示例#1
0
    def __new__(mcs, name, bases, dict):
        def gen_test_shape_eq(config, x, shape):
            def test(self):
                if (type(x) is tuple):
                    config.set_grid_dims(dims=x)
                else:
                    config.set_grid_dims(num_generations=x)
                self.assertEqual(config.initial_grid.shape, shape)
                self.assertEqual(config.grid_dims, shape)
            return test

        #1d
        #below limit, valid and below current, valid and above current
        gens = [0, 50, 150]
        expected = [(2,3), (51,101), (151, 301)]
        config = CAConfig(TESTDESCRIPTIONS_PATH + "1dbasic.py")
        config.fill_in_defaults()
        for g, e in zip(gens, expected):
            testname = "test_config_setdims_1d_{g}".format(g=g)
            dict[testname] = gen_test_shape_eq(config, g, e)
            dict[testname] = gen_test_shape_eq(config, g, e)

        #2d
        config = CAConfig(TESTDESCRIPTIONS_PATH + "2dbasic.py")
        config.fill_in_defaults()
        shapes = [(2,3), (3,3), (50, 50), (300,300), (300,420)]
        for s in shapes:
            testname = "test_config_setdims_2d_{g}".format(g=g)
            dict[testname] = gen_test_shape_eq(config, s, s)
            dict[testname] = gen_test_shape_eq(config, s, s)

        return type.__new__(mcs, name, bases, dict)
示例#2
0
 def test_minimal_2d_comments_fill(self):
     ca_config = CAConfig(self.filepath2d)
     self.assertIsInstance(ca_config, CAConfig)
     ca_config.fill_in_defaults()
     #values from defaults
     # + 1 from the inclusion of generation 0
     expected_gens = 100
     expected_dims = (200, 200)
     expected_grid = np.zeros(expected_dims)
     expected_nhood = np.ones((3,3))
     self.assertEqual(ca_config.num_generations, expected_gens)
     self.assertEqual(ca_config.grid_dims, expected_dims)
     self.assertTrue(np.array_equal(ca_config.initial_grid, expected_grid))
     self.assertTrue(np.array_equal(ca_config.nhood_arr, expected_nhood))
示例#3
0
 def test_minimal_1d_comments_fill(self):
     ca_config = CAConfig(self.filepath1d)
     self.assertIsInstance(ca_config, CAConfig)
     ca_config.fill_in_defaults()
     #values from defaults
     expected_rulenum = 0
     expected_gens = 100
     # + 1 for generation 0
     expected_dims = (expected_gens + 1, expected_gens*2 + 1)
     expected_grid = np.zeros(expected_dims)
     expected_nhood = np.array([1,1,1])
     self.assertEqual(ca_config.rule_num, expected_rulenum)
     self.assertEqual(ca_config.num_generations, expected_gens)
     self.assertEqual(ca_config.grid_dims, expected_dims)
     self.assertTrue(np.array_equal(ca_config.initial_grid, expected_grid))
     self.assertTrue(np.array_equal(ca_config.nhood_arr, expected_nhood))
示例#4
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 def test_minimal_2d_vars_fill(self):
     ca_config = CAConfig(self.filepath2d)
     prerun_ca(ca_config)
     ca_config = load(ca_config.path)
     self.assertIsInstance(ca_config, CAConfig)
     self.assertEqual(ca_config.states, (0,1,2))
     #values from file
     ca_config.fill_in_defaults()
     #values from defaults
     expected_gens = 100
     expected_dims = (200, 200)
     expected_grid = np.zeros(expected_dims)
     expected_nhood = np.ones((3,3))
     self.assertEqual(ca_config.num_generations, expected_gens)
     self.assertEqual(ca_config.grid_dims, expected_dims)
     self.assertTrue(np.array_equal(ca_config.initial_grid, expected_grid))
     self.assertTrue(np.array_equal(ca_config.nhood_arr, expected_nhood))