Merge pull request #151 from DiamondLightSource/docsfix

docs fixing

Author: dkazanc

docs/source/conf.py

@@ -22,6 +22,8 @@
     "nvtx",
     "cupy",
     "cupyx",
+    "tomobar.methodsDIR_CuPy",
+    "tomobar.methodsIR_CuPy",
     "PIL",
     "skimage",
     "scipy",
@@ -40,6 +42,15 @@ def __repr__(self):
 
 sys.modules["cupy"] = CustomMock()
 sys.modules["numpy"] = CustomMock()
+sys.modules["cupyx.scipy.interpolate"] = CustomMock()
+sys.modules["cupyx.scipy.ndimage"] = CustomMock()
+sys.modules["cupyx.scipy.fft"] = CustomMock()
+sys.modules["cupyx.scipy.fftpack"] = CustomMock()
+sys.modules["scipy.fftpack"] = CustomMock()
+sys.modules["tomobar.methodsDIR_CuPy"] = CustomMock()
+sys.modules["tomobar.methodsIR_CuPy "] = CustomMock()
+sys.modules["skimage.registration"] = CustomMock()
+
 
 # ------------------------------------------------------------------------------
 

docs/source/reference/api.rst

@@ -8,7 +8,7 @@ This section contains the API reference and usage information for HttomolibGPU.
 
 HTTomolibGPU Modules
 ---------------------
-v.2.0
+v.2.1
 '''''
 
 .. toctree::

httomolibgpu/cupywrapper.py

@@ -2,6 +2,7 @@
 try:
     import cupy as cp
     import nvtx
+    import cupyx
 
     try:
         cp.cuda.Device(0).compute_capability
@@ -17,3 +18,4 @@
     import numpy as cp
 
     nvtx = Mock()
+    cupyx = Mock()

httomolibgpu/misc/morph.py

@@ -24,8 +24,11 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-
 nvtx = cupywrapper.nvtx
+cupyx = cupywrapper.cupyx
+
+from cupyx.scipy.interpolate import interpn
+
 from typing import Literal
 
 __all__ = [
@@ -77,6 +80,9 @@ def __sino_360_to_180(
         raise ValueError("overlap must be less than data.shape[2]")
     if overlap < 0:
         raise ValueError("only positive overlaps are allowed.")
+    
+    if rotation not in ['left', 'right']:
+        raise ValueError('rotation parameter must be either "left" or "right"')
 
     n = dx // 2
 
@@ -90,16 +96,14 @@ def __sino_360_to_180(
             weights * data[:n, :, :overlap]
             + (weights * data[n : 2 * n, :, :overlap])[:, :, ::-1]
         )
-    elif rotation == "right":
+    if rotation == "right":
         weights = cp.linspace(1.0, 0, overlap, dtype=cp.float32)
         out[:, :, : dz - overlap] = data[:n, :, :-overlap]
         out[:, :, -dz + overlap :] = data[n : 2 * n, :, :-overlap][:, :, ::-1]
         out[:, :, dz - overlap : dz] = (
             weights * data[:n, :, -overlap:]
             + (weights * data[n : 2 * n, :, -overlap:])[:, :, ::-1]
         )
-    else:
-        raise ValueError('rotation parameter must be either "left" or "right"')
 
     return out
 
@@ -134,8 +138,7 @@ def data_resampler(
 @nvtx.annotate()
 def __data_resampler(
     data: cp.ndarray, newshape: list, axis: int = 1, interpolation: str = "linear"
-) -> cp.ndarray:
-    from cupyx.scipy.interpolate import interpn
+) -> cp.ndarray:    
 
     if data.ndim != 3:
         raise ValueError("only 3D data is supported")

httomolibgpu/prep/alignment.py

@@ -25,6 +25,9 @@
 
 cp = cupywrapper.cp
 nvtx = cupywrapper.nvtx
+cupyx = cupywrapper.cupyx
+
+from cupyx.scipy.ndimage import map_coordinates
 
 from typing import Dict, List
 
@@ -72,7 +75,7 @@ def distortion_correction_proj_discorpy(
     Returns
     -------
     cp.ndarray
-        3D array. Distortion-corrected image(s).
+        3D array. Distortion-corrected array.
     """
     if cupywrapper.cupy_run:
         return __distortion_correction_proj_discorpy(
@@ -121,9 +124,7 @@ def __distortion_correction_proj_discorpy(
     -------
     cp.ndarray
         3D array. Distortion-corrected image(s).
-    """
-
-    from cupyx.scipy.ndimage import map_coordinates
+    """   
 
     # Check if it's a stack of 2D images, or only a single 2D image
     if len(data.shape) == 2:

httomolibgpu/prep/normalize.py

@@ -24,9 +24,10 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-
 nvtx = cupywrapper.nvtx
 
+from cupy import mean
+
 from numpy import float32
 from typing import Tuple
 
@@ -86,8 +87,7 @@ def __normalize(
     minus_log: bool = True,
     nonnegativity: bool = False,
     remove_nans: bool = True,
-) -> cp.ndarray:
-    from cupy import mean
+) -> cp.ndarray:    
 
     _check_valid_input(data, flats, darks)
 

httomolibgpu/prep/phase.py

@@ -24,9 +24,11 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-
 nvtx = cupywrapper.nvtx
 
+from httomolibgpu.cuda_kernels import load_cuda_module
+from cupyx.scipy.fft import fft2, ifft2, fftshift
+
 from numpy import float32
 from typing import Union
 import math
@@ -117,8 +119,6 @@ def __paganin_filter_savu(
     pad_method: str = "edge",
     increment: float = 0.0,
 ) -> cp.ndarray:
-    from httomolibgpu.cuda_kernels import load_cuda_module
-    from cupyx.scipy.fft import fft2, ifft2
 
     # Check the input data is valid
     if data.ndim != 3:
@@ -327,8 +327,7 @@ def __paganin_filter_tomopy(
     dist: float = 50.0,
     energy: float = 53.0,
     alpha: float = 1e-3,
-) -> cp.ndarray:
-    from cupyx.scipy.fft import fft2, ifft2, fftshift
+) -> cp.ndarray:   
 
     # Check the input data is valid
     if tomo.ndim != 3:

httomolibgpu/prep/stripe.py

@@ -24,8 +24,10 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-
 nvtx = cupywrapper.nvtx
+
+from cupyx.scipy.ndimage import median_filter, binary_dilation, uniform_filter1d
+
 from typing import Union
 
 __all__ = [
@@ -96,8 +98,6 @@ def _rs_sort(sinogram, size, dim):
     """
     Remove stripes using the sorting technique.
     """
-    from cupyx.scipy.ndimage import median_filter
-
     sinogram = cp.transpose(sinogram)
 
     #: Sort each column of the sinogram by its grayscale values
@@ -245,8 +245,6 @@ def _rs_sort2(sinogram, size, matindex, dim):
     """
     Remove stripes using the sorting technique.
     """
-    from cupyx.scipy.ndimage import median_filter
-
     sinogram = cp.transpose(sinogram)
     matcomb = cp.asarray(cp.dstack((matindex, sinogram)))
 
@@ -321,9 +319,6 @@ def _rs_large(sinogram, snr, size, matindex, drop_ratio=0.1, norm=True):
     """
     Remove large stripes.
     """
-    from cupyx.scipy.ndimage import median_filter
-    from cupyx.scipy.ndimage import binary_dilation
-
     drop_ratio = max(min(drop_ratio, 0.8), 0)  # = cp.clip(drop_ratio, 0.0, 0.8)
     (nrow, ncol) = sinogram.shape
     ndrop = int(0.5 * drop_ratio * nrow)
@@ -372,10 +367,6 @@ def _rs_dead(sinogram, snr, size, matindex, norm=True):
     """
     Remove unresponsive and fluctuating stripes.
     """
-    from cupyx.scipy.ndimage import median_filter
-    from cupyx.scipy.ndimage import binary_dilation
-    from cupyx.scipy.ndimage import uniform_filter1d
-
     sinogram = cp.copy(sinogram)  # Make it mutable
     (nrow, _) = sinogram.shape
     # sinosmooth = cp.apply_along_axis(uniform_filter1d, 0, sinogram, 10)

httomolibgpu/recon/algorithm.py

@@ -24,14 +24,15 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-
 nvtx = cupywrapper.nvtx
-from numpy import float32, complex64
-from typing import Optional, Type
 
 from tomobar.methodsDIR_CuPy import RecToolsDIRCuPy
 from tomobar.methodsIR_CuPy import RecToolsIRCuPy
 
+from numpy import float32, complex64
+from typing import Optional, Type
+
+
 __all__ = [
     "FBP",
     "LPRec",

httomolibgpu/recon/rotation.py

@@ -24,9 +24,14 @@
 from httomolibgpu import cupywrapper
 
 cp = cupywrapper.cp
-
 nvtx = cupywrapper.nvtx
 
+from httomolibgpu.cuda_kernels import load_cuda_module
+from cupyx.scipy.ndimage import shift, gaussian_filter
+from skimage.registration import phase_cross_correlation
+from cupyx.scipy.fftpack import get_fft_plan
+from cupyx.scipy.fft import rfft2
+
 import math
 from typing import List, Literal, Optional, Tuple, Union
 
@@ -96,8 +101,7 @@ def __find_center_vo(
     step: float = 0.25,
     ratio: float = 0.5,
     drop: int = 20,
-) -> float:
-    from cupyx.scipy.ndimage import gaussian_filter
+) -> float:    
 
     if data.ndim == 2:
         data = cp.expand_dims(data, 1)
@@ -185,7 +189,6 @@ def _search_fine(sino, srad, step, init_cen, ratio, drop):
 
 @nvtx.annotate()
 def _create_mask(nrow, ncol, radius, drop):
-    from httomolibgpu.cuda_kernels import load_cuda_module
 
     du = 1.0 / ncol
     dv = (nrow - 1.0) / (nrow * 2.0 * np.pi)
@@ -258,11 +261,6 @@ def _calculate_chunks(
 
 @nvtx.annotate()
 def _calculate_metric(list_shift, sino1, sino2, sino3, mask, out):
-    from httomolibgpu.cuda_kernels import load_cuda_module
-    from cupyx.scipy.ndimage import shift
-    from cupyx.scipy.fftpack import get_fft_plan
-    from cupyx.scipy.fft import rfft2
-
     # this tries to simplify - if shift_col is integer, no need to spline interpolate
     assert list_shift.dtype == cp.float32, "shifts must be single precision floats"
     assert sino1.dtype == cp.float32, "sino1 must be float32"
@@ -351,8 +349,6 @@ def _calculate_metric(list_shift, sino1, sino2, sino3, mask, out):
 
 @nvtx.annotate()
 def _downsample(sino, level, axis):
-    from httomolibgpu.cuda_kernels import load_cuda_module
-
     assert sino.dtype == cp.float32, "single precision floating point input required"
     assert sino.flags["C_CONTIGUOUS"], "list_shift must be C-contiguous"
 
@@ -612,7 +608,6 @@ def _search_overlap(
         Initial position of the searching window where the position
         corresponds to the center of the window.
     """
-    from cupyx.scipy.ndimage import gaussian_filter
 
     if denoise is True:
         # note: the filtering makes the output contiguous
@@ -650,8 +645,6 @@ def _calc_metrics(mat1, mat2, win_width, side, use_overlap, norm):
     assert mat1.flags.c_contiguous, "only contiguos arrays supported"
     assert mat2.flags.c_contiguous, "only contiguos arrays supported"
 
-    from httomolibgpu.cuda_kernels import load_cuda_module
-
     _calc_metrics_module = load_cuda_module(
         "calc_metrics",
         name_expressions=[
@@ -766,9 +759,7 @@ def __find_center_pc(
     tol: float = 0.5,
     rotc_guess: Union[float, Optional[str]] = None,
 ) -> float:
-    from cupyx.scipy.ndimage import shift
-    from skimage.registration import phase_cross_correlation
-
+    
     imgshift = 0.0 if rotc_guess is None else rotc_guess - (proj1.shape[1] - 1.0) / 2.0
 
     proj1 = shift(proj1, [0, -imgshift], mode="constant", cval=0)