def test_directions_2d(self):
x = np.linspace(0., 20., 100)
y = np.linspace(0., 15., 80)
rng = np.random.RandomState(1479373475)
x_rand = rng.rand(len(x))
y_rand = rng.rand(len(y))
# random values repeated along y-axis
field_x = np.tile(x_rand, (len(y), 1)).T
# random values repeated along x-axis
field_y = np.tile(y_rand, (len(x), 1))
gamma_x_x = variogram.estimate_structured((x, y), field_x,
direction='x')
gamma_x_y = variogram.estimate_structured((x, y), field_x,
direction='y')
gamma_y_x = variogram.estimate_structured((x, y), field_y,
direction='x')
gamma_y_y = variogram.estimate_structured((x, y), field_y,
direction='y')
self.assertAlmostEqual(gamma_x_y[1], 0.)
self.assertAlmostEqual(gamma_x_y[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_x_y[-1], 0.)
self.assertAlmostEqual(gamma_y_x[1], 0.)
self.assertAlmostEqual(gamma_y_x[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_y_x[-1], 0.)
def test_masked_1d(self):
x = np.arange(1, 11, 1, dtype=np.double)
# literature values
z = np.array((41.2, 40.2, 39.7, 39.2, 40.1,
38.3, 39.1, 40.0, 41.1, 40.3), dtype=np.double)
z_ma = np.ma.masked_array(z, mask=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
gamma = variogram.estimate_structured(x, z_ma)
self.assertAlmostEqual(gamma[0], .0000, places=4)
self.assertAlmostEqual(gamma[1], .4917, places=4)
self.assertAlmostEqual(gamma[2], .7625, places=4)
z_ma = np.ma.masked_array(z, mask=[1, 0, 0, 0, 0, 0, 0, 0, 0, 0])
gamma = variogram.estimate_structured(x, z_ma)
self.assertAlmostEqual(gamma[0], .0000, places=4)
self.assertAlmostEqual(gamma[1], .4906, places=4)
self.assertAlmostEqual(gamma[2], .7107, places=4)
def test_mixed(self):
x = np.arange(1, 11, 1, dtype=np.double)
z = np.array((41.2, 40.2, 39.7, 39.2, 40.1,
38.3, 39.1, 40.0, 41.1, 40.3), dtype=np.double)
gamma = variogram.estimate_structured(x, z)
self.assertAlmostEqual(gamma[1], .4917, places=4)
x = np.arange(1, 5, 1, dtype=np.double)
z = np.array((10, 20, 30, 40), dtype=LONGTYPE)
gamma = variogram.estimate_structured(x, z)
self.assertAlmostEqual(gamma[1], 50., places=4)
x = np.arange(1, 5, 1, dtype=np.double)
z = np.array((10, 20, 30, 40), dtype=LONGTYPE)
gamma = variogram.estimate_structured(x, z)
self.assertAlmostEqual(gamma[1], 50., places=4)
def test_uncorrelated_3d(self):
x = np.linspace(0., 100., 30)
y = np.linspace(0., 100., 30)
z = np.linspace(0., 100., 30)
rng = np.random.RandomState(1479373475)
field = rng.rand(len(x), len(y), len(z))
gamma = variogram.estimate_structured((x, y, z), field, 'x')
gamma = variogram.estimate_structured((x, y, z), field, 'y')
gamma = variogram.estimate_structured((x, y, z), field, 'z')
var = 1. / 12.
self.assertAlmostEqual(gamma[0], 0., places=2)
self.assertAlmostEqual(gamma[len(gamma)//2], var, places=2)
self.assertAlmostEqual(gamma[-1], var, places=2)
def test_uncorrelated_2d(self):
x = np.linspace(0., 100., 80)
y = np.linspace(0., 100., 60)
rng = np.random.RandomState(1479373475)
field = rng.rand(len(x), len(y))
gamma_x = variogram.estimate_structured((x, y), field, direction='x')
gamma_y = variogram.estimate_structured((x, y), field, direction='y')
var = 1. / 12.
self.assertAlmostEqual(gamma_x[0], 0., places=2)
self.assertAlmostEqual(gamma_x[len(gamma_x)//2], var, places=2)
self.assertAlmostEqual(gamma_x[-1], var, places=2)
self.assertAlmostEqual(gamma_y[0], 0., places=2)
self.assertAlmostEqual(gamma_y[len(gamma_y)//2], var, places=2)
self.assertAlmostEqual(gamma_y[-1], var, places=2)
def test_directions_3d(self):
x = np.linspace(0., 10., 20)
y = np.linspace(0., 15., 25)
z = np.linspace(0., 20., 30)
rng = np.random.RandomState(1479373475)
x_rand = rng.rand(len(x))
y_rand = rng.rand(len(y))
z_rand = rng.rand(len(z))
field_x = np.tile(x_rand.reshape((len(x), 1, 1)), (1, len(y), len(z)))
field_y = np.tile(y_rand.reshape((1, len(y), 1)), (len(x), 1, len(z)))
field_z = np.tile(z_rand.reshape((1, 1, len(z))), (len(x), len(y), 1))
gamma_x_x = variogram.estimate_structured((x, y, z), field_x,
direction='x')
gamma_x_y = variogram.estimate_structured((x, y, z), field_x,
direction='y')
gamma_x_z = variogram.estimate_structured((x, y, z), field_x,
direction='z')
gamma_y_x = variogram.estimate_structured((x, y, z), field_y,
direction='x')
gamma_y_y = variogram.estimate_structured((x, y, z), field_y,
direction='y')
gamma_y_z = variogram.estimate_structured((x, y, z), field_y,
direction='z')
gamma_z_x = variogram.estimate_structured((x, y, z), field_z,
direction='x')
gamma_z_y = variogram.estimate_structured((x, y, z), field_z,
direction='y')
gamma_z_z = variogram.estimate_structured((x, y, z), field_z,
direction='z')
self.assertAlmostEqual(gamma_x_y[1], 0.)
self.assertAlmostEqual(gamma_x_y[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_x_y[-1], 0.)
self.assertAlmostEqual(gamma_x_z[1], 0.)
self.assertAlmostEqual(gamma_x_z[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_x_z[-1], 0.)
self.assertAlmostEqual(gamma_y_x[1], 0.)
self.assertAlmostEqual(gamma_y_x[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_y_x[-1], 0.)
self.assertAlmostEqual(gamma_y_z[1], 0.)
self.assertAlmostEqual(gamma_y_z[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_y_z[-1], 0.)
self.assertAlmostEqual(gamma_z_x[1], 0.)
self.assertAlmostEqual(gamma_z_x[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_z_x[-1], 0.)
self.assertAlmostEqual(gamma_z_y[1], 0.)
self.assertAlmostEqual(gamma_z_y[len(gamma_x_y)//2], 0.)
self.assertAlmostEqual(gamma_z_y[-1], 0.)
def test_np_int(self):
x = np.arange(1, 5, 1, dtype=np.int)
z = np.array((10, 20, 30, 40), dtype=np.int)
gamma = variogram.estimate_structured(x, z)
self.assertAlmostEqual(gamma[1], 50., places=4)
def test_doubles(self):
x = np.arange(1, 11, 1, dtype=np.double)
z = np.array((41.2, 40.2, 39.7, 39.2, 40.1,
38.3, 39.1, 40.0, 41.1, 40.3), dtype=np.double)
gamma = variogram.estimate_structured(x, z)
self.assertAlmostEqual(gamma[1], .4917, places=4)