Add raven filter parameters test

Author: neon60

tests/test_prep/stripe_cpu_reference.py

@@ -2,7 +2,7 @@
 import pyfftw
 import pyfftw.interfaces.numpy_fft as fft
 
-def raven_filter_cpu(
+def raven_filter_numpy(
         sinogram,
         uvalue: int = 20,
         nvalue: int = 4,

tests/test_prep/test_stripe.py

@@ -12,7 +12,7 @@
     raven_filter,
 )
 from numpy.testing import assert_allclose
-from .stripe_cpu_reference import raven_filter_cpu
+from .stripe_cpu_reference import raven_filter_numpy
 
 def test_remove_stripe_ti_on_data(data, flats, darks):
     # --- testing the CuPy implementation from TomoCupy ---#
@@ -72,16 +72,39 @@ def test_stripe_raven_cupy(data, flats, darks):
 
     data = normalize(data, flats, darks, cutoff=10, minus_log=True)
 
-    data_after_raven_gpu = raven_filter(cp.copy(data))
-    data_after_raven_cpu = cp.asarray(raven_filter_cpu(cp.copy(data).get()))
+    data_after_raven_gpu = raven_filter(cp.copy(data)).get()
+    data_after_raven_cpu = raven_filter_numpy(cp.copy(data).get())
 
-    cp.testing.assert_allclose(data_after_raven_cpu, data_after_raven_gpu, rtol=0, atol=4e-01)
+    assert_allclose(data_after_raven_cpu, data_after_raven_gpu, rtol=0, atol=4e-01)
 
     data = None  #: free up GPU memory
     # make sure the output is float32
     assert data_after_raven_gpu.dtype == np.float32
     assert data_after_raven_gpu.shape == data_after_raven_cpu.shape
 
+@pytest.mark.parametrize("uvalue", [20, 50, 100])
+@pytest.mark.parametrize("nvalue", [2, 4, 6])
+@pytest.mark.parametrize("vvalue", [2, 4])
+@pytest.mark.parametrize("pad_x", [0, 10, 20])
+@pytest.mark.parametrize("pad_y", [0, 10, 20])
+@cp.testing.numpy_cupy_allclose(rtol=0, atol=3e-01)
+def test_stripe_raven_parameters_cupy(ensure_clean_memory, xp, uvalue, nvalue, vvalue, pad_x, pad_y):
+    # because it's random, we explicitly seed and use numpy only, to match the data
+    np.random.seed(12345)
+    data = np.random.random_sample(size=(256, 5, 512)).astype(np.float32) * 2.0 + 0.001
+    data = xp.asarray(data)
+
+    if xp.__name__ == "numpy":
+        results = raven_filter_numpy(
+            data, uvalue=uvalue, nvalue=nvalue, vvalue=vvalue, pad_x=pad_x, pad_y=pad_y
+        ).astype(np.float32) 
+    else:
+        results = raven_filter(
+            data, uvalue=uvalue, nvalue=nvalue, vvalue=vvalue, pad_x=pad_x, pad_y=pad_y
+        ).get()
+
+    return xp.asarray(results)
+
 @pytest.mark.perf
 def test_stripe_removal_sorting_cupy_performance(ensure_clean_memory):
     data_host = (
@@ -155,25 +178,6 @@ def test_raven_filter_performance(ensure_clean_memory):
 
     assert "performance in ms" == duration_ms
 
-# @pytest.mark.perf
-# def test_raven_filter_cpu_performance(ensure_clean_memory):
-#     data_host = (
-#         np.random.random_sample(size=(1801, 5, 2560)).astype(np.float32) * 2.0 + 0.001
-#     )
-#     data = cp.asarray(data_host, dtype=np.float32)
-# 
-#     # do a cold run first
-#     raven_filter_cpu(cp.copy(data).get())
-# 
-#     start = time.perf_counter_ns()
-#     for _ in range(10):
-#         raven_filter_cpu(cp.copy(data).get())
-# 
-#     duration_ms = float(time.perf_counter_ns() - start) * 1e-6 / 10
-# 
-#     assert "performance in ms" == duration_ms
-
-
 def test_remove_all_stripe_on_data(data, flats, darks):
     # --- testing the CuPy implementation from TomoCupy ---#
     data = normalize(data, flats, darks, cutoff=10, minus_log=True)