class TestVariogramMethods(unittest.TestCase):
def setUp(self):
# set up default values, whenever c and v are not important
np.random.seed(42)
self.c = np.random.gamma(10, 4, (30, 2))
np.random.seed(42)
self.v = np.random.normal(10, 4, 30)
self.V = Variogram(self.c, self.v, normalize=False, n_lags=10)
def test_clone_method(self):
# copy variogram
copy = self.V.clone()
# test against bins and experimental
assert_array_almost_equal(copy.experimental, self.V.experimental)
assert_array_almost_equal(copy.bins, self.V.bins)
def test_data_no_force(self):
lags, var = self.V.data(n=10, force=False)
assert_array_almost_equal(
lags, [0., 4.7, 9.4, 14.1, 18.8, 23.5, 28.2, 32.9, 37.6, 42.3],
decimal=2)
assert_array_almost_equal(var, [
0., 11.82, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97
],
decimal=2)
def test_data_with_force(self):
# should work if _dist is corccupted
self.V._dist = self.V._dist * 5.
self.V.cof = None
lags, var = self.V.data(n=10, force=True)
assert_array_almost_equal(
lags, [0., 4.7, 9.4, 14.1, 18.8, 23.5, 28.2, 32.9, 37.6, 42.3],
decimal=2)
assert_array_almost_equal(var, [
0., 11.82, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97
],
decimal=2)
def test_data_normalized(self):
V = self.V.clone()
V.normalize = True
lags, var = V.data(n=5, force=True)
assert_array_almost_equal(lags, [0., 10.58, 21.15, 31.73, 42.3],
decimal=2)
assert_array_almost_equal(var, [0., 13.97, 13.97, 13.97, 13.97],
decimal=2)
def test_parameter_property_matern(self):
V = self.V.clone()
# test matern
param = [42.3, 15.42, 0.11, 0.]
V.set_model('matern')
assert_array_almost_equal(V.parameters, param, decimal=2)
def test_parameter_property_stable(self):
V = self.V.clone()
# test stable
param = [42.3, 15.79, 0.45, 0.]
V.set_model('stable')
assert_array_almost_equal(V.parameters, param, decimal=2)
class TestVariogramMethods(unittest.TestCase):
def setUp(self):
# set up default values, whenever c and v are not important
np.random.seed(42)
self.c = np.random.gamma(10, 4, (30, 2))
np.random.seed(42)
self.v = np.random.normal(10, 4, 30)
self.V = Variogram(self.c, self.v, normalize=False, n_lags=10)
def test_get_empirical(self):
bins = self.V.bins
exp = self.V.experimental
emp_x, emp_y = self.V.get_empirical()
# test
assert_array_almost_equal(bins, emp_x)
assert_array_almost_equal(exp, emp_y)
def test_get_empirical_center(self):
V = Variogram(self.c, self.v)
# overwrite bins
V.bins = [4, 8, 9, 12, 15]
emp_x, emp_y = V.get_empirical(bin_center=True)
assert_array_almost_equal(emp_x, [2., 6., 8.5, 10.5, 13.5])
def test_clone_method(self):
# copy variogram
copy = self.V.clone()
# test against bins and experimental
assert_array_almost_equal(copy.experimental, self.V.experimental)
assert_array_almost_equal(copy.bins, self.V.bins)
def test_data_no_force(self):
lags, var = self.V.data(n=10, force=False)
assert_array_almost_equal(
lags, [0., 4.7, 9.4, 14.1, 18.8, 23.5, 28.2, 32.9, 37.6, 42.3],
decimal=2)
assert_array_almost_equal(var, [
0., 11.82, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97
],
decimal=2)
def disabled_test_data_with_force(self):
# Distance can no longer be explicitly set
# it would require setting the whole MetricSpace, with a
# non-sparse diagonal matrix
# should work if _dist is corccupted
self.V._dist = self.V._dist * 5.
self.V.cof = None
lags, var = self.V.data(n=10, force=True)
assert_array_almost_equal(
lags, [0., 4.7, 9.4, 14.1, 18.8, 23.5, 28.2, 32.9, 37.6, 42.3],
decimal=2)
assert_array_almost_equal(var, [
0., 11.82, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97, 13.97
],
decimal=2)
def test_data_normalized(self):
V = self.V.clone()
V.normalize = True
lags, var = V.data(n=5, force=True)
assert_array_almost_equal(lags, [0., 10.58, 21.15, 31.73, 42.3],
decimal=2)
assert_array_almost_equal(var, [0., 13.97, 13.97, 13.97, 13.97],
decimal=2)
def test_parameter_property_matern(self):
V = self.V.clone()
# test matern
param = [42.3, 16.2, 0.1, 0.]
V.set_model('matern')
assert_array_almost_equal(V.parameters, param, decimal=2)
def test_parameter_property_stable(self):
V = self.V.clone()
# test stable
param = [42.3, 15.79, 0.45, 0.]
V.set_model('stable')
assert_array_almost_equal(V.parameters, param, decimal=2)
class TestPyKrigeInterface(unittest.TestCase):
def setUp(self):
# use real sample data in the interface
df = pd.read_csv(os.path.join(os.path.dirname(__file__), 'sample.csv'))
self.c = df[['x', 'y']].values
self.v = df.z.values
self.V = Variogram(self.c,
self.v,
model='matern',
normalize=False,
use_nugget=True)
if not PYKRIGE_AVAILABLE:
print('PyKrige not found, will skip all pykrige interface tests')
def test_model_interface(self):
if not PYKRIGE_AVAILABLE: # pragma: no cover
return True
# get the function
model = pykrige_interface.pykrige_model(self.V)
# use the transform function.
xi = np.arange(1, 85)
yi = self.V.transform(xi)
assert_array_almost_equal(yi, model([], xi), decimal=6)
def test_model_interface_from_list(self):
if not PYKRIGE_AVAILABLE: # pragma: no cover
return True
# get the function
model = pykrige_interface.pykrige_model(self.V)
# use the transform function
xi = list(range(1, 85))
yi = self.V.transform(np.array(xi))
assert_array_almost_equal(yi, model([], xi), decimal=6)
def test_parameters(self):
if not PYKRIGE_AVAILABLE: # pragma: no cover
return True
p = pykrige_interface.pykrige_params(self.V)
params = self.V.parameters
self.assertAlmostEqual(p[0], params[1], places=4)
self.assertAlmostEqual(p[1], params[0], places=4)
self.assertAlmostEqual(p[2], params[2], places=4)
def test_as_kwargs(self):
if not PYKRIGE_AVAILABLE: # pragma: no cover
return True
args = pykrige_interface.pykrige_as_kwargs(self.V)
pars = pykrige_interface.pykrige_params(self.V)
# test
self.assertEqual(args['variogram_model'], 'custom')
assert_array_almost_equal(pars, args['variogram_parameters'])
xi = np.arange(1, 80)
yi = self.V.transform(xi)
assert_array_almost_equal(yi,
args['variogram_function']([], xi),
decimal=6)
def test_as_kwargs_adjust_maxlag(self):
if not PYKRIGE_AVAILABLE: # pragma: no cover
return True
V = self.V.clone()
# now maxlag should be changed
args = pykrige_interface.pykrige_as_kwargs(V, adjust_maxlag=True)
# should be None
self.assertIsNone(V.maxlag)
# transform should change
xi = np.arange(1, 20)
yi = V.transform(xi)
# test changed values
assert_array_almost_equal(yi, args['variogram_function']([], xi))
def test_as_kwargs_adjust_nlags(self):
if not PYKRIGE_AVAILABLE: # pragma: no cover
return True
args = pykrige_interface.pykrige_as_kwargs(self.V, adjust_nlags=True)
self.assertEqual(args['nlags'], self.V.n_lags)
