more tests and some renaming

tests/outlier_detection/utils.py

@@ -0,0 +1,95 @@
+import warnings
+
+import pytest
+import numpy as np
+import scipy.signal
+
+from stcal.outlier_detection.utils import (
+    _abs_deriv,
+    compute_weight_threshold,
+    medfilt,
+)
+
+
+@pytest.mark.parametrize("shape,diff", [
+    ([5, 5], 100),
+    ([7, 7], 200),
+])
+def test_abs_deriv(shape, diff):
+    arr = np.zeros(shape)
+    # put diff at the center
+    np.put(arr, arr.size // 2, diff)
+    # since abs_deriv with a single non-zero value is the same as a
+    # convolution with a 3x3 cross kernel use it to test the result
+    expected = scipy.signal.convolve2d(arr, [[0, 1, 0], [1, 1, 1], [0, 1, 0]], mode='same')
+    result = _abs_deriv(arr)
+    np.testing.assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize("shape,mean,maskpt,expected", [
+    ([5, 5], 11, 0.5, 5.5),
+    ([5, 5], 11, 0.25, 2.75),
+    ([3, 3, 3], 17, 0.5, 8.5),
+])
+def test_compute_weight_threshold(shape, mean, maskpt, expected):
+    arr = np.ones(shape, dtype=np.float32) * mean
+    result = compute_weight_threshold(arr, maskpt)
+    np.testing.assert_allclose(result, expected)
+
+
+def test_compute_weight_threshold_outlier():
+    """
+    Test that a large outlier doesn't bias the threshold
+    """
+    arr = np.ones([7, 7, 7], dtype=np.float32) * 42
+    arr[3, 3] = 9000
+    result = compute_weight_threshold(arr, 0.5)
+    np.testing.assert_allclose(result, 21)
+
+
+def test_compute_weight_threshold_zeros():
+    """
+    Test that zeros are ignored
+    """
+    arr = np.zeros([10, 10], dtype=np.float32)
+    arr[:5, :5] = 42
+    result = compute_weight_threshold(arr, 0.5)
+    np.testing.assert_allclose(result, 21)
+
+
+@pytest.mark.parametrize("shape,kern_size", [
+    ([7, 7], [3, 3]),
+    ([7, 7], [3, 1]),
+    ([7, 7], [1, 3]),
+    ([7, 5], [3, 3]),
+    ([5, 7], [3, 3]),
+    ([42, 42], [7, 7]),
+    ([42, 42], [7, 5]),
+    ([42, 42], [5, 7]),
+    ([42, 7, 5], [3, 3, 3]),
+    ([5, 7, 42], [5, 5, 5]),
+])
+def test_medfilt_against_scipy(shape, kern_size):
+    arr = np.arange(np.prod(shape), dtype='uint32').reshape(shape)
+    result = medfilt(arr, kern_size)
+
+    # The use of scipy.signal.medfilt is ok here ONLY because the
+    # input has no nans. See the medfilt docstring
+    expected = scipy.signal.medfilt(arr, kern_size)
+
+    np.testing.assert_allclose(result, expected)
+
+
+@pytest.mark.parametrize("arr,kern_size,expected", [
+    ([2, np.nan, 0], [3], [1, 1, 0]),
+    ([np.nan, np.nan, np.nan], [3], [0, np.nan, 0]),
+])
+def test_medfilt_nan(arr, kern_size, expected):
+    with warnings.catch_warnings():
+        warnings.filterwarnings(
+            "ignore",
+            message="All-NaN slice",
+            category=RuntimeWarning
+        )
+        result = medfilt(arr, kern_size)
+    np.testing.assert_allclose(result, expected)