removing paganin savu related tests

Author: dkazanc

httomolibgpu/prep/phase.py

@@ -45,6 +45,7 @@
     "paganin_filter_tomopy",
 ]
 
+
 ##-------------------------------------------------------------##
 # Adaptation of retrieve_phase (Paganin filter) from TomoPy
 def paganin_filter_tomopy(
@@ -247,4 +248,4 @@ def _reciprocal_coord(pixel_size: float, num_grid: int) -> cp.ndarray:
     n = num_grid - 1
     rc = cp.arange(-n, num_grid, 2, dtype=cp.float32)
     rc *= 2 * math.pi / (n * pixel_size)
-    return rc
+    return rc

httomolibgpu/recon/algorithm.py

@@ -152,7 +152,7 @@ def FBP3d_tomobar(
     """
     Perform Filtered Backprojection (FBP) reconstruction using ASTRA toolbox :cite:`van2016fast` and
     ToMoBAR :cite:`kazantsev2020tomographic` wrappers.
-    This is a 3D recon from the CuPy array directly and using a custom built SINC filter for filtration in Fourier space, 
+    This is a 3D recon from the CuPy array directly and using a custom built SINC filter for filtration in Fourier space,
     see more in :ref:`method_FBP3d_tomobar`.
 
     Parameters
@@ -166,7 +166,7 @@ def FBP3d_tomobar(
     detector_pad : int
         Detector width padding with edge values to remove circle/arc type artifacts in the reconstruction.
     filter_freq_cutoff : float
-        Cutoff frequency parameter for the SINC filter, the lower values may produce better contrast but noisy reconstruction. The filter change will also affect the dynamic range of the reconstructed image. 
+        Cutoff frequency parameter for the SINC filter, the lower values may produce better contrast but noisy reconstruction. The filter change will also affect the dynamic range of the reconstructed image.
     recon_size : int, optional
         The [recon_size, recon_size] shape of the reconstructed slice in pixels.
         By default (None), the reconstructed size will be the dimension of the horizontal detector.

tests/test_prep/test_phase.py

@@ -4,118 +4,12 @@
 import numpy as np
 import pytest
 from cupy.cuda import nvtx
-from httomolibgpu.prep.phase import paganin_filter_savu, paganin_filter_tomopy
+from httomolibgpu.prep.phase import paganin_filter_tomopy
 from numpy.testing import assert_allclose
 
 eps = 1e-6
 
 
-def test_paganin_savu_filter(data):
-    # --- testing the Paganin filter on tomo_standard ---#
-    filtered_data = paganin_filter_savu(data).get()
-
-    assert filtered_data.ndim == 3
-    assert_allclose(np.mean(filtered_data), -770.5339, rtol=eps)
-    assert_allclose(np.max(filtered_data), -679.80945, rtol=eps)
-
-    #: make sure the output is float32
-    assert filtered_data.dtype == np.float32
-    assert filtered_data.flags.c_contiguous
-
-
-def test_paganin_filter_savu_energy100(data):
-    filtered_data = paganin_filter_savu(data, energy=100.0).get()
-
-    assert_allclose(np.mean(filtered_data), -778.61926, rtol=1e-05)
-    assert_allclose(np.min(filtered_data), -808.9013, rtol=eps)
-
-    assert filtered_data.ndim == 3
-    assert filtered_data.dtype == np.float32
-
-
-def test_paganin_filter_savu_padmean(data):
-    filtered_data = paganin_filter_savu(data, pad_method="mean").get()
-
-    assert_allclose(np.mean(filtered_data), -765.3401, rtol=eps)
-    assert_allclose(np.min(filtered_data), -793.68787, rtol=eps)
-    # test a few other slices to ensure shifting etc is right
-    assert_allclose(
-        filtered_data[0, 50, 1:5],
-        [-785.60736, -786.20215, -786.7521, -787.25494],
-        rtol=eps,
-    )
-    assert_allclose(
-        filtered_data[0, 50, 40:42], [-776.6436, -775.1906], rtol=eps, atol=1e-5
-    )
-    assert_allclose(
-        filtered_data[0, 60:63, 90],
-        [-737.75104, -736.6097, -735.49884],
-        rtol=eps,
-        atol=1e-5,
-    )
-
-
-@pytest.mark.perf
-def test_paganin_filter_savu_performance(ensure_clean_memory):
-    # Note: low/high and size values taken from sample2_medium.yaml real run
-
-    # this test needs ~20GB of memory with 1801 - we'll divide depending on GPU memory
-    dev = cp.cuda.Device()
-    mem_80percent = 0.8 * dev.mem_info[0]
-    size = 1801
-    required_mem = 20 * 1024 * 1024 * 1024
-    if mem_80percent < required_mem:
-        size = int(np.ceil(size / required_mem * mem_80percent))
-        print(f"Using smaller size of ({size}, 5, 2560) due to memory restrictions")
-
-    data_host = np.random.random_sample(size=(size, 5, 2560)).astype(np.float32) * 2.0
-    data = cp.asarray(data_host, dtype=np.float32)
-
-    # run code and time it
-    # cold run first
-    paganin_filter_savu(
-        data,
-        ratio=250.0,
-        energy=53.0,
-        distance=1.0,
-        resolution=1.28,
-        pad_y=100,
-        pad_x=100,
-        pad_method="edge",
-        increment=0.0,
-    )
-    dev = cp.cuda.Device()
-    dev.synchronize()
-
-    start = time.perf_counter_ns()
-    nvtx.RangePush("Core")
-    for _ in range(10):
-        paganin_filter_savu(
-            data,
-            ratio=250.0,
-            energy=53.0,
-            distance=1.0,
-            resolution=1.28,
-            pad_y=100,
-            pad_x=100,
-            pad_method="edge",
-            increment=0.0,
-        )
-    nvtx.RangePop()
-    dev.synchronize()
-    duration_ms = float(time.perf_counter_ns() - start) * 1e-6 / 10
-
-    assert "performance in ms" == duration_ms
-
-
-def test_paganin_filter_savu_1D_raises(ensure_clean_memory):
-    _data = cp.ones(10)
-    with pytest.raises(ValueError):
-        paganin_filter_savu(_data)
-
-    _data = None  #: free up GPU memory
-
-
 # paganin filter tomopy
 def test_paganin_filter_tomopy_1D_raises(ensure_clean_memory):
     _data = cp.ones(10)