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Commit 86875044 authored by leufen1's avatar leufen1
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removed import statement in tests

parent c6de1c5b
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2 merge requests!253include current develop,!252Resolve "release v1.3.0"
Pipeline #59584 passed with warnings
...@@ -3,10 +3,8 @@ import pandas as pd ...@@ -3,10 +3,8 @@ import pandas as pd
import pytest import pytest
import xarray as xr import xarray as xr
from mlair.helpers.statistics import DataClass, TransformationClass
from mlair.helpers.statistics import standardise, standardise_inverse, standardise_apply, centre, centre_inverse, \ from mlair.helpers.statistics import standardise, standardise_inverse, standardise_apply, centre, centre_inverse, \
centre_apply, \ centre_apply, apply_inverse_transformation
apply_inverse_transformation
lazy = pytest.lazy_fixture lazy = pytest.lazy_fixture
...@@ -115,50 +113,3 @@ class TestCentre: ...@@ -115,50 +113,3 @@ class TestCentre:
data = centre_apply(data_orig, mean) data = centre_apply(data_orig, mean)
mean_expected = np.array([2, -5, 10]) - np.array([2, 10, 3]) 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 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
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