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Commit bc62a0f5 authored by leufen1's avatar leufen1
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updated test for statistics

parent a997bce5
Branches cmake_add_soversion
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3 merge requests!500Develop,!499Resolve "release v2.3.0",!492Resolve "new metric bias / mean error"
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Stage: docs
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This commit is part of merge request !499. Comments created here will be created in the context of that merge request.
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test/test_helpers/test_statistics.py
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...@@ -375,6 +375,7 @@ class TestCalculateErrorMetrics: ...@@ -375,6 +375,7 @@ class TestCalculateErrorMetrics:
x_array1 = xr.DataArray(d1, coords=coords, dims=coords.keys()) x_array1 = xr.DataArray(d1, coords=coords, dims=coords.keys())
x_array2 = xr.DataArray(d2, coords=coords, dims=coords.keys()) x_array2 = xr.DataArray(d2, coords=coords, dims=coords.keys())
expected = {"mse": xr.DataArray(np.array([1, 2, 0, 0, 1./3]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"]), expected = {"mse": xr.DataArray(np.array([1, 2, 0, 0, 1./3]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"]),
"me": xr.DataArray(np.array([-1./3, -2./3, 0, 0, -1./3]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"]),
"rmse": np.sqrt(xr.DataArray(np.array([1, 2, 0, 0, 1./3]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"])), "rmse": np.sqrt(xr.DataArray(np.array([1, 2, 0, 0, 1./3]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"])),
"mae": xr.DataArray(np.array([1, 4./3, 0, 0, 1./3]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"]), "mae": xr.DataArray(np.array([1, 4./3, 0, 0, 1./3]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"]),
"ioa": xr.DataArray(np.array([0.3721, 0.4255, 1, 1, 0.4706]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"]), "ioa": xr.DataArray(np.array([0.3721, 0.4255, 1, 1, 0.4706]), coords={"value": [0, 1, 2, 3, 4]}, dims=["value"]),
...@@ -383,6 +384,7 @@ class TestCalculateErrorMetrics: ...@@ -383,6 +384,7 @@ class TestCalculateErrorMetrics:
assert check_nested_equality(expected, calculate_error_metrics(x_array1, x_array2, "index"), 3) is True assert check_nested_equality(expected, calculate_error_metrics(x_array1, x_array2, "index"), 3) is True
expected = {"mse": xr.DataArray(np.array([1.2, 0.4, 0.4]), coords={"index": [0, 1, 2]}, dims=["index"]), expected = {"mse": xr.DataArray(np.array([1.2, 0.4, 0.4]), coords={"index": [0, 1, 2]}, dims=["index"]),
"me": xr.DataArray(np.array([-0.8, -0.4, 0.4]), coords={"index": [0, 1, 2]}, dims=["index"]),
"rmse": np.sqrt(xr.DataArray(np.array([1.2, 0.4, 0.4]), coords={"index": [0, 1, 2]}, dims=["index"])), "rmse": np.sqrt(xr.DataArray(np.array([1.2, 0.4, 0.4]), coords={"index": [0, 1, 2]}, dims=["index"])),
"mae": xr.DataArray(np.array([0.8, 0.4, 0.4]), coords={"index": [0, 1, 2]}, dims=["index"]), "mae": xr.DataArray(np.array([0.8, 0.4, 0.4]), coords={"index": [0, 1, 2]}, dims=["index"]),
"ioa": xr.DataArray(np.array([0.8478, 0.9333, 0.9629]), coords={"index": [0, 1, 2]}, dims=["index"]), "ioa": xr.DataArray(np.array([0.8478, 0.9333, 0.9629]), coords={"index": [0, 1, 2]}, dims=["index"]),
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