我的代码是:
for generation in np.arange(100):
print(len(TC.keys()))
keys = list(TC.keys())
keys = np.array(keys)
for cell in np.ndenumerate(keys): # for each tumor cell
cell_id = cell[1]
division, migration, death = TC[cell_id].action(TC_pdivision, TC_pmigration, TC_pdeath)
if death:
del TC[cell_id] # remove tumor cell from dictionary
else:
# find free spot
ngh = TC[cell_id].freeSpots(cells,TC[cell_id].neighbors(TC[cell_id].x,TC[cell_id].y,TC[cell_id].z))
if np.size(ngh)>0:
ngh = np.reshape(ngh,[int(np.shape(ngh)[0]/3),3])
x,y,z = spot(ngh)
cell_id_new = positionToID(x,y,z)
if migration:
TC[cell_id_new] = TC.pop(cell_id)
elif division:
TC[cell_id_new] = TumorCell(x, y, z)
也就是说,每个肿瘤的位置在三个维度(x,y,z)中定义。每个肿瘤细胞是词典中的一个条目。我正在使用PositionToID函数转换(x,y,z):
def positionToID(x,y,z):
id = int(x +(y-1)* gridSize +(z-1)* gridSize * gridSize)
返回ID
因此,肿瘤细胞的定义如下:
TC[id] = [some_tumor_cell_properties]
函数邻居在所有26个相邻单元格中生成(x,y,z),并且自由点为:
def freeSpots(self, cells, ngh):
freeSpots = np.array([])
for neighbor in ngh:
currNeighbor = tuple(neighbor)
if currNeighbor not in cells:
freeSpots = np.append(freeSpots, np.array(currNeighbor))
return freeSpots
负责检查每个相邻的电池是否被占用。 Freespots速度很快,因此此功能不是问题。
我想,问题出在迭代器上。我试图通过提取字典TC(肿瘤细胞)的键并将其转换为numpy.array来遍历所有肿瘤细胞。接下来,我将ndenumare应用于所有单元的迭代。
有什么办法可以提高代码的性能?预先感谢您的帮助。