def __new__(mcs, name, bases, dict):
def gen_test_success(gens, config, transfunc):
def test(self):
config.num_generations = gens
g = Grid1D(config, transfunc)
self.assertIsInstance(g, Grid1D)
return test
def gen_test_valerr(gens, config, transfunc):
def test(self):
config.num_generations = gens
self.assertRaises(ValueError,
lambda c=config: Grid1D(config, transfunc))
return test
config = CAConfig("test/testdescriptions/1dbasic.py")
config.states = 0, 1
testgens = range(-1, 6)
def transfunc(self, grid, neighbourcounts):
return grid
for g in testgens:
testname = "test_1d_numgens_{i}".format(i=g)
config.num_generations = g
if g < 1:
dict[testname] = gen_test_valerr(g, config, transfunc)
else:
dict[testname] = gen_test_success(g, config, transfunc)
return type.__new__(mcs, name, bases, dict)
def __new__(mcs, name, bases, dict):
def gen_test_array_eq(a, b):
def test(self):
self.assertTrue(np.array_equal(a, b))
return test
config = CAConfig("test/testdescriptions/1dbasic.py")
config.num_generations = 20
config.states = 0, 1
config.dimensions = 1
config.nhood_arr = [1, 1, 1]
def transfunc(self, grid, neighbourcounts):
return grid[0]
numgens = 20
sizes = range(numgens * 2, numgens * 2 + 3)
for s in sizes:
testname = "test_initial_grid_set_{s}".format(s=s)
initialgrid = np.array([np.random.randint(0, 2, s)])
config.initial_grid = initialgrid
g = Grid1D(config, transfunc)
#check that the first lines match
if s < g.grid.shape[1]:
dict[testname] = gen_test_array_eq(g.grid[0, :s],
initialgrid[0])
else:
dict[testname] = gen_test_array_eq(
g.grid[0], initialgrid[0, :g.grid.shape[1]])
return type.__new__(mcs, name, bases, dict)