def __new__(mcs, name, bases, dict):
def gen_test_success(d, config, transfunc, expectedshape):
def test(self):
config.grid_dims = d
g = Grid2D(config, transfunc)
self.assertIsInstance(g, Grid2D)
self.assertEqual(g.grid.shape, expectedshape)
return test
def gen_test_valerr(d, config, transfunc):
def test(self):
config.grid_dims = d
l = lambda c=config: Grid2D(c, transfunc)
self.assertRaises(ValueError, l)
return test
config = CAConfig('test/testdescriptions/2dbasic.py')
dimensions = 2
config.states = 0,1,2
config.nhood_arr = [1,1,1]
shapes = range(0, 5)
def transfunc(grid, neighbourcounts):
return grid
for i in shapes:
for j in shapes:
testname = "test_grid_2d_{i}{j}".format(i=i, j=j)
testdims = (i, j)
if i < 3 or j < 3:
dict[testname] = gen_test_valerr(testdims, config, transfunc)
else:
dict[testname] = gen_test_success(testdims, config, transfunc, (i,j))
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)
def __new__(mcs, name, bases, dict):
def gen_test_array_equal(a,b):
def test(self):
self.assertTrue(np.array_equal(a, b))
return test
config = CAConfig('test/testdescriptions/2dbasic.py')
dimensions = 2
config.states = 0,1,2
config.nhood_arr = [1,1,1]
def transfunc(self, grid, neighbourcounts):
return grid
#matching dimensions
config.grid_dims = (20,20)
testname = "test_gridset_2d_{i}{j}".format(i=config.grid_dims[0], j=config.grid_dims[1])
config.initial_grid = np.random.randint(0, 3, config.grid_dims)
g = Grid2D(config, transfunc)
dict[testname] = gen_test_array_equal(g.grid, config.initial_grid)
#non matching
testsize = [(19,20), (20,21), (21,21), (19,19)]
for s in testsize:
testname = "test_gridset_2d_{i}{j}".format(i=s[0], j=s[1])
config.grid_dims = s
ig = np.random.randint(0, 3, s)
config.initial_grid = ig
g = Grid2D(config, transfunc)
if s < config.grid_dims:
dict[testname] = gen_test_array_equal(g.grid[:s[0], :s[1]], ig)
else:
dict[testname] = gen_test_array_equal(g.grid, ig[:config.grid_dims[0], :config.grid_dims[1]])
return type.__new__(mcs, name, bases, dict)