def setUp(self):
# define the target field
def func(x, y):
return np.sin(0.02 * np.pi * y) * np.cos(0.02 * np.pi * x)
# create a grid
self.grid_x, self.grid_y = np.mgrid[0:100:100j, 0:100:100j]
# sample the field
np.random.seed(42)
self.x = np.random.randint(100, size=300)
np.random.seed(1337)
self.y = np.random.randint(100, size=300)
self.z = func(self.x, self.y)
# build the Variogram and Kriging class
self.V = Variogram(list(zip(self.x, self.y)),
self.z,
model='gaussian',
n_lags=15,
maxlag=0.4)
self.ok = OrdinaryKriging(self.V,
min_points=2,
max_points=5,
perf=True)
def test_max_points_smaller_min_points(self):
with self.assertRaises(ValueError) as e:
ok = OrdinaryKriging(self.V, min_points=3, max_points=5)
ok.max_points = 2
self.assertEqual(str(e.exception),
'max_points can\'t be smaller than min_points.')
def test_max_points_negative(self):
with self.assertRaises(ValueError) as e:
ok = OrdinaryKriging(self.V, max_points=10)
ok.max_points = - 2
self.assertEqual(
str(e.exception), 'max_points can\'t be negative.'
)
def krig(vario, grid, **kwargs):
# get the grid
xx, yy = grid
ok = OrdinaryKriging(vario, **kwargs)
field = ok.transform(xx.flatten(), yy.flatten())
sigma = ok.sigma
return field.reshape(xx.shape), sigma.reshape(xx.shape)
def test_mode_settings(self):
# estimate mode
ok = OrdinaryKriging(self.V, mode='estimate')
self.assertIsNotNone(ok._prec_g)
self.assertIsNotNone(ok._prec_dist)
# exact mode
ok.mode = 'exact'
self.assertIsNone(ok._prec_g)
self.assertIsNone(ok._prec_dist)
def test_mode_unknown(self):
with self.assertRaises(ValueError) as e:
OrdinaryKriging(self.V, mode='foo')
self.assertEqual(
str(e.exception), "mode has to be one of 'exact', 'estimate'."
)
def test_max_points_type_check(self):
with self.assertRaises(ValueError) as e:
OrdinaryKriging(self.V, max_points=16.0)
self.assertEqual(
str(e.exception), 'max_points has to be an integer.'
)
def test_min_points_larger_max_points(self):
with self.assertRaises(ValueError) as e:
OrdinaryKriging(self.V, min_points=10, max_points=5)
self.assertEqual(
str(e.exception), 'min_points can\'t be larger than max_points.'
)
def test_min_points_negative(self):
with self.assertRaises(ValueError) as e:
OrdinaryKriging(self.V, min_points=-2)
self.assertEqual(
str(e.exception), 'min_points can\'t be negative.'
)
def test_solver_AttributeError(self):
with self.assertRaises(AttributeError) as e:
OrdinaryKriging(self.V, solver='peter')
self.assertEqual(
str(e.exception), "solver has to be ['inv', 'numpy', 'scipy']"
)
def test_precision_ValueError(self):
with self.assertRaises(ValueError) as e:
OrdinaryKriging(self.V, precision=0)
self.assertEqual(
str(e.exception), 'The precision has be be > 1'
)
def test_precision_TypeError(self):
with self.assertRaises(TypeError) as e:
OrdinaryKriging(self.V, precision='5.5')
self.assertEqual(
str(e.exception), 'precision has to be of type int'
)
def ordinary_kriging(x, y, z, grid, model='spherical', estimator='matheron',
n_lags=15, maxlag='median', min_points=5, max_points=15,
mode='exact', precision=1000, **settings):
# build coordinates
coords = __point_array(x, y)
# fit a Variogram
V = Variogram(coords, z, model=model, estimator=estimator, n_lags=n_lags,
maxlag=maxlag, normalize=False)
# get the shape and build the Kriging
shape = grid[0].shape
ok = OrdinaryKriging(V, min_points=min_points, max_points=max_points,
mode=mode, precision=precision)
# apply
return ok.transform(grid[0].flatten(), grid[1].flatten()).reshape(shape)
def setUp(self):
# Generate some random but spatially correlated data
# with a range of ~20
np.random.seed(42)
c = np.random.sample((50, 2)) * 60
np.random.seed(42)
v = np.random.normal(10, 4, 50)
V = Variogram(c, v).describe()
V["effective_range"] = 20
OK = OrdinaryKriging(V, coordinates=c, values=v)
self.c = np.random.sample((500, 2)) * 60
self.v = OK.transform(self.c)
self.c = self.c[~np.isnan(self.v), :]
self.v = self.v[~np.isnan(self.v)]
def test_ordinary(self):
if not GSTOOLS_AVAILABLE: # pragma: no cover
return True
x = np.array([self.c[0][0]])
y = np.array([self.c[0][1]])
# run ordinary kriging with skgstat
ok = OrdinaryKriging(self.V, min_points=3)
sk_res = ok.transform(x, y)
# get the gstools Krige class
krige = self.V.to_gs_krige()
gs_res, _ = krige.structured([x, y])
# test
assert_array_almost_equal(sk_res.flatten(),
gs_res.flatten(),
decimal=1)
def test_coordinates_with_duplicates(self):
c = self.c.copy()
# create two duplicates
c[14] = c[42]
c[8] = c[42]
V = Variogram(c, self.v)
ok = OrdinaryKriging(V)
# two instances should be removed
self.assertEqual(len(ok.coords), 50 - 2)
class TestPerformance(unittest.TestCase):
"""
The TestPerformance class is not a real unittest. It will always be true.
It does apply some benchmarking, which could be included into the testing
framework, as soon as the OrdinaryKriging class is finalized. From that
point on, new code should not harm the performance significantly.
"""
def setUp(self):
# define the target field
def func(x, y):
return np.sin(0.02 * np.pi * y) * np.cos(0.02 * np.pi * x)
# create a grid
self.grid_x, self.grid_y = np.mgrid[0:100:100j, 0:100:100j]
# sample the field
np.random.seed(42)
self.x = np.random.randint(100, size=300)
np.random.seed(1337)
self.y = np.random.randint(100, size=300)
self.z = func(self.x, self.y)
# build the Variogram and Kriging class
self.V = Variogram(list(zip(self.x, self.y)),
self.z,
model='gaussian',
n_lags=15,
maxlag=0.4)
self.ok = OrdinaryKriging(self.V,
min_points=2,
max_points=5,
perf=True)
def _run_benchmark(self, points):
xi = self.grid_x.flatten()[:points]
yi = self.grid_y.flatten()[:points]
# run
res = self.ok.transform(xi, yi)
self.ok.perf_dist *= 1000
self.ok.perf_mat *= 1000
self.ok.perf_solv *= 1000
print('Benchmarking OrdinaryKriging...')
print('-------------------------------')
print('Points:', points)
print('Solver:', self.ok.solver)
print('Mode:', self.ok.mode)
print('Build distance matrix: %.1f ms (%.4f ms each)' %
(np.sum(self.ok.perf_dist), np.std(self.ok.perf_dist)))
print('Build variogram matrix: %.1f ms (%.4f ms each)' %
(np.sum(self.ok.perf_mat), np.std(self.ok.perf_mat)))
print('Solve kriging matrix: %.1f ms (%.4f ms each)' %
(np.sum(self.ok.perf_solv), np.std(self.ok.perf_solv)))
print('---------------------------------------------')
def test_20points_exact(self):
self.ok.mode = 'exact'
self.ok.solver = 'inv'
self._run_benchmark(points=20)
def test_100points_exact(self):
self.ok.mode = 'exact'
self.ok.solver = 'inv'
self._run_benchmark(points=100)
def test_20points_estimate(self):
self.ok.mode = 'estimate'
self.ok.solver = 'inv'
self._run_benchmark(points=20)
def test_100points_estimate(self):
self.ok.mode = 'estimate'
self.ok.solver = 'inv'
self._run_benchmark(points=100)
def test_max_points_smaller_min_points(self):
with self.assertRaises(ValueError) as e:
OrdinaryKriging(self.V, min_points=10, max_points=5)
self.assertEqual(
str(e), 'max_points can\'t be larger smaller min_points.')
def test_min_points_type_check(self):
with self.assertRaises(ValueError) as e:
OrdinaryKriging(self.V, min_points=4.0)
self.assertEqual(str(e), 'min_points has to be an integer.')
def test_coordinates_and_values(self):
ok = OrdinaryKriging(self.V)
assert_array_almost_equal(self.c, ok.coords)
