def gridprint (z, C_true):
picgrid=pres.Gridpic(x,k,c_ary,threeD,pos,C_true)
C = picgrid.calc_cval()
# Variables
point_data = np.zeros((nx + 1, ny + 1, nz + 1))
# cell_data = np.zeros((nx, ny, nz))
distribution = np.zeros((nx, ny, nz))
for i in range(len(C)):
x_dis = i % x
z_dis = i // ((x)**2)
y_dis = (i %((x) ** 2)) // x
point_data[x_dis][y_dis][z_dis]=C[i]
hl.gridToVTK("c_grid"+str(z), x_c, y_c, z_c, cellData={"distribution": distribution}, pointData={"point_data": point_data})
def gridprint (z, C_true):
picgrid=pres.Gridpic(x,k,c_ary,threeD,pos,C_true)
C = picgrid.calc_cval()
# Variables
point_data = np.zeros((nx + 1, ny + 1, nz + 1))
# cell_data = np.zeros((nx, ny, nz))
distribution = np.zeros((nx, ny, nz))
for i in range(len(C)):
x_dis = i % x
z_dis = i // ((x)**2)
y_dis = (i %((x) ** 2)) // x
point_data[x_dis][y_dis][z_dis]=C[i]
hl.gridToVTK("c_grid"+str(z), x_c, y_c, z_c, cellData={"distribution": distribution}, pointData={"point_data": point_data})
density[pos_x][pos_y][pos_z] += 1.0
elif rand_num < 4.0 / 6.0:
if 0 < pos_y:
if boundary[pos_x][pos_y - 1][pos_z] == 1:
molecule[pos_x][pos_y][pos_z] = 0.0
pos_y -= 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
elif rand_num < 5.0 / 6.0:
if pos_z < nz - 1:
if boundary[pos_x][pos_y][pos_z + 1] == 1:
molecule[pos_x][pos_y][pos_z] = 0.0
pos_z += 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
else:
if 0 < pos_z:
if boundary[pos_x][pos_y][pos_z - 1] == 1:
molecule[pos_x][pos_y][pos_z] = 0.0
pos_z -= 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
hl.gridToVTK(
"output/diffusion_obstacle" + str(i),
x,
y,
z,
cellData={"particle": molecule, "density": density, "boundary": boundary},
)
point_data = np.zeros((nx + 1, ny + 1, nz + 1))
#cell_data = np.zeros((nx, ny, nz))
distribution = np.zeros((nx, ny, nz))
D = 10.0
k = 0.5
lam = np.sqrt(D/k)
cell_radius = 2.0
def distribution_fct(x,y):
x0 = 5.0
y0 = 5.0
r = max(cell_radius,np.sqrt((x-x0)**2 + (y-y0)**2))
value = np.exp(-r/lam)/r
return value
for i in range(0,nx):
for j in range(0,ny):
distribution[i][j][0] = distribution_fct(x[i],x[j])
#print "x:" + str(x[i])
#print "y:" + str(y[j])
#print "value:" + str(distribution[i][j][0])
hl.gridToVTK("./test", x, y, z, cellData = {"distribution" : distribution}, pointData = {"temp" : point_data})
#! /usr/bin/env python
import hl
import numpy as np
# Dimensions
nx, ny, nz = 2, 2, 2
lx, ly, lz = 1.0, 1.0, 1.0
dx, dy, dz = lx/nx, ly/ny, lz/nz
ncells = nx * ny * nz
npoints = (nx + 1) * (ny + 1) * (nz + 1)
# Coordinates
x = np.arange(0, lx + dx, dx, dtype='float64')
y = np.arange(0, ly + dy, dy, dtype='float64')
z = np.arange(0, lz + dz, dz, dtype='float64')
# Variables
point_data = np.zeros((nx + 1, ny + 1, nz + 1))
cell_data = np.zeros((nx, ny, nz))
cell_data[1][1][1] = 1.0
point_data[2][1][1] = 1.0
hl.gridToVTK("./visualization", x, y, z, cellData = {"pressure" : cell_data}, pointData = {"temp" : point_data})
molecule[pos_x][pos_y][pos_z] = 0.0
pos_x -= 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
elif ( rand_num < 3.0/6.0):
if (pos_y < ny - 1):
molecule[pos_x][pos_y][pos_z] = 0.0
pos_y += 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
elif ( rand_num < 4.0/6.0):
if ( 0 < pos_y):
molecule[pos_x][pos_y][pos_z] = 0.0
pos_y -= 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
elif ( rand_num < 5.0/6.0):
if (pos_z < nz - 1):
molecule[pos_x][pos_y][pos_z] = 0.0
pos_z += 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
else:
if ( 0 < pos_z):
molecule[pos_x][pos_y][pos_z] = 0.0
pos_z -= 1
molecule[pos_x][pos_y][pos_z] = 1.0
density[pos_x][pos_y][pos_z] += 1.0
hl.gridToVTK("output/diffusion_" + str(i), x, y, z, cellData = {"particle" : molecule, "density" : temp})
point_data = np.zeros((nx + 1, ny + 1, nz + 1))
#cell_data = np.zeros((nx, ny, nz))
distribution = np.zeros((nx, ny, nz))
D = 10.0
k = 0.5
lam = np.sqrt(D / k)
cell_radius = 2.0
def distribution_fct(x, y):
x0 = 5.0
y0 = 5.0
r = max(cell_radius, np.sqrt((x - x0)**2 + (y - y0)**2))
value = np.exp(-r / lam) / r
return value
for i in range(0, nx):
for j in range(0, ny):
distribution[i][j][0] = distribution_fct(x[i], x[j])
#print "x:" + str(x[i])
#print "y:" + str(y[j])
#print "value:" + str(distribution[i][j][0])
hl.gridToVTK("./test",
x,
y,
z,
cellData={"distribution": distribution},
pointData={"temp": point_data})
