How to use the pykrige.core._calculate_variogram_model function in PyKrige
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bsmurphy / PyKrige / tests / test_core.py View on Github 
"power",
variogram_models.power_variogram_model,
False,
)
assert_allclose(res, np.array([1.0, 1.5, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([1.0, 1.4142, 1.7321, 2.0]),
"power",
variogram_models.power_variogram_model,
False,
)
assert_allclose(res, np.array([1.0, 0.5, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([1.2642, 1.7293, 1.9004, 1.9634]),
"exponential",
variogram_models.exponential_variogram_model,
False,
)
assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([0.5769, 1.4872, 1.9065, 1.9914]),
"gaussian",
variogram_models.gaussian_variogram_model,
False,
)
assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)def test_core_calculate_variogram_model():
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([2.05, 2.95, 4.05, 4.95]),
"linear",
variogram_models.linear_variogram_model,
False,
)
assert_allclose(res, np.array([0.98, 1.05]), 0.01, 0.01)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([2.05, 2.95, 4.05, 4.95]),
"linear",
variogram_models.linear_variogram_model,
True,
)
assert_allclose(res, np.array([0.98, 1.05]), 0.01, 0.01)def test_core_calculate_variogram_model():
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([2.05, 2.95, 4.05, 4.95]),
"linear",
variogram_models.linear_variogram_model,
False,
)
assert_allclose(res, np.array([0.98, 1.05]), 0.01, 0.01)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([2.05, 2.95, 4.05, 4.95]),
"linear",
variogram_models.linear_variogram_model,
True,
)
assert_allclose(res, np.array([0.98, 1.05]), 0.01, 0.01)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([1.0, 2.8284271, 5.1961524, 8.0]),
"power",
variogram_models.power_variogram_model,
False,
)
assert_allclose(res, np.array([1.0, 1.5, 0.0]), 0.001, 0.001)"linear",
variogram_models.linear_variogram_model,
True,
)
assert_allclose(res, np.array([0.98, 1.05]), 0.01, 0.01)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([1.0, 2.8284271, 5.1961524, 8.0]),
"power",
variogram_models.power_variogram_model,
False,
)
assert_allclose(res, np.array([1.0, 1.5, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([1.0, 1.4142, 1.7321, 2.0]),
"power",
variogram_models.power_variogram_model,
False,
)
assert_allclose(res, np.array([1.0, 0.5, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([1.2642, 1.7293, 1.9004, 1.9634]),
"exponential",
variogram_models.exponential_variogram_model,
False,
)
assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)"power",
variogram_models.power_variogram_model,
False,
)
assert_allclose(res, np.array([1.0, 0.5, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([1.2642, 1.7293, 1.9004, 1.9634]),
"exponential",
variogram_models.exponential_variogram_model,
False,
)
assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([0.5769, 1.4872, 1.9065, 1.9914]),
"gaussian",
variogram_models.gaussian_variogram_model,
False,
)
assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([3.33060952, 3.85063879, 3.96667301, 3.99256374]),
"exponential",
variogram_models.exponential_variogram_model,
False,
)
assert_allclose(res, np.array([3.0, 2.0, 1.0]), 0.001, 0.001)"gaussian",
variogram_models.gaussian_variogram_model,
False,
)
assert_allclose(res, np.array([2.0, 3.0, 0.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([3.33060952, 3.85063879, 3.96667301, 3.99256374]),
"exponential",
variogram_models.exponential_variogram_model,
False,
)
assert_allclose(res, np.array([3.0, 2.0, 1.0]), 0.001, 0.001)
res = core._calculate_variogram_model(
np.array([1.0, 2.0, 3.0, 4.0]),
np.array([2.60487044, 3.85968813, 3.99694817, 3.99998564]),
"gaussian",
variogram_models.gaussian_variogram_model,
False,
)
assert_allclose(res, np.array([3.0, 2.0, 1.0]), 0.001, 0.001)
Popular PyKrige functions
- pykrige.core
- pykrige.core._adjust_for_anisotropy
- pykrige.core._calculate_variogram_model
- pykrige.core._find_statistics
- pykrige.core._initialize_variogram_model
- pykrige.core._krige
- pykrige.core._make_variogram_parameter_list
- pykrige.core.calc_cR
- pykrige.core.calcQ1
- pykrige.core.calcQ2
- pykrige.core.great_circle_distance
- pykrige.ok.OrdinaryKriging
- pykrige.ok3d.OrdinaryKriging3D
- pykrige.uk.UniversalKriging
- pykrige.uk3d.UniversalKriging3D
- pykrige.variogram_models
- pykrige.variogram_models.exponential_variogram_model
- pykrige.variogram_models.gaussian_variogram_model
- pykrige.variogram_models.linear_variogram_model
- pykrige.variogram_models.power_variogram_model