fixes for #142 and outlier tests (#143)

* fixes for #142 and outlier tests

* stops main pulls in iris tests action

Author: dkazanc

.github/workflows/tests_run.yml

@@ -22,10 +22,7 @@ jobs:
 
     steps:
       - name: Checkout repository code
-        uses: actions/checkout@v3
-        with:
-          ref: "main"
-          fetch-depth: 0
+        uses: actions/checkout@v4
 
       - name: Create conda environment
         uses: mamba-org/setup-micromamba@v1

httomolibgpu/prep/normalize.py

@@ -85,7 +85,7 @@ def __normalize(
     cutoff: float = 10.0,
     minus_log: bool = True,
     nonnegativity: bool = False,
-    remove_nans: bool = False,
+    remove_nans: bool = True,
 ) -> cp.ndarray:
     from cupy import mean
 
@@ -116,6 +116,7 @@ def __normalize(
         kernel += "if (isinf(v)) v = 0.0f;\n"
         kernel_name += "_remnan"
     kernel += "if (v > cutoff) v = cutoff;\n"
+    kernel += "if (v < -cutoff) v = cutoff;\n"
     kernel += "out = v;\n"
 
     normalisation_kernel = cp.ElementwiseKernel(

tests/test_misc/test_corr.py

@@ -145,7 +145,7 @@ def test_remove_outlier3d(data):
     assert_allclose(np.mean(filtered_data), 808.753494, rtol=eps)
     assert_allclose(np.mean(filtered_data, axis=(1, 2)).sum(), 145575.628906)
     assert_allclose(np.median(filtered_data), 976.0)
-    assert_allclose(np.median(filtered_data, axis=(1, 2)).sum(), 175741.5)
+    assert_allclose(np.median(filtered_data, axis=(1, 2)).sum(), 175742)
 
     assert filtered_data.dtype == np.uint16
 
@@ -160,10 +160,10 @@ def test_remove_outlier2d(data):
     filtered_data = remove_outlier(data, kernel_size=3, axis=0, dif=1.5).get()
 
     assert filtered_data.ndim == 3
-    assert_allclose(np.mean(filtered_data), 808.861578504, rtol=eps)
-    assert_allclose(np.mean(filtered_data, axis=(1, 2)).sum(), 145595.0841308)
-    assert_allclose(np.max(filtered_data), 1080)
-    assert_allclose(np.min(filtered_data), 80)
+    assert_allclose(np.mean(filtered_data), 809.049971, rtol=eps)
+    assert_allclose(np.mean(filtered_data, axis=(1, 2)).sum(), 145628.994824)
+    assert_allclose(np.max(filtered_data), 1136)
+    assert_allclose(np.min(filtered_data), 62)
 
     assert filtered_data.dtype == np.uint16
     assert filtered_data.flags.c_contiguous