R = firstL(K, R, k, pbc)
r = (k.shape[1] - 2) * (k.shape[2] - 2) * nnz2 # start row
K, R = lastL(K, R, k2, r)
k2 = 0
K.assemble()
R.assemble()
print("entro3")
ksp = PETSc.KSP()
ksp.create(comm=PETSc.COMM_SELF)
ksp.setFromOptions()
print("entro4")
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
pc.create(comm=PETSc.COMM_SELF)
print("entro4")
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1 = time.time()
ksp.solve(R, P)
t2 = time.time()
p = P.getArray().reshape(nz, ny, nx)
if rank == 0:
keff, Q = getKeff(p, k[1:-1, 1:-1, 1:-1], pbc, Nz)
return keff
return
# Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)

591
tools/postprocessK/kperm/computeFlows.py
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@ -2,50 +2,72 @@ import numpy as np
import math
def getKref(k,rank,pn,ref):
Nz = k.shape[0]
nz = Nz//pn
if ref==1:
return getK(k,rank,pn)
if (rank>0) and (rank<pn-1):
k=k[rank*nz-1:(rank+1)*nz+1,:,:]
k=refinaPy(k, ref)
if ref!=1:
k=k[(ref-1):-(ref-1),:,:]
nz,ny,nx=k.shape[0],k.shape[1],k.shape[2]
ki=np.zeros((nz,ny+2,nx+2))
ki[:,1:-1,1:-1]=k
nnz=nz
if rank==0:
k=k[:(rank+1)*nz+1,:,:]
k=refinaPy(k, ref)
if ref!=1:
k=k[:-(ref-1),:,:]
nz,ny,nx=k.shape[0],k.shape[1],k.shape[2]
ki=np.zeros((nz+1,ny+2,nx+2))
ki[1:,1:-1,1:-1]=k
ki[0,:,:]=ki[1,:,:]
nnz=nz
if rank==(pn-1):
k=k[rank*nz-1:,:,:]
k=refinaPy(k, ref)
if ref!=1:
k=k[(ref-1):,:,:]
nz,ny,nx=k.shape[0],k.shape[1],k.shape[2]
ki=np.zeros((nz+1,ny+2,nx+2))
ki[:-1,1:-1,1:-1]=k
ki[-1,:,:]=ki[-2,:,:]
nnz=(Nz//pn)*ref
return ki, Nz*ref, nnz
def getKref(k, rank, pn, ref):
Nz = k.shape[0]
nz = Nz // pn
if ref == 1:
return getK(k, rank, pn)
if (rank > 0) and (rank < pn - 1):
k = k[rank * nz - 1 : (rank + 1) * nz + 1, :, :]
k = refinaPy(k, ref)
if ref != 1:
k = k[(ref - 1) : -(ref - 1), :, :]
nz, ny, nx = k.shape[0], k.shape[1], k.shape[2]
ki = np.zeros((nz, ny + 2, nx + 2))
ki[:, 1:-1, 1:-1] = k
nnz = nz
if rank == 0:
k = k[: (rank + 1) * nz + 1, :, :]
k = refinaPy(k, ref)
if ref != 1:
k = k[: -(ref - 1), :, :]
nz, ny, nx = k.shape[0], k.shape[1], k.shape[2]
ki = np.zeros((nz + 1, ny + 2, nx + 2))
ki[1:, 1:-1, 1:-1] = k
ki[0, :, :] = ki[1, :, :]
nnz = nz
if rank == (pn - 1):
k = k[rank * nz - 1 :, :, :]
k = refinaPy(k, ref)
if ref != 1:
k = k[(ref - 1) :, :, :]
nz, ny, nx = k.shape[0], k.shape[1], k.shape[2]
ki = np.zeros((nz + 1, ny + 2, nx + 2))
ki[:-1, 1:-1, 1:-1] = k
ki[-1, :, :] = ki[-2, :, :]
nnz = (Nz // pn) * ref
return ki, Nz * ref, nnz
def getK(k, rank, pn):
# k=np.load(kfile)
# nn=int(np.cbrt(k.shape[0]))
# k=k.reshape((nn,nn,nn))
Nz, Ny, Nx = k.shape[0], k.shape[1], k.shape[2]
nz = Nz // pn
if rank == pn - 1:
nnz = Nz - (pn - 1) * nz
ki = np.zeros((nnz + 2, Ny + 2, Nx + 2))
else:
nnz = nz
ki = np.zeros((nz + 2, Ny + 2, Nx + 2))
if (rank > 0) and (rank < pn - 1):
ki[:, 1:-1, 1:-1] = k[rank * nz - 1 : (rank + 1) * nz + 1, :, :]
if rank == 0:
ki[1:, 1:-1, 1:-1] = k[: (rank + 1) * nz + 1, :, :]
ki[0, :, :] = ki[1, :, :]
if rank == (pn - 1):
ki[:-1, 1:-1, 1:-1] = k[rank * nz - 1 :, :, :]
ki[-1, :, :] = ki[-2, :, :]
return ki, Nz, nz
"""
def getK(k,rank,pn):
#k=np.load(kfile)
@ -69,188 +91,307 @@ def getK(k,rank,pn):
ki[:-1,1:-1,1:-1]=k[rank*nz-1:,:,:]
ki[-1,:,:]=ki[-2,:,:]
return ki, Nz, nz
'''
def getK(k,rank,pn):
"""
#k=np.load(kfile)
#nn=int(np.cbrt(k.shape[0]))
#k=k.reshape((nn,nn,nn))
Nz, Ny,Nx=k.shape[0],k.shape[1],k.shape[2]
nz=Nz//pn
if rank==pn-1:
nnz= Nz-(pn-1)*nz
ki=np.zeros((nnz+2,Ny+2,Nx+2))
else:
nnz=nz
ki=np.zeros((nz+2,Ny+2,Nx+2))
if (rank>0) and (rank<pn-1):
ki[:,1:-1,1:-1]=k[rank*nz-1:(rank+1)*nz+1,:,:]
if rank==0:
ki[1:,1:-1,1:-1]=k[:(rank+1)*nz+1,:,:]
ki[0,:,:]=ki[1,:,:]
if rank==(pn-1):
ki[:-1,1:-1,1:-1]=k[rank*nz-1:,:,:]
ki[-1,:,:]=ki[-2,:,:]
return ki, Nz, nz
'''
def refinaPy(k, ref):
if ref==1:
return k
nx,ny,nz=k.shape[2],k.shape[1],k.shape[0]
krz=np.zeros((ref*nz,ny,nx))
for i in range(ref):
krz[i::ref,:,:]=k
k=0
krzy=np.zeros((ref*nz,ny*ref,nx))
for i in range(ref):
krzy[:,i::ref,:]=krz
if nx==1:
return krzy
krz=0
krzyx=np.zeros((ref*nz,ny*ref,nx*ref))
for i in range(ref):
krzyx[:,:,i::ref]=krzy
return krzyx #krzyx[(ref-1):-(ref-1),:,:]
def centL(K,R,kkm,r):
nx, ny, nz=kkm.shape[2]-2,kkm.shape[1]-2,kkm.shape[0]-2
for k in range(nz):
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1]) ])
K.setValues(r, r, t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r-1,-t[1])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r+nx*ny,-t[4])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
r+=1
return K, R
def firstL(K,R,kkm,pbc):
# Right side of Rmat
r=0
nx, ny, nz=kkm.shape[2]-2,kkm.shape[1]-2,kkm.shape[0]-2
k=0
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),4*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1]) ]) #atento aca BC 2Tz
K.setValues(r, r,t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r+nx*ny,-t[4])
R.setValues(r, t[5]*pbc)
r+=1
# Left side of Rmat
for j in range(ny):
for i in range(1,nx):
r=j*nx+i
K.setValues(r,r-1,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]))
for j in range(1,ny):
for i in range(nx):
r=j*nx+i
K.setValues(r,r-nx,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]))
for k in range(1,nz):
for j in range(ny):
for i in range(nx):
r=k*ny*nx+j*nx+i
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1]) ])
K.setValues(r, r,t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r-1,-t[1])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r+nx*ny,-t[4])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
return K,R
def lastL(K,R,kkm,r):
# Right side of Rmat
nx, ny, nz=kkm.shape[2]-2,kkm.shape[1]-2,kkm.shape[0]-2
for k in range(nz-1):
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1])])
K.setValues(r, r, t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r-1,-t[1])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r+nx*ny,-t[4])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
r=r+1
auxr=r
k=-3
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),4*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1])]) #guarda aca BC en t[4] va por 2 por dx/2
K.setValues(r, r,t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r-1,-t[1])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
r+=1
# Right side of Mat
for j in range(ny):
for i in range(nx-1):
r=j*nx+i+auxr
K.setValues(r,r+1,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]))
for j in range(ny-1):
for i in range(nx):
r=j*nx+i+auxr
K.setValues(r,r+nx,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]))
return K,R
if ref == 1:
return k
nx, ny, nz = k.shape[2], k.shape[1], k.shape[0]
krz = np.zeros((ref * nz, ny, nx))
for i in range(ref):
krz[i::ref, :, :] = k
k = 0
krzy = np.zeros((ref * nz, ny * ref, nx))
for i in range(ref):
krzy[:, i::ref, :] = krz
if nx == 1:
return krzy
krz = 0
krzyx = np.zeros((ref * nz, ny * ref, nx * ref))
for i in range(ref):
krzyx[:, :, i::ref] = krzy
return krzyx # krzyx[(ref-1):-(ref-1),:,:]
def centL(K, R, kkm, r):
nx, ny, nz = kkm.shape[2] - 2, kkm.shape[1] - 2, kkm.shape[0] - 2
for k in range(nz):
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
)
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r + nx * ny, -t[4])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
r += 1
return K, R
def firstL(K, R, kkm, pbc):
# Right side of Rmat
r = 0
nx, ny, nz = kkm.shape[2] - 2, kkm.shape[1] - 2, kkm.shape[0] - 2
k = 0
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
4
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
) # atento aca BC 2Tz
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r + nx * ny, -t[4])
R.setValues(r, t[5] * pbc)
r += 1
# Left side of Rmat
for j in range(ny):
for i in range(1, nx):
r = j * nx + i
K.setValues(
r,
r - 1,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
)
for j in range(1, ny):
for i in range(nx):
r = j * nx + i
K.setValues(
r,
r - nx,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
)
for k in range(1, nz):
for j in range(ny):
for i in range(nx):
r = k * ny * nx + j * nx + i
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
)
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r + nx * ny, -t[4])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
return K, R
def lastL(K, R, kkm, r):
# Right side of Rmat
nx, ny, nz = kkm.shape[2] - 2, kkm.shape[1] - 2, kkm.shape[0] - 2
for k in range(nz - 1):
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
)
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r + nx * ny, -t[4])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
r = r + 1
auxr = r
k = -3
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
4
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
) # guarda aca BC en t[4] va por 2 por dx/2
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
r += 1
# Right side of Mat
for j in range(ny):
for i in range(nx - 1):
r = j * nx + i + auxr
K.setValues(
r,
r + 1,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
)
for j in range(ny - 1):
for i in range(nx):
r = j * nx + i + auxr
K.setValues(
r,
r + nx,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
)
return K, R

21
tools/postprocessK/kperm/flow.py
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@ -1,17 +1,14 @@
import numpy as np
def getKeff(pm, k, pbc, Nz):
nx = k.shape[2] # Pasar k sin bordes de k=0
ny = k.shape[1]
def getKeff(pm,k,pbc,Nz):
nx = k.shape[2] #Pasar k sin bordes de k=0
ny = k.shape[1]
tz = 2*k[1,:,:]*k[0, :,:]/(k[0, :,:]+k[1,:,:])
q=((pm[0,:,:]-pm[1,:,:])*tz).sum()
area=ny*nx
l=Nz
keff=q*l/(pbc*area)
return keff,q
tz = 2 * k[1, :, :] * k[0, :, :] / (k[0, :, :] + k[1, :, :])
q = ((pm[0, :, :] - pm[1, :, :]) * tz).sum()
area = ny * nx
l = Nz
keff = q * l / (pbc * area)
return keff, q

312
tools/solver/Ndar.py
Unescape Escape View File

@ -1,179 +1,167 @@
import numpy as np
import petsc4py
import math
import time
#from mpi4py import MPI
# from mpi4py import MPI
from tools.postprocessK.kperm.computeFlows import *
from tools.postprocessK.flow import getKeff
from petsc4py import PETSc
import sys
def PetscP(datadir,ref,k,saveres,Rtol,comm):
if comm==0:
pcomm=PETSc.COMM_SELF
rank=0
pn=1
else:
pcomm=PETSc.COMM_WORLD
rank=pcomm.rank
pn=pcomm.size
t0=time.time()
if pn==1:
if not isinstance(k,np.ndarray):
k = np.load(datadir+'k.npy')
if k.shape[2]==1:
refz=1
else:
refz=ref
nz, ny, nx=k.shape[0]*ref,k.shape[1]*ref,k.shape[2]*refz
n=nx*ny*nz
K = PETSc.Mat().create(comm=pcomm)
K.setType('seqaij')
K.setSizes(((n,None),(n,None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7,4)) # Idem anterior
K.setUp()
R = PETSc.Vec().createSeq((n,None),comm=pcomm) #PETSc.COMM_WORLD
R.setUp()
k2, Nz, nnz2=getKref(k,1,2,ref)
k, Nz, nnz=getKref(k,0,2,ref)
pbc=float(Nz)
K,R = firstL(K,R,k,pbc)
r=(k.shape[1]-2)*(k.shape[2]-2)*nnz2 #start row
K,R =lastL(K,R,k2,r)
k2=0
else:
if not isinstance(k,np.ndarray):
k = np.load(datadir+'k.npy')
k, Nz, nnz=getKref(k,rank,pn,ref)
pbc=float(Nz)
nz, ny, nx=(k.shape[0]-2),(k.shape[1]-2),(k.shape[2]-2)
n=nx*ny*nz
K = PETSc.Mat().createAIJ(((n,None),(n,None)), nnz=(7,4), comm=pcomm)
K.setUp()
R = PETSc.Vec().createMPI((n,None),comm=pcomm)
R.setUp()
r=nx*ny*nnz*rank #start row
if rank==0:
K,R = firstL(K,R,k,pbc)
if (rank>0) and (rank<pn-1):
K,R=centL(K,R,k,r)
k=0
if rank==(pn-1):
K,R =lastL(K,R,k,r)
k=0
K.assemble()
R.assemble()
ksp = PETSc.KSP()
ksp.create(comm=pcomm)
ksp.setTolerances(rtol=Rtol, atol=1.0e-100, max_it=999999999)
ksp.setFromOptions()
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
pc.create(comm=pcomm)
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1=time.time()
ksp.solve(R, P)
t2=time.time()
p=P.getArray().reshape(nz,ny,nx)
if rank==0:
keff,Q=getKeff(p,k[1:-1,1:-1,1:-1],pbc,Nz)
if saveres==True:
for i in range(1,pn):
from mpi4py import MPI
comm=MPI.COMM_WORLD
pi=comm.recv(source=i)
p=np.append(p,pi,axis=0)
np.save(datadir+'P',p)
f=open(datadir+"RunTimes.out","a")
f.write("ref: "+str(ref)+"\n")
f.write("Matrix creation: "+str(t1-t0)+"\n")
f.write("Solver: "+str(t2-t1)+"\n")
f.write("Keff: "+str(keff)+"\n")
f.write("N_cores: "+str(pn)+"\n")
f.close()
try:
res=np.loadtxt(datadir+'SolverRes.txt')
res=np.append(res,np.array([keff,ref,t2-t0,pn]))
except:
res=np.array([keff,ref,t2-t0,pn])
np.savetxt(datadir+'SolverRes.txt',res,header='Keff, ref, Runtime, N_cores')
print(datadir[-3:],' keff= '+str(keff), ' rtime= '+str(t2-t0))
return keff
else:
if saveres==True:
from mpi4py import MPI
comm=MPI.COMM_WORLD
comm.send(p, dest=0)
return
#Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)
def PetscP(datadir, ref, k, saveres, Rtol, comm):
if comm == 0:
pcomm = PETSc.COMM_SELF
rank = 0
pn = 1
else:
pcomm = PETSc.COMM_WORLD
rank = pcomm.rank
pn = pcomm.size
t0 = time.time()
if pn == 1:
if not isinstance(k, np.ndarray):
k = np.load(datadir + "k.npy")
if k.shape[2] == 1:
refz = 1
else:
refz = ref
nz, ny, nx = k.shape[0] * ref, k.shape[1] * ref, k.shape[2] * refz
n = nx * ny * nz
K = PETSc.Mat().create(comm=pcomm)
K.setType("seqaij")
K.setSizes(((n, None), (n, None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7, 4)) # Idem anterior
K.setUp()
R = PETSc.Vec().createSeq((n, None), comm=pcomm) # PETSc.COMM_WORLD
R.setUp()
k2, Nz, nnz2 = getKref(k, 1, 2, ref)
k, Nz, nnz = getKref(k, 0, 2, ref)
pbc = float(Nz)
K, R = firstL(K, R, k, pbc)
r = (k.shape[1] - 2) * (k.shape[2] - 2) * nnz2 # start row
K, R = lastL(K, R, k2, r)
k2 = 0
else:
if not isinstance(k, np.ndarray):
k = np.load(datadir + "k.npy")
k, Nz, nnz = getKref(k, rank, pn, ref)
pbc = float(Nz)
nz, ny, nx = (k.shape[0] - 2), (k.shape[1] - 2), (k.shape[2] - 2)
n = nx * ny * nz
K = PETSc.Mat().createAIJ(((n, None), (n, None)), nnz=(7, 4), comm=pcomm)
K.setUp()
R = PETSc.Vec().createMPI((n, None), comm=pcomm)
R.setUp()
r = nx * ny * nnz * rank # start row
if rank == 0:
K, R = firstL(K, R, k, pbc)
if (rank > 0) and (rank < pn - 1):
K, R = centL(K, R, k, r)
k = 0
if rank == (pn - 1):
K, R = lastL(K, R, k, r)
k = 0
K.assemble()
R.assemble()
ksp = PETSc.KSP()
ksp.create(comm=pcomm)
ksp.setTolerances(rtol=Rtol, atol=1.0e-100, max_it=999999999)
ksp.setFromOptions()
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
pc.create(comm=pcomm)
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1 = time.time()
ksp.solve(R, P)
t2 = time.time()
p = P.getArray().reshape(nz, ny, nx)
if rank == 0:
keff, Q = getKeff(p, k[1:-1, 1:-1, 1:-1], pbc, Nz)
if saveres == True:
for i in range(1, pn):
from mpi4py import MPI
comm = MPI.COMM_WORLD
pi = comm.recv(source=i)
p = np.append(p, pi, axis=0)
np.save(datadir + "P", p)
f = open(datadir + "RunTimes.out", "a")
f.write("ref: " + str(ref) + "\n")
f.write("Matrix creation: " + str(t1 - t0) + "\n")
f.write("Solver: " + str(t2 - t1) + "\n")
f.write("Keff: " + str(keff) + "\n")
f.write("N_cores: " + str(pn) + "\n")
f.close()
try:
res = np.loadtxt(datadir + "SolverRes.txt")
res = np.append(res, np.array([keff, ref, t2 - t0, pn]))
except:
res = np.array([keff, ref, t2 - t0, pn])
np.savetxt(
datadir + "SolverRes.txt", res, header="Keff, ref, Runtime, N_cores"
)
print(datadir[-3:], " keff= " + str(keff), " rtime= " + str(t2 - t0))
return keff
else:
if saveres == True:
from mpi4py import MPI
comm = MPI.COMM_WORLD
comm.send(p, dest=0)
return
# Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)
try:
if sys.argv[5]=='1':
from mpi4py import MPI
icomm = MPI.Comm.Get_parent()
PetscP(sys.argv[1],int(sys.argv[2]),'0',True,float(sys.argv[4]),1) #multip cores not Tupac
#icomm = MPI.Comm.Get_parent()
icomm.Disconnect()
else:
PetscP(sys.argv[1],int(sys.argv[2]),'0',True,float(sys.argv[4]),0) #1 core read k map
except IndexError:
try:
PetscP(sys.argv[1],int(sys.argv[2]),'0',True,1e-4,1) # multip core as executable
except IndexError:
nada=0
# PetscP(sys.argv[1],int(sys.argv[2]),sys.argv[3],False,1e-4,0) #1 core, k field as argument
if sys.argv[5] == "1":
from mpi4py import MPI
icomm = MPI.Comm.Get_parent()
PetscP(
sys.argv[1], int(sys.argv[2]), "0", True, float(sys.argv[4]), 1
) # multip cores not Tupac
# icomm = MPI.Comm.Get_parent()
icomm.Disconnect()
else:
PetscP(
sys.argv[1], int(sys.argv[2]), "0", True, float(sys.argv[4]), 0
) # 1 core read k map
except IndexError:
try:
PetscP(
sys.argv[1], int(sys.argv[2]), "0", True, 1e-4, 1
) # multip core as executable
except IndexError:
nada = 0
# PetscP(sys.argv[1],int(sys.argv[2]),sys.argv[3],False,1e-4,0) #1 core, k field as argument

310
tools/solver/Ndar_temp.py
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@ -1,175 +1,161 @@
print('importo0')
print("importo0")
import numpy as np
#import petsc4py
print('importo1')
# import petsc4py
print("importo1")
import math
import time
#from mpi4py import MPI
# from mpi4py import MPI
from tools.postprocessK.kperm.computeFlows import *
print('importo2')
print("importo2")
print('importo4')
print("importo4")
from tools.postprocessK.flow import getKeff
import sys
def PetscP(datadir,ref,k,saveres,Rtol,comm):
from petsc4py import PETSc
#petsc4py.init('-ksp_max_it 9999999999')
print('importo3')
if comm==0:
pcomm=PETSc.COMM_SELF
rank=0
pn=1
else:
pcomm=PETSc.COMM_WORLD
rank=pcomm.rank
pn=pcomm.size
t0=time.time()
if pn==1:
if not isinstance(k,np.ndarray):
k = np.load(datadir+'k.npy')
if k.shape[2]==1:
refz=1
else:
refz=ref
nz, ny, nx=k.shape[0]*ref,k.shape[1]*ref,k.shape[2]*refz
n=nx*ny*nz
K = PETSc.Mat().create(comm=pcomm)
K.setType('seqaij')
K.setSizes(((n,None),(n,None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7,4)) # Idem anterior
K.setUp()
R = PETSc.Vec().createSeq((n,None),comm=pcomm) #PETSc.COMM_WORLD
R.setUp()
k2, Nz, nnz2=getKref(k,1,2,ref)
k, Nz, nnz=getKref(k,0,2,ref)
pbc=float(Nz)
K,R = firstL(K,R,k,pbc)
r=(k.shape[1]-2)*(k.shape[2]-2)*nnz2 #start row
K,R =lastL(K,R,k2,r)
k2=0
else:
if not isinstance(k,np.ndarray):
k = np.load(datadir+'k.npy')
k, Nz, nnz=getKref(k,rank,pn,ref)
pbc=float(Nz)
nz, ny, nx=(k.shape[0]-2),(k.shape[1]-2),(k.shape[2]-2)
n=nx*ny*nz
K = PETSc.Mat().createAIJ(((n,None),(n,None)), nnz=(7,4), comm=pcomm)
K.setUp()
R = PETSc.Vec().createMPI((n,None),comm=pcomm)
R.setUp()
r=nx*ny*nnz*rank #start row
if rank==0:
K,R = firstL(K,R,k,pbc)
if (rank>0) and (rank<pn-1):
K,R=centL(K,R,k,r)
k=0
if rank==(pn-1):
K,R =lastL(K,R,k,r)
k=0
K.assemble()
R.assemble()
ksp = PETSc.KSP()
ksp.create(comm=pcomm)
ksp.setTolerances(rtol=Rtol, atol=1.0e-100, max_it=999999999)
ksp.setFromOptions()
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
pc.create(comm=pcomm)
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1=time.time()
ksp.solve(R, P)
t2=time.time()
p=P.getArray().reshape(nz,ny,nx)
if rank==0:
keff,Q=getKeff(p,k[1:-1,1:-1,1:-1],pbc,Nz)
if saveres==True:
for i in range(1,pn):
from mpi4py import MPI
comm=MPI.COMM_WORLD
pi=comm.recv(source=i)
p=np.append(p,pi,axis=0)
np.save(datadir+'P',p)
f=open(datadir+"RunTimes.out","a")
f.write("ref: "+str(ref)+"\n")
f.write("Matrix creation: "+str(t1-t0)+"\n")
f.write("Solver: "+str(t2-t1)+"\n")
f.write("Keff: "+str(keff)+"\n")
f.write("N_cores: "+str(pn)+"\n")
f.close()
try:
res=np.loadtxt(datadir+'SolverRes.txt')
res=np.append(res,np.array([keff,ref,t2-t0,pn]))
except:
res=np.array([keff,ref,t2-t0,pn])
np.savetxt(datadir+'SolverRes.txt',res,header='Keff, ref, Runtime, N_cores')
print(datadir[-3:],' keff= '+str(keff), ' rtime= '+str(t2-t0))
return keff
else:
if saveres==True:
from mpi4py import MPI
comm=MPI.COMM_WORLD
comm.send(p, dest=0)
return
#Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)
ddir='./test/0/'
ref=1
def PetscP(datadir, ref, k, saveres, Rtol, comm):
from petsc4py import PETSc
# petsc4py.init('-ksp_max_it 9999999999')
print("importo3")
if comm == 0:
pcomm = PETSc.COMM_SELF
rank = 0
pn = 1
else:
pcomm = PETSc.COMM_WORLD
rank = pcomm.rank
pn = pcomm.size
t0 = time.time()
if pn == 1:
if not isinstance(k, np.ndarray):
k = np.load(datadir + "k.npy")
if k.shape[2] == 1:
refz = 1
else:
refz = ref
nz, ny, nx = k.shape[0] * ref, k.shape[1] * ref, k.shape[2] * refz
n = nx * ny * nz
K = PETSc.Mat().create(comm=pcomm)
K.setType("seqaij")
K.setSizes(((n, None), (n, None))) # Aca igual que lo que usas arriba
K.setPreallocationNNZ(nnz=(7, 4)) # Idem anterior
K.setUp()
R = PETSc.Vec().createSeq((n, None), comm=pcomm) # PETSc.COMM_WORLD
R.setUp()
k2, Nz, nnz2 = getKref(k, 1, 2, ref)
k, Nz, nnz = getKref(k, 0, 2, ref)
pbc = float(Nz)
K, R = firstL(K, R, k, pbc)
r = (k.shape[1] - 2) * (k.shape[2] - 2) * nnz2 # start row
K, R = lastL(K, R, k2, r)
k2 = 0
else:
if not isinstance(k, np.ndarray):
k = np.load(datadir + "k.npy")
k, Nz, nnz = getKref(k, rank, pn, ref)
pbc = float(Nz)
nz, ny, nx = (k.shape[0] - 2), (k.shape[1] - 2), (k.shape[2] - 2)
n = nx * ny * nz
K = PETSc.Mat().createAIJ(((n, None), (n, None)), nnz=(7, 4), comm=pcomm)
K.setUp()
R = PETSc.Vec().createMPI((n, None), comm=pcomm)
R.setUp()
r = nx * ny * nnz * rank # start row
if rank == 0:
K, R = firstL(K, R, k, pbc)
if (rank > 0) and (rank < pn - 1):
K, R = centL(K, R, k, r)
k = 0
if rank == (pn - 1):
K, R = lastL(K, R, k, r)
k = 0
K.assemble()
R.assemble()
ksp = PETSc.KSP()
ksp.create(comm=pcomm)
ksp.setTolerances(rtol=Rtol, atol=1.0e-100, max_it=999999999)
ksp.setFromOptions()
P = R.copy()
ksp.setType(PETSc.KSP.Type.CG)
pc = PETSc.PC()
pc.create(comm=pcomm)
pc.setType(PETSc.PC.Type.JACOBI)
ksp.setPC(pc)
ksp.setOperators(K)
ksp.setUp()
t1 = time.time()
ksp.solve(R, P)
t2 = time.time()
p = P.getArray().reshape(nz, ny, nx)
if rank == 0:
keff, Q = getKeff(p, k[1:-1, 1:-1, 1:-1], pbc, Nz)
if saveres == True:
for i in range(1, pn):
from mpi4py import MPI
comm = MPI.COMM_WORLD
pi = comm.recv(source=i)
p = np.append(p, pi, axis=0)
np.save(datadir + "P", p)
f = open(datadir + "RunTimes.out", "a")
f.write("ref: " + str(ref) + "\n")
f.write("Matrix creation: " + str(t1 - t0) + "\n")
f.write("Solver: " + str(t2 - t1) + "\n")
f.write("Keff: " + str(keff) + "\n")
f.write("N_cores: " + str(pn) + "\n")
f.close()
try:
res = np.loadtxt(datadir + "SolverRes.txt")
res = np.append(res, np.array([keff, ref, t2 - t0, pn]))
except:
res = np.array([keff, ref, t2 - t0, pn])
np.savetxt(
datadir + "SolverRes.txt", res, header="Keff, ref, Runtime, N_cores"
)
print(datadir[-3:], " keff= " + str(keff), " rtime= " + str(t2 - t0))
return keff
else:
if saveres == True:
from mpi4py import MPI
comm = MPI.COMM_WORLD
comm.send(p, dest=0)
return
# Ver: A posteriori error estimates and adaptive solvers for porous media flows (Martin Vohralik)
ddir = "./test/0/"
ref = 1
icomm = MPI.Comm.Get_parent()
print('aca')
PetscP(ddir,ref,'0',True,0.000001,1)
#icomm = MPI.Comm.Get_parent()
print("aca")
PetscP(ddir, ref, "0", True, 0.000001, 1)
# icomm = MPI.Comm.Get_parent()
icomm.Disconnect()

151
tools/solver/comp_Kperm_scale.py
Unescape Escape View File

@ -3,106 +3,105 @@ import os
import time
from tools.solver.Ndar import PetscP
def comp_kperm_sub(parser,rundir,nr):
def comp_kperm_sub(parser, rundir, nr):
k=np.load(rundir+'k.npy')
k = np.load(rundir + "k.npy")
ref=int(parser.get('Solver',"ref"))
ref = int(parser.get("Solver", "ref"))
t0 = time.time()
t0=time.time()
S_min_post = int(parser.get("K-Postprocess", "MinBlockSize"))
nimax = 2 ** int(parser.get("K-Postprocess", "Max_sample_size"))
S_min_post = int(parser.get('K-Postprocess','MinBlockSize'))
nimax =2** int(parser.get('K-Postprocess','Max_sample_size'))
S_min_post = S_min_post * ref
S_min_post=S_min_post*ref
if S_min_post == 0:
sx = 2 # k.shape[0]
else:
sx = get_min_nbl(k, nimax, nr, S_min_post)
if S_min_post==0:
sx=2 #k.shape[0]
else:
sx = get_min_nbl(k,nimax,nr,S_min_post)
if sx == 1:
sx = 2
if sx==1:
sx=2
tkperm = getKpost(k, sx, rundir, ref)
tkperm=getKpost(k, sx,rundir,ref)
ttotal = time.time() - t0
return
ttotal=time.time()-t0
return
def getKpost(kf, sx, rundir, ref):
ex = int(np.log2(kf.shape[0]))
esx = int(np.log2(sx))
scales = 2 ** np.arange(esx, ex)
datadir = rundir + "KpostProcess/"
try:
os.makedirs(datadir)
except:
nada = 0
tkperm = np.zeros((scales.shape[0]))
def getKpost(kf, sx,rundir,ref):
for il in range(scales.shape[0]):
l = scales[il]
nblx, nbly, nblz = kf.shape[0] // l, kf.shape[1] // l, kf.shape[2] // l
sx, sy, sz = l, l, l
if kf.shape[2] == 1:
nblz = 1
sz = 1
if l == 2:
refDeg = 2
else:
refDeg = ref
tkperm[il] = time.time()
Kperm = np.zeros((nblx, nbly, nblz))
try:
Kperm = np.load(datadir + "Kperm" + str(l // ref) + ".npy")
except:
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
ex=int(np.log2(kf.shape[0]))
esx=int(np.log2(sx))
Kperm[i, j, k] = PetscP(
"",
refDeg,
kf[
i * sx : (i + 1) * sx,
j * sy : (j + 1) * sy,
k * sz : (k + 1) * sz,
],
False,
1e-4,
0,
)
scales=2**np.arange(esx,ex)
datadir=rundir+'KpostProcess/'
try:
os.makedirs(datadir)
except:
nada=0
tkperm[il] = time.time() - tkperm[il]
np.save(datadir + "Kperm" + str(sx) + ".npy", Kperm)
tkperm=np.zeros((scales.shape[0]))
np.savetxt(rundir + "tkperm_sub.txt", tkperm)
for il in range(scales.shape[0]):
l=scales[il]
nblx, nbly, nblz = kf.shape[0]//l, kf.shape[1]//l, kf.shape[2]//l
sx,sy,sz=l,l,l
if kf.shape[2]==1:
nblz=1
sz=1
if l==2:
refDeg=2
else:
refDeg=ref
return tkperm
tkperm[il]=time.time()
Kperm = np.zeros((nblx,nbly,nblz))
try:
Kperm=np.load(datadir+'Kperm'+str(l//ref)+'.npy')
except:
for i in range(nblx):
for j in range(nbly):
for k in range(nblz):
Kperm[i,j,k]=PetscP('',refDeg,kf[i*sx:(i+1)*sx,j*sy:(j+1)*sy,k*sz:(k+1)*sz],False,1e-4,0)
def get_min_nbl(kc, nimax, nr, smin):
tkperm[il]= time.time()-tkperm[il]
np.save(datadir+'Kperm'+str(sx)+'.npy',Kperm)
np.savetxt(rundir+'tkperm_sub.txt',tkperm)
return tkperm
def get_min_nbl(kc,nimax,nr,smin):
if kc.shape[2]==1:
dim=2.0
else:
dim=3.0
if nr>0:
y=(1/dim)*np.log2(nr*kc.size/(nimax*(smin**dim)))
else:
y=0
y=int(y)
s=int((2**y) * smin)
if s<smin:
s=smin
return s
if kc.shape[2] == 1:
dim = 2.0
else:
dim = 3.0
if nr > 0:
y = (1 / dim) * np.log2(nr * kc.size / (nimax * (smin ** dim)))
else:
y = 0
y = int(y)
s = int((2 ** y) * smin)
if s < smin:
s = smin
return s

591
tools/solver/computeFlows.py
Unescape Escape View File

@ -2,51 +2,73 @@ import numpy as np
import math
def getKref(k,rank,pn,ref):
Nz = k.shape[0]
nz = Nz//pn
if ref==1:
return getK(k,rank,pn)
if (rank>0) and (rank<pn-1):
k=k[rank*nz-1:(rank+1)*nz+1,:,:]
k=refinaPy(k, ref)
if ref!=1:
k=k[(ref-1):-(ref-1),:,:]
nz,ny,nx=k.shape[0],k.shape[1],k.shape[2]
ki=np.zeros((nz,ny+2,nx+2))
ki[:,1:-1,1:-1]=k
#print(ki.shape)
nnz=nz-2
if rank==0:
k=k[:(rank+1)*nz+1,:,:]
k=refinaPy(k, ref)
if ref!=1:
k=k[:-(ref-1),:,:]
nz,ny,nx=k.shape[0],k.shape[1],k.shape[2]
ki=np.zeros((nz+1,ny+2,nx+2))
ki[1:,1:-1,1:-1]=k
ki[0,:,:]=ki[1,:,:]
nnz=nz
if rank==(pn-1):
k=k[rank*nz-1:,:,:]
k=refinaPy(k, ref)
if ref!=1:
k=k[(ref-1):,:,:]
nz,ny,nx=k.shape[0],k.shape[1],k.shape[2]
ki=np.zeros((nz+1,ny+2,nx+2))
ki[:-1,1:-1,1:-1]=k
ki[-1,:,:]=ki[-2,:,:]
nnz=(Nz//pn)*ref
return ki, Nz*ref, nnz
def getKref(k, rank, pn, ref):
Nz = k.shape[0]
nz = Nz // pn
if ref == 1:
return getK(k, rank, pn)
if (rank > 0) and (rank < pn - 1):
k = k[rank * nz - 1 : (rank + 1) * nz + 1, :, :]
k = refinaPy(k, ref)
if ref != 1:
k = k[(ref - 1) : -(ref - 1), :, :]
nz, ny, nx = k.shape[0], k.shape[1], k.shape[2]
ki = np.zeros((nz, ny + 2, nx + 2))
ki[:, 1:-1, 1:-1] = k
# print(ki.shape)
nnz = nz - 2
if rank == 0:
k = k[: (rank + 1) * nz + 1, :, :]
k = refinaPy(k, ref)
if ref != 1:
k = k[: -(ref - 1), :, :]
nz, ny, nx = k.shape[0], k.shape[1], k.shape[2]
ki = np.zeros((nz + 1, ny + 2, nx + 2))
ki[1:, 1:-1, 1:-1] = k
ki[0, :, :] = ki[1, :, :]
nnz = nz
if rank == (pn - 1):
k = k[rank * nz - 1 :, :, :]
k = refinaPy(k, ref)
if ref != 1:
k = k[(ref - 1) :, :, :]
nz, ny, nx = k.shape[0], k.shape[1], k.shape[2]
ki = np.zeros((nz + 1, ny + 2, nx + 2))
ki[:-1, 1:-1, 1:-1] = k
ki[-1, :, :] = ki[-2, :, :]
nnz = (Nz // pn) * ref
return ki, Nz * ref, nnz
def getK(k, rank, pn):
# k=np.load(kfile)
# nn=int(np.cbrt(k.shape[0]))
# k=k.reshape((nn,nn,nn))
Nz, Ny, Nx = k.shape[0], k.shape[1], k.shape[2]
nz = Nz // pn
if rank == pn - 1:
nnz = Nz - (pn - 1) * nz
ki = np.zeros((nnz + 2, Ny + 2, Nx + 2))
else:
nnz = nz
ki = np.zeros((nz + 2, Ny + 2, Nx + 2))
if (rank > 0) and (rank < pn - 1):
ki[:, 1:-1, 1:-1] = k[rank * nz - 1 : (rank + 1) * nz + 1, :, :]
if rank == 0:
ki[1:, 1:-1, 1:-1] = k[: (rank + 1) * nz + 1, :, :]
ki[0, :, :] = ki[1, :, :]
if rank == (pn - 1):
ki[:-1, 1:-1, 1:-1] = k[rank * nz - 1 :, :, :]
ki[-1, :, :] = ki[-2, :, :]
return ki, Nz, nz
"""
def getK(k,rank,pn):
#k=np.load(kfile)
@ -70,187 +92,306 @@ def getK(k,rank,pn):
ki[:-1,1:-1,1:-1]=k[rank*nz-1:,:,:]
ki[-1,:,:]=ki[-2,:,:]
return ki, Nz, nz
'''
def getK(k,rank,pn):
"""
#k=np.load(kfile)
#nn=int(np.cbrt(k.shape[0]))
#k=k.reshape((nn,nn,nn))
Nz, Ny,Nx=k.shape[0],k.shape[1],k.shape[2]
nz=Nz//pn
if rank==pn-1:
nnz= Nz-(pn-1)*nz
ki=np.zeros((nnz+2,Ny+2,Nx+2))
else:
nnz=nz
ki=np.zeros((nz+2,Ny+2,Nx+2))
if (rank>0) and (rank<pn-1):
ki[:,1:-1,1:-1]=k[rank*nz-1:(rank+1)*nz+1,:,:]
if rank==0:
ki[1:,1:-1,1:-1]=k[:(rank+1)*nz+1,:,:]
ki[0,:,:]=ki[1,:,:]
if rank==(pn-1):
ki[:-1,1:-1,1:-1]=k[rank*nz-1:,:,:]
ki[-1,:,:]=ki[-2,:,:]
return ki, Nz, nz
'''
def refinaPy(k, ref):
if ref==1:
return k
nx,ny,nz=k.shape[2],k.shape[1],k.shape[0]
krz=np.zeros((ref*nz,ny,nx))
for i in range(ref):
krz[i::ref,:,:]=k
k=0
krzy=np.zeros((ref*nz,ny*ref,nx))
for i in range(ref):
krzy[:,i::ref,:]=krz
if nx==1:
return krzy
krz=0
krzyx=np.zeros((ref*nz,ny*ref,nx*ref))
for i in range(ref):
krzyx[:,:,i::ref]=krzy
return krzyx #krzyx[(ref-1):-(ref-1),:,:]
def centL(K,R,kkm,r):
nx, ny, nz=kkm.shape[2]-2,kkm.shape[1]-2,kkm.shape[0]-2
for k in range(nz):
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1]) ])
K.setValues(r, r, t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r-1,-t[1])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r+nx*ny,-t[4])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
r+=1
return K, R
def firstL(K,R,kkm,pbc):
# Right side of Rmat
r=0
nx, ny, nz=kkm.shape[2]-2,kkm.shape[1]-2,kkm.shape[0]-2
k=0
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),4*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1]) ]) #atento aca BC 2Tz
K.setValues(r, r,t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r+nx*ny,-t[4])
R.setValues(r, t[5]*pbc)
r+=1
# Left side of Rmat
for j in range(ny):
for i in range(1,nx):
r=j*nx+i
K.setValues(r,r-1,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]))
for j in range(1,ny):
for i in range(nx):
r=j*nx+i
K.setValues(r,r-nx,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]))
for k in range(1,nz):
for j in range(ny):
for i in range(nx):
r=k*ny*nx+j*nx+i
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1]) ])
K.setValues(r, r,t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r-1,-t[1])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r+nx*ny,-t[4])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
return K,R
def lastL(K,R,kkm,r):
# Right side of Rmat
nx, ny, nz=kkm.shape[2]-2,kkm.shape[1]-2,kkm.shape[0]-2
for k in range(nz-1):
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1])])
K.setValues(r, r, t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r+1,-t[0])
K.setValues(r,r-1,-t[1])
K.setValues(r,r+nx,-t[2])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r+nx*ny,-t[4])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
r=r+1
auxr=r
k=-3
for j in range(ny):
for i in range(nx):
t=np.array([2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k+1,j,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j,i+1]),4*kkm[k+1,j+1,i+1]*kkm[k+2,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+2,j+1,i+1]),2*kkm[k+1,j+1,i+1]*kkm[k,j+1,i+1]/(kkm[k+1,j+1,i+1]+kkm[k,j+1,i+1])]) #guarda aca BC en t[4] va por 2 por dx/2
K.setValues(r, r,t[0]+t[1]+t[2]+t[3]+t[4]+t[5])
K.setValues(r,r-1,-t[1])
K.setValues(r,r-nx,-t[3])
K.setValues(r,r-nx*ny,-t[5])
R.setValues(r, 0)
r+=1
# Right side of Mat
for j in range(ny):
for i in range(nx-1):
r=j*nx+i+auxr
K.setValues(r,r+1,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j+1,i+2]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+1,i+2]))
for j in range(ny-1):
for i in range(nx):
r=j*nx+i+auxr
K.setValues(r,r+nx,-2*kkm[k+1,j+1,i+1]*kkm[k+1,j+2,i+1]/(kkm[k+1,j+1,i+1]+kkm[k+1,j+2,i+1]))
return K,R
if ref == 1:
return k
nx, ny, nz = k.shape[2], k.shape[1], k.shape[0]
krz = np.zeros((ref * nz, ny, nx))
for i in range(ref):
krz[i::ref, :, :] = k
k = 0
krzy = np.zeros((ref * nz, ny * ref, nx))
for i in range(ref):
krzy[:, i::ref, :] = krz
if nx == 1:
return krzy
krz = 0
krzyx = np.zeros((ref * nz, ny * ref, nx * ref))
for i in range(ref):
krzyx[:, :, i::ref] = krzy
return krzyx # krzyx[(ref-1):-(ref-1),:,:]
def centL(K, R, kkm, r):
nx, ny, nz = kkm.shape[2] - 2, kkm.shape[1] - 2, kkm.shape[0] - 2
for k in range(nz):
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
)
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r + nx * ny, -t[4])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
r += 1
return K, R
def firstL(K, R, kkm, pbc):
# Right side of Rmat
r = 0
nx, ny, nz = kkm.shape[2] - 2, kkm.shape[1] - 2, kkm.shape[0] - 2
k = 0
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
4
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
) # atento aca BC 2Tz
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r + nx * ny, -t[4])
R.setValues(r, t[5] * pbc)
r += 1
# Left side of Rmat
for j in range(ny):
for i in range(1, nx):
r = j * nx + i
K.setValues(
r,
r - 1,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
)
for j in range(1, ny):
for i in range(nx):
r = j * nx + i
K.setValues(
r,
r - nx,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
)
for k in range(1, nz):
for j in range(ny):
for i in range(nx):
r = k * ny * nx + j * nx + i
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
)
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r + nx * ny, -t[4])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
return K, R
def lastL(K, R, kkm, r):
# Right side of Rmat
nx, ny, nz = kkm.shape[2] - 2, kkm.shape[1] - 2, kkm.shape[0] - 2
for k in range(nz - 1):
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
)
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r + 1, -t[0])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r + nx, -t[2])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r + nx * ny, -t[4])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
r = r + 1
auxr = r
k = -3
for j in range(ny):
for i in range(nx):
t = np.array(
[
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j, i + 1]),
4
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 2, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 2, j + 1, i + 1]),
2
* kkm[k + 1, j + 1, i + 1]
* kkm[k, j + 1, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k, j + 1, i + 1]),
]
) # guarda aca BC en t[4] va por 2 por dx/2
K.setValues(r, r, t[0] + t[1] + t[2] + t[3] + t[4] + t[5])
K.setValues(r, r - 1, -t[1])
K.setValues(r, r - nx, -t[3])
K.setValues(r, r - nx * ny, -t[5])
R.setValues(r, 0)
r += 1
# Right side of Mat
for j in range(ny):
for i in range(nx - 1):
r = j * nx + i + auxr
K.setValues(
r,
r + 1,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 1, i + 2]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 1, i + 2]),
)
for j in range(ny - 1):
for i in range(nx):
r = j * nx + i + auxr
K.setValues(
r,
r + nx,
-2
* kkm[k + 1, j + 1, i + 1]
* kkm[k + 1, j + 2, i + 1]
/ (kkm[k + 1, j + 1, i + 1] + kkm[k + 1, j + 2, i + 1]),
)
return K, R

21
tools/solver/flow.py
Unescape Escape View File

@ -1,17 +1,14 @@
import numpy as np
def getKeff(pm, k, pbc, Nz):
nx = k.shape[2] # Pasar k sin bordes de k=0
ny = k.shape[1]
def getKeff(pm,k,pbc,Nz):
nx = k.shape[2] #Pasar k sin bordes de k=0
ny = k.shape[1]
tz = 2*k[1,:,:]*k[0, :,:]/(k[0, :,:]+k[1,:,:])
q=((pm[0,:,:]-pm[1,:,:])*tz).sum()
area=ny*nx
l=Nz
keff=q*l/(pbc*area)
return keff,q
tz = 2 * k[1, :, :] * k[0, :, :] / (k[0, :, :] + k[1, :, :])
q = ((pm[0, :, :] - pm[1, :, :]) * tz).sum()
area = ny * nx
l = Nz
keff = q * l / (pbc * area)
return keff, q

12
tools/solver/solverEjec.py
Unescape Escape View File

@ -8,12 +8,8 @@ from mpi4py import MPI
from petsc4py import PETSc
if sys.argv[3]=='0':
icomm = MPI.Comm.Get_parent()
PetscP(sys.argv[1],int(sys.argv[2]),'0',True)
icomm.Disconnect()
if sys.argv[3] == "0":
icomm = MPI.Comm.Get_parent()
PetscP(sys.argv[1], int(sys.argv[2]), "0", True)
icomm.Disconnect()

114
utilities/ConTest_collect.py
Unescape Escape View File

@ -5,81 +5,83 @@ from tools.generation.config import DotheLoop, get_config
def collect_scalar(filename):
njobs = DotheLoop(-1)
rdir='./data/'
res=np.array([])
for job in range(njobs):
res=np.append(res,np.loadtxt(rdir+str(job)+'/'+filename))
njobs = DotheLoop(-1)
rdir = "./data/"
res = np.array([])
for job in range(njobs):
res = np.append(res, np.loadtxt(rdir + str(job) + "/" + filename))
res=res.reshape(njobs,-1)
return res
res = res.reshape(njobs, -1)
return res
def get_stats(res,col,logv):
parser, iterables = get_config()
def get_stats(res, col, logv):
seeds=iterables['seeds']
n_of_seeds=len(seeds)
ps = iterables['ps']
n_of_ps=len(ps)
stats=np.zeros((n_of_ps,3))
x=res[:,col]
if logv==True:
x=np.log(x)
parser, iterables = get_config()
for i in range(n_of_ps):
seeds = iterables["seeds"]
n_of_seeds = len(seeds)
ps = iterables["ps"]
n_of_ps = len(ps)
stats = np.zeros((n_of_ps, 3))
x = res[:, col]
if logv == True:
x = np.log(x)
stats[i,0]=ps[i]
stats[i,1]=np.nanmean(x[i*n_of_seeds:(i+1)*n_of_seeds])
stats[i,2]=np.nanvar(x[i*n_of_seeds:(i+1)*n_of_seeds])
for i in range(n_of_ps):
stats[i, 0] = ps[i]
stats[i, 1] = np.nanmean(x[i * n_of_seeds : (i + 1) * n_of_seeds])
stats[i, 2] = np.nanvar(x[i * n_of_seeds : (i + 1) * n_of_seeds])
if logv==True:
stats[:,1]=np.exp(stats[:,1])
if logv == True:
stats[:, 1] = np.exp(stats[:, 1])
return stats
return stats
def plot_keff(stats):
ylabel=r'$K_{eff}$'
xlabel=r'$p$'
fsize=14
plt.figure(1)
plt.semilogy(stats[:,0],stats[:,1])
plt.xlabel(xlabel,fontsize=fsize)
plt.ylabel(ylabel,fontsize=fsize)
plt.grid()
plt.savefig('Keff_p.png')
plt.close()
plt.figure(2)
plt.plot(stats[:,0],stats[:,2])
plt.xlabel(xlabel,fontsize=fsize)
plt.ylabel(ylabel,fontsize=fsize)
plt.grid()
plt.savefig('vKeff_p.png')
plt.close()
return
ylabel = r"$K_{eff}$"
xlabel = r"$p$"
fsize = 14
plt.figure(1)
plt.semilogy(stats[:, 0], stats[:, 1])
plt.xlabel(xlabel, fontsize=fsize)
plt.ylabel(ylabel, fontsize=fsize)
plt.grid()
plt.savefig("Keff_p.png")
plt.close()
plt.figure(2)
plt.plot(stats[:, 0], stats[:, 2])
plt.xlabel(xlabel, fontsize=fsize)
plt.ylabel(ylabel, fontsize=fsize)
plt.grid()
plt.savefig("vKeff_p.png")
plt.close()
return
def searchError(filename):
njobs = DotheLoop(-1)
rdir='./data/'
for job in range(njobs):
nclus=np.loadtxt(rdir+str(job)+'/'+filename)[:,4]
for i in range(1,nclus.shape[0]):
if nclus[0]!=nclus[i]:
print(job,nclus[0],nclus[i])
njobs = DotheLoop(-1)
rdir = "./data/"
for job in range(njobs):
nclus = np.loadtxt(rdir + str(job) + "/" + filename)[:, 4]
for i in range(1, nclus.shape[0]):
if nclus[0] != nclus[i]:
print(job, nclus[0], nclus[i])
return
return
filename='resTestCon.txt'
filename = "resTestCon.txt"
searchError(filename)
res=collect_scalar(filename)
'''
res = collect_scalar(filename)
"""
stats = get_stats(res,0,True)
plot_keff(stats)
np.savetxt('Stats.txt',stats)
'''
"""

78
utilities/Var_analytical.py
Unescape Escape View File

@ -4,61 +4,61 @@ from scipy import integrate
import matplotlib.pyplot as plt
def VarLgauss(lc,blks,d):
def VarLgauss(lc, blks, d):
scl=(blks/lc)**2
return (scl**-d)*((np.sqrt(2*np.pi*scl)*erf(np.sqrt(scl/2)) +2*np.exp(-0.5*scl)-2)**d)
scl = (blks / lc) ** 2
return (scl ** -d) * (
(np.sqrt(2 * np.pi * scl) * erf(np.sqrt(scl / 2)) + 2 * np.exp(-0.5 * scl) - 2)
** d
)
def VarLgaussSimp(lc,blks,d):
def VarLgaussSimp(lc, blks, d):
A=lc/blks #lc/L
B=np.sqrt(2*np.pi) # square root of 2*pi
C=erf((blks/lc)/np.sqrt(8)) # erf( (L/lc) / square root of 2)
return (A*B*C)**d
A = lc / blks # lc/L
B = np.sqrt(2 * np.pi) # square root of 2*pi
C = erf((blks / lc) / np.sqrt(8)) # erf( (L/lc) / square root of 2)
return (A * B * C) ** d
def arg_exp(t, lc, blks, d):
def arg_exp(t,lc,blks,d):
scl = (blks / lc) ** 2
scl=(blks/lc)**2
aux1 = np.pi * erf(np.sqrt(scl / (4 * t)))
aux2 = np.sqrt(np.pi) * (1 - np.exp(-scl / (4 * t))) * np.sqrt(4 * t / scl)
return t * np.exp(-t) * ((aux1 - aux2) ** d)
aux1=np.pi*erf(np.sqrt(scl/(4*t)))
aux2=np.sqrt(np.pi)*(1-np.exp(-scl/(4*t)))*np.sqrt(4*t/scl)
return t*np.exp(-t)*((aux1-aux2)**d)
def VarLexp3d(lc, blks): # ic=5.378669493723924333 para lc 16
d = 3
# a=1/64/np.pi
t = np.arange(0.000000001, 50, 0.001)
var = np.empty(0)
for blk in blks:
y = arg_exp(t, lc, blk, d)
var = np.append(var, 64 * np.pi * ((lc / (2 * np.pi * blk)) ** d) * np.trapz(y))
def VarLexp3d(lc,blks): #ic=5.378669493723924333 para lc 16
d=3
#a=1/64/np.pi
t=np.arange(0.000000001,50,0.001)
var=np.empty(0)
for blk in blks:
y=arg_exp(t,lc,blk,d)
var=np.append(var,64*np.pi*((lc/(2*np.pi*blk))**d)*np.trapz(y))
return var
return var
def argVarLexp2d(lc,blk):
scl=float(blk/(2*lc))
f = lambda y, x: np.exp(-1*np.sqrt(x**2 +y**2))
def argVarLexp2d(lc, blk):
scl = float(blk / (2 * lc))
f = lambda y, x: np.exp(-1 * np.sqrt(x ** 2 + y ** 2))
res=integrate.dblquad(f,-scl , scl, lambda x: -scl, lambda x: scl,epsabs=1.49e-8, epsrel=1.49e-8)#0,1,lambda x: 0, lambda x: 1)
return ((lc/blk)**2)*res[0]
def VarLexp2d(lc,blks):
#if lc==1.33:
# blks=np.append(np.arange(1,2,0.1),blks[1:])
res=np.empty(0)
for blk in blks:
res=np.append(res,argVarLexp2d(lc,blk))
return res
res = integrate.dblquad(
f, -scl, scl, lambda x: -scl, lambda x: scl, epsabs=1.49e-8, epsrel=1.49e-8
) # 0,1,lambda x: 0, lambda x: 1)
return ((lc / blk) ** 2) * res[0]
def VarLexp2d(lc, blks):
# if lc==1.33:
# blks=np.append(np.arange(1,2,0.1),blks[1:])
res = np.empty(0)
for blk in blks:
res = np.append(res, argVarLexp2d(lc, blk))
return res

243
utilities/collect.py
Unescape Escape View File

@ -3,132 +3,141 @@ import matplotlib.pyplot as plt
from tools.generation.config import DotheLoop, get_config
import os
def collect_scalar(filename,rdir):
njobs = DotheLoop(-1)
res=np.array([])
for job in range(njobs):
res=np.append(res,np.loadtxt(rdir+str(job)+'/'+filename))
res=res.reshape(njobs,-1)
return res
def get_stats(res,col,logv):
parser, iterables = get_config()
seeds=iterables['seeds']
n_of_seeds=len(seeds)
ps = iterables['ps']
n_of_ps=len(ps)
stats=np.zeros((n_of_ps,3))
x=res[:,col]
if logv==True:
x=np.log(x)
for i in range(n_of_ps):
stats[i,0]=ps[i]
stats[i,1]=np.nanmean(x[i*n_of_seeds:(i+1)*n_of_seeds])
stats[i,2]=np.nanvar(x[i*n_of_seeds:(i+1)*n_of_seeds])
if logv==True:
stats[:,1]=np.exp(stats[:,1])
return stats
def collect_Conec(scales,rdir):
parser, iterables = get_config(rdir+'config.ini')
ps = iterables['ps']
njobs = DotheLoop(-1,parser, iterables)
res=dict()
for job in range(njobs):
for scale in scales:
try:
fdir=rdir+str(job)+'/ConnectivityMetrics/'+str(scale)+'.npy'
jobres=np.load(fdir).item()
params=DotheLoop(job,parser,iterables)
indp=int(np.where(ps == params[2])[0])
for ckey in jobres.keys():
try:
res[params[0],params[1],scale,ckey,indp]=np.append(res[params[0],params[1],scale,ckey,indp],jobres[ckey].reshape(-1))
except KeyError:
res[params[0],params[1],scale,ckey,indp]=jobres[ckey].reshape(-1)
except IOError:
pass
return res
def ConValidat(conkey,scale,ddir):
scales=[scale]
resdict=collect_Conec(scales,ddir)
parser, iterables = get_config(ddir+'config.ini')
params=DotheLoop(0,parser,iterables)
con=params[0]
lc=params[1]
x,y,yv=constasP(con,lc,scales[0],conkey,resdict,iterables)
try:
os.makedirs('./plots/'+ddir)
except:
pass
plt.figure(1)
plt.plot(x,y,marker='x')
plt.xlabel('p')
plt.ylabel(conkey)
plt.grid()
plt.savefig('./plots/'+ddir+conkey+str(scale)+'.png')
plt.close()
return
def collect_scalar(filename, rdir):
njobs = DotheLoop(-1)
res = np.array([])
for job in range(njobs):
res = np.append(res, np.loadtxt(rdir + str(job) + "/" + filename))
res = res.reshape(njobs, -1)
return res
def get_stats(res, col, logv):
parser, iterables = get_config()
seeds = iterables["seeds"]
n_of_seeds = len(seeds)
ps = iterables["ps"]
n_of_ps = len(ps)
stats = np.zeros((n_of_ps, 3))
x = res[:, col]
if logv == True:
x = np.log(x)
for i in range(n_of_ps):
stats[i, 0] = ps[i]
stats[i, 1] = np.nanmean(x[i * n_of_seeds : (i + 1) * n_of_seeds])
stats[i, 2] = np.nanvar(x[i * n_of_seeds : (i + 1) * n_of_seeds])
if logv == True:
stats[:, 1] = np.exp(stats[:, 1])
return stats
def collect_Conec(scales, rdir):
parser, iterables = get_config(rdir + "config.ini")
ps = iterables["ps"]
njobs = DotheLoop(-1, parser, iterables)
res = dict()
for job in range(njobs):
for scale in scales:
try:
fdir = rdir + str(job) + "/ConnectivityMetrics/" + str(scale) + ".npy"
jobres = np.load(fdir).item()
params = DotheLoop(job, parser, iterables)
indp = int(np.where(ps == params[2])[0])
for ckey in jobres.keys():
try:
res[params[0], params[1], scale, ckey, indp] = np.append(
res[params[0], params[1], scale, ckey, indp],
jobres[ckey].reshape(-1),
)
except KeyError:
res[params[0], params[1], scale, ckey, indp] = jobres[
ckey
].reshape(-1)
except IOError:
pass
return res
def ConValidat(conkey, scale, ddir):
scales = [scale]
resdict = collect_Conec(scales, ddir)
parser, iterables = get_config(ddir + "config.ini")
params = DotheLoop(0, parser, iterables)
con = params[0]
lc = params[1]
x, y, yv = constasP(con, lc, scales[0], conkey, resdict, iterables)
try:
os.makedirs("./plots/" + ddir)
except:
pass
plt.figure(1)
plt.plot(x, y, marker="x")
plt.xlabel("p")
plt.ylabel(conkey)
plt.grid()
plt.savefig("./plots/" + ddir + conkey + str(scale) + ".png")
plt.close()
return
def showValidateResults(conkeys):
for conkey in conkeys:
ConValidat(conkey,128,'./data_Val2D/')
ConValidat(conkey,16,'./data_Val3D/')
for conkey in conkeys:
ConValidat(conkey, 128, "./data_Val2D/")
ConValidat(conkey, 16, "./data_Val3D/")
return
return
def constasP(con,lc,scale,conkey,res,iterables):
def constasP(con, lc, scale, conkey, res, iterables):
x = iterables['ps']
x = iterables["ps"]
y = np.zeros((x.shape))
vy = np.zeros((x.shape))
for i in range((x.shape[0])):
y = np.zeros((x.shape))
vy = np.zeros((x.shape))
for i in range((x.shape[0])):
y[i]=np.mean(res[con,lc,scale,conkey,i])
vy[i]=np.mean(res[con,lc,scale,conkey,i])
y[i] = np.mean(res[con, lc, scale, conkey, i])
vy[i] = np.mean(res[con, lc, scale, conkey, i])
return x,y,vy
return x, y, vy
def plot_keff(stats):
ylabel=r'$K_{eff}$'
xlabel=r'$p$'
fsize=14
plt.figure(1)
plt.semilogy(stats[:,0],stats[:,1])
plt.xlabel(xlabel,fontsize=fsize)
plt.ylabel(ylabel,fontsize=fsize)
plt.grid()
plt.savefig('Keff_p.png')
plt.close()
plt.figure(2)
plt.plot(stats[:,0],stats[:,2])
plt.xlabel(xlabel,fontsize=fsize)
plt.ylabel(ylabel,fontsize=fsize)
plt.grid()
plt.savefig('vKeff_p.png')
plt.close()
return
showValidateResults(['P','S','npx','Plen'])
ylabel = r"$K_{eff}$"
xlabel = r"$p$"
fsize = 14
plt.figure(1)
plt.semilogy(stats[:, 0], stats[:, 1])
plt.xlabel(xlabel, fontsize=fsize)
plt.ylabel(ylabel, fontsize=fsize)
plt.grid()
plt.savefig("Keff_p.png")
plt.close()
plt.figure(2)
plt.plot(stats[:, 0], stats[:, 2])
plt.xlabel(xlabel, fontsize=fsize)
plt.ylabel(ylabel, fontsize=fsize)
plt.grid()
plt.savefig("vKeff_p.png")
plt.close()
return
showValidateResults(["P", "S", "npx", "Plen"])

161
utilities/collect1R.py
Unescape Escape View File

@ -4,88 +4,85 @@ from tools.generation.config import DotheLoop, get_config
import os
def get_conScales(ddir,scales,Cind):
ns=len(scales)
res=np.zeros((ns))
for i in range(ns):
y=np.load(ddir+str(scales[i])+'.npy').item()[Cind]
res[i]=np.mean(y)
plt.figure(1)
if 0 in res:
plt.semilogx(scales,res,marker='x')
else:
res=np.log(res)
plt.semilogx(scales,res,marker='o')
plt.grid()
plt.xlabel('L')
plt.ylabel(Cind)
plt.savefig(ddir+Cind+'.png')
plt.close()
return
def compGlobal(ddir,ddirG,scales,Cind):
ns=len(scales)
res=np.zeros((ns))
for i in range(ns):
y=np.load(ddir+str(scales[i])+'.npy').item()[Cind]
yG=np.load(ddirG+str(scales[i])+'.npy').item()[Cind]
res[i]=np.nanmean(y/yG)
plt.figure(1)
if 0 in res or Cind=='npx':
plt.semilogx(scales,res,marker='x')
else:
res=np.log(res)
plt.semilogx(scales,res,marker='o')
plt.grid()
plt.xlabel('L')
plt.ylabel(Cind)
plt.savefig(ddirG+Cind+'_CGvsC.png')
plt.close()
return
def get_conScalesScatter(ddir,scales,Cind):
ns=len(scales)
res=np.array([])
x=np.array([])
for i in range(ns):
y=np.load(ddir+str(scales[i])+'.npy').item()[Cind]
res=np.append(res,y.reshape(-1))
x=np.append(x,np.ones((y.size))*scales[i])
plt.figure(1)
if 0 in res or Cind=='npx':
plt.semilogx(x,res,marker='x',linestyle='')
else:
res=np.log(res)
plt.semilogx(x,res,marker='o',linestyle='')
plt.grid()
plt.xlabel('L')
plt.ylabel(Cind)
plt.savefig(ddir+Cind+'_scatter.png')
plt.close()
return
scales=2**np.arange(7,13)
scales=[32,64,128,256,512]
Cinds=['P','S','npx','Plen','PX','SX','PlenX']
def get_conScales(ddir, scales, Cind):
ns = len(scales)
res = np.zeros((ns))
for i in range(ns):
y = np.load(ddir + str(scales[i]) + ".npy").item()[Cind]
res[i] = np.mean(y)
plt.figure(1)
if 0 in res:
plt.semilogx(scales, res, marker="x")
else:
res = np.log(res)
plt.semilogx(scales, res, marker="o")
plt.grid()
plt.xlabel("L")
plt.ylabel(Cind)
plt.savefig(ddir + Cind + ".png")
plt.close()
return
def compGlobal(ddir, ddirG, scales, Cind):
ns = len(scales)
res = np.zeros((ns))
for i in range(ns):
y = np.load(ddir + str(scales[i]) + ".npy").item()[Cind]
yG = np.load(ddirG + str(scales[i]) + ".npy").item()[Cind]
res[i] = np.nanmean(y / yG)
plt.figure(1)
if 0 in res or Cind == "npx":
plt.semilogx(scales, res, marker="x")
else:
res = np.log(res)
plt.semilogx(scales, res, marker="o")
plt.grid()
plt.xlabel("L")
plt.ylabel(Cind)
plt.savefig(ddirG + Cind + "_CGvsC.png")
plt.close()
return
def get_conScalesScatter(ddir, scales, Cind):
ns = len(scales)
res = np.array([])
x = np.array([])
for i in range(ns):
y = np.load(ddir + str(scales[i]) + ".npy").item()[Cind]
res = np.append(res, y.reshape(-1))
x = np.append(x, np.ones((y.size)) * scales[i])
plt.figure(1)
if 0 in res or Cind == "npx":
plt.semilogx(x, res, marker="x", linestyle="")
else:
res = np.log(res)
plt.semilogx(x, res, marker="o", linestyle="")
plt.grid()
plt.xlabel("L")
plt.ylabel(Cind)
plt.savefig(ddir + Cind + "_scatter.png")
plt.close()
return
scales = 2 ** np.arange(7, 13)
scales = [32, 64, 128, 256, 512]
Cinds = ["P", "S", "npx", "Plen", "PX", "SX", "PlenX"]
for job in range(5):
ddir='./testConx/'+str(job)+'/ConnectivityMetrics/'
ddirG='./testConx/'+str(job)+'/GlobalConnectivityMetrics/'
for Cind in Cinds:
get_conScales(ddir,scales,Cind)
get_conScales(ddirG,scales,Cind)
compGlobal(ddir,ddirG,scales,Cind)
#get_conScalesScatter(ddir,scales,Cind)
ddir = "./testConx/" + str(job) + "/ConnectivityMetrics/"
ddirG = "./testConx/" + str(job) + "/GlobalConnectivityMetrics/"
for Cind in Cinds:
get_conScales(ddir, scales, Cind)
get_conScales(ddirG, scales, Cind)
compGlobal(ddir, ddirG, scales, Cind)
# get_conScalesScatter(ddir,scales,Cind)

3
utilities/conditional_decorator.py
Unescape Escape View File

@ -4,4 +4,5 @@ def conditional_decorator(dec, condition):
# Return the function unchanged, not decorated.
return func
return dec(func)
return decorator
return decorator

285
utilities/flow.py
Unescape Escape View File

@ -2,173 +2,216 @@ import numpy as np
from scipy.sparse import diags
from scipy.stats import mstats
from scipy.sparse.linalg import bicg, bicgstab, cg, dsolve #,LinearOperator, spilu, bicgstab
from scipy.sparse.linalg import (
bicg,
bicgstab,
cg,
dsolve,
) # ,LinearOperator, spilu, bicgstab
from scikits.umfpack import spsolve, splu
import time
def getDiss(k,vx,vy,vz):
diss = (vx[1:,:,:]**2+vx[:-1,:,:]**2+vy[:,1:,:]**2+vy[:,:-1,:]**2+vz[:,:,1:]**2+vz[:,:,:-1]**2)/(2*k)
return diss
def getDiss(k, vx, vy, vz):
diss = (
vx[1:, :, :] ** 2
+ vx[:-1, :, :] ** 2
+ vy[:, 1:, :] ** 2
+ vy[:, :-1, :] ** 2
+ vz[:, :, 1:] ** 2
+ vz[:, :, :-1] ** 2
) / (2 * k)
return diss
def ComputeVol(k,P,saveV):
k=refina(k, P.shape[0]//k.shape[0])
Px,Py,Pz = getPfaces(k,P)
vx,vy,vz = getVfaces(k,P, Px,Py, Pz)
diss = getDiss(k,vx,vy,vz)
if saveV==False:
vy, vz= 0, 0
else:
vy, vz= 0.5*(vy[:,1:,:]+vy[:,:-1,:]), 0.5*(vz[:,:,1:]+vz[:,:,:-1])
vx= 0.5*(vx[1:,:,:]+vx[:-1,:,:])
def ComputeVol(k, P, saveV):
k = refina(k, P.shape[0] // k.shape[0])
Px, Py, Pz = getPfaces(k, P)
vx, vy, vz = getVfaces(k, P, Px, Py, Pz)
diss = getDiss(k, vx, vy, vz)
if saveV == False:
vy, vz = 0, 0
else:
vy, vz = 0.5 * (vy[:, 1:, :] + vy[:, :-1, :]), 0.5 * (
vz[:, :, 1:] + vz[:, :, :-1]
)
vx = 0.5 * (vx[1:, :, :] + vx[:-1, :, :])
return k, diss, vx, vy, vz, Px, Py, Pz
return k, diss, vx,vy,vz, Px, Py, Pz
def comp_Kdiss_Kaverage(k, diss, vx, Px, Py, Pz):
mgx, mgy, mgz = np.mean(Px[-1,:,:]-Px[0,:,:])/k.shape[0],np.mean(Py[:,-1,:]-Py[:,0,:])/k.shape[1],np.mean(Pz[:,:,-1]-Pz[:,:,0])/k.shape[2]
kave=np.mean(vx)/mgx
kdiss=np.mean(diss)/(mgx**2+mgy**2+mgz**2)
return kdiss, kave
mgx, mgy, mgz = (
np.mean(Px[-1, :, :] - Px[0, :, :]) / k.shape[0],
np.mean(Py[:, -1, :] - Py[:, 0, :]) / k.shape[1],
np.mean(Pz[:, :, -1] - Pz[:, :, 0]) / k.shape[2],
)
kave = np.mean(vx) / mgx
kdiss = np.mean(diss) / (mgx ** 2 + mgy ** 2 + mgz ** 2)
return kdiss, kave
def getKeff(pm, k, pbc, Nz):
def getKeff(pm,k,pbc,Nz):
nx = k.shape[2] # Pasar k sin bordes de k=0
ny = k.shape[1]
nx = k.shape[2] #Pasar k sin bordes de k=0
ny = k.shape[1]
tz = 2 * k[1, :, :] * k[0, :, :] / (k[0, :, :] + k[1, :, :])
q = ((pm[0, :, :] - pm[1, :, :]) * tz).sum()
area = ny * nx
l = Nz
keff = q * l / (pbc * area)
return keff, q
tz = 2*k[1,:,:]*k[0, :,:]/(k[0, :,:]+k[1,:,:])
q=((pm[0,:,:]-pm[1,:,:])*tz).sum()
area=ny*nx
l=Nz
keff=q*l/(pbc*area)
return keff,q
def getPfaces(k,P):
nx,ny,nz=k.shape[0],k.shape[1],k.shape[2]
Px,Py,Pz= np.zeros((nx+1,ny,nz)),np.zeros((nx,ny+1,nz)),np.zeros((nx,ny,nz+1))
def getPfaces(k, P):
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
Px, Py, Pz = (
np.zeros((nx + 1, ny, nz)),
np.zeros((nx, ny + 1, nz)),
np.zeros((nx, ny, nz + 1)),
)
Px[1:-1,:,:] = (k[:-1,:,:]*P[:-1,:,:]+k[1:,:,:]*P[1:,:,:])/(k[:-1,:,:]+k[1:,:,:])
Px[0,:,:]=nx
Px[1:-1, :, :] = (k[:-1, :, :] * P[:-1, :, :] + k[1:, :, :] * P[1:, :, :]) / (
k[:-1, :, :] + k[1:, :, :]
)
Px[0, :, :] = nx
Py[:,1:-1,:] = (k[:,:-1,:]*P[:,:-1,:]+k[:,1:,:]*P[:,1:,:])/(k[:,:-1,:]+k[:,1:,:])
Py[:,0,:],Py[:,-1,:] =P[:,0,:], P[:,-1,:]
Py[:, 1:-1, :] = (k[:, :-1, :] * P[:, :-1, :] + k[:, 1:, :] * P[:, 1:, :]) / (
k[:, :-1, :] + k[:, 1:, :]
)
Py[:, 0, :], Py[:, -1, :] = P[:, 0, :], P[:, -1, :]
Pz[:,:,1:-1] = (k[:,:,:-1]*P[:,:,:-1]+k[:,:,1:]*P[:,:,1:])/(k[:,:,:-1]+k[:,:,1:])
Pz[:,:,0],Pz[:,:,-1] =P[:,:,0], P[:,:,-1]
Pz[:, :, 1:-1] = (k[:, :, :-1] * P[:, :, :-1] + k[:, :, 1:] * P[:, :, 1:]) / (
k[:, :, :-1] + k[:, :, 1:]
)
Pz[:, :, 0], Pz[:, :, -1] = P[:, :, 0], P[:, :, -1]
return Px, Py, Pz
return Px, Py, Pz
def getVfaces(k,P, Px,Py, Pz):
nx,ny,nz=k.shape[0],k.shape[1],k.shape[2]
vx,vy,vz= np.zeros((nx+1,ny,nz)),np.zeros((nx,ny+1,nz)),np.zeros((nx,ny,nz+1))
vx[1:,:,:] = 2*k*(Px[1:,:,:]-P) #v= k*(deltaP)/(deltaX/2)
vx[0,:,:] = 2*k[0,:,:]*(P[0,:,:]-Px[0,:,:])
def getVfaces(k, P, Px, Py, Pz):
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
vx, vy, vz = (
np.zeros((nx + 1, ny, nz)),
np.zeros((nx, ny + 1, nz)),
np.zeros((nx, ny, nz + 1)),
)
vx[1:, :, :] = 2 * k * (Px[1:, :, :] - P) # v= k*(deltaP)/(deltaX/2)
vx[0, :, :] = 2 * k[0, :, :] * (P[0, :, :] - Px[0, :, :])
vy[:,1:,:] = 2*k*(Py[:,1:,:]-P)
vy[:,0,:] = 2*k[:,0,:]*(P[:,0,:]-Py[:,0,:])
vz[:,:,1:] = 2*k*(Pz[:,:,1:]-P)
vz[:,:,0] = 2*k[:,:,0]*(P[:,:,0]-Pz[:,:,0])
return vx,vy,vz
vy[:, 1:, :] = 2 * k * (Py[:, 1:, :] - P)
vy[:, 0, :] = 2 * k[:, 0, :] * (P[:, 0, :] - Py[:, 0, :])
vz[:, :, 1:] = 2 * k * (Pz[:, :, 1:] - P)
vz[:, :, 0] = 2 * k[:, :, 0] * (P[:, :, 0] - Pz[:, :, 0])
return vx, vy, vz
def refina(k, ref):
if ref==1:
return k
nx,ny,nz=k.shape[0],k.shape[1],k.shape[2]
if ref == 1:
return k
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
krx=np.zeros((ref*nx,ny,nz))
for i in range(ref):
krx[i::ref,:,:]=k
k=0
krxy=np.zeros((ref*nx,ny*ref,nz))
for i in range(ref):
krxy[:,i::ref,:]=krx
krx=0
if nz==1:
return krxy
krx = np.zeros((ref * nx, ny, nz))
for i in range(ref):
krx[i::ref, :, :] = k
k = 0
krxy = np.zeros((ref * nx, ny * ref, nz))
for i in range(ref):
krxy[:, i::ref, :] = krx
krx = 0
if nz == 1:
return krxy
krxyz=np.zeros((ref*nx,ny*ref,nz*ref))
for i in range(ref):
krxyz[:,:,i::ref]=krxy
krxy=0
krxyz = np.zeros((ref * nx, ny * ref, nz * ref))
for i in range(ref):
krxyz[:, :, i::ref] = krxy
krxy = 0
return krxyz
return krxyz
def computeT(k):
nx = k.shape[0]
ny = k.shape[1]
nz = k.shape[2]
tx = np.zeros((nx+1,ny, nz))
ty = np.zeros((nx,ny+1, nz))
tz = np.zeros((nx,ny, nz+1))
tx[1:-1,:,:] = 2*k[:-1,:,:]*k[1:,:,:]/(k[:-1,:,:]+k[1:,:,:])
ty[:,1:-1,:] = 2*k[:,:-1,:]*k[:,1:,:]/(k[:,:-1,:]+k[:,1:,:])
tz[:,:,1:-1] = 2*k[:,:,:-1]*k[:,:,1:]/(k[:,:,:-1]+k[:,:,1:])
nx = k.shape[0]
ny = k.shape[1]
nz = k.shape[2]
tx = np.zeros((nx + 1, ny, nz))
ty = np.zeros((nx, ny + 1, nz))
tz = np.zeros((nx, ny, nz + 1))
tx[1:-1, :, :] = 2 * k[:-1, :, :] * k[1:, :, :] / (k[:-1, :, :] + k[1:, :, :])
ty[:, 1:-1, :] = 2 * k[:, :-1, :] * k[:, 1:, :] / (k[:, :-1, :] + k[:, 1:, :])
tz[:, :, 1:-1] = 2 * k[:, :, :-1] * k[:, :, 1:] / (k[:, :, :-1] + k[:, :, 1:])
return tx, ty, tz
return tx, ty, tz
def Rmat(k):
pbc = k.shape[0]
tx, ty, tz = computeT(k)
pbc=k.shape[0]
tx, ty , tz = computeT(k)
tx[0,:,:],tx[-1,:,:] = 2*tx[1,:,:],2*tx[-2,:,:]
rh=np.zeros((k.shape[0],k.shape[1],k.shape[2]))
tx[0, :, :], tx[-1, :, :] = 2 * tx[1, :, :], 2 * tx[-2, :, :]
rh[0,:,:]=pbc*tx[0,:,:]
rh=rh.reshape(-1)
d=(tz[:,:,:-1]+tz[:,:,1:]+ty[:,:-1,:]+ty[:,1:,:]+tx[:-1,:,:]+tx[1:,:,:]).reshape(-1)
a=(-tz[:,:,:-1].reshape(-1))[1:]
#a=(tx.reshape(-1))[:-1]
b=(-ty[:,1:,:].reshape(-1))[:-k.shape[2]]
c=-tx[1:-1,:,:].reshape(-1)
rh = np.zeros((k.shape[0], k.shape[1], k.shape[2]))
rh[0, :, :] = pbc * tx[0, :, :]
rh = rh.reshape(-1)
d = (
tz[:, :, :-1]
+ tz[:, :, 1:]
+ ty[:, :-1, :]
+ ty[:, 1:, :]
+ tx[:-1, :, :]
+ tx[1:, :, :]
).reshape(-1)
a = (-tz[:, :, :-1].reshape(-1))[1:]
# a=(tx.reshape(-1))[:-1]
b = (-ty[:, 1:, :].reshape(-1))[: -k.shape[2]]
c = -tx[1:-1, :, :].reshape(-1)
return a, b, c, d, rh
return a, b, c, d, rh
def PysolveP(k, solver):
a, b, c, d, rh = Rmat(k)
nx, ny, nz = k.shape[0], k.shape[1],k.shape[2]
offset = [-nz*ny,-nz, -1, 0, 1, nz, nz*ny]
km=diags(np.array([c, b, a, d, a, b, c]), offset, format='csc')
a, b, c, d = 0, 0 ,0 , 0
p = solver(km, rh)
if type(p)==tuple:
p=p[0]
p=p.reshape(nx, ny, nz)
keff,q = getKeff(p,k,nz,nz)
return keff
solvers=[bicg, bicgstab, cg, spsolve]
snames=['bicg', 'bicgstab',' cg',' spsolve']
solvers=[ cg, spsolve]
snames=[' cg',' spsolve']
a, b, c, d, rh = Rmat(k)
nx, ny, nz = k.shape[0], k.shape[1], k.shape[2]
offset = [-nz * ny, -nz, -1, 0, 1, nz, nz * ny]
km = diags(np.array([c, b, a, d, a, b, c]), offset, format="csc")
a, b, c, d = 0, 0, 0, 0
p = solver(km, rh)
if type(p) == tuple:
p = p[0]
p = p.reshape(nx, ny, nz)
keff, q = getKeff(p, k, nz, nz)
return keff
solvers = [bicg, bicgstab, cg, spsolve]
snames = ["bicg", "bicgstab", " cg", " spsolve"]
solvers = [cg, spsolve]
snames = [" cg", " spsolve"]
for job in range(15):
kff=np.load('./otrotest/'+str(job)+'/k.npy')
print('************* JOB : '+str(job)+' ******************')
print(' ')
for i in range(len(solvers)):
t0=time.time()
keff=PysolveP(kff, solvers[i])
print('Solver: '+snames[i]+' Keff = ' +str(keff)+' time: '+str(time.time()-t0))
kff = np.load("./otrotest/" + str(job) + "/k.npy")
print("************* JOB : " + str(job) + " ******************")
print(" ")
for i in range(len(solvers)):
t0 = time.time()
keff = PysolveP(kff, solvers[i])
print(
"Solver: "
+ snames[i]
+ " Keff = "
+ str(keff)
+ " time: "
+ str(time.time() - t0)
)

50
utilities/plotCon.py
Unescape Escape View File

@ -2,21 +2,37 @@ import numpy as np
import matplotlib.pyplot as plt
nps = 13
ps = np.linspace(0.1, 0.5, nps)
nps=13
ps=np.linspace(0.1,.5,nps)
clabels=['Intermediate','high','low']
Cind='spanning'
scale=128
clabels = ["Intermediate", "high", "low"]
Cind = "spanning"
scale = 128
for con in range(3):
ci=np.zeros(nps)
for ip in range(nps):
folder=con*nps+ip
ci[ip]=np.mean(np.load('./test_old/'+str(folder)+'/GlobalConnectivityMetrics/'+str(scale)+'.npy',allow_pickle=True).item()[Cind])
ci_new=np.mean(np.load('./test_new/'+str(folder)+'/GlobalConnectivityMetrics/'+str(scale)+'.npy',allow_pickle=True).item()[Cind])
'''
ci = np.zeros(nps)
for ip in range(nps):
folder = con * nps + ip
ci[ip] = np.mean(
np.load(
"./test_old/"
+ str(folder)
+ "/GlobalConnectivityMetrics/"
+ str(scale)
+ ".npy",
allow_pickle=True,
).item()[Cind]
)
ci_new = np.mean(
np.load(
"./test_new/"
+ str(folder)
+ "/GlobalConnectivityMetrics/"
+ str(scale)
+ ".npy",
allow_pickle=True,
).item()[Cind]
)
"""
print(ip,ci[ip],ci_new)
if ci_new!=0:
ci[ip]=ci[ip]/ci_new
@ -25,13 +41,11 @@ for con in range(3):
if ci_new==0 and ci[ip]==0:
ci[ip]=1.0
'''
"""
plt.plot(ps,ci,label=clabels[con]+'-'+str(con))
plt.plot(ps, ci, label=clabels[con] + "-" + str(con))
plt.legend()
plt.grid()
plt.show()
#plt.savefig(str(scale)+Cind+'.png')
# plt.savefig(str(scale)+Cind+'.png')

41
utilities/plotCon_scale.py
Unescape Escape View File

@ -2,26 +2,33 @@ import numpy as np
import matplotlib.pyplot as plt
rdir = "./lc8/"
nps = 50
ps = np.linspace(0.0, 1.0, nps)
rdir='./lc8/'
nps=50
ps=np.linspace(0.0,1.0,nps)
clabels=['Intermediate','high','low']
Cind='npx'
scale=128
scales=[64,128,256,512,1024]
con=3
con=con-1
clabels = ["Intermediate", "high", "low"]
Cind = "npx"
scale = 128
scales = [64, 128, 256, 512, 1024]
con = 3
con = con - 1
for scale in range(len(scales)):
ci=np.zeros(nps)
for ip in range(nps):
folder=con*nps+ip
ci[ip]=np.mean(np.load(rdir+str(folder)+'/ConnectivityMetrics/'+str(scales[scale])+'.npy',allow_pickle=True).item()[Cind])
ci = np.zeros(nps)
for ip in range(nps):
folder = con * nps + ip
ci[ip] = np.mean(
np.load(
rdir
+ str(folder)
+ "/ConnectivityMetrics/"
+ str(scales[scale])
+ ".npy",
allow_pickle=True,
).item()[Cind]
)
plt.plot(ps[2:-2],ci[2:-2],label=str(scales[scale]))
plt.plot(ps[2:-2], ci[2:-2], label=str(scales[scale]))
plt.legend()
plt.grid()
plt.savefig(rdir+str(con+1)+'-'+Cind+'.png')
plt.savefig(rdir + str(con + 1) + "-" + Cind + ".png")

22
utilities/plotKeff.py
Unescape Escape View File

@ -2,22 +2,20 @@ import numpy as np
import matplotlib.pyplot as plt
rdir = "./data/"
nps = 5
ps = np.linspace(0.1, 0.5, nps)
rdir='./data/'
nps=5
ps=np.linspace(.1,.5,nps)
clabels=['Intermediate','high','low']
clabels = ["Intermediate", "high", "low"]
for con in range(3):
keff=np.zeros(nps)
for ip in range(nps):
folder=con*nps+ip
keff[ip]=np.loadtxt(rdir+str(folder)+'/SolverRes.txt')[2]
keff = np.zeros(nps)
for ip in range(nps):
folder = con * nps + ip
keff[ip] = np.loadtxt(rdir + str(folder) + "/SolverRes.txt")[2]
plt.plot(ps,keff,label=clabels[con])
plt.plot(ps, keff, label=clabels[con])
plt.legend()
plt.grid()
plt.savefig('rTimeSolver.png')
plt.savefig("rTimeSolver.png")

89
utilities/plot_Dist.py
Unescape Escape View File

@ -2,43 +2,64 @@ import numpy as np
import matplotlib.pyplot as plt
rdir = "./data/"
rdir='./data/'
clabels = [r"$K_{perm}$", r"$K_{diss}$", r"$K_{average}$", r"$K_{1/3}$"]
names = ["Kperm", "Kdiss", "Kaverage", "Kpower"]
cases = [
r"$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$",
r"$Lognormal \ \sigma^{2}_{\log(k)} = 7$",
r"$Binary p = 0.2; k+/k- = 10^4$",
]
clabels=[r'$K_{perm}$',r'$K_{diss}$',r'$K_{average}$',r'$K_{1/3}$']
names=['Kperm','Kdiss','Kaverage','Kpower']
cases=[r'$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$',r'$Lognormal \ \sigma^{2}_{\log(k)} = 7$', r'$Binary p = 0.2; k+/k- = 10^4$']
scales=np.array([4,8,16,32,64])
lcs=[16,16,8]
est=3
ranges=[(-0.5,0.5),(-5,5),(-4,4)]
scales = np.array([4, 8, 16, 32, 64])
lcs = [16, 16, 8]
est = 3
ranges = [(-0.5, 0.5), (-5, 5), (-4, 4)]
for i in range(3):
for scale in range(len(scales)):
if est == 0:
keff = np.log(
np.load(rdir + str(i) + "/kperm/" + str(scales[scale]) + ".npy")
)
if est == 1:
keff = np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kd" + str(scales[scale]) + ".npy"
)
)
if est == 2:
keff = np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kv" + str(scales[scale]) + ".npy"
)
)
if est == 3:
keff = np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kpo" + str(scales[scale]) + ".npy"
)
)
for scale in range(len(scales)):
if est==0:
keff=np.log(np.load(rdir+str(i)+'/kperm/'+str(scales[scale])+'.npy'))
if est==1:
keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kd'+str(scales[scale])+'.npy'))
if est==2:
keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kv'+str(scales[scale])+'.npy'))
if est==3:
keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kpo'+str(scales[scale])+'.npy'))
plt.hist(keff.reshape(-1),label=r'$\lambda = $'+' ' +str(scales[scale]),density=True,histtype='step',range=ranges[i])
#plt.semilogx(scales/512.0,kpost[:,1],label=clabels[1],marker='s')
#plt.semilogx(scales/512.0,kpost[:,2],label=clabels[2],marker='^')
#plt.semilogx(scales/512.0,kpost[:,3],label=clabels[3],marker='o')
#plt.vlines(lcs[i]/512.0,kpost[:,0].min(),kpost[:,0].max(),label=r'$lc = $'+str(lcs[i]))
plt.xlabel(r'$\log(K_{eff})$')
plt.ylabel(r'$P(K_{eff})$')
plt.legend()
plt.grid()
plt.title(cases[i]+' '+str(names[est]))
plt.tight_layout()
plt.savefig(rdir+str(i)+'/Kpost_dist_scales_'+names[est]+'.png')
plt.close()
plt.hist(
keff.reshape(-1),
label=r"$\lambda = $" + " " + str(scales[scale]),
density=True,
histtype="step",
range=ranges[i],
)
# plt.semilogx(scales/512.0,kpost[:,1],label=clabels[1],marker='s')
# plt.semilogx(scales/512.0,kpost[:,2],label=clabels[2],marker='^')
# plt.semilogx(scales/512.0,kpost[:,3],label=clabels[3],marker='o')
# plt.vlines(lcs[i]/512.0,kpost[:,0].min(),kpost[:,0].max(),label=r'$lc = $'+str(lcs[i]))
plt.xlabel(r"$\log(K_{eff})$")
plt.ylabel(r"$P(K_{eff})$")
plt.legend()
plt.grid()
plt.title(cases[i] + " " + str(names[est]))
plt.tight_layout()
plt.savefig(rdir + str(i) + "/Kpost_dist_scales_" + names[est] + ".png")
plt.close()

187
utilities/plot_Kmap.py
Unescape Escape View File

@ -2,119 +2,114 @@ import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
def plotK(kk,pdir,logn):
y=np.arange(kk.shape[0])
x=np.arange(kk.shape[1])
newcolors = np.zeros((2,4))
alto = np.array([0.0, 0.0, 0.0, 1])
bajo = np.array([191/256.0, 191/256.0, 191/256.0, 1]) #[108.0/256, 122.0/256, 137.0/256, 1])
alto = np.array([204.0/254, 0.0, 0.0, 1])
bajo = np.array([0.0, 0.0, 153.0/254, 1]) #[108.0/256, 122.0/256, 137.0/256, 1])
newcolors[0, :] = bajo
newcolors[1, :] = alto
newcmp = ListedColormap(newcolors)
if logn==True:
kk=np.log(kk)
vmin,vmax=-2*np.var(kk)+np.mean(kk),2*np.var(kk)+np.mean(kk)
#print(vmax)
colormap='viridis'
plt.pcolormesh(x,y,kk,cmap=colormap)#,vmin=vmin,vmax=vmax)
else:
#colormap='binary'
plt.pcolormesh(x,y,kk,cmap=newcmp)
cbar=plt.colorbar()
cbar.set_label('k')
#plt.title('Guassian N(0,1)')
plt.savefig(pdir+'k.png')
plt.close()
'''
def plotK(kk, pdir, logn):
y = np.arange(kk.shape[0])
x = np.arange(kk.shape[1])
newcolors = np.zeros((2, 4))
alto = np.array([0.0, 0.0, 0.0, 1])
bajo = np.array(
[191 / 256.0, 191 / 256.0, 191 / 256.0, 1]
) # [108.0/256, 122.0/256, 137.0/256, 1])
alto = np.array([204.0 / 254, 0.0, 0.0, 1])
bajo = np.array([0.0, 0.0, 153.0 / 254, 1]) # [108.0/256, 122.0/256, 137.0/256, 1])
newcolors[0, :] = bajo
newcolors[1, :] = alto
newcmp = ListedColormap(newcolors)
if logn == True:
kk = np.log(kk)
vmin, vmax = -2 * np.var(kk) + np.mean(kk), 2 * np.var(kk) + np.mean(kk)
# print(vmax)
colormap = "viridis"
plt.pcolormesh(x, y, kk, cmap=colormap) # ,vmin=vmin,vmax=vmax)
else:
# colormap='binary'
plt.pcolormesh(x, y, kk, cmap=newcmp)
cbar = plt.colorbar()
cbar.set_label("k")
# plt.title('Guassian N(0,1)')
plt.savefig(pdir + "k.png")
plt.close()
"""
if logn==True:
plt.hist(kk.reshape(-1),range=(2*vmin,2*vmax),histtype='step',bins=250,density=True)
plt.xlabel('k')
plt.ylabel('p(k)')
plt.savefig(pdir+'histo.png')
'''
return
def plotK_imshow(kk,pdir,logn):
kk=np.rot90(kk)
y=np.arange(kk.shape[0])
x=np.arange(kk.shape[1])
newcolors = np.zeros((2,4))
alto = np.array([0.0, 0.0, 0.0, 1])
bajo = np.array([191/256.0, 191/256.0, 191/256.0, 1]) #[108.0/256, 122.0/256, 137.0/256, 1])
alto = np.array([204.0/254, 0.0, 0.0, 1])
bajo = np.array([0.0, 0.0, 153.0/254, 1]) #[108.0/256, 122.0/256, 137.0/256, 1])
newcolors[0, :] = bajo
newcolors[1, :] = alto
newcmp = ListedColormap(newcolors)
if logn==True:
kk=np.log(kk)
vmin,vmax=-3*np.var(kk)+np.mean(kk),3*np.var(kk)+np.mean(kk)
#print(vmax)
colormap='viridis'
plt.imshow(kk,vmin=vmin,vmax=vmax) #,cmap='binary'
else:
#colormap='binary'
plt.imshow(kk,cmap='binary') #,cmap='binary'
plt.colorbar()
#cbar.set_label('k')
#plt.title('Guassian N(0,1)')
plt.tight_layout()
plt.savefig(pdir+'k.png')
plt.close()
'''
"""
return
def plotK_imshow(kk, pdir, logn):
kk = np.rot90(kk)
y = np.arange(kk.shape[0])
x = np.arange(kk.shape[1])
newcolors = np.zeros((2, 4))
alto = np.array([0.0, 0.0, 0.0, 1])
bajo = np.array(
[191 / 256.0, 191 / 256.0, 191 / 256.0, 1]
) # [108.0/256, 122.0/256, 137.0/256, 1])
alto = np.array([204.0 / 254, 0.0, 0.0, 1])
bajo = np.array([0.0, 0.0, 153.0 / 254, 1]) # [108.0/256, 122.0/256, 137.0/256, 1])
newcolors[0, :] = bajo
newcolors[1, :] = alto
newcmp = ListedColormap(newcolors)
if logn == True:
kk = np.log(kk)
vmin, vmax = -3 * np.var(kk) + np.mean(kk), 3 * np.var(kk) + np.mean(kk)
# print(vmax)
colormap = "viridis"
plt.imshow(kk, vmin=vmin, vmax=vmax) # ,cmap='binary'
else:
# colormap='binary'
plt.imshow(kk, cmap="binary") # ,cmap='binary'
plt.colorbar()
# cbar.set_label('k')
# plt.title('Guassian N(0,1)')
plt.tight_layout()
plt.savefig(pdir + "k.png")
plt.close()
"""
if logn==True:
plt.hist(kk.reshape(-1),range=(2*vmin,2*vmax),histtype='step',bins=250,density=True)
plt.xlabel('k')
plt.ylabel('p(k)')
plt.savefig(pdir+'histo.png')
'''
return
"""
def plot_hist(k,pdir,logn):
return
plt.figure(1)
if logn==True:
k=np.log(k)
vmin,vmax=-4*np.var(k)+np.mean(k),4*np.var(k)+np.mean(k)
plt.hist(k.reshape(-1),range=(vmin,vmax))
else:
plt.hist(k.reshape(-1))
plt.xlabel('k')
plt.ylabel('Counts')
plt.savefig(pdir+'-histo.png')
plt.close()
return
rdir='./perco_lc8/'
for i in range(11):
k=np.load(rdir+str(i)+'/k.npy')[:,:,0]
log='False'
plotK_imshow(k,rdir+str(i)+'Map',log)
#plot_hist(k,rdir+'Res/'+resname,log)
def plot_hist(k, pdir, logn):
plt.figure(1)
if logn == True:
k = np.log(k)
vmin, vmax = -4 * np.var(k) + np.mean(k), 4 * np.var(k) + np.mean(k)
plt.hist(k.reshape(-1), range=(vmin, vmax))
else:
plt.hist(k.reshape(-1))
plt.xlabel("k")
plt.ylabel("Counts")
plt.savefig(pdir + "-histo.png")
plt.close()
return
rdir = "./perco_lc8/"
for i in range(11):
k = np.load(rdir + str(i) + "/k.npy")[:, :, 0]
log = "False"
plotK_imshow(k, rdir + str(i) + "Map", log)
# plot_hist(k,rdir+'Res/'+resname,log)

117
utilities/plot_VelDiss.py
Unescape Escape View File

@ -3,67 +3,70 @@ import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
def plotK_imshow(kk,pdir,logn,xlabel,minfact,maxfact):
kk=np.rot90(kk)
if logn==True:
#kk=np.log(kk)
vmin,vmax=minfact,maxfact
#print(vmax)
colormap='viridis'
plt.imshow(kk,vmin=vmin,vmax=vmax) #,cmap='binary'
else:
#colormap='binary'
plt.imshow(kk,cmap='binary') #,cmap='binary'
plt.colorbar()
#cbar.set_label(xlabel)
plt.title(xlabel)
plt.tight_layout()
plt.savefig(pdir+'.png',dpi=1200)
plt.close()
return
def plot_hist(k,pdir,logn,xlabel,minfact,maxfact,llg):
if logn==True:
vmin,vmax=minfact,maxfact
#plt.hist(k.reshape(-1),bins=100,range=(vmin,vmax),histtype='step',normed=1,label=llg)#,range=(vmin,vmax))
plt.hist(k.reshape(-1),bins=100,histtype='step',normed=1,label=llg)#,range=(vmin,vmax))
else:
plt.hist(k.reshape(-1))
plt.xlabel(xlabel)
plt.ylabel('Counts')
return
ps=np.linspace(0,100,50)
rdir='./testlc8/'
rdir='./lc0/'
def plotK_imshow(kk, pdir, logn, xlabel, minfact, maxfact):
kk = np.rot90(kk)
if logn == True:
# kk=np.log(kk)
vmin, vmax = minfact, maxfact
# print(vmax)
colormap = "viridis"
plt.imshow(kk, vmin=vmin, vmax=vmax) # ,cmap='binary'
else:
# colormap='binary'
plt.imshow(kk, cmap="binary") # ,cmap='binary'
plt.colorbar()
# cbar.set_label(xlabel)
plt.title(xlabel)
plt.tight_layout()
plt.savefig(pdir + ".png", dpi=1200)
plt.close()
return
def plot_hist(k, pdir, logn, xlabel, minfact, maxfact, llg):
if logn == True:
vmin, vmax = minfact, maxfact
# plt.hist(k.reshape(-1),bins=100,range=(vmin,vmax),histtype='step',normed=1,label=llg)#,range=(vmin,vmax))
plt.hist(
k.reshape(-1), bins=100, histtype="step", normed=1, label=llg
) # ,range=(vmin,vmax))
else:
plt.hist(k.reshape(-1))
plt.xlabel(xlabel)
plt.ylabel("Counts")
return
ps = np.linspace(0, 100, 50)
rdir = "./testlc8/"
rdir = "./lc0/"
plt.figure(1)
for j in range(1):
for i in range(0,50,1):
for i in range(0, 50, 1):
log=True
log = True
label=r'$\log_{10}(vx/<vx>)$'
label = r"$\log_{10}(vx/<vx>)$"
folder=j*50+i
folder = j * 50 + i
V=np.load(rdir+str(folder)+'/V.npy')[0][:,:,0]
perco=np.load(rdir+str(folder)+'/ConnectivityMetrics/1024.npy',allow_pickle=True).item()['spanning'][0,0,0]
V=np.log10(np.abs(V)) #/np.mean(np.abs(V)))
leg='p = '+str(ps[i])[:4]+'% ('+str(perco)+')'
plot_hist(V,rdir+str(folder)+'/HisTabsV',log,label,-.8,.5,leg)
plotK_imshow(V[512:1536,512:1536],rdir+str(i)+'/V',log,label,-4,1)
plt.legend(loc='upper left')
plt.savefig(rdir+str(folder)+'VelHistogramB.png')
plt.close()
'''
V = np.load(rdir + str(folder) + "/V.npy")[0][:, :, 0]
perco = np.load(
rdir + str(folder) + "/ConnectivityMetrics/1024.npy", allow_pickle=True
).item()["spanning"][0, 0, 0]
V = np.log10(np.abs(V)) # /np.mean(np.abs(V)))
leg = "p = " + str(ps[i])[:4] + "% (" + str(perco) + ")"
plot_hist(V, rdir + str(folder) + "/HisTabsV", log, label, -0.8, 0.5, leg)
plotK_imshow(V[512:1536, 512:1536], rdir + str(i) + "/V", log, label, -4, 1)
plt.legend(loc="upper left")
plt.savefig(rdir + str(folder) + "VelHistogramB.png")
plt.close()
"""
label=r'$\log_{10}(|v_x|/<|v_x|>)$'
V=np.load(rdir+str(i)+'/V.npy')[0][:,:,0]
@ -80,6 +83,4 @@ plotK_imshow(V[1024:2048,512:1024],rdir+str(i)+'/Vy',log,label,0,1)
'''
"""

22
utilities/plot_lc.py
Unescape Escape View File

@ -2,22 +2,20 @@ import numpy as np
import matplotlib.pyplot as plt
rdir = "./lc_vslcbin/"
nps = 41
ps = np.linspace(0.1, 0.5, nps)
rdir='./lc_vslcbin/'
nps=41
ps=np.linspace(.1,.5,nps)
clabels=['Intermediate','high','low']
clabels = ["Intermediate", "high", "low"]
for con in range(1):
keff=np.zeros(nps)
for ip in range(nps):
folder=con*nps+ip
keff[ip]=np.loadtxt(rdir+str(folder)+'/lc.txt')[2]
keff = np.zeros(nps)
for ip in range(nps):
folder = con * nps + ip
keff[ip] = np.loadtxt(rdir + str(folder) + "/lc.txt")[2]
plt.plot(ps,keff,label=clabels[con])
plt.plot(ps, keff, label=clabels[con])
plt.legend()
plt.grid()
plt.savefig('lc2.png')
plt.savefig("lc2.png")

74
utilities/plot_val_Kpost.py
Unescape Escape View File

@ -2,32 +2,60 @@ import numpy as np
import matplotlib.pyplot as plt
rdir = "./data/"
rdir='./data/'
clabels = [r"$K_{perm}$", r"$K_{diss}$", r"$K_{average}$"]
cases = [
r"$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$",
r"$Lognormal \ \sigma^{2}_{\log(k)} = 7$",
r"$Binary p = 0.2; k+/k- = 10^4$",
]
clabels=[r'$K_{perm}$',r'$K_{diss}$',r'$K_{average}$']
cases=[r'$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$',r'$Lognormal \ \sigma^{2}_{\log(k)} = 7$', r'$Binary p = 0.2; k+/k- = 10^4$']
scales=np.array([4,8,16,32,64,128,256,512])
lcs=[16,16,8]
scales = np.array([4, 8, 16, 32, 64, 128, 256, 512])
lcs = [16, 16, 8]
for i in range(3):
kpost=np.zeros((len(scales),3))
for scale in range(len(scales)):
kpost[scale,0]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/kperm/'+str(scales[scale])+'.npy'))))
kpost[scale,1]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kd'+str(scales[scale])+'.npy'))))
kpost[scale,2]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kv'+str(scales[scale])+'.npy'))))
plt.semilogx(scales/512.0,kpost[:,0],label=clabels[0],marker='x')
plt.semilogx(scales/512.0,kpost[:,1],label=clabels[1],marker='s')
plt.semilogx(scales/512.0,kpost[:,2],label=clabels[2],marker='^')
plt.vlines(lcs[i]/512.0,kpost[:,0].min(),kpost[:,0].max(),label=r'$lc = $'+str(lcs[i]))
plt.xlabel(r'$\lambda / L$')
plt.ylabel(r'$<K_{eff}>_G$')
plt.legend()
plt.grid()
plt.title(cases[i])
plt.tight_layout()
plt.savefig(rdir+str(i)+'/Kpost_mean.png')
plt.close()
kpost = np.zeros((len(scales), 3))
for scale in range(len(scales)):
kpost[scale, 0] = np.exp(
np.nanmean(
np.log(np.load(rdir + str(i) + "/kperm/" + str(scales[scale]) + ".npy"))
)
)
kpost[scale, 1] = np.exp(
np.nanmean(
np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kd" + str(scales[scale]) + ".npy"
)
)
)
)
kpost[scale, 2] = np.exp(
np.nanmean(
np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kv" + str(scales[scale]) + ".npy"
)
)
)
)
plt.semilogx(scales / 512.0, kpost[:, 0], label=clabels[0], marker="x")
plt.semilogx(scales / 512.0, kpost[:, 1], label=clabels[1], marker="s")
plt.semilogx(scales / 512.0, kpost[:, 2], label=clabels[2], marker="^")
plt.vlines(
lcs[i] / 512.0,
kpost[:, 0].min(),
kpost[:, 0].max(),
label=r"$lc = $" + str(lcs[i]),
)
plt.xlabel(r"$\lambda / L$")
plt.ylabel(r"$<K_{eff}>_G$")
plt.legend()
plt.grid()
plt.title(cases[i])
plt.tight_layout()
plt.savefig(rdir + str(i) + "/Kpost_mean.png")
plt.close()

112
utilities/plot_val_Kpost_varianve.py
Unescape Escape View File

@ -3,42 +3,90 @@ import matplotlib.pyplot as plt
from Var_analytical import *
rdir='./data/'
rdir = "./data/"
clabels=[r'$K_{perm}$',r'$K_{diss}$',r'$K_{average}$',r'$K_{1/3}$','analitycal Gaussian cov']
cases=[r'$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$',r'$Lognormal \ \sigma^{2}_{\log(k)} = 7$', r'$Binary p = 0.2; k+/k- = 10^4$']
clabels = [
r"$K_{perm}$",
r"$K_{diss}$",
r"$K_{average}$",
r"$K_{1/3}$",
"analitycal Gaussian cov",
]
cases = [
r"$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$",
r"$Lognormal \ \sigma^{2}_{\log(k)} = 7$",
r"$Binary p = 0.2; k+/k- = 10^4$",
]
scales=np.array([4,8,16,32,64,128])
variances=[0.1,7,13.572859162824695]
x=scales/512.0
lcs=[16,16,8]
va=VarLgauss(16/2.45398,scales,3)
scales = np.array([4, 8, 16, 32, 64, 128])
variances = [0.1, 7, 13.572859162824695]
x = scales / 512.0
lcs = [16, 16, 8]
va = VarLgauss(16 / 2.45398, scales, 3)
for i in range(3):
kpost = np.zeros((len(scales), 4))
for scale in range(len(scales)):
kpost[scale, 0] = (
np.nanvar(
np.log(np.load(rdir + str(i) + "/kperm/" + str(scales[scale]) + ".npy"))
)
/ variances[i]
)
kpost[scale, 1] = (
np.nanvar(
np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kd" + str(scales[scale]) + ".npy"
)
)
)
/ variances[i]
)
kpost[scale, 2] = (
np.nanvar(
np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kv" + str(scales[scale]) + ".npy"
)
)
)
/ variances[i]
)
kpost[scale, 3] = (
np.nanvar(
np.log(
np.load(
rdir
+ str(i)
+ "/KpostProcess/Kpo"
+ str(scales[scale])
+ ".npy"
)
)
)
/ variances[i]
)
plt.loglog(x, (x ** 3) * kpost[:, 0], label=clabels[0], marker="x")
plt.loglog(x, (x ** 3) * kpost[:, 1], label=clabels[1], marker="s")
plt.loglog(x, (x ** 3) * kpost[:, 2], label=clabels[2], marker="^")
plt.loglog(x, (x ** 3) * kpost[:, 3], label=clabels[3], marker="o")
if i == 0 or i == 1:
plt.loglog(x, (x ** 3) * va, label=clabels[4], marker="", linestyle="--")
kpost=np.zeros((len(scales),4))
for scale in range(len(scales)):
kpost[scale,0]=np.nanvar(np.log(np.load(rdir+str(i)+'/kperm/'+str(scales[scale])+'.npy')))/variances[i]
kpost[scale,1]=np.nanvar(np.log(np.load(rdir+str(i)+'/KpostProcess/Kd'+str(scales[scale])+'.npy')))/variances[i]
kpost[scale,2]=np.nanvar(np.log(np.load(rdir+str(i)+'/KpostProcess/Kv'+str(scales[scale])+'.npy')))/variances[i]
kpost[scale,3]=np.nanvar(np.log(np.load(rdir+str(i)+'/KpostProcess/Kpo'+str(scales[scale])+'.npy')))/variances[i]
plt.loglog(x,(x**3)*kpost[:,0],label=clabels[0],marker='x')
plt.loglog(x,(x**3)*kpost[:,1],label=clabels[1],marker='s')
plt.loglog(x,(x**3)*kpost[:,2],label=clabels[2],marker='^')
plt.loglog(x,(x**3)*kpost[:,3],label=clabels[3],marker='o')
if i==0 or i==1:
plt.loglog(x,(x**3)*va,label=clabels[4],marker='',linestyle='--')
plt.vlines(lcs[i]/512.0,((x**3)*kpost[:,0]).min(),((x**3)*kpost[:,0]).max(),label=r'$lc = $'+str(lcs[i]))
plt.xlabel(r'$\lambda / L$')
plt.ylabel(r'$(\lambda / L)^3 \sigma^{2}_{\log(K_{eff})} / \sigma^{2}_{\log(k)}$')
plt.legend()
plt.grid()
plt.title(cases[i])
plt.tight_layout()
plt.savefig(rdir+str(i)+'/Kpost_var.png')
plt.close()
plt.vlines(
lcs[i] / 512.0,
((x ** 3) * kpost[:, 0]).min(),
((x ** 3) * kpost[:, 0]).max(),
label=r"$lc = $" + str(lcs[i]),
)
plt.xlabel(r"$\lambda / L$")
plt.ylabel(r"$(\lambda / L)^3 \sigma^{2}_{\log(K_{eff})} / \sigma^{2}_{\log(k)}$")
plt.legend()
plt.grid()
plt.title(cases[i])
plt.tight_layout()
plt.savefig(rdir + str(i) + "/Kpost_var.png")
plt.close()

89
utilities/plot_val_Kpost_with_power (copy).py
Unescape Escape View File

@ -2,43 +2,64 @@ import numpy as np
import matplotlib.pyplot as plt
rdir = "./data/"
rdir='./data/'
clabels = [r"$K_{perm}$", r"$K_{diss}$", r"$K_{average}$", r"$K_{1/3}$"]
names = ["Kperm", "Kdiss", "Kaverage", "Kpower"]
cases = [
r"$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$",
r"$Lognormal \ \sigma^{2}_{\log(k)} = 7$",
r"$Binary p = 0.2; k+/k- = 10^4$",
]
clabels=[r'$K_{perm}$',r'$K_{diss}$',r'$K_{average}$',r'$K_{1/3}$']
names=['Kperm','Kdiss','Kaverage','Kpower']
cases=[r'$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$',r'$Lognormal \ \sigma^{2}_{\log(k)} = 7$', r'$Binary p = 0.2; k+/k- = 10^4$']
scales=np.array([4,8,16,32,64])
lcs=[16,16,8]
est=3
ranges=[(-0.5,0.5),(-5,5),(-4,4)]
scales = np.array([4, 8, 16, 32, 64])
lcs = [16, 16, 8]
est = 3
ranges = [(-0.5, 0.5), (-5, 5), (-4, 4)]
for i in range(3):
for scale in range(len(scales)):
if est == 0:
keff = np.log(
np.load(rdir + str(i) + "/kperm/" + str(scales[scale]) + ".npy")
)
if est == 1:
keff = np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kd" + str(scales[scale]) + ".npy"
)
)
if est == 2:
keff = np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kv" + str(scales[scale]) + ".npy"
)
)
if est == 3:
keff = np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kpo" + str(scales[scale]) + ".npy"
)
)
for scale in range(len(scales)):
if est==0:
keff=np.log(np.load(rdir+str(i)+'/kperm/'+str(scales[scale])+'.npy'))
if est==1:
keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kd'+str(scales[scale])+'.npy'))
if est==2:
keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kv'+str(scales[scale])+'.npy'))
if est==3:
keff=np.log(np.load(rdir+str(i)+'/KpostProcess/Kpo'+str(scales[scale])+'.npy'))
plt.hist(keff.reshape(-1),label=r'$\lambda = $'+' ' +str(scales[scale]),density=True,histtype='step',range=ranges[i])
#plt.semilogx(scales/512.0,kpost[:,1],label=clabels[1],marker='s')
#plt.semilogx(scales/512.0,kpost[:,2],label=clabels[2],marker='^')
#plt.semilogx(scales/512.0,kpost[:,3],label=clabels[3],marker='o')
#plt.vlines(lcs[i]/512.0,kpost[:,0].min(),kpost[:,0].max(),label=r'$lc = $'+str(lcs[i]))
plt.xlabel(r'$\log(K_{eff})$')
plt.ylabel(r'$P(K_{eff})$')
plt.legend()
plt.grid()
plt.title(cases[i]+' '+str(names[est]))
plt.tight_layout()
plt.savefig(rdir+str(i)+'/Kpost_dist_scales_'+names[est]+'.png')
plt.close()
plt.hist(
keff.reshape(-1),
label=r"$\lambda = $" + " " + str(scales[scale]),
density=True,
histtype="step",
range=ranges[i],
)
# plt.semilogx(scales/512.0,kpost[:,1],label=clabels[1],marker='s')
# plt.semilogx(scales/512.0,kpost[:,2],label=clabels[2],marker='^')
# plt.semilogx(scales/512.0,kpost[:,3],label=clabels[3],marker='o')
# plt.vlines(lcs[i]/512.0,kpost[:,0].min(),kpost[:,0].max(),label=r'$lc = $'+str(lcs[i]))
plt.xlabel(r"$\log(K_{eff})$")
plt.ylabel(r"$P(K_{eff})$")
plt.legend()
plt.grid()
plt.title(cases[i] + " " + str(names[est]))
plt.tight_layout()
plt.savefig(rdir + str(i) + "/Kpost_dist_scales_" + names[est] + ".png")
plt.close()

93
utilities/plot_val_Kpost_with_power.py
Unescape Escape View File

@ -2,36 +2,75 @@ import numpy as np
import matplotlib.pyplot as plt
rdir = "./data/"
rdir='./data/'
clabels = [r"$K_{perm}$", r"$K_{diss}$", r"$K_{average}$", r"$K_{1/3}$"]
cases = [
r"$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$",
r"$Lognormal \ \sigma^{2}_{\log(k)} = 7$",
r"$Binary p = 0.2; k+/k- = 10^4$",
]
clabels=[r'$K_{perm}$',r'$K_{diss}$',r'$K_{average}$',r'$K_{1/3}$']
cases=[r'$Lognormal \ \sigma^{2}_{\log(k)} = 0.1$',r'$Lognormal \ \sigma^{2}_{\log(k)} = 7$', r'$Binary p = 0.2; k+/k- = 10^4$']
scales=np.array([4,8,16,32,64,128,256,512])
lcs=[16,16,8]
scales = np.array([4, 8, 16, 32, 64, 128, 256, 512])
lcs = [16, 16, 8]
for i in range(3):
kpost=np.zeros((len(scales),4))
for scale in range(len(scales)):
kpost[scale,0]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/kperm/'+str(scales[scale])+'.npy'))))
kpost[scale,1]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kd'+str(scales[scale])+'.npy'))))
kpost[scale,2]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kv'+str(scales[scale])+'.npy'))))
kpost[scale,3]=np.exp(np.nanmean(np.log(np.load(rdir+str(i)+'/KpostProcess/Kpo'+str(scales[scale])+'.npy'))))
plt.semilogx(scales/512.0,kpost[:,0],label=clabels[0],marker='x')
plt.semilogx(scales/512.0,kpost[:,1],label=clabels[1],marker='s')
plt.semilogx(scales/512.0,kpost[:,2],label=clabels[2],marker='^')
plt.semilogx(scales/512.0,kpost[:,3],label=clabels[3],marker='o')
plt.vlines(lcs[i]/512.0,kpost[:,0].min(),kpost[:,0].max(),label=r'$lc = $'+str(lcs[i]))
plt.xlabel(r'$\lambda / L$')
plt.ylabel(r'$<K_{eff}>_G$')
plt.legend()
plt.grid()
plt.title(cases[i])
plt.tight_layout()
plt.savefig(rdir+str(i)+'/Kpost_mean.png')
plt.close()
kpost = np.zeros((len(scales), 4))
for scale in range(len(scales)):
kpost[scale, 0] = np.exp(
np.nanmean(
np.log(np.load(rdir + str(i) + "/kperm/" + str(scales[scale]) + ".npy"))
)
)
kpost[scale, 1] = np.exp(
np.nanmean(
np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kd" + str(scales[scale]) + ".npy"
)
)
)
)
kpost[scale, 2] = np.exp(
np.nanmean(
np.log(
np.load(
rdir + str(i) + "/KpostProcess/Kv" + str(scales[scale]) + ".npy"
)
)
)
)
kpost[scale, 3] = np.exp(
np.nanmean(
np.log(
np.load(
rdir
+ str(i)
+ "/KpostProcess/Kpo"
+ str(scales[scale])
+ ".npy"
)
)
)
)
plt.semilogx(scales / 512.0, kpost[:, 0], label=clabels[0], marker="x")
plt.semilogx(scales / 512.0, kpost[:, 1], label=clabels[1], marker="s")
plt.semilogx(scales / 512.0, kpost[:, 2], label=clabels[2], marker="^")
plt.semilogx(scales / 512.0, kpost[:, 3], label=clabels[3], marker="o")
plt.vlines(
lcs[i] / 512.0,
kpost[:, 0].min(),
kpost[:, 0].max(),
label=r"$lc = $" + str(lcs[i]),
)
plt.xlabel(r"$\lambda / L$")
plt.ylabel(r"$<K_{eff}>_G$")
plt.legend()
plt.grid()
plt.title(cases[i])
plt.tight_layout()
plt.savefig(rdir + str(i) + "/Kpost_mean.png")
plt.close()

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