incomp-flame-post/code/pycompact/pycompact.py
2019-08-08 12:32:23 +09:00

472 lines
10 KiB
Python

import numpy as np
from compact import compact
class CompactScheme:
def __init__ (self, nx, ny, nz, px, py, pz, lx, ly, lz):
pi8 = np.arccos(-1., dtype=np.float64)
self.shape = (nz, ny, nx)
self.px = px
self.py = py
self.pz = pz
h = pi8 * lx / nx
self.hx = h
self.hy = h
self.hz = h
# Allocate LU
compact.lxf = np.zeros(nx, dtype=np.float64)
compact.lxs = np.zeros(nx, dtype=np.float64)
compact.wxf = np.zeros(nx, dtype=np.float64)
compact.wxs = np.zeros(nx, dtype=np.float64)
compact.lyf = np.zeros(ny, dtype=np.float64)
compact.lys = np.zeros(ny, dtype=np.float64)
compact.wyf = np.zeros(ny, dtype=np.float64)
compact.wys = np.zeros(ny, dtype=np.float64)
compact.lzf = np.zeros(nz, dtype=np.float64)
compact.lzs = np.zeros(nz, dtype=np.float64)
compact.wzf = np.zeros(nz, dtype=np.float64)
compact.wzs = np.zeros(nz, dtype=np.float64)
bcx = 0 if px else 1
bcy = 0 if py else 1
bcz = 0 if pz else 1
compact.ludcmp_calculate(nx, ny, nz, bcx, bcy, bcz)
def test_ludcmp (self):
import pprint
pp = pprint.PrettyPrinter(indent=4)
# First Derivative Non-periodic BC
l1 = compact.test_nonp_lud1(self.shape[-1])
# Second Derivative Non-periodic BC
l2 = compact.test_nonp_lud2(self.shape[-1])
print ("Test Internally Calculated Non-periodic Coefs")
print (np.linalg.norm((l1 - compact.lxf)/compact.lxf))
print (np.linalg.norm((l2 - compact.lxs)/compact.lxs))
def py_rhs_1_np (self, x):
dx = np.zeros(x.shape)
h1 = 1./self.hx
r1 = 7./3.
r2 = 1./12.
r3 = 3.
a = -1.25
b = 1.
c = 0.25
nd, n = x.shape
dx[:, -2] = x[:, -1] - x[:, -3]
dx[:, -1] = - (a*x[:, -1] + b*x[:, -2] + c*x[:, -3])
dx[:, 0] = (a*x[:, 0] + b*x[:, 1] + c*x[:, 2])
dx[:, 1] = x[:, 2] - x[:, 0]
dx[:,-2] = dx[:,-2]*h1*r3
dx[:,-1] = dx[:,-1]*h1
dx[:,0] = dx[:,0]*h1
dx[:,1] = dx[:,1]*h1*r3
for i in range(2,n-2):
t1=x[:,i+1]-x[:,i-1]
t2=x[:,i+2]-x[:,i-2]
dx[:,i]=h1*(r1*t1+r2*t2)
return dx
def py_tdslv(self, r, l):
nd, n = r.shape
r[:,0] = r[:,0] * l[0]
for i in range(1,n):
r[:,i] = l[i] * (r[:,i] - r[:,i-1])
for i in range(n-1)[::-1]:
r[:,i] = r[:,i] - l[i] * r[:,i+1]
def test_dfnonp (self):
x = np.sin(1.1 * np.arange(512) * self.hx).reshape((1,-1))
exact = 1.1 * np.cos(1.1 * np.arange(512) * self.hx).reshape((1,-1))
print ("First Non-periodic RHS Test")
dx = self.py_rhs_1_np(x)
dx_fortran = compact.rhs1np(self.hx, x)
print (np.linalg.norm((dx - dx_fortran) / dx_fortran))
print (((dx - dx_fortran) / dx_fortran).min())
print (((dx - dx_fortran) / dx_fortran).max())
print ("First Non-periodic TD SOLVE Test")
l1 = compact.test_nonp_lud1(512)
self.py_tdslv(dx, l1)
compact.tdslv(dx_fortran,l1)
print ("dx - exact")
print (np.linalg.norm((dx - exact) / exact))
print (((dx - exact) / exact).min())
print (((dx - exact) / exact).max())
print ("dx_fortran - exact")
print (np.linalg.norm((dx_fortran - exact) / exact))
print (((dx_fortran - exact) / exact).min())
print (((dx_fortran - exact) / exact).max())
'''
import pprint
pp = pprint.PrettyPrinter(indent=4)
pp.pprint ((dx - exact) / exact)
pp.pprint ((zip ((dx - exact).ravel(), dx.ravel(), exact.ravel())))
'''
def verify_nonp_lud1(self):
print ("Non-periodic coef first derivative")
nx = 512
aa = np.ones(nx) * 3.
aa[0] = 0.5
aa[1] = 4.
aa[-2] = 4.
aa[-1] = 0.5
coef = compact.stdlu(aa)
coef_verify = self.py_stdlu(aa)
print (np.linalg.norm((coef - coef_verify)/coef_verify))
def verify_nonp_lud2(self):
print ("Non-periodic coef second derivative")
nx = 512
aa = np.ones(nx) * 3.
aa[0] = 2./11.
aa[1] = 10.
aa[-2] = 10.
aa[-1] = 2./11.
coef = compact.stdlu(aa)
coef_verify = self.py_stdlu(aa)
print (np.linalg.norm((coef - coef_verify)/coef_verify))
def py_stdlu(self, aa):
coef = np.ones(aa.shape)/aa[0]
print ("coef.size = ", coef.size)
for i in range(1,coef.size):
coef[i]=1.0/(aa[i]-coef[i-1])
return coef
def ddx (self, src):
if src.shape != self.shape:
print ("error")
nz, ny, nx = self.shape
xsrc = np.zeros((ny, nx,), dtype=np.float64, order="F")
# dst = np.zeros((nx, ny, nz,), order="F")
dst = np.zeros((nz, ny, nx,), dtype=np.float64,)
if self.px: # Periodic BC
for i in range(nz):
xsrc[:] = src[i]
dst[i] = compact.dfp(self.hx, xsrc, 1)
else:
for i in range(nz):
xsrc[:] = src[i]
dst[i] = compact.dfnonp(self.hx, xsrc, 1)
# return np.swapaxes(dst, 1, 2)
return dst
def ddy (self):
return
def ddz (self):
return
def port_nonp_coef (self):
# SUBROUTINE nonp_lud(xyz,xx)
nz, ny, nx = self.shape
xx = nx
lxf = np.zeros(xx)
lxs = np.zeros(xx)
aa = np.zeros(xx)
aa[:] = 3.
aa[0]=0.5
aa[1]=4.
aa[-2]=4.
aa[-1]=0.5
# first derivative
compact.stdlu(aa,lxf)
aa[:] = 5.5
aa[0]=2./11.
aa[1]=10.
aa[-2]=10.
aa[-1]=2./11.
# second derivative
compact.stdlu(aa,lxs)
compact.lxf = lxf
compact.lxs = lxs
def read_old_data (fname):
import struct
import sys
import os
with open(fname, 'rb') as f1 :
f1.seek(0)
raw_info = f1.read(4+8*6+4)[4:-4]
t = struct.unpack('d', raw_info[ 0: 8])[0]
nx = struct.unpack('q', raw_info[ 8:16])[0]
ny = struct.unpack('q', raw_info[16:24])[0]
nz = struct.unpack('q', raw_info[24:32])[0]
count = nx*ny*nz
bSize = count*8 # size in bytes for a variable
dummy_len = (4+8*3+4) + (4+8*2+4) + (4+8*2+4) + (4+8*2+4) + 4
dummy = f1.read(dummy_len)
#dummy = f1.read(4)
print (t, nx, ny, nz)
#raw_field = f1.read(4+bSize*5+4)[4:-4]
V = np.fromfile(f1, dtype=np.float64, count=(3*count)).reshape((3,nz,ny,nx))
s = np.fromfile(f1, dtype=np.float64, count=(2*count)).reshape((nz,ny,nx,2))
print (V.order)
print (s.order)
print (V.shape)
print (s.shape)
V.order="F"
s.order="F"
print (V.shape)
print (s.shape)
u = V[0]
v = V[1]
w = V[2]
Y0 = s.T[0].T
Y1 = s.T[1].T
return t, nx, ny, nz, u, v, w, Y0, Y1
def read_data (fname):
import struct
import sys
import os
with open(fname, 'rb') as f1 :
f1.seek(0)
raw_info = f1.read(4+8*6+4)[4:-4]
t = struct.unpack('d', raw_info[ 0: 8])[0]
nx = struct.unpack('q', raw_info[ 8:16])[0]
ny = struct.unpack('q', raw_info[16:24])[0]
nz = struct.unpack('q', raw_info[24:32])[0]
count = nx*ny*nz
bSize = count*8 # size in bytes for a variable
dummy_len = (4+8*3+4) + (4+8*2+4) + (4+8*2+4) + (4+8*2+4) + 4
dummy = f1.read(dummy_len)
#dummy = f1.read(4)
#raw_field = f1.read(4+bSize*5+4)[4:-4]
V = np.fromfile(f1, dtype=np.float64, count=(3*count)).reshape((3,nz,ny,nx))
s = np.fromfile(f1, dtype=np.float64, count=(2*count)).reshape((2,nz,ny,nx))
print (V.order)
print (s.order)
print (V.shape)
print (s.shape)
V.order="F"
s.order="F"
print (V.shape)
print (s.shape)
u = V[0]
v = V[1]
w = V[2]
Y0 = s[0]
Y1 = s[1]
return t, nx, ny, nz, u, v, w, Y0, Y1
def validate_trigonometric():
writeToFile = False
shape = (256, 256, 512)
nz, ny, nx = shape
pi8 = np.arccos(-1.)
l_0 = 2.0
hyp=l_0*pi8/ny
hxp=hyp
hzp=hyp
cs = CompactScheme(nx, ny, nz, False, True, True, 4., 2., 2.)
cs.test_ludcmp()
cs.verify_nonp_lud1()
cs.verify_nonp_lud2()
cs.test_dfnonp()
print ("Test ddx")
Y1 = np.zeros(shape)
true = np.zeros(shape)
XX = np.arange(nx) * hxp
YY = np.arange(ny) * hyp
ZZ = np.arange(nz) * hzp
print ("1-D sine test")
cos_fortran = compact.dfnonp(hxp, np.sin(1.1*XX).reshape((1,-1)), 1)
cos_exact = 1.1 * np.cos(1.1*XX).reshape((1,-1))
print (cos_fortran.min(), cos_fortran.max())
print (cos_exact.min(), cos_exact.max())
print (np.linalg.norm((cos_fortran - cos_exact)/cos_exact))
# print (((cos_fortran - cos_exact)/cos_exact))
print ("3-D trigonometric test")
zz, yy, xx = np.meshgrid(ZZ, YY, XX)
Y1[:] = np.sin(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz)[:]
true[:] = (1.1 * np.cos(1.1 * xx) * np.sin(3.0 * yy) * np.sin(2.0 * zz))[:]
dY1 = cs.ddx(Y1)[:]
print (dY1.min(), dY1.max())
print (true.min(), true.max())
relerr = (dY1 - true) / true
print (np.nanmin(relerr), np.nanmax(relerr))
if writeToFile:
y = np.memmap("phi", dtype=np.float64, mode="w+", shape=cs.shape)
y[:] = Y1[:]
dydxtrue = np.memmap("dphitrue", dtype=np.float64, mode="w+", shape=cs.shape)
dydxtrue[:] = true[:]
dydx = np.memmap("dphi", dtype=np.float64, mode="w+", shape=cs.shape)
dydx[:] = dY1[:]
# cs.verify_nonp_coef()
def test_dns_data():
import sys
file_name = sys.argv[1]
t, nx, ny, nz, u, v, w, Y0, Y1 = read_data(file_name)
cs = CompactScheme(nx, ny, nz, False, True, True, 4, 2, 2)
y = np.memmap("yr", dtype=np.float64, mode="w+", shape=cs.shape)
y[:] = Y1[:]
print (y.min(), y.max())
dydx = np.memmap("dyr", dtype=np.float64, mode="w+", shape=cs.shape)
dydx[:] = cs.ddx(Y1)[:]
dydx.flush()
print (dydx.min(), dydx.max())
if __name__ == "__main__":
validate_trigonometric()
# test_dns_data()