diff --git a/test/test_configuration/test_defaults.py b/test/test_configuration/test_defaults.py
index ae81ef2ef0a15ad08f14ad19312f04040ab71263..90227ed21e544feb90b3b426edc07e0283624177 100644
--- a/test/test_configuration/test_defaults.py
+++ b/test/test_configuration/test_defaults.py
@@ -36,9 +36,6 @@ class TestAllDefaults:
assert DEFAULT_END == "2017-12-31"
assert DEFAULT_WINDOW_HISTORY_SIZE == 13
assert DEFAULT_OVERWRITE_LOCAL_DATA is False
- assert isinstance(DEFAULT_TRANSFORMATION, TransformationClass)
- assert DEFAULT_TRANSFORMATION.inputs.transform_method == "standardise"
- assert DEFAULT_TRANSFORMATION.targets.transform_method == "standardise"
assert DEFAULT_TARGET_VAR == "o3"
assert DEFAULT_TARGET_DIM == "variables"
assert DEFAULT_WINDOW_LEAD_TIME == 3
diff --git a/test/test_helpers/test_statistics.py b/test/test_helpers/test_statistics.py
index 76adc1bdd210e072b4fc9be717269c6ceb951fec..2e9b0db62d24edaaaff19eff590597f89b65ca2c 100644
--- a/test/test_helpers/test_statistics.py
+++ b/test/test_helpers/test_statistics.py
@@ -3,7 +3,6 @@ import pandas as pd
import pytest
import xarray as xr
-from mlair.helpers.statistics import DataClass, TransformationClass
from mlair.helpers.statistics import standardise, standardise_inverse, standardise_apply, centre, centre_inverse, \
centre_apply, \
apply_inverse_transformation
@@ -72,7 +71,7 @@ class TestStandardise:
(lazy('xarray'), 'index')])
def test_apply_standardise_inverse(self, data_orig, dim):
mean, std, data = standardise(data_orig, dim)
- data_recovered = apply_inverse_transformation(data, mean, std)
+ data_recovered = apply_inverse_transformation(data, "standardise", mean, std)
assert np.testing.assert_array_almost_equal(data_orig, data_recovered) is None
@pytest.mark.parametrize('data_orig, mean, std, dim', [(lazy('pandas'), lazy('pd_mean'), lazy('pd_std'), 0),
@@ -106,7 +105,7 @@ class TestCentre:
(lazy('xarray'), 'index')])
def test_apply_centre_inverse(self, data_orig, dim):
mean, _, data = centre(data_orig, dim)
- data_recovered = apply_inverse_transformation(data, mean, method="centre")
+ data_recovered = apply_inverse_transformation(data, mean=mean, method="centre")
assert np.testing.assert_array_almost_equal(data_orig, data_recovered) is None
@pytest.mark.parametrize('data_orig, mean, dim', [(lazy('pandas'), lazy('pd_mean'), 0),
@@ -115,50 +114,3 @@ class TestCentre:
data = centre_apply(data_orig, mean)
mean_expected = np.array([2, -5, 10]) - np.array([2, 10, 3])
assert np.testing.assert_almost_equal(data.mean(dim), mean_expected, decimal=1) is None
-
-
-class TestDataClass:
-
- def test_init(self):
- dc = DataClass()
- assert all([obj is None for obj in [dc.data, dc.mean, dc.std, dc.max, dc.min, dc.transform_method, dc._method]])
-
- def test_init_values(self):
- dc = DataClass(data=12, mean=2, std="test", max=23.4, min=np.array([3]), transform_method="f")
- assert dc.data == 12
- assert dc.mean == 2
- assert dc.std == "test"
- assert dc.max == 23.4
- assert np.testing.assert_array_equal(dc.min, np.array([3])) is None
- assert dc.transform_method == "f"
- assert dc._method is None
-
- def test_as_dict(self):
- dc = DataClass(std=23)
- dc._method = "f(x)"
- assert dc.as_dict() == {"data": None, "mean": None, "std": 23, "max": None, "min": None,
- "transform_method": None}
-
-
-class TestTransformationClass:
-
- def test_init(self):
- tc = TransformationClass()
- assert hasattr(tc, "inputs")
- assert isinstance(tc.inputs, DataClass)
- assert hasattr(tc, "targets")
- assert isinstance(tc.targets, DataClass)
- assert tc.inputs.mean is None
- assert tc.targets.std is None
-
- def test_init_values(self):
- tc = TransformationClass(inputs_mean=1, inputs_std=2, inputs_method="f", targets_mean=3, targets_std=4,
- targets_method="g")
- assert tc.inputs.mean == 1
- assert tc.inputs.std == 2
- assert tc.inputs.transform_method == "f"
- assert tc.inputs.max is None
- assert tc.targets.mean == 3
- assert tc.targets.std == 4
- assert tc.targets.transform_method == "g"
- assert tc.inputs.min is None