def test_dasymap11(self):
cols = 3
rows = 5
cat_map = np.array([[float('nan'), 0, 1], [float('nan'), 1, 0],
[2, 2, 1], [0, 2, 2], [float('nan'), 0, 2]],
dtype='f')
zone_map = np.array(
[[0, 0, 0], [1, 2, 3], [1, 2, 3], [1, 2, 3],
[float('nan'), float('nan'),
float('nan')]],
dtype='f')
expect_result = np.array(
[[0, 0, 0], [0, 8.47458, 0], [100, 45.7627, 15.625],
[0, 45.7627, 84.375], [float('nan'),
float('nan'),
float('nan')]],
dtype='f')
weights = np.array([0.0, 0.1, 0.9])
demands = np.array([0.0, 100.0, 100.0, 100.0])
result = dasymap.calculate(np.NaN, cat_map, weights, zone_map, demands)
self.assertTrue(np.allclose(expect_result, result, equal_nan=True))
def test_dasymap10a(self):
cols = 3
rows = 4
cat_map = np.array([[1, 1, 1], [2, 2, 2], [2, 2, 2], [1, 1, 1]])
zone_map = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
weights = np.array([0, 0.1, 0.9])
demands = np.array([0, 100, 100, 100])
result = dasymap.calculate(np.NaN, cat_map, weights, zone_map, demands)
expected = np.array([[5, 5, 5], [45, 45, 45], [45, 45, 45], [5, 5, 5]])
self.assertTrue(np.allclose(expected, result, equal_nan=True))
def test_dasymap7b(self):
cols = 3
rows = 4
cat_map = np.array([[1, 0, 0], [2, 1, 0], [2, 2, 1], [2, 2, 2]])
zone_map = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0]])
weights = np.array([1, 0, 2, 3])
demands = np.array([9, 300, 300, 300])
result = dasymap.calculate(np.NaN, cat_map, weights, zone_map, demands)
expected = np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0], [1, 1, 1]])
self.assertTrue(np.allclose(expected, result, equal_nan=True))
def test_dasymap2(self):
cols = 3
rows = 4
cat_map = np.array([[2, 1, 1], [2, 2, 1], [2, 2, 2], [2, 2, 2]])
zone_map = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
weights = np.array([0, 1, 2])
demands = np.array([0, 300, 300, 300])
result = dasymap.calculate(np.NaN, cat_map, weights, zone_map, demands)
expected = np.array([[75, 100, 90], [75, 66.6666, 90],
[75, 66.6666, 60], [75, 66.6666, 60]])
self.assertTrue(np.allclose(expected, result, equal_nan=True))
def test_dasymap4(self):
cols = 3
rows = 4
cat_map = np.array([[1, 0, 1], [2, 0, 1], [2, 0, 1], [2, 0, 2]])
zone_map = np.array([[1, 2, 3], [1, 2, 3], [1, 2, 3], [1, 2, 3]])
weights = np.array([0, 1, 2])
demands = np.array([0, 300, 300, 300])
result = dasymap.calculate(np.NaN, cat_map, weights, zone_map, demands)
expected = np.array([[300 * 1.0 / 7.0, 75, 300 * 1.0 / 5.0],
[300 * 2.0 / 7.0, 75, 300 * 1.0 / 5.0],
[300 * 2.0 / 7.0, 75, 300 * 1.0 / 5.0],
[300 * 2.0 / 7.0, 75, 300 * 2.0 / 5.0]])
self.assertTrue(np.allclose(expected, result, equal_nan=True))