fixing few suggestions, applying black formatting

Author: dkazanc

httomolibgpu/misc/corr.py

@@ -82,7 +82,9 @@ def median_filter(
     else:
         raise ValueError("The input array must be a 3D array")
 
-    data = data_checker(data, verbosity=True, method_name="median_filter_or_remove_outlier")
+    data = data_checker(
+        data, verbosity=True, method_name="median_filter_or_remove_outlier"
+    )
 
     if kernel_size not in [3, 5, 7, 9, 11, 13]:
         raise ValueError("Please select a correct kernel size: 3, 5, 7, 9, 11, 13")

httomolibgpu/misc/denoise.py

@@ -82,7 +82,7 @@ def total_variation_ROF(
         If the input array is not float32 data type.
     """
 
-    data = data_checker(data,verbosity=True,method_name="total_variation_ROF")
+    data = data_checker(data, verbosity=True, method_name="total_variation_ROF")
 
     return ROF_TV(
         data, regularisation_parameter, iterations, time_marching_parameter, gpu_id
@@ -129,7 +129,7 @@ def total_variation_PD(
         If the input array is not float32 data type.
     """
 
-    data_checker(data,verbosity=True,method_name="total_variation_PD")
+    data_checker(data, verbosity=True, method_name="total_variation_PD")
 
     methodTV = 0
     if not isotropic:

httomolibgpu/misc/supp_func.py

@@ -77,14 +77,14 @@ def _zeros_check(
     method_name: Optional[str] = None,
 ) -> bool:
     """
-    This function finds all zeros present in the data. If the amount of zeros is larger than percentage_threshold it prints the warning. 
+    This function finds all zeros present in the data. If the amount of zeros is larger than percentage_threshold it prints the warning.
 
     Parameters
     ----------
     data : cp.ndarray
-        Input CuPy or Numpy array either float32 or uint16 data type.
+        Input CuPy or Numpy array.
     verbosity : bool
-        If enabled, then the printing of the warning happens when data contains infs or nans
+        If enabled, then the printing of the warning happens when data contains infs or nans.
     percentage_threshold: float:
         If the number of zeros in input data is more than the percentage of all data points, then print the data warning
     method_name : str, optional.
@@ -103,7 +103,7 @@ def _zeros_check(
     warning_zeros = False
     zero_elements_total = int(xp.count_nonzero(data == 0))
 
-    nonzero_elements_total = 1    
+    nonzero_elements_total = 1
     for tot_elements_mult in data.shape:
         nonzero_elements_total *= tot_elements_mult
     if (zero_elements_total / nonzero_elements_total) * 100 >= percentage_threshold:
@@ -123,21 +123,21 @@ def data_checker(
 ) -> bool:
     """
     Function that performs the variety of checks on input data, in some cases also correct the data and prints warnings.
-    Currently it checks for: the presence of infs and nans in data; the number of zero elements. 
+    Currently it checks for: the presence of infs and nans in data; the number of zero elements.
 
     Parameters
     ----------
     data : xp.ndarray
-        Input CuPy or Numpy array either float32 or uint16 data type.
+        Input CuPy or Numpy array.
     verbosity : bool
-        If enabled, then the printing of the warning happens when data contains infs or nans
+        If enabled, then the printing of the warning happens when data contains infs or nans.
     method_name : str, optional.
         Method's name for which input data is tested.
 
     Returns
     -------
     cp.ndarray
-        Returns corrected or not data array
+        Returns corrected or not data array.
     """
 
     data = _naninfs_check(
@@ -151,4 +151,4 @@ def data_checker(
         method_name=method_name,
     )
 
-    return data
+    return data

httomolibgpu/prep/alignment.py

@@ -88,7 +88,9 @@ def distortion_correction_proj_discorpy(
     if len(data.shape) == 2:
         data = cp.expand_dims(data, axis=0)
 
-    data = data_checker(data, verbosity=True, method_name="distortion_correction_proj_discorpy")        
+    data = data_checker(
+        data, verbosity=True, method_name="distortion_correction_proj_discorpy"
+    )
 
     # Get info from metadata txt file
     xcenter, ycenter, list_fact = _load_metadata_txt(metadata_path)

httomolibgpu/prep/normalize.py

@@ -145,7 +145,7 @@ def _check_valid_input_normalise(data, flats, darks) -> None:
         flats = flats[cp.newaxis, :, :]
     if darks.ndim == 2:
         darks = darks[cp.newaxis, :, :]
-    
-    data_checker(data,verbosity=True,method_name="normalize_data")
-    data_checker(flats,verbosity=True,method_name="normalize_flats")
-    data_checker(darks,verbosity=True,method_name="normalize_darks")
+
+    data_checker(data, verbosity=True, method_name="normalize_data")
+    data_checker(flats, verbosity=True, method_name="normalize_flats")
+    data_checker(darks, verbosity=True, method_name="normalize_darks")

httomolibgpu/prep/phase.py

@@ -300,7 +300,7 @@ def paganin_filter_tomopy(
             f"Invalid number of dimensions in data: {tomo.ndim},"
             " please provide a stack of 2D projections."
         )
-    
+
     tomo = data_checker(tomo, verbosity=True, method_name="paganin_filter_tomopy")
 
     dz_orig, dy_orig, dx_orig = tomo.shape

httomolibgpu/prep/stripe.py

@@ -144,7 +144,7 @@ def remove_stripe_ti(
     _, _, dx_orig = data.shape
     if (dx_orig % 2) != 0:
         # the horizontal detector size is odd, data needs to be padded/cropped, for now raising the error
-        raise ValueError("The horizontal detector size must be even")    
+        raise ValueError("The horizontal detector size must be even")
 
     gamma = beta * ((1 - beta) / (1 + beta)) ** cp.abs(
         cp.fft.fftfreq(data.shape[-1]) * data.shape[-1]
@@ -387,7 +387,7 @@ def raven_filter(
     """
     if data.dtype != cp.float32:
         raise ValueError("The input data should be float32 data type")
-    
+
     data = data_checker(data, verbosity=True, method_name="raven_filter")
 
     # Padding of the sinogram

httomolibgpu/recon/rotation.py

@@ -112,7 +112,7 @@ def find_center_vo(
 
     data = data_checker(data, verbosity=True, method_name="find_center_vo")
 
-    angles_tot, detY_size, detX_size = data.shape    
+    angles_tot, detY_size, detX_size = data.shape
 
     if ind is None:
         ind = detY_size // 2  # middle slice index
@@ -459,8 +459,8 @@ def find_center_360(
     """
     if data.ndim != 3:
         raise ValueError("A 3D array must be provided")
-    
-    data = data_checker(data, verbosity=True, method_name="find_center_360")    
+
+    data = data_checker(data, verbosity=True, method_name="find_center_360")
 
     # this method works with a 360-degree sinogram.
     if ind is None:
@@ -781,8 +781,8 @@ def find_center_pc(
         Rotation axis location.
     """
 
-    proj1 = data_checker(proj1, verbosity=True, method_name="find_center_pc")   
-    proj2 = data_checker(proj2, verbosity=True, method_name="find_center_pc")   
+    proj1 = data_checker(proj1, verbosity=True, method_name="find_center_pc")
+    proj2 = data_checker(proj2, verbosity=True, method_name="find_center_pc")
 
     imgshift = 0.0 if rotc_guess is None else rotc_guess - (proj1.shape[1] - 1.0) / 2.0
 
@@ -802,4 +802,6 @@ def find_center_pc(
     center = (proj1.shape[1] + shiftr[0][1] - 1.0) / 2.0
 
     return np.float32(center + imgshift)
+
+
 ##%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

tests/test_misc/test_supp_func.py

@@ -6,9 +6,7 @@
     _zeros_check,
     data_checker,
 )
-from numpy.testing import assert_allclose, assert_equal
-
-eps = 1e-6
+from numpy.testing import assert_equal
 
 
 def test_naninfs_check1():
@@ -36,7 +34,7 @@ def test_naninfs_check1_numpy():
 
 
 def test_naninfs_check2():
-    _data_input = cp.ones(shape=(10, 10, 10), dtype=cp.float32) * 100
+    _data_input = cp.ones(shape=(10, 10, 10)).astype(cp.float32) * 100
     _data_input[1, 1, 1] = -cp.inf
     _data_input[1, 1, 2] = cp.inf
     _data_input[1, 1, 3] = cp.nan
@@ -82,7 +80,7 @@ def test_naninfs_check2_numpy():
 
 
 def test_naninfs_check3():
-    _data_input = cp.ones(shape=(10, 10, 10), dtype=cp.uint16) * 100
+    _data_input = cp.ones(shape=(10, 10, 10)).astype(cp.uint16) * 100
     _data_output = _naninfs_check(_data_input.copy())
 
     assert _data_output.dtype == _data_input.dtype
@@ -97,8 +95,16 @@ def test_naninfs_check3_numpy():
     assert _data_output.shape == (10, 10, 10)
 
 
+def test_naninfs_check4_numpy():
+    _data_input = np.ones(shape=(10, 10, 10), dtype=np.uint8) * 100
+    _data_output = _naninfs_check(_data_input.copy())
+
+    assert _data_output.dtype == _data_input.dtype
+    assert _data_output.shape == (10, 10, 10)
+
+
 def test_zeros_check1():
-    _data_input = cp.ones(shape=(10, 10, 10), dtype=cp.float32) * 100
+    _data_input = cp.ones(shape=(10, 10, 10)).astype(cp.float32) * 100
     warning_zeros = _zeros_check(_data_input.copy())
 
     assert warning_zeros == False
@@ -112,7 +118,7 @@ def test_zeros_check1_numpy():
 
 
 def test_zeros_check2():
-    _data_input = cp.ones(shape=(10, 10, 10), dtype=cp.float32) * 100
+    _data_input = cp.ones(shape=(10, 10, 10)).astype(cp.float32) * 100
     _data_input[2:7, :, :] = 0.0
     warning_zeros = _zeros_check(_data_input.copy())
 
@@ -128,7 +134,7 @@ def test_zeros_check2_numpy():
 
 
 def test_zeros_check3():
-    _data_input = cp.ones(shape=(10, 10, 10), dtype=cp.float32) * 100
+    _data_input = cp.ones(shape=(10, 10, 10)).astype(cp.float32) * 100
     _data_input[3:7, :, :] = 0.0
     warning_zeros = _zeros_check(_data_input.copy())
 
@@ -144,7 +150,7 @@ def test_zeros_check3_numpy():
 
 
 def test_data_checker_numpy():
-    _data_input = np.ones(shape=(10, 10, 10), dtype=np.float32)
+    _data_input = np.ones(shape=(10, 10, 10)).astype(np.float32)
     _data_input[1, 1, 1] = -np.inf
     _data_input[1, 1, 2] = np.inf
     _data_input[1, 1, 3] = np.nan
@@ -166,8 +172,9 @@ def test_data_checker_numpy():
     assert _data_output.dtype == _data_input.dtype
     assert _data_output.shape == (10, 10, 10)
 
+
 def test_data_checker():
-    _data_input = cp.ones(shape=(10, 10, 10)).astype(cp.float32) * 100.0    
+    _data_input = cp.ones(shape=(10, 10, 10)).astype(cp.float32) * 100.0
     _data_input[1, 1, 1] = -cp.inf
     _data_input[1, 1, 2] = cp.inf
     _data_input[1, 1, 3] = cp.nan
@@ -187,4 +194,4 @@ def test_data_checker():
         0.0,
     )
     assert _data_output.dtype == _data_input.dtype
-    assert _data_output.shape == (10, 10, 10)    
+    assert _data_output.shape == (10, 10, 10)

tests/test_recon/test_algorithm.py

@@ -34,6 +34,7 @@ def test_reconstruct_FBP_2d_astra(data, flats, darks, ensure_clean_memory):
     assert recon_data.dtype == np.float32
     assert recon_data.shape == (recon_size, 128, recon_size)
 
+
 def test_reconstruct_FBP3d_tomobar_1(data, flats, darks, ensure_clean_memory):
     recon_data = FBP3d_tomobar(
         normalize_cupy(data, flats, darks, cutoff=10, minus_log=True),