def test_get_voxels_in_large_range(grid: RectangularGrid):
for voxel, distance in grid.get_voxels_in_range(Point(x=4, y=4, z=4), 10):
assert distance <= 10
assert grid.is_valid_voxel(voxel)
def test_valid_voxel(grid: RectangularGrid, voxel, valid):
assert grid.is_valid_voxel(voxel) == valid
def chemotaxis(
self,
molecule,
drift_lambda,
drift_bias,
tissue,
grid: RectangularGrid,
):
# 'molecule' = state.'molecule'.concentration
# prob = 0-1 random number to determine which voxel is chosen to move
# 1. Get cells that are alive
for index in self.alive():
prob = rg.random()
# 2. Get voxel for each cell to get molecule in that voxel
cell = self[index]
vox = grid.get_voxel(cell['point'])
# 3. Set prob for neighboring voxels
p = []
vox_list = []
p_tot = 0.0
i = -1
# calculate individual probability
for x in [0, 1, -1]:
for y in [0, 1, -1]:
for z in [0, 1, -1]:
p.append(0.0)
vox_list.append([x, y, z])
i += 1
zk = vox.z + z
yj = vox.y + y
xi = vox.x + x
if grid.is_valid_voxel(Voxel(x=xi, y=yj, z=zk)):
if tissue[zk, yj, xi] in [
TissueType.SURFACTANT.value,
TissueType.BLOOD.value,
TissueType.EPITHELIUM.value,
TissueType.PORE.value,
]:
p[i] = logistic(molecule[zk, yj, xi],
drift_lambda, drift_bias)
p_tot += p[i]
# scale to sum of probabilities
if p_tot:
for i in range(len(p)):
p[i] = p[i] / p_tot
# chose vox from neighbors
cum_p = 0.0
for i in range(len(p)):
cum_p += p[i]
if prob <= cum_p:
cell['point'] = Point(
x=random.uniform(grid.xv[vox.x + vox_list[i][0]],
grid.xv[vox.x + vox_list[i][0] + 1]),
y=random.uniform(grid.yv[vox.y + vox_list[i][1]],
grid.yv[vox.y + vox_list[i][1] + 1]),
z=random.uniform(grid.zv[vox.z + vox_list[i][2]],
grid.zv[vox.z + vox_list[i][2] + 1]),
)
self.update_voxel_index([index])
break